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Owing to the integration of energy digitization and artificial intelligence technology, smart energy grids can realize the stable, efficient and clean operation of power systems. However, the emergence of cyber-physical attacks, such as dynamic load-altering attacks (DLAAs) has introduced great challenges to the security of smart energy grids. Thus, this study developed a novel cyber-physical collaborative security framework for DLAAs in smart energy grids. The proposed framework integrates attack prediction in the cyber layer with the detection and localization of attacks in the physical layer. First, a data-driven method was proposed to predict the DLAA sequence in the cyber layer. By designing a double radial basis function network, the influence of disturbances on attack prediction can be eliminated. Based on the prediction results, an unknown input observer-based detection and localization method was further developed for the physical layer. In addition, an adaptive threshold was designed to replace the traditional precomputed threshold and improve the detection performance of the DLAAs. Consequently, through the collaborative work of the cyber-physics layer, injected DLAAs were effectively detected and located. Compared with existing methodologies, the simulation results on IEEE 14-bus and 118- bus power systems verified the superiority of the proposed cyber-physical collaborative detection and localization against DLAAs.

With the increasingly severe problem of air pollution and energy crisis, new energy power generation technology in ship has quickly become the focus of attention. Compared with traditional ships, hybrid shipboard microgrid systems can achieve pollution-free, renewable and high use value. However, the integration of electricity-gas-heat in hybrid energy shipboard microgrid system also poses challenges to current optimization methods. Therefore, this paper develops a bi-level optimization dispatch model for hybrid shipboard microgrid system based on multi-objective particle swarm optimization algorithm. Taking the diesel generators, photovoltaic generation system, energy storage system (ESS) and thermal energy storage equipment into account, a hybrid shipboard microgrid system model considering electricity-gas-heat coupling is constructed. Based on this, a bi-level optimization dispatch model is established to reduce total cost, GHG (GHG) emissions and lifespan loss of ESS. The upper-level model achieves the optimization dispatch of power generation equipment and loads; a lower-level optimization model with the goal of reducing the lifespan loss of ESS is constructed. The improved multi-objective and single-objective particle swarm optimization algorithms are introduced to find the optimal dispatch solutions for bi-level optimization dispatch model. Finally, simulation results show that the proposed optimization method can not only reduce the cost and GHG emissions by 8.7% and 10.9%, but also improve the cycle life of ESS by 9.2%.

As a result of the growing complexity of industrial Internet applications, traditional hardware-based network designs are encountering challenges in terms of programmability and dynamic adaptability as they struggle to meet the real-time, high-reliability transmission requirements for the vast quantities of data generated in industrial environments. This paper proposes a holistic software-defined deterministic network (HSDDN) design solution. This solution uses a centralized controller to implement a comprehensive software definition, ranging from the network layer down to the physical layer. Within the wireless access domain, we decouple the standard radio-frequency modules from baseband processing to realize a software-defined physical layer, which then allows us to adjust the data transmission cycles and tag the trigger rates to meet demand for low-power, high-concurrency transmission. Within the wired network domain, we integrate software-defined networking with time-sensitive networking and propose a coordinated design strategy to address routing and the deterministic scheduling problem. We define a set of constraints to ensure collaborative transmission of the periodic and aperiodic data flows. To guarantee load balancing across all paths and timeslots, we introduce the Jain’s fairness index as the optimization objective and then construct a nondeterministic polynomial-time (NP)-hard joint optimization problem. Furthermore, an algorithm called Tabu search for routing and scheduling with dual-stages (TSRS-DS) is proposed. Simulation experiments demonstrate the effectiveness of the proposed HSDDN architecture.

