On the effects of cognitive mobility prediction in wireless multi-hop ad hoc networks
Abstract
In this paper, we address an important problem in mobile ad hoc networks, namely, the intrinsic inefficiency of the standard transmission control protocol (TCP), which has not been designed to work in these types of networks. After an initial training phase, we predict the mobility status of the network through a probabilistic approach, and we propose a series of ad hoc strategies to counteract the TCP inefficiency based on this prediction. Via simulation, we show the performance improvements in various wireless scenarios, in terms of increased average throughput and decreased length of the outage intervals. The significant performance improvements shown here will be verified in a future work by implementing our approach in a real testbed.
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Several ML-based approaches have addressed limitations of inferring congestion by estimating throughput, RTT, and mobility in TCP-based networks, table entries rate in NDNs, and congestion level in DTNs.
“For the aforementioned reasons, several ML-based approaches have addressed the limitations of inferring the congestion in various network architectures by estimating different network parameters: throughput [238, 316, 371], RTT [22,128], and mobility [309] in TCP-based networks, table entries rate in NDNs [230], and congestion level in DTNs [412].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [309]
The mobility classifier was used to implement a TCP variant that outperforms TCP-NewReno in terms of throughput and outage probability.
“This mobility classifier was used to implement a TCP variant that outperforms TCP-NewReno in terms of throughput and outage probability.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [309]
The simplified BN model achieves good accuracy for classifying mobility in WANETs when varying the radio propagation stability.
“The results reveal that the simplified BN model achieves a good accuracy for classifying mobility in WANETs, when varying the radio propagation stability.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [309]
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- Authors
- Mezzavilla, M., Quer, G., & Zorzi, M.
- Affiliations
- University of Padua (IT)
- University of California San Diego (US)
- Venue
- 2014 IEEE International Conference on Communications (ICC)
- Publisher
- IEEE
- Published
- 2014-06
- Volume
- 3626
- Pages
- 1638-1644
- ISSN
- 1550-3607 (ISSN-L)
- OpenAlex
- W2027341604
- Cited by
- 12
- References
- 30
- Citations by year
- 2022: 1 · 2018: 2 · 2017: 1 · 2016: 3 · 2015: 2 · 2014: 3
- FWCI
- 2.34
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Communications (publishedVersion)
- Virtual Community of Pathological Anatomy (University of Castilla La Mancha) (submittedVersion)
- Padua Research Archive (University of Padova) (submittedVersion)
- Funders
- Army Research Office
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Traffic and Congestion Control
- Physical Sciences › Computer Science › Computer Networks and Communications › Mobile Ad Hoc Networks
- Physical Sciences › Computer Science › Computer Networks and Communications › Wireless Networks and Protocols
- Keywords
- mobile ad hoc networks, throughput improvement, multi-hop wireless networks
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