Self-Organized Data-Gathering Scheme for Multi-Sink Sensor Networks Inspired by Swarm Intelligence
SASO '07 Proceedings of the First International Conference on Self-Adaptive and Self-Organizing Systems
Energy Efficient Routing in Multiple Sink Sensor Networks
ICCSA '07 Proceedings of the The 2007 International Conference Computational Science and its Applications
A Load-Balance Routing Algorithm for Multi-Sink Wireless Sensor Networks
ICCSN '09 Proceedings of the 2009 International Conference on Communication Software and Networks
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Learning to act using real-time dynamic programming
Artificial Intelligence
Energy-Conserving Dynamic Routing in Multi-sink Heterogeneous Sensor Networks
CMC '10 Proceedings of the 2010 International Conference on Communications and Mobile Computing - Volume 03
Hi-index | 12.05 |
In many researches on load balancing in multi-sink WSN, sensors usually choose the nearest sink as destination for sending data. However, in WSN, events often occur in specific area. If all sensors in this area all follow the nearest-sink strategy, sensors around nearest sink called hotspot will exhaust energy early. It means that this sink is isolated from network early and numbers of routing paths are broken. In this paper, we propose an adaptive learning scheme for load balancing scheme in multi-sink WSN. The agent in a centralized mobile anchor with directional antenna is introduced to adaptively partition the network into several zones according to the residual energy of hotspots around sink nodes. In addition, machine learning is applied to the mobile anchor to make it adaptable to any traffic pattern. Through interactions with the environment, the agent can discovery a near-optimal control policy for movement of mobile anchor. The policy can achieve minimization of residual energy's variance among sinks, which prevent the early isolation of sink and prolong the network lifetime.