Faculty Publications
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Item A novel meta-heuristic differential evolution algorithm for optimal target coverage in wireless sensor networks(Springer Verlag service@springer.de, 2019) Naik, C.; Shetty D, D.A wireless sensor network (WSN) faces various issues one of which includes coverage of the given set of targets under limited energy. There is a need to monitor different targets in the sensor field for effective information transmission to the base station from each sensor node which covers the target. The problem of maximizing the network lifetime while satisfying the coverage and energy parameters or connectivity constraints is known as the Target Coverage Problem in WSN. As the sensor nodes are battery driven and have limited energy, the primary challenge is to maximize the coverage in order to prolong network lifetime. The problem of assigning a subset of sensors, such that all targets are monitored is proved to be NP-complete. The Objective of this paper is to assign an optimal number of sensors to targets to extend the lifetime of the network. In the last few decades, many meta-heuristic algorithms have been proposed to solve clustering problems in WSN. In this paper, we have introduced a novel meta-heuristic based differential evolution algorithm to solve target coverage in WSN. The simulation result shows that the proposed meta-heuristic method outperforms the random assignment technique. © 2019, Springer Nature Switzerland AG.Item ECABBO: Energy-efficient clustering algorithm based on Biogeography optimization for wireless sensor networks(Institute of Electrical and Electronics Engineers Inc., 2019) Nomosudro, P.; Mehra, J.; Naik, C.; Shetty D, D.Cluster-based communication design is an assuring technique in wireless sensor networks to reduce energy consumption and enhance scalability. The requirement of data collection from neighbor nodes, data gathering, and data forwarding to the sink overloads each cluster head. Therefore, it is a highly significant issue to elect a set of optimal cluster heads from the normal sensor nodes. In this paper, the Biogeography Based Optimization for energy-efficient clustering is introduced for cluster head selection. The simulation outcomes show that the algorithm improves the network endurance as compared to other protocols such as Genetic algorithm, Low energy adaptive clustering hierarchy, and Clustered Routing for Selfish Sensors. © 2019 IEEE.Item Intelligent Interference Minimization Algorithm for Optimal Placement of Sensors using BBO(Springer, 2020) Naik, C.; Shetty D, P.In wireless sensor networks, the performance metric such as energy conservation becomes paramount. One of the fundamental problems of energy drains is due to the interference of sensors during sensing, transmission, and receiving data. The issue of placing sensors on a region of interest to minimize the sensing and communication interference with a connected network is NP-complete. In order to overcome the existing problem, we have proposed a new work for interference minimization technique for optimal placement of sensors by employing biogeography-based optimization scheme. An efficient habitats representation, objective function derivation, migration, and mutation operators are adopted in the scheme. The simulations are performed to obtain the optimal position for sensor placement. Finally, the energy-saving of the network is compared with and without interference aware sensor nodes placement. © 2020, Springer Nature Singapore Pte Ltd.Item Differential evolution meta-heuristic scheme for k-coverage and m-connected optimal node placement in wireless sensor networks(Machine Intelligence Research (MIR) Labs contact@mirlabs.org, 2019) Naik, C.; Shetty D, D.A wireless sensor network (WSN) faces a wide range of issues, which includes coverage of the given set of targets under specified connectivity constraint. There is a need to monitor different targets in the sensor field for effective information communication to the base station from each wireless sensor node which monitors the target by maintaining required connectivity among them. The problem of ensuring every target covered by at least k sensors and each sensor directly communicate with m sensors is termed as k-coverage and m-connectivity problem in wireless sensor networks. As the wireless sensor nodes are battery driven and have limited energy, the primary challenge is to have an optimal placement of sensor nodes in the field of deployment to minimize energy consumption. The objective of this work is to deploy the optimal number of sensor nodes with k-coverage and m-connectivity constraints in an area of interest. In the last few years, many meta-heuristic algorithms have been proposed to solve different problems like clustering and localization in WSN. In this paper, we introduce a meta-heuristic based differential evolution algorithm to solve k-coverage and m-connectivity problem in WSN. The simulation result shows that the proposed meta-heuristic method out performs the genetic algorithm. © MIR Labs.Item FLAG: fuzzy logic augmented game theoretic hybrid hierarchical clustering algorithm for wireless sensor networks(Springer, 2022) Naik, C.; Shetty D, P.D.Stability of the wireless sensor network (WSN) is the most critical factor in real-time and data-sensitive applications like military and surveillance systems. Many energy optimization techniques and algorithms have been proposed to extend the stability of a wireless sensor network. Clustering is a well regarded method in the research communities among them. Hence, this paper presents hybrid hierarchical artificial intelligence based clustering techniques, named FLAG and I-FLAG. The first phase of these algorithms use game-theoretic technique to elect suitable cluster heads (CHs) and later phase of the algorithms use fuzzy inference system to select appropriate super cluster heads (SCHs) among CHs. The I-FLAG is an improved version of FLAG where additional parameters like energy and distance are considered to elect CHs. Simulations are performed to check superiority of the proposed algorithms over the existing protocols like LEACH, CHEF, and CROSS. Simulation results show that the average stability period of WSN is better in FLAG and I-FLAG compared to other protocols, and so is the throughput of WSN during the stability period. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.