FROMS: Feedback Routing for Optimizing Multiple Sinks in WSN with Reinforcement Learning
Abstract
In the domain of wireless sensor networks (WSNs), information routing is both a fundamental and challenging problem. In this work, we describe how information local to each node can be shared without overhead as feedback to neighboring nodes, enabling efficient routing to multiple sinks. Such a situation arises in WSNs with multiple, possibly mobile users collecting data from a monitored area. We formulate the problem as a reinforcement learning task, and apply Q-Routing techniques to derive a solution. Evaluation of the resulting FROMS protocol demonstrates its ability to significantly decrease the network overhead over existing approaches.
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Forster et al. use a Q-learning approach in a multicast routing protocol called FROMS.
“In [151], Forster et al. use a Q-learning approach in a multicast routing protocol, called FROMS (Feedback Routing for Optimizing Multiple Sinks).”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [151]
FROMS shows up to 5 times higher delivery rates than the directed diffusion algorithm in the presence of node failure, and 20% less network overhead per packet due to route aggregation.
“FROMS shows up to 5 times higher delivery rates than the popular directed diffusion algorithm [205] in the presence of node failure, and 20% less network overhead per packet due to route aggregation.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [151]
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- Authors
- Forster, A., & Murphy, A. L.
- Affiliations
- Università della Svizzera italiana (CH)
- ORCID
- Murphy, Amy L.
- Venue
- 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information
- Publisher
- IEEE
- Published
- 2007
- Pages
- 371-376
- OpenAlex
- W2542764750
- Cited by
- 81
- References
- 10
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 4 · 2023: 3 · 2022: 5 · 2021: 11 · 2020: 8 · 2019: 11 · 2018: 8 · 2017: 1 · 2016: 2 · 2015: 3 · 2014: 3 · 2013: 7 · 2012: 2
- FWCI
- 1.62
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Intelligent Sensors, Sensor Networks and Information Processing (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Energy Efficient Wireless Sensor Networks
- Physical Sciences › Computer Science › Computer Networks and Communications › Security in Wireless Sensor Networks
- Physical Sciences › Computer Science › Computer Networks and Communications › Mobile Ad Hoc Networks
- Keywords
- wireless sensor networks, multi-sink routing, reinforcement learning, Q-routing, energy efficiency, network overhead reduction, mobile sink, information sharing
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