Retinal vessel segmentation is extremely important for risk prediction and treatment of many major diseases. Therefore, accurate segmentation of blood vessel features from retinal images can help assist physicians in diagnosis and treatment. Convolutional neural networks are good at extracting local feature information, but the convolutional block receptive field is limited. Transformer, on the other hand, performs well in modeling long-distance dependencies. Therefore, in this paper, a new network model MTPA_Unet is designed from the perspective of extracting connections between local detailed features and making complements using long-distance dependency information, which is applied to the retinal vessel segmentation task. MTPA_Unet uses multi-resolution image input to enable the network to extract information at different levels. The proposed TPA module not only captures long-distance dependencies, but also focuses on the location information of the vessel pixels to facilitate capillary segmentation. The Transformer is combined with the convolutional neural network in a serial approach, and the original MSA module is replaced by the TPA module to achieve finer segmentation. Finally, the network model is evaluated and analyzed on three recognized retinal image datasets DRIVE, CHASE DB1, and STARE. The evaluation metrics were 0.9718, 0.9762, and 0.9773 for accuracy; 0.8410, 0.8437, and 0.8938 for sensitivity; and 0.8318, 0.8164, and 0.8557 for Dice coefficient. Compared with existing retinal image segmentation methods, the proposed method in this paper achieved better vessel segmentation in all of the publicly available fundus datasets tested performance and results. Automatic segmentation of the retinal vessels plays an important role in the clinical evaluation and diagnosis of many ocular-related diseases. Since the fundus is the only part of the human body where arterioles and capillaries can be directly and centrally observed with the naked eye, morphological information of these retinal vessels, such as thickness, curvature and density, can reflect the occurrence of disease to some extent ,2. StudiHowever, in current clinical practice, the manual examination is usually relied upon to obtain information on these retinal fundus lesions. This task is not only time-consuming and laborious but also requires a high level of medical skills from the physician. Therefore, the automatic and accurate segmentation of retinal vessels from retinal fundus images to assist physicians in examination and diagnosis is very important and meaningful work. Many researchers have applied machine learning methods to retinal vessel segmentation tasks, such as using high-pass filtering for vessel enhancement  and GaboWith the rapid development in the field of computer vision, deep learning techniques have played an important function in the field of image processing. Compared with traditional machine learning methods, deep convolutional neural networks  have a hAlthough convolutional neural networks (CNN) have a strong feature extraction capability, they still suffer from the problem of a limited convolutional kernel field of receptiive. Therefore, CNN is limited to processing local information but cannot focus on global contextual information. In addition, the difficulty of the retinal vessel segmentation task is how to obtain to perform accurate pixel-level classification rather than image-level classification. To solve the above problems, some researchers have introduced the Transformer  frameworA TPA module is proposed to replace the MSA structure in the traditional Transformer, which not only considers the relationship between long-distance pixels but also focuses on the acquisition of blood vessel pixel position information. The network model is adapted to the fine segmentation task of retinal blood vessels with a small number of samples.The MTPA_Unet network model is proposed, and the Transformer and convolutional neural network are combined to design and apply it to the retinal blood vessel segmentation task. MTPA_Unet can alleviate the limitations exhibited by CNN in modeling long-term dependencies and achieve higher retinal vessel segmentation accuracy.Perform ablation experiments and comparative experiments on three datasets, DRIVE, CHASE DB1 and STARE, and analyze the results. The results show that the network model proposed in this paper achieves better vessel segmentation performance.In this paper, we propose an MTPA_Unet network model for retinal vessel segmentation. It consists of a serial combination of Transformer and CNN. Specifically, we first propose a TPA module to replace the traditional Transformer’s multi-headed attention module. Considering that the Transformer structure is not well adapted to retinal datasets with a small number of samples. Therefore, a lightweight positional attention module is added behind The rest of this paper is organized as follows: In recent years, CNN-based methods have achieved great success in the field of semantic segmentation and medical image segmentation due to their efficient feature extraction capability and powerful feature representation. In addition to the classic U-Net , FCN 1111, ResNeIn addition, it is also an important work to make improvements between the accuracy and cost of the attention mechanism; CC-Net  uses a nZhang et al.  proposedThe above methods have contributed to different tasks of retinal blood vessels, innovating the method from different perspectives. However, the segmentation of retinal blood vessels with higher accuracy is the goal that researchers have been pursuing, which is also the starting point of the method in this paper. Compared with these methods using convolutional neural networks, the advantage of this paper is that it pays more attention to the complementary meaning of global information, so Transformer is introduced in the method to assist better segmentation.Transformer  first plSince CNN has great advantages in extracting local information, it is insufficient for feature extraction of long-distance dependencies. Therefore, in order to balance between long-distance dependencies and short-distance dependencies, this paper uses a combination of traditional CNN and Transformer architectures to achieve high-precision segmentation of retinal fundus vessels. The general structure of our proposed MTPA_Unet network model is discussed as follows.The input to the network is derived from slices of the original retinal vessel images. Due to the high detail information of fundus vessel endings, feature map inputs of different scales are used to enhance the feature extraction capability of the network. These feature maps were input layer by layer into each stage of the Transformer structure, with each layer having image input sizes of 64 × 64, 32 × 32, 16 × 16, and 8 × 8 pixels. Each stage consists of Patch embedding, position encoding, and TPA modules. Furthermore, the results extracted by the Transformer are passed to the encoder in the corresponding CNN network, i.e., the output of each stage corresponds as the input of the encoder block. After the initial extraction of the image features by the Transformer, the advantages of the Transformer for long-distance dependency acquisition are exploited and the shortcomings of the encoder are compensated. Since short-distance dependencies occupy a more important proportion in retinal vessel segmentation, we perform further fine-grained segmentation of the feature map. The encoder block consists of a feature extraction module and a downsampling module. Here, the output of each layer of the encoder block is fused and passed into the underlying multilayer pooling module together with the encoder block of the last layer in order to make use of the information in the shallow layers and to assist in better segmentation. Residual connections are used between the corresponding encoder and decoder blocks in each layer to reduce noise interference. Finally, the processed feature maps are then fed to the decoder for feature reconstruction and recovery by upsampling operations. The parts are described in detail as follows. The overall structure of the network is shown in The encoder block consists of a feature extraction module (FE) and a downsampling module (DS). For the input retinal vessel image, it is first passed through the feature extraction module. The FE module is designed to extract the retinal image vessel features, while the number of channels of the input feature map is adjusted, doubling from 32 channels of the input layer by layer to 256 channels of the fourth layer. The process is as follows: for the input feature maps, a 1 × 1 convolutional layer is first used for dimensionality reduction. The image information is extracted using a 3 × 3 convolution and a 3 × 3 transposed convolution for the reduced-dimensional feature map, respectively, and the extracted information is fused. A 1 × 1 bottleneck layer is subsequently used, and the normalization layer is selected for batch normalization and finally activated by the ReLU function. The feature map is output before it is output with the previous input superimposed. The detailed structure is shown in Since the pooling layer has the advantage of good feature degradation and feature invariance, we introduce a downsampling module (DS) after the FE module, which allows the model to extract a wider range of features and serves for further feature extraction and downsampling of the image. The DS module consists of an adaptive pooling, three consecutive batch normalization layers, a ReLU activation function, and a Conv layer. The detailed structure is shown in The Transformer-Position Attention (TPA) module consists of a modified multi-headed attention Patch Embedding layer: The PE layer is used to serialize the input image. Specifically, the input image dimension is x and Since each stage of the TPA module works on a different size of the input feature map, the PE layer is able to downsample the feature map and gradually expand the channel dimension to achieve a hierarchical feature representation. We use PE before each layer of the TPA module except for the first stage, with the aim of using the PE module to scale the feature map spatial dimension and channel dimension. The spatial dimension is reduced by a factor of 4 and the channel dimension is increased by a factor of 2. This process is implemented using a 3 × 3 convolution with a step size of 2 and a padding of 1. The output of each PE layer can be formalized as Equations TMSA=quations  and 6),,TMSA strx in M. In order not to increase extra computational effort, when calculating the correlation of a pixel in the whole image with position x, the feature vectors that are in the same row and column as position x are first