Abstract Autonomous robotics working in the uncertain environment have drawn increasing interests from researchers. Here, an issue of online motion optimization under unknown environment is considered while preserving the safety and improving the flexible manoeuvrability of robot–environment interaction. This problem is addressed by improving the conventional dynamic movement primitives (DMPs) framework with force tracking constraints. First, an initial motion is learned through the DMPs. At the stage of skill generalization, a temporal coupling term combining with force constraints scheme which is inspired by the barrier Lyapunov function and finite‐time prescribed performance is deduced and adds to the original DMPs, so as to remain the contacting force staying within a predefined limit while aligning the motion along with surface of unknown environment adaptively. In this way, not only the contacting force can be guaranteed within a safe margin, but the shape of generalizing motion is preserved. Then the convergence and stability of the proposed DMPs are proved which is grounded on Laplace transformation‐based stability analysis to ensure the performance and safety. Finally, the proposed method is instantiated combined with conventional PID controller through the compared simulations to verify its effectiveness.

This paper proposes a novel optimization scheme to support stable and reliable vehicle-to-everything connections in two-tier networks, where the uplink channel of the cellular user is reused by underlay vehicle-to-vehicle communications. However, considering complex channel fading and high-speed vehicle movement, the certainty assumption is impractical and fails to maintain power control strategy in reality in the traditional statical vehicular networks. Rather than the perfect channel state information assumption, the first-order Gauss-Markov process which is a probabilistic model affected by vehicle speed and fading is introduced to describe imperfect channel gains. Moreover, interference management is a major challenge in reusing communications, especially in uncertain channel environments. Power control is an effective way to realize interference management, and optimal power allocation can ensure that interference of the user meets the communication requirements. In this study, the sum-rate-oriented power control scheme and minimum-rate-oriented power control scheme were implemented to manage interference and satisfy different design objectives. Since both of these schemes are non-convex and intractable, the Bernstein approximation and successive convex approximation methods were adopted to transform the original problems into convex ones. Furthermore, a novel distributed robust power control algorithm was developed to determine the optimal solutions. The performance of the algorithm was evaluated through numerical simulations, and the results indicate that the proposed algorithm is effective in vehicular communication networks with uncertain channel environments.

Abstract Photovoltaics face the threat of many potential faults in daily operation, which calls for accurate fault diagnosis to avoid huge economical losses. This paper investigates practical and troublesome scenarios, where the photovoltaics station has a large number of unlabeled samples and only a few are labelled. In the labelled set, the sample sizes of different types are unbalanced. To this end, a new fault sample rebalancing framework based on semi‐supervised learning (FSR‐SSL) is proposed. Specifically, a dual‐threshold selection mechanism is proposed to choose trusted pseudo‐label samples from unlabeled data to expand the training set. Moreover, a fault sample rebalancing strategy is designed to further filter the obtained trusted pseudo‐label samples, thereby flexibly adding different amounts of pseudo‐label data to various types. As the training rounds increase, the fault samples are gradually rebalanced and the model learning bias caused by type imbalance is well overcome. The extensive numerical experiments show that the proposed FSR‐SSL method reaches 99% accuracy. Compared with existing methods, the accuracy is increased by up to 33%.

Abstract Accurate and real‐time diagnosis of the inverter is crucial for the reliability, safety and generation efficiency of the photovoltaic (PV) system. Recently, deep learning (DL) is widely used for accurate diagnosis, which automatically extracts useful features instead of relying on experts. For real‐time diagnosis, in an emerging edge computing paradigm, massive data generated by end‐device are processed in nearby edge nodes, which reduces energy consumption and latency. However, large amounts of data are required for model training in the cloud, which leads to tremendous burdens of transmission and computation. To tackle these issues, a data‐driven diagnosis method based on compressed sensing (CS) and convolutional neural network (CNN) is proposed for open‐circuit faults of PV inverters. Based on CS, raw signals are compressed in edge nodes with the optimal compression ratio determined by experiments. The proposed CS‐CNN is evaluated on an edge‐cloud semi‐physical experiment platform. Compared with common compression methods, the accuracy of CS is improved by more than 3%, and the time consumption is only one‐fifth. Compared with the CNN of the same structure, the transmission and computation time is largely reduced by an order of magnitude. The developed CS‐CNN compresses 85% of data with a test accuracy of 99.18%.

