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With the rapid development of edge intelligence (EI) and machine learning (ML), the applications of Cyber-Physical Systems (CPS) have been discovered in all aspects of the life world. As one of its most essential branches, Medical CPS (MCPS) determines human health and medical treatment in the Internet of Everything (IOE) era. Knowledge sharing is the critical point of MCPS and has also been humanity's best dream through the ages. This paper explores a novel knowledge-sharing model in MCPS and takes a pulmonary nodule detection task as a significant case for building an Unet-based mask generator. A Classification-guided Module (CGM)-based discriminator with knowledge from EMRs is set against a generator to offer a promising result for each mask from the inexperienced participant of federated ML. After an iterative communication between the federated server and its clients for knowledge sharing, the segmented sub-image owns a coincident attribute distribution with that of the EMRs from the experts. Besides, the adversarial network augment the data to normalize the data distribution for all the clients as a remission for none independent identically distributed (non-IID) data problem. We implement a detection framework on the simulated EI environment following an existing adaptive synchronization strategy based on data sharing and median loss function. On 1304 scans of the merged dataset, our proposed framework can help boost the detection performance for most of the existing methods of pulmonary nodule detection.

The diffusion of toxic gas, biochemical material, and radio-active contamination, known as continuous objects, endangers the safe production of petrochemical and nuclear industry. Industrial wireless sensor networks are a new paradigm, which shows great potential in monitoring evolving hazardous phenomena in unfriendly industrial field. To prolong the lifetime of IWSNs, existing research focuses on energy-efficient boundary nodes selection. However, sensor state cannot be scheduled proactively due to the difficulty of predicting the spatiotemporal evolution of diffusive hazard. In this article, we propose a predictive boundary tracking algorithm based on motion behavior learning (MBLPT) for continuous objects in IWSNs. Considering the multiple behavior patterns exhibited by continuous objects, MBLPB establishes data-driven models for motion state recognition, and then utilizes Bayesian model averaging for future boundary prediction. The prediction of MBLPT provides the knowledge for establishing a wake-up zone, in which sleeping nodes are activated in advance to participate in tracking the upcoming boundary. Simulation results demonstrate that MBLPB achieves superior energy efficiency under acceptable tracking accuracy.

Rescue assignments strategy are crucial for multiple Autonomous Underwater Vehicle (multi-AUV) systems in three dimensional (3-D) complex underwater environments. Considering the requirements of rescue missions, multi-AUV systems need to be cost-effective, fast-rescuing, and less concerned about the relationship between rescue missions. The real-time rescue plays a vital role in the multi-AUV system with the characteristics mentioned above. In this paper, we propose an efficient Reward acting on Reinforcement Learning and Particle Swarm Optimization (R-RLPSO), to provide a strategy of real-time rescue assignment for the multi-AUV system in the 3-D underwater environment. This strategy consists of the following three parts. Firstly, we present a reward-based real-time rescue assignment algorithm. Secondly, we propose an Attraction Rescue Area containing a Rescue Area. For the waypoints in each Attraction Rescue Area, the reward is calculated by a linear reward function. Thirdly, to speed up the convergence of the R-RLPSO and mark the rescue states of Attraction Rescue Area and rescue area, we develop a Reward Coefficient based on the reward of all Attraction Rescue Areas and Rescue Areas. Finally, simulation results show that the system based on R-RLPSO is more cost-effective and time-saving than that of based on comparison algorithms ISOM and IACO.

The massive growth in interconnected devices from a multiplicity of networks goes hand-in-hand with the emergence of the Internet of Things (IoT) paradigm. As a critical component of the IoT, sensor networks have become ubiquitous and widely used in various application domains. However, open-ended wireless communication brings severe threats to user privacy and security. Attackers from outside the network can trace back along the data stream to capture the source node, which poses a significant threat to the privacy of the data source. A feasible defense method is to interfere with the attacker’s tracking process through forged data streams. However, the related traditional solutions generally have shortcomings in terms of balancing security and efficiency. Therefore, this article proposes a Pseudo-Packet Scheduling Algorithm (PPSA), which aims at reasonably regulating the process of pseudo-packet generation to interfere with the adversary’s tracking to the data source. The algorithm comprises three phases. First, the sink node performs geographic information acquisition and neighbor node discovery with a flood-based method. Then, the sink node uses a self-adapting proxy selection method to construct backbone routes with both randomness and low latency to receive actual packets. Finally, the nodes on both sides of the backbone routes follow a pseudo-packet scheduling strategy to interfere with the adversary’s tracking of the source locations. The experimental results showcase that our proposed scheme effectively controls the additional energy consumption and transmission delays within acceptable ranges while ensuring adequate location privacy.

