Staircases are some of the most common building structures in urban environments. Stair detection is an important task for various applications, including the environmental perception of exoskeleton robots, humanoid robots, and rescue robots and the navigation of visually impaired people. Most existing stair detection algorithms have difficulty dealing with the diversity of stair structure materials, extreme light and serious occlusion. Inspired by human perception, we propose an end-to-end method based on deep learning. Specifically, we treat the process of stair line detection as a multitask involving coarse-grained semantic segmentation and object detection. The input images are divided into cells, and a simple neural network is used to judge whether each cell contains stair lines. For cells containing stair lines, the locations of the stair lines relative to each cell are regressed. Extensive experiments on our dataset show that our method can achieve 81.49 For the first type of method, the staircase is defined as a collection of parallel lines. Lines are extracted by applying Canny edge detection, Hough transform and other traditional computer vision algorithms in RGB images or depth images. For the second type of method, the staircase is defined as a collection of parallel planes in three-dimensional space, and the planes are extracted by applying a plane segmentation algorithm to the point cloud data.With a long research history in computer vision, stair detection is a fundamental problem and has a wide range of applications. Two kinds of mainstream methods are available for stair detection: line extraction methods10. After performing line extraction, the features of the stairs need to be matched. Khaliluzzaman et al.11 regard the endpoints of the stairs as three line segments converging at one point for feature extraction. Carbonara and Guaragnella12 creatively regard the stair structure as a periodic signal in the spatial domain, and its period is the distance between two continuous edges. Then, the 2D fast Fourier transform (FFT) is applied to transform the observed signal to the frequency domain to obtain an image that contains only the edges of stairs. Khaliluzzaman et al.13 propose a framework based on a unique geometrical feature of a stair. The unique geometrical feature is that every step’s height gradually decreases from the bottom to the top of the staircase. Huang and Tang14 propose a method to identify stairs by using the statistical properties of projection histogram. Relatively few deep learning computer vision methods are available for stair detection. The main idea of these methods is to extract the region of interest (ROI) containing stairs in the input image through object detection algorithms such as Yolo15 and RCNN16. Then, the traditional computer vision methods are applied to extract lines in the ROI18. These methods divide the stair detection task into two steps, which makes it difficult to ensure real-time performance. In addition, some classification methods utilize deep learning to determine whether an image contains stairs and whether the ROI is upstairs or downstairs19. Such a classification method does not achieve pixel-level stair localization. It can only be used to provide voice reminders for visually impaired people and not for robot environment perception.Line extraction methods mainly include traditional computer vision methods and deep learning methods. The main idea of traditional methods is to use visual clues obtained through edge detection algorithms such as the Canny algorithm, the Sobel filter and line detection algorithms such as the Hough transform21. Sinha et al.22 present an algorithm for stair detection from point clouds based on a new minimal 3D map representation and the estimation of step-like features that are grouped based on adjacency in order to emerge dominant staircase structures. Oh and Choi23 propose a stair plane extraction algorithm based on supervoxel clustering. Tang et al.24 use the random sample consensus (RANSAC) algorithm to extract planes and then model the corresponding stairs. Stahlschmidt et al.25 obtain the ground plane through the analysis and processing of point clouds and then detect a group of continuous rising planes as stair features.The main idea of plane extraction method is to extract potential planes from the input point clouds and filter the planes belonging to stairs in a certain way. Point cloud segmentation is a common plane extraction method, and many methods have been developed for stair feature matching. Classifying planes by obtaining their normal vector and eliminating the planes that do not belong to the stairs is a common methodThese two types of methods have long provided relatively reliable stair detection abilities for robots used in urban environments and for visually impaired people. However, there are still some important and challenging problems to be addressed.1 directly discard the regions without stairs in the input images by a priori ROI. Oh and Choi23 directly remove large planes to reduce the number of input point clouds when scanning the environment.The working environment of stair detection determines that a related algorithm usually runs on some small embedded devices. This requires an extremely low computational cost to achieve better real-time performance. To solve this problem, the method used by most algorithms is reducing the input data, namely, reducing the size of the input image or the number of three-dimensional point clouds. For example, Shahrabadi et al.The working nature of stair detection determines that a related algorithm should have high reliability and accuracy. The most challenging problem with stair detection is the adaptability of an algorithm to deal with extreme lighting conditions, special structures, severe occlusions and special materials, as shown in Fig. . For the method based on line extraction, these problems will be fatal. The reason for this is that the limitations of traditional computer vision based artificial feature extraction make the algorithms difficult to adapt to complex and changeable scenarios. For the method based on plane extraction, the acquisition of point clouds depends on light detection and ranging (LiDAR) or depth cameras, which are not affected by stair texture features and lighting conditions. However, LiDAR and binocular sensors are often expensive and still cannot solve the problem of severe occlusion.In addition, when detecting objects with texture features and structures that are similar to stairs, these objects are often misidentified as stairs. The reason for this is that an algorithm based on feature extraction cannot obtain high-level semantic information about stairs like humans, which often leads to false detection and missed detection.26 established the dominant position of convolutional neural networks (CNNs) in computer vision in 2012, CNNs have rapidly developed in various fields of computer vision due to their strong learning abilities and unique perception modes. The reason for introducing CNNs into stair detection is that an artificial neural network can learn the texture features and high-level semantic information of stairs simultaneously and obtain better robustness by learning dataset that contain various detection scenarios.With the above motivations, we propose an end-to-end method based on deep learning that has an extremely fast speed and solves the problem regarding adaptability to different scenarios with monocular vision. Since AlexNetOur method is also based on line extraction. The network input is a RGB image obtained from visual sensor, and the output is a group of extracted stair lines. All the intermediate processes are computed within the neural network. The key problem of designing this neural network is the representation of staircase features. Approximately two schemes are available for this purpose. (1) The stair detection task is regarded as a semantic segmentation task. The pixels belonging to the stair lines can be taken as positive samples, and the background pixels can be taken as negative samples. However, because the stair lines are usually very thin, the numbers of positive and negative samples will be seriously unbalanced, and the network may have difficulty converging. Additionally, the semantic segmentation framework usually incurs a high computational cost. (2) The stair detection task is regarded as an object detection task, and each stair line is given an external rectangular box. It is easy to know that most boxes will be narrow and long. After experiments, we find that the object detection network has difficulty learning the features of stair lines.27 in the network to improve its perception ability in scenarios without visual clues, such as scenarios with occlusion and scenarios with extreme lighting.After comprehensively considering these two schemes, we propose a feature representation method of coarse-grained semantic segmentation combined with object detection, as shown in Fig. 28 module by applying group dilated convolution with different dilation rates in the horizontal and vertical directions, and a channel attention mechanism is also applied so that the network can learn to extract long-range information features with different aspect ratios.In summary, the contribution of this work can be summarized in three parts: (1) we provide a stair dataset with fine annotations for stair detection research. The training set contains 2670 images, and the validation set contains 424 images. Each label contains the locations of the two endpoints and the classification (convex/concave) of each stair line. (2) We propose a novel stair detection method based on line extraction. To the best of our knowledge, this is the first deep learning-based end-to-end stair detection network . Compared with the line extraction method based on traditional computer vision, our approach not only achieves extremely fast detection speed but also solves the problem regarding the difficulty of detecting staircases in challenging scenarios. (3) We design a module based on dilated convolution and group convolution. Specifically, we build an inceptionIn this section, we describe the details of our method, including the network architecture of StairNet and the group dilated convolution with different dilation rates in the horizontal and vertical directions. Finally, we briefly introduce the design of the employed loss function.As described above, we propose a feature representation method involving coarse-grained semantic segmentation combined with object detection for stair detection. Our model takes a 512 29 to judge whether the cell contains convex lines and whether the cell contains concave lines.The output target values for the classification branch are stored in a 3D tensor of size 64 The output target values for the location branch are stored in a 3D tensor of size 64 30, several squeeze-and-excitation (SE)-ResNeXt blocks32 with dilated convolution and an ASPP module. Each part is introduced in detail below:As shown in Fig. 33. A convolution with stride >1 can extract features while downsampling. Tensor slicing takes a value every other pixel in the feature map and four feature maps are obtained, then the divided feature maps are concatenated together and the number of channels becomes four times of the original layers. Finally a 1 34.In the shallow calculation process of a deep neural network, due to the large size of the input image, downsampling is usually required. Common downsampling methods include pooling, convolution with stride >1 and tensor slicing. Pooling causes the loss of details due to the reduction in resolution, which is unfavorable for segmentation32, we add the channel attention mechanism of SENet and apply dilated convolution to obtain larger receptive field. ResNeXt32 uses standard group convolution in its bottlenecks, and the group convolution can reduce the computational cost and achieve higher accuracy than ResNet35. For the details of our bottleneck, see Section “Dilated group convolution with different dilation rates”. Inspired by ENet36, standard SE-ResNeXt blocks and SE-ResNeXt blocks with dilated convolution are connected in series to form our backbone. See Table Based on the ResNeXt block27 applies dilated convolution with dilation rates of , ,  and  to extract