Adaptive Chaotic Ant Colony Optimization for Energy Optimization in Smart Sensor Networks

Smart sensor network has the characteristics of low cost, low power consumption, real time, strong adaptability, etc., and it has a wide range of application prospects in the agricultural field. However, the smart sensor node is limited by its own energy; it also faces many bottlenecks in agricultur...

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Vydáno v:Journal of sensors Ročník 2021; číslo 1
Hlavní autoři: Jia, Wenxian, Liu, Menghan, Zhou, Jie
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Hindawi 05.07.2021
John Wiley & Sons, Inc
Témata:
Adaptive algorithms
Algorithms
Ant colony optimization
Artificial intelligence
Clustering
Communication
Data collection
Design
Energy consumption
Energy efficiency
Genetic algorithms
Optimization
Particle swarm optimization
Power consumption
Sensors
Smart sensors
Transition probabilities
Unmanned aerial vehicles
ISSN:1687-725X, 1687-7268
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Shrnutí:Smart sensor network has the characteristics of low cost, low power consumption, real time, strong adaptability, etc., and it has a wide range of application prospects in the agricultural field. However, the smart sensor node is limited by its own energy; it also faces many bottlenecks in agricultural applications. Therefore, balancing the energy consumption of nodes and extending the life of the network are important considerations in the design of efficient routing for smart sensor networks. Aiming at the problem of energy constraints, this paper proposes an intelligent sensor network clustering algorithm based on adaptive chaotic ant colony optimization (ACACO). ACACO introduces logical chaotic mapping to interfere with the pheromone on the initial path and uses the adaptive strategy to improve the transition probability formula. After selecting the best next hop node, the advancing ants are released to update the local pheromone, and the current pheromone content is adjusted by the chaos factor. When the ants determine the path, they release subsequent ants to update the global pheromone. The simulation results show that ACACO has obvious advantages over genetic algorithm (GA) and particle swarm optimization (PSO).
Bibliografie:ObjectType-Article-1
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ISSN:1687-725X
1687-7268
DOI:10.1155/2021/5051863