searched in the feature map N and saved in the set Due to the presence of more capillaries in retinal fundus images, more details need to be captured in the information extraction. Therefore, we do not simply use Equation , and theThe attention map and the set Equation :(8)Y=∑i=By using this feature correlation calculation twice, it is possible to obtain global contextual information about each pixel location. This more comprehensive information extraction enhances the Transformer and introduces little computational effort. The structure of the TPA module is shown in P) and the true value (denoted as G), and the Dice coefficient formula is defined as Equation DiceCEquation . A constThe retinal images used in this paper are from three publicly available datasets, respectively, the DRIVE, CHASEDB1, and STARE dataset. The DRIVE dataset  consistsThe STARE dataset  consistsThe CHASE DB1 dataset  consistsIn this paper, we also use the necessary preprocessing to enhance the vessel contours in the original retinal images. In this paper, we used the preprocessing methods proposed by Jiang et al. , which aTP) is the number of vessel pixels that are correctly segmented, true negative (TN) is the number of background pixels that are correctly segmented, false positive (FP) is the number of background pixels that are incorrectly segmented as vessel pixels, and false negative (FN) is the number of vessel pixels that are incorrectly segmented as background pixels.To quantitatively evaluate the accuracy of the method in this paper for the retinal vessel segmentation task, the performance of the evaluation metrics such as Dice coefficient, Accuracy, Sensitivity, and Specificity were analyzed using a confusion matrix. The corresponding equations for each evaluation metric are expressed in Equations –15). In. In15). The method in this paper is implemented using the Pytorch framework for deep learning. The model training was implemented on a Quadro RTX 6000 server with a GPU memory size of 24 GB and an operating system of Ubuntu64. The initial learning rate of 0.001 was used for training. We used the model with the best validation performance in the test, and the Dice loss function was used for the loss function.For the DRIVE and CHASE DB1 datasets, the number of iterations of the model is set to 100, the training batch size is 32, and the threshold is set to 0.5. Since there are only 20 images in the STARE dataset, the experiments are conducted using the leave-one-out method to make the training effect as good as possible. That is, one image is used for training at a time, and the remaining 19 samples are used for testing. The training batch size was set to 64, the number of iterations of the model was set to 100, and the threshold value was set to 0.48.In order to verify the effectiveness of the utilization of the shallow information in the model MTPA_Unet, the TPA module, and the Transformer for the retinal vessel segmentation task in this paper, in the same experimental setting, we performed retinal vessel segmentation experiments in DRIVE, CHASE DB1, and STARE datasets, respectively, using the U-network as the baseline model. The performance of these modules was quantified by designing ablation experiments. First, the baseline network is based on a modification of the U-Net incorporating residual connectivity and a multiscale pooling module, denoted as BaseLine. Based on this, the output of each encoder block is fused and passed to the multiscale pooling module to utilize shallow coarse-grained feature information. Next, the Transformer is added to the BaseLine+SCI to compensate for the contextual information, and a multi-scale network input is used. Finally, the MSA is replaced by the TPA structure, which is the model MTPA_Unet in this paper. MTPA_Unet (w/o pre) means operating directly on the original image without any preprocessing.The structure of the ablation experiments performed on the DRIVE and CHASE DB1 datasets are presented in For the STARE dataset, the same ablation experiments were designed. For clarity, the test results of the MTP_Unet model on 20 images are listed in For the models BaseLine, BaseLine+SCI, and BaseLine+SCI+MT were trained and tested on the STARE dataset using the leave-one-out method. For the sake of simplicity, only the average values obtained on the five evaluated metrics are shown here. The experimental results are shown in In addition to using these evaluation metrics to measure the effectiveness of the models, to see more clearly the segmentation effect of the retinal fundus vessels, the segmentation effect of each model on the same data set is shown in It can be seen from the performance of each model on the three datasets in To further illustrate the model validity, the method in this paper conducts comparison experiments with some existing state-of-the-art methods on three datasets. Classical advanced methods such as U-Net++ , R2U-NetAs can be seen from Similarly, the segmentation capability of the model is visualized with visualized images of retinal vessel segmentation results. The method in this paper is compared with the current better performing UNet++ , CA-Net We evaluate the cost of the network model to obtain better segmentation performance from the perspective of the model parameters. Ordinary CNN networks usually do not introduce too much computation, while Transformer leads to a higher number of parameters due to the complex multi-headed attention computation. To demonstrate that our final model MTPA_Unet does not introduce too many parameters, the obtained high precision experimental results do not only rely on the complex model to be achieved. The model with the introduction of the Transformer structure is compared with the number of MTPA_Unet parameters with the addition of the position attention module. As can be seen in To further judge the model performance, Receiver Operating Characteristic (ROC) curves and Precision Recall (PR) curves were calculated for each ablation structure network model and displayed in visualization in The MTPA_Unet retinal vessel segmentation network model proposed in this paper jointly uses Transformer and convolutional neural network to help improve the performance of the network model. Since the connection between two distant pixels on an image is important for more accurate retinal vessel segmentation, the convolutional neural network is utilized to extract the long-distance dependencies while taking advantage of the convolutional neural network for local information extraction. The proposed TPA module can further enhance the acquisition of retinal vessel location information, having richer feature information to be fully used in the refinement process. The multi-resolution image input and the utilization of shallow feature information further alleviate the problems of blurred boundaries of segmentation results and inaccurate capillary segmentation. We trained and tested the MTPA_Unet network model proposed in this paper on the DRIVE, CHASEDB1, and STARE datasets. The evaluation shows that the model has achieved good results in terms of Accuracy and Dice. Comparison experiments were also designed to compare and analyze the evaluation results with other popular methods to visually demonstrate the segmentation details of each network model on the retinal vessel task. The comparison of the segmentation results and the analytical discussion show that the MTPA_Unet network model proposed in this paper is more advantageous compared with other methods. Future research will aim to further improve the accuracy of the network model for the retinal vessel segmentation task without sacrificing time and storage.


Smart home technologies have attracted more users in recent years due to significant advancements in their underlying enabler components, such as sensors, actuators, and processors, which are spreading in various domains and have become more affordable. However, these IoT-based solutions are prone to data leakage; this privacy issue has motivated researchers to seek a secure solution to overcome this challenge. In this regard, wireless signal eavesdropping is one of the most severe threats that enables attackers to obtain residents’ sensitive information. Even if the system encrypts all communications, some cyber attacks can still steal information by interpreting the contextual data related to the transmitted signals. For example, a “fingerprint and timing-based snooping (FATS)” attack is a side-channel attack (SCA) developed to infer in-home activities passively from a remote location near the targeted house. An SCA is a sort of cyber attack that extracts valuable information from smart systems without accessing the content of data packets. This paper reviews the SCAs associated with cyber–physical systems, focusing on the proposed solutions to protect the privacy of smart homes against FATS attacks in detail. Moreover, this work clarifies shortcomings and future opportunities by analyzing the existing gaps in the reviewed methods. The smart home is a concept that has existed for a few decades ,2,3 and IoT systems depend heavily on wireless communication solutions. Since a home network carries a remarkable volume of residents’ personal information, any data breach incident can be a catastrophe for users—for instance, the unauthorized disclosure of lifestyle, health conditions, political views, and financial situations ,13. In mSide-channel attacks (SCAs) exploit systems without having complete control over the devices; the attackers lack access to the source codes, communication contents, and detailed knowledge about system functionality. Encryption solutions cannot prevent these attacks, since the attack model is not interested in the content of the communications. Conversely, physical aspects, system responses, and contextual data related to a smart device’s wireless communication provide data-rich resources to infer critical information. A fingerprint and timing-based snooping (FATS) attack is an SCA that eavesdrops on emitted signals from a home perimeter and converts them into insights about the in-home activities of the residents. Passively violating the victims’ privacy puts them in a problematic situation. First, they cannot detect the attack during its operation; secondly, the leaked data can be used for more severe threats, e.g., blackmailing and selling data to a third party. To overcome the risk of FATS attacks, researchers have attempted to develop proactive defensive strategies for sabotaging the attack’s performance since it cannot be blocked or stopped. The foundation of this method of privacy protection is concealing the wireless traffic patterns of the actual events and obfuscating the correlations between the transmissions, either by