features in parallel to capture object and image context information at multiple scales. We apply a module similar to the ASPP module and replace the dilation rates with , ,  and . The reason for applying dilated convolution with different dilation rates in the horizontal and vertical directions is that the distributions of stair lines in the dataset are usually transverse. Therefore, dilated convolution with a larger transverse dilation rate is conducive to the detection of stair lines. Specifically, we count the aspect ratios of all stair lines in the whole dataset, and the histogram is shown in Fig. To further expand the receptive field and enhance the learning ability of our network with respect to semantic stair information, we apply an ASPP module. Reference18) and 2,24 to exAs mentioned in “Similar to the dilation rates in the ASPP module in “Stair detection is a typical multitask of classification and regression. Our loss function inherits the multitask loss idea used for most object detection tasks. The loss function includes a classification loss and a location loss. The specific formula is as follows:For the classification loss In this section, we describe the details of the conducted experiment, including the introduction of the dataset, the model evaluation method, the training strategy, ablation experiments, performance experiments and comparison experiments with traditional methods on the dataset.This section includes the following three parts: the introduction of our dataset, the training strategy and the evaluation metrics we designed.The images in the dataset are collected from actual scenes, and all of them are padded and resized to 512 The annotation form of the dataset is as follows:cls x1 y1 x2 y2/n...Each stair line is represented by the above five-tuple data, where cls represents the class of the stair line, 0 represents a convex line and 1 represents a concave line. X1 and y1 represent the coordinates of the left endpoint of the stair line, and x2 and y2 represent the coordinates of the right endpoint of the stair line. The coordinate system in our dataset uses the pixel coordinate system and the coordinate origin is located in the upper left corner of the image, with the x direction pointing to the right and y direction pointing down. The label of an image is stored in a text file and associated by the file name.We train the model on a workstation with an i7-9700 CPU and an RTX 3080 GPU by using the PyTorch framework. As mentioned above, the input size of the network is 512 In terms of data enhancement, we mainly use a random mirror with a probability of 0.5 and random occlusion with a probability of 0.5. Random mirror is used to eliminate the uneven distribution of ROIs in the training images. Random occlusion is used to simulate the situation in which the stairs are often blocked by pedestrians and other objects in reality.37 as the evaluation method, and the background class is not calculated.In essence, our task is still an object detection problem based on coarse-grained segmentation. There are only four kinds of cells in our task, namely, cells with only convex lines, cells with only concave lines, cells with both kinds of lines and cells with no lines. Since the background cells are easy to classify and a few cells with both lines are difficult to locate, to objectively evaluate the performance of the model, we use the frequency weighted intersection over union (FWIOU)38, we apply Eq.  is used to measure the proximity between two boxes. Similarly, we need an index to measure the proximity of two line segments. The location of a line segment is determined by its two endpoints, so the problem can be transformed into measuring the proximity between endpoints. Inspired by referencepply Eq.  to conveIn the following content, we use the accuracy, recall and FWIOU metrics when c(x) = 0.5, as well as the mean FWIOU (mFWIOU), as the evaluation indicators of the model. The mFWIOU is defined by the mean value of the FWIOUs obtained under 19 confidence values when c(x) = 0.05–0.95 with a step size of 0.05.In this section, we verify our method with several ablation studies. The experiments are all conducted with the same settings as those described above. We take the network stacked with standard SE-ResNeXt blocks as the baseline. Based on this, we study the influence of the focus module, ASPP module and SE-ResNeXt blocks with dilated convolution on the performance of the model. The experimental results are shown in Table The results show that the dilated convolution can significantly improve the performance of the model. In addition, the focus module and ASPP module slightly improve the performance of the model.In this section, we present model performance experiments conducted on the validation set, which are mainly performed to determine the inference speed and accuracy of the model. We provide three versions of StairNet, including StairNet 1The results show that our three models can meet the real-time requirements of the desktop platform, and the 0.5To objectively evaluate the accuracy of the model, we divide the data in the validation set into daytime dataset, night dataset and art dataset according to their collection conditions, and the detection difficulty also increases in sequence. The accuracy results are shown in Table The results show that the performance of the 0.5Figure 10, we apply Gabor filter, Canny algorithm and Hough transform to make our implementation. First, we use Gabor filter to enhance the features in the 9018, we apply YoLov530 to obtain the ROI containing stairs, and then the traditional computer vision algorithms are applied to extract lines in the ROI.In this section, we compare our method with traditional methods. For the method based on traditional computer visionBefore the performance evaluation, we adjusted the parameters of Gabor filter, Canny algorithm and Hough transform to fit our validation set. After the evaluation, we convert the outputs of the above two methods to matrix outputs that are consistent with StairNet. In addition, because StairNet can distinguish the convex lines and the concave lines but the above two methods cannot, we convert the classification output of StairNet into two classes: cells with lines and cells without lines. The comparison experiments are conducted on our validation set, using the evaluation metrics described in “The results show that the performance of the StairNet is far superior to the traditional methods in terms of accuracy and recall. Our validation set contains a variety of scenarios and our evaluation metrics are very strict, so it is difficult to find a set of parameters to adapt to a variety of detection scenarios for traditional methods. For the inference speed, our StairNet 1We propose a novel fully convolutional neural network architecture for stair detection. First, considering the slender shape of stair lines, we creatively propose a new feature representation. We regard the stair detection task as a combination of semantic segmentation and object detection. The input images are divided into cells, a simple neural network is used to judge whether each cell contains stair lines and regress the locations. In the ablation experiments, the baseline network has an accuracy of 79.96%, which proves the effectiveness of our proposed feature representation method. Next, according to some prior knowledge of the stair lines, we propose several optimization methods, including: (1) the texture features of stairs should be learned in the shallow layers of the network. Therefore, we use a focus module as the initial module of the network. (2) Because stair lines are typical long-range information features and are often distributed horizontally in the images, we use the dilation convolution and ASPP module to expand the receptive field while reducing the number of downsampling. And we set different dilation rates in the horizontal and vertical directions to make the network learn from different scales and directions. In the ablation experiments, the effectiveness of our proposed optimization methods are proved. Then, we compress the original model and conduct performance experiments on the models. The results show that the accuracy decreases slightly as the model is compressed, but the inference speed is greatly improved. The experiments of 0.58, it is not possible to migrate to the embedded system. In order to overcome the poor adaptability of traditional computer vision algorithms under challenging scenarios, including extreme lighting conditions, special structures, severe occlusions and special materials, there are some improvements. For example, some studies first extract the ROI containing stairs in the input image through object detection algorithms, and then the traditional computer vision algorithms are applied to extract lines in the ROI18. This single object detection requires only a small-scale dataset to be trained and reduces the interference of complex scenarios to some extent. However, the detection task is divided into two stages, which are carried out on CPU and GPU respectively, and the real-time performance is poor. In order to fundamentally solve the problems, we propose an end-to-end detection method based on deep learning for the first time, which does not rely on any external algorithm and achieves better real-time performance while improving the detection robustness. This depends on the collection and production of our dataset, which contains a total of 3094 images, and each stair line is labeled at the pixel level according to the novel stair feature representation method we proposed. In the experiments, we find that the dataset of this scale is enough for network convergence and learning.There have been some previous studies based on line extraction methods. Most of them use traditional computer vision algorithms, such as Canny edge detection, Hough transform, etc. Because stairs have well described geometric features, and there is not enough large-scale stair dataset for deep learning training in the industry, the detection methods based on traditional computer vision have been used as a means of stair detection for a long time. Although, these algorithms often run on CPU and have good real-time performance on the host end39. This is due to the maturity and miniaturization of depth cameras today. These methods can extract the stair features in different dimensions, and RGB map and depth map have their own advantages in different scenarios. However, These methods require rules designed artificially to integrate the results of RGB map and depth map. There is still the problem of poor adaptability, and the algorithms need to process two images at the same time, which has higher requirements on the computing power of the device. In this paper, the method of deep learning is used to detect stairs quickly and accurately under the condition of monocular vision. However, the experimental results show that the performance on the night dataset and the performance on the art dataset are lower than the performance on the daytime dataset. The reason is that the information from monocular RGB images is limited, especially at night, it is difficult to distinguish stairs in RGB images. In future research, we will consider combining depth information for stair detection, and study the integration of texture information from RGB map and depth information from depth map in neural network.There are also some methods based on line extraction which extract stair lines from both RGB and depth mapsWe proposed a novel fully convolutional neural network architecture that regards the stair detection task as a combination of semantic segmentation and object detection, where the aim is to quickly and accurately detect stair lines in monocular vision in an end-to-end manner. In addition, we provide a dataset with fine annotations for stair detection research. Experiments conducted on this dataset demonstrate the effectiveness of our method. Finally, experiments performed on a Jetson NX show that our model can run in real time on an embedded platform and serve as an edge computing solution for stair detection in various devices. In future work, the depth data will also be used for stair detection and the neural network architecture can effectively process RGB-D data to obtain higher accuracy.


