Recent years evidenced an increase in the total number of skin cancer cases, and it is projected to grow exponentially. This paper proposes a computer-aided diagnosis system for the classification of a malignant lesion, where the acquired image is primarily pre-processed using novel methods. Digital artifacts such as hair follicles and blood vessels are removed, and thereafter, the image is enhanced using a novel method of histogram equalization. Henceforth, the pre-processed image undergoes the segmentation phase, where the suspected lesion is segmented using the Neutrosophic technique. The segmentation method employs a thresholding-based method along with a pentagonal neutrosophic structure to form a segmentation mask of the suspected skin lesion. The paper proposes a deep neural network base on Inception and residual blocks with softmax block after each residual block which makes the layer wider and easier to learn the key features more quickly. The proposed classifier was trained, tested, and validated over PH2, ISIC 2017, ISIC 2018, and ISIC 2019 datasets. The proposed segmentation model yields an accuracy mark of 99.50%, 99.33%, 98.56% and 98.04% for these datasets, respectively. These datasets are augmented to form a total of 103,554 images for training, which make the classifier produce enhanced classification results. Our experimental results confirm that the proposed classifier yields an accuracy score of 99.50%, 99.33%, 98.56%, and 98.04% for PH2, ISIC 2017, 2018, and 2019, respectively, which is better than most of the pre-existing classifiers. The technical development and scientific innovations in the field of medical science have led to improvement in the surgical condition of a patient; thereby, it has decreased the mortality rate and increased the satisfactory index of patients. Although new discoveries have aided the health care system with advanced diagnosis methods for several diseases, accurate diagnosis and timely treatment of a cancer patient remains exacting and challenging for researchers all around the world. Among several other types of cancers, skin cancer has been the most frequently diagnosed cancer as stated in by the National Institute of Skin Cancer (NISC) . SupportSkin cancer can be categorized into three major types, i.e., nevus, malignant, and benign . Nevus iIn this article, we have proposed a novel computer-aided diagnosis system (CAD) for digitally diagnosing the suspected lesion. A novel and effective pre-processing phase is introduced in the article which not only removes the artifacts  and reflections from the acquired images, but also enhances the image and automatically adjusting contrast and brightness of the image. Pre-processing is the fundamental and most important phase of CADs as availability of artifacts, such as hair follicles and blood vessel, affect the visual inspection of the lesion and hamper the accuracy of diagnosis, and thus, digitally removing such artifacts not only reduces the pain and labor of removing those artifacts over suspected lesion but also enhances the diagnosis outcome. Inspired from uncertainty theory, we want to incorporate the vagueness principle into image segmentation techniques to discard the ambiguity portion of a segmented image and to capture the best fitted region more efficiently. Obviously, some crucial questions to be asked are, if the size of the image is large, how we can cut or capture the desired actual feasible region in a logical way using the pixel values? Additionally, in case of segmentation how we can capture the hesitation portion using uncertainty logic? Most importantly, how can we link the matrix presentation of a segmented image with the pixel values? In case of iteration process where mathematical theory will be utilized to get the original affected region? This research article deploys a determinant based image segmentation method namely absolute value computational algorithm using each pixel values for three channels to detect the most affected region from the captured image. Initially, we will follow an algorithm based on 3 × 3 determinant constructions of the segmented image using the pixel values. We will scale down the total image into finite number of determinants and will calculate all the absolute values for each of the three channels. Furthermore, we will set the threshold value using the geometric mean concept, which will capture the affected region roughly from the original figure. After that, we will incorporate neutrosophic theory to judge the exact affected portion after 1st phase of segmentation. Neutrosophic number can grab all three components of an uncertain number, namely truth, false, and hesitation portions, very efficiently and logically. Here, we can utilize the pentagonal linguistic neutrosophic number for the second phase of segmentation to set the exact threshold value such that the image segmentation process can be performed properly to get more accurate approximation. Then, the article utilizes the segmented region for classification of lesion using proposed classifier in the Keras  frameworThe fundamental motive of this research article is to develop a trustworthy CAD method based on principles of neutrosophy and deep learning for precise segmentation and classification of skin lesion. The forthcoming section in this article portrays materials and methods in This section explains the proposed method in detail, which uses different stages of computer aided diagnosis, namely: pre-processing, segmentation, lesion localization, and classification. Novel methods for pre-processing of lesion is introduced in this paper, which not only enhances the image quality but also digitally removes the artifacts. A unique segmentation phase is proposed in the article using neutrosophy and determinant to calculate the threshold value for segmentation of the lesion. Thereafter, a modified classifier is employed for classification of skin lesion into a malignant and non-malignant class. A complete flowchart of the proposed method is pictorially represented in Neutrosophic Set : A set Additionally, Single-Valued Pentagonal Neutrosophic Number (SVPNN) : A SVPNN f, we have applied a filter f, such that noise at pixel  is flattened.:In order to remove noise from the acquired input image m by applying the filter over the image, which is: Thus, we get a modified image In order to maintain the energy in the filter = 1, m,n] and  is more than i,j] is modified to 0 to avoid obsoletion of features. The modified brightness filter can be shown as:However, due to the variation in the intensity of the image, a global filter alone cannot be employed to reduce digital image noise. Moreover, the image is smoothened to a great extent with an increased value of Thus, our modified equation for removal of extra unwanted noise is:As mentioned in Equation (9), digital noise is removed, and the image is smoothened without losing the key features which might be lost if we had only employed a Gaussian filter.m is binarized and it was checked if the selected pixels are continuous or discrete. Continuous and regular block of mask are not considered to be hairs follicles and they are set as the background of the mask. Thereafter, the binarized mask is alternatively diluted and convolved to form prominent lines of hair, which could be masked out from the original image f. This method of hair removal also enables to remove marks from the dermoscopic ruler and frames. Artifacts such as presence of hair follicles (both thin and thick), presence of blood vessels, and reflection of dermoscopic gel and dermoscopic frames are digitally removed by the proposed method, where the noise free image Finally, the pre-processed image undergoes the process of image enhancement by histogram equalization to adjust the brightness and contrast of each pixel. The enhanced image is further used for lesion localization and segmentation. Additionally, the efficiency of the proposed method is illustrated in the result and analysis section, which clearly depicts the efficiency of the proposed pre-processing method. Initially, the pre-processed image Thereafter, y-axis as 0, Henceforth, the segmentation mask is formed, but we can observe a fuzziness in boundaries of the segmented lesion. We are confused in case of the next phase of segmentation, as all the pixel values lie within a small bandwidth. Moreover, we are in a dilemma which pixel should be captured and which one should be discarded. Thus, for the next phase of segmentation, we will utilize the linguistic pentagonal neutrosophic number to select the threshold value of the next phase. Structure of PNN is pictorially represented in Case-1: If ith pixel of the segmented image, then the pixel is discarded.Case-2: If ith pixel of the segmented image, then the pixel is accepted for the next round.The conception of neutrosophic number is being proposed here to tackle the ambiguity portion and to fix the threshold value Classification is the fundamental phase of computer-aided diagnosis, where the acquired skin lesion image is classified to be malignant or non-malicious by using our proposed and efficient deep learning model. It is trained over a publicly available and standard dataset of ISIC. The forthcoming subsections deal with a detailed description of datasets that are employed for training and the hyperparameter used to fetch the best-fit results of classification.With the advancement in medical vision and computer-aided diagnostic systems, the classification of a malignant lesion has significantly improved. However, training a classifier to detect a particular class is a strenuous task. The ISIC 2017, ISIC 2018, and ISIC 2019 datasets are used for training the classifier, with a total of 103,524 images, out of which 15,464 are melanoma images, while 88,060 belong to non-malignant images. A huge set of training data increased the efficiency of the classification. To unify the dataset to overcome the problem of classification for various images of multiple dimensions, the training images are resized to This article proposes a classifier based on the inception and residual block with a softmax block after each residual block, which makes the network wider and less deep, thereby decreasing the training time. Pictorial representation of the proposed neural network is shown in The proposed classifier fetches the best fit results for classification under the following set of hyperparameters: batch size = 32, subdivision = 16, learning rate = 0.045, decay rate = 0.5, epsilon = 1.0, and momentum = 0.7. The classification result is generated for 5000 epochs while saving results for each 50th epoch. This criterion is evaluated to fetch enhanced classification results, as the learning rate is increased by saving epochs after 50th phase. The RMSProp optimizer is used for updating weight and biases during backpropagation and categorical_crossentropy is used as a loss function, as it seems to perform better with ‘RMSProp’. The classifier is validated after each 50th epoch to check the enhanced efficiency of lesion detection. The configuration file is generated after training over a huge dataset of ISIC and is used to classify skin lesion images of the test dataset. The optimal hyperparameters are selected by employing Bayesian optimization methods. The key benefit of Bayesian approaches is that they can inform the choice of the subsequent hyperparameter values to be evaluated by using the results from historical runs or gradually increasing prior knowledge in the form of pairs of hyperparameter values and objective function scores. This reduces the time and computation costs required to attain acceptable objective function scores.Efficient processing of dermoscopic images and accurate classification of skin lesions has emerged as a vital field of research. The accumulation of relevant datasets and accurate training of the classifier under specific parameter has always being a perplexing job. The proposed method is trained over the publicly accessible