Big Data in Motion: A Vehicle-Assisted Urban Computing Framework for Smart Cities

Abstract

Smart cities are envisioned to facilitate the well-being of the society through efficient management of the Internet of Things resources and the data produced by these resources. However, the enormous number of such devices would result in unprecedented growth in data, creating capacity issues related to the acquisition, transfer from one location to another, storage, and finally the analysis. The traditional networks are not sufficient to support the transfer of this huge amount of data, proving to be costly both in terms of delay and energy consumption. Alternative means of data transfers are thus required to support this big data produced by smart cities. In this paper, we have proposed an efficient data-transfer framework based on volunteer vehicles whereby we employ vehicles to carry data in the direction of the destination. The framework promotes self-belonging, social awareness, and energy conservation through urban computing encouraging participation by citizens. The proposed framework can also facilitate the research community to benchmark their own route selection algorithms easily. Further, we performed an extensive evaluation of the proposed framework based on realistic models of vehicles, routes, data-spots, data chunks to be transmitted and the energy consumed. Our results show the efficacy of the proposed data transfer framework as the energy consumed through vehicles is significantly less than that consumed by transmission over the Internet thereby reducing the carbon footprint. The results also offer several insights into the optimal configuration of a vehicular data transfer network based on analysis of delay, energy consumption, and data-spot utilization.

Keywords

• Big-data transfer
• data center
• delay
• energy
• simulation
• Anylogic