A Neural Network-Based Sustainable Data Dissemination through Public Transportation for Smart Cities

Rashmi Munjal, William Liu, Xue Jun Li and Jairo Gutierrez
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Rashmi Munjal: School of Engineering, Computer, and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand
William Liu: School of Engineering, Computer, and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand
Xue Jun Li: School of Engineering, Computer, and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand
Jairo Gutierrez: School of Engineering, Computer, and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand

Sustainability, 2020, vol. 12, issue 24, 1-26

Abstract: In recent years, there has been a big data revolution in smart cities dues to multiple disciplines such as smart healthcare, smart transportation, and smart community. However, most services in these areas of smart cities have become data-driven, thus generating big data that require sharing, storing, processing, and analysis, which ultimately consumes massive amounts of energy. The accumulation process of these data from different areas of a smart city is a challenging issue. Therefore, researchers have started aiming at the Internet of vehicles (IoV), in which smart vehicles are equipped with computing and storage capabilities to communicate with surrounding infrastructure. In this paper, we propose a subcategory of IoV as the Internet of buses (IoB), where public buses enable a service as a data carrier in a smart city by introducing a neural network-based sustainable data dissemination system (NESUDA), where opportunistic sensing comprises delay-tolerant data collection, processing and disseminating from one place to another place around the city. The objective was to use public transport to carry data from one place to another and to reduce the traffic from traditional networks and energy consumption. An advanced neural network (NN) algorithm was applied to locate the realistic arrival time of public buses for data allocation. We used the Auckland transport (AT) buses data set from the transport agency to validate our model for the level of accuracy in predicted bus arrival time and scheduled arrival time to disseminate data using bus services. Data were uploaded onto buses as per their dwelling time at each stop and terminals within the coverage area of deployed RSU. The offloading capacity of our proposed data dissemination system showed that it could be utilized to effectively complement traditional data networks. Moreover, the maximum offloading capacity at each parent stop could reach up to 360 GB with a huge saving of energy consumption.

Keywords: big data; data scheduling; delay tolerant network (DTN); bus arrival time; public transport; bus dwell time; neural network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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