Learning Automata-based Opportunistic Data Aggregation and Forwarding Scheme for Alert Generation in Vehicular Ad Hoc Networks
Kumar, N. K.
; Chilamkurti, N. C.
Rodrigues, J. R.
Computer Communications Vol. 39, Nº Feb.2014, pp. 22 - 32, January, 2014.
ISSN (print): 0140-3664
Journal Impact Factor: 1,695 (in 2014)
Digital Object Identifier: 10.1016/j.comcom.2013.09.005
Due to the highly mobile and continuously changing topology, the major problem in Vehicular Ad Hoc Networks (VANETs) is how and where the collected information is to be transmitted. An intelligent approach can adaptively select the next hop for data forwarding and aggregation from the other nodes in the networks. To address these issues, we propose a Leaning Automata-based Opportunistic Data Aggregation and Forwarding (LAODAF) scheme for alert generation in VANETs. Learning automata (LA) operates separately in each vehicle in which it is deployed and collects data in its respective region. Once data is aggregated, LA adaptively selects the destination for data transfer, based on the newly defined metric known as Opportunistic Aggregation and Forwarding (OAF). LA predicts the mobility of the vehicle and adaptively selects the path for forwarding, based on the value of OAF. Moreover, it updates its action probability vector and learning rate based on the values of OAF. This will reduce network congestion and the load on the network as it is aggregated and forwarded only when required. An algorithm for opportunistic data aggregation and forwarding is also proposed. The proposed strategy is evaluated using various metrics, a number of successful transmissions, connectivity, link breakage rate, traffic density, packet reception ratio, and delay.