With the deep integration of wearable devices and information technology, health data collection has become more efficient and accurate. This has brought about significant changes to health management and public health. However, due to the high sensitivity and privacy of health data, data sharing faces major challenges. Most existing studies focus on encryption algorithms and access control to ensure data security. They often ignore the credibility of data providers, which affects data quality and reduces user participation. To address these issues, this article proposes a blockchain-based and reputation-enhanced health data trading model. A fine-grained reputation value calculation method based on the Beta distribution is introduced to objectively evaluate the behavior of data providers. Based on this, a consensus mechanism linked to reputation value is designed to improve consensus efficiency and avoid centralization of node selection. Furthermore, this article uses evolutionary game theory to analyze the reward and punishment mechanism in the trading process. It explores the dynamic balance between platform cost and user willingness to share. Experimental results show that the model ensures secure health data sharing, while effectively improving data usability, system fairness, and user participation.

With the exponential growth of mobile devices, Mobile Crowdsensing (MCS) has emerged as a new paradigm for various types of tasks. However, most existing studies focus primarily on the utility of either buyers or sellers, with limited exploration of the task types for buyers and the user types for sellers. Therefore, this paper investigates the incentive mechanisms for different types of sellers under varying task types. In this study, we classify sellers into two groups: teams and individuals, and classify buyers' tasks into two categories: simple tasks and complex tasks. Considering the characteristics of different task types and seller groups, we design two auction schemes: the Two-Stage Auction Scheme for Simple Tasks (TDAS-S) and the Two-Stage Auction Scheme for Complex Tasks (TDAS-C). In the first stage, we design a seller auction model based on the Stackelberg game to ensure the maximization of the seller's utility. In the second stage, we design an auction model for buyers, utilizing a variant of the Vickrey Auction and marginal contribution theory to pay the selected sellers in both TDAS-S and TDAS-C, ensuring the maximization of the buyer's utility. We further prove that the proposed scheme satisfies properties such as individual rationality, truthfulness, and budget balance. Finally, through extensive experiments on real-world datasets, we demonstrate that our scheme effectively balances the utility conflicts between buyers and sellers, allowing each party to maximize their respective utilities.

Traditional respiratory monitoring methods typically use wearable sensors. In recent years, Radio Frequency Identification (RFID) tags have emerged as potential respiratory monitoring devices. However, existing research only detects respiration under static or simple body movements such as forward and backward motions, which limits the availability of RFID. In this article, an RFID-based respiratory monitoring system under body motion disturbances is proposed. In order to eliminate the effect of body movement on the signal, reference tags are placed on the user’s shoulders. The phase between different tags is calibrated and aligned to remove the discreteness. Then, a sliding window peak-seeking algorithm is designed to calculate respiratory rate over time. The system measures respiratory rate during daily exercise and maintains an accuracy of no less than 86.12%, even when influenced by lying down and turning. Additionally, the system can analyze estimated respiratory rates and intervals to provide alert information.

The sharing of pathological data is of utmost importance in various applications such as remote diagnosis, graded diagnosis, illness treatment, and specialist system development. However, ensuring reliable, secure, privacy-preserving, and efficient sharing of pathological data pose significant challenges. This paper presents a novel solution that leverages blockchain technology to ensure reliability in pathological data sharing. Additionally, it employs conditional proxy re-encryption (C-PRE) and public key encryption with equality test technology to control the scope and preserve the privacy of shared data. To assess the practicality of our solution, we have implemented a prototype system using Hyperledger Fabric and conducted evaluations with various metrics. We also compared the solution with relevant schemes. The results demonstrate that the proposed solution effectively meets the requirements for pathological data sharing and is practical in production scenarios.

Blockchain is commonly employed in access control to provide safe medical data exchange because of the characteristics of decentralization, nontamperability, and traceability. Patients share personal health data by granting access rights to users or medical institutions. The major purpose of the existing access control techniques is to identify users who are permitted to access medical data. They hardly ever recognize internal assailants from legitimate entities. Medical data will involve multilayer access within the authorized organizations. Considering the cost of permissions management and the problem of insider malicious node attacks, users hope to implement authorization constraints within the authorized institutions. It can prevent their data from being maliciously disclosed by end‐users from different authorized healthcare domains. For the purpose to achieve the fine‐grained permissions propagation control of medical data in sharing institutions, a trust‐based authorization access control mechanism is suggested in this study. Trust thresholds are assigned to different privileges based on their sensitivity and used to generate zero‐knowledge proof to be broadcasted among blockchain nodes. This method evaluates the trust of each user through the dynamic trust calculation model. And meanwhile, smart contract is employed to verify whether the user’s trust can activate some permissions and ensure the privacy of the user’s trust in the process of authorization verification. In addition, the authorization transaction between users and institutions is recorded on the blockchain for patient traceability and accountability. The feasibility and effectiveness of the scheme are demonstrated through comprehensive comparisons and extensive experiments.

