Name
Abstract | ||
|---|---|---|
Due to the combination of constrained power, low duty cycle, and high mobility, neighbor discovery is one of the most challenging problems in wireless sensor networks. Existing discovery designs can be divided into two types: pairwise-based and group-based. The former schemes suffer from high discovery delay, while the latter ones accelerate the discovery process but incur too much energy overhead, far from practical. In this article, we propose a novel efficient group-based discovery method based on relative distance, which makes a delicate trade-off between discovery delay and energy consumption. Instead of directly referring to the wake-up schedules of a whole group of nodes, efficient group-based discovery selectively recommends nodes that are most likely to be neighbors, in which the probability is calculated based on the nodes' relative distance. Moreover, the sequence of received signal strengths are employed to estimate the relative distance for avoiding the effect of the node distribution. Extensive simulations are conducted to verify the effectiveness of the design. The results indicate that efficient group-based discovery statistically achieves a good trade-off between energy cost and discovery latency. Efficient group-based discovery also shows one order of magnitude reduction in discovery delay with a maximum of 6.5% increase in energy consumption compared with typical discovery methods. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1177/1550147717719056 | INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS |
Keywords | Field | DocType |
Wireless sensor networks, neighbor discovery, group-based discovery, relative distance | Key distribution in wireless sensor networks,Computer science,Duty cycle,Computer network,Wi-Fi array,Mobile wireless sensor network,Neighbor Discovery Protocol,Wireless sensor network | Journal |
Volume | Issue | ISSN |
13 | 7 | 1550-1477 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pengpeng Chen | 1 | 123 | 17.75 |
| Ying Chen | 2 | 0 | 0.34 |
| Shouwan Gao | 3 | 8 | 2.24 |
| Qiang Niu | 4 | 8 | 7.67 |
| jun gu | 5 | 3 | 3.25 |