Energy efficient and sustainable Steiner tree based path planning for mobile data collection in internet of things

Efficient data collection in Internet of Things enabled wireless sensor networks (WSNs) is essential for sustainability of the network. Nevertheless, the task is challenging as it is NP-hard in nature. In this paper, we address the issue by proposing a novel Steiner Tree–based Path Planning (STPP) scheme. The algorithm works in two phases, (1) A primal dual approximation algorithm is used to construct the Steiner tree, which is then used to ascertain the RPs. (2) Then, Christofide's algorithm is used to construct a tour using the previously obtained RPs. We model RP selection as a combinatorial optimization problem, obtain approximation bounds, and the computational complexity is also analyzed. We also analyze the practical constraints of centralized structure and describe the paths to extended distributed and dynamic forms. Extensive simulations over varying network sizes and communication ranges demonstrate that STPP achieves on average a 17% reduction in tour length, a 22% decrease in RP count, and a 19% improvement in network lifetime compared to state-of-the-art approaches.