School of Computer and Information Engineering, Chuzhou University, Chuzhou 239000, China.
Department of Computer Science and Information Engineering, Tamkang University, New Taipei City 25137, Taiwan.
Sensors (Basel). 2022 May 29;22(11):4120. doi: 10.3390/s22114120.
Barrier coverage is a fundamental issue in wireless sensor networks (WSNs). Most existing works have developed centralized algorithms and applied the Boolean Sensing Model (BSM). However, the critical characteristics of sensors and environmental conditions have been neglected, which leads to the problem that the developed mechanisms are not practical, and their performance shows a large difference in real applications. On the other hand, the centralized algorithms also lack scalability and flexibility when the topologies of WSNs are dynamically changed. Based on the Elfes Sensing Model (ESM), this paper proposes a distributed Joint Surveillance Quality and Energy Conservation mechanism (JSQE), which aims to satisfy the requirements of the desired surveillance quality and minimize the number of working sensors. The proposed JSQE first evaluates the sensing probability of each sensor and identifies the location of the weakest surveillance quality. Then, the JSQE further schedules the sensor with the maximum contribution to the bottleneck location to improve the overall surveillance quality. Extensive experiment results show that our proposed JSQE outperforms the existing studies in terms of surveillance quality, the number of working sensors, and the efficiency and fairness of surveillance quality. In particular, the JSQE improves the surveillance quality by 15% and reduces the number of awake sensors by 22% compared with the relevant TOBA.
障碍物覆盖是无线传感器网络（WSNs）的一个基本问题。大多数现有工作都开发了集中式算法，并应用了布尔感知模型（BSM）。然而，传感器的关键特性和环境条件被忽视了，这导致开发的机制不实用，并且它们的性能在实际应用中存在很大差异。另一方面，当 WSN 的拓扑结构动态变化时，集中式算法也缺乏可扩展性和灵活性。基于 Elfes 感知模型（ESM），本文提出了一种分布式联合监测质量和节能机制（JSQE），旨在满足所需监测质量的要求，并最小化工作传感器的数量。所提出的 JSQE 首先评估每个传感器的感知概率，并识别出监测质量最差的位置。然后，JSQE 进一步调度对瓶颈位置贡献最大的传感器，以提高整体监测质量。大量实验结果表明，在所提出的 JSQE 中，在监测质量、工作传感器数量以及监测质量的效率和公平性方面都优于现有研究。特别是，与相关的 TOBA 相比，JSQE 提高了 15%的监测质量，并减少了 22%的唤醒传感器数量。
