Multi-attribute decision making approach for energy efficient sensor placement and clustering in wireless sensor networks

Chandra Naik () and D. Pushparaj Shetty ()
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Chandra Naik: Alva’s Institute of Engineering and Technology
D. Pushparaj Shetty: National Institute of Technology

Telecommunication Systems: Modelling, Analysis, Design and Management, 2025, vol. 88, issue 1, No 3, 17 pages

Abstract: Abstract Energy conservation is the most critical problem in wireless sensor networks due to its battery-operated tiny devices called sensors. These sensors are placed randomly in a region of interest to monitor certain events and targets. The random placement of sensors creates interference among them and leads to a quick energy drain of sensors. Minimizing interference while maintaining target coverage and connectivity in wireless sensor networks is less studied in the literature. There are many studies on clustering in wireless sensor network using different schemes and techniques to handle energy problems in wireless sensor networks. However, these studies never consider the interference during the sensor placement and clustering. The interference of nodes causes a message drop and results in quick energy drain during data transfer between member nodes and cluster heads. Therefore, in the proposed work, a novel interference-aware sensor deployment scheme is developed followed by a clustering technique on deployed sensors. The parameters such as interference, coverage, and connectivity of the sensors are considered for the sensor deployment. In clustering, the cluster heads are identified using various parameters like energy of the nodes, distance between the nodes and base station, communication range of the nodes, average distance between the nodes to their member nodes. Both the sensor deployment and the clustering adopt a well known multi-attribute decision making method E_TOPSIS for ranking potential positions for deployment of the sensors and ranking the sensor nodes for electing cluster heads. The sensor deployment scheme is compared with TOPSIS and SAW methods and the clustering technique is compared with TOPSIS, SAW, and Modified LEACH for stability period and network lifetime. The results show that the stability period for clustering using E_TOPSIS is 34.1%, 73.65%, and 83.5% better than TOPSIS, SAW, and Modified LEACH methods respectively.

Keywords: Wireless sensor networks; TOPSIS; MADM; Sensors deployment; Clustering; Entropy; MCDM (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11235-024-01250-2

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