Abstract
We address the problem of estimating the neighborhood cardinality of nodes in dynamic wireless networks. Different from previous studies, we consider networks with high densities (a hundred neighbors per node) and where all nodes estimate cardinality concurrently. Performing concurrent estimations on dense mobile networks is hard; we need estimators that are not only accurate, but also fast, asynchronous (due to mobility) and lightweight (due to concurrency and high density). To cope with these requirements, we propose Estreme, a neighborhood cardinality estimator with extremely low overhead that leverages the rendezvous time of low-power medium access control (MAC) protocols. We implemented Estreme on the Contiki OS and show a significant improvement over the state-of-the-art. With Estreme, 100 nodes can concurrently estimate their neighborhood cardinality with an error of ≈10%. State-of-the-art solutions provide a similar accuracy, but on networks consisting of a few tens of nodes and where only a fraction of nodes estimate the cardinality concurrently.
Original language | English |
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Title of host publication | IPSN'14 |
Subtitle of host publication | Proceedings of the 13th international symposium on Information processing in sensor networks |
Publisher | ACM/IEEE |
Pages | 179-189 |
Number of pages | 11 |
ISBN (Print) | 978-1-4799-3146-0 |
Publication status | Published - 2014 |
Event | 13th IEEE/ACM International Conference on Information Processing in Sensor Networks, IPSN 2014 - Berlin, Germany Duration: 15 Apr 2014 → 17 Apr 2014 |
Conference
Conference | 13th IEEE/ACM International Conference on Information Processing in Sensor Networks, IPSN 2014 |
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Country/Territory | Germany |
City | Berlin |
Period | 15/04/14 → 17/04/14 |
Keywords
- Wireless Communications
- Modeling
- performance evaluation