The sintering process is a crucial thermochemical process in the blast furnace iron-making system. Tumble strength (TS), as a vital performance to assess sinter quality, is difficult to monitor due to the lack of timely measurement. Constructing a data-driven model for TS is an alternative for monitoring TS. However, the time-varying dynamic sintering process makes the task of modelling challenging. And the data are incomplete and insufficient in practice for modelling since there are unknown time delays in the system and lack actual TS value. The digital twin (DT) technique is a powerful tool to simulate the system dynamics with the real-time interaction between physical processes and virtual agents in cyberspace. This paper introduces a DT-enabled equivalent of the sintering system and proposes online data-driven modelling for TS monitoring. The time delay in the system is estimated for variable sequence alignment based on a modified maximum information coefficient method. The data used for modelling is enriched based on a multi-source information fusion technique. An adaptive update method is proposed to deal with the time-varying dynamics. The iterative forgetting factor-based algorithm is designed for the support vector regression method and guarantees a fast computational speed. Implementation and validation of the model on a DT-enabled sintering system show the efficiency of the proposed method. The accuracy of TS monitoring reaches 99.6% by analysis of 3 months’ data.

Chemical contents, the important quality indicators are crucial for the modeling of sintering process. However, the lack of these data can result in the biasedness of state estimation in sintering process. It, thus, greatly reduces the accuracy of modeling. Although there are some general imputation methods to tackle the data lackness, they rarely consider the interoutputs correlation and the negative impacts caused by incorrect prefilling. In this article, a novel sparse multioutput Gaussian convolution process (MGCP) modeling framework is proposed for data imputation. MGCP can flexibly mine the relationships between the outputs by a convolution of a sharing latent function and different Gaussian kernels. Moreover, the penalization terms are designed to weaken the false relationship between these outputs. To generalize the MGCP to a long-period case, dynamic time warping term is introduced to keep the global similarity between the original and estimated time series. Compared with several existing methods, the proposed method shows great superiority in sintering raw material contents estimation with real-world data.

In the above article [1], the authors found some mistakes: (P1) a controller composed of a consensus part and an obstacle avoidance part was proposed, but only the consensus part was theoretically analyzed. Therefore, the obtained result was incomplete; (P2) the method in [1] was applicable to linear systems; however, the designed obstacle avoidance matrix $V$ made the system to be a nonlinear system when two sub-controllers were considered as a whole. In addition, the time-varying matrix $V$ cannot guarantee that its eigenvalues were greater than 0 all the time. These led to the error of Theorem 3 in [1]. Therefore, the corrected results are given here.

Supervisory control and data acquisition (SCADA) is a widely implemented structure to achieve remote measurement and control in many iron and steel plants. In traditional consideration, more attention on physical network separation methods is paid to isolate the SCADA system from management network to keep SCADA in a considered ”safe” state. In addition, lots of security solution providers are focusing on the network side security assurance without involving the SCADA communication level protection. This paper investigates a new trusted-ID referenced key scheme for securing SCADA communications efficiently. The advanced encryption standard algorithm is used in the data transmission for its fast calculating speed, and the elliptic curve cryptography digital signature algorithm is used to confirm the data package that is from the right ID which can avoid the measured values and the control instructions to be maliciously modified by attacker. This solution for securing SCADA communication provides an efficient way to protect the data and protocol between the controllers and the remote terminal units (RTUs), and offers an authentication for the communication, which can avoid Man-In-The-Middle attack. Random numbers are used as a session key that can avoid the replay attack. cipher-block chaining mode message authentication code calculation is used to meet the data integrity requirement. Gong Needham Yahalom logic is used to prove the security of this solution, and an example is given to verify its validity.

