Sathyamoorthy Malathy, Dhanaraj Rajesh Kumar, Vanitha C N, Sayeed Md Shohel
Department of Information Technology, KPR Institute of Engineering and Technology, Coimbatore, India.
Symbiosis Institute of Computer Studies and Research (SICSR), Symbiosis International (Deemed University), Pune, India.
Sci Rep. 2025 Jul 1;15(1):21808. doi: 10.1038/s41598-025-06001-0.
Numerous researches on wireless sensor networks was conducted to design proficient algorithms not only to minimalize the exploitation of energy and delay, but also to increase the network lifetime and throughput. Optimization techniques will provide the proper balance among the parameters considered and help us to achieve the target of lifetime prolongation in wireless sensor networks. The proposed scheme comprises of two phases namely optimal cluster head selection and an effective data transmission phase. In phase 1, Spatial Ant Lion optimization will focus on optimal cluster head selection based on minimal computation on smaller population of nodes. The members inside the cluster are divided into two categories such as ant and the antlions. The ants are present in the innermost region of the cluster will gather and send the data to one of the antlions chosen as the cluster head. The fitness value is calculated only for the antlions based on energy retained in the node, distance between the sink to antlion and from the ants to antlion. Then the antlion with the highest fitness value will be selected as cluster head. After a waiting period the fitness value will be reevaluated to choose the new cluster head. In phase 2 utilitarian data transmission algorithm is proposed for effective data transmission. If the distance from the cluster head to the sink is lesser then the single hop data transmission will be employed otherwise two-hop data transmission employed for data transmission. The experimental evaluation was conducted considering parameters such as the length and width of the area, number of nodes, routing protocol, sink node placement, antlion population size, network topology, energy parameters (including free space energy, transmitter's energy, receiver's energy, and energy spent for data transfer), and the maximum number of iterations. Results demonstrate that the proposed model achieved a 1.86% increase in throughput, a 2.56% reduction in delay, and a 3.18% improvement in energy efficiency when compared to existing schemes such as PSO, ALO, and SA-AOA.
人们对无线传感器网络进行了大量研究，以设计高效的算法，不仅将能量消耗和延迟降至最低，还能延长网络寿命并提高吞吐量。优化技术将在所考虑的参数之间提供适当的平衡，并帮助我们实现无线传感器网络中延长寿命的目标。所提出的方案包括两个阶段，即最优簇头选择和有效的数据传输阶段。在第一阶段，空间蚁狮优化将基于对较少节点群体的最小计算来专注于最优簇头选择。簇内的成员分为两类，即蚂蚁和蚁狮。位于簇最内部区域的蚂蚁会收集数据并将其发送到被选为簇头的蚁狮之一。仅根据节点中保留的能量、汇聚节点到蚁狮的距离以及蚂蚁到蚁狮的距离为蚁狮计算适应度值。然后，具有最高适应度值的蚁狮将被选为簇头。经过一段等待期后，将重新评估适应度值以选择新的簇头。在第二阶段，提出了功利数据传输算法以进行有效的数据传输。如果簇头到汇聚节点的距离较小，则采用单跳数据传输，否则采用两跳数据传输进行数据传输。实验评估考虑了诸如区域的长度和宽度、节点数量、路由协议、汇聚节点放置、蚁狮群体大小、网络拓扑、能量参数（包括自由空间能量、发射器能量、接收器能量以及数据传输所花费的能量）和最大迭代次数等参数。结果表明，与粒子群优化（PSO）、蚁群优化（ALO）和模拟退火 - 人工鱼群算法（SA - AOA）等现有方案相比，所提出的模型在吞吐量上提高了1.86%，在延迟上降低了2.56%，在能量效率上提高了3.
