EDGO: UAV-based effective data gathering scheme for wireless sensor networks with obstacles

One of the most important requirements for effective UAV-WSN operations is to perform data collection in timely and safe manner. Identifying an effective path in an environment with various obstacles and ensuring that the path may efficiently cover the selected stop points for effective data collection are both necessary and difficult. We propose a UAV-based effective data gathering scheme for wireless sensor networks with obstacles, where a UAV is employed as a mobile sink to collect data from the ground sensor nodes (EDGO). The main novelty includes a UAV-WSN collaborative approach for data gathering, which incorporates a convenient method for UAV trajectory design in a three-dimensional environment with obstacles. We propose an improved heuristic evolutionary approach based on genetic algorithm to determine the optimized trajectory for the UAV to gather data. In contrast to existing methods, the proposed method focus to reduce the length and angle cost of the path, minimize the energy consumption, and delay, and includes different evolutionary operations to generate a collision free path for UAV. Our approach retains the infeasible path through sufficient modifications, which improves the diversity of paths, so that it is possible to jump out of the local optima. The results reveal the effectiveness of the EDGO scheme against the other related approaches in terms of path cost and data collection efficiency. The network lifetime is extended by approximately 11% and offers a reduction of 42% and 35% in the UAV path length and travel time, respectively, when compared to the existing schemes.