adding a deliberate random delay before packet transmissions or by an injection of numerous fake packets identical to the actual ones into the network traffic.The challenges in devising a privacy-preserving mechanism for smart homes against FATS attacks go beyond enhancing the privacy rate or diminishing the attack accuracy rate. A practical method must provide an optimal trade-off between the provided privacy rate, the system latency caused by the method, and energy demands. An investigation of the existing protection solutions indicates that many of these approaches’ drawbacks lie in their poor performances regarding energy consumption and system response time. Reporting actual events with a delay harms the quality of service, and random injections of fake packets drain the system’s energy resources.This article provides a novel overview of the existing protection solutions against “fingerprint and timing-based snooping (FATS)” attacks as passive side-channel attacks that effectively invade the data privacy of smart homes. The aim of this work is to elaborate on the latest solutions’ advantages and discuss their shortcomings to provide comprehensive insight on how to tackle the privacy-related vulnerability of smart homes.This paper consists of six sections. The first section covers a brief overview of the problem’s background and states the work’s contributions. A smart home is a residential building with several interconnected smart systems; these embedded systems provide some advanced digital services to users, such as remote healthcare monitoring, the intelligent management of utilities, high-level security surveillance, etc. ,15. The Privacy-related threats to smart homes are among the most significant risks that need to be addressed. However, an extensive list of cyber attacks might compromise the functionality of smart home systems. Ensuring the security of the personal information of smart home residents is a vital requirement to eliminate the threats associated with the wide acceptance of such systems . The datConfidentiality in a wireless system refers to properly concealing the contents of data packets, which include either controlling messages or information about the functionality of smart devices, and preventing unauthorized access by intruders . ImplemeData privacy indicates that the protected information belongs to a person rather than a device or system. Residents of an intelligent building share extensive data about their matters with the system, and numerous embedded sensors in various smart devices are in charge of collecting data about users’ Activities of Daily Living (ADL). These comprehensive accumulated data enable the system’s intelligent engines to evaluate the situation of users and make services to comply with their desired needs. Likewise, the system’s ability to acquire data provides a valuable resource for intruders to discover sensitive information about residents .Based on the definition, privacy means the right someone has to keep their personal life or personal information secret or known only to a small group of people , which iIn the case of FATS attacks, intrusion detection systems do not help due to the passive nature of the attack. The attacker quietly collects the data and abuses them; the catastrophic results of the snooping appear once it is too late for the victims to properly react. Moreover, encryption methods fail to secure the intended information by the attackers since the data leak from the contextual aspects of the wireless transmissions. Therefore, the above situations emphasize the need for a robust, proactive defense mechanism within home systems to protect users’ information. Conducting a side-channel attack means exploiting the physical aspects of a device to discover its associated critical information. The primary assumption in SCAs is that data are constantly leaking; therefore, attackers have opportunities to take malicious advantages of the system .SCAs are divided into active or passive attacks. Active SCAs require physical access to the targeted device/system or physical proximity. An example of an active SCA is the fault analysis attack, in which hackers inject predefined inputs and observe the system’s response; through this process, they intend to discover how the device works. Another example is analyzing the sounds produced by a device and discovering their correlations with the system’s functionality, which is more applicable to systems with mechanical actuators. A potential countermeasure to these attacks is physical security solutions. In contrast, passive SCAs are not recognizable by the victims during the attack. They quietly exploit the external aspects of a device. For example, an eavesdropping attack remotely listens to the network traffic and captures transmitted data packets for further analysis. Even though the captured packets have been encrypted, attackers can extract valuable information from the contextual aspects of the signals carrying those data packets. A suitable solution to overcome the risk of passive SCA attacks is to employ proactive defensive methods to prevent the attackers from interpreting the stolen packets .A typical smart device developed for a smart home usually has several standard building blocks, such as a processor, I/O ports, data storage disk, wireless communications modules , power supply unit, and, depending on the device’s application, a group of sensors and actuators. For attackers who utilize SCAs, each device component is prone to exposing valuable data. Therefore, in the last decades, numerous malicious techniques have been developed for invading various aspects of intelligent devices ,33,34. As illustrated in Side-channel attacks are divided into three classes from a physical security perspective: invasive, semi-invasive, and non-invasive. An invasive attack physically manipulates the targeted device which destroys the device most of the time. Damaging the components to study their functions and conduct chemical examinations are examples of this class of SCAs. Similarly, semi-invasive SCAs need physical manipulations but do not dismantle the devices—for instance, opening the enclosure to gain direct access to the PCB or disassembling some parts. In contrast, non-invasive SCAs only exploit accessible information via data ports, external power cables, wireless communications, emitted electromagnetic radiations, or produced sounds. These items provide valuable contextual data about the system and its functionality . Figure A simple power analysis (SPA) monitors the alterations of a device’s consumption of power when executing various algorithms and reveals a series of patterns that can be correlated to specific activities. Attackers attempt to match these extracted patterns with known algorithms to identify the device’s function. For example, this method can differentiate between encryption methods since their energy demands are different. One might note that occasional current spikes and noises are challenges that limit the performance of this approach .A differential power analysis (DPA) is an improved power analysis method that applies statistical error-correcting approaches. This method monitors the power consumption of a device to discover the encryption key. The primary difference between the DPA and SPA methods is that the DPA method analyses the device’s power consumption for two types of operations, non-cryptographic and cryptographic; then, it compares the results to discover the system’s critical information. A DPA is a powerful tool that threatens all sorts of hardware protected by cryptographic approaches ,38.A fault analysis is an approach in which the attacker injects various types of faulty inputs into a smart device and then investigates the outcome of the system. A few examples of physical hardware manipulations are increasing the device’s temperature, applying a laser beam at a specific frequency, and injecting fake inputs to surge the likelihood of the signals’ collision ,40.An electromagnetic analysis takes advantage of the emitted power radiations from devices protected by encryption approaches while performing encryption and decryption processes to discover the correlation between EM radiations and the ciphertext. This attack does not require proximity of the attacker system to the target device, depending on the radiation receiver equipment’s strength, which makes it suitable for remote performance .An acoustic analysis takes the produced sounds (noises) by the electromechanical components of a device as the input and attempts to obtain the system’s secret information via analyzing the acoustic oscillations. The advantages of this attack are the capability of the attack algorithm to precisely distinguish the slightly different sounds and its use of relatively simple equipment, such as a digital sound recording device or a smartphone ,43,44.A timing analysis concentrates on temporal data related to the wireless signal transmissions of a system. The attack aims to discover the temporal correlations in the network traffic and recognize the patterns, which reveals critical information about the system’s behavior. This malicious approach is a fitting option for an eavesdropping attacker who has a global view over the system’s communications, especially if they are interested in a system’s contextual information .A traffic analysis includes a wide range of options for scrutinizing the system’s network traffic. This approach mostly focuses on transmitted data packets over the wireless network. The number of packets, the packets’ associated signal fingerprints, and the correlation between the packets forwarded from different devices are valuable contextual data for an attacker in identifying the sender, receiver, and their locations .Due to their passive nature, eavesdropping-based cyber attacks are among the most challenging threats for cyber–physical systems. However, several cryptographic solutions have been developed to secure the content of communications. A sub-class of these attacks aims at the contextual data of the system; therefore, encryption methods would be irrelevant to ensuring system data security while encountering such threats. In this paper, we investigate an attack of this sort called a fingerprint and timing-based snooping (FATS) attack . This atThe stolen data from a home network may contain sensitive information about the residents’ lifestyle, political views, financial situation, personal health conditions, sexual conduct, routines, future schedules, shopping preferences, and more. Therefore, an attacker would be capable of launching subsequent severe attacks to harm their victims using this information. Notable examples of probable risks include blackmailing, selling information