Förster, Anna Murphy, Amy L. 2009 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. This paper explores in depth the requirements of this set of application scenarios and proposes, FROMS, a machine learning-based approach. The primary benefits are the flexibility to optimize routing on a variety of properties such as route length, battery levels, etc., ease of recovery after node failures, and native support for sink mobility. We provide extensive simulation results supporting these claims, clearly showing the benefits of FROMS in terms of low routing overhead, extended network lifetimes, and other key metrics for the WSN environment. application/pdf https://susi.usi.ch/global/documents/318310 https://localhost:5000/ark:/12658/srd1318310 https://susi.usi.ch/documents/318310/files/ITR0904.pdf eng info:eu-repo/semantics/altIdentifier/ark/12658/srd1318310 info:eu-repo/semantics/openAccess License undefined info:eu-repo/classification/udc/004 FROMS : a failure tolerant and mobility enabled multicast routing paradigm with reinforcement learning for WSNs http://purl.org/coar/resource_type/c_816b