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🔥 Content Introduction
1. Introduction
Wireless Sensor Networks (WSNs) consist of numerous sensor nodes deployed in a monitoring area, forming a self-organizing network through wireless communication, widely applied in environmental monitoring, military reconnaissance, industrial automation, and other fields. The quality of network coverage directly affects the monitoring effectiveness and service performance of WSNs; good coverage ensures that information within the monitoring area is accurately collected and transmitted. Traditional coverage optimization methods, such as genetic algorithms and particle swarm optimization, have shortcomings when dealing with complex problems, such as easily falling into local optima and slow convergence speed. The Dung Beetle Optimization Algorithm (DBOA), as an emerging intelligent optimization algorithm inspired by the behaviors of dung beetles rolling dung balls and competing for resources, features a simple structure and strong global search capability, providing a new solution for WSNs coverage optimization. Research based on DBOA for WSNs coverage optimization not only helps improve network performance but also promotes the application of intelligent optimization algorithms in the field of wireless communication.
2. Principles of Dung Beetle Optimization Algorithm
2.1 Inspiration for the Algorithm
Dung beetles primarily engage in rolling dung balls in nature; they search for suitable dung balls and transport them to appropriate locations for reproduction or food storage. During this process, there is competition among dung beetles for dung ball resources; stronger beetles may seize dung balls from others, while beetles also adjust their rolling direction based on environmental information (such as light and terrain). DBOA simulates these behaviors of dung beetles, analogizing solutions in optimization problems to dung balls, and searches for optimal solutions by simulating the rolling, competition, and movement processes of dung beetles.
2.2 Basic Process of the Algorithm
In DBOA, the dung beetle population is first initialized, with each beetle representing a potential solution to the problem. Then, the fitness of each beetle is calculated, reflecting the quality of the solution; for example, in WSNs coverage optimization, network coverage can be used as the fitness function. Next, the rolling behavior of the beetles is simulated, updating their positions according to certain rules to move towards potentially better solutions; at the same time, competitive behavior is simulated, allowing stronger beetles to seize the “dung balls” (i.e., solutions) from others, achieving a balance between global search and local development. This process is repeated until stopping conditions are met (such as reaching the maximum number of iterations or convergence of fitness values), at which point the optimal solution obtained is the approximate optimal solution to the problem.
3. WSNs Coverage Optimization Process Based on Dung Beetle Optimization Algorithm
3.1 Problem Modeling
3.2 Algorithm Application
Applying DBOA to WSNs coverage optimization, the node positions are treated as variables in the dung beetle optimization algorithm, where each beetle’s position represents a deployment scheme for a set of nodes. In each iteration, the fitness of the beetles is calculated based on coverage rate, and then the positions of the beetles are updated according to the rules of DBOA, continuously adjusting the node deployment scheme. As iterations progress, the algorithm gradually finds the node deployment scheme that maximizes network coverage, achieving coverage optimization.
⛳️ Running Results
🔗 References
📣 Partial Code
🎈 Some theoretical references are from online literature; please contact the author for removal if there is any infringement.
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