In this paper, we investigate the resource optimization algorithm design in a multicarrier relay system with simultaneous wireless information and power transfer (SWIPT). The relay is capable of harvesting energy from the source's signals by using the power splitting method. The non-ideal energy consumption including both the non-ideal power amplifier and non-ideal circuit power consumption is considered. First, we study the transmission rate maximization problem (TRMP) in an asymmetric decode-and-forward (DF) relay transmission, where the transmission power at the source, the transmission power at the relay, the power splitting ratio, and the transmission time are jointly optimized. The formulated problem is a non-convex problem, and it is generally quite difficult to solve it. By exploiting the structure of the problem, we propose two methods (logarithmic operation on constraints and logarithmic change of variables) to transform it into the corresponding difference of convex (DC) optimization problems. Then, we extend the TRMP to an amplify-and-forward (AF) relay transmission. Furthermore, we propose an effective algorithm to solve the DC optimization problem and prove that the algorithm can converge to a stationary point. Finally, extensive simulations are conducted to verify the performance of the proposed algorithm. The simulation results show that the asymmetric DF relay transmission achieves the highest sum rate and the AF relay transmission achieves a much lower sum rate than both the asymmetric and symmetric DF relay transmissions under different conditions.

The paper studies the consensus of event-triggered for second-order multi-agent systems (MASs) with or without input time delay. Based on the designed triggering function, a distributed event-triggered control strategy is presented to drive the system to achieve consensus. For each agent, controller update only needs the neighbor's information communication and its own error value, thus continuous communication among agents is not necessary. Communication energy can be saved as the agents send their state information only at infrequent event instants. To demonstrate the asymptotical stability of the closed-loop error system, S-procedure approach is used by combing Lyapunov stability theories with linear matrix inequality (LMI) technique. Two sufficient conditions are derived by solving LMIs for achieving second-order consensus under the cases of with or without input time delay, respectively.Numerical examples are presented to verify the effectiveness of the proposed controllers.

In this paper, we study the problems of consensus and obstacle avoidance of multi-agent system with limited energy and communication faults. Based on algebraic Riccati equations, a distributed energy-optimal collision-free controller is proposed under communication faults. Compared with common controllers that can achieve consensus asymptotically, this controller not only makes each agent avoid neighboring agents, but also minimizes the energy consumed by each agent. Through analysis, the range of energy consumption is obtained, and the maximum energy consumption is optimized. Finally, some simulation results also verify the validity of the theoretical results.

Target tracking is an important application of underwater wireless sensor networks (UWSNs). Due to the energy constraint and energy imbalanced dissipation of underwater nodes, it is a challenge to maximize the energy efficiency and balance energy consumption simultaneously. In this paper, we propose an adaptive sampling algorithm for target tracking in UWSNs to address this issue. First, for maximizing the energy efficiency, we design an adaptive sampling interval adjustment (ASIA) method using a two-input-single-output fuzzy logic controller. In this method, the sampling interval is adaptively adjusted to make the actually uncertainty equal to the uncertainty threshold, which minimizes the sampling frequency and then reduces the energy consumption of information exchange. Second, for balancing the energy consumption, we develop a dynamic uncertainty threshold adjustment (DUTA) method using a single-input-single-output fuzzy logic controller. According to the residual energy of network nodes, the DUTA method dynamically adjusts the uncertainty threshold in the ASIA method, which changes the sampling frequency for avoiding premature death of nodes. Finally, the simulations show that, compared to the existing adaptive sampling algorithm, the proposed algorithm not only saves about 36% of energy but also alleviates the imbalance of energy consumption in different parts of the tracking area.

