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Adaptive trust-based routing protocol for large scale WSNs

Citation

Khalid, N and Bai, Q and Al-Anbuky, A, Adaptive trust-based routing protocol for large scale WSNs, IEEE Access, 7 pp. 143539 - 143549. ISSN 2169-3536 (2019) [Refereed Article]


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Official URL: https://ieeexplore.ieee.org/document/8853253

DOI: doi:10.1109/ACCESS.2019.2944648

Abstract

Due to the dynamic and uncertain behaviors of nodes in Wireless Sensor Networks (WSNs), reliable data delivery becomes challenging task. With the absence of global information and centralised decision maker, the nodes in distributed WSNs need to rely on the surrounding nodes. This reliance requires the nodes to select the most reliable partner to work with in relaying the packets. Thus, the evaluation criteria and evaluation process has become a crucial agenda. Recent approaches adopt the concept of trust in selecting the next forwarder. However, most of them are restricted to certain criteria and the evaluation are conducted for single node. Inefficient consideration on the factors involved and inability to have wider view of the network could lead to inaccurate selection of forwarder, which eventually causes packet loss or re-transmission that consumes more resources. In this paper, we present an Adaptive Trust-based Routing Protocol (ATRP) that encompasses direct trust, indirect trust, and witness trust that considers multiple factors (resources and security) in its trustworthiness using pairwise comparison. The proposed mechanism allows further evaluations on more potential nodes, at several hops that helps to balance the energy consumption and prolong the network lifetime. Simulation results demonstrate longer lifetime, less delay, less packet loss and low energy consumption when compared to existing protocols.

Item Details

Item Type:Refereed Article
Keywords:adaptive routing, wireless network, agent-based modelling, trust-based coordination, trust-based, routing protocol, multi criteria, WSNs
Research Division:Information and Computing Sciences
Research Group:Artificial Intelligence and Image Processing
Research Field:Adaptive Agents and Intelligent Robotics
Objective Division:Information and Communication Services
Objective Group:Communication Networks and Services
Objective Field:Mobile Data Networks and Services
UTAS Author:Bai, Q (Dr Quan Bai)
ID Code:138230
Year Published:2019
Web of Science® Times Cited:1
Deposited By:Information and Communication Technology
Deposited On:2020-03-27
Last Modified:2020-04-04
Downloads:0

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