datasets of ISIC 2017, ISIC 2018, and ISIC 2019. These datasets consist reliable and easily available ground truth images that were drawn by a panel of expert dermatologist, which enable the researchers to compare and evaluate the proposed methodology. A huge set of dermoscopic data (after the process of data augmentation) not only helps the classifiers to train under rigorous conditions, but also increases the validation size, thereby producing an enhanced report of classification. The augmentation of data produces more training images from the same sample set by addition of digital noise and artifacts and rotating the image at specific angles, therefore helping the model to learn more features and classify the skin lesion accurately. Refs. ,22 emploThis section specifies different evaluation metrics that are used to validate and evaluate each stage of the CAD system: pre-processing, localization, segmentation, and classification. These metrics are accepted and employed globally by well-known researchers in similar domains.I and a pre-processed image Is is calculated by the MSE. The square root of the MSE is the RMSE. The equations for both the MSE and RMSE are given as follows:The efficiency of the pre-processing stage is evaluated by employing the universal image quality index (UIQI), peak signal to noise ratio (PSNR), root mean squared error (RMSE), and mean squared error (MSE). These matrices are used to calculate the enhancement capacity of the dermoscopic image in pre-processing stage, which might increase the efficiency of lesion detection in later stages. These parameters also measure the efficiency of the pre-processing method by measuring its capacity to remove artifacts digitally. The mean difference between an acquired image with noise The quality of image is widely measured by the peak signal to noise ratio (PSNR). The quality of image enhancement is marked by a high PSNR score. It is usually expressed in terms of the logarithmic decibel (dB) scale. The mathematical representation of the PSNR is given below in Equation (15):UIQI is the estimation of linear correlation of acquired dermoscopic image along with the pre-processed image, based on luminance, contrast, and structure features in the pre-processing stage. σ represents the mean of the input image, mean of pre-processed image, and standard deviation of the acquired image. The mathematical representation UIQI is illustrated below:TP) value, whereas if it is predicted to be non-malignant, it is defined as a false negative (FN). If a non-malignant lesion is predicted to be melanoma, it is classified as a false positive (FP), as it raises a false malignant flag, whereas if it is predicted as non-malignant, the classification is defined as a true negative (TN).The performance of an algorithm is visualized by a 2 × 2 table, which is known as a confusion matrix or error matrix. It encapsulates a detailed report of prediction results on a classification problem. Each row represents an occurrence of the predicted class and each column indicates occurrence of actual class. It is employed for localization and classification of malignant lesion. If a malignant lesion is predicted to be malignant, it is defined as a true positive (IoU), which is represented by Equation (17):Evaluation of melanoma lesion localization is executed by an overlapping predicted region over the ground truth region to find the intersection area; this greedy method is termed as intersection-over-union (Mean average precision (mAP) is used for evaluating localization phase by computing the mean precision of detection of melanoma area. Equation (18) shows the detailed mathematical representation:Sn), Specificity (Sp), Accuracy (Ac), Dice index coefficient (Dc), and Jaccard score (Js) are used. These standardized criteria of evaluation are utilized for the validation of segmentation and classification performance in the ISBI challenge, which is considered to be a standard platform for publishing practical implementation of diagnosis of a melanoma lesion. Sensitivity (Sn) represents the ratio of accurate detection/segmentation of dermoscopic lesion, whereas Specificity (Sp) indicates the proportion of accurate detection/segmentation of non-melanoma pixels. Overall performance of diagnosis is quantified by a measure of the Accuracy (Ac) metric. The dice index coefficient (Dc) is used to measure the performance of detection/segmentation by comparing ground truth results. Similarly, the Jaccard score (Js) is a measure of the intersection of the union of segmented lesion with ground truth results. The area under curve (AUC) metric is used for assessing the performance by calculating the area of the ROC curve. A high value of AUC represents better classification architecture for the prediction of true values as true and false entities as false. Precision is the accuracy of the classification method to generate only valuable data, that is, it determines the rate of accurate detection. On the other hand, recall is the ability of the classifier to detect true values as true, which is also named the true positive rate. The weighted mean of recall and precision is termed the F1 score or harmonic mean. F1 is used to measure accuracy of machine learning architecture in a single score metric by evaluating both recall and precision. The Matthew correlation coefficient (MCC) quantifies the correlation between segmented and annotated area of lesion, the outcome of MCC ranges from −1 to 1. A larger value of MCC represents efficient detection/segmentation of skin lesions. The mathematical representation of all these matrices is shown as follows: For evaluating the performance of skin lesion classification, evaluation metrics such as Sensitivity  and compared with other well-known classifiers, such as the YOLO, K-nearest neighbor (KNN), Support vector machine (SVM), Decision Tree (DT), Multilayer perceptron (MLP), Bayesian network (BN), random forest (RF), logistic, and naïve bayes (NB) algorithms. The performance is evaluated for Accuracy, Sensitivity, Specificity, Precision, and F1-Score to determine the efficiency of classification by different classifiers. The performance analysis is tabulated in The supremacy of the proposed classifier is evidently illustrated by the comparisons with well-known classifiers for the accurate identification of skin lesion. Our classifier yields a high score for all the evaluation parameters when compared with recently developed and extensively used classifiers. Due to its ability to distinguish the minor difference of pixel coloration for classification, it stands to be the most efficient and trustworthy for the classification of dermoscopic images. This article also focuses on the importance of an adequate pre-processing method for the removal of artifacts from the acquired image and thereafter enhancing the image, which will help the classifier to fetch such high values of accuracy. Notwithstanding the fact that segmentation of skin lesion using neutrosophy and a third-order determinant also helped to achieve high value of classification. The complete workflow of the proposed method is represented in The performance analysis of the classification of skin lesions using five-fold cross-validation is represented in M and N are dimensions of the image (I). The asymmetry distribution of pixels of a malignant lesion is market by Skewness of the image, which is mathematically represented in Equation (30), where Kurtosis (represented in Equation (31)) of an image. It is an important statistical parameter which shows that how often outliers occur.Statistical tests were conducted of a few of the dermoscopic skin lesion from the PH2, ISIC 2017, ISIC 2018, and ISIC 2019 datasets to check if the lesion can be classified into a specific class, based on statistical parameters, such as mean, skewness, and kurtosis. The mean value of an image represents the average pixel density, which can be an important parameter to classify a skin lesion as melanoma, because melanoma lesion does lie into specific colors ranging from bluish grey to dark brown. A mathematical representation of the mean value is shown in Equation (29), where A statistical comparison of eight different dermoscopic images from the PH2, ISIC 2017, ISIC 2018, and ISIC 2019 datasets based on statistical parameters such as mean, skewness, and kurtosis is tabulated in p-value is calculated using the Freidman test with Bergmann and Hommel’s correction. If the value of ‘p’ is less than 0.05, then it shows that the null hypothesis (H0) is proven wrong, thereby proving the alternative hypothesis (Ha), which states that the proposed algorithm outperforms all of the state-of-the-art classifiers, as there exists a significant difference in the mean accuracy. H0 is used to prove that there exists no difference in the mean accuracy of the classifiers when the models are classified using the test dataset. The t-test is performed because the classifiers run over the same dataset, thereby producing a t-value. The performance of each classifier is compared in a pair-wise manner against the proposed method. Hence, the first row of the table contains not-a-number (NaN). The mean rank for each classifier is calculated by ranking the performance of classification for each image in the test dataset by several classifiers . The proposed method outperforms in classification all the dermoscopic test images; thereby, its mean rank  is 1.12. The mean and standard deviation of confidence value  for each dermoscopic image in the test dataset is encapsulated under the mean and SD columns in p-value for all the classifiers is very low, which signifies the mean accuracy difference; moreover, the mean confidence score of the proposed method is 0.86, which is higher than any existing method, thereby portraying the efficiency of the proposed method.The mean accuracy obtained using the proposed method was higher than that for the state-of-the-art techniques. However, to assess how significantly different the accuracy is, we also performed a statistical analysis. The statistical test for all the classifiers based on the test datasets is encapsulated in In this article, an effective pre-processing model was proposed to digitally remove the artifacts present in the acquired image; the image is enhanced by the method of histogram equalization. After refining the image, it undergoes the segmentation phase, where a mathematical-based algorithm using thresholding and pentagonal neutrosophy is demonstrated to achieve enhanced segmentation results. Henceforth, the segmented image is classified using the proposed classifier. The model was trained with a huge dataset, which was proposed after data augmentation and balancing. The outcomes of the proposed methods were evaluated using publicly accessible datasets: PH2, ISIC 2017, ISIC 2018, and ISIC 2019. A vivid and vast range of test parameters proved that the proposed methods, for each stage of CAD, outperformed the state-of-the-art methods. The proposed method had a significantly higher score in sensitivity and specificity in the field of diagnosis of melanoma lesion. The classification results mentioned in More pronounced output can be achieved in the near future, when an extensive and varied range of datasets with several classes will be available, along with modified CAD equipment, which will increase the image quality during image acquisition. A broad range of classifications for various skin diseases  and several other types of skin cancers  should be performed in future research. In future research, we also aim to deploy the proposed algorithms into a smartphone application, such that the diagnosis of skin cancer is made easily available without the need for any invasive techniques. Additionally, we also aim to proposes a smartphone-based dermoscopic tool which will increase the accuracy of diagnosis when diagnosed using a smartphone. The digital diagnosis of skin lesions is an extensive area of research and it has a great potential in the near future.