Multi-user cooperative transmission is an attractive architecture for underwater acoustic sensor networks (UASNs). Cooperative transmission depends on careful allocations of resources such as relay selection, but traditional relay selection requires precise measurements of channel state information, which is infeasible for multi-user cooperative transmission due to the unique features and hardware restrictions of UASNs. In this paper, we model multi-user relay selection under a multiuser multi-armed bandit (MU-MAB) framework, whereby users are not provided any prior knowledge about underwater acoustic channel conditions. We first exploit a novel MU-MAB algorithm, DSMU-MAB, for relay selection, assuming that the reward distributions are initially unknown but remain constant. Second, we consider an evolving environment in which the reward distributions undergo changes in time, and DSMU-rMAB, a derivative of DSMU-MAB, is proposed, which can be robust to abrupt changes in underwater communication environments. The proposed algorithms not only help sources find the suitable relays to achieve a high quality transmission and avoid collisions among users but also reduce the mass of information exchanged among users. We established the effectiveness of our proposed algorithms using theoretical and numerical analyses.

In this paper, the optimally rigid topology control problem in wireless sensor networks is considered to improve the algebraic rigidity properties. This problem is first formulated as a constrained optimization problem which can be solved by two stages. A minimally rigid network is constructed in the first stage, then the optimally rigid topology in the second stage. A potential game approach is proposed for solving the optimization problem by choosing a different performance metric as the potential function. It can be seen that the proposed algorithm can significantly improve the network performance, such as reducing communication complexity and transmit power, prolonging network lifetime, and so on. Finally, some simulations demonstrate the effectiveness of the proposed algorithms from multiple perspectives: topology complexity, average degree, consensus convergence speed, average radius, average link length, and network lifetime.

A distributed single-input multiple-output (SIMO) sonar system makes use of a sound source and a distributed star receiver network to localize targets. Compared with the terrestrial target localization, there are more challenges for underwater target localization. The underwater sound speed varies with depth, temperature, and salinity. As a result, the propagation path of the acoustic signal is not a straight line. It makes existing straight-line based distance measurements less accurate. Furthermore, time synchronization, which is closely related to time-based localization, has to be carried out because of the absence of GPS and clock differences of underwater devices. In this paper, we take the sound speed variation and time synchronization into account and propose two underwater target localization algorithms, namely, the space-alternating generalized expectation maximization-based underwater target localization (SAGE-UTL) and the nonlinear weighted least squares based underwater target localization (NWLS-UTL), for the distributed SIMO sonar system. The SAGE-UTL algorithm is designed for the situation, where the central receiver of the star receiver network needs to remain silent for its own safety. On the other hand, the NWLS-UTL algorithm is designed for the other situation, where the silence of the central receiver is not required. We assume that all the receivers are not synchronized, and the speed variation can be approximately modeled by a depth-dependent sound speed profile. Our proposed algorithms can jointly achieve target localization and time synchronization. We evaluate the SAGE-UTL and the NWLS-UTL algorithms with several numerical simulations. The results show good performance of our proposed algorithms compared with the Cramér-Rao bound and the tailored benchmark algorithms, such as the approximate nonlinear least squares algorithm and the approximate space-alternating generalized expectation maximization based underwater target localization algorithm.

Teleoperating cyber-physical system (TCPS) has been considered as a promising technology to stretch artificial intelligences to remote locations. Many applications of TCPS demand operator and slaves to keep state consensus on the shared information. However, the cyberand physicalconstrained characteristics on TCPS make it difficult to realize such a consensus. This paper investigates the consensus problem for single-master-multi-slave TCPS with time-varying delay and actuator saturation. According to the communication structures of slaves, centralized and decentralized controllers are, respectively, designed to drive the consensus of master and slave robots. To simplify the information fusion in decentralized controller design, we use min-weighted rigid graph-based topology optimization algorithm to reduce the communication redundancy in slave site. Under time-varying delay and actuator saturation constraints, the sufficient stability conditions are presented to show that the centralized and decentralized controllers can stabilize the single-master-multi-slave TCPS. Moreover, the stability conditions are rearranged into a form of linear matrix inequalities, and then, the required initial stability conditions for master and slaves are developed. Finally, simulations and experiments are demonstrated to show the validity of the method. It is shown that the consensus controllers can guarantee the asymptotic stability of single-master-multi-slave TCPS, while the topology optimization can reduce the redundancy of communication links.

