Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
A two-tier data dissemination model for large-scale wireless sensor networks
Proceedings of the 8th annual international conference on Mobile computing and networking
DEAR: a device and energy aware routing protocol for heterogeneous ad hoc networks
Journal of Parallel and Distributed Computing - Special issue on Routing in mobile and wireless ad hoc networks
Minimum-energy asynchronous dissemination to mobile sinks in wireless sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
Reinforcement Learning in Autonomic Computing: A Manifesto and Case Studies
IEEE Internet Computing
Predictive QoS routing to mobile sinks in wireless sensor networks
IPSN '09 Proceedings of the 2009 International Conference on Information Processing in Sensor Networks
ROME: routing over mobile elements in WSNs
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
EMMON: A WSN System Architecture for Large Scale and Dense Real-Time Embedded Monitoring
EUC '11 Proceedings of the 2011 IFIP 9th International Conference on Embedded and Ubiquitous Computing
Using feedback in collaborative reinforcement learning to adaptively optimize MANET routing
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
An intelligent agent-based routing structure for mobile sinks in WSNs
IEEE Transactions on Consumer Electronics
HOLSR: a hierarchical proactive routing mechanism for mobile ad hoc networks
IEEE Communications Magazine
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Wireless sensor networks (WSNs) may suffer from congestion at the nodes near the sink, and partition due to the failure of crucial nodes. In urban environments, mobile devices, such as vehicles and smart phones, present in the vicinity of the sensor field could be opportunistically used for data forwarding. Such devices, controlled by third parties, introduce paths that may appear for only very small intervals. This paper discusses how exploiting such unstable paths to shift the routing-related processing and communication load to more capable mobile devices can alleviate traffic congestion, improve fault tolerance and reduce WSN energy consumption.