Multi-Attribute Decision Making for Energy-Efficient Public Transport Network Selection in Smart Cities

Rashmi Munjal, William Liu, Xuejun Li, Jairo Gutierrez and Peter Han Joo Chong
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Rashmi Munjal: School of Engineering, Computer, and Mathematical Sciences, Auckland University of Technology, Auckland 1142, New Zealand
William Liu: School of Engineering, Computer, and Mathematical Sciences, Auckland University of Technology, Auckland 1142, New Zealand
Xuejun Li: School of Engineering, Computer, and Mathematical Sciences, Auckland University of Technology, Auckland 1142, New Zealand
Jairo Gutierrez: School of Engineering, Computer, and Mathematical Sciences, Auckland University of Technology, Auckland 1142, New Zealand
Peter Han Joo Chong: School of Engineering, Computer, and Mathematical Sciences, Auckland University of Technology, Auckland 1142, New Zealand

Future Internet, 2022, vol. 14, issue 2, 1-29

Abstract: Smart cities use many smart devices to facilitate the well-being of society by different means. However, these smart devices create great challenges, such as energy consumption and carbon emissions. The proposed research lies in communication technologies to deal with big data-driven applications. Aiming at multiple sources of big data in a smart city, we propose a public transport-assisted data-dissemination system to utilize public transport as another communication medium, along with other networks, with the help of software-defined technology. Our main objective is to minimize energy consumption with the maximum delivery of data. A multi-attribute decision-making strategy is adopted for the selction of the best network among wired, wireless, and public transport networks, based upon users’ requirements and different services. Once public transport is selected as the best network, the Capacitated Vehicle Routing Problem (CVRP) will be implemented to offload data onto buses as per the maximum capacity of buses. For validation, the case of Auckland Transport is used to offload data onto buses for energy-efficient delay-tolerant data transmission. Experimental results show that buses can be utilized efficiently to deliver data as per their demands and consume 33% less energy in comparison to other networks.

Keywords: big data; delay-tolerant network (DTN); multi-attribute decision making; public transport; energy consumption (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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