to a third party, terrorism, defamation, and government surveillance.Tier 0 detects smart devices based on their unique radio signatures. The fingerprint of data transmission is a set of RF waveform features that differentiate the source of signals even if the signal senders have a similar manufacturer and model. The attack identifies basic events, such as home occupancy or sleeping, in this step.Tier 1 clusters the identified nodes by investigating time intervals between signal transmissions. The assumption is that devices which are spatially located close to each other activate at a proximate time. Thus, the produced clusters represent either the location of their member devices, e.g., a room, or their purposes, e.g., cooking.Tier 2 performs clustering labelling using the extracted features from the formed clusters. Note that this process prioritizes logical categorization rather than device locations. For example, the washing machine will be categorized as a laundry event, although it might be located in the kitchen or basement.Tier 3 performs another round of classification in which the attack algorithm uses the extracted feature vectors of the node clusters and the training data to label those clusters as the devices. For example, the attack’s trained model expects the stove to be in the kitchen; therefore, it examines the clusters placed in the kitchen cluster with the stove’s features vector extracted from the actual dataset. In the case of a proper match, the unknown device cluster would be recognized as the stove.Implementing FATS attacks consists of multi-tiers in which a combination of machine-learning techniques, such as classification, clustering, and features-based matching, takes place. The attack clusters the captured signals based on their radio fingerprints and attempts to find correlations between data packets’ forwarding timestamps. Eventually, the algorithm identifies the connected devices, rooms, and in-home events; from this point onwards, the hacker obtains unauthorized monitoring access to the smart home. The four tiers of this attack are as follows:The FATS attack procedure combines several statistical and machine-learning techniques to discover the identities of rooms, devices, and activities. First, it clusters the records based on the radio fingerprints. Then, using the associated timestamps, it creates a temporal matrix representing the time proximity between the transmissions. Next, it converts the temporal matrix to a metric distance matrix using Dijkstra’s shortest path algorithm . It applThe attack converts the inputs to the devices’ geospatial information. Then, it attempts to identify devices, rooms, and activities by matching the produced feature vectors from the created unknown clusters to the known vectors for its model using some classifier models. As a result, it successfully violates the residents’ privacy without cracking the secret key of the encryption. The attack labels the room clusters in the next tier by computing the maximal min-cost bipartite mapping ; the criInvestigations into the proposed methods developed to protect smart homes encountering FATS attacks indicate that researchers have attempted different defensive strategies, and in some cases, they have succeeded in remarkably improving the privacy rate. Although preserving privacy is the primary goal for every proposed solution, the implications of the employed approaches on the other system parameters are critically important. For example, solutions that impose latency on the system communications decrease its quality of service; this side effect is especially unacceptable for a few delay-sensitive home systems, such as systems for the detection of fire and elderly people falling. In addition, the methods’ energy demands have to be justifiable. A suitable solution must have an optimal balance between the three system parameters, namely the privacy rate, communications latency, and energy consumption.Confusing the pattern recognition mechanism in FATS attacks is the main idea of the defensive strategies to mitigate the risk of data breaches; for this purpose, the preferred approach is to obfuscate the signal traffic of the smart home’s wireless network. Since the attack algorithm’s foundation is finding the temporal correlations between transmitted signals, manipulating the actual patterns sabotages the attack’s performance.Strategies for developing a protection method fall into two categories. In the first category, solutions delay sending the data packets. The delay durations are randomly determined; therefore, the attack would have difficulty finding the actual time correlations between the transmitted signals. On the other hand, methods in the second category randomly inject some fake packets into the network traffic, and the characteristics of these dummy packets are identical to the actual ones; therefore, the attack cannot differentiate between them. As a result, the accuracy of the FATS attack diminishes because it will be challenging for the attacker to properly understand the actual events occurring in the home.The temporal manipulation of signal traffic refers to altering the actual time correlations of signals transmitted by devices that collaboratively report an event. To do this, devices use a random interval generator algorithm to delay their packet transmissions for a random amount of time. The primary assumption of the attack is that devices collaborating in reporting an event send out their messages at a proximate time to each other; therefore, the deliberate change in transmission times prevents the attacker from finding the real activity patterns, resulting in a decline in the attack’s accuracy. The drawback of this strategy stems from the excessive latency imposed on the system’s communications. Some of the smart home’s sub-systems are delay-sensitive, e.g., healthcare-monitoring systems, because delayed communications affect their response time, a critical factor in their performance. Injecting a series of dummy data packets into the home’s wireless traffic is an alternative approach to mislead the FATS attack. In this strategy, the system tasks the smart devices to generate a random number of data packets identical to actual ones and transmit them in random intervals. The forged messages are encrypted; therefore, the attack cannot distinguish them from the actual packets; therefore, it considers them in its pattern recognition processes and this mistake leads the attack algorithm to make false conclusions. The falsehood of the results is either due to the attack’s failure to detect the patterns of actual events  or to reporting some activities that never occurred . Both of these mistakes affect the correctness rate of the attack. As shown in Data privacy and security in wireless network systems (WSN) and the Internet of Things (IoT) have been studied extensively in recent years. In this domain, several research studies have focused on the sub-category of smart buildings and attempt to address concerns such as source anonymity , data eaIn , the autAn argument for the effectiveness of this scheme is that establishing a transmission framework from the beginning of the process results in a uniform distribution of signals in the network traffic; therefore, finding any time correlations in the transmitted signals is nearly impossible for the attack. Failing to recognize actual traffic patterns disarms the FATS attack completely. Simulation results of the ConstRate scheme support this claim by showing a near-perfect privacy protection rate. On the contrary, the drawback of the ConstRate scheme is its detrimental effect on the system’s response time and energy efficiency. The determination of the waiting interval is random in this approach. Thus, on the one hand, the shorter waiting intervals enforce a higher number of fake packet injections and surge the system’s energy demand. However, on the other hand, the longer intervals increase the transmission delays for the actual messages, which prolongs the overall latency within the system. Neither of these consequences comply with the required optimal trade-off for the critical factors of the system. In response to the shortcomings of the ConstRate scheme, researchers proposed the ProbRate scheme in . In thisThe chosen intervals get shorter over time by following an exponential distribution, producing shorter packet-forwarding delays, resulting in less overall latency in the system. This method keeps the smart home’s privacy rate high despite the attack’s capability of viewing all the traffic, recognizing the distribution pattern, and obtaining its mean over time. Observing a repetitive pattern in the network traffic decreases the attack’s capacity to identify the needed time correlations, and similar to the previous scheme, the ProbeRate scheme offers near-perfect privacy protection for smart homes against FATS attacks. Although the ProbRate scheme reduces delays, it does not eliminate the overall system latency. The fact is that this issue is still problematic for delay-sensitive systems in smart homes. Additionally, by reducing the waiting times for the packet injections, the number of injection windows increases, which increases the likelihood of injecting fake messages; thus, the systems’ energy consumption escalates.The FitProbRate (FPR) scheme is another approach for preserving privacy in a smart home facing FATS attacks as proposed in . This scAccording to the authors’ reported results, the FitProbRate scheme’s system latency is approximately one-tenth that of the ProbRate scheme, which indicates a remarkable improvement in terms of maintaining the quality of service of the smart home. Conversely, the proposed scheme does not offer any improvements regarding the energy overhead issue. In this scheme, smart devices must inject at least a dummy packet in every injection window that is empty of actual messages to disrupt the performance of the attack’s pattern recognition algorithm. As a negative effect, for a long period of silence in the home, e.g., at night, the system consumes a massive amount of energy for injecting unnecessary fake packets since there are no actual patterns to conceal. Moreover, similar to earlier schemes, the number of injected fake packets is an uncontrollable random value which undesirably affects the energy demand of the scheme. In a subsequent work , the autThe latency issue of the privacy-protection method is significantly reduced in , in whicThe SDASL consists of two phases, sample data analysis and supervised learning. In the first step, the model simulates the dissemination of fake messages using the distribution of the radio frequencies (RF); the output is referred to as the FDR. Firstly, the similarity in the extracted