The Industrial Internet of Things (IIoT) connects industrial assets to ubiquitous smart sensors and actuators to enhance manufacturing and industrial processes. Data-driven condition monitoring is an essential technology for intelligent manufacturing systems to identify anomalies from malfunctioning equipment, prevent unplanned downtime, and reduce the operation costs by predictive maintenance without interrupting normal machine operations. However, data-driven condition monitoring requires massive data collected from smart sensors to be transmitted to the cloud for further processing, thereby contributing to network congestion and affecting the network performance. Furthermore, unbalanced training data with very few labelled anomalies limit supervised learning models because of the lack of sufficient fault data for the training process in anomaly detection algorithms. To address these issues, we proposed an IIoT-based condition monitoring system with an edge-to-cloud architecture and computed the relative wavelet energy as feature vectors on the edge layer to reduce the network traffic overhead. We also proposed an unsupervised deep long short-term memory (LSTM) network module for anomaly detection. We implemented the proposed IIoT condition monitoring system for a manufacturing machine in a real shop site to evaluate our proposed solution. Our experimental results verify the effectiveness of our approach which can not only reduce the network traffic overhead for the IIoT but also detect anomalies accurately. With the advances in the smart sensor and Industrial Internet of Things (IIoT) technologies, the development of intelligent manufacturing has been accelerated dramatically ,2,3. In The condition of manufacturing systems needs to be monitored because they have become more complex with multiple functions ,8. A malThe IIoT extends information technology to operational technology by combining networked sensors and intelligent devices for applications in manufacturing systems, including data collection to drive artificial intelligence and adding intelligence to manufacturing equipment, processes, and management . IIoT teTo obtain a more stable and efficient IIoT system, edge computing devices are deployed between cloud servers and the IIoT sensors and close to the sensors on the manufacturing machines to monitor the machine’s condition ,13. EdgeAnomaly detection can identify the deteriorating condition of the machine by observing the exceptional patterns during the normal operation of the equipment. The anomalies are the early signs of system failure, which may lead to systematic faults or equipment breakdown ,15. HoweTraditional statistical methods are used for anomaly detection at the early stages. Statistical anomaly detection techniques assume that normal data instances occur in high probability regions of a stochastic model, whereas anomalies occur in the low probability regions of the stochastic model . HoweverMachine learning methods process data for anomaly detection with analytical model building automatically and simplify sensor dataset handling to detect the new types of anomalies by self-training. The machine learning methods for anomaly detection are classified into supervised and unsupervised approaches. Supervised algorithms are trained models with datasets that are already labeled as normal and abnormal. The algorithm then processes the real sensor data and detects anomalies based on the trained model. A supervised algorithm only detects the normal or abnormal categories on which it has been trained with the pre-labeled datasets. Thus, a supervised machine learning algorithm cannot recognize a pattern that has not been previously trained. The new type of anomaly should be trained by the supervised models continuously to obtain a more robust detection system for every possible data distribution and pattern. This condition is not always practical for real industrial manufacturing situations. Unsupervised machine learning algorithms, different from supervised learning approaches, learn the normal behavior without the pre-labeled datasets, and then statistical techniques are applied to determine if a specific data point is an anomaly because very few anomalies are available during the normal operations of the manufacturing. The unsupervised anomaly detection methods can detect any type of anomaly, even those never seen before.The anomaly detection should work with large amounts of different data types, which are often unstructured. Therefore, traditional machine learning models face the challenges of dealing with these unstructured data. These models are trained on structured data that have input features with corresponding output labels, so the unstructured data cannot be directly used as input features. However, traditional machine learning methods build a single model of prediction for all anomaly detection entries, and the model parameters are difficult to fit through the high-dimensional data and sequence datasets. The datasets used by traditional methods to train the model may not contain abnormal signals because anomalies are rare in real manufacturing scenarios. In addition, anomalies that have occurred in the past may not be a perfect indication of problems occurring in the future because anomaly detection detects faults under time-varying operational conditions the in long-term running. Recently, many studies have been conducted with deep learning on condition monitoring for manufacturing machines, the fault features of which are automatically extracted from the sensor data by the deep learning methods, and the feature extractor is not reconstructed for every specific fault diagnosis. The deep learning approach uses the available data to learn the underlying model of the time series, enabling it to perform more complex anomaly detection tasks. However, most methods take directly raw time-series vibration signals as input. The large amount of data is a great burden on the system. The high volume of the sensor data is also challenging to the system’s ability for data transmission and processing. We proposed an edge-to-cloud condition monitoring platform for anomaly detection. The relative wavelet energy-based LSTM model is implemented, and the relative wavelet energy is computed on the edge layer to reduce the network traffic overhead and accelerate the model deployment.The remainder of the paper is organized as follows: The methodology of the IIoT-based anomaly detection using relative wavelet energy and the LSTM model is discussed in Machine condition monitoring is performed through IIoT devices organized as a hierarchy network. The proposed edge-to-cloud condition monitoring system with an unsupervised learning approach is illustrated in The smart sensor devices construct the sensor layer. Each sensor device consists of sensors, a signal conditioning circuit, communication components, a power management unit, and a microcontroller. The IIoT devices have been deployed in different places of the machines to collect sensor data in real-time. These sensor data represent the running state of the machines, and the devices transfer the sensor data through the IIoT sensor networks.The IIoT gateway is deployed to connect the IIoT sensor network to the centralized cloud. To connect the cloud servers, the gateway supports a wired Ethernet connection and wireless networking protocols over the Internet. In addition, the gateway connects the smart sensor devices through RS485 using a Modbus RTU protocol. Modbus is a popular industrial protocol because it is simple and universal, especially for the compatibility of legacy industrial systems. This finding indicates that a Modbus connection can provide digital communications in older systems with new devices.The overview of the proposed anomaly detection algorithm is shown in Satisfying the IIoT data processing requirements by the traditional heterogeneous network architecture is very challenging because the massive sensor data are generated with the growth of the sensors’ number. Edge computing technology helps to extend cloud-computing capabilities to the edge layer of the network. It can manage the data transmission and enable data preprocessing to avoid network congestion and reduce the computing loads on the clouds.The developing fault trend of the manufacturing systems is slow, whereas a high sampling frequency is required to capture the failure-generated signal, which is modulated at a high frequency. To maintain a trade-off between the sensitivity of failure detection and the number of data, we identify the optimal discrete sampling window size and interval to measure the sensor signal.i is represented as i}i=1,2,…, is the start time for each sampling window and The time series Time and frequency-based analysis methods are also used with the time series sensor data collected during the manufacturing process for condition monitoring. The signal analysis for failure detection does not only focus on all global properties of a signal in scope but also on information about local features of the signal, such as changes in frequency. Since changes in environmental and operating conditions can influence the vibration sources of the anomaly detection and make the signals non-stationary with a changing frequency and amplitude characteristics, traditional Fourier spectral analysis, such as fast Fourier transform (FFT) and short-time Fourier transform (STFT), cannot adequately be applied to non-stationary signals ,19. In eIn our study, we apply the discrete wavelet transform (DWT)  to decomj is defined as follows:jE described as follows:jC(k) is the DWT coefficient at the decomposed level j. The relative wavelet energy is used as the input feature to the prognostic algorithm. The calculated relative wavelet energy features are passed from the edge layer for anomaly detection on the cloud.Relative wavelet energy describes information about the relative energy associated with different frequency ranges and can be considered a time-scale density ,22. ThisLong short-term memory (LSTM) is a type of deep