In mobile wireless sensor networks, energy consumption and area coverage are two well-known optimization problems. An efficient and restricted sensor movement is essential so that redundant area coverage, as well as consumed energy, can be reduced to mitigate these two issues in mobile wireless sensor networks. To make equilibrium between energy consumption and the total area coverage by the sensor nodes is a difficult task. In this context, optimized path planning for sensor movement is crucial to reach the target. The article presents a Dynamic Fuzzy Inference System Based Reverse Glowworm Swarm Optimization (FIS-RGSO) of energy and coverage in smart green mobile wireless sensor networks. The objective of this article is to achieve minimum energy consumption by the sensors through their optimum movements so that sensors can cover maximum area and increase their lifetime. The proposed approach improves the sustainability and performance of green sensor networks in terms of a lifetime and energy-efficiency by implementing restricted and organized sensor movements based on the decision taken by the Fuzzy Inference System, which leads to minimum energy consumption and less distance traversing. The simulation results reveal that our proposed model reduces the consumed energy in a range of 5%–45% as compared with the existing method in reverse glowworm swarm optimization (RGSO) algorithm. The total distance covered by the sensors is also minimized by almost 7%–62% as compared with the existing one. The proposed method has experimented extensively and the result shows it performs better than the existing one in terms of the total number of live sensors that exist after execution. Therefore, the proposed methodology is realized as an energy-efficient model in wireless sensor networks that proliferate network lifetime.

在移动无线传感器网络中，能耗和区域覆盖是两个众所周知的优化问题。有效且受限制的传感器运动至关重要，因此可以减少多余的区域覆盖范围以及所消耗的能量，以减轻移动无线传感器网络中的这两个问题。要在能量消耗和传感器节点的总覆盖范围之间取得平衡，是一项艰巨的任务。在这种情况下，优化传感器运动路径规划对于实现目标至关重要。本文提出了一种基于动态模糊推理系统的逆向萤火虫群优化（FIS-RGSO），其能量和覆盖范围适用于智能绿色移动无线传感器网络。本文的目的是通过传感器的最佳运动来实现最低的能耗，从而使传感器能够覆盖最大的面积并延长其使用寿命。所提出的方法通过基于模糊推理系统做出的决定来实施受限且有组织的传感器运动，从而在使用寿命和能源效率方面提高了绿色传感器网络的可持续性和性能，从而实现了最低的能耗和更少的距离遍历。仿真结果表明，与现有的反向萤火虫群优化算法（RGSO）相比，我们提出的模型将能耗降低了5％–45％。与现有传感器相比，传感器所覆盖的总距离也被最小化了近7％–62％。所提出的方法已经进行了广泛的实验，结果表明，在执行后存在的实时传感器总数方面，该方法的性能优于现有方法。因此，在无线传感器网络中，所提出的方法可实现为节能模型，从而延长网络寿命。