This paper proposes a pricing strategy for energy management in the electricity market composed of one generation company, multiple competing utility companies, and consumers with heating, ventilation, and air conditionings, which participate in demand response to keep the electricity real-time balance. The utility company plays the role of an intermediary agent between the generation company and consumers. We model the cost of the utility companies from the demand-side regulation and give the condition of a unique optimal regulation price. For the multiple competing utility companies, we demonstrate that the retail pricing game belongs to the supermodular game and the game exists a pure Nash equilibrium. The uniqueness of the Nash equilibrium is also proved, and then a distributed algorithm is developed to search for the optimal equilibrium price. Furthermore, we study the influence of the wholesale price on the equilibrium. Finally, numerical results demonstrate the efficiency of the proposed pricing strategy.

The real-time position of an autonomous underwater vehicle (AUV) is always of great interest. With advances of technologies, underwater wireless sensor networks (UWSNs) become promising tools for AUV tracking. Due to the energy constraint of underwater nodes, energy saving is a key issue that affects all aspects of the design of a tracking scheme. In this paper, we propose a novel energy-efficient tracking scheme for an AUV to locate itself in time by UWSNs. We first design a tracking protocol considering the energy consumptions in both the AUV and sensor nodes (SNs). Particularly, the protocol is designed in two aspects: 1) the passive listening mechanism and duty-cycle strategy for the AUV and 2) the detection-based ranging packet transmission for SNs. Since the tracking protocol will inevitably affect the packet delivery between the AUV and SNs, we analyze the packet delivery success rate (PDSR) to shed light on the impact of system parameters on the tracking performance. To cope with non-linearity of the model and the intermittent observations mainly arisen from the effect of the tracking protocol, we adopt two extended versions of the original intermittent Kalman filter for tracking. They are intermittent extended Kalman filter and intermittent unscented Kalman filter. Simulation results demonstrate the effectiveness of the proposed tracking scheme, and reveal that the PDSR analysis provides a design guidance for parameter selection in system configuration.

A precision positioning system with energy efficiency is of great necessity for guaranteeing personnel safety in underground mines. The location information of the miners’ should be transmitted to the control center timely and reliably; therefore, a heterogeneous network with the backbone based on high speed Industrial Ethernet is deployed. Since the mobile wireless nodes are working in an irregular tunnel, a specific wireless propagation model cannot fit all situations. In this paper, an underground localization system is designed to enable the adaptation to kinds of harsh tunnel environments, but also to reduce the energy consumption and thus prolong the lifetime of the network. Three key techniques are developed and implemented to improve the system performance, including a step counting algorithm with accelerometers, a power control algorithm and an adaptive packets scheduling scheme. The simulation study and experimental results show the effectiveness of the proposed algorithms and the implementation.

In this paper, a mobile anchor node assisted RSSI localization scheme in underwater wireless sensor networks (UWSNs) is proposed, which aims to improve location accuracy and shorten location time. First, to improve location accuracy, we design a support vector regression (SVR) based interpolation method to estimate the projection of sensor nodes on the linear trajectory of the mobile anchor node. The proposed method increases the accuracy of the nonlinear regression model of noisy measured data and synchronously decreases the estimation error caused by the discreteness of measured data. Second, to shorten location time, we develop a curve matching method to obtain the perpendicular distance from sensor nodes to the linear trajectory of the mobile anchor node. The location of the sensor node can be calculated based on the projection and the perpendicular distance. Compared with existing schemes that require the anchor node to travel at least two trajectories, the proposed scheme only needs one-time trajectory to locate sensor nodes, and the location time is shortened with the reduction in the number of trajectories. Finally, simulation results prove that the proposed scheme can obtain more accurate sensor node location in less time compared with the existing schemes.

