Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Power management in energy harvesting sensor networks
ACM Transactions on Embedded Computing Systems (TECS) - Special Section LCTES'05
Decentralised coordination of low-power embedded devices using the max-sum algorithm
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Agent Technologies for Sensor Networks
IEEE Intelligent Systems
A scalable method for multiagent constraint optimization
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Model based Bayesian exploration
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Good error-correcting codes based on very sparse matrices
IEEE Transactions on Information Theory
The generalized distributive law
IEEE Transactions on Information Theory
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
On the optimality of solutions of the max-product belief-propagation algorithm in arbitrary graphs
IEEE Transactions on Information Theory
Modelling the asynchronous dynamic evolution of architectural types
SOAR'09 Proceedings of the First international conference on Self-organizing architectures
Distributed constraint optimisation for resource limited sensor networks
Science of Computer Programming
Risk-neutral bounded max-sum for distributed constraint optimization
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Multiagent reinforcement learning in a distributed sensor network with indirect feedback
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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In this paper, we consider the self-organisation of sensors within a network deployed for wide area surveillance. We present a decentralised coordination algorithm based upon the max-sum algorithm and demonstrate how self-organisation can be achieved within a setting where sensors are deployed with no a priori information regarding their local environment. These energy-constrained sensors first learn how their actions interact with those of neighbouring sensors, and then use the max-sum algorithm to coordinate their sense/sleep schedules in order to maximise the effectiveness of the sensor network as a whole. In a simulation we show that this approach yields a 30% reduction in the number of vehicles that the sensor network fails to detect (compared to an uncoordinated network), and this performance is close to that achieved by a benchmark centralised optimisation algorithm (simulated annealing).