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Pilot agent implied efficient data communication in pervasive acoustic wireless sensor network

Author

Listed:
  • Sushovan Das

    (College of Engineering and Mgmt)

  • Uttam Kr. Mondal

    (Vidyasagar University)

Abstract

Pervasive Acoustic Wireless Sensor Networks (PAWSNs) represent a growing and dynamic field with significant potential in areas like environmental monitoring, surveillance, and healthcare. Within PAWSNs, efficient data communication is essential due to high energy requirements and data redundancy caused by multipath phenomena linked to acoustic signals. A novel solution to these issues is the implementation of a pilot agent that optimizes data transmission. This Pilot Agent is an intelligent, autonomous entity within sensor nodes that utilizes semantic understanding and context awareness to process acoustic data. By interpreting the collected acoustic data, the Pilot Agent discards irrelevant or redundant information, transmitting only important and contextually pertinent data. Using its authenticator, path, and energy optimizer functions, the Pilot Agent selects the best path from authenticated routes, leading to decreased energy consumption and improved communication efficiency. The Pilot Agent considers factors such as hop distance, equivalent distance, and energy distance, along with efficiency metrics such as Energy Efficiency Ratio $$ \eta _E $$ η E and Path Optimization Efficiency $$ \eta _P $$ η P , to determine the best route for data transmission. In experiments, XBEE radio parameters are utilized for simulation with router nodes to assess energy and verify the proposed model’s efficiency.

Suggested Citation

  • Sushovan Das & Uttam Kr. Mondal, 2025. "Pilot agent implied efficient data communication in pervasive acoustic wireless sensor network," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 88(2), pages 1-15, June.
    • Handle: RePEc:spr:telsys:v:88:y:2025:i:2:d:10.1007_s11235-025-01281-3
    DOI: 10.1007/s11235-025-01281-3
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    References listed on IDEAS

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    1. Efi Dvir & Mark Shifrin & Omer Gurewitz, 2024. "Cooperative Multi-Agent Reinforcement Learning for Data Gathering in Energy-Harvesting Wireless Sensor Networks," Mathematics, MDPI, vol. 12(13), pages 1-34, July.
    2. Sushovan Das & Uttam Kr. Mondal, 2024. "Energy efficient acoustic sensor data integration in hybrid mode operated pervasive wireless sensor network," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 87(1), pages 61-72, September.
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