frequency rates from the sample dataset and dummy messages is critical. Then the central controller updates the smart devices with the produced FDR. In the second phase, a supervised learning model is employed to perform three tasks: data collection, labelling, and learning model parameter updating. Every device in the home network must be upgraded with a copy of the final prediction model; the required inputs for this model are time and the network’s traffic status. A logistic regression function makes the final decision using the real-time inputs and a threshold set of 0.5; computation results over the passing mark indicate that the fake packet must be injected. The SDASL method requires frequent communication between the central controller and the smart devices. Reportedly, the SDASL method decreases the FATS attack’s accuracy by 30% after 13 days of model training, which means a 70% privacy rate for the home. The achieved privacy protection is significantly lower than that of the aforementioned statistical-based schemes, but it resolves the need for injection delays. Energy-wise, the results state that for every actual data packet, 13 fake packets were injected, meaning the energy cost of the SDASL method is thirteen times more than that of an unprotected home.The authors in  briefly The reporting of fake events by the attack increases its true positive rate (TPR); therefore, the attack’s accuracy rate decreases effectively. This approach is called the actual activity mimicking (AAM) method and provides the most optimal trade-off for the triad of privacy protection, communication latency, and energy consumption. In this solution, the policy of immediately forwarding the actual data packet eliminates the latency issue. Moreover, it improves the privacy rate by deceiving the FATS attack to report unreal events, which decreases the TPR of the attack model. In addition, despite the randomness of the injections, this method increases the overlapping chance for fake and actual activity patterns, using a probabilistic-based mechanism that concentrates the injections on periods of the day with a higher likelihood of actual activities. This strategy enhances the privacy rate, consumes energy resources more efficiently, and complies with the zero-delay requirement for quality-of-service matters.This attack performs passively; therefore, it is undetectable during the attack period.The attack extracts information from contextual data of the home’s communications; thus, encryption methods cannot resist it.This malicious algorithm requires a minimum of inputs including the signals’ fingerprints and timestamps of the transmissions; hence, blocking its access to these data is challenging.Preserving the privacy of smart homes is a critical requirement that should be fulfilled to prevent unintended consequences of potential data breaches. FATS attacks effectively let attackers discover smart home residents’ private matters related to their in-home activities. The essential characteristics of this attack can be summarized as follows:All things considered, since the attack is unstoppable, it is evident that mitigating the risk of this threat would be a proactive protection solution to ensure the traffic patterns of the snooped data are altered in a way that the malicious algorithm cannot interpret them precisely.Investigating the existing protection methods indicates that the predominant tactic in these approaches is to maximize the obfuscation of the network traffic to challenge the attack’s pattern-recognition ability. It seems reasonable to assume that the poor performance of the attack in detecting activity patterns causes problems in subsequent phases. This aim is obtained by techniques such as the temporal manipulation of the transmitted signals or injections of random fake data packets, both effectively altering the traffic’s patterns. Nonetheless, this achievement comes with a cost for the system which appears as either latency in communications or an energy overhead. Notably, the home privacy rate has a reverse relationship with the attack’s accuracy; therefore, any decline in the attack’s correctness rate means an equal gain for the privacy rate. The two primary metrics to compute the attack’s correctness rate are the event detection rate (EDR) and the true positive rate (TPR) . The EDRThis study’s investigations have shown that most reviewed works emphasize decreasing the attack’s EDR as their primary aim. The logic behind this choice seems to be simple but effective; if the attack does not detect the events, it cannot identify them. The results of a few reviewed protection schemes support this argument, such as the ConstRate, ProbeRate, and FitProbRate schemes that provide near-perfect privacy for home systems. However, overlooking the drawbacks of these solutions would be costly. These approaches alter the time correlations of the transmitted signals by delaying the forwarding of them, which means there is an unbearable latency in the real-time services of the home. This issue affects the response time of delay-sensitive systems and harms their effectiveness. Examples include incidents such as an elderly person falling, the detection of a fire which requires an immediate report, or in a more straightforward case, the late performance of smart locks which can inconvenience users.Moreover, concealing the actual activities and traffic patterns demands the injection of numerous fake packets; the number of these injections is not deterministic to comply with the necessity of randomness in the protection procedures. The results have shown that to provide an adequate privacy rate, the number of fake packets is many times more than that of the actual packets. In this regard, the notation of FVR refers to the ratio of the dummy packets to the actual ones. Since transmitting both types of data packets equally consume energy resources, the system’s energy overhead can be massive, which undermines the affordability of the solutions.Unlike other methods, the actual activity mimicking method targets the TPR to decrease the attack’s accuracy. Unlike others that attempt to conceal the traffic patterns of the actual events, this method encourages the attack to detect activities as much as possible. Therefore, this matter provides the opportunity to blend more fake activity patterns into the final outcome of the attack which diminishes the TPR. Injecting fewer fake packets lowers the system’s energy consumption and eliminates delays in wireless communications, which are the advantages of this method over the other solutions.In sum, the critical metric for evaluating a privacy protection method is the circumstances of the trade-off between the provided privacy rate, the latency caused, and the energy consumption of the solution. A method will be ideal if it maximizes the privacy rate while keeping the two other factors as low as possible. The first reason for the necessity of considering this trade-off is the existence of delay-sensitive smart home sub-systems and the need to comply with quality-of-service requirements. The second justification stems from the unintended increase in operating costs caused by the massive energy overhead. Smart homes are IoT-based systems that provide in-home services to residents for their convenience and for control over their domestic lives. However, these technologies have some cyber security flaws that expose them to data leakage, such as wireless snooping-based side-channel attacks. This issue implies the necessity of protecting residents’ personal information by employing a robust privacy-preserving mechanism to prevent the consequences of probable data breach incidents.A side-channel attack is a sort of cyber attack that exploits the contextual data of cyber–physical systems to infer their functionalities and secret information without gaining access to their source codes or their communication contents.FATS attacks are effective SCAs that attackers launch on smart homes to eavesdrop on the home’s wireless traffic to obtain the in-home activities passively. Reportedly, the success rate of FATS attacks in detecting and identifying in-home events reaches over 80%.In this paper, we have reviewed the fundamental strategies to counter FATS attacks and the existing methods in the literature to investigate their strengths and weaknesses, providing novel insight into research gaps and potential opportunities to tackle this imposed threat on smart homes. As far as our research has revealed, the principal protection strategies that the existing solutions have been built upon include the temporal manipulation of signal traffic, the injection of fake patterns into the traffic, or a combination of these techniques.The obfuscation of the home network’s wireless traffic patterns is the predominant approach for confusing the attack algorithm, which diminishes the event detection rate (EDR) and, consequently, the accuracy of the attack. This aim is obtained by manipulating data packet-forwarding processes. Delaying the reporting of events, fake packet injections, or a combination of these are applied techniques for this purpose.Randomness is a key part of the traffic-obscuring techniques that makes it hard for an attack to find the real correlations between the signals sent by the devices.Delay-based methods are not preferred because they hurt the quality of service of the system. This is because the communication latency is too much for delay-sensitive services in the home and it stops them from working effectively.Privacy protection methods impact the energy efficiency of the system. The evidence indicates that to achieve the desired privacy rate, the number of fake injections is usually multiple times more than that of the actual packets; moreover, since the randomness of the dummy packet injections puts the number of transmissions out of one’s control, the system’s energy resources might suffer from a massive overhead caused by the protection technique.The achieved trade-off in the triad of privacy rate, system latency, and energy consumption is a comprehensive metric for evaluating the adequacy of a privacy-preserving method.Reviewing the existing protection methods against FATS attacks indicates the following findings:Changing the assumptions related to smart home configurations, increasing the number of residents, and improving the capabilities of attacks can lead to further research questions and provide future work opportunities in protecting the privacy of smart building users. However, establishing an optimal trade-off between the system’s critical parameters by maximizing home privacy, compliance with the QoS requirements, and energy demand affordability should be the common focal point of all efforts in the future.


