XgBoosted Neighbor Referring in Low-Duty-Cycle Wireless Sensor Networks

Abstract

As one of the basic protocols of the wireless sensor network (WSN), neighbor discovery aims at initializing the network topology and maintaining the runtime connectivity. It is a quite big challenge to reduce the neighbor discovery latency in low-duty-cycle WSN, because the long dormancy of nodes handicaps the quick neighborhood establishment in their vicinity. Recently preliminary efforts have been directed toward the referring-based neighbor discovery, which proactively refers or recommends potential neighbors to nearby nodes in order to reduce the total discovery latency. Without delicate considerations, however, such proactive references will incur a great waste of node energy. In response to this limitation, we design and implement xBOND, a novel referring-based neighbor discovery protocol for the WSN with low-duty cycles. The xBOND protocol involves four perspectives of featuring the physical proximity of nodes. These features can be easily evaluated, and thus the node willing to do neighbor recommendation does not need to mull over them. With the features evaluated, xBOND leverages the XGBoost (a powerful classifier in machine learning) to achieve precise neighbor recommendations during neighbor discovery and then a better tradeoff between energy overhead and discovery latency. We also concretize the xBOND so that it can complete the neighbor discovery in a distributed way. Finally, we validate xBOND through extensive simulation experiments that show significant efficiency gains in discovery latency and energy consumption over the state-of-the-art referring-based approach.