neural network algorithm ,24, withi) is used as the input of the LSTM, and each input data point is an m-dimensional vector of the relative wavelet energy at the time instance ti, which is generated from the edge layer. The LSTM model predicts the output vector A sequence of the relative wavelet energy R and standard deviation are metrics to measure the spread of values in a dataset, as shown in This section verifies our proposed method by detecting machine anomalies with an edge-to-cloud condition monitoring system in a real workplace environment. The experimental work of the manufacturing machine condition monitoring has been conducted with an electronic component insertion machine, which is used for electronic product assembly by inserting the axial or radial electronic components and the functional parts of various shapes, as shown in Electronic component insertion machines are used in electronics assembly by inserting various discrete components into printed circuit boards. The system needs a higher degree of stability for the system operating accuracy to improve manufacturing efficiency. However, environmental factors, such as larger ground and building floor vibrations or the misconduct of the operators and changes in the operating conditions, may cause equipment anomalies in the machine’s accuracy and affect the stability of the system. The anomaly detection of the vibration conditions in the operation of the machines is helpful in adjusting the parameters and calibrating the system in time.According to the research on the generic vibration criteria used for vibration-sensitive technical facilities ,27, suchPrior to calculating the relative wavelet energy to obtain the local features of the system’s running condition, the wavelet transform should be applied. Five wavelet transform decomposition levels were selected to achieve a good frequency resolution depending on how the system energy is distributed in the frequency domain and on the system’s complexity. As shown in The relative wavelet energy for each level is calculated by Equation (1), then inputted to the LSTM to establish the optimal model for anomaly prediction in the condition monitoring system. The RWE–LSTM models are trained by an unsupervised method with training and validation datasets. The proposed model is a stacked LSTM with three subsequent layers and one fully connected layer. The function activation is the sigmoid function, and the model is set for 400 epochs. Considering the computational complexity and the model training performance, the Mean Squared Error (MSE) and Mean Absolute Error (MAE) loss functions are used for the model training and validation. The training and validation loss function graph for the LSTM model in the training period is shown in The current proposed IIoT architecture for condition monitoring is based on the edge-to-cloud scenario to reduce the data transmission and algorithm computing on the cloud servers efficiently. However, the IoT network’s architecture will face challenges since the number of sensors and the complexity of the application scenarios increase. There are various advanced network architectures proposed for the next generation of IoT applications, such as the Multiple IoT scenario (MIoT) ,29 and MIn this paper, we presented an IIoT-based condition monitoring system with an unsupervised learning approach to detect anomaly conditions for manufacturing applications. The discrete sampling window method is applied in the IIoT gateway on the edge layer to reduce the network traffic overhead for the IIoT while maintaining detection accuracy. The unsupervised learning with the deep LSTM network module is used for the time series of the long-term condition monitoring in the manufacturing process. We demonstrated the proposed system’s architecture and algorithms on real manufacturing machines and showed that our condition monitoring hardware platform works stably and efficiently, and the anomaly detection model can detect unknown spatial and temporal anomalies on the manufacturing machines.


The kernel functions used in this study are fixed bisquare kernel, fixed tricube kernel, and adaptive bisquare kernel. Optimum bandwidth selection using the Cross-Validation (CV) method. The results obtained the best model is the GWGPR model with a fixed bisquare kernel because it produces the smallest AIC value of 194.92. Variables significantly affecting the number of maternal postpartum mortality in East Java in 2020 vary in each district/city where there are three regional groups. The percentage of pregnant women who had a pregnancy visit K1, the percentage of pregnant women who had a pregnancy visit K4, the percentage of households receiving cash assistance, and the ratio of hospitals and health centres have a significant effect on Kabupaten Blitar, Mojokerto, Gresik, Bangkalan, Blitar City, Mojokerto City, Surabaya City. While the five predictor variables together significantly affect districts/cities included in group 3, such as Ponorogo, Trenggalek, Tulungagung, Kediri, Malang, Lumajang, Jember, Banyuwangi, Bondowoso and so on. Some of the highlights of the proposed approach are:  Specifications tableRegression analysis is a statistical method used to determine the relationship between variables. In a study, it is often found that the object of research is in the form of count data influenced by several explanatory variables. As the case of the number of maternal deaths is a rare event with discrete data, the appropriate regression analysis model to explain the relationship between the dependent variable in the form of discrete and rare data with independent variables in the form of discrete, continuous, categorical, or mixed data is the Poisson regression model. The Poisson regression model is a non-linear regression model used to model count or discrete data, with the main requirement being that the response variable has a Poisson distribution However, there are times when the equidispersion assumption is only sometimes met. Namely, here can also be cases of overdispersion  or underdispersion  in data modeling with a Poisson distribution Murakami, Daisuke et al. (2021) used the Geographically Weighted Generalized Poisson Regression method to analyze the covid-19 case in Tokyo, Japan. In this study, the bandwidth used was an adaptive and fixed Gaussian kernel Multicollinearity in the regression model can be determined using the Variance Inflation Factor (VIF), which is expressed in the following equation:In some research, it is often found that the object of research is in the form of count data influenced by several explanatory variables. A regression model based on the Poisson distribution can determine the relationship pattern between variables. The Poisson distribution is a discrete distribution that measures the probability of a certain number of events or events occurring within a certain period. The probability of a Poisson distribution with a random variable The Poisson regression model is a non-linear regression model used to model count or discrete data, with the main requirement being that the response variable has a Poisson distribution. Poisson regression modeling the expected value of the response variable, with link function to model Poisson regression in the Generalized Linear Model approach, is:Since the mean Thus, the Poisson regression model for the response variable Y, which is Poisson distributed with parameter Parameter estimation of the Poisson regression model can be done using the Maximum Likelihood Estimation (MLE) method. The maximum likelihood estimator for the parameter The log-likelihood function of the Poisson regression model is:Because of Furthermore, to obtain the estimator Testing the parameters of the Poisson regression model is carried out to test whether the regression model parameters have a significant effect on the response variable p = 1, 2, …, kHypothesis:The test statistic used:The After testing the parameters simultaneously, we will continue with partial parameter testing. The partial parameter testing hypothesis is as follows:Hypothesis:The test statistic used:p has a significant effect on the response variable.The rejection region of Pearson Chi-Square values divided by the degrees of freedom. A value or quotient more significant than 1 indicates the presence of overdispersion The presence of overdispersion or underdispersion in Poisson regression can be detected using the devians, and The Generalized Poisson Regression (GPR) model is used for counting data that experience equidispersion violations. The Generalized Poisson Regression model has the same form as Poisson Regression as follows:In the GPR model, the values of the parameters The function of the GPR model are as follows:Next, Newton Raphson iteration is performed to maximize the log-likelihood function formulated as follows:Perform Newton Raphson iteration based on equation:The iteration process will stop if it has found an estimated value that converges to a value as follows:Furthermore, the estimation of the parameter ϕ using the Newton-Raphson iteration method is:A more accessible approach to estimating the parameter phi is to use the estimation of moments, equating the Pearson Chi-Square statistic with the degrees of freedom Thus, the following iteration is obtained:p = 1, 2, …, kGPR parameter testing is done with the Maximum Likelihood Ratio Test (MLRT) with the following hypothesis:The test statistic used:The The test statistic used:p has a significant effect on the response variable.The rejection region of Spatial heterogeneity occurs when the same predictor variable has an unequal effect on different locations within a study area. Spatial heterogeneity can be tested using the Breusch-Pagan (BP) test. Hypotheses used in the Breusch- Pagan test is as follows.Where the vector element y and predictor variables Geographically weighted regression is used to analyze spatial heterogeneity, where each parameter is calculated at each observation location, so each region or observation location has different regression parameter values. Systematically