A phenotype-based multi-objective evolutionary algorithm for maximizing lifetime in wireless sensor networks with bounded hop

Relay node placement with a hop count bound is a crucial problem in enhancing connectivity, lifetime, and reliability in multi-hop wireless sensor networks. However, existing approaches focus solely on minimizing the number of used relay nodes without considering the energy consumption among nodes....

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Germany), 2024-08, Vol.28 (15-16), p.8681-8699
Main Authors: Ngoc, Bui Hong, Tam, Nguyen Thi, Binh, Huynh Thi Thanh, Vinh, Le Trong
Format: Article
Language:English
Subjects:
Application of Soft Computing
Artificial Intelligence
Communication
Computational Intelligence
Connectivity
Control
Energy consumption
Engineering
Evolutionary algorithms
Fault tolerance
Genetic algorithms
Mathematical Logic and Foundations
Mechatronics
Multiple objective analysis
Network reliability
Nodes
Placement
Relay
Robotics
Sensors
Wireless sensor networks
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Summary:Relay node placement with a hop count bound is a crucial problem in enhancing connectivity, lifetime, and reliability in multi-hop wireless sensor networks. However, existing approaches focus solely on minimizing the number of used relay nodes without considering the energy consumption among nodes. This work investigates a relay node placement problem in multi-hop wireless sensor networks with two objectives: minimize the number of used relay nodes, and minimize the maximum node energy consumption to prolong the network’s lifetime while still ensuring the network’s connectivity. In particular, we consider a hop count bound as a delay constraint to elevate the network’s reliability. We propose a multi-objective evolutionary algorithm called GPrim to solve our problem. The algorithm is a combination of edge-set encoding and NSGA-II framework. Leveraging problem-specific properties, we introduce objective-oriented heuristics incorporated into initialization, crossover, and mutation operators to improve the algorithm’s convergence. Simulation results on 3D datasets show that the proposed algorithm performs significantly better than existing algorithms on all measured metrics.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-023-08923-1