With the advent of the digital information age, new data services such as virtual reality, industrial Internet, and cloud computing have proliferated in recent years. As a result, it increases operator demand for 5G bearer networks by providing features such as high transmission capacity, ultra-long transmission distance, network slicing, and intelligent management and control. Software-defined networking, as a new network architecture, intends to increase network flexibility and agility and can better satisfy the demands of 5G networks for network slicing. Nevertheless, software-defined networking still faces the challenge of network intrusion. We propose an abnormal traffic detection method based on the stacking method and self-attention mechanism, which makes up for the shortcoming of the inability to track long-term dependencies between data samples in ensemble learning. Our method utilizes a self-attention mechanism and a convolutional network to automatically learn long-term associations between traffic samples and provide them to downstream tasks in sample embedding. In addition, we design a novel stacking ensemble method, which computes the sample embedding and the predicted values of the heterogeneous base learner through the fusion module to obtain the final outlier results. This paper conducts experiments on abnormal traffic datasets in the software-defined network environment, calculates precision, recall and F1-score, and compares and analyzes them with other algorithms. The experimental results show that the method designed in this paper achieves 0.9972, 0.9996, and 0.9984 in multiple indicators of precision, recall, and F1-score, respectively, which are better than the comparison methods. The advancement of communication technology has altered the face of human civilization as it enters the digital information era. The advancement of information technology will have an impact on the ease of living in human civilization. With the arrival of the 5G era, human society’s level of informatization will become even higher. In comparison to 4G, the 5G network’s application scenarios will span the sectors of mobile Internet, Internet of Vehicles, and the Industrial Internet. Simultaneously, operators have set greater standards for 5G networks, including huge transmission capacity, ultra-long transmission distance, network slicing, and intelligent management and control. Among them, the software-defined network (SDN) is a new type of network design idea that intends to increase network flexibility and agility and can better fulfill the network slicing demands of 5G networks. The central concept of software-defined networking is to decouple past network architecture into the control plane and the data plane and to previous abstract network functions into applications in the network operating system in the control plane . From toHowever, software-defined networks are vulnerable to cyber-attacks in the same way that traditional networks are. As previously said, SDN introduces the SDN controller, which provides unified API services for the application plane and the infrastructure layer, allowing the network to be centralized, programmable, and open. These characteristics, such as permitting mismatched network packets to be submitted to the controller to request forwarding rules, raise security issues for SDN. A network assault is frequently exhibited as anomalous traffic. The term “abnormal traffic” refers to network traffic behavior that deviates from the expected typical pattern. Server overload induced by DOS assaults, worms’ privileged access, and server attacks will result in anomalous traffic . SDN netWhen aberrant traffic is detected, abnormal traffic detection technology monitors network traffic transmission immediately, sends an alarm, or takes active reaction steps. The real-time monitoring of SDN traffic may maintain the security, confidentiality, and integrity of SDN network information while also promoting the development and implementation of SDN technology . As a reHinton, Geoffrey E. et al.  proposedWe propose an abnormal traffic detection method based on the stacking method and self-attention mechanism (TSMASAM) that combines the self-attention mechanism and ensemble learning to make up for the inability of ensemble learning to learn the associations between data. First, we propose a neural network composed of a self-attention mechanism and a deep convolutional network which aims to automatically learn the correlation between traffic samples, capture the feature space’s internal structure, and provide it downstream in the form of a sample embedding task. Secondly, we design a novel stacking integration method, which aims to detect and identify abnormal network traffic by integrating the sample embedding obtained above and the inspection results of the heterogeneous base learner. Finally, we design a new loss function, which fully and comprehensively considers the basic learner’s influence on the model’s overall performance by introducing the basic learner’s loss value and the regular term composed of it and preventing the model from falling into an overfitting state.We propose a neural network composed of a self-attention mechanism and a deep convolutional network, which learns from samples and converts them into sample embeddings.We propose a stacking ensemble learning method composed of the autoencoder and base learner, using the autoencoder to remove irrelevant information in the samples and the stacking method to integrate the detection results of sample embedding and the base learner.We design a novel loss function to observe the operation of the model through the introduced regularization term and base learner loss value. We use a network traffic dataset under an SDN architecture to evaluate the model’s performance. The results show that the model has a better abnormal traffic detection effect than the comparison model.The main contributions of this paper are as follows:The structure of this paper is as follows: The concept of abnormal traffic detection technology began in the 1980s, which refers to a network security technology that monitors network traffic transmission, promptly issues an alarm or takes active response measures when abnormal traffic is found. Anomaly detection obtains a feature model by modeling and analyzing traffic characteristics, thereby judging whether the network traffic is normal. Anomaly detection technology can be roughly divided into: that based on traffic feature matching, based on traffic feature statistics, and based on machine learning. There are essential differences in the modeling logic of the three traffic characteristics, which bring about different detection scenarios and inspection effects. Algorithms based on traffic feature matching require professionals to analyze and summarize the characteristics of abnormal traffic and then match them with the observed traffic characteristic. The advantage of this approach is the higher accuracy of identifying known attacks. However, algorithms based on traffic feature matching rely on expert knowledge and are difficult to deal with unseen abnormal network traffic. The anomaly detection algorithm based on data statistics is based on the normal distribution of network traffic characteristics. When the observed network traffic characteristics deviation from the benchmark exceeds a certain threshold, it is regarded as abnormal network traffic. The advantage of the algorithm based on data feature statistics is that it is simple to implement and can also identify abnormal traffic in unknown networks, but it is prone to misjudgment. Algorithms based on machine learning have a stronger learning ability and can learn from incomplete traffic characteristics to abnormal traffic, however, the models generally have high computational complexity and are not suitable for high-response environments. However, with the substantial improvement in computing technology, methods based on machine learning have received attention and research from all parties.Based on the traffic feature matching algorithm, the abnormal network traffic is known quickly, but it cannot effectively deal with the unknown abnormal network traffic. Ref.  proposedThe algorithm based on data statistics can quickly identify abnormal traffic and deal with unknown abnormal network traffic, but it is prone to misjudgment. A histogram-based outlier detection (HBOS) algorithm was proposed to score data in linear time. Since HBOS assumes no dependencies between features, the algorithm is technically faster than other methods but less accurate . HBOS derithm of  focuses rithm of  proposedrithm of  developerithm of  via generithm of  developerithm of  implemenrithm of  also devrithm of  developerithm of  here exprithm of  believedrithm of  proposedrithm of  used a nAlgorithms based on machine learning have a strong learning ability and can effectively deal with unknown abnormal network traffic. Ref.  proposedTSMASAM is a deep learning model based on ensemble learning and the self-attention mechanism. Combining the self-attention mechanism and ensemble learning makes up for the relationship between data that cannot be learned by ensemble learning. The module frame is shown in The experimental flow is shown in Definition 1.The dataset is represented as Definition 2.Definition 3.In the abnormal traffic detection model, input data whereinmeans that the function H(X) predicts that the data sampleis abnormal, andimplies that the function H(X) predicts that the data pointis normal.Q, K, and V is to find the correlation coefficient with other features by calculation, calculate a weight for each feature, and then obtain a weighted result to judge the relationship between each feature and other features. At most, the detection efficiency of traffic is improved by learning the information in these attention values. The main work of self-attention is to calculate the dot product of the query Q and all K, scale it, derive the weight of the value V through the softmax function, and then multiply the value V and the weight to obtain the attention value:ith inputs, that is, the correlation between each input. After obtaining the calculated attention value To explore the correlation between multiple sample features, the model introduces a self-attention mechanism to automatically learn the correlation between samples, captures the feature space’s internal structure, and uses a convolutional network to construct a sample embedding to express the relationship between data samples. Given a set of input samples We use an autoencoder to denoise the original to obtain representative feature information in the samples. After processing by the autoencoder, we obtain the reconstructed information vector The stacking ensemble detection network learns to train the base learner by reconstructing the data In the second layer of the stacking ensemble detection network, we designed a fusion module to train the fusion module by using the long-term dependencies between samples captured by the self-attention mechanism and the prediction results of the base learner as a new dataset. Based on the embedded vector Y is the label set, Y. We design a new loss function to fully consider the sharing of each base model to the model as a whole. The loss function of our method is calculated based on the cross-entropy loss function:The TSMASAM designed in this paper is implemented based on Python3, Pytorch1.2 and Numpy. The four CPU models are Intel(R), Xeon(R), CPU E5-2620 v2 @ and 2.10GHz, the graphics card model is Matrox G200eR2, and the PyTorch version is the 1.2.0 server environment. The InSDN dataset is generated from environment simulations under four virtual machines; the first virtual machine is Kali Linux, which represents the attacker server; the second virtual machine is Ubuntu 16.4, which acts as the ONOS controller; the