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Energy-Efficient Cluster Formation in IoT-Enabled Wireless Body Area Network.

Asim ZebSonia WakeelTaj RahmanInayat KhanMuhammad Irfan UddinBadam Niazi
Published in: Computational intelligence and neuroscience (2022)
Wireless sensor network is widely used in different IoT-enabled applications such as health care, underwater sensor networks, body area networks, and various offices. A sensor node may face operational difficulties due to low computing capacity. Moreover, mobility has become an open challenge in the healthcare wireless body area network that is highly affected by message loss due to topological manipulation. In this article, an enhanced version of the well-known algorithm MT-MAC is proposed, namely DT-MAC, to ensure successful message delivery. It considers node handover mechanism among virtual clusters to ensure network integrity and also uses the concept of minimum connected dominating set for network formation to achieve efficient energy utilization. It is then compared with well-known algorithms such as MT-MAC. The simulation results show that an increase in little latency of roughly 3 percent in using the proposed protocol improves the MT-MAC's packet delivery by 13-17 percent and the response time by around 15 percent. Therefore, the algorithm is best fitted for real-time applications where the high packet delivery and response time are required.
Keyphrases
  • healthcare
  • machine learning
  • deep learning
  • randomized controlled trial
  • low cost
  • network analysis
  • social media
  • psychometric properties