Autism Spectrum Disorder (ASD) is a complicated collection of neurodevelopmental illnesses characterized by a variety of developmental defects. It is a binary classification system that cannot cope with reality. Furthermore, ASD, data label noise, high dimension, and data distribution imbalance have all hampered the existing classification algorithms. As a result, a new ASD was proposed. This strategy employs label distribution learning (LDL) to deal with label noise and uses support vector regression (SVR) to deal with sample imbalance. The experimental results show that the proposed method balances the effects of majority and minority classes on outcomes. It can effectively deal with imbalanced data in ASD diagnosis, and it can help with ASD diagnosis. This study presents a cost-sensitive approach to correct sample imbalance and uses a support vector regression (SVR)-based method to remove label noise. The label distribution learning approach overcomes high-dimensional feature classification issues by mapping samples to the feature space and then diagnosing multiclass ASD. This technique outperforms previous methods in terms of classification performance and accuracy, as well as resolving the issue of unbalanced data in ASD diagnosis. Autism spectrum disorder (ASD) is a series of complex neurodevelopmental disorders, and its clinical manifestations are mainly social interaction disorders, verbal communication disorders, and stereotyped repetitive movements , 2. StatStudies have shown that autism spectrum disorders are related to the abnormal brain function in patients and resting-state functional magnetic resonance imaging, which reflects functional changes such as brain metabolic activity in patients under a resting state, is reflected using blood oxygen-dependent levels , 5. ResoThe class imbalance problem under high-dimensional features is another challenge involved in the auxiliary diagnosis of multiclass ASD . The neuLabel distribution learning (LDL) is designed to cope with label noise in this technique, while support vector regression (SVR) is also used to handle the sample imbalance. According to the results of the trials, the proposed technique optimizes the effects of majority and minority classes on outcomes. It can handle skewed data in ASD diagnosis and can assist with ASD diagnosis. This study provided a cost-sensitive technique for correcting sample imbalance using a support vector regression (SVR)-based method to reduce label noise. The label distribution learning approach addresses high-dimensional feature classification challenges by mapping data to the feature space and then diagnosing multiclass ASD. In terms of classification performance and accuracy, our proposed strategy outperforms earlier methods, as well as eliminates the challenge of unbalanced data in ASD diagnosis.However, the improved model is still biased towards the majority class to some extent, and the imbalanced data problem should be improved further as a future study. Researchers can further try to improve the data sampling method or use the synthetic minority sample method as future prespectiveThe paper is framed into several sections where Label distribution learning (LDL) is a machine learning method that has emerged in recent years . It intrLabel distribution learning requires that the training data contain label distribution information. However, in real life, people often label samples in the form of single-label or multilabel, making it difficult to directly obtain label distribution information. Nonetheless, the labels of these data still contain relevant information about the distribution of the labels. Marker enhancement enhances the supervised information of samples through the implicit correlation between different sample markers, thereby achieving better results in marker distribution learning . For exaxi ∈ Rq to represent the ith sample, where q represents the dimension of the feature vector; X =  ∈ Rq×N; li=T represents the logical token corresponding to xi, where K represents the number of possible tokens; and lij ∈ {0, 1}. Similarly, di=T ∈ RK represents the label distribution of the ith sample, where dij ∈  represents the jth value of the label distribution of the ith sample, satisfying ∑j=1Kdij=1, D =  ∈ RK × N.The main symbols in this paper are expressed as follows: Use The label distribution learning algorithm for multiclass autism auxiliary diagnosis proposed in this paper is shown in (1)p fuzzy clusters, and find the center of each cluster, so that the sum of the weighted distances from all training samples to the cluster center is the smallest. Equation  represents the distance measure, and each sample the membership degree represents the strength of the association between the sample and the cluster. The clustering result of traditional FCM is greatly affected by the initial value and cannot ensure convergence to the global optimal solution, but in label enhancement, the clustering result of FCM is only used as a transitional bridge. Although the clustering result fluctuates, however, it has little effect on the results of label enhancement, and the gaps between the Chebyshev distance and the KL divergence (Kullback–Leibler divergence) of the results of multiple label enhancements are both below 10−6.Among them, (2)Construct an association matrix A between markers and clusters. The elements in the matrix represent the degree of association between markers and clusters. The calculation method of the association matrix is as follows:Aj is the jth row of the matrix and Aj is the sum of the membership degree vectors of the samples of the jth class. After the rows are normalized, the association matrix A can be regarded as a fuzzy relationship matrix of clustering and labeling.In the formula, (3)According to the fuzzy logic reasoning mechanism, the fuzzy synthesis operation is performed on the association matrix and the membership degree, and the membership degree of the sample to the label is obtained . After nLabel distribution learning describes the degree of correlation between each label and sample by introducing a descriptive degree, so it can obtain richer semantic information from the data than multilabel and more accurately express the relative importance difference of multiple labels of the same sample. However, the basic requirement of labeled distribution learning is to have labeled distributed datasets, which is often difficult to meet in reality. The marker distribution data can be obtained by transforming a given multimarket form sample by a marker enhancement method. The label enhancement method based on FCM (fuzzy C-means) and fuzzy operation is adopted . The basThe marker enhancement based on FCM and fuzzy operation introduces cluster analysis as a bridge. Through the compound operation between the membership degree of the sample to the cluster and the membership degree of the cluster to the marker, the membership degree of the sample to the marker, that is, the marker, is obtained distributed. In this process, the topological relationship of the sample space is mined through fuzzy clustering, and this relationship is projected to the label space through the association matrix, so that the simple logical labeling generates richer semantic information and transforms it into a label distribution.This paper uses both the evaluation metric of the label distribution and the evaluation metric of the multiclassification task for algorithm evaluation. All evaluation indicators and calculation formulas are shown in Pj is the precision of the jth class, xnor is the XOR calculation, Dis is the distance, Sim is the similarity, and mAP is the macro-averaging precision.In http://fcon_1000.projects.nitrc.org/indi/abide/). All rs-fMRI datasets used in this paper were obtained from the ABIDE website  tool to extract the average time-series signals of each brain region, calculate the Pearson coefficient between the brain regions, and obtain the functional connectivity matrixTake each row of the functional connectivity matrix as the feature description of each brain region, take the upper triangular matrix of the functional connectivity matrix, and connect the rows in series to obtain the corresponding eigenvectorsAlthough brain regions are spatially isolated from each other, the neural activity between them influences each other. This paper uses the brain functional connectivity matrix between brain regions as a classification feature . The calThe proposed CSLDSVR method is compared with six existing LDL algorithms and two multiclassification algorithms. Two multiclassification algorithms are decision tree and K-nearest neighbor (KNN), both of which are classic multiclassification algorithms , 34. Theε, and the kernel bandwidth of the Gaussian kernel. The specific range of parameters is shown in The CSLDSVR algorithm proposed in this paper has four parameters, namely, the weight coefficient C, the type of kernel function, the size of the insensitive region To verify the performance improvement of the cost-sensitive mechanism, the algorithm in this paper is compared with the LDSVR without the cost-sensitive mechanism. As shown in ε take different values on five different datasets. Comparing and studying two graphs of the same parameter and different indicators, such as Figures In this section, we study the effect of parameter changes on the performance of the algorithm CSLDSVR. It is found that for different datasets, the parameter values for obtaining the optimal solution are not the same, which also shows that in the diagnosis of autism, the data distribution of different data centers is different, and the parameters for building the model should also be different. Moreover, it is found that for a dataset with fewer samples, the result is more sensitive to the change of the parameters, such as for the KKI dataset with only 48 samples, the fluctuation is the largest when the parameter value changes.It can be seen that the parameters of the CSLDSVR algorithm should be based on the characteristics of the dataset, and the corresponding parameter values should be set to build a model. If the parameter settings are reasonable, CSLDSVR can overcome the high dimensionality and category imbalance of the autism dataset. Thus, the whole section contains the strategy of how ASD detection is done by evaluating several strategies such as SVR and LDL by considering certain parameters. Therefore, the label distribution learning approach overcomes high-dimensional feature classification issues by mapping samples to the feature space and then diagnosing multiclass ASD. This technique outperforms previous methods in terms of classification performance and accuracy, as well as resolving the issue of unbalanced data in ASD diagnosis.This research presents a cost-sensitive marker distribution to enable an ASD-aided diagnostic approach for vector regression based on functional connectivity characteristics collected from rs-fMRI. Since ASD patients' brain function differs from that of healthy persons,so rs-fMRI is a useful method for capturing brain activity. In this study, researchers have introduced the label distribution learning that solves the label noise problem in multiclassification ASD diagnosis. Furthermore, the new technique have been implemented, which provides class balancing and in addition balances the effect of the majority and minority classes on the objective function using the labeled distribution support vector regression method. The new method employed in this study effectively solves the imbalanced data problem in ASD diagnosis by overcoming the imbalance of the influence of the majority and minority classes on the results obtained in the paper. Besides, it presents a cost-sensitive approach to correct sample imbalance and uses a support vector regression (SVR)-based method to remove label noise. The label distribution learning approach overcomes high-dimensional feature classification issues by mapping samples to the feature space and then diagnosing multiclass ASD. The overall result obtained in this technique outperforms previous methods in terms of classification performance and accuracy, as well as resolves the issue of unbalanced data in ASD diagnosis. However, the improved model is still biased towards the majority class to some extent, and the imbalanced data problem should be improved further as a future study. Researchers can further try to improve the data sampling method or use the synthetic minority sample method, etc. as future prespective. However, relatively high-level distances must also be introduced, which necessitates more prior knowledge. Since prior knowledge is no longer used, the Euclidean distance is used instead. Other advanced distances have their set of benefits that will be refined in future research.