“Request Triggered Recharging” has been a flexible type of scheduling schemes to allow the mobile charging vehicle (MCV) to supply energy for sensor nodes on demand. However, in most existing works, MCV always passively waits for the arrival of the unpredictable requests that may cause it missing the best departure time to serve nodes. To solve this problem, we propose a balanced distribution strategy for the number of recharging requests based on dynamic dual thresholds (BDRR). First, the adjustable double recharging request thresholds (DRRTs) are set for each node to ensure that all the requesting nodes can be successfully charged. Then, the method for setting the energy replenishment value (MSERV) is proposed to enable the distribution of the moments at which nodes send out their recharging requests being concentrated within each period. Furthermore, an efficient traversal path for the MCV is constructed by safe or dangerous scheduling strategy, and the charging capacity reduction scheme (CCRS) is also executed to help survive more nodes in need. Finally, a passer-by recharging scheme (PRS) is introduced to further improve the energy efficiency (EE) of the MCV. Simulation results show that BDRR outperforms the compared algorithms in terms of surviving rate of sensors as well as the EE of MCV with different network scales.

Federated learning (FL) is a popular distributed learning framework that is proposed to address privacy concerns in traditional machine learning. However, recent research has highlighted an issue where model or gradient updates can be exploited to infer sensitive information from the training data, resulting in severe privacy leakage. Existing defenses against gradient leakage attacks often suffer from high computation overhead or compromised model performance. In addition, most defense methods lack sufficient protection for labels. To overcome the above shortcomings, we develop a compressed sensing (CS)-based practical and efficient privacy-preserving FL scheme. In order to provide simultaneous protection for both original data and labels, we propose a CS-based gradient perturbation method, which eliminates the information in the gradient that is commonly exploited by attackers to extract labels, and increases the discrepancy between the perturbed and original gradients. Meanwhile, double aggregation is adopted together to ensure individual gradients are not easily disclosed by attackers. We also design a novel gradient reconstruction method that adaptively estimates the true gradient sparsity used for decompressing, thereby improving the model performance in practical scenarios. Furthermore, our CS-based gradient compression reduces communication overhead and requires low computation overhead as it only involves fast matrix multiplication. Extensive experiment results demonstrate the strong privacy protection effects of our proposed scheme compared to other approaches across various settings, with advantages in terms of communication overhead, computation overhead, and model accuracy.

With the proliferation of mobile smart devices, such as smartphones, mobile crowdsensing (MCS) has gained significant attention and widespread application. However, the increasing risk of personal privacy breaches has become a significant concern in MCS. Typically, workers are required to disclose their location information to participate in task assignments, making the protection of sensitive data, like location, a crucial factor influencing worker engagement. To address the issue of location privacy leakage in the task allocation process, this article proposes a location privacy protection method (VGDP) based on local differential privacy. In VGDP, the server utilizes a clustering algorithm to construct a task map based on the Voronoi diagram using task locations. Each task location is then mapped to its corresponding task area to ensure the privacy of the location information. Encoding technology is employed to encode the relative locations of all workers within the area, while a double random response mechanism is utilized to obfuscate the relative location codes, thereby safeguarding their location privacy. Furthermore, a personalized privacy budget allocation mechanism is employed to enhance the effectiveness of privacy protection. Once workers upload their perturbed location information to the server, the server selects winners based on the perturbed locations to facilitate task allocation. Additionally, this article proposes a high-reward payment method to augment workers’ enthusiasm for participation. Experimental results demonstrate that the proposed method exhibits promising performance in terms of data availability and location privacy.

With the widespread popularity of smartphones, watches, and other devices, mobile crowd sensing has garnered significant public attention. Application service providers publish crowd sensing tasks, and users actively participate in collecting relevant sensing data, which are then submitted to servers. However, these data contain users’ personal privacy. Therefore, this article proposes a trajectory privacy protection method based on differential privacy(CTDP). First, the article conducts clustering based on the features of user trajectory data to extract feature regions of the user trajectory. Then, a personalized privacy budget allocation method is developed based on the number of trajectory points in the feature region and the user's privacy requirements for sensitive trajectory points. A set of confusion points is generated within the feature range and a score is calculated based on its similarity to the trajectory points. Subsequently, the sampling probability is calculated based on the score and privacy budget of each confusion point, and finally the confusion points are selected through random sampling. The internationally recognized real dataset Cabspotting data was used for experimental evaluation. The experimental results indicate that the method proposed in this article exhibits excellent performance in terms of data availability while providing sufficient privacy guarantees.

