In this correspondence, we show that solutions to the multiple description coding problem and the broadcast channel coding problem share a common encoding procedure: successive source encoding. We use this connection as the basis for establishing connections between the achievable multiple description rate region and Marton's region for broadcast channels. Specifically, we show that Marton's encoding scheme can be viewed as a multiple description coding procedure. We also explore the dual problem, namely, the relationship between successive channel decoding in multiple access communication and distributed source coding. By illuminating these connections to multiple description, we hope to motivate a solution to what remains a mostly unsolved problem

We consider the use of distributed source and network coding in networks of redundant, unreliable sensors. A design algorithm is described for distributed joint source and network coding that creates an optimal balance between compression efficiency and network robustness.

This article considers the problem of distributing sensors to observe a random field while meeting a series of energy and coverage constraints. In the network under consideration, N identical sensors are to be distributed, with each sensor sending data to a single fusion center at a fixed rate Δ R . The problem of sensor allocation to the coverage area is reduced to the problem of allocating sensors to cells by first partitioning the coverage field into cells, and then assuming that sensors in the same cells make the same observations. It is shown that the resulting sensor allocation problem is equivalent to a discrete form of the rate allocation problem, a well-known problem in the field of information theory. General guidelines for solving this problem are provided.

In this paper, we study the optimal way of distributing sensors in a random field to minimize the estimation distortion. We show that this problem is equivalent to certain problems in optimal sampling design and multiterminal source coding. In our model, we partition the field into cells within which the observations are assumed to be the same. A number of identical sensors that can report the observed data to the fusion center at communication rate ¢R are to be allocated to cells so that the distortion achieved at the fusion center is minimized. We find optimal solutions for the Gaussian, binary and simplified Gaussian vector cases.

We consider a distributed sensor network in which several observations are communicated to the fusion center using limited transmission rate. The observation must be separately encoded so that the target can be estimated with minimum average distortion. We address the problem from an information theoretic perspective and establish the inner and outer bound of the admissible rate-distortion region. We derive an upper bound on the sum-rate distortion function and its corresponding rate allocation schemes by exploiting the contra-polymatroid structure of the achievable rate region. The quadratic Gaussian case is analyzed in detail and the optimal rate allocation schemes in the achievable rate region are characterized. We show that our upper bound on the sum-rate distortion function is tight for the quadratic Gaussian CEO problem in the case of same signal-to-noise ratios at the sensors.