Abstract
In a typical smart city, drones can collect (or sense) massive amount of data, that is sent to a computing capability for further analysis to make useful decision making without human intervention. This data is relayed to the Cloud for processing and analysis due to its large-scale infrastructural capabilities. However, the key goal of the drone deployment in smart city scenarios or urban environments is to provide timely and quick response alongside providing an energy-efficient service delivery. Thus, we need a sustainable solution that can be deployed locally (closer to the data source) in a smart city, to process or analyze the data (generated from smart city sources) and provide timely decision making for smart city applications. Edge computing, popularly known as the “cloud close to the ground”, can provide computational and processing facilities at edge of the network in a smart city. Hence, Edge computing act as an effective alternative solution to process and analyze the data closer to the point of it’s generation. Looking into the above discussion, We propose a novel drone-edge coalesce that provides an energy-aware data processing mechanism for sustainable service delivery in the multi-drone smart city networks. In this model, the edge computing layer is deployed to process and store the data sensed and collected by drones in a smart city. In this context, an adaptive edge node selection mechanism has been designed on the basis of decision tree approach. In this coalesce, we have to deal with the conventional problems related to the collision and congestion while providing low-latency and sustainable data transmission in a smart city. So, We have designed an energy-aware multi-purpose algorithm that avoids collisions and provides a congestion free data transmission. The proposed coalesce is validated in a simulated environment on the basis of several performance metrics such as, throughput, end-to-end delay and energy consumption.
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