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Author: search.filters.author.Shetty D, D.P.Subject: search.filters.subject.Sensor nodes

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    Optimal sensors placement scheme for targets coverage with minimized interference using BBO
    (Springer Science and Business Media Deutschland GmbH, 2022) Naik, C.; Shetty D, D.P.
    A Wireless Sensor Network (WSN) consists of a group of energy-constrained tiny devices called sensors which have sensing, processing, and communicating capabilities. These sensors are deployed in a region of interest for monitoring targets or detecting events, and forwarding the processed data to the sink nodes or gateways. In any wireless network scenario, the targets are to be covered by at least one sensor in the network in order to detect certain events. Maximizing coverage along with improving energy efficiency of the network is a fundamental issue in WSN. Therefore, a Biogeography Based Optimization (BBO) meta-heuristic technique is employed to place sensors in the region of interest. The proposed scheme solves a multi-objective problem using classical weighted sum approach. A fitness function is derived from combination of conflicting objectives, minimum interference, maximum target coverage, and selection of minimum number of sensor nodes along with connectivity of the network as a constraint. The scheme selects minimum number of sensors to deploy in the field of interest which maximizes the target coverage by minimizing the interference of sensors. The proposed scheme is tested on random and grid deployment scenarios. Finally, the scheme is compared with Genetic Algorithm and Random Scheme. The average interference energy loss on BBO-based scheme is found to be 16% less than that of the GA-based scheme, and 60% less than that of a Random-based scheme. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
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    Multi-attribute decision making approach for energy efficient sensor placement and clustering in wireless sensor networks
    (Springer, 2025) Naik, C.; Shetty D, D.P.
    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. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.