As the key technology for Internet of things, wireless sensor networks have received more attentions in recent years. Mobile localization is one of the significant topics in wireless sensor networks. In wireless sensor network, non-line-of-sight propagation is a common phenomenon leading to the growing non-line-of-sight error. It is a fatal impact for the localization accuracy of the mobile target. In this article, a novel method based on the nearest neighbor variable estimation is proposed to mitigate the non-line-of-sight error. First, the linear regression model of the extended Kalman filter is used to obtain the residual of the distance measurement value. After that, the residual analysis is used to complete the identification of the measurement value state. Then, by analyzing the statistical characteristics of the non-line-of-sight residual, the nearest neighbor variable estimation is proposed to estimate the probability density function of residual. Finally, the improved M-estimation is proposed to locate the mobile robot. Experiment results prove that the accuracy and robustness of the proposed algorithm are better than other methods in the mixed line-of-sight/non-line-of-sight environment. The proposed algorithm effectively inhibits the non-line-of-sight error.

Wireless body area network is a promising technology that brings healthcare to a new level of personalization. The applications of wireless body area network are not limited to healthcare monitoring applications but vastly used in entertainment applications. The applications are emerging at a fast pace and attract the attention of researchers. IEEE 802.15.6 provides a communication standard which specifies the physical layer and media access control layer operations for wireless body area networks. A fixed superframe structure is used for handling of heterogeneous traffics of wireless body area networks through pre-defined user priorities. This leads to inefficient use of superframe time duration because of fixed time phases for different types of data traffic. In this article, a novel group-based classification of traffic is introduced to avoid contention and inefficient use of superframe duration. A group-based media access control is developed to adjust the superframe duration according to high priority traffic whereas the rest of the traffic is controlled using node-based buffering. The experimental results showed that the proposed media access control outperformed adaptive beaconing medium access control and priority media access control, in terms of stability period, delay, throughput, transmission loss, and residual energy.

In wireless sensor network, the storage amount of information is high, and the transmission and scheduling of control information is reasonable. The node communication model, network structure model, and energy consumption model are constructed. On this basis, the high-density data in wireless sensor network are scheduled to optimize the time for nodes to perform tasks. The nodes in the network are fully scheduled to control the generation time of packets in the network and the generation time of packets in the network. Experimental results show that in different iterations, the proposed method has lower node delay and node energy consumption, with values less than 0.2 and 2, respectively, and the maximum data fusion quality can reach 98, with high fusion benefits, so as to improve the transmission and scheduling efficiency and quality of high-density data in wireless sensor network.

Internet of Things integrates various technologies, including wireless sensor networks, edge computing, and cloud computing, to support a wide range of applications such as environmental monitoring and disaster surveillance. In these types of applications, IoT devices operate using limited resources in terms of battery, communication bandwidth, processing, and memory capacities. In this context, load balancing, fault tolerance, and energy and memory efficiency are among the most important issues related to data dissemination in IoT networks. In order to successfully cope with the abovementioned issues, two main approaches—data-centric storage and distributed data storage—have been proposed in the literature. Both approaches suffer from data loss due to memory and/or energy depletion in the storage nodes. Even though several techniques have been proposed so far to overcome the abovementioned problems, the proposed solutions typically focus on one issue at a time. In this article, we propose a cross-layer optimization approach to increase memory and energy efficiency as well as support load balancing. The optimization problem is a mixed-integer nonlinear programming problem, and we solve it using a genetic algorithm. Moreover, we integrate the data-centric storage features into distributed data storage mechanisms and present a novel heuristic approach, denoted as Collaborative Memory and Energy Management, to solve the underlying optimization problem. We also propose analytical and simulation frameworks for performance evaluation. Our results show that the proposed method outperforms the existing approaches in various IoT scenarios.

Power load forecasting is an important guarantee of safe, stable, and economic operation of power systems. It is appropriate to use interval data to represent fuzzy information in power load forecasting. The dual possibilistic regression models approximate the observed interval data from the outside and inside directions, respectively, which can estimate the inherent uncertainty existing in the given fuzzy phenomenon well. In this article, efficient dual possibilistic regression models of support vector machines based on solving a group of quadratic programming problems are proposed. And each quadratic programming problem containing fewer optimization variables makes the training speed of the proposed approach fast. Compared with other interval regression approaches based on support vector machines, such as quadratic loss support vector machine approach and two smaller quadratic programming problem support vector machine approach, the proposed approach is more efficient on several artificial datasets and power load dataset.