Traditional solutions for human action recognition usually rely on sensor or video methods. However, these methods have some limitations, such as inconvenient portability, light intensity influence, privacy protection, etc. In this article, an RFID-based nonwearable human action recognition scheme is proposed. In order to reduce the occlusion effect of the human body on the signal and increase the diversity of the reflected signal, a tags array is constructed. The data of phase and RSSI are fused as feature data to enhance the diversity of data. Furthermore, a combined processing method is proposed to eliminate thermal noise generated by the equipment and reduce the interference caused by the environment. Then, an action segmentation algorithm is designed to align the RF signals of human action. Finally, an efficient human action signal classification model is constructed using the spatiotemporal graph convolutional neural network (STGCN). Extensive experiments demonstrate that the overall accuracy rate of the system for human action recognition is 92.8%. Compared with the comparative mainstream recognition algorithms, STGCN shows better classification performance in terms of identification precision. In addition, multimodal RFID data fusion also improves the accuracy of identification.

Due to the breakthrough of wireless power transfer technology, wireless rechargeable sensor networks (WRSNs) have the potential to provide sustainable work. Most existing researches on WRSNs usually focus on the cases that mobile charging vehicle moves freely through the sensors. However, for some applications, such as bridge monitoring, WRSNs are implemented in a three-dimensional space with obstacles, so the charging path may be blocked by the obstacles. To cope with this problem, charging scheduling to replenish a wireless rechargeable sensor network for bridge monitoring by an unmanned aerial vehicle (UAV) is studied. The problem is formulated as an optimization problem through optimizing UAV navigation path and sensor energy allocation collaboratively. This optimization problem is hard to be solved as both path navigation and energy allocation are required to be optimized simultaneously. To circumvent this challenge, an improved ant colony system algorithm (IM-ACS) is proposed to plan the trajectory of the UAV between sensors. By integrating enhancement factors and dynamic pheromone intensity coefficients, the convergence of the algorithm is accelerated. Then, a two-stage algorithm is proposed to schedule charging sequence and assign energy with limited energy carried by the UAV in each charging period. Experiments and simulations show that the proposed approach achieves shorter feasible trajectory paths and longer network lifetime than those obtained by the compared methods.

Traditionally, RFID is frequently used in identification and localization. In this paper, an extension application of RFID is designed to recognize gestures. Currently, gesture recognition is mainly used for feature extraction through wearable sensors and video cameras, which have shortcomings such as inconvenience to carry and interference with obstacles. This paper proposes a gesture recognition system based on radio frequency identification (RFID), where users do not need to wear devices. In the proposed model, the interference information generated by the gesture action on the tag signal is used as the fingerprint feature of the action. To obtain satisfactory recognition, the signal diversity is first increased through the tag array. Then, the RSSI and phase signal are normalized to eliminate offset and noise before training. Furthermore, a residual neural network (ResNet) is carefully built as a gesture classification model. The experimental results show that the recognition system achieves more recognition accuracy than existing methods, and the average gesture recognition accuracy reaches 95.5%.

A medical cyber–physical system (MCPS) is a unique cyber–physical system (CPS), which combines embedded software control devices, networking capabilities, and complex physiological dynamics of patients in the modern medical field. In the process of communication, device, and information system interaction of MCPS, medical cyber–physical data are generated digitally, stored electronically, and accessed remotely by medical staff or patients. With the advent of the era of medical big data, a large amount of medical cyber–physical data is collected, and its sharing provides great value for diagnosis, pathological analysis, epidemic tracking, pharmaceutical, insurance, and so on. This overview will present MCPS’s architectures and frameworks from different perspectives, modeling and verification methods, identification and sign sensing technologies, key communications’ technologies, data storage and analysis technologies, monitoring systems, data security and privacy protection technologies, and key research perspectives and directions. We can have a comprehensive understanding of the important characteristics and technical route of MCPS, and grasp its research status and progress.

As the background of application in the field of smart health care, the flexible interaction between patients and medical system is provided by medical cyber-physical systems (MCPSs) to realize all-round three-dimensional medical service. According to the controllable and credible requirements of MCPS, it needs a secure and reliable device identity authentication mechanism to build the security barrier. Based on the blockchain technology, a lightweight authentication scheme is designed for sensor/execution devices, users, and gateway nodes in MCPS. The security analysis and experimental results show that the scheme can resist the existing attacks with better efficiency; thus, our proposed scheme can be efficiently applied to the medical field.

