How to distribute sensors in a random field?
Proceedings of the 3rd international symposium on Information processing in sensor networks
Power-bandwidth-distortion scaling laws for sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Robustness vs. efficiency in sensor networks
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
FAWNA: a high-speed mobile communication network architecture
AcessNets '06 Proceedings of the 1st international conference on Access networks
IWCMC '07 Proceedings of the 2007 international conference on Wireless communications and mobile computing
Distributed keyless security for correlated data with applications in visual sensor networks
Proceedings of the 9th workshop on Multimedia & security
Successively Structured Gaussian Two-terminal Source Coding
Wireless Personal Communications: An International Journal
Low-complexity coding and source-optimized clustering for large-scale sensor networks
ACM Transactions on Sensor Networks (TOSN)
Distributed field estimation with randomly deployed, noisy, binary sensors
IEEE Transactions on Signal Processing
Energy planning for progressive estimation in multihop sensor networks
IEEE Transactions on Signal Processing
On the minimum sum rate of Gaussian multiterminal source coding: new proofs
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 3
The quadratic Gaussian CEO problem with Byzantine agents
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 2
Field estimation from randomly located binary noisy sensors
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Robust distributed source coder design by deterministic annealing
IEEE Transactions on Signal Processing
Source-channel communication in sensor networks
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Brief paper: Min-max optimal data encoding and fusion in sensor networks
Automatica (Journal of IFAC)
Selective measurement transmission in distributed estimation with data association
IEEE Transactions on Signal Processing
On the sum rate of Gaussian multiterminal source coding: new proofs and results
IEEE Transactions on Information Theory
On the performance of single LDGM codes for iterative data fusion over the multiple access channel
MACOM'10 Proceedings of the Third international conference on Multiple access communications
EURASIP Journal on Wireless Communications and Networking - Special issue on signal processing-assisted protocols and algorithms for cooperating objects and wireless sensor networks
In-Network Computations of Machine-to-Machine Communications for Wireless Robotics
Wireless Personal Communications: An International Journal
ACM Transactions on Sensor Networks (TOSN)
Hi-index | 755.03 |
We consider a new problem in multiterminal source coding motivated by the following decentralized communication/estimation task. A firm's Chief Executive Officer (CEO) is interested in the data sequence {X(t)} t=1∞ which cannot be observed directly, perhaps because it represents tactical decisions by a competing firm. The CEO deploys a team of L agents who observe independently corrupted versions of {X(t)}t=1∞. Because {X(t)} is only one among many pressing matters to which the CEO must attend, the combined data rate at which the agents may communicate information about their observations to the CEO is limited to, say, R bits per second. If the agents were permitted to confer and pool their data, then in the limit as L→∞ they usually would be able to smooth out their independent observation noises entirely. Then they could use their R bits per second to provide the CEO with a representation of {X(t)} with fidelity D(R), where D(·) is the distortion-rate function of {X(t)}. In particular, with such data pooling D can be made arbitrarily small if R exceeds the entropy rate H of {X(t)}. Suppose, however, that the agents are not permitted to convene, Agent i having to send data based solely on his own noisy observations {Yi(t)}. We show that then there does not exist a finite value of R for which even infinitely many agents can make D arbitrarily small. Furthermore, in this isolated-agents case we determine the asymptotic behavior of the minimal error frequency in the limit as L and then R tend to infinity