Faculty Publications

Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736

Publications by NITK Faculty

Browse

Has files: NoSubject: search.filters.subject.Biogeography-based optimization

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    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.
  • No Thumbnail Available
    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.