This article develops a robust global strategy for identifying the linear parameter varying (LPV) errors-in-variables (EIVs) systems subjected to randomly missing observations and outliers. The parameter interpolated LPV autoregressive exogenous model with an uncertain/noisy input is investigated and a nonlinear state-space model is considered for the input generation model (IGM). The parameters estimation of the LPV EIV systems with nonideal observations is realized using the expectation-maximization algorithm which is particular effective for the incomplete data issue. To ensure the robustness in the identification, the Student's t-distribution which is characterized by its adjustable degree of freedom, is used to handle the measurement non-normality. Since the posterior distributions of the latent states in the IGM are also involved in the identification process and they are difficult to calculate directly, the particle filter is introduced to recursively approximate them instead. Finally, the verification examples are given to demonstrate the effectiveness of the developed strategy.

In recent years, developments in data collection schemes based on multiple autonomous underwater vehicles (AUVs) are facilitating the realization of the so-called underwater acoustic sensor networks (UASNs). As yet, the lack of suitable collaboration mechanisms among multiple AUVs, which are based on functional or resource distributions, prevents effective information sharing and yields increased data collection delays, thus reducing the capacity of the networks. In this article, to address these shortcomings, we propose a state prediction-based data collection (SPDC) algorithm for UASNs. The principle of operation is as follows. First, some cluster pairs named observation clusters obtain and exchange the state information about AUVs between the adjacent subregions. Based on the shared information, the AUVs predict each other’s status and adjust their data collection areas. Then, the AUVs use a heuristic strategy to complete the path planning based on the updated access area. Finally, a scheduling data forwarding mechanism reduces the diving number of the AUVs, by reasonably allocating the overlapped data unloading intervals between the AUVs and a mobile sink. Experimental results prove that the proposed algorithm shows satisfactory performance in reducing data collection delays and in improving the total network lifetime.

The development of the IIOT paradigm brings the possibility of a significant transformation within the manufacturing industry. This paradigm is based on sensing large amounts of data, so that it can be employed by intelligent control systems eliciting optimal decisions in real time. Ensuring the accuracy and reliability of intelligent wireless sensing and control systems is crucial towards achieving this goal. In this paper, a novel solution is proposed to denoise data while simultaneously preserving actual anomalies. The proposed approach operates by measuring both the neighbor and background contrasts to compute a noise score. The trust level of each data point is then calculated through a correlation measure, to purge spurious data. Extensive experiments on real data-sets demonstrate that the proposed approach yields effective performance, as compared to existing methods, and it meets the requirements of low latency, facilitating the normal operation of the monitored control systems.

Smart agriculture enables the efficiency and intelligence of production in physical farm management. Though promising, due to the limitation of the existing data collection methods, it still encounters few challenges required to be considered. Mobile crowd sensing (MCS) embeds three beneficial characteristics: 1) cost-effectiveness; 2) scalability; and 3) mobility and robustness. With the Internet of Things becoming a reality, smartphones are widely becoming available even in remote areas. Hence, both the MCS characteristics and the plug-and-play widely available infrastructure provide huge opportunities for MCS-enabled smart agriculture, opening up several new opportunities at the application level. In this article, we extensively evaluate agriculture mobile crowd sensing (AMCS) and provide insights for agricultural data collection schemes. In addition, we offer a comparative study with the existing agriculture data collection solutions and conclude that AMCS has significant benefits in terms of flexibility, collecting implicit data, and low-cost requirements. However, we note that AMCSs may still possess limitations regarding data integrity and quality to be considered a future work. To this end, we perform a detailed analysis of the challenges and opportunities that concerns MCS-enabled agriculture by putting forward seven potential applications of AMCS-enabled agriculture. Finally, we propose general research based on agricultural characteristics and discuss a special case based on the solar insecticidal lamp maintenance problem.

Due to the flammability, explosibility and toxicity of continuous objects (e.g. chemical gas, oil spill, radioactive waste) in petrochemical and nuclear industry, boundary tracking of continuous objects is becoming a critical issue for industrial wireless sensor networks (IWSNs) to achieve disaster reduction. In this article, we fully exploit the collective intelligence and machine learning capability of senor nodes, and propose a continuous objects boundary tracking algorithm for IWSNs. The proposed algorithm first determines an upper bound of the event region covered by continuous objects. A full binary tree-based partition is performed within the event region for coarse-grained boundary area mapping. To study the irregularity of continuous objects in detail, the boundary tracking problem is further transformed into a binary classification problem. A hierarchical soft margin support vector machines training strategy is designed to address the binary classification problem in a distributed fashion. Simulation results demonstrate that the proposed algorithm achieves high tracking accuracy with less boundary nodes. Without additional fault-tolerant mechanisms, the proposed algorithm is inherently robust to the false sensor readings reported by low proportion of faulty nodes, which is expected in harsh industrial environments.

