Zhiyi Qu

Target tracking is an essential issue in wireless sensor networks (WSNs). Compared with single-target tracking, how to guarantee the performance of multi-target tracking is more challenging because the system needs to balance the tracking resource for each target according to different target properties and network status. However, the balance of tracking task allocation is rarely considered in those prior sensor-scheduling algorithms, which may result in the degradation of tracking accuracy for some targets and additional system energy consumption. To address this issue, we propose in this paper an improved Q-learning-based sensor-scheduling algorithm for multi-target tracking (MTT-SS). First, we devise an entropy weight method (EWM)-based strategy to evaluate the priority of targets being tracked according to target properties and network status. Moreover, we develop a Q-learning-based task allocation mechanism to obtain a balanced resource scheduling result in multi-target-tracking scenarios. Simulation results demonstrate that our proposed algorithm can obtain a significant enhancement in terms of tracking accuracy and energy efficiency compared with the existing sensor-scheduling algorithms.

As a key technology in wireless sensor networks (WSNs), target tracking plays an essential role in many applications. To improve energy efficiency, clustering is widely used in tracking to organize the network to achieve data fusion and reduce communication costs. Many existing studies make dynamic adjustments based on static clusters to track moving targets. However, the additional overhead caused by frequent cluster reconstruction and redundant data transmission is rarely considered. To address this issue, we propose a tracking-anchor-based clustering method (TACM) in this paper, in which tracking anchors are introduced to provide activation indications for sensors according to the target position. We use the rough fuzzy C-means (RFCM) algorithm to locate the anchors and use the membership table to activate sensors to form a cluster. Since there are no sending, receiving, and fusing data tasks for anchors, they are lightly burdened and can significantly reduce the frequency of being rotated. Moreover, the state of cluster members (CMs) is scheduled using the linear 0–1 programming to reduce redundant transmissions. The simulation results demonstrate that, compared with some existing clustering methods, the proposed TACM effectively reduces the energy consumption when tracking a moving target, thus prolonging the network lifetime.

In large-scale wireless sensor networks (WSNs), spectrum efficiency and energy consumption are two challengeable problems to be solved due to the increasing traffic requirement and its limited energy. Successive interference cancellation (SIC) technology, which supports multiple parallel transmissions, gives an opportunity to improve the efficiency of data collection. However, those classical schemes suffer from high energy consumption, which presents a severe challenge to energy-constrained WSNs. To improve the transmission and energy efficiency of WSNs, we study an energy-efficient scheduling algorithm joint with SIC and power control in this paper. We first develop a non-uniform M-Level scheduling model to identify the beneficial opportunities and guarantee the transmission quality of SIC concurrency. Then, we formulate an energy efficiency maximization scheduling (EEMS) utility and further transform it into a combinatorial optimization problem. To reduce the computational complexity and obtain meaningful insights, two low-complexity optimization algorithms are proposed from the perspective of SIC scheduling and power control, respectively. Finally, extensive results demonstrate that the SIC scheduling design enables our proposed algorithm to achieve 51% higher throughput than the algorithm without SIC. Moreover, the energy efficiency of our proposed algorithm is improved by 37% compared with the existing algorithms, which indicates that our algorithm can search for the optimal configuration of SIC-concurrent transmissions and better manage the concurrent transmission power.

As a key technology, clustering has been an effective way in large-scale wireless sensor networks (WSNs) to extend the lifetime. However, the static cluster structure in most of the traditional method is formed without considering the development of the event. In this paper, we propose an Energy-Efficient Dynamic Clustering (EEDC) protocol for event monitoring applications in large scale WSN. In EEDC, a dynamic clustering method using Rough Fuzzy C-Means and Genetic algorithm (RFCM-GA) is designed. Firstly, the idea of fuzzy set and rough set in RFCM are used to form the overlapping cluster, which can guarantee the quality of coverage of the developing event. Secondly, we use GA to perform a parallel search in each cluster to find the optimal set of candidate cluster heads (CCHs). RFCM-GA can use its powerful global search capabilities and fast convergence speed to obtain the best clustering results. Simulation results demonstrate that EEDC has higher energy efficiency and prolongs the network lifetime compared to the existing approaches.