Kaddi Mohammed, Omari Mohammed, Salameh Khouloud, Alnoman Ali
LDDI Laboratory, Mathematics and Computer Science Department, University of Adrar, Adrar 01000, Algeria.
Computer Science and Engineering Department, American University of Ras Al Khaimah, Ras Al Khaimah P.O. Box 10021, United Arab Emirates.
Sensors (Basel). 2024 Aug 13;24(16):5234. doi: 10.3390/s24165234.
Survivability is a critical concern in WSNs, heavily influenced by energy efficiency. Addressing severe energy constraints in WSNs requires solutions that meet application goals while prolonging network life. This paper presents an Energy Optimization Approach (EOAMRCL) for WSNs, integrating the Grey Wolf Optimization (GWO) for enhanced performance. EOAMRCL aims to enhance energy efficiency by selecting the optimal duty-cycle schedule, transmission power, and routing paths. The proposed approach employs a centralized strategy using a hierarchical network architecture. During the cluster formation phase, an objective function, augmented with GWO, determines the ideal cluster heads (CHs). The routing protocol then selects routes with minimal energy consumption for data transmission to CHs, using transmission power as a metric. In the transmission phase, the MAC layer forms a duty-cycle schedule based on cross-layer routing information, enabling nodes to switch between active and sleep modes according to their network allocation vectors (NAVs). This process is further optimized by an enhanced CSMA/CA mechanism, which incorporates sleep/activate modes and pairing nodes to alternate between active and sleep states. This integration reduces collisions, improves channel assessment accuracy, and lowers energy consumption, thereby enhancing overall network performance. EOAMRCL was evaluated in a MATLAB environment, demonstrating superior performance compared with EEUC, DWEHC, and CGA-GWO protocols, particularly in terms of network lifetime and energy consumption. This highlights the effectiveness of integrating GWO and the updated CSMA/CA mechanism in achieving optimal energy efficiency and network performance.
可生存性是无线传感器网络中的一个关键问题，受能量效率的影响很大。解决无线传感器网络中严重的能量限制需要在满足应用目标的同时延长网络寿命的解决方案。本文提出了一种用于无线传感器网络的能量优化方法（EOAMRCL），集成了灰狼优化算法（GWO）以提高性能。EOAMRCL旨在通过选择最佳占空比调度、传输功率和路由路径来提高能量效率。所提出的方法采用集中式策略，使用分层网络架构。在簇形成阶段，一个结合了GWO的目标函数确定理想的簇头（CH）。然后，路由协议以传输功率为指标，选择能耗最小的路由将数据传输到簇头。在传输阶段，MAC层根据跨层路由信息形成占空比调度，使节点能够根据其网络分配向量（NAV）在活跃和睡眠模式之间切换。这一过程通过增强的CSMA/CA机制进一步优化，该机制结合了睡眠/激活模式，并使配对节点在活跃和睡眠状态之间交替。这种集成减少了冲突，提高了信道评估准确性，降低了能耗，从而提高了整体网络性能。EOAMRCL在MATLAB环境中进行了评估，与EEUC、DWEHC和CGA-GWO协议相比表现出卓越的性能，特别是在网络寿命和能耗方面。这突出了集成GWO和更新的CSMA/CA机制在实现最佳能量效率和网络性能方面的有效性。
