Geographic Random Forwarding (GeRaF) for Ad Hoc and Sensor Networks: Energy and Latency Performance
IEEE Transactions on Mobile Computing
Foundations and Trends® in Networking
An overview of network coding for dynamically changing networks
International Journal of Autonomous and Adaptive Communications Systems
A self-adaptive probabilistic packet filtering scheme against entropy attacks in network coding
Computer Networks: The International Journal of Computer and Telecommunications Networking
An efficient dynamic-identity based signature scheme for secure network coding
Computer Networks: The International Journal of Computer and Telecommunications Networking
Live peer-to-peer streaming with scalable video coding and networking coding
MMSys '10 Proceedings of the first annual ACM SIGMM conference on Multimedia systems
SenseCode: Network coding for reliable sensor networks
ACM Transactions on Sensor Networks (TOSN)
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The drive toward the implementation and massive deployment of wireless sensor networks calls for ultralow-cost and low-power nodes. While the digital subsystems of the nodes are still following Moore's Law, there is no such trend regarding the performance of analog components. This work proposes a