third Ubuntu 16.4 machine, as a Mininet and OVS switch; the fourth virtual machine is a Metasploitable 2-based Linux machine that serves as an exploit service to demonstrate the exploit. The controller of SDN is implemented using the open source tool ONOS. This dataset contains anomalous traffic from inside and outside attackers targeting the controller.This paper evaluates the performance of TSMASAM through two experiments. This paper applies the network traffic simulation dataset  under thTo verify the generalization ability, not only the InSDN dataset  but alsoTrue positives (TP): TP represents the proportion of abnormal behavior correctly identified as abnormal behavior;False positives (FP): FP represents the proportion of normal behavior incorrectly identified as abnormal behavior;False negatives (FN): FN represents the proportion of abnormal behavior incorrectly identified as normal behavior;True negatives (TN): TN represents the proportion of normal behavior correctly identified as normal behavior;There are only two types of anomaly detection targets in this paper. The positive examples are normal data, and the negative ones are abnormal. The classification results of the experiments can be divided into the following four categories:After classifying the results, this paper evaluates the algorithm’s performance by Precision, Recall, and F1-score.The experimental results of detection performance are shown in The abscissa represents the epoch number , and the ordinate represents the loss value of the model. It can be seen from the figure that the loss value of TSMASAM shows a small range of fluctuation in the early training period, and then quickly converges and maintains a low oven for a long time.The experimental results are shown in The experimental results of detection performance are analyzed as follows: the traditional machine learning algorithm has limited ability and poor generalization ability, which makes the model’s learning of traffic characteristics too limited. The learning of datasets with long-term associations is not sufficient, so the performance is lower.Due to the large randomness of traffic, it is difficult to learn suitable feature information. The detection method based on ensemble learning ensures the diversity of weak classifiers and fully considers each base model in decision making so that the results obtained are better than traditional ones. The machine learning method works well.The method proposed in this paper learns the long-term dependencies between data samples through the self-attention mechanism and convolutional network and transfers them to the ensemble learning model in the form of sample embedding so that the ensemble learning model can more accurately model the process. Therefore, the detection mechanism proposed in this paper is more stable, and the detection effect is not weaker than other integrated learning methods.The analysis of the control group experiment is as follows: introducing a self-attention mechanism in ensemble learning to capture long-term dependencies between data samples can improve the model’s detection performance. After adding the self-attention mechanism, the method in this paper improves the original detection index by 15.37% on average. In the experiments of base learners, our method integrates the detection capabilities of each base learner well. In experiments on different datasets, our method shows good generalization ability and performs well on KDD99 and UNSW-NB15. Since the method in this paper seeks to improve the detection performance, such as introducing a self-attention mechanism, the computational complexity is increased. Therefore, it is not suitable for application scenarios with high response speeds.While SDN technology brings a certain degree of convenience to people, it also brings security risks due to its own design. In order to protect the security of supporting SDN technology, this paper proposes an intrusion detection algorithm, TSMASAM, based on ensemble learning. TSMASAM introduces a self-attention mechanism to capture the correlation between data features to improve the integration effect of the model; TSMASAM achieves the detection and identification of abnormal network traffic by integrating the sample embedding obtained above and the inspection results of the heterogeneous base learner. The purpose is to effectively improve the effect of an integrated detection of abnormal traffic in industrial scenarios using SDN technology.The dataset used in this paper is generated by simulating the SDN network built in the virtual machine environment, and the traffic data of the controller is collected. Therefore, the model in this paper is mainly oriented to the abnormal traffic detection of the controller in the SDN network. The model proposed in this paper increases the training time and running time to a certain extent in order to consider the influence of the base learner on the model. In the real environment, the intrusion detection algorithm also pays attention to timeliness, so the next step is to study how to maintain the algorithm’s performance while shortening the running time.


In remote sensing, the conversion of at-sensor radiance to surface reflectance for each pixel in a scene is an essential component of many analysis tasks. The empirical line method (ELM) is the most used technique among remote sensing practitioners due to its reliability and production of accurate reflectance measurements. However, the at-altitude radiance ratio (AARR), a more recently proposed methodology, is attractive as it allows reflectance conversion to be carried out in real time throughout data collection, does not require calibrated samples of pre-measured reflectance to be placed in scene, and can account for changes in illumination conditions. The benefits of AARR can substantially reduce the level of effort required for collection setup and subsequent data analysis, and provide a means for large-scale automation of remote sensing data collection, even in atypical flight conditions. In this study, an onboard, downwelling irradiance spectrometer integrated onto a small unmanned aircraft system (sUAS) is utilized to characterize the performance of AARR-generated reflectance from hyperspectral radiance data under a variety of challenging illumination conditions. The observed error introduced by AARR is often on par with ELM and acceptable depending on the application requirements and natural variation in the reflectance of the targets of interest. Additionally, a number of radiometric and atmospheric corrections are proposed that could increase the accuracy of the method in future trials, warranting further research. Conversion of recorded radiance imagery to reflectance is essential to the production of accurate and useful information for a variety of remote sensing applications. The empirical line method (ELM) ,2 is theWhile many previous studies have taken a modelling approach in characterizing the performance of AARR, this research focuses on real-world application; a miniature high resolution spectrometer was integrated into a newly designed sUAS-mountable downwelling irradiance sensor, coined the RIT Downwelling Irradiance Sensor (RIT DIS), in order to collect the data required for AARR. The initial version of the RIT DIS has produced encouraging results for AARR with multispectral sensors, where the MicaSense RedEdge-M multispectral camera was used to capture scene radiance . This paWhile ELM was previously shown to slightly outperform AARR , the datThis paper is organized into the following sections; Remote sensing data are captured in raw digital count by the sensor and is then commonly converted to radiance using a variety of radiometric calibration techniques. Reflectance conversion is the next step in the processing chain. It allows practitioners and scientists in a variety of fields to discern further important details about specific materials in the target scene. As mentioned previously, the most popular method to perform this conversion is the empirical line method (ELM), however the at-altitude radiance ratio (AARR) promises to facilitate the data collection process by reducing the number of personnel and on-site equipment required. Therefore, it has the potential to progress the automation of the data collection process for sUAS-based remote sensing.Before exploring these reflectance conversion methods in the following sections, another important element to consider is the bidirectional reflectance distribution function (BRDF) and the impact it has on reflectance measurements. BRDF represents the reflectance factor of a particular material as a function of illumination angle, detector imaging angle and other environmental factors . A portaSince the reflectance factor of materials is a function of illumination angle and detector imaging angle, it must be considered in all reflectance measurements. In sUAS-based remote sensing, the imaging angle is most often at nadir . It should be noted that the field of view (FOV) of the sensor on the platform will have a direct impact on the imaging angle and will cause it to vary for each recorded pixel. The attitude of the platform is also changing throughout the flight as it tries to stabilize, thus the imaging angle is not perfectly nadir across the sensor’s aperture. Yet, the illumination angle of the target surface varies as the solar zenith angle changes throughout the collect. Consequently, it is commonplace for remote sensing reflectance conversion methods to make the assumption that the diffuse reflectance is the main component being measured, ignoring the specular component. This assumption implies that the materials in the target scene have a constrained BRDF, independent of detector imaging angle, which is a property of Lambertian surfaces. Although nearly all materials on the Earth’s surface are not Lambertian, the actual BRDF and an assumed Lambertian BRDF of materials are consistently in agreement when the solar zenith angle is between 30 and 45 degrees and the imaging angle is at zenith ,12,13. TIt is important to note that radiance reflected by the specular component of reflectance must come from a particular direction and might not always be from direct sunlight; they can come from specular reflections from a variety of objects such as leaves blowing in the wind, windows, sand grains and wave facets in the water. Specular reflections from direct sunlight will be much brighter than specular reflections from surrounding objects, but based on the nearly nadir imaging angles typical of sUAS-based sensors and the position of the sun in the sky during these collects, true specular reflections from direct sunlight should not contribute to measured radiance. The specular reflections from other objects in or near the scene can and will affect radiance measured by the sensor, but they should only affect the recorded radiance of certain pixels in the scene, not all, as they are typically less than fraction of a pixel in size. On average, the recorded radiance of a target within the scene across all pixels will be less likely to be affected by these specular reflections, which further explains why the omission of the specular component of reflectance is reasonable.ELM identifies a linear relationship between radiance and reflectance and uses it as a basis for reflectance conversion ,2. This ® panel. The reflectance measurements of the in-scene calibration panels can then be recorded. It is important to note that other reflectance measurements of in-scene calibration panels can be used as a basis for this second approach, such as those measured in a lab setting or on previous collect days; however, it is common practice to take measurements in the field on the same day since they can be substantially influenced by changes in illumination conditions, as discussed in ELM requires a number of