Multi‐hop routing in vehicular ad‐hoc networks (VANETs) and wireless sensor networks has attracted significant interest of researchers in the wireless ad‐hoc networks community. Most multi‐hop routing protocols in VANET are based around the idea of choosing the next destination, which will provide the shortest‐delay to reach a destination. To ensure better monitoring and reporting of road condition information, this study proposes location‐based data forwarding through roadside sensors using k ‐shortest path routing combined with Q‐learning. Q‐learning is used for exploration of the sensing field to determine those sensors which have a higher queuing delay during peak hours as well as those which have comparatively lower delays. The use of Q‐learning for exploration (sans routing) enables faster convergence for the sensors as compared to those techniques which utilise naive Q‐learning for shortest path routing. Secondly, multi‐hop routing is being combined with source coding (Huffman and Arithmetic coding) to compress the data payload of packets. This has shown some promising results for the VANETs employing dedicated short‐range communication.
Location‐based data delivery between vehicles and infrastructure
IET Intelligent Transport Systems ; 14 , 5 ; 288-296
2020-05-01
9 pages
Article (Journal)
Electronic Resource
English
routing protocols , vehicular ad hoc networks , learning (artificial intelligence) , source coding , Huffman codes , arithmetic codes , telecommunication computing , data compression , k‐shortest path routing , queuing delay , sans routing , naive Q‐learning , location‐based data delivery , wireless sensor networks , multihop routing protocols , VANET , road condition information , roadside sensors , vehicular ad‐hoc networks , wireless ad‐hoc networks community , location‐based data forwarding , Huffman coding , Arithmetic coding , packet data payload , short‐range communication
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