the Geographically Weighted Regression (GWR) model with response variable i.Fixed BisquareIn spatial analysis, the role of bandwidth for GWR models is essential because the weight value represents the location of observations between one data and another. There are three types of bandwidth functions used in this study:ii.Fixed TricubeThe form of fixed bisquare is expressed by the following formula:iii.Adaptive Bisquarewith The following formula expresses the form of the fixed tricube:One of the methods used to determine the optimum bandwidth size is the Cross-Validation (CV) method. CV method is defined as follows:Spatial regression modeling is performed on response variables in the form of count data that experience underdispersion or overdispersion and depend on the characteristics of the observed location using Geographically Weighted Generalized Poisson Regression. The difference between global Poisson regression and GWGPR is in parameter estimation. The global Poisson regression model has the same parameter estimator value for each observation location, so it is global. The GWGPR model produces a different parameter estimator value for each observation location area, so it is local The GWGPR model is the development of Generalized Pois-son Regression by considering the weights in the form of latitude and longitude of the observation location points. The GWGPR model follows the distribution of Generalized Poisson Regression so that the probability function for each GWGPR modeling uses a generalized linear model with a “g” function (link function) that connects the mean of the response variable with the linear predictor (η). The link function used in the GWGPR model is the log link. The GWGPR model with In the GWGPR model, the method used to estimate the model parameters is the Maximum Likelihood Estimation (MLE) method, with the GWGPR model likelihood function as follows:Furthermore, the likelihood function in By substituting the value 1.Determine the initial estimated values of the parameters2.g) with k estimated parametersForm a gradient vector  method. Testing the parameters of the GWGPR model is carried out to determine the significance of the Test Statistic:Partial GWGPR model parameter testing is conducted to determine the effect of individual predictor variables on the response variable at each location. The partial parameter testing hypothesis is as follows.The Statistics:Akaike Information Criterion (AIC) is one of the criteria to determine the best model. A small AIC value indicates that the model is getting better. The best model selection using the AIC value criterion is as follows:Maternal Mortality Rate (MMR) is the number of female mortalities that occur during pregnancy or its handling during pregnancy, childbirth, or within 42 days after the end of preg nancy (postpartum period) but not mortality caused by accidents or injuries. MMR is an essential indicator in determining the welfare of society, especially in the health sector. The Ministry of Health in 2020 revealed that the number of maternal mortalities in Indonesia is still relatively high, reaching 4627 people. East Java Province ranks second with the number of maternal postpartum mortality at 284. The number of maternal postpartum mortality in East Java Province over five years is presented in the following figure.Several studies have found that the factor that has a significant effect on the number of maternal postpartum mortality is the percentage of pregnant women who had a pregnancy visit K4 Based on the description above, in this study, several variables are used that are thought to affect the number of maternal postpartum mortality in East Java in 2020. Y is the number of maternal postpartum mortality, Descriptive analysis was carried out to obtain the characteristics of the number of maternal postpartum mortality (Y) and the factors that influence it. In 2020 the number of maternal postpartum mortality in East Java was 284. Descriptive statistics of all variables used and presented in An overview of the distribution of cases of the number of maternal postpartum mortality in East Java in 2020 is presented in One of the requirements that must be met in Poisson regression analysis is multicollinearity detection. The presence of multicollinearity can be known from each predictor variable's VIF (Variance Inflation Factor) value. A VIF value of more than 10 indicates a case of multicollinearity.0 : Data follows Poisson distributionH1 : Data does not follow Poisson distribution Significance level: HThe distribution suitability test is used to determine whether the data is Poisson distributed or not. To conduct the test, you can use the Kolmogorov Smirnov Test with the following hypothesis:Test StatisticSpatial heterogeneity testing is conducted to determine whether there are characteristics between observation location points. Spatial heterogeneity can be tested using the Breusch- Pagan (BP) test with the following hypothesis test:The test statistic value is 17.802 and the The existence of spatial effects causes spatial heterogeneity, so spatial weighting is needed. The best spatial weighting is obtained from the minimum Cross-Validation (CV) value's bandwidth value. Based on the result, obtaining the estimated value of the Poisson regression model parameters can be done using the Maximum Likelihood Estimation (MLE) method with Newton- Raphson iterations. The estimated values of the Poisson regression model parameters are presented in From the Poisson regression modeling, it is known that the data on the number of maternal postpartum mortality experience overdispersion cases because DBased on The partial parameter test results show that the variables that have a significant effect are the percentage of households receiving cash assistance :Testing the parameters of the GWGPR model is carried out to determine the significance of the parameters β with the following hypothesisBased on the result, the devians value D The predictor variable is said to have a significant effect on the response variable if the value of•Variable •Variable •Variable The grouping of districts/cities in East Java based on significant variables is presented in Based partial parameter testing, as an example, we will present parameter testing at the 3rd research location Based on the GWGPR model in Kabupaten Trenggalek, every 5% increase in the percentage of pregnant women who had a pregnancy visit K1 will reduce the average number of maternal postpartum mortality in East Java by Based on One of the criteria for determining the best model is to look at the Akaike Information Criterion (AIC) value obtained. The smaller the AIC value, the better the model used. The following are the AIC values for the Poisson, GPR and GWGPR regression models.The AIC value of the Poisson regression model is 225.19, the AIC value of the GPR model is 216.4872, and the GWGPR model with the best kernel (fixed bisquare) has an AIC value of 194.92. This means that the GWGPR model is the best because it has the smallest AIC value compared to other models , Table 7The average number of maternal postpartum mortality in each district/city in East Java Province was 7.47 in 2020, with the highest death of 30 cases in Kabupaten Jember and the lowest case in Sampang, Kediri city, Madiun City and Batu city. The high variance value of 31.553 indicates a reasonably high difference in maternal postpartum mortality in each district/city. The best kernel function is the fixed bisqure kernel function with a CV value of 1367.024. Based on the results obtained, the GWGPR model is the best model to model the case of the number of maternal postpartum mortality in East Java in 2020 because it produces the smallest AIC value of 194.92 with three groupings obtained based on the results of partial testing for GWGPR modeling.The data used in this study are secondary data derived from the 2020 East Java Provincial Health Profile published by the East Java Provincial Health Office and some secondary data derived from the official website of the Badan Pusat Statistik (BPS) East Java.Ghanim Al-Hasani: Methodology, GWPR Methods; Murakami, Daisuke: Spatial, Validity tests; Gunardi: Conceptualization, Conceptual.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Recently, novel networking architectures have emerged to cope with the fast-evolving and new Internet utilisation patterns. Information-Centric Networking (ICN) is a prominent example of this architecture. By perceiving content as the core element of the networking functionalities, ICN opens up a whole new avenue of information exchange optimisation possibilities. This paper presents an approach that progresses the base operation of ICN and leverages content identification right at the network layer, allowing to selectively retrieve partial pieces of information from content already present in ICN in-network caches. Additionally, this proposal enables information producers to seamlessly offload some content processing tasks into the network. The concept is discussed and demonstrated through a proof-of-concept prototype targeting an Internet of Things (IoT) scenario, where consumers retrieve specific pieces of the whole information generated by sensors. The obtained results showcase reduced traffic and storage consumption at the core of the network. Over the years, we have witnessed the evolution of the Internet from its original purpose of interconnecting a reduced set of computers, to the provisioning of worldwide connectivity to more than 18 billion devices ,3, whichParallel to this, the advances in sensor technologies have promoted the emergence of new usage scenarios under the umbrella of the Internet of Things (IoT) ,5,6,7. TFurthermore, the suitability of the TCP/IP stack for supporting IoT has been questioned , creatinDifferent ICN implementations follow different design choices . This work focuses on Interest-based ICN solutions, particularly on the Named Data Networking (NDN) protocol . This prThus, the contributions of the current paper are two-fold: (i) to provide the means for