Alano: An Efficient Neighbor Discovery Algorithm in an Energy-Restricted Large-Scale Network

Abstract

Neighbor discovery is a fundamental step in constructing wireless sensor networks and many algorithms have been proposed aiming to minimize its latency. Recent developments of intelligent devices call for new algorithms, which are subject to energy restrictions. In energy-restricted large-scale networks, a node has limited power supply and can only discover other nodes that are within its range. Additionally, the discovery process may fail if excessive communications take place in a wireless channel. These factors make neighbor discovery a very challenging task and only a few of the proposed neighbor discovery algorithms can be applied to energy-restricted large-scale networks. In this paper, we propose Alano, a nearly optimal algorithm for a large-scale network, which uses the nodes' distribution as a key input. When nodes have the same energy constraint, we modify Alano by the Relaxed Difference Set (RDS), and present a Traversing Pointer (TP) based Alano when the nodes' energy constraints are different. We compare Alano with the state-of-the-art algorithms through extensive evaluations, and the results show that Alano achieves at least 31.35% lower discovery latency and has higher performance regarding quality (discovery rate) and scalability.