Gro¨tzsch's 3-color theorem and its counterparts for the torus and the projective plane
Journal of Combinatorial Theory Series B
Near-optimal sensor placements in Gaussian processes
ICML '05 Proceedings of the 22nd international conference on Machine learning
A utility-based sensing and communication model for a glacial sensor network
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
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
Maximizing a Submodular Set Function Subject to a Matroid Constraint (Extended Abstract)
IPCO '07 Proceedings of the 12th international conference on Integer Programming and Combinatorial Optimization
Three-coloring triangle-free planar graphs in linear time
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
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
Balanced data gathering strategy based on ant colony algorithm in WSNs
International Journal of Wireless and Mobile Computing
Near-optimal continuous patrolling with teams of mobile information gathering agents
Artificial Intelligence
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In large wireless sensor networks, the problem of assigning radio frequencies to sensing agents such that no two connected sensors are assigned the same value (and will thus interfere with one another) is a major challenge. To tackle this problem, we develop a novel decentralised coordination algorithm that activates only a subset of the deployed agents, subject to the connectivity graph of this subset being provably 3-colourable in linear time, hence allowing the use of a simple decentralised graph colouring algorithm. Crucially, while doing this, our algorithm maximises The sensing coverage achieved by the selected sensing agents, which is given by an arbitrary non-decreasing submodular set function. We empirically evaluate our algorithm by benchmarking it against a centralised greedy algorithm and an optimal one, and show that the selected sensing agents manage to achieve 90% of the coverage provided by the optimal algorithm, and 85% of the coverage provided by activating all sensors. Moreover, we use a simple decentralised graph colouring algorithm to show the frequency assignment problem is easy in the resulting graphs; in all considered problem instances, this algorithm managed to fin a colouring in less than 5 iterations on average. We then show how the algorithm can be used in dynamic settings, in which sensors can fail or new sensors can be deployed. In this setting, our algorithm provides 250% more coverage over time compared to activating all available sensors simultaneously.