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Bioinspired Mobility-Aware Clustering Optimization in Flying Ad Hoc Sensor Network for Internet of Things: BIMAC-FASNET

Author

Listed:
  • Abdu Salam
  • Qaisar Javaid
  • Masood Ahmad

Abstract

Flying ad hoc sensor network (FASNET) for Internet of Things (IoT) consists of multiple unmanned aerial vehicles (multi-UAVs) with high mobility, quick changes in topology, and diverse direction. The flying multi-UAVs were operated remotely by human beings or automatically by an onboard system. The applications of multi-UAVs are remote sensing, tracking, observing, and monitoring. It has a different nature compared to ordinary ad hoc network. The speed and diverse directions of multi-UAVs make it harder to route information in a desired way. Different issues may arise due to differences in unmanned aerial vehicle mobility, speed, diverse direction, and quick changes in topology. The researchers proposed conventional ad hoc routing protocols which has poor aspects for the flying ad hoc networks. They tried to tackle the issue by using the clustering approach that divides the network structure into multiple clusters, each with its own cluster head (CH). During the selection of CH and balance cluster formation, they consider only location awareness, neighborhood range, residual energy, and connection to the base station (BS) while ignoring the multi-UAVs distance, speed, direction, degree, and communication load. In this paper, we proposed bioinspired mobility-aware clustering optimization scheme based on bee intelligence foraging behavior for routing, considering relative mobility, residual energy, degree, and communication load during CH selection and balanced cluster formation. First, the clustering problem in network is formulated to dynamic optimization problem. An algorithm is designed based on bee intelligence, applied to select optimal UAVs CH and balanced cluster. The simulation results show that the proposed BIMAC-FASNET scheme performs better among existing clustering protocols in terms of link-connection lifetime, reaffiliation rate, communication load, number of UAVs per cluster, CH lifetime, and cluster formation time.

Suggested Citation

  • Abdu Salam & Qaisar Javaid & Masood Ahmad, 2020. "Bioinspired Mobility-Aware Clustering Optimization in Flying Ad Hoc Sensor Network for Internet of Things: BIMAC-FASNET," Complexity, Hindawi, vol. 2020, pages 1-20, September.
  • Handle: RePEc:hin:complx:9797650
    DOI: 10.1155/2020/9797650
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    Cited by:

    1. Abdu Salam & Qaisar Javaid & Masood Ahmad & Ishtiaq Wahid & Muhammad Yeasir Arafat, 2023. "Cluster-Based Data Aggregation in Flying Sensor Networks Enabled Internet of Things," Future Internet, MDPI, vol. 15(8), pages 1-24, August.
    2. Shi Wang, 2021. "Data allocation optimization for sensor information of internet of things," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(4), pages 790-800, August.
    3. Abdu Salam & Qaisar Javaid & Masood Ahmad, 2021. "Bio-inspired cluster–based optimal target identification using multiple unmanned aerial vehicles in smart precision agriculture," International Journal of Distributed Sensor Networks, , vol. 17(7), pages 15501477211, July.

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