A Sustainable Internet Data Dissemination Architecture by Utilizing the Existing Public Transport Networks
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The traditional world has transformed into a digital society where almost anything can be accessed from anywhere. However, this digital society is responsible for the explosive growth of high-volume, high-velocity, and high-variety information assets, with high demand for “Big Data”. Not only this, most services in this digital world have become data-driven, thus generating big data that require sharing, storing, processing, and analysis, which ultimately overburden existing infrastructure-based networks, increase the energy demands exponentially, and leads to CO2 carbon emissions, which could finally end up seriously harming the environment. Several challenges are being posed such as the accumulation process of these data from different areas of a smart city and alleviating bandwidth of infrastructure in an energy-efficient manner. Therefore, to tackle these challenges, we proposed Public Transport Assisted Data Dissemination System (PTDD), where public buses enable a service as a data carrier in a smart city as an alternative communication channel and this opportunistic sensing comprises delay tolerant data collection, processing, and disseminating from one place to another place around the city. The biggest motivation is that the public transport network is an existing infrastructure and can be utilized efficiently for energy-efficient data dissemination. The main contribution of the thesis is highlighted below: Firstly, we identified all the characterizations of public transport including their movement pattern to analyze their great potential to form a data dissemination network. Next, an advanced neural network (NN) algorithm was applied to locate the realistic arrival time of public buses for the 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. Secondly, we defined a heterogeneous network architecture called Software-Defined Connectivity Architecture (SDCA) that utilizes the flow of transport network using buses to start the forwarding process from nearby parking/offloading spots to disseminate data along with conventional networks. The controller selects the optimal network using Multi-Attribute Decision Making with defining a utility function and Analytical Hierarchical Process (AHP) method for criteria weights to rank the network. Data were uploaded onto buses as per their dwelling time at each stop and terminals within the coverage area of deployed RSU. Based on the analytical, and numerical analysis, we understand that optimal networks can be selected as per different services and demands of the user. Finally, we proposed an energy consumption model for energy-efficient data dissemination using PTDD. We used different optimization techniques to minimize the energy cost to fulfil all demands of all bus stops. We used the SAS optimization tool and Cplex solver for finding out the best energy-efficient network. This work provides strong evidence that significant energy savings can be achieved while still guaranteeing data delivery in the heterogeneous network