Real-time obstacle avoidance for manipulators and mobile robots
International Journal of Robotics Research
Gossip-Based Computation of Aggregate Information
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
A scheme for robust distributed sensor fusion based on average consensus
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Design patterns from biology for distributed computing
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Distributed algorithms for reaching consensus on general functions
Automatica (Journal of IFAC)
Composable Information Gradients in Wireless Sensor Networks
IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
GUIDE-gradient: A Guiding Algorithm for Mobile Nodes in WLAN and Ad-hoc Networks
Wireless Personal Communications: An International Journal
Geographic Gossip: Efficient Averaging for Sensor Networks
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
Local search and the local structure of NP-complete problems
Operations Research Letters
Think globally, act locally: on the reshaping of information landscapes
Proceedings of the 12th international conference on Information processing in sensor networks
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A common task of mobile wireless ad-hoc networks is to distributedly extract information from a monitored process. We define process information as a measure that is sensed and computed by each mobile node in a network. For complex tasks, such as searching in a network and coordination of robotic swarms, we are typically interested in the spatial distribution of the process information. Spatial distributions can be thought of as information potentials that recursively consider the richness of information around each node. This paper describes a localized mechanism for determining the information potential on each node based on local process information and the potential of neighboring nodes. The mechanism allows us to distributedly generate a spectrum of possible information potentials between the extreme points of a local view and distributed averaging. In this work, we describe the mechanism, prove its exponential convergence, and characterize the spectrum of information potentials. Moreover, we use the mechanism to generate information potentials that are unimodal, i.e., feature a single extremum. Unimodality is a very valuable property for chemotactic search, which can be used in diverse application tasks such as directed search of information and rendezvous of mobile agents.