Background: the organisation of a COVID-19 vaccination campaign for healthcare workers (HCWs) within a university hospital presents a challenge of a particularly large scale and urgency. Here, we evaluate the in-hospital vaccination process and centre for HCWs at LMU University Hospital in Munich, Germany. Methods: We executed a mixed-method process evaluation of the vaccination centre at LMU University Hospital during the first COVID-19 vaccination campaign. In a programme monitoring, we continuously assessed the implementation of the centre’s operational management including personnel resources. In evaluating the outreach to and satisfaction of the target group with the centre and process, we executed two anonymous surveys aimed at the HCWs vaccinated at the in-hospital centre (1) as well as centre staff members (2). Results: staff numbers and process time per person were reduced several times during the first vaccination campaign. Lessons concerning appointment scheduling were learned. HCWs vaccinated at the in-hospital centre were satisfied with the process. A longer waiting time between admission and inoculation, perceived dissatisfying accessibility as well as an increased frequency of observed adverse events were linked to a reduced satisfaction. Comparatively subpar willingness to adhere to non-pharmaceutical measures was observed. Centre staff reported high satisfaction and a workload relatively equal to that of their regular jobs. Our outcomes provide references for the implementation of an in-hospital vaccination centre in similar settings.  Vaccinations are among the most effective preventive measures against COVID-19 . Once thAs one of the largest hospitals in Germany, the Ludwig Maximilian University (LMU) Hospital was faced with assembling a large-scale vaccination centre within days following the authorisation of the first COVID-19 vaccines by the European Medicines Agency . Due to Therefore, this paper aims to explore the feasibility of the first-of-its-kind large-scale COVID-19 vaccination centre at the LMU University hospital and assess managerial and implementation aspects that may help facilitate the organisation of in-hospital vaccination centres in similar settings, especially in the context of future outbreak prevention strategies. Further, we analyse the satisfaction of HCWs with the vaccination process and identify potential associated factors that can serve as guidance for the design of vaccination centres for HCWs.LMU with the first part of the project exploring the COVID-19 vaccination intent and associated factors in HCWs (1 survey) and the second part, presented here, with 2 surveys, focusing on the implementation of the vaccination centre [Within the scope of the LMU University Hospital as well as of this analysis, we define all hospital employees, including non-medical hospital staff and medical students, as HCWs. We present a process evaluation consisting of an appraisal of the vaccination centre’s organisation. In addition, we executed 2 online-based anonymous surveys evaluating the HCWs’ satisfaction with the organisation of the vaccinations process. The surveys were part of an extensive evaluation of the whole vaccination campaign at LMU University Hospital within the scope of the prospective study IMPFn centre . The resn centre . Of the 2 surveys presented here, one was aimed at HCWs who had received at least 1 inoculation (vaccinees) at the in-hospital centre, while the other one targeted HCWs working as staff in the centre. The vaccination centre began operations on 28 December 2020, and remained open until 18 June 2021. This period constitutes the first vaccination campaign against COVID-19 at the LMU University hospital, and is subject to the contents of the following analyses. The in-hospital vaccination centre was organised in accordance with the guidelines provided by the Bavarian State Ministry of Health as well as with the recommendations of the Bavarian State Office for Health and Food Safety ,8. The cIn the admission area located at the entrance, the vaccinees were registered and their COVID-19 vaccination history and recovery status were prompted. Next, the inoculations were given in private cubicles mainly by physicians. Lastly, vaccinees were asked to rest for 15 min in the observation area, located near the emergency equipment and the nearest exit to the emergency department. The vaccinations were prepared mainly by nursing staff and, for hygienic reasons, outside the main area. Opening hours of the centre were from 9 am to 3 pm with occasional extension to 5 pm. We planned for up to 72 vaccinations per hour. ® vaccine [All HCWs were offered inoculation against COVID-19 in accordance with their professional risk of exposure and with the health authorities’ prioritisation scheme. During the first vaccination campaign, the centre inoculated solely with the Comirnaty vaccine . For any vaccine .Here, we assess the first COVID-19 vaccination campaign (December 2020–June 2021) at the LMU University hospital, taking into account documentation, observation and emails addressed to the vaccination centre’s inbox. LMU [The perception of the vaccination process and centre was evaluated within the scope of the prospective study IMPFLMU . For colSurvey 1 was aimed at hospital employees with at least 1 COVID-19 shot at the in-hospital vaccination centre. It assessed satisfaction with the vaccination process and potentially associated factors. The design of the questionnaire was informed by the SAGE Working Group’s guidance on vaccine hesitancy and consisted of 5 sections ,13. The Survey 2 targeted employees that had worked at the in-hospital vaccination centre since 2020. The design of the questionnaire was informed primarily by the evaluation needs of the hospital and consisted of 4 sections: general organisation and perception of the vaccination process, information about the vaccination process and sociodemographic data. The questionnaire was distributed by the vaccination centre’s management team to a mailing list including all persons on the centre’s duty roster. The questionnaire was available between 14.04.2021 and 30.06.2021. Due to the restricted sample size of the vaccination centre staff, we limited the analysis to a descriptive report including the mean values and standard deviation of Likert scale variables as well as Cronbach’s Alpha for reliability testing.Between December 2020 and June 2021, we administered 20,250 vaccine doses amongst the 11,005 active and permanent employees, of which 13,790 (68%) were given to female HCWs, consistent with the higher proportion of women among hospital staff. There were no serious incidents or adverse events after immunization. Vasovagalreactions or near-syncopes were the most common incidents, with a frequency of about 1:1000 vaccinations. Organisational adjustments were required during the campaign and within the course of continuous resource evaluation, which referred especially to the personnel management, due to an initial overestimation of staff and time needed per inoculation. Within the first days, we recognised that only five instead of ten minutes per inoculation were needed. This allowed for a substantial reduction of the physicians needed for vaccinations from 12 to 6 per 72 scheduled vaccinees per hour. We also switched from a voluntary deployment system to requesting medical staff from individual departments with support from the executive board of the hospital. With growing experience of the staff and decreased need for consultation concerning safety and side effects, we were able to further reduce the inoculation time to 4 minutes, which summed up to 5 vaccinating physicians per 72 scheduled vaccinees/hour, including a 30 min break for each physician. We also found that the consultant who initially used to be permanently on-site as the centre’s manager for emergencies and medical inquiries was needed for occasional telephone consultations only.Administration was initially covered by eight employees and was also reduced, first to six, and later to five employees, similar to the medical personnel adjustments. The time needed to process a registration for vaccination summed up to four minutes per patient as well. Further, the preparation of the vaccinations equated to 2.5 min per dose and required a total of 3 people daily. Lastly, one additional person acted as on-site operation manager, monitoring and directing the processes, onboarding new employees and coordinating organisational problems and logistics. In total, 14 employees covered the vaccination centre on site. Administration and vaccine preparation were each supervised on demand by a designated person. Other staff needed for work in the vaccinations centre´s environment, such as cleaning, security, IT, logistics and engineering staff, should be taken into account.The finalised layout of the vaccination centre is presented in We documented no-show and extra-show rates on a daily basis. We observed a maximum no-show rate of 5.8% for the first shot and 5.2% for the second shot. Further, we aimed to accommodate HCWs with an impromptu inquiry, i.e., without an appointment. This occurred mostly at the beginning or end of a vaccination days’ series, with a maximum of 10.5% extra-shows for the first, and 12.1% for the second vaccination. Ultimately, we performed a mean of 70 vaccinations/hour (range: 49–84/h). In addition, many HCWs presented at the beginning or after their working hours, as well as during their lunch breaks, independently from the time their appointment was scheduled for. As these HCWs would present within the day they had been scheduled, the irregularity was not documented as no-show or extra-show, but, nevertheless, led to unequal distribution of the work load for the staff on site.The vaccination appointment booking was initially set up with a low-barrier digital environment without special requirements for personal authorisation via login data. This method swiftly proved to be error-prone, thus triggering an adjustment of the system towards booking via personalised login and automatic generation of the second appointment as well as an appointment confirmation via SMS. The follow-up (second) appointment was scheduled in accordance with the recommendations by the German Standing Committee on Vaccination (STIKO) and the Federal Institute for Vaccines and Biomedicines (PEI) ,15. The The email set up for inquiries received up to 80 messages/day (approx. 0.7% of employees). The questions or messages were medical in around 20% of cases, and organisational, e.g., related to scheduling, in approx. 