Asynchronous distributed averaging on communication networks
IEEE/ACM Transactions on Networking (TON)
Distributed event-region detection in wireless sensor networks
EURASIP Journal on Advances in Signal Processing
A framework for assessing residual energy in wireless sensor network
International Journal of Sensor Networks
Decentralized Synchronization and Estimation in Wireless Networks
NEW2AN '08 / ruSMART '08 Proceedings of the 8th international conference, NEW2AN and 1st Russian Conference on Smart Spaces, ruSMART on Next Generation Teletraffic and Wired/Wireless Advanced Networking
A scalable fault tolerant diffusion scheme for data fusion in sensor networks
Proceedings of the 3rd international conference on Scalable information systems
Power constrained distributed estimation with cluster-based sensor collaboration
IEEE Transactions on Wireless Communications
ACC'09 Proceedings of the 2009 conference on American Control Conference
Fast distributed average consensus algorithms based on advection-diffusion processes
IEEE Transactions on Signal Processing
Distributed consensus with quantized data via sequence averaging
IEEE Transactions on Signal Processing
Adaptive filter algorithms for accelerated discrete-time consensus
IEEE Transactions on Signal Processing
Consensus acceleration in multiagent systems with the Chebyshev semi-iterative method
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Efficient in-network processing through local ad-hoc information coalescence
DCOSS'06 Proceedings of the Second IEEE international conference on Distributed Computing in Sensor Systems
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In this paper, we develop algorithms for distributed computation of averages of the node data over networks with arbitrary but fixed connectivity. The algorithms we develop are linear dynamical systems that generate sequences of improving approximations to the desired computation at each node, via iterative processing and broadcasting. The algorithms are locally constructed at each node by exploiting only locally available and macroscopic information about the network topology. We present methods for optimizing the convergence rates of these algorithms to the desired computation, and evaluate their performance characteristics in the context of a problem of signal estimation from multinode noisy observations. By conducting simulations based on simple power-loss propagation models, we perform a preliminary comparison of the algorithms we develop against other types of distributed algorithms for computing averages, and identify transmit-power optimized algorithmic implementations as a function of the size and density of the sensor network.