Identification of optimal location points for efficient data gathering in IoT environment. (3rd May 2021) - Wala, Tanuj; Chand, Narottam; Sharma, Ajay K.
Summary: Recent development in sensor technologies encourages the adoption of mobile data collectors (MDCs), that is, vehicles or robots, to collect data in the Internet of Things (IoT) environment. However, the slow travel speed of the MDCs leads to significant delays in the transmission of data. This work proposes an optimal region location point based route planning (ORLP‐RP) scheme to construct a shorter tour with minimized data delivery latency. Primary goal of the work is to distinguish the overlapped wireless sensor network (WSN) clusters and specify the overlapped regions to find optimal location points within each identified region to ensure one‐hop data gathering from each cluster. Thereafter, MDC trajectory is formed by using the nearest‐neighbor (NN) heuristic approach that generates a near optimal solution. In performance evaluation, the ORLP‐RP scheme has been compared with the existing algorithms such as EAPC, RDP, MOPSO, and NDCMC for data collection in terms of the number of optimal location points, total path length, data gathering latency, network lifespan, energy consumption, and data gathering ratio. Simulation results demonstrate the effectiveness of the proposed approach by improving the number of optimal location points with an average up to 58%, total path length improvement by 17%, energy consumption and network lifespan improvement on average by 18% and 12%, data gathering ratio by the MDC up to 9%, and reducing the delay of the MDC path by 21% compared with considered approaches. Thus, simulation results indicate better performance of the proposed scheme at all the indices listed above. Abstract : The major goal of this paper is to optimize the problem of energy consumption and data delivery delay in energy‐constrained and delay‐sensitive applications. The optimal region location points based route planning (ORLP‐RP) scheme is proposed in large and disconnected networks to determine an optimal number of data collection points that ensure the one‐hop data collection from each cluster head to preserve energy consumption. Further, to reduce the trajectory length, the nearest‐neighbor heuristic approach is used to collect data from the optimal points.