Journal
AD HOC NETWORKS
Volume 9, Issue 5, Pages 940-965Publisher
ELSEVIER
DOI: 10.1016/j.adhoc.2010.11.006
Keywords
Sensor networks; Routing; Energy-aware; Multicast; Mobile sinks; Node failures; Reinforcement learning
Funding
- EU Cooperating Objects Network of Excellence (CONET)
- Swiss National Science Foundation [5005-67322]
Ask authors/readers for more resources
A growing class of wireless sensor network (WSN) applications require the use of sensed data inside the network at multiple, possibly mobile base stations. Standard WSN routing techniques that move data from multiple sources to a single, fixed base station are not applicable, motivating new solutions that efficiently achieve multicast and handle mobility. This paper explores in depth the requirements of this set of application scenarios and proposes FROMS, a machine learning-based multicast routing paradigm. Its primary benefits are flexibility to optimize routing over a variety of properties such as route length, battery levels, ease of recovery after node failures, and native support for sink mobility. We provide theoretical, simulation and experimentation results supporting these claims, showing the benefits of FROMS in terms of low routing overhead, extended network lifetime, and other key metrics for the WSN environment. (C) 2010 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available