The position of nodes is the critical foundation for information transfer between "person to things" and "things to things" at any time and from any location in wireless multi-hop networks. The hop-based wireless positioning technology has received widespread attention because it does not requiring additional ranging devices. However, most existing hop-based positioning algorithms ignore the network topology irregularities issues, which are frequently observed in multi-hop networks and may lead to poor positioning performance. In this paper, we present a novel wireless positioning algorithm, named NRAP, for irregular networks based on the nearest reliable anchors to mitigate the impact of topology irregularities. Specifically, a more accurate per-hop distance Article Title estimation model is firstly adopted. Then, NRAP divides the entire network into multiple sub-networks with the node to be positioned and its nearest four neighbor anchor nodes. Moreover, a hybrid particle swarm optimization and natural selection algorithm are employed to search in each sub-network to find the optimal estimated position of the node to be positioned. We evaluate and analyze the performance of NRAP under various network topologies and parameters in comparison with the many state-of-the-art works, and the results further demonstrated the superior performance than these benchmarks.

The position of nodes is the critical foundation for information transfer between "person to things" and "things to things" at any time and from any location in wireless multi-hop networks. The hop-based wireless positioning technology has received widespread attention because it does not requiring additional ranging devices. However, most existing hop-based positioning algorithms ignore the network topology irregularities issues, which are frequently observed in multi-hop networks and may lead to poor positioning performance. In this paper, we present a novel wireless positioning algorithm, named NRAP, for irregular networks based on the nearest reliable anchors to mitigate the impact of topology irregularities. Specifically, a more accurate per-hop distance estimation model is firstly adopted. Then, NRAP divides the entire network into multiple sub-networks with the node to be positioned and its nearest four neighbor anchor nodes. Moreover, a hybrid particle swarm optimization and natural selection algorithm are employed to search in each sub-network to find the optimal estimated position of the node to be positioned. We evaluate and analyze the performance of NRAP under various network topologies and parameters in comparison with the many state-of-the-art works, and the results further demonstrated the superior performance than these benchmarks.