Nowadays, wireless sensor networks (WSNs) are utilised in military-based applications like border surveillance. However, existing border surveillance methods have difficulties with energy efficiency, latency, security, connectivity, optimal path selection and coverage. In this paper, a Voronoi Modified Red Fox (VMRF) algorithm is proposed as a solution to these problems. Initially, secure cluster head (CH) selection and clustering is performed using Secure Spatial Intelligence-Enhanced Voronoi Clustering (SIEVC) to boost energy efficiency, security, and extend network coverage and connectivity. The SIEVC algorithm dynamically selects CHs based on past and present trust, identity trust, and energy trust to identify malicious nodes and form optimal clusters for improved network coverage and connectivity. It also employs dynamic cluster size adjustment to maintain proximity between CHs and cluster members and utilizes node alternation to ensure equitable cluster sizes. This approach minimizes energy depletion, enhances network longevity, and improves load balancing. The algorithm introduces a node alternation mechanism to balance cluster sizes and prevent energy holes. This approach ensures secure and efficient CH selection and promotes even energy distribution. Then the proposed modified red fox (MRF) optimization method, based on the fitness metric, computes the energy-efficient and safe path for data transmission. Trust, energy, distance, link quality and traffic intensity are the factors that the fitness function takes into account. Finally, the data is transmitted to the base station (BS) through CH along the path with the highest fitness value. Then the proposed VMRF algorithm is evaluated using the NS-2 platform, and the outcomes are compared with existing protocols. Based on the evaluations, the VMRF algorithm performs better than existing ones in terms of delay, energy consumption, throughput, packet delivery ratio (PDR), malicious node detection ratio, and residual energy.

如今，无线传感器网络 (WSN) 用于边境监视等军事应用。然而，现有的边界监控方法在能源效率、延迟、安全性、连接性、最佳路径选择和覆盖方面存在困难。本文提出了一种Voronoi Modified Red Fox (VMRF)算法来解决这些问题。最初，使用安全空间智能增强型 Voronoi 聚类 (SIEVC) 执行安全簇头 (CH) 选择和聚类，以提高能源效率、安全性并扩展网络覆盖范围和连接性。 SIEVC算法根据过去和现在的信任、身份信任和能量信任动态选择CH，以识别恶意节点并形成最佳集群，以提高网络覆盖和连通性。它还采用动态集群大小调整来保持 CH 和集群成员之间的邻近性，并利用节点交替来确保公平的集群大小。这种方法可以最大限度地减少能源消耗、延长网络寿命并改善负载平衡。该算法引入了节点交替机制来平衡集群大小并防止能量空洞。这种方法可确保安全高效的 CH 选择并促进均匀的能量分配。然后，提出的改进的红狐（MRF）优化方法基于适应度度量，计算节能且安全的数据传输路径。信任、能量、距离、链路质量和流量强度是适应度函数考虑的因素。最后，数据沿着适应度值最高的路径通过CH传输到基站(BS)。然后使用 NS-2 平台评估所提出的 VMRF 算法，并将结果与​​现有协议进行比较。评估结果表明，VMRF算法在时延、能耗、吞吐量、包投递率（PDR）、恶意节点检测率、剩余能量等方面均优于现有算法。