calibration panels to be placed in the target scene, and a field spectrometer is used to measure the reflectance of these panels prior to the data collection event. Before measuring the in-scene panels, the field spectrometer is calibrated for reflectance by first measuring the radiance of a white reference panel. This panel must be clean, in pristine condition and have calibrated reflectance measurements supplied by a provider such as Labsphere, Inc.  for a SpectralonWith the reflectance measurements of the in-scene calibration panels in hand, the collection flight can commence. The sUAS-platform must image these panels at least once or at various points during the flight which allows their digital counts or radiance values to be extracted during post processing. Any realistic number of panels of varying spectral reflectance can be used to determine the ELM relationship; commonly one or two panels are used, referred to as 1-point ELM and 2-point ELM, respectively. For 3 or more calibration panels, a line of best fit is applied using an assortment of regression techniques. For this research, 1-point and 2-point ELM are used as references for our experiments as they are currently the preferred methods for reflectance conversion; arbitrary examples are shown in x and y denote the pixel coordinates and For 2-point ELM, two panels are deployed in the field, one dark and the other bright. The relationship for 2-point ELM can then be calculated using Equations –3) [1,2 [1,23) [e sensor .x and y denote the pixel coordinates and For 1-point ELM, only one bright panel is placed in the scene. Since two points are required to compute a linear relationship, Equation  is assumquations  and 5)  bλ from 2-nm-sr. This causes 1-point ELM to overestimate the reflectance of weak reflectors by a maximum of 0.002 reflectance units and underestimate the reflectance of strong reflectors by a maximum of 0.004 reflectance units. Thus, the assumption should not have a substantial impact on ELM-estimated reflectance under clear conditions at typical sUAS altitudes.For 1-point ELM, the assumption of zero path radiance implies that The widespread use of ELM in remote sensing research is easily explained by its reliable performance and relatively low error ,2. HowevAARR, a more recently proposed method for reflectance conversion , is the Equation , in whicSince sUAS-based remote sensing typically operates at low altitudes between 30 and 90 m, the path radiance will contribute negligibly to the total at-sensor radiance . ReconsiEquation , is formEquation  and rearon ρd,λIn practice, AARR can be applied using data collected by a ground-based sensor . HoweverTo make this work, the Equation  is assumThe Nano-Hyperspec and RIT DIS used in this research to collect at-sensor radiance and downwelling irradiance, respectively, were introduced in ®, a cintered polytetrafluoroethylene (PTFE) developed by Labsphere, Inc. in 1986. It has an almost uniform spectral reflectance of ~50% from 400–1500 nm and exhibits Lambertian properties [The Headwall Nano-Hyperspec, a push-broom hyperspectral sensor, was used to capture the scene radiance data for this research. The Nano is calibrated for radiance at Headwall. The camera is depicted in operties . A targeThe RIT DIS, a new sUAS-mounted sensor system, was built to measure and record downwelling irradiance throughout a collection flight. It enables the application of the AARR method with any radiance-calibrated sensor that is responsive in the VIS-NIR range. It was first applied to multispectral imagery , and is The RIT DIS has been lightly modified since its use in the previous multispectral study. The specifications of the main component, the Ocean Insight (formerly Ocean Optics) Flame-S-VIS-NIR-ES spectrometer, are listed in The RIT DIS is mounted to the top of the sUAS at a fixed angle of 4° aft of zenith, which offsets the common forward pitch of the platform during forward flight while collecting data, keeping the center of the cosine corrector’s FOV as close as to zenith as possible. This angle was chosen carefully by analyzing historical attitude data from this specific sUAS. It is important to note that the attitude of the platform can vary significantly during the collect since its stability is affected by a variety of flight conditions. Since the sensor is mounted at a fixed position, the sensor cannot account for these changes in platform attitude. The effects of this design on downwelling irradiance measurements were explored and are documented in The HR-1024i, depicted in As expressed previously, data were collected over three days with varying illumination conditions in order to assess the robustness of each reflectance conversion method. During each day, two flights were completed; one at 30 m and another at 90 m of altitude. All data collection events were completed at the Tait Preserve in Penfield, NY  and Mercury-Neon (HgNe) were used to derive a Gaussian kernel approximation to the blurring inherent to the lower resolution of the Nano-Hyperspec. The spectral response curves of isolated spectral lines in the samples were averaged to determine a simple blur kernel that can be convolved with the downwelling spectrum, in an effort to diminish the effects of the acute spectral features caused by the spectral resolution differences between the two instruments. This kernel is a simple Gaussian fitted to the average spectral response curve, resulting in a full width at half maximum (FWHM) of ~4.5 nm; the effects of this blur are illustrated in This analysis also identified significant errors in the wavelength calibration of the Nano-Hyperspec. To correct this error so that both instruments were in agreement, the same spectral emission lamps were also measured by the RIT DIS. These allowed a wavelength shift parameter to be determined from the wavelength errors of each instrument, which helped increase the accuracy of wavelength matching between them.In practice, this wavelength shift parameter was applied to the upwelling channels, the blur kernel was convolved with the downwelling irradiance spectrum and then the interpolation of the upwelling radiance spectrum to the shifted channels followed. As mentioned, this is a rudimentary approach to this problem, but the derivation of a transfer function between the spectral response of each of the instruments would be the ultimate solution. This has been previously researched for reflectance measurements using the AARR approach when operating instruments with comparable spectral profiles , but neeFollowing interpolation, each line of the upwelling radiance hypercube is matched with the analogous downwelling irradiance spectrum using the timestamps provided by the internal GPS of each instrument, achieving a downwelling irradiance hypercube for each upwelling image. It should be noted that while the Headwall software does produce ortho-rectified imagery, this processing must be done using the raw data; the transfer function for ortho-rectification is not provided to the end-user, limiting the ability for each pixel to be accurately mapped to the downwelling spectrum that was recorded at the same time. AARR can then finally be performed using Equation , which iFor ELM, reflectance imagery is generated by Headwall’s software using 1-point ELM, so additional processing steps were only required for 2-point ELM. This task is initiated by extracting calibration panel data from one of the first Nano-Hyperspec radiance images. The lightest gray panel and the black panel were used as bright and dark targets, respectively. The radiance data for each panel are then averaged spatially, and the corresponding reflectance data measured by the SVC HR-1024i are interpolated to the wavelengths of the Nano-Hyperspec. This does not require a transfer function like the RIT DIS does, since the reflectance spectrum is smooth and slowly varying. Together, these are used to form the 2-point ELM relationship in Equations , which iF-distribution with p and p is the length of the reflectance vector and n is the number of samples.F-distribution. For the purposes of this study, a common selection of p-value drop below 0.05 when Prior to statistical analysis, the measured reflectance of each of the in-scene target panels must be extracted reflectance imagery generated by the methodology described in ate case , beginniEquation  is satisAdditionally, since the Equation .(12)RMSIn this section, the results are separated by each of the three data collection days, labelled by their corresponding sky conditions. Each section describes the conditions during the collect in further detail, indicating important data such as the data and time, solar zenith angle, and cloud types that were present in the sky. There was no precipitation present during any of the collects. The figures show the distributions of the sampled reflectance using each conversion method with separate figures for each in-scene panel, and indicate those distributions using a 2-standard deviation envelope. The three tables in each section present both the As mentioned previously in The environmental conditions for data collection were not ideal; the non-uniform dispersion of light cloud layers in the atmosphere are typically undesirable. These conditions are presented visually in The data collected on this day represented atypical conditions for remote sensing as a variety of cloud layers were present, as depicted in Interestingly, all three methods seemed to struggle in measuring the reflectance of the red, blue and light gray panels, while they all observed an average RMSE of ±0.022 amongst the other four.Conditions during the final data collection for this paper presented mainly diffuse illumination on account of the uniformity of the overcast sky, presented in ® or other panels with consistent BRDFs is suggested for following studies.While the results of the Examining the  studies . CertainFurther, the observed RMSEs for AARR were on par with ELM, and are oftentimes lower depending on the panel in question. For most cases, except for a select few for the red and light gray panels, the RMSE of AARR reflectance measurements are below ±0.10, with a large majority below ±0.050 and many below ±0.020. This is well within the reflectance variation of many materials commonly analyzed in remote sensing such as vegetation [As discussed in The radiometric calibration of the spectrometer used in the RIT DIS can be improved, as the effects of non-linearity, stray light, and exposure time have been left largely uncorrected. Non-linearity, dark current and exposure time effects could be corrected through photon transfer curve characterization, and will have a significant impact on the accuracy of the detector. Changes in detector behaviour with temperature should be analyzed and corrected for, as it will also have underlying impacts on the radiometric calibration. Further, atmospheric corrections can be developed both physics-based approaches  and thosAdditionally, there are areas outside of downwelling measurement that could help improve the performance of AARR. The Nano-Hyperspec used in this study will be better calibrated for radiance, as there is a tremendous amount of ongoing work to characterize every aspect of the sensor, including flat-fielding and smile correction. As discussed in In summary, the observed error introduced by the AARR method was shown to be on par with ELM and acceptable depending on the application requirements and natural variation in the reflectance of the targets of interest. The vastness of areas for improvement for the AARR technique is compelling for future research, which is important for sUAS-based remote sensing since it can substantially reduce the level of effort required for collection setup and subsequent data analysis, and provide a means for large-scale automation of remote sensing data collection in all flight conditions.


