VLA-CR: a variable action-set learning automata-based cognitive routing protocol for IoT
VLA-CR: a variable action-set learning automata-based cognitive routing protocol for IoT
Internet of Things (IoT) is a heterogeneous, mixed, and uncertain ubiquitous network, which has significantly affected the concept of wireless networking. A large number of wireless devices have connected through the IoT and shared large amounts of data. So, efficient routing and forwarding data packets from the wireless devices toward gateways, which have connected to the Internet, is one of the most important issues in IoT. This paper has focused on routing and forwarding data packets in IoT. Firstly, a learning automata-based cognitive framework has been applied to integrate cognition into IoT; because current IoT lacks intelligence and cannot satisfy the increasing application performance requirements, and also adding cognition into IoT equips it with a brain and high level intelligence. Then, a new routing and forwarding protocol, which benefits from cross-layer optimization between routing and Media Access Control (MAC) layer protocols, has been proposed. In the proposed protocol a network of variable action-set learning automata establishes a route between source nodes and a corresponding gateway, by making a directed acyclic graph toward the gateway. Then, using a set of learning automata, MAC layer protocol parameters are configured to properly forward data packets hop by hop. The proposed protocol has been named VLA-CR (Variable Action-set Learning Automata-based Cognitive Routing Protocol). Based on Martingale theorem, the convergence of VLA-CR has been proved. Finally, extensive simulation experiments have been conducted to show the performance of the proposed protocol. Simulation results show the superiority of VLA-CR over several existing routing protocol in terms of end-to-end reliability, end-to-end delay, power consumption, and routing time.
162-176
Gheisari, Soulmaz
ec5925da-f424-4f49-8b72-d4d045ee7ba6
27 October 2020
Gheisari, Soulmaz
ec5925da-f424-4f49-8b72-d4d045ee7ba6
Gheisari, Soulmaz
(2020)
VLA-CR: a variable action-set learning automata-based cognitive routing protocol for IoT.
Computer Communications, 164, .
(doi:10.1016/j.comcom.2020.10.015).
Abstract
Internet of Things (IoT) is a heterogeneous, mixed, and uncertain ubiquitous network, which has significantly affected the concept of wireless networking. A large number of wireless devices have connected through the IoT and shared large amounts of data. So, efficient routing and forwarding data packets from the wireless devices toward gateways, which have connected to the Internet, is one of the most important issues in IoT. This paper has focused on routing and forwarding data packets in IoT. Firstly, a learning automata-based cognitive framework has been applied to integrate cognition into IoT; because current IoT lacks intelligence and cannot satisfy the increasing application performance requirements, and also adding cognition into IoT equips it with a brain and high level intelligence. Then, a new routing and forwarding protocol, which benefits from cross-layer optimization between routing and Media Access Control (MAC) layer protocols, has been proposed. In the proposed protocol a network of variable action-set learning automata establishes a route between source nodes and a corresponding gateway, by making a directed acyclic graph toward the gateway. Then, using a set of learning automata, MAC layer protocol parameters are configured to properly forward data packets hop by hop. The proposed protocol has been named VLA-CR (Variable Action-set Learning Automata-based Cognitive Routing Protocol). Based on Martingale theorem, the convergence of VLA-CR has been proved. Finally, extensive simulation experiments have been conducted to show the performance of the proposed protocol. Simulation results show the superiority of VLA-CR over several existing routing protocol in terms of end-to-end reliability, end-to-end delay, power consumption, and routing time.
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Accepted/In Press date: 22 October 2020
e-pub ahead of print date: 26 October 2020
Published date: 27 October 2020
Identifiers
Local EPrints ID: 494347
URI: http://eprints.soton.ac.uk/id/eprint/494347
ISSN: 0140-3664
PURE UUID: 450cbd53-2c3a-42cd-8da8-b1ae260df31e
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Date deposited: 04 Oct 2024 17:00
Last modified: 05 Oct 2024 02:17
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Author:
Soulmaz Gheisari
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