Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Journal of Global Optimization
An introduction to low-density parity-check codes
Theoretical aspects of computer science
FOCS '02 Proceedings of the 43rd Symposium on Foundations of Computer Science
An algebraic approach to network coding
IEEE/ACM Transactions on Networking (TON)
Decentralized erasure codes for distributed networked storage
IEEE/ACM Transactions on Networking (TON) - Special issue on networking and information theory
Growth codes: maximizing sensor network data persistence
Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications
Boundary recognition in sensor networks by topological methods
Proceedings of the 12th annual international conference on Mobile computing and networking
XORs in the air: practical wireless network coding
IEEE/ACM Transactions on Networking (TON)
Multicast throughput order of network coding in wireless ad-hoc networks
SECON'09 Proceedings of the 6th Annual IEEE communications society conference on Sensor, Mesh and Ad Hoc Communications and Networks
The capacity of wireless networks
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
The capacity of low-density parity-check codes under message-passing decoding
IEEE Transactions on Information Theory
Design of capacity-approaching irregular low-density parity-check codes
IEEE Transactions on Information Theory
A Random Linear Network Coding Approach to Multicast
IEEE Transactions on Information Theory
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Network coding (NC) within wireless sensor networks (WSNs) can be viewed as the mapping of efficient channel codes to the data generated within the network. In particular, this perspective of code-on-network-graphs (CNG) can be exploited to map source data generated within WSN (of size K) to a variable nodes subset in low-density parity check (LDPC) codes. The resulting fixed size symbol stream when transmitted through the network suffers erasures. At sink, an average of z source symbols can be recovered by employing belief propagation decoding. In this paper, we determine CNG code ensembles that achieve maximal recovery (z/ K) for different erasure rates and network topological constraints corresponding to node transmission range. An analytic framework to predict code performance under transmission range constraints is developed. Additionally, necessary condition for code stability was derived using fixed-point stability analysis. Optimal solutions for a WSN with 1000 nodes are determined using differential evolution algorithm. We outline a distributed algorithm for generating a sequence of encoded symbols adhering to the designed code ensemble. The performance of the designed CNG code is demonstrated to be superior to random NC and growth code based ensembles, as well as resilient to network size and inter-connectivity variations.