Human activity recognition (HAR) is an important task in the field of human-computer interaction. Given the penetration of WiFi devices in our daily lives, HAR using WiFi channel state information (CSI) is a more cost-efficient and comfortable approach. However, most existing approaches ignore the correlation between CSI sub-carriers, which makes their models inefficient and need to rely on deeper and more complex networks to further improve performance. To solve these problems, we propose a reconstructed WiFi CSI phase based HAR approach (WiPhase), which contains a two-stream model to fuse both temporal features and sub-carrier correlation features of reconstructed CSI phase. Specifically, a gated pseudo-Siamese network (GPSiam) is designed to capture the temporal features of the reconstructed sparse CSI phase integration representation (CSI-PIR), and a dynamic resolution based graph attention network (DRGAT) is designed to capture the nonlinear correlation between CSI sub-carriers by the reconstructed CSI phase graph. Furthermore, dendrite network (DD) makes the final decision by combining the features output from GPSiam and DRGAT. Experimental results show that WiPhase outperforms the existing state-of-the-art approaches.

Mobile charging is feasible to deal with the energy-constrained problem in wireless rechargeable sensor networks (WRSNs). The mobile chargers (MCs) are usually employed to charge the sensors sequentially according to the charging schemes. Existing studies assume that each sensor should be charged to its maximum energy capacity or to a fixed upper threshold before the next one can be charged. However, they neglect to control the charging time adaptively of each sensor according to the charging demand. Hence, in this paper, we assume that the charging time of each sensor can be controlled, and we study the joint optimization of charging sequence and charging time problem (JCSCT). Correspondingly, we propose a novel deep reinforcement learning with hybrid action space approach for JCSCT (DRLH-JCSCT), which utilizes deep q-network (DQN) to generate the charging sequence, and adopts deep deterministic policy gradient (DDPG) to determine the charging time. An attention-based encoder–decoder model is integrated in the actor network of DDPG, and a modified bi-directional gate recurrent unit network (MBGRU) is utilized as the decoder. We also design a novel reward function to evaluate the quality of the charging actions. Simulations demonstrate the improved charging performance of the proposed approach, with a longer network lifetime and fewer failed sensors compared with the existing mobile charging scheduling approaches.

Sensor scheduling for energy-efficient collaborative target tracking in wireless sensor networks (WSNs) is an important problem to deal with the limited network resources. With the recent development and emerging applications of energy acquisition technologies, it has become possible to overcome the bottleneck of battery energy in WSNs using the energy harvesting devices, where theoretically the lifetime of the network could be extended to the infinite. However, the energy harvesting WSN also poses new challenges for sensor scheduling algorithm over the infinite horizon under the limited sensor energy harvesting capabilities. In this article, a novel multistep prediction-based adaptive dynamic programming (MSPADP) approach is proposed for collaborative target tracking in energy harvesting WSNs to schedule sensors over an infinite horizon, according to the ADP mechanism. The “action” module of MSPADP is designed to obtain the sensor scheduling for multiple steps starting from the current step, and implemented by the minimal-cost first search (MCFS) decision tree scheme, and the “critic network” module of MSPADP is iteratively performed to optimize the performance for the remaining infinite steps using neural network. Extended Kalman filter (EKF) is adopted to predict and estimate the target state. The performance index is defined by the tracking accuracy derived from EKF and the energy consumption predicted by the candidate sensor schedule. Theoretical analysis shows the optimality of MSPADP, and simulation results demonstrate its superior tracking performance compared with single-step prediction-based ADP (SSPADP), multistep prediction-based dynamic programming (MSPDP), and multistep prediction-based pruning (MSPP) sensor scheduling approaches. Note to Practitioners-Collaborative target tracking is a typical problem in wireless sensor networks (WSNs) where the sensors need to be scheduled to address the constraints of the limited network resources, such as sensor energy usually supplied by the battery. In the recent years, energy harvesting device has been developed and applied to WSNs to overcome the energy restriction. As the energy harvesting capabilities of the sensors are limited, sensor scheduling remains as a challenging problem and is studied in this article. A novel multistep prediction-based adaptive dynamic programming (MSPADP) approach is proposed for collaborative target tracking, by scheduling sensors for the current time step based on the predictions of the subsequent steps over an infinite horizon. It runs iteratively in two modules: obtaining the previous optimal multistep sensor scheduling and updating the remaining infinite-step performance. Simulation results show its superior tracking performance compared with single-step prediction-based ADP (SSPADP), multistep prediction-based dynamic programming (MSPDP), and multistep prediction-based pruning (MSPP) approaches, and lay a good foundation for the practical applications.