Bimonthly    Since 1986
ISSN 1004-9037
Publication Details
Edited by: Editorial Board of Journal of Data Acquisition and Processing
P.O. Box 2704, Beijing 100190, P.R. China
Sponsored by: Institute of Computing Technology, CAS & China Computer Federation
Undertaken by: Institute of Computing Technology, CAS
Published by: SCIENCE PRESS, BEIJING, CHINA
Distributed by:
China: All Local Post Offices
 
   
      07 April 2023, Volume 38 Issue 2   
    Article

    MACHINE LEARNING BASED ENERGY EFFICIENT ROUTING PROTOCOLS ANALYSIS FOR MOBILE AD-HOC NETWORKS
    1 Mrs. Ashwini V. Biradar, 2 Dr. Sharanabasappa C Gandage
    Journal of Data Acquisition and Processing, 2023, 38 (2): 4619-4625 . 

    Abstract

    Without the aid of centralized administration or specific support services, a group of wireless mobile hosts form a temporary network under the name Mobile Ad-hoc Network (MANET). Consumption of energy is the most important problem in MANETs because a majority of mobile hosts rely on limited battery resources. The lifespan and throughput of the network increase as a result of lower energy use. Regarding concerns with energy conservation, the effectiveness of existing techniques is lower. This study combines a proactive MANET routing technology with an energy consumption approach to get around these restrictions. The nodes' mobility and level of energy both affect the routing protocol. Route discovery in AODV routing is carried out via the flooding approach, which involves broadcasting route request (RREQ) packets to all nodes within a sender's transmission range. Packet collisions and network congestion frequently result from the unneeded re-transmission of RREQ packets and the reply (RREP) packets generated in response. We have suggested an optimized route-discovering mechanism for AODV in this project. The essential concept is to choose the best cluster of RREQ packet forwarders using the K-Means clustering algorithm rather than broadcasting. This method's goal is to lessen network congestion and end-to-end delay by decreasing the delivery of unneeded control packets.

    Keyword

    Mobile ad-hoc network, K-Means Clustering, Machine Learning, Clustering, Ad-hoc on demand distance vector etc.


    PDF Download (click here)

SCImago Journal & Country Rank

ISSN 1004-9037

         

Home
Editorial Board
Author Guidelines
Subscription
Journal of Data Acquisition and Processing
Institute of Computing Technology, Chinese Academy of Sciences
P.O. Box 2704, Beijing 100190 P.R. China
E-mail: info@sjcjycl.cn
 
  Copyright ©2015 JCST, All Rights Reserved