DMTC: Optimize Energy Consumption in Dynamic Wireless Sensor Network Based on Fog Computing and Fuzzy Multiple Attribute Decision-Making

Advances in wireless technologies and small computing devices, wireless sensor networks can be superior technology in many applications. Energy supply constraints are one of the most critical measures because they limit the operation of the sensor network; therefore, the optimal use of node energy h...

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Bibliographic Details
Published in:Wireless communications and mobile computing 2021, Vol.2021 (1)
Main Authors: Varmaghani, Abbas, Matin Nazar, Ali, Ahmadi, Mohsen, Sharifi, Abbas, Jafarzadeh Ghoushchi, Saeid, Pourasad, Yaghoub
Format: Article
Language:English
Subjects:
Access control
Algorithms
Cloud computing
Clustering
Communication
Data collection
Decision making
Design
Electronic devices
Energy consumption
Energy efficiency
Energy management
Methods
Optimization
Sensors
Stress concentration
Traffic congestion
Wireless networks
Wireless sensor networks
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Summary:Advances in wireless technologies and small computing devices, wireless sensor networks can be superior technology in many applications. Energy supply constraints are one of the most critical measures because they limit the operation of the sensor network; therefore, the optimal use of node energy has always been one of the biggest challenges in wireless sensor networks. Moreover, due to the limited lifespan of nodes in WSN and energy management, increasing network life is one of the most critical challenges in WSN. In this investigation, two computational distributions are presented for a dynamic wireless sensor network; in this fog-based system, computing load was distributed using the optimistic and blind method between fog networks. The presented method with the main four steps is called Distribution-Map-Transfer-Combination (DMTC) method. Also, Fuzzy Multiple Attribute Decision-Making (Fuzzy MADM) is used for clustering and routing network based on the presented distribution methods. Results show that the optimistic method outperformed the blind one and reduced energy consumption, especially in extensive networks; however, in small WSNs, the blind scheme resulted in an energy efficiency network. Furthermore, network growth leads optimistic WSN to save higher energy in comparison with blinded ones. Based on the results of complexity analysis, the presented optimal and blind methods are improved by 28% and 48%, respectively.
ISSN:1530-8669
1530-8677
DOI:10.1155/2021/9953416