As the research topics in ocean emerge, Underwater Acoustic Sensor Networks (UASNs) have become ever more relevant. Consequently, challenges arise with the security and privacy of the UASNs. Compared to the active attacks, the characteristics of passive attacks are more difficult to discriminate. Thus, the focus of this study is on the passive attacks in UASNs, where a Push-based Probabilistic method for Source Location Privacy Protection (PP-SLPP) is proposed. The fake packet technology and the multi-path technology are utilized in the PP-SLPP scheme to counter the passive attacks, so as to protect the source location privacy in UASNs. Moreover, the Ekman drift current model is employed to simulate the underwater environment. And the mean shift algorithm and the k-means algorithm are adopted in the dynamic layer and static layer of the Ekman drift current model, respectively, to increase the stability of the clusters. Finally, an Autonomous Underwater Vehicle (AUV) swarm is implemented to collect data in clusters. Through the comparison with existing data collection schemes in UASNs, the simulation results have demonstrated that the PP-SLPP scheme can achieve a longer safety period, with a minor compromise of energy consumption and delay.

Demand response is an effective way for ensuring safety and stabilization of power grid by maintaining the balance between the supply and the demand of power grid, and this paper focuses on using electric water heaters for demand response. In addition to considering comfort and price factors as did in previous works, this paper considers the overshoot temperature and its influence on demand response. First, a theoretical model of the heating and cooling processes of the electric water heater is established; second, the demand response process using electric water heaters is analyzed, including the influences of the physical parameters and the settings of electric water heaters on the demand response process; third, a model is established considering the demand response requirement, the comfort of owners of electric water heaters, and the electricity price, simultaneously; fourth, an optimization method based on deep reinforcement learning is proposed for demand response using electric water heaters. Meanwhile, the influence of parameters on the results of demand response is discussed in details. Experimental results show the effectiveness of the proposed method.

Pair-wise ranging between nodes plays a crucial role in Internet of Underwater Things (IoUT) networks, and it typically impacts the overall performance of such networks. As the sound speed depends on several parameters, time-of-flight-based techniques cannot work well under varying sound speeds in actual underwater conditions. Pair-wise ranging algorithms that consider stratification should be studied to improve ranging accuracy. However, there is a tradeoff between underwater acoustic ranging accuracy and number of calculations. This makes it challenging to implement underwater acoustic ranging algorithms in IoUT networks. In this paper, we propose an underwater acoustic horizontal ranging algorithm that rapidly and accurately estimates the horizontal range from a sender to a receiver under an arbitrary sound-speed profile in the deep sea. Simulation results show that the proposed algorithm accurately calculates the horizontal range with a low computational complexity. We validate the performance of the proposed algorithm using the data collected during an ultrashort baseline precision test in the South China Sea.

Industrial Internet of Things inevitably leads to the implementation of highly data-intensive devices, where the associated sensing nodes accelerate the energy consumption rate, which ultimately produces an energy bottleneck. To address this issue, this paper proposes a dynamic collaborative charging algorithm that acts on both the mobile nodes and the static nodes in a sensing node network. The proposed scheme is to design a collaborative group of charging robots that can rendezvous with the sensing nodes. The group includes aerial charging vehicles – able to charge the underpowered mobile nodes, and terrestrial charging vehicles, which charge their targeted static nodes. The aim of this study is to optimize the charging effect and the energy cost in the rendezvous process. This approach consists of two sub-algorithms: a charging algorithm for mobile nodes and a charging algorithm for static nodes. The charging algorithm for mobile nodes is designed so that each underpowered mobile node can be charged by a dedicated aerial charging vehicle. For this purpose, a deep learning model is trained to divide the underpowered mobile nodes into appropriate clusters, each of which is equipped with a mobile base station. The rendezvous process is then constructed as a mixed continuous/discrete optimization problem, which is solved by using firefly algorithm. In addition, the charging algorithm for static nodes ensures that the terrestrial charging vehicles traverse their routes, charging static nodes as they proceed. This traversing process was formulated as a multi-objective optimization problem, solved by using genetic algorithm. Through various experiments and case studies, the results have demonstrated both the feasibility and the efficiency of the proposed algorithms.