DocumentCode
3611152
Title
Packet Size-Aware Broadcasting in VANETs With Fuzzy Logic and RL-Based Parameter Adaptation
Author
Celimuge Wu ; Xianfu Chen ; Yusheng Ji ; Fuqiang Liu ; Ohzahata, Satoshi ; Yoshinaga, Tsutomu ; Kato, Toshihiko
Author_Institution
Grad. Sch. of Inf. Syst., Univ. of Electro-Commun., Tokyo, Japan
Volume
3
fYear
2015
fDate
7/7/1905 12:00:00 AM
Firstpage
2481
Lastpage
2491
Abstract
Most existing multi-hop broadcast protocols for vehicular ad hoc networks do not consider the problem of how to adapt transmission parameters according to the network environment. Besides the propagation environment that determines the channel bit error rate, packet payload size has a significant effect on the packet loss rate. In this paper, we first discuss the effect of packet size on the packet reception ratio, and then propose a broadcast protocol that is able to specify the best relay node by taking into account the data payload size. The proposed protocol employs a fuzzy logic-based algorithm to jointly consider multiple metrics (link quality, inter-vehicle distance, and vehicle mobility) and uses a redundancy transmission approach to ensure high reliability. Since the fuzzy membership functions are tuned by using reinforcement learning, the protocol can adapt to various network scenarios. We use both real-world experiments and computer simulations to evaluate the proposed protocol.
Keywords
broadcast communication; error statistics; fuzzy logic; learning (artificial intelligence); parameter estimation; protocols; redundancy; telecommunication computing; vehicular ad hoc networks; wireless channels; RL-based parameter adaptation; VANET; channel bit error rate; fuzzy logic; multihop broadcast protocol; packet loss rate; packet payload size; packet reception ratio; packet size effect; packet size-aware broadcasting; propagation environment; redundancy transmission approach; reinforcement learning; transmission parameters; vehicular ad hoc network; Broadcasting; Payloads; Protocols; Relays; Reliability; Vehicles; Vehicular ad hoc networks; Vehicular ad hoc networks; broadcast protocol; fuzzy logic; reinforcement learning;
fLanguage
English
Journal_Title
Access, IEEE
Publisher
ieee
ISSN
2169-3536
Type
jour
DOI
10.1109/ACCESS.2015.2502949
Filename
7335572
Link To Document