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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. [ABSTRACT FROM AUTHOR]