The property of performing data processing near the source of data (i.e., at the edge of the network) enables fog computing that can effectively reduce computation latency, bandwidth and energy consumption, especially for big data network scenarios. For the sake of achieving efficient and secure big sensory data collection in fog-assisted Internet of Things (IoT), this paper proposes an efficient privacy preserving data collection and computation offloading scheme. In the proposed scheme, first, the designed layer-aware fog computing architecture provides effective support for efficient and secure data collection and fog computation offloading. Then the proposed sampling perturbation encryption method protects data privacy against eavesdroppers and active attackers without sacrificing data correlation, and it also facilitates the simultaneous execution of decrypting and decompressing operations on encrypted sampling data. Furthermore, the developed data processing method at fog nodes reduces the amount of redundant data transmissions significantly, and the formulated optimization model for the measurement matrix ensures the high precision of data reconstruction at the end user. Particularly, a completion time minimization problem is formulated for fog computation offloading, and an efficient offloading decision algorithm is developed to find the minimum completion time by determining the optimal offloading proportion with joint optimal allocation of local CPU, external CPU and channel bandwidth resources. Finally, the illustrative results reveal that the proposed scheme is an efficient data collection and computation offloading scheme with a strong privacy preservation property. For example, when the temporal compression ratio is 0.5, the redundant data can be reduced by 65 percent at fog node with a low relative recovery error 0.0139. At the same time when the task size is 9 Mb, the completion time of compression computation task at fog node can be reduced by 14.6 percent compared with other computation offloading method.

The application of IPv6 to the community medical Internet of things (CMIoT) is helpful to solve the heterogeneous network fusion problem. However, how to effectively implement the application of IPv6 technology in the CMIoT is still a problem that needs to be constantly explored. While the IPv6 technology can effectively solve the problem of identifying the nodes of the CMIoT, the big data in the CMIoT becomes a more prominent problem. On the basis of the research on the application of IPv6 technology to the Internet of things (IoT), this paper proposes a kind of IPv6 proxy scheme for the interconnection between the IoT subnet and the IPv6 Internet. At the same time, the big data problem of the IoT is discussed, and a big data processing architecture based on cloud platform is designed for effective processing of the big data of the CMIoT. Combined with the Ptolemy II modeling and simulation platform, the IPv6 proxy scheme and the medical big data processing architecture based on cloud platform are modeled and simulated. The simulation and analysis results show that the IPv6 proxy scheme can not only achieve more efficient implementation of IoT subnet nodes accessing the IPv6 Internet based on the IPv6 technology, but also play a certain role in the big data processing of CMIoT.

According to the practical requirements of high recovery precision and low latency in wireless big sensory data networks, this paper proposes an accelerated distributed rate control method for minimizing the recovery error of big sensory data. This method can guarantee the error minimization of reconstructed data and converge to the optimal value fast with a lower latency. In order to achieve these effects, an accelerated distributed solving algorithm is constructed by designing accelerated subgradient method for dual decomposition. This solving algorithm achieves convergence rate O(1/t2) in practical implementation, which significantly improves the convergence rate of regular solving algorithms. Meanwhile, the convergence analysis testifies the convergence property of the proposed distributed solving algorithm, and this algorithm is applicable to other convex optimization problems. Finally, the performance evaluation shows that the proposed accelerated method can converge to the unique optimal value successfully and the convergence speed is faster than the regular optimization method, and this proposed method can be extended to networks of different sizes without sacrificing the accelerated effect.

Since the temporal and spatial correlations of sensor readings are existent in wireless sensor networks (WSNs), this paper develops a clustered spatio-temporal compression scheme by integrating network coding (NC) and compressed sensing (CS) for correlated data. The proper selections of NC coefficients and measurement matrix are designed for this scheme. This design guarantees the reconstruction of clustered compression data successfully with an overwhelming probability and unifies the operations of NC and CS into real field successfully. Moreover, in contrast to other spatio-temporal schemes with the same computational complexity, the proposed scheme possesses lower reconstruction error by employing the independent encoding in each sensor node (including the cluster head nodes) and joint decoding in sink node. At the same time it has lower computational complexity as compared with JSM-based spatio-temporal scheme by exploiting the temporal and spatial correlations of original sensing data step by step. Finally, the simulation results verify that the clustered spatio-temporal compression scheme outperforms the other two compression schemes significantly in terms of recovery error and compression gain.