History-Aware Adaptive Backoff for Neighbor Discovery in Wireless Networks

The ability of discovering neighboring nodes, namely neighbor discovery, is essential for the self-organization of wireless ad hoc networks. In this paper, we propose a history-aware adaptive back off algorithm for neighbor discovery assuming collision detection and feedback mechanisms. Given successful discovery feedback, undiscovered nodes can adjust their contention window. With collision feedback and historical information, only transmission nodes enter the re-contention process, and decrease their contention window to accelerate neighbor discovery process after collision. Then, we give theoretical analysis of our algorithm on the discovery time and energy consumption, and derive the optimal size of contention windows by two rounds of optimization. Finally, we validate our theoretical analysis by simulations, and show the performance improvement over existing algorithms.