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A predictive energy-efficient adaptive routing methodology for Mobile Ad hoc Networks

  • Neelam Malyadri
  • , M. Ramakrishna
  • , Rajesh Nandalike
  • , Pundalik Chavan
  • , S. Supreeth
  • , P. Dayananda*
  • , S. Rohith
  • *Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    Abstract

    A Mobile Ad hoc Network (MANET) is a self-configuring, infrastructure-less network of mobile devices connected wirelessly. In this paper, a novel approach, the Predictive Energy-efficient Adaptive Routing Technique (PEAR) approaches to the infrastructure-less network of mobile devices, addressing the intrinsic challenges of MANETs. PEAR is a network management system that uses real-time predictive analytics to forecast and adapt to topological changes due to node mobility, ensuring robust and proactive route management. It incorporates energy-aware routing decisions, conserving battery life and extending operational longevity, and uses a multi-factor routing algorithm. PEAR ensures loop-free routing to minimize latency and maintain scalability, making it adept at handling the growing complexity of MANET applications. PEAR outperformed Mobile Ad hoc On-demand Routing, showing throughput improvements of 2.5% at 5 m/s and 3.125% at 30 m/s, routing overhead reductions of 66.67% at 5 m/s and 50% at 30 m/s, energy consumption decreases by 66.67% at 5 m/s and 55.56% at 30 m/s, and a significant reduction in average delay by 66.67% at 5 m/s and 55.56% at 30 m/s, proving its superior efficiency and reliability in mobile networks.

    Original languageEnglish
    Article numbere70001
    JournalIET Networks
    Volume14
    Issue number1
    DOIs
    Publication statusPublished - 01-01-2025

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 7 - Affordable and Clean Energy
      SDG 7 Affordable and Clean Energy

    All Science Journal Classification (ASJC) codes

    • Computer Networks and Communications
    • Management Science and Operations Research
    • Control and Optimization

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