Target localization, which aims to estimate the location of an unknown target, is one of the key issues in applications of underwater acoustic sensor networks (UASNs). However, the constrained property of an underwater environment, such as restricted communication capacity of sensor nodes and sensing noises, makes target localization a challenging problem. This paper relies on fractional sensor nodes to formulate a support vector learning-based particle filter algorithm for the localization problem in communication-constrained underwater acoustic sensor networks. A node-selection strategy is exploited to pick fractional sensor nodes with short-distance pattern to participate in the sensing process at each time frame. Subsequently, we propose a least-square support vector regression (LSSVR)-based observation function, through which an iterative regression strategy is used to deal with the distorted data caused by sensing noises, to improve the observation accuracy. At the same time, we integrate the observation to formulate the likelihood function, which effectively update the weights of particles. Thus, the particle effectiveness is enhanced to avoid “particle degeneracy” problem and improve localization accuracy. In order to validate the performance of the proposed localization algorithm, two different noise scenarios are investigated. The simulation results show that the proposed localization algorithm can efficiently improve the localization accuracy. In addition, the node-selection strategy can effectively select the subset of sensor nodes to improve the communication efficiency of the sensor network.

The wireless technology is regarded as a paradigm shifter in the process industry. A star-linear industry wireless sensor network (IWSN) for narrow process industries is proposed in this paper. Based on the proposed IWSN, we focus on time-efficient convergecast solutions. We present algorithms to achieve optimal convergecast performance in terms of time slots use. In the proposed IWSN, the field devices (FDs) constitute a set of TDMA (time division multiple access) based star topology clusters, and the cluster heads present a multihop linear backbone. Time slots are scarce communication resource for convergecast in a narrow IWSN. Aiming to use slots efficiently, we design optimal algorithms to improve the polling scheduling in the cluster and the packets forwarding over the backbone. In a cluster, we design a multicycle scheduling algorithm and a fair polling algorithm to improve slots utility of the communication reliability and integrity. Over the backbone, an optimal slots allocating algorithm is designed to maximize the slots performance in terms of the end-to-end communication reliability, based on which a slot-efficient multisuperframe scheduling algorithm is presented. Performance analysis and simulations show that our solution outperforms traditional ones in terms of communication reliability and real-time.

This work proposes three switched control strategies for aggregated heating, ventilation, and air conditioning (HVAC) systems in commercial buildings to track the automatic generation control (AGC) signal in smart grid. The existing control strategies include the direct load control strategy and the setpoint regulation strategy. The direct load control strategy cannot track the AGC signal when the state of charge (SOC) of the aggregated thermostatically controlled loads (TCLs) exceeds their regulation capacity, while the setpoint regulation strategy provides flexible regulation capacity, but causes larger tracking errors. To improve the tracking performance, we took the advantages of the two control modes and developed three switched control strategies. The control strategies switch between the direct load control mode and the setpoint regulation mode according to different switching indices. Specifically, we design a discrete-time controller and optimize the controller parameter for the setpoint regulation strategy using the Fibonacci optimization algorithm, enabling us to propose two switched control strategies across multiple time steps. Furthermore, we extend the switched control strategies by introducing a two-stage regulation in a single time step. Simulation results demonstrate that the proposed switched control strategies can reduce the tracking errors for frequency regulation.

