Reinforcement Learning Framework for Opportunistic Routing in WSNs
This work addresses adaptive routing for wireless sensor networks, but it appears incremental as it builds on existing opportunistic routing techniques with a learning-based approach.
The paper tackled the problem of non-adaptive opportunistic routing in wireless sensor networks by proposing a reinforcement learning framework that combines learning and routing to explore optimal paths, resulting in optimized packet routing even with unknown network structures.
Routing packets opportunistically is an essential part of multihop ad hoc wireless sensor networks. The existing routing techniques are not adaptive opportunistic. In this paper we have proposed an adaptive opportunistic routing scheme that routes packets opportunistically in order to ensure that packet loss is avoided. Learning and routing are combined in the framework that explores the optimal routing possibilities. In this paper we implemented this Reinforced learning framework using a customer simulator. The experimental results revealed that the scheme is able to exploit the opportunistic to optimize routing of packets even though the network structure is unknown.