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.
Highlights
Nowadays, a huge surge in Internet traffic raises many concerns over the capacity of the infrastructure
All the mobile devices are equipped with wireless network interfaces and which introduce new demands in the wireless network leading to the digital society with the emerging trend of “big data” with features of high-volume, high-velocity, and high-variety
Recently, much attention has been focused on accommodating big data needs by leveraging the traffic burden from the traditional network to other networks, which is known as data offloading
Summary
A huge surge in Internet traffic raises many concerns over the capacity of the infrastructure. We used Auckland Transport (AT) historical data to analyze bus daily movement patterns and applied an advanced neural network algorithm to understand the variance between predicted arrival time and schedule travel time for more realistic information to authenticate that public transport can be used as an energy-efficient communication channel. The remainder of the paper is structured as follows: in Section 2, we present the literature on existing work done on utilizing public transport for carrying delay-tolerant data and their arrival predictions.
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