EVOLUTIONARY OPTIMISATION METHOD FOR THE STRUCTURE OF A WIRELESS SENSOR NETWORK

Abstract

The paper focuses on the evolutionary optimisation of wireless sensor network (WSN) topology using a genetic algorithm (GA) as the main computational tool. The research addresses the critical problem of sensor node placement within a monitored area, which directly impacts coverage efficiency, energy consumption, and overall network resilience. The optimisation task is formulated as a structural problem that aims to determine node coordinates to achieve maximum coverage with a limited number of nodes, avoid redundant overlaps of sensing zones, and comply with minimum inter-node distance constraints to prevent coverage merging. To this end, the authors propose a GA-based approach that incorporates adaptive crossover and mutation probabilities, enabling the algorithm to escape premature convergence and to explore the solution space more effectively.

A block diagram of the proposed method is presented, detailing the algorithmic stages of initial population generation, fitness evaluation, selection, crossover, and mutation. The fitness function is designed to minimise the overlap of coverage areas while maximising spatial distribution uniformity. The method was validated through a series of simulation experiments under conditions of random node deployment with varying sensing radii (20 m, 30 m, and 40 m) and different minimum inter-node distance thresholds. Visualisations of intermediate and final optimisation stages are provided, demonstrating how the algorithm progressively improves deployment quality with increasing generations. The results show that the proposed method effectively reduces redundant coverage and enhances network structure adaptability. The global optimum was achieved at generation G=117, yielding a balanced deployment of nodes across the target area.

The experimental findings highlight the effectiveness of evolutionary approaches for WSN design, showing that the GA can provide near-optimal solutions in complex and large-scale environments where traditional analytical or brute-force methods fail. Furthermore, the adaptability of the algorithm makes it suitable for dynamic scenarios requiring rapid deployment with limited prior knowledge of terrain or application-specific constraints. The study also emphasises the potential integration of the proposed method with other evolutionary paradigms, such as particle swarm optimisation and differential evolution, to further improve accuracy and convergence speed.

In conclusion, the work demonstrates that GA-based optimisation of WSN topology is a promising tool for achieving efficient coverage, reducing energy waste, and ensuring the reliability of sensor networks. Future research directions include hybridisation with other metaheuristics, real-world deployment testing, and applications in large-scale Internet of Things (IoT) systems where adaptive and scalable network design is essential.