A Sensing Data Collection Strategy in Software-Defined Mo-bile-Edge Vehicular Networks
Lionel Nkenyereye1, Jong-Wook Jang2

1Lionel Nkenyereye, Department of Computer Engineering, Dong-Eui University, Korea.
2Jong-Wook Jang, Department of Computer Engineering, Dong-Eui University, Korea.
Manuscript received on 09 May 2019 | Revised Manuscript received on 19 May 2019 | Manuscript Published on 23 May 2019 | PP: 1339-1344 | Volume-7 Issue-6S5 April 2019 | Retrieval Number: F12320476S519/2019©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The car is seeing as an intelligent transportation sensing platform suitable to transfer wirelessly urban sensing data to a remote processing server. This paper comes out with the study on urban sensing data collection strategy in a Software-Defined Mobile Edge vehicular net-working. The two schemes for data collection in urban using a car are cooperative vehicular and edge cell trajectory prediction mode. In cooperative vehicular scenario, the vehicle observe its neighboring vehicles and sets up vehicular cluster for cooperative sensing data collection. The data collection output can be transmitted from vehicles participating in the cooperative sensing data collection strategy to the vehicle on which the sensing data collection request originate through V2V communication. The vehicle on which computation originate will reassemble the computation output and send to the closest RSU. In case the neighboring vehicles are unable to handle the urban sensing request, the edge cell trajectory prediction decision based on the SDEVN architecture is selected. The SDEVN (Software Defined Edge Vehicular Network) Controller determines how much effort the sensing data collection request requires and calculates the number of RSUs required to support coverage of one RSU to the other. Thus, The RSU which extracts resources level and location information, then send that information to the SDEVN controller to compute the movement trajectory of the neighboring’s vehicles. The SDEVN forecasts and determines the position and then allows reconnection to the following RSU of each vehicle. The goal is to maximize the number of vehicles that participate in executing a sensing data collection request (task) which consisting in sensing the environmental conditions towards vehicle’s destination. We prove that urban sensing data collection cost through a car sensing platform for urban data collection algorithm to find an optimal strategy. We set up a simulation scenario based on realistic traffic and communication features and demonstrate the scalability of the proposed solution. The proposed vehicular architecture is based on the edge cell which includes RSU, RSU controller, and SDEVN controller, represent the first comprehensive vehicular ad hoc computing (VANET) implementing based on Software defined mobile edge vehicular network concept.
Keywords: Software-Defined Networks, Software-Defined Mobile Edge Computing, Sensing Data Collection, Car as Urban Sensing Platform, Vehicular ad hoc Networks, Optimal Strategy, Edge Computing.
Scope of the Article: Systems and Software Engineering