Neighbor discovery is a fundamental service for initialization and managing network dynamics in wireless sensor networks and mobile sensing applications. In this paper, we present a novel design principle named Talk More Listen Less (TMLL) to reduce idle-listening in neighbor discovery protocols by learning the fact that more beacons lead to fewer wakeups. We propose an extended neighbor discovery model for analyzing wakeup schedules in which beacons are not necessarily placed in the wakeup slots. Furthermore, we are the first to consider channel occupancy rate in discovery protocols by introducing a new metric to trade off among duty-cycle, latency and channel occupancy rate. Guided by the TMLL principle, we have designed Nihao, a family of energy-efficient asynchronous neighbor discovery protocols for symmetric and asymmetric cases. We compared Nihao with existing state of the art protocols via analysis and real-world testbed experiments. The result shows that Nihao significantly outperforms the others both in theory and practice.
Many wireless applications urgently demand an efficient neighbor discovery protocol to build up bridges connecting user themselves or to some service providers. However, due to intrinsic constraints of wireless devices, e.g., limited energy and error of clock synchronization, there is still absence of effective and efficient neighbor discovery protocols in the literature. In this work, we propose neighbor discovery protocols for the following two problems. First, we study Asynchronous Symmetry Neighbor Discovery problem, in which potential neighbor devices with asynchronous time clocks but the same duty cycle aim to find each other. Second, we propose an efficient protocol (utilizing Bouncing strategy) named BlindDate with guaranteed worst-case performance @math where @math is a small fraction of the length of a time slot unit and @math is the duty cycle. Third, we extend this strategy to address Asynchronous Asymmetry Neighbor Discovery problem, in which both the time clock and the duty cycles of potential neighbors are considered to be heterogeneous. We conduct extensive experiments and simulations to examine the feasibility and efficiency of the proposed protocols, and results show that BlindDate greatly outperforms existing approaches in average-case. Compared with known protocols, BlindDate also achieves a better worst-case discovery latency bound (e.g., @math percent performance gain comparing with Searchlight [1] ). The rapid deployment of millions of mobile sensors and smartphones has resulted in a demand for opportunistic encounter-based networking to support mobile social networking applications and proximity-based gaming. However, the success of these emerging networks is limited by the lack of effective and energy efficient neighbor discovery protocols. While probabilistic approaches perform well for the average case, they exhibit long tails resulting in high upper bounds on neighbor discovery time. Recent deterministic protocols, which allow nodes to wake up at specific timeslots according to a particular pattern, improve on the worst case bound, but do so by sacrificing average case performance. In response to these limitations, we have designed Searchlight, a highly effective asynchronous discovery protocol that is built on three basic ideas. First, it leverages the constant offset between periodic awake slots to design a simple probing-based approach to ensure discovery. Second, it allows awake slots to cover larger sections of time, which ultimately reduces total awake time drastically. Finally, Searchlight has the option to employ probabilistic techniques with its deterministic approach that can considerably improve its performance in the average case when all nodes have the same duty cycle. We validate Searchlight through analysis and real-world experiments on smartphones that show considerable improvement (up to 50 ) in worst-case discovery latency over existing approaches in almost all cases, irrespective of duty cycle symmetry.