selectively accessing specific parts of a given content, enabling the (dis)aggregation of IoT content while still leveraging ICN in-network caching; and (ii) to enable an in-network processing approach that allows the offloading of the (dis)aggregation process to the network itself.These contributions are materialised and assessed through the development of a proof-of-concept prototype following the NDN principles. The assessment is performed through simulation, allowing to extend the scope of the evaluation to a larger scale.The remainder of this paper is structured as presented in In NDN, the communication process is initiated by consumers, which send a request . This packet is forwarded by intermediary nodes towards an entity holding the content, according to the information stored in the Forwarding Information Base (FIB) and following a configured forwarding strategy. The network nodes maintain a Pending Interest Table (PIT) with information about the forwarded requests. Subsequent requests for the same content are aggregated in the PIT. Responses  follow the reverse request path based on the state information stored in the PITs. When a node forwards a data packet, the Interest is considered to be satisfied, and the corresponding PIT entries are removed. Content objects are signed by the producers, ensuring both integrity and authenticity of the content and can be cached in the intermediary Content Stores (CS).FreshnessPeriod parameter in data packets to indicate how long a certain packet could be held in the network content stores before marking it as “non-fresh”. While “non-fresh” data is still valid, it is an indication that the producer may have issued newer data.In fact, the NDN caching mechanism has a prominent role in providing an increased data delivery performance and has been identified as a key enabler towards a name-based IoT . HoweverThe caching of transient IoT content has been explored in different stages of the caching process: (i) caching strategies considering the popularity of IoT data and their lifetime ,21; (ii)The integration of edge computing and networking solutions has emerged as a promising technology with the potential to impact the different elements of an end-to-end system . The intrinsic capabilities of NDN makes it a suitable candidate for joint edge computing and communication solutions, whereas IoT stands out as one of the scenarios that could better profit edge approaches.In particular, NDN has been explored for processing IoT data at the edge, thus reducing the latency perceived by the users and the network overhead that would be required for unnecessary transport of raw IoT through the network core ,25. EdgeIn addition to edge computing, in-network processing has been increasingly gaining significance, empowered by the evolution toward a programmable data plane  and furtapproach . Moreoveapproach ,35. Furtapproach ; the resIn addition, with the goal of bringing computation closer to consumers and reduce the usage of network resources while providing better latency, Named Function as Service (NFaaS)  takes a The aggregation of IoT content (as a way to save scarce resources and reduce traffic load) has been the focus of numerous research works . DiffereNovel ICN-based frameworks have been proposed to handle IoT data with increased focus on specific criteria  toward providing improved data delivery performance for constrained devices. Although some of the existing solutions share our motivation, to the best of our knowledge there is no prior work that targets the selective retrieval of content as part of the forwarding process by leveraging in-network computation. In this context, the solution proposed in this paper will provide an extendable and name-independent selective content retrieval mechanism that inherently supports function offloading, through a distributed in-network processing approach. Moreover, the proposed mechanism will do so while retaining the benefits associated to NDN’s intrinsic caching mechanisms.The current section uses a motivational scenario to showcase the need to support efficient IoT content retrieval by enhancing the basic features of the NDN architecture. Different considerations and approaches to realise these enhancements are discussed.In general, IoT devices provide different information, typically generating small pieces of content. As such, the aggregation of IoT information is a shared vision for IoT content dissemination optimisation. This raises the question of how the ICN functionalities can be better leveraged for the (dis)aggregation of IoT content.Wheel Count”, “Direction” and “Speed” . The purpose of this information is to be sent through the network and absorbed by actuators, such as a railroad crossing controller that is able to lower or raise the road gate when an incoming train is detected. The scenario also considers different end-points that may be interested in different pieces of the aggregated content . The challenge ahead lies in how to enable network nodes to selectively retrieve these pieces of content from the intrinsic ICN in-network caching, reducing the extra network overhead and releasing the information producers (possibly with constrained resources) from the content processing tasks.This question is the motivation for conducting this research. The foundations for the development of this work can be observed from Consumer A requests the aggregated content of the whole train , which is not available in the intermediary node content store and has to be provided by the information producer. Next, Consumers B and C request Train Speed and Train Speed from Sensor Z contents, respectively. Although the actual information is already available at the Edge Router , the said router does not have the knowledge to extract the desired information. Consequently, these requests have to be satisfied by the Producer.To place the previous scenario into perspective, let us consider it from a basic NDN point of view, as shown in B and C are satisfied by the edge router, which extracts the necessary information from the cached Train Data content.To avoid the unnecessary forwarding of requests towards the source, it is possible to add some intelligence to intermediary routers, enabling them to extract the requested information from the aggregated data available in the content store. Such behaviour is shown in A and B request partial pieces of the aggregated Train Data content. Since the content is not available in the cache, these requests, as well as the request from Consumer C, have to be satisfied by the Producer. As a consequence, Speed from Sensor Z information is retrieved three times  and the Speed information is retrieved twice .Still, with such an enhancement, the order of arrival of the incoming requests at the intermediary routers also impacts the retrieval mechanism, as represented in Edge Router forwards the request for the smaller piece of content, and the producer replies with the larger aggregated content (i.e. Train Data); (ii) the intelligence is on the router: the Edge Router takes the decision of extending the request to a larger piece of content (/asp/train) and the Producer responds accordingly. Notwithstanding, these kinds of approaches require more detailed agreements between the producer and the network .This highlights the need for a more advanced response strategy, one that recognises that the piece of content being requested is part of a larger aggregated content and that preemptively pushes this larger content towards the edge of the network for later processing. Two different approaches are envisioned, as shown in As it has been already established, the goal of this work is to provide network-assisted content (dis)aggregation in different scenarios, as befitting consumers, producers, or network operators. The balance between these three parties defines the possible targets of enhancements. As such, the current section explores different strategies for realising a mechanism that fulfils the vision presented in The producer provides detailed content (dis)aggregation information ;A network entity has the content (dis)aggregation intelligence .According to the source of the (dis)aggregation information, the resulting strategies fall into two categories:Consumer: consumers hold the (dis)aggregation knowledge and generate complex queries that identify the desired piece of content. For such an approach to work, the intermediary routers must implement and support a (dis)aggregation protocol;Router: intermediary routers hold the (dis)aggregation knowledge and processes incoming Interests and return the associated data. Consumers should have the required knowledge to request pieces of content ;Third party: a third party entity holds the (dis)aggregation information. In this approach, the (dis)aggregation duties are offloaded to the third party entity, which must implement a (dis)aggregation protocol known to users and/or routers.Depending on the holder of the (dis)aggregation knowledge, the (dis)aggregation information strategies fall into four categories:Additionally, hybrid approaches in which the dis-aggregation knowledge is spread over different networking entities are also envisioned.After discussing the different strategies for enabling efficient IoT content retrieval, the current section presents a solution that falls into the “Smart Router” approach. Particularly, we propose to provide the NDN intermediary nodes (or at least those at the edge of the network) with the capabilities to execute queries on cached content, thus removing this burden from the producer of the information. This section explains the design choices that guided the development of the solution, provides the implementation details of the proof-of-concept prototype, and discusses the main security concerns associated with the proposed solution.As discussed earlier, the proposed solution was deployed over the NDN networking architecture. In doing so, keeping the proposed enhancements as generic as possible without affecting the NDN design principles, is a major design requirement. The