Multiobjective Multiple Mobile Sink Scheduling via Evolutionary Fuzzy Rough Neural Network for Wireless Sensor Networks

The sensor nodes in wireless sensor networks have the deficiency of limited energy, and the multihop transmission of information will lead to a premature paralysis of nodes near the sink. The use of the mobile sink can balance the energy consumption and greatly prolong the lifetime. Therefore, this...

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Bibliographic Details
Published in:IEEE transactions on fuzzy systems Vol. 30; no. 11; pp. 4630 - 4641
Main Authors: Zhao, Jianwei, Cao, Bin, Liu, Xin, Yang, Peng, Singh, Amit Kumar, Lv, Zhihan
Format: Journal Article
Language:English
Published: New York IEEE 01.11.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Artificial neural networks
Energy consumption
Energy utilization
Evolutionary algorithms
Fuzzy inference
Fuzzy logic
Fuzzy neural networks
Fuzzy rough neural network
Fuzzy rough neural network (FRNN)
Fuzzy sets
Gene expression
Genetic algorithms
Multi-Objective Evolutionary Algorithm
multi-objective evolutionary algorithm (MOEA)
multiobjective evolutionary algorithm
Multiobjective evolutionary algorithms
Multiobjective optimization
multiple mobile sink scheduling
Multiple mobile sinks
Multiple objective analysis
network lifetime
neural evolution
Neural evolutions
Neural networks
Nodes
Optimisations
Optimization
Paralysis
Rough neural networks
Rough sets
Scheduling
Sensor nodes
Sensors
Task complexity
Task scheduling
Wireless communication
Wireless communications
Wireless networks
Wireless sensor networks
ISSN:1063-6706, 1941-0034, 1941-0034
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Summary:The sensor nodes in wireless sensor networks have the deficiency of limited energy, and the multihop transmission of information will lead to a premature paralysis of nodes near the sink. The use of the mobile sink can balance the energy consumption and greatly prolong the lifetime. Therefore, this article studies the scheduling strategy of multiple mobile sinks and proposes a heuristic strategy based on interval type-2 fuzzy rough neural network. The energy and lifetime of sensor nodes, as well as location information of the mobile sink and special nodes are taken as input features. Through neural network learning, the outputs determine whether to move, moving direction, moving distance, and residence time, which can complete the scheduling task. The scheduling problem is regarded as a multiobjective optimization problem, and the network lifetime, the moving path length, and the network interpretability are optimized at the same time, so as to obtain a lightweight network with good interpretability and performance. Based on the parallel multiobjective evolutionary algorithm, a multiobjective neural evolutionary framework is constructed. This framework can balance multiple objectives and complete complex scheduling tasks. Compared with static sinks, random-moving sinks, sinks with manually designed strategy, gene expression programming-based sinks, as well as the other state-of-the-art multiobjective evolutionary algorithms, the proposed framework can achieve superior results.
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ISSN:1063-6706
1941-0034
1941-0034
DOI:10.1109/TFUZZ.2022.3163909