Irregularities in microphone distribution enrich the diversity of spatial differences to decorrelate interferences from the beamforming target. However, the large degrees of freedom of irregular placements make it difficult to analyse and optimize array performance. This article proposes fast and feasible optimal irregular array design methods with improved beamforming performance for human speech. Important geometric features are extracted to be used as the input vector of the neural network structure to determine the optimal irregular arrangements of sensors. In addition, a hyperbola design method is proposed to directly cluster microphones in the hyperbola areas to produce rich differential distance entropies and yield significant signal-to-noise ratio improvements. These methods can be easily applied to guide non-computer-aided optimal irregular array designs for human speech in acoustic scenes such as immersive cocktail party environments.

In this article, we consider a directional data forwarding problem to multiple destinations under distinct deadline constraints in vehicular ad hoc networks. We present a simple yet effective data forwarding algorithm based on only vehicle-to-vehicle communications in infrastructure-less and map-less environments. Our algorithm consists of two phases: relay selection and proliferation. We design a relay selection algorithm that encourages a shared ride for data delivery toward a certain common intermediate point from time to time for forwarding efficiency. It chooses a strong next relay candidate among nearby connected vehicles by considering their current position, velocity, and also the current progress toward the destination. In case that one of the progress lagging indicators becomes signaled, the number of vehicle relays increases under control depending on the degree of deterioration during a packet replication procedure called proliferation. Embedding two essential parts in designing a timely data forwarding scheme validates its accurate on-time data delivery performance and forwarding efficiency in network overhead based on real-world data-driven experiments.

Partial discharges are the main insulation defects encountered in gas-insulated switchgears. When it occurs inside the gas-insulated switchgear cavity, it degrades insulation, and, sooner or later, causes a breakdown. Therefore, it is important to discover insulation defects as early as possible, locate the discharge, and perform both defect identification and maintenance. Current ultra high frequency-based partial discharge location methods mainly use time delay. To obtain accurate delay times, however, a very high sampling rate is needed, which requires expensive hardware and greatly limits its application. Therefore, in this article, a localization method based on received signal strength indicator ranging is proposed, and location estimation is carried out. An easily implementable particle swarm optimization algorithm with high positioning accuracy is selected to compensate for the low positioning accuracy of current received signal strength indicator ranging methods. To further improve positioning accuracy, the convergence conditions of the particle swarm optimization are investigated, and, considering their constraints, an improved particle swarm optimization algorithm is proposed. By combining the characteristics of ultra high frequency wireless sensor array positioning, the particle size is optimized. The simulation results show that the location accuracy using the ultra high frequency switchgear partial discharge location method based on received signal strength indicator ranging with the improved particle swarm optimization algorithm performs significantly better.

We design a greedy routing scheme specifically for GPS-free large-scale wireless sensor networks deployed on surfaces of complex-connected three-dimensional settings. Compared with other greedy embedding–based surface network routing scheme, the proposed one is cut free such that no pair of nodes suffers a long detour to reach each other. The routing scheme is designed to be resilient to node or link failures especially under random node or link failure model where each node in a network has an equal and independent probability of failure during some time interval. The proposed algorithm is fully distributed and scalable to both the size and the topological complexity of a network. Each sensor node requires only limited and constant storage. Simulation results show the proposed routing scheme with a higher successful delivery ratio, a lower average stretch factor, and a lower normalized communication cost compared with other resilient routing methods.

In order to improve the control ability of the human body in the process of physical training, it is necessary to carry out the inverse simulation analysis of the physical training process and establish the process control model of the physical training. The complex problem of high-dimensional spatial motion planning involved in physical training is decomposed into a series of sub-problems in low-dimensional space, and the inertial attitude parameter fusion is carried out according to the position and pose state of the human body in the end of the workspace during the process of physical training. The design of sensor node and base station in the system can realize real-time collection of motion parameters of motion collectors. The multi-dimensional control of physical training process is carried out by fuzzy constraint and inverse integral control, and the attitude parameters of human body are adjusted by means of mechanical analysis model and inertial parameter analysis method. The simulation results show that the inversion simulation control has better convergence, higher control quality, and better inverse simulation performance in the process of physical training, which can effectively guide physical training and improve the effect of physical training.