Spatial gossip and resource location protocols
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fractionally cascaded information in a sensor network
Proceedings of the 3rd international symposium on Information processing in sensor networks
Sweeps over wireless sensor networks
Proceedings of the 5th international conference on Information processing in sensor networks
Design patterns from biology for distributed computing
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
A tutorial on spectral clustering
Statistics and Computing
Composable Information Gradients in Wireless Sensor Networks
IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
Reaching consensus in wireless networks with probabilistic broadcast
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Higher dimensional consensus: learning in large-scale networks
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
Randomized consensus algorithms over large scale networks
IEEE Journal on Selected Areas in Communications
On distributed computation of information potentials
FOMC '12 Proceedings of the 8th International Workshop on Foundations of Mobile Computing
Mode-seeking on graphs via random walks
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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In large-scale resource-constrained systems, such as wireless sensor networks, global objectives should be ideally achieved through inexpensive local interactions. A technique satisfying these requirements is information potentials, in which distributed functions disseminate information about the process monitored by the network. Information potentials are usually computed through local aggregation or gossiping. These methods however, do not consider the topological properties of the network, such as node density, which could be exploited to enhance the performance of the system. This paper proposes a novel aggregation method with which a potential becomes sensitive to the network topology. Our method introduces the notion of affinity spaces, which allow us to uncover the deep connections between the aggregation scope (the radius of the extended neighborhood whose information is aggregated) and the network's Laplacian (which captures the topology of the connectivity graph). Our study provides two additional contributions: (i) It characterizes the convergence of information potentials for static and dynamic networks. Our analysis captures the impact of key parameters, such as node density, time-varying information, as well as of the addition (or removal) of links and nodes. (ii) It shows that information potentials are decomposed into wave-like eigenfunctions that depend on the aggregation scope. This result has important implications, for example it prevents greedy routing techniques from getting stuck by eliminating local-maxima. Simulations and experimental evaluation show that our main findings hold under realistic conditions, with unstable links and message loss.