Fog computing is an extension of cloud computing that provides computing services closer to user end-devices at the network edge. One of the challenging topics in fog networks is the placement of tasks on fog nodes to obtain the best performance and resource usage. The process of mapping tasks for resource-constrained devices is known as the service or fog application placement problem . The highly dynamic fog infrastructures with mobile user end-devices and constantly changing fog nodes resources  require distributed/decentralized service placement (orchestration) algorithms to ensure better resilience, scalability, and optimal real-time performance. However, recently proposed service placement algorithms rarely support user end-device mobility, constantly changing the resource availability of fog nodes and the ability to recover from fog node failures at the same time. In this article, we propose a distributed agent-based orchestrator model capable of flexible service provisioning in a dynamic fog computing environment by considering the constraints on the central processing unit (CPU), memory, battery level, and security level of fog nodes. Distributing the decision-making to multiple orchestrator fog nodes instead of relying on the mapping of a single central entity helps to spread the load and increase scalability and, most importantly, resilience. The prototype system based on the proposed orchestrator model was implemented and tested with real hardware. The results show that the proposed model is efficient in terms of response latency and computational overhead, which are minimal compared to the placement algorithm itself. The research confirms that the proposed orchestrator approach is suitable for various fog network applications when scalability, mobility, and fault tolerance must be guaranteed. Fog computing is an extension of cloud computing that uses heterogeneous and geographically distributed fog nodes to provide services and computation closer to data-generating user end-devices, thereby reducing the amount of data forwarded to the cloud, and minimizing bandwidth requirements and request-response time. According to the OpenFog Reference Architecture for Fog Computing , fog comLayered architecture is the most common representation of the fog computing paradigm. Specifically, a three-layer architecture is often used to represent a fog computing infrastructure , as showAlthough the number of fog computing layers varies in different publications, it can be seen that Internet of Things (IoT) and Cloud layers are present in all of them, while the main differences are manifested as different ways of structuring the Fog Layer . As geneIt is geographically distributed and produces data that are forwarded to the fog layer for processing and storage. The fog layer consists of various fog node devices that are able to process and store user requests. These devices, fixed (static) or mobile, are connected to the cloud servers and can send requests to data centers. The cloud layer consists of several data centers, which are able to perform complex computations and store a large amount of data .The following six characteristics are essential in distinguishing fog computing from other computing paradigms : low latFog architectures should ensure that processing of data generated by end-devices is done at the most suitable fog nodes, depending on various application requirements  and fog nodes constraints. Since fog nodes are distributed and heterogeneous in nature, it is a challenge to select a fog node where the data processing application should be placed for best performance and resource usage. Therefore, fog computing requires new methodologies for fog application placement optimization, dealing with limited hardware resources and large-scale deployments, unstable connectivity and platform/operators heterogeneity, and (node or link) failures . MoreoveThis article is organized as follows. According to recent systematic literature reviews ,3,11,12,The service placement decision can be made offline or online. The offline approach considers that all system requirements and constraints are known beforehand, while online placement decisions are made during the run-time of the system. As noticed in , in mostThe dynamicity of the system can be two-fold: the dynamicity of fog infrastructure and the dynamicity of applications. As described in , fog netFinally, one of the most important features of the fog network, which should be addressed by the application placement approach, is user end-nodes and fog nodes mobility. As concluded in , mobilitDespite the huge number of research papers that solve the aforementioned problems related to service and application placement, only a few of them support fully distributed, dynamic, mobility-supporting, and resilient service placement at the same time.Some recent studies propose the dynamic placement of services, thereby supporting mobility of end-users, but use centralized or clustered (not fully distributed) approaches like Yousefpour et al. , Mahmud The other studies, including Lee et al. , Aral anAs concluded in the review , only a To our knowledge, the most similar works include Casadei and Viroli , which pThe research presented in this paper contributes to the field of resilient service placement in dynamic fog environments. We propose a distributed service placement approach and an orchestration model that includes distributed decision-making agents in every fog node, which is capable of flexible service provisioning and is resilient to fog node failures. The proposed architecture was implemented as a prototype system, which continuously monitors the incoming tasks and the state of fog nodes to make placement decisions in real-time. Unlike the majority of prior works that rely on a central control entity, we propose the distributed orchestration model using orchestrator agents in every fog node. The service placement or migration decision is made at the nearest fog node, thereby considering the state of other fog nodes. This enables proper decision making, saves energy, reduces delay, and improves the overall performance of the system. Furthermore, our proposed architecture is resilient to fog node failures by continuously monitoring the state of each fog node and migrating/restoring services to healthy fog nodes.This work evaluates the efficiency of the proposed model and its ability to deal with constantly changing computational resources, fog node failures, end-node mobility, and withdrawal. The effectiveness of the approach is evaluated in terms of response time latency (delay) and computational overhead, which are influenced by the inter-agent communications of the distributed orchestrator.In this section, we discuss the goals of designing an agent-based distributed service placement approach and describe the architecture of the whole system.The design of the proposed architecture is based on three key objectives. The first objective is to improve the computational performance and power efficiency of the fog node devices by efficiently distributing tasks (services) among them. The second is to react to the rapid changes in the fog environment due to the moving user end-devices and constantly changing resources of the fog nodes, including complete failures of the fog nodes. The third is to discover the available resources of nearby fog nodes or the cloud automatically and use them to allocate tasks when required.Each fog node can communicate with other nearby fog nodes using various types of wireless or even wired communications such as Wi-Fi, Bluetooth, ZigBee, Ethernet, etc. Fog nodes can be directly connected to the Internet and Cloud network, though this is not a necessary requirement. Furthermore, fog nodes are heterogeneous and can have different computational resources, including CPU utilization levels, memory utilization levels, battery levels (energy), and security levels.Each fog node in this system can be used to perform a set of predefined tasks (services) requested by the user end-devices, as shown in The fog node (task receiver) identifies the type of task and the resources needed for such task.The fog node checks the free resources left in each neighboring fog node (including itself) that can serve the request and calculates the best placement for the requested service, using the combinatorial placement algorithm presented in our previous work .The task is then placed on one of the fog nodes by activating the appropriate service needed for the user end-device. The service might be activated at the task receiver node itself or in one of the nearby nodes or the cloud. If the service is not yet installed at the selected fog node, then it is downloaded from the cloud.All fog nodes broadcast their available computational resources to their neighbors, so each node always knows how many resources are left in the remaining infrastructure. When a user end-device requests the nearest fog node to perform a task (makes a service request), the following steps are performed:The resources of any fog node changed significantly. These can include changes in the battery level and the security level. In such a case, the fog node with depleted battery or lower than required security level  initiates a new placement calculation procedure, and currently active services are relocated and distributed to the neighboring fog nodes to ensure system stability and resilience.The fog node unexpectedly fails and cannot provide any services it is currently running. This situation is detected by other fog nodes based on missing heartbeat messages from the failed node. The remaining fog nodes update their tables of available computational resources by removing resources of the failed fog node. When user end-devices will request unavailable services again (after predefined timeout), the healthy fog nodes will be aware of missing neighbor and will calculate the best placement using the remaining resources.The end-device moves between fog nodes, which results in disconnection from one fog node and connection to another fog node. If the end-device disconnects from the fog node, the latter detects this after some timeout, stops the unnecessary service(s), thereby freeing related resources, and informs other fog nodes about this. When the end-device requests services from the other nearby fog node on its movement path, this fog node already knows how many resources are left in the whole network and can find service placement for the new request.The already placed services can be relocated (activated/deactivated) to different fog nodes depending on the changes of hosting fog node computational resources, fog node failures, and the changes in the position of the user end-devices. Basically, there are few events that can initiate a new placement procedure:All aforementioned actions are performed by the Orchestrator components, which are deployed on every fog node. The orchestrator consists of software agents, including the Synchronization Agent, Decision Making Agent, Resource Monitoring Agent, Request Processing Agent, and Deployment Agent, as depicted in The Resource Monitoring Agent constantly monitors local computational resource changes and notifies the Decision Making Agent and the Synchronization Agent if this has occurred. The Decision Making Agent stores a resource table for all fog nodes and keeps it up-to-date with notifications from the Monitoring and Synchronization Agents. When resource-related changes occur . The disconnection is detected by the service(s), currently provided to the End-Device ;send remote deployment requests when needed.All coordination between fog nodes is performed by the Synchronization Agents, which:The service placement algorithm considers the properties and constraints of all the components of the fog network to make the appropriate service placement decision. These include node properties, network properties, service (task) properties, and user application requirements.(1) Node properties:Each fog node in our model at any given time can have different capacities of computational resources, which include a CPU utilization level from 1–100%, a random access memory (RAM) utilization level from 1–100%, a remaining battery level from 1–100%, and a level of security from 1–3, which describes how strong the data and communications protected in a particular fog node are.(2) Network properties:The service placement decision must take into account the ability of the end-user device to connect to the fog node(s) where the service(s) will be placed. Since fog nodes are distributed for better coverage of the area, the placement algorithm does not consider fog nodes that are too far away from the user end-device. This constraint reduces the complexity of the placement calculations and decreases latency.