IEEE Transactions on Signal Processing
SIAM Journal on Control and Optimization
Distributed consensus algorithms in sensor networks: quantized data and random link failures
IEEE Transactions on Signal Processing
Distributed anonymous function computation in information fusion and multiagent systems
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Anytime Reliable Transmission of Real-Valued Information through Digital Noisy Channels
SIAM Journal on Control and Optimization
Information theoretic bounds for distributed computation over networks of point-to-point channels
IEEE Transactions on Information Theory
Distributed Average Consensus With Dithered Quantization
IEEE Transactions on Signal Processing - Part I
Distributed Symmetric Function Computation in Noisy Wireless Sensor Networks
IEEE Transactions on Information Theory
Toward a theory of in-network computation in wireless sensor networks
IEEE Communications Magazine
| Hi-index | 22.14 |
Iterative distributed algorithms are studied for computing arithmetic averages over networks of agents connected through memoryless broadcast erasure channels. These algorithms do not require the agents to have any knowledge about the global network structure or size. Almost sure convergence to state agreement is proved, and the communication and computational complexities of the algorithms are analyzed. Both the number of transmissions and the number of computations performed by each agent of the network are shown to grow not faster than poly-logarithmically in the desired precision. The impact of the graph topology on the algorithms' performance is analyzed as well. Moreover, it is shown how, in the presence of noiseless communication feedback, one can modify the algorithms, significantly improving their performance versus complexity trade-off.