Due to the lack of a physical line of defense, intrusion detection becomes one of the key issues in applications of underwater wireless sensor networks (UWSNs), especially when the confidentiality has prime importance. However, the resource-constrained property of UWSNs such as sparse deployment and energy constraint makes intrusion detection a challenging issue. This paper considers a virtual-lattice-based approach to the intrusion detection problem in UWSNs. Different from most existing works, the UWSNs consist of two kinds of nodes, i.e., sensor nodes (SNs), which cannot move autonomously, and actuator nodes (ANs), which can move autonomously according to the performance requirement. With the cooperation of SNs and ANs, the intruder detection probability is defined. Then, a virtual lattice-based monitor (VLM) algorithm is proposed to detect the intruder. In order to reduce the redundancy of communication links and improve detection probability, an optimal and coordinative lattice-based monitor patrolling (OCLMP) algorithm is further provided for UWSNs, wherein an equal price search strategy is given for ANs to find the shortest patrolling path. Under VLM and OCLMP algorithms, the detection probabilities are calculated, while the topology connectivity can be guaranteed. Finally, simulation results are presented to show that the proposed method in this paper can improve the detection accuracy and save the energy consumption compared with the conventional methods.

Industrial wireless sensor networks (IWSNs) have become a viable solution for diverse application. However, commercial products and real-world deployments of IWSNs are faced with harsh reliability, real time, and predictability issues. The problem is more challenging in the narrow process industry. A novel two-tier wireless network consisting of subnetworks (FNs) and a backbone (BN) in the field is proposed in this paper. Along exploring time and frequency diversity, we present an optional polling slots allocation method in the FN to maximize communication reliability and integrity. Since time slots are scarce in the communication network for narrow process, we design a slot-reuse strategy with time slots consumption of 2 * N to construct a N -hop multipath BN without violating industrial standard. Furthermore, a multicycle scheduling strategy for polling and convergecast is presented to reduce the workload over the BN. Performance analysis and simulations show that our solution outperforms traditional ones in terms of communication reliability and integrity.

Maritime search and rescue (MSR), as the last defense line for the life at sea, plays a great role in shipping industry and ocean environment protection. MSR is attracting more and more attention from technical and scientific aspects which are expected to help locate the victim goals quickly, precisely, and efficiently. So far, there have been some new techniques applied to MSR, such as computer vision. However, these techniques have a common drawback that they are too dependent on the effort of the search and rescue party, so that the victim goals can only passively wait for their search. To change this kind of passive status, wireless sensor network (WSN) is the deficiencies of the current maritime search and rescue. To make WSN localization suitable for the MSR, an improved microelectromechanical systems MEMS aided algorithm on the basis of triangle and centroid algorithm is proposed to locate and track the search targets in real time and more precisely. Finally, the simulation results show that the proposed method and algorithm are feasible and efficient.

Currently, maritime search and rescue (MSR) is mainly depending on the search party, while the searching objects are waiting passively. Therefore, a new method of MSR which is based on the wireless sensor network (WSN) techniques is proposed in this paper. WSN could be self-organized into network and transmit nodes information, such as position information, for search party to accomplish the search and rescue work. However, the application encounters the problems of dynamic adaptability and life cycle limitation at sea. An energy dynamic distribution and optimization algorithm (EDDO), which is based on genetic algorithm (GA), is presented to handle with these problems. The algorithm satisfies the connectivity and energy saving of the network, and the GA with elitism-based immigrants approach is put forward to optimize the poor individuals when the positions of some nodes have changed. Simulation results show that the algorithm can quickly adapt to a dynamic network and reduce energy consumption at the same time.

Heterogeneous wireless sensor networks (HWSNs) can achieve more tasks and prolong the network lifetime. However, they are vulnerable to attacks from the environment or malicious nodes. This paper is concerned with the issues of a consensus secure scheme in HWSNs consisting of two types of sensor nodes. Sensor nodes (SNs) have more computation power, while relay nodes (RNs) with low power can only transmit information for sensor nodes. To address the security issues of distributed estimation in HWSNs, we apply the heterogeneity of responsibilities between the two types of sensors and then propose a parameter adjusted-based consensus scheme (PACS) to mitigate the effect of the malicious node. Finally, the convergence property is proven to be guaranteed, and the simulation results validate the effectiveness and efficiency of PACS.