(3) Service properties:Each service demands some resources, provided by fog nodes: CPU, RAM, and security level. The service requirements are predefined and each fog node knows these requirements for each possible service. If the user end-device requests an unknown service, the fog node uses cloud services to get all service-related data.The service placement algorithm must ensure that the service that is being deployed will have enough resources and its deployment will not negatively affect already deployed services.n available services in k fog nodes according to the given constraints and conditions. In this method, the QoS parameters of the The service placement optimization method used in this research was proposed in . This muThe main objective of the optimization process is to find the service placement The objective functions may include the overall security of the whole system ented in ).The objective functions contradict each other, and usually there is no single solution that minimizes all the objective functions at the same time. To solve this problem, the two-stage optimization process presented in The Integer Multi-Objective Particle Swarm Optimization (IMOPSO) process is used to find a Pareto set of solutions to the problem. The members of this set are non-dominated service placements, meaning that each of them is better than all the other placements by at least one objective function.The second step uses the Analytical Hierarchy Process (AHP)  to choosGenerate Initialize Pareto set: Assign the initial velocities Initialize the global best position and the global best score: Assign the initial best scores and best positions to all particles: a.Calculate the values of each objective function: b.If c.If d.If e.If f.Calculate new velocity: g.Update the position of the particle: h.If the particle is out of the range: With each particle Analyze set If maximum number of iterations is not reached go to step 6.The set The pseudo code of the IMOPSO method is presented below:n—total number of services; k—total number of available fog nodes; m—total number of evaluation criteria (objective functions); r—number of particles used in IMOPSO algorithm; n services among k fog nodes (position of the These are the meanings of the main notations: Load a Construct the weight coefficient matrix With each criterion (objective function) Use AHP decision-making method and get alternative priority vector Choose the best service distribution alternative The pseudo code of the AHP process is presented below:R; The main notations used in this pseudo code are: A more detailed description of the service placement optimization algorithm with an explanation of the meanings of all notations is presented in . In thisWe used a two-fold approach to evaluate the properties of the proposed agent-based orchestrator model. Real hardware was used to evaluate the real-world characteristics of the building blocks of the proposed model, i.e., the performance of the communication protocols, capabilities of the underlying hardware, etc., was measured using prototype hardware sensors and actuators. On the other hand, it is very difficult to use real hardware while evaluating the scaling properties of the model and trying to reproduce results in order to repeat the exact same experiments with different parameters. To achieve repeatability, we implemented the proposed model as a multi-agent system in which all agents act exactly the same as they would in the real system, but they are not interacting with real hardware. In this system, external events, measurements of environment parameters, and activations of end devices are emulated by special agents. All the performance characteristics emulated during the experiment were measured using real hardware devices in order to bring the overall model performance evaluation results as close as possible to the reality.The hardware setup used for the experimental evaluation is presented in Two fog nodes were implemented using the Raspberry Pi 4 Model B computers with 4 GB of RAM. They were running Raspbian 10 OS and JDK 1.8.0_333. The multi-agent system was implemented using the Jade 4.5 library. To evaluate the scalability of the proposed model in some experiments, the role of fog nodes was performed by a personal computer equipped with Intel Core i5-4570 CPU with 16 GB of RAM and running Windows 10 Pro OS with JDK 1.8.0_333. All fog nodes were connected to the local area network (LAN) using a gigabit Ethernet switch integrated into the wireless router. The prototypes of the end-devices were implemented using ESP8266 microcontrollers, which used integrated Wi-Fi capabilities to connect directly to the same wireless router. All communications between sensors and actuators are performed using the TCP/IP protocol and through the corresponding agents running inside the fog nodes.The main objective of the experimental evaluation was to find out how the nature of the multi-agent system, i.e., lots of small agents performing single dedicated roles and communicating with each other, may impact the overall performance of the proposed model. Two scenarios, described in To register the latency of the agent-based system, a special GlobalTimer class was developed. This class uses the System.nanoTime method and stores the time and additional related information on the events. To reduce the impact of the log subsystem on the system performance, all log information is stored only in RAM and printed to the console only after the explicit request. The logging class can provide reports of all recorded events grouped by the involved agent’s name, the instance of related event, and the system time. Each agent in the system uses the same instance of GlobalTimer class and logs all significant events, such as creation and death of the agent, times when each inter-agent communication message was received and sent, and times when the important cycles of agent life  were started and finished. Using the logging object, it is possible to view how one concrete event was processed by the agents, which agents were involved in the process, how long it took to perform various tasks inside the agents, etc.Agent communications within fog nodes and simple decisions made by them  n-dimensional paraboloids as the objective functions: n-dimensional paraboloids, i.e., n-dimensions, all the properties are known, the minima point is at their vertices, and they are easy enough to calculate, which corresponds to the real objective functions . To make the Pareto set not trivial in each experiment, depending on the dimensions of the area of the definition of the problem, different vertices were used for each objective function, i.e., Additional experiments were carried out to determine the performance characteristics of the service placement solving algorithm, as it has the greatest impact on the overall performance of the agent-based orchestrator architecture. The service placement finding method was fully implemented in Java and integrated into the Decision Making Agent. To make the service placement problem more deterministic, we used a set of four (The judgment matrix was chosen in such a way that one of the objectives was of very high importance compared to the other ones (consistency ratio of the matrix is 4.5%):Therefore, it is possible to predict the result of the optimization process and to be able to evaluate the accuracy of the optimization results. The IMOPSO method uses initial random places for all particles, so the optimization process may slightly vary in performance each time. We used 20 attempts for each set of parameters and calculated the average performance values. The computational time of the service placement optimization method has a linear dependence on the initial number of particles and the number of epochs.The computational time of the service placement optimization method has a linear dependence on the complexity of the problem in terms of the number of fog nodes and services. The IMOPSO part of the optimization process is linear by design, and the experimental results confirm that the AHP part also scales linearly with the increase in the complexity of the problem. This trend was observed by finding the recommended parameters of the optimization process for each step of complexity of the problem  shows that it is not feasible to use this method to solve problems with more than a few tens of services.The most significant observations emerging from these experiments are the following:The experimental evaluation of the prototype system, based on the proposed distributed orchestrator model, shows low latency and small computational overhead, which are caused by the synchronization and communication part of the orchestrator algorithm. Even the Raspberry Pi 4-based fog nodes are capable of quickly deploying or redeploying the needed services once the placement calculations are made by the placement optimization algorithm. For example, the delay caused by agent intercommunications, stopping of three services, and starting of these services in a new fog node takes less than 55 ms on Raspberry Pi 4 computers. Moreover, this time is very little dependent on CPU and RAM capabilities of fog nodes, as very similar results are achieved when using a more capable PC.The results of the study show that the selected service placement algorithm generates the highest computational overhead , which is even greater when the number of deployed services and the number of suitable-for-deployment fog nodes are high. However, experimental results show that if an average PC is used for computations, then the placement problem with up to 30 services and 10 fog nodes is solved in less than 1 s, which is fully acceptable in real-life conditions. Moreover, the optimization algorithm scales linearly with increasing complexity of the situation, making it possible to use it in more complex situations with more powerful hardware.However, in real conditions, the numbers of services and fog nodes are quite low, since fog nodes are distributed evenly and widely to cover a larger area, and user-demanded services can be deployed only in the few neighboring nodes. Even if this is not the case, our proposed orchestrator model is suitable for incorporating any kind of optimization algorithm, which improves the overall performance even more.The experimental results show that even using the fully distributed orchestrator approach, the performance of the proposed system is in line with the proposals, based on the centralized or federated approach. Furthermore, the resilience in our model is much higher because any fog node can make service placement decisions. This means that the system is fully resilient to any fog node failure regardless of the number of healthy fog nodes. In contrast, federated  and espThe main advantage of the proposed distributed orchestrator model is its resilience to fog node failures and support for user mobility (through redistribution of the services) and ability to adapt to the constantly changing computational resources of fog nodes (through constant resource monitoring and synchronization between fog nodes) with minimal computational overhead. Our model could be applied to a wide range of low-intensity IoT applications with mobile users and no hard real-time constraints, e.g., person-oriented applications using body array networks (BAN) and personal area networks (PAN), smart home and environments monitoring and control applications, etc.In order to apply the proposed model for larger fog networks with more strict response requirements, future studies should aim to select a faster service placement optimization algorithm, of which complexity plays a major role in the overall placement latency.This paper proposes a distributed agent-based orchestrator model for fog computing. This model is based on orchestrator agents, which are distributed across all fog nodes and make service placement decisions, considering the CPU, memory, battery, and security constraints of fog nodes and the requirements of user applications.The major contribution of our work is the novel orchestration model, which, unlike the majority of prior works, does not rely on a central control entity. Since there is no central decision-making entity, service(s) placement decision is made by the closest to the user orchestrator agent, which knows how many resources are left in the fog network via agent synchronization and monitoring mechanism. Such an approach enables scalability and resilience to fog node failures, as well as supporting the mobility of the end-users and adaptability to the constantly changing computational resources .This work also revealed that the greatest impact on performance is the efficiency of the service placement optimization algorithm, while the communication and synchronization overhead plays only an insignificant role. Therefore, more research should focus on finding a more efficient optimization algorithm for service placement.


