Smart and intelligent network selection approach to support location-dependent and context-aware service migration

Issue title: Impact of Sensor Data in Intelligent Environments

Article type: Research Article

Authors: Ul-Amin, Riaz^{a; b; *} | Sventek, Joe^c | Mackenzie, Lewis^a | Abid, Adnan^d

Affiliations: [a] University of Glasgow, UK | [b] University of Okara, Pakistan | [c] University of Oregon, USA | [d] University of Management and Technology, Pakistan

Correspondence: [*] Corresponding author. E-mail: riazulamin@gmail.com.

Abstract: Vehicular networking has gained considerable interest within the research community and industry. The automotive industry is supporting the notion of pervasive connectivity by agreeing to equip vehicles with devices required for vehicular ad hoc networking. Equipped with these devices, mobile nodes in vehicular ad hoc networks (VANETs) are capable of hosting many types of applications as services for other nodes in the network. This research focuses on addressing the challenges of location-dependence, intermittent network connectivity and irregular network traffic flows in unplanned areas for VANETs to host and operate non-safety-critical VANETs services. We assume unplanned areas as the one that lack communication infrastructure and planning. Such areas observe irregular vehicular traffic on the roads as well as on the networks. This research investigates the shortcomings of location-dependence, intermittent network connectivity and irregular network traffic flows and addresses them by exploiting location-dependent service migration over an integrated network in an efficient and cost-effective manner.

Keywords: LDCAMS, cooperative & cost effective network selection algorithm (CACENSA), VANET deployment, VANET services, network Integration, WAVE, UMTS

DOI: 10.3233/AIS-200559

Journal: Journal of Ambient Intelligence and Smart Environments, vol. 12, no. 3, pp. 219-237, 2020