LOAD BALANCING OPTIMIZATION FOR RPL BASED EMERGENCY RESPONSE USING Q-LEARNING

MATTER: International Journal of Science and Technology

View Publication Info
 
 
Field Value
 
Title LOAD BALANCING OPTIMIZATION FOR RPL BASED EMERGENCY RESPONSE USING Q-LEARNING
 
Creator Sebastian, A.
Sivagurunathan, Dr. S.
 
Subject Internet of Things
RPL
Load Balancing Optimization
Disaster Response
Multi Agent Q-Learning
 
Description Internet of Things technology has given rise to Smart Cities, Smart Health, Smart Transport Logistics, Smart Production and Supply chain management, Smart Home and many more. For IoT deployments, ROLL-WG has standardized Routing Protocol for Low Power and Lossy Networks (RPL) for urban environment (RFC 5548). RPL is designed to address the needs of constrained IoT environment. RPL uses Objective Functions (ETX & Hop Count) to optimize route selection. Many new Objective Functions for IoT applications are suggested by researchers to optimize path selection. Load Balancing Optimization for emergency response is least explored. In this article, we propose load balancing optimization for RPL based emergency response using Q-learning (LBO-QL). We have tested the proposed model in Contiki OS and Cooja simulator. Proposed model provides improved efficiency in Packet Delivery Ratio, Traffic Control Overhead and Power consumption. Hence, DODAG optimization using Q-Learning for disaster response is effective in optimized usage of constrained resources for disaster response operations with improved efficiency and reliability. Article DOI: https://dx.doi.org/10.20319/mijst.2018.42.7492This work is licensed under the Creative Commons Attribution-Non-commercial 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
 
Publisher GRDS Publishing
 
Date 2018-08-23
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier https://grdspublishing.org/index.php/matter/article/view/1476
 
Source MATTER: International Journal of Science and Technology; Vol 4 No 2 (2018): Regular Issue
2454-5880
 
Language eng
 
Relation https://grdspublishing.org/index.php/matter/article/view/1476/1252
 
Rights Copyright (c) 2018 Author(s)
 

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