A dense single-node 3D seismic survey has been carried out around the Scrovegni Chapel in Padua , in order to give new insights about the archaeological setting of the area. The survey made use of nearly 1500 vertical nodes deployed over two rectangular grids. 38 shot positions were fired all around the two receiver patches. The fundamental mode Rayleigh wave signal is here analysed: traveltimes are directly inferred from the signal phases, and phase velocity maps are obtained using Eikonal tomography. Also surface wave amplitudes are used, to produce autospectrum gradient maps. The joint analysis of phase velocity and autospectrum gradient allowed the identification of several buried features, among which possible remains of radial walls of the adjacent Roman amphitheater, structures belonging to a medieval convent, and the root area of an eradicated tree. Finally, depth inversion of 1D dispersion curves allowed the reconstruction of a quasi-3D shear-wave velocity model. The Chapel stands on the remains of a Roman amphitheater. In particular, the building has two wholly separated levels, one above and the other below the surface (hypogeum). Below the main facade, the south-western wall of the hypogeum corresponds to one of the elliptical walls of the Roman amphitheater. During the centuries, the construction of different buildings and their buried remains affected this complex archaeological area, as evidenced by historical documents2. From an archaeological point of view, several excavations carried out between 1880 and 2013 brought to light different elements, such as a system of corridors among three concentric elliptical walls, a possible gallery running along the minor axis of the amphitheater, the remains of about 3 m thick concrete slab foundation and a few standing radial walls on the south-western side. No evidence of expected galleries along the central axis of the amphitheater has ever been found, although their existence has been hypothesized, and no new remains of radial walls in other locations confirm their supposed orientation, apparently tilted with respect to the longitudinal walls of the Chapel1.Today, we only have poor elements to thoroughly understand the exact role of the hypogeum in the Chapel and its relation with the buried undiscovered remains of the Roman amphitheater, among other most recent remains distributed in this area6 or low-frequency seismic ambient noise10. SWT applied to small-scale near-surface data is recently gaining popularity, because of its capability to resolve shallow lateral velocity variations. In most cases, small scale applications make use of active (controlled-source) data17 or passive records20 originally acquired for exploration purposes, since they guarantee a dense receiver coverage. Small scale applications specifically designed for SWT are very rare24. More specifically, at the time of this writing, no SWT study has ever been carried out for archaeological prospection, where the required resolution is metric to sub-metric.This study aims at giving new insights into the knowledge of this area, with a specific focus on the buried remains of the Roman amphitheater. This objective is here achieved through the analysis of seismic surface waves, in particular by applying surface wave traveltime tomography to a dense 3D seismic dataset. Surface wave tomography (SWT) is a widespread technique, originally used in global seismology to image crustal and upper mantle structures. At that large scale, the active sources used for the inversion are natural seismic eventsIn this study we perform a 3D dense single-node seismic survey, including both active and passive measurements, with the main purpose of analysing the Rayleigh wave propagation. In particular, this piece of work focuses on the analysis of the active seismic data. Both surface-wave phases and amplitudes are processed in parallel, since their joint analysis is capable of imaging both smooth and sharp lateral velocity variations. Depth inversion of local dispersion curves is finally applied to derive a high-resolution quasi-3D shear-wave velocity model of the near surface, to be used for archaeological interpretation.25  brings to systematic errors that add up to the estimated uncertainties.The analysis of surface-wave phases and amplitudes described in the section “26  and amplitude . For this reason, the joint analysis of phase velocity and autospectrum gradient maps is a critical step for a correct interpretation of the results. The quasi-3D The present study is successful in imaging small-scale structures at the archaeological scale with the analysis of surface waves only. This has been possible thanks to a very dense 3D single-node acquisition, which allowed a regular and fine sampling of the wave propagation, overcoming the limitations due to the high level of noise in the data. In fact, the a posteriori tests on resolution  collapsed and/or it could have been filled with dense material, indiscernible from the outside material. Linear structures were found outside the amphitheater on the southern-eastern side, oriented as the supposed direction of radial walls. It is plausible that these structures are remains of partially-preserved radial walls, or remains of later structures of which we do not have any record.Finally, some unexpected features, only partially connected to the archaeological character of this survey, were found. The low-velocity zone imaged inside the amphitheater could be hardly attributed to a structure of the amphitheater, both because of its size and position. Instead, the presence of a former pond in that area is confirmed by historical pictures dating back to the early twentieth century, when the interior of the amphitheater was a garden. The sharply defined low-velocity anomaly outside the amphitheater wall, undoubtedly attributed to the root zone of an eradicated tree, is extremely clear both in terms of shape and phase-velocity/autospectrum gradient amplitudes, in spite of its limited size. This confirms the strong potential for surface wave analysis to detect small-size shallow velocity anomalies, suggesting the opportunity to use this methodology in different fields, such as civil engineering, soil science and biology.17. Traveltimes for different frequencies are directly extracted from the phase of the surface wave signal, taking advantage of the fine sampling in both spatial directions. However, the phase distribution needs to be representative of only one mode of propagation . More generally, any coherent noise  needs to be removed from the signal, since it perturbs both phase and amplitude variations with offset, generating periodic patterns, as explained in detail by Barone et al.29.The processing scheme of the raw 3D data is similar to the one proposed by Barone et al.Preliminary analyses on the recorded dataset are needed to study the characteristics of the signal, including the frequency content of Rayleigh waves, the presence of higher order modes and/or of coherent noise and the degree of heterogeneity in the two survey areas, and to identify the LMO velocities used for the correction in the 3D processing  correction to raw traces to (i) reduce phase jumps related to the 29. This operation is repeated for each shot point, and phase velocities are finally computed as the inverse of the average phase slownesses over the different shots. This tomography method is very fast since it does not involve a true inversion . Moreover, the regular receiver geometry of our dataset is perfectly suitable for this method, since it makes unnecessary the most critical and user-dependent step of Eikonal Tomography, which is the 2D traveltime interpolation over a regular grid. Another relevant aspect of Eikonal tomography is that it does not require absolute traveltimes , but it only takes into account traveltime differences between adjacent receivers (for this reason we used relative traveltime maps). Finally, this method permits the extraction of velocity standard deviations, which represent an estimate of the error for the obtained velocities. On the other side, Eikonal tomography is very sensitive to outliers17. For this reason, we performed a statistical analysis to identify and remove outliers from single-shot phase velocity maps: velocity values outside the range from spatial mean plus/minus three times the standard deviation have been discarded.The traveltime maps for one frequency and all shots are input into an Eikonal Tomography scheme to extract phase velocity maps. Eikonal tomography derives phase slownesses at one frequency from the traveltime gradient magnitude30. For this reason, the obtainable lateral resolution is intrinsically limited by the signal wavelength which, for the lower frequencies, is much higher than the sensor spacing. The analysis of signal amplitudes may help detect sudden lateral velocity variations caused by small size objects or discontinuities31. Several methods to measure amplitude variations are available. We focused on the autospectrum method32, which analyses the autospectral density at each frequency G(f), defined as:Y and A are the complex spectrum and amplitude spectrum of a seismic record, respectively. The autospectral density is a measure of the surface wave energy for a certain frequency: significant spatial variations of this parameter could indicate the presence of a scatterer (negative variation) or an amplifying zone (positive variation)31. For this reason, this study focuses on the analysis of autospectrum gradient maps. The procedure followed here consists in the retrieval of autospectral density maps for all shots, which are normalized by their maximum value. Then, the autospectrum gradient is computed, and an average is computed from the gradient magnitude maps from different shots. From this analysis we obtain a robust estimate of the energy spatial variations, even though the positive/negative sign of the anomalies is not preserved. Traces included in the analysis should not be filtered, because they should include backscattering. Moreover, geometrical spreading effects should be removed before Fourier transformation, and the near-offset region should be excluded as for the phase velocity analysis.The analysis described so far was mainly focused on the signal phase, with the purpose of extracting velocity information. However, most tomography approaches, including Eikonal tomography, are based on the high frequency approximation33, that uses the neighborhood algorithm34. All details about the model space parameterization are given in the supplementary information (Supplementary Text Depth inversion is necessary to produce a quasi-3D shear-wave velocity model of the near-surface. In order to ensure inversion stability, phase velocity maps were initially smoothed with a Gaussian filter. The length of the filter is frequency-dependent: we used Supplementary Information.