Bhargavi Kondisetty Venkata Naga Aruna, Varma Gottumukkala Partha Saradhi, Hemalatha Indukuri, Dilli Ravilla
Computer Science Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad 500075, Telengana, India.
Computer Science Engineering, Koneru Lakshmaiah Education Foundation, Vijayawada 520002, Andhra Pradesh, India.
Sensors (Basel). 2024 Sep 26;24(19):6238. doi: 10.3390/s24196238.
Positioning, coverage, and connectivity play important roles in next-generation wireless network applications. The coverage in a wireless sensor network (WSN) is a measure of how effectively a region of interest (ROI) is monitored and targets are detected by the sensor nodes. The random deployment of sensor nodes results in poor coverage in WSNs. Additionally, battery depletion at the sensor nodes creates coverage holes in the ROI and affects network coverage. To enhance the coverage, determining the optimal position of the sensor nodes in the ROI is essential. The objective of this study is to define the optimal locations of sensor nodes prior to their deployment in the given network terrain and to increase the coverage area using the proposed version of an enhanced particle swarm optimization (EPSO) algorithm for different frequency bands. The EPSO algorithm avoids the deployment of sensor nodes in close proximity to each other and ensures that every target is covered by at least one sensor node. It applies a probabilistic coverage model based on the Euclidean distances to detect the coverage holes in the initial deployment of sensor nodes and guarantees a higher coverage probability. Delaunay triangulation (DT) helps to enhance the coverage of a given network terrain in the presence of targets. The combination of EPSO and DT is applied to cover the holes and optimize the position of the remaining sensor nodes in the WSN. The fitness function of the EPSO algorithm yielded converged results with the average number of iterations of 78, 82, and 80 at 3.6 GHz, 26 GHz, and 38 GHz frequency bands, respectively. The results of the sensor deployment and coverage showed that the required coverage conditions were met with a communication radius of 4 m compared with 6-120 m with the existing works.
定位、覆盖范围和连通性在下一代无线网络应用中发挥着重要作用。无线传感器网络（WSN）中的覆盖范围是衡量传感器节点对感兴趣区域（ROI）进行有效监测以及检测目标的程度的指标。传感器节点的随机部署导致WSN中的覆盖效果不佳。此外，传感器节点的电池耗尽会在ROI中形成覆盖空洞，并影响网络覆盖范围。为了提高覆盖范围，确定传感器节点在ROI中的最佳位置至关重要。本研究的目的是在给定的网络地形中部署传感器节点之前确定其最佳位置，并使用针对不同频段提出的增强粒子群优化（EPSO）算法版本来增加覆盖区域。EPSO算法避免传感器节点彼此靠近部署，并确保每个目标至少被一个传感器节点覆盖。它应用基于欧几里得距离的概率覆盖模型来检测传感器节点初始部署中的覆盖空洞，并保证更高的覆盖概率。德劳内三角剖分（DT）有助于在有目标的情况下增强给定网络地形的覆盖范围。将EPSO和DT相结合，以覆盖空洞并优化WSN中其余传感器节点 的位置。EPSO算法的适应度函数分别在3.6 GHz、26 GHz和38 GHz频段产生了收敛结果，平均迭代次数分别为78、82和80次。传感器部署和覆盖结果表明，与现有工作中6 - 120 m的通信半径相比，4 m的通信半径满足了所需的覆盖条件。
