Cluster-Based Data Aggregation in Flying Sensor Networks Enabled Internet of Things

Abdu Salam, Qaisar Javaid, Masood Ahmad, Ishtiaq Wahid and Muhammad Yeasir Arafat ()
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Abdu Salam: Department of Computer Science, Abdul Wali Khan University, Mardan 23200, Pakistan
Qaisar Javaid: Department of Computer Science and Software Engineering, International Islamic University, Islamabad 44000, Pakistan
Masood Ahmad: Department of Computer Science, Abdul Wali Khan University, Mardan 23200, Pakistan
Ishtiaq Wahid: Department of Computer Science, Abdul Wali Khan University, Mardan 23200, Pakistan
Muhammad Yeasir Arafat: IT Research Institute, Chosun University, 309 Pilmun-daero, Dong-gu, Gwangju 61452, Republic of Korea

Future Internet, 2023, vol. 15, issue 8, 1-24

Abstract: Multiple unmanned aerial vehicles (UAVs) are organized into clusters in a flying sensor network (FSNet) to achieve scalability and prolong the network lifetime. There are a variety of optimization schemes that can be adapted to determine the cluster head (CH) and to form stable and balanced clusters. Similarly, in FSNet, duplicated data may be transmitted to the CHs when multiple UAVs monitor activities in the vicinity where an event of interest occurs. The communication of duplicate data may consume more energy and bandwidth than computation for data aggregation. This paper proposes a honey-bee algorithm (HBA) to select the optimal CH set and form stable and balanced clusters. The modified HBA determines CHs based on the residual energy, UAV degree, and relative mobility. To transmit data, the UAV joins the nearest CH. The re-affiliation rate decreases with the proposed stable clustering procedure. Once the cluster is formed, ordinary UAVs transmit data to their UAVs-CH. An aggregation method based on dynamic programming is proposed to save energy consumption and bandwidth. The data aggregation procedure is applied at the cluster level to minimize communication and save bandwidth and energy. Simulation experiments validated the proposed scheme. The simulation results are compared with recent cluster-based data aggregation schemes. The results show that our proposed scheme outperforms state-of-the-art cluster-based data aggregation schemes in FSNet.

Keywords: clustering; data aggregation; dynamic programming; flying sensor network; internet of things (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
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