80% of cases. Initially, medical inquiries referred mainly to safety and expected side effects of the vaccine, while later on, reports of assumed and observed AEFIs as well as questions related to individual diseases, pregnancy, breastfeeding and COVID-19 antibodies dominated. The frequently adapted recommendations regarding the intervals between inoculations as well as between inoculations and SARS-CoV-2 infection were a source of numerous inquiries. The answers to organisational inquiries as well as the administrative work initiating from these inquiries was mainly covered by one person from the hospital’s administration staff while medical inquiries were handled by the centre’s manager as a medical expert. Of 11,005 employees, 1662 participated in the survey for vaccinees. Of those, 1035 filled out the questionnaire in full . We obseThe initial testing showed a significant association of age and sex with the reported satisfaction as well as with the observation of AEFIs after the first and second inoculation. Similarly, occupation showed a significant association with satisfaction and reported AEFIs after the first vaccination dose. Variables with significant associations in the initial testing were used for further analyses in the adjusted models.The four items for general satisfaction with the vaccination process as well as the nine items for the satisfaction with the individual aspects of the vaccination process demonstrated good reliability .The better fitting unadjusted model showed a link between satisfaction with the vaccination process and accessibility of the vaccination centre and waiting time. Vaccinees dissatisfied with the location of the vaccination centre had a 9.542 higher likelihood of perceiving the vaccination process as rather ill-organised. Further, vaccinees who only partially agreed that the vaccination centre was well accessible had a 5.519 higher likelihood of perceiving the vaccination process as partially ill-organised. Similarly, HCWs not willing to travel over 1 h to the vaccination centre had a 9.502 higher likelihood of perceiving the vaccination process as rather ill-organised . Vaccinen = 177). The items for perceived safety and confidence with the vaccination process provided similarly consistent results at the upper end of the Likert scale.We measured the satisfaction of participants with the written information provided upon inoculation . All forWe measured the attitudes and attitude changes towards COVID-19 non-pharmaceutical interventions (NPI) after receiving both vaccine doses . All iteThere was a weak significant association between experiencing AEFIs after the first inoculation and reporting a lower satisfaction with the process . The datOverall, 74 vaccination centre staff members participated in the survey, with 54 of them filling out the questionnaire in full . Here, wThe satisfaction of the staff was measured with seven items, where the majority presented a consistent mean above 4.50 . The iteSimilarly, the nine items for spatial arrangement and staff management demonstrated comparable consistency, with all items presenting mean values above 4.0 . The lowThe eight items on the quality and helpfulness of the information delivered throughout the process also demonstrated consistent mean values above 4.0 . The iteFurther, we asked staff members about their perception of the vaccinees’ knowledgeability regarding COVID-19 vaccines . The fivThe 10 items for satisfaction with the working atmosphere showed consistent mean values . Only thTo our knowledge, this paper provides the first published insights into the organisation and evaluation of a large-scale in-hospital vaccination centre in Germany on the basis of the experience gathered through implementing and operating a COVID-19 vaccination campaign for 11,005 HCWs.The currently available literature concerned with the topic of organising a COVID-19 vaccination centre is still narrow and mainly concerned with mass vaccination sites for the general population ,17,18. AThe number of vaccine doses inoculated in the centre every day corresponds to the rate in the COVID-19 hospital-based or mass vaccination sites ,19,20. IFurther considering personnel management, planning and scheduling staff on a voluntary basis assumes a certain degree of predictability and neglects motivation loss over time, as observed at the beginning of the first campaign. We therefore recommend the later adopted option of a planned roster, as this allows for better reliability. Additionally, the arrival of vaccinees at specific times of the day should lead to further adjustments in the personnel planning in the future, in order to cover the bottleneck timeslots more efficiently. Further, as other case studies have pointed out, an onsite manager who continuously monitors and, as appropriate, adapts the workflow, is highly beneficial to the agile management required in the setting .The lessons learned during the first vaccination campaign, especially concerning personnel, facilitated the setup of the second vaccination phase (October–December 2021), to the extent that we were able to offer a mean of 17 influenza shots/hour (range: 3–32 shots), in addition to COVID-19 inoculations, without staff changes. However, no-show rates rose rapidly, to almost 16% (no extra-shows), likely reflecting several factors specific to the second vaccination campaign: as this period coincided with a sharp rise in SARS-CoV-2 incidence, it is possible that many HCWs had to delay their scheduled vaccination due to an infection ; lastly,Our findings indicate a direct association between the accessibility of a vaccine and the satisfaction with the vaccination process. Especially regarding geographical barriers, the results emphasise the need to improve access and reduce physical impediments even among vaccine-receptive populations. Although the Vaccine Hesitancy Matrix observes the contextual influences independently from the vaccination-specific issues, our findings accentuate the benefit of considering those simultaneously, as geographical and temporal barriers may serve as guidance in the design of vaccination programmes . AdmittiIn a similar manner, there is a need for consideration of the experience of AEFIs as a factor potentially influencing COVID-19 vaccine-related decisions in the future, as vaccine adaptation and emerging variants of concern may pose the need for further COVID-19 large-scale and mass vaccination campaigns . The facLMU project which examined this topic several months prior to the surveys presented here. The overall adherence to the extension of NPI at the hospital confirms the data from the first questionnaire that showed an association between a positive vaccination status and rather agreeing to the extension of the NPIs validity beyond 2021 incl. PCR testing [In terms of COVID-19 behaviour, our results present referential data to the first questionnaire of the IMPF testing . HoweverFurther, the results presented here underline the outcomes of the first questionnaire stating that HCWs with a positive vaccination status are less worried about getting infected with SARS-CoV-2 in their personal or professional environment .The overall satisfaction of the vaccination centre staff and the perception of the workload as neither lower nor higher than usual testify to the fidelity of the implementation. Similar to the report of De Micco et al., our results indicate a strong sense of team spirit and commitment by the centre’s staff, hence underlining the role of leadership and personnel management beyond the formative fulfilment of the required tasks . This asSeveral limitations need to be considered when interpreting the results of this work.It is uncertain whether the described personnel management can be transferred to other hospitals or settings, especially regarding the number of physicians instead of medical assistants or nursing staff involved, which rather reflects the staff structure of a university hospital than the requirements of vaccination centres. Still, our results provide an indication of the human resources needed for a large-scale vaccination centre, where the majority of the roles may also be assigned to other HCWs with similar qualifications to perform the given tasks, e.g., planning for five qualified medical assistants instead of five physicians to execute the inoculations.In terms of administrative support, it should be noted that due to the rapid setup, the initial email inquiries were only partially saved, hence we can only provide a general assessment of number and content rather than a detailed analysis.LMU project, it can be hypothesised that the weaker response could be partially owing to a depleted motivation of HCWs to participate in COVID-19 vaccination surveys. To a certain extent, this hypothesis could be broadened to include a general exhaustion with the topic, since COVID-19 was the predominant issue at the LMU University Hospital and beyond during the period of the survey. Further, the voluntary design of the survey certainly accounts for low participation. We acknowledge that a different or an addition dissemination approach might have facilitated the participation in both surveys: an example of such a strategy would have been to promote the surveys on site using posters presenting a QR code and/or link to both surveys.Both surveys were available to the target groups for approx. 2 months, thus not covering the complete period of the vaccination campaign. Changes in attitude towards COVID-19 vaccines could have potentially been driven by newly distributed information, adaptations of the recommended vaccination scheme, prioritisation or other factors but were not considered in the survey design. Further, the consistently high uniformity of answers to a majority of the questions did not allow for a meaningful and powerful inferential analysis, hence reducing the evaluation to a rather descriptive report. In terms of design it should be noted that the high satisfaction reported by participants may in part be due to an acquiescence bias despite the specific definition of the middle-point in the Likert scale to every item, or other response biases . FurtherIn terms of representativeness, the age and sex distribution of participants in the survey for vaccinees is similar to the distribution in the whole target population, thus making the results fairly representative for the HCWs inoculated at the in-hospital vaccination centre.As a long-term observation was outside of the scope of this evaluation, the displayed outcome lacks information on potential fluctuations of the perception of the vaccination centre and process. That is to be considered against the background of limited vaccine availability at the beginning of the campaign and gradually increasing availability subsequently. However, we feel that a follow-up data collection would not have been meaningful due to the fast-paced changes in recommended inoculated vaccines as well in the general pandemic situation that may have implied further confounding factors which one could have not accounted for.Nevertheless, our work presents valuable insights into the specifics of organising and managing a large-scale in-hospital vaccination centre. As other studies have showed, HCW vaccination campaigns require a tailored yet accessible and agile approach in order to facilitate the uptake of vaccines . A GermaImplementing and managing a large-scale in-hospital vaccination campaign requires a specific focus on the geographical and temporal accessibility of the vaccination centre. An agile personnel management is necessary both in terms of the centre staff as well as on the hospital-wide level, as demands may rapidly change and AEFIs may noticeably affect the working ability of vaccinated HCWs, which may, in turn, affect the provision of care to patients. The potential effect of self-reported AEFI experiences following a COVID-19 vaccination on future decisions on vaccination uptake may represent a particularly relevant topic for research. Additionally, self-reported AEFIs following inoculation need to be considered as a confounding variable in the evaluation of the satisfaction with vaccination campaigns and programs, as these appear to significantly affect the perception of the setting where the respective inoculation has taken place. Regardless of the high effectiveness of vaccinations, campaigns among HCWs should still aim to facilitate the adherence to non-pharmaceutical preventive measures such as wearing a mask, testing regularly, and other personal hygiene standards. This is a crucial factor to be considered in occupational health promotion, as our results underline the need for a strategically selected and tactically implemented set of measures that facilitate the achievement of a paramount goal rather than observing and evaluating a single measure. Future research should aim to examine health promotion campaigns in occupational settings as a whole and observe potential interactive and inversely proportional coherences between the adherences to different health promotional activities.