solution focuses on network-assisted dis-aggregation of content, leaving the generation of the aggregated content out of the scope of the proposal, and leading to what will be denoted as the Content-in-Content (CiC) retrieval mechanism. In realising this concept, the intelligence of the network will be placed on the forwarding nodes, particularly on those at the edge of the network. The goal is to allow the processing offloading from the likely constrained IoT sensors and to have the information closer to the end-user. As for the dis-aggregation process, for simplicity, it will be assumed that the content is structured and a query language could be used for extracting the desired part of the content. Notwithstanding, the proposed mechanism should be extendable to non-structured content, relying upon more advanced content extraction functionalities . Finally, the solution should provide enhanced operations such as the ones described in The proof-of-concept prototype was developed using the NDN C++ library with eXperimental eXtensions (ndn-cxx) and the NDN Forwarding Daemon (NFD) implementations (version 0.6.5).ApplicationParameters field. This field can carry an arbitrary amount of information and is used to parametrise the request for Data. The intention of this field is to allow consumers to customise the Data they are willing to receive. As such, this field is processed by the information Producer. The purpose of this field matches the requirements of this work, and as such we propose to let intermediary nodes interpret and process it.In our proposal, content selection queries are embedded into the Interest packets, using the ApplicationParameters field is present and the original data packet is processed accordingly, potentially generating a new data packet with the resulting information, as shown in ApplicationParameters) and offloads the processing to the network.To provide enhanced nodes with the ability to analyse this field, the processing functionalities were implemented as part of the forwarding decision, and therefore all the logic was included in a new forwarding strategy. The forwarding strategy in the NDN architecture determines whether, when, and where to forward an Interest packet. The proposed strategy is based on the well known best-route strategy  provided as part of the NFD implementation . FollowiApplicationParameters field for CiC-enabled prefixes, the producer will reply with the data identified in the Interest name, leaving the content selection functionality to the edge networking entities. This approach is in line with the concepts sketched in Edge Router, while the Router node does not have CiC enhancements. First, Consumer A requests (Message 1) a piece of the Train Data content  by including a query in the ApplicationParameters field. Since the content is not available at the intermediary content stores, the Edge Router and the Router forward the Interest towards the Producer (Messages 2–3). Since the producer knows that the prefix /asp is CiC-enabled, it replies with the complete Train Data content (Message 4), which is forwarded by the Router towards the Edge Router (Message 5). Before satisfying the pending Interest, the Edge Router checks whether it contains a query embedded in the ApplicationParameters field, processes the original content and generates a new data packet with the extracted information (Message 6). Subsequent requests (Messages 7 and 9) are analysed, and in the presence of a query, the same approach is followed, or otherwise, the complete content is provided.By following this design, the proposed mechanism inherently supports the request for a larger piece of information. By neglecting the jsoncons (https://danielaparker.github.io/jsoncons/—(accessed on September 2022)) header-only library was used to implement the JSON-related functionalities in the developed prototype.To showcase the selective content retrieval, it is assumed that the content to be retrieved is in JSON format, which is a common format to provide structured IoT content. Notwithstanding, the main operational principles are generic and could be extended to other types of content. Accordingly, JSON Path , a queryInformation security and privacy are major concerns when considering the deployment of any networking solution. Requirements such as access control, confidentiality, integrity, and authenticity are fundamental to fostering the wide adoption of any particular solution. This becomes particularly relevant when considering scenarios, such as the IoT, which tightly couple the physical and digital worlds . AlthougIn the particular context of the current proposal, there are other security challenges associated with the execution of in-network function execution for ICN that should be taken into account, as highlighted in Refs. ,51. The Intermediary nodes are allowed to process cached data and produce “new” data. This breaks the original ICN signature verification process since the signer of the information will not be its producer. To address this issue we envision solutions relying on schematised trust  and the Train Data information, it may be allowed to check temperature-related data. As such, in addition to name-based access control [The possibility of selectively accessing individual pieces of content also challenges access control mechanisms. For example, in our scenario, while a particular consumer may not have access rights for the whole  control ,55, fineHowever, these security-related mechanisms fall into general concerns of the NDN operation itself and as such, it should be the target of deeper efforts particularly focused on those aspects. For that reason, the proper design and enforcement of security mechanisms are left out of the scope of this paper.(1)How does the proposed mechanism affect the amount of information exchanged between the edge of the network and the information producers?Answered by analysing the number of hops followed by data packets before reaching the consumers.(2)How does it impact the overall cache utilisation?Answered by determining the space used for storing content in all the network content stores.(3)How do applications perceive the effects of the mechanism?Answered by determining the processing delay as observed by the applications .The proposed solution promises to impact the retrieval of content in the following aspects: (i) reduce the traffic load by lowering the amount of information exchanged between the edge of the network and the information producers; (ii) better leverage cached content by extracting partial information from the content, when possible; and (iii) provide content processing at the network layer. Consequently, the evaluation targets these aspects by answering the following research questions:To analyse these metrics and to properly answer the corresponding research question, different network topology sizes should be taken into consideration. Additionally, to better assess the impact of the proposed solution in terms of cache utilisation, different freshness values should be considered. As such, a simulation-based evaluation better fits the assessment requirements. Accordingly, the developed proof-of-concept prototype was ported to the ndnSIM  simulatiThe simulation scenario features a variable-depth binary tree topology. As represented in The simulation results are presented in Train Data content is exchanged over the network. The freshness has no impact on this metric because the updated content replaces the older content, but still, the amount of used space remains the same.As expected, the content store utilisation a is largFor the freshness value of 0, which means that the caching is completely neglected, it can be seen from Finally, as expected, the application delay c is largIt is important to highlight that, besides the savings in terms of network utilisation (average number of hops), the most critical impact of the proposed mechanism is that the processing is performed at the network itself, thus saving the precious resources from the possibly constrained IoT devices.ApplicationParameters field of Interest packets. The developed proof-of-concept prototype was ported to the ndnSIM simulator to assess the impact of the proposed mechanism. For evaluation purposes, a use case scenario in which JSON encoded information is generated by sensors and partially retrieved by interested parties, was followed. The results showcase the suitability of the proposed approach.This paper discussed the ability for retrieving particular pieces of content from within other content, with particular emphasis on its application in an IoT scenario. Different strategies for realising such an approach were presented. A novel Content in Content forwarding strategy was designed and implemented, which enabled the network to extract the requested information from larger pieces of content, through the exploitation of the Furthermore, we are now witnessing how digitalization efforts, such as Industry 4.0, have profoundly refactored how communications are being perceived by a myriad of different vertical sectors. As a result, such actors are, on the one hand, not only converting and integrating new ways and technologies for information inter-exchange into their businesses, but also, on the other hand, producing new utilisation and consolidation requirements towards such technologies. These requirements will help shape the next generations of IoT solutions, by not only incorporating the principles of newly established Internet architecture designs, but also by further extending them with new operational capabilities on their own. This stands as a great opportunity for more clean-slate oriented network designs to come forward, becoming established as a strong proposition towards the IoT value chain. As a result, such designs themselves will not only need to evolve considering incremental enhancements of their own key capabilities, but they will have to increasingly target higher degrees of flexibility that render them capable of being integrated in widely different application scenarios. This comes as a key addition to the already on-going efforts targeting interoperability between these new systems and well established architectures, such as IP .Concretely, IoT composes heterogeneously rich environments, manifesting a plethora of available in-networking resources operating at cloud, edge, and fog levels, targeting improved QoS/QoE and resource efficiency . The des


Heart sounds and heart rate (pulse) are the most common physiological signals used in the diagnosis of cardiovascular diseases. Measuring these signals using a device and analyzing their interrelationships simultaneously can improve the accuracy of existing methods and propose new approaches for the diagnosis of cardiovascular diseases. In this study, we have presented a novel smart stethoscope based on multimodal physiological signal measurement technology for personal cardiovascular health monitoring. The proposed device is designed in the shape of a compact personal computer mouse for easy grasping and attachment to the surface of the chest using only one hand. A digital microphone and photoplehysmogram sensor are installed on the bottom and top surfaces of the device, respectively, to measure heart sound and pulse from the user’s chest and finger simultaneously. In addition, a high-performance Bluetooth Low Energy System-on-Chip ARM microprocessor is used for pre-processing of measured data and communication with the smartphone. The prototype is assembled on a manufactured printed circuit board and 3D-printed shell to conduct an in vivo experiment to test the performance of physiological signal measurement and usability by observing users’ muscle fatigue variation. Heart sound auscultation, electrocardiogram (ECG) recording, and heart rate measurement are the most popular methods for observing and analyzing the cardiovascular status in in vitro diagnostics . Each teRecently, with the popularity of wearable devices in personal management, the use of health bands and smartwatches to record the daily health status of cardiovascular system by measuring pulse and ECG from the user’s wrist is widely available. The pulse signal is measured by a photoplethysmogram (PPG) sensor commonly designed at the bottom of the health band or smartwatches and attached to the skin surface of the wrist. The measured signal is processed and delineated to the heart rate to observe the heart pump and blood flow status . To measThe cardiovascular system is the most complex system in the human body, that includes the heart, blood vessels, and blood. However, existing personal cardiovascular monitoring devices, such as stethoscopes, Holter, and health bands, can measure only one type of signal from the cardiovascular system. Therefore, for cardiovascular disease diagnosis, the acquired data from these devices can only be used as a reference for initial screening and are not suitable for diagnostics. To improve the accuracy of existing methods and identify novel approaches for the diagnosis of cardiovascular diseases, wearable devices based on multimodal technology were developed for measuring heart sound, ECG, and pulse simultaneously; however, analysis of their interrelationship has been reported in several studies . Kulm etBased on previous research, we have presented a novel multimodal technology-based smart stethoscope designed for personal cardiovascular health monitoring. To improve convenience, mobility, and durability, the proposed smart stethoscope is designed in the shape of a compact personal computer (PC) mouse that can be grasped by one hand and attached to the user’s chest easily. The stethoscope bell and PPG sensor are attached to the bottom and top of the device, respectively, to simultaneously measure the heart sound and pulse from the surface of the user’s chest and finger, respectively. In addition, a high-performance Bluetooth Low Energy (BLE) System-on-Chip (SoC) ARM processor is implemented in the device for system control, data pre-processing, and wireless data communication. The prototype is used to conduct an in vivo experiment with 20 young adults to verify the performance of physiological signal measurement and test the usability of the device by evaluating muscle fatigue levels.The goal of this study is to develop a smart stethoscope that can be held by one hand to measure heart sounds and pulses simultaneously. Therefore, the reference for the shape of the proposed smart stethoscope was taken from a standard PC mouse because of its perfect grip and appropriate size suitable for use by every individual . Figure 2S interface that allows the microphone to connect directly to the microprocessor without an audio codec to decrease data loss during extra processing. In addition, the microphone has a high signal-to-noise ratio (SNR) and wideband frequency response with sensitivity tolerance that is suitable for recording heart sounds because of its frequency range of 20–400 Hz [To achieve two core functions, namely heart sound and pulse measurement, two high-performance digital sensors, namely a micro-electromechanical systems (MEMS) digital microphone sensor and PPG sensor, were individually selected and designed in the proposed smart stethoscope. The selected microphone sensor  consists of an MEMS sensor, signal conditioning, analog-to-digital (ADC) converter, decimation and antialiasing filters, and 24-bit I0–400 Hz . A wearaThe proposed smart stethoscope is designed to measure heart sound and pulse simultaneously. In addition, the measured data must be transmitted to a smartphone in real time. Therefore, a multi-tasking support, high-performance, and low-power Bluetooth SoC ARM processor  is designated as the main microprocessor. This chip includes an 80 MHz ARM Cortex-M33 core that provides a full digital signal processing instruction set and floating-point unit to rapidly process physiological signals. Its integrated security subsystem provides leading security features that significantly reduce the risk of IoT security breaches and compromised intellectual property to protect personal physiological data. Depending on the multi-tasking function, the heart sound and pulse signal processing are designed to be two parallel channels for collecting two signals in synchronization. In addition, a ping-pong buffer was designed for this system to process the heart sound continuously, from receiving sensor data to extracting the data features . In addi2C) interface. A 4.7 V 350 mAh Lithium recharge battery with a high-speed recharging circuit was designed for power supply. In addition, to increase the usage time, the operating voltage of the microprocessor and sensors was set to 1.8 V, which is the minimum value for most of the semiconductor elements. In addition, the USB recharging connector was upgraded to C type owing to its popularity and rich connection support for future improvement. The manufactured PCB is illustrated in The structure of the proposed multimodal smart stethoscope in a 3D design is shown in The assembled prototype of the proposed multimodal smart stethoscope was used to conduct an in vivo experiment that included two tasks: first, to verify the performance of the prototype in heart sound and pulse signal measurement in comparison with a professional physiological signals system, and second, to test the usability by evaluating the muscle fatigue level of the arm when using the prototype. Twenty healthy adult subjects  were invited to participate in this study. The purpose and procedure of this study and experiment were informed to the subjects, and a consent was signed before the experiment. Before the experiment began, the subjects were required to sit comfortably on a chair and relax for approximately five minutes.To verify the heart sound and pulse signals measured by the prototype and measure the electromyography (EMG) signal from the arm of the users for fatigue level evaluation, a multi-channel physiological data acquisition system MP160  with a contact acoustic transducer (SS17LA), PPG sensor (TSD200C), and EMG sensor were used for comparison with the prototype and to measure the EMG signals. As shown in In addition, the EMG signals measured from the two tasks were calculated with the root mean square (RMS) in time domain and average frequency (MEF) in frequency domain as the metrics to evaluate the potential of EMG in measuring subjects’ muscle fatigue . The equThis study presented the design of a novel multimodal smart stethoscope for monitoring personal cardiovascular health status by simultaneously measuring the user’s heart sounds and pulse. Based on previous research, the shape of the stethoscope was designed to be similar to a compact PC mouse. The assembly was mounted on one PCB for easy grasp, and attachment to the chest surface using one hand helped to improve ruggedness and usability. A digital microphone and PPG sensor were equipped on the bottom and top surfaces of the device, respectively, to measure heart sounds and pulse variation from the user’s chest and finger simultaneously. In addition, to improve the signal processing capacity and convenience, a high-performance BLE Soc ARM microprocessor was implemented in the device for measured data pre-processing and communication with the smartphone. The prototype was assembled with a manufactured PCB and 3D-printed shell to conduct an in vivo experiment with 20 male and female subjects. The performance of heart sound and pulse signal measurement along with usability was measured by evaluating the users’ muscle fatigue levels. The experimental results showed that the prototype can measure heart sound and pulse signals simultaneously with high accuracy, which is similar to the professional physiological acquisition system. The experimental results also proved that the lightweight prototype decreases the fatigue level during heart sound measurement compared to standard stethoscopes in heart sound measurement. In future work, various cardiovascular disease patients will be asked to participate in the study to improve the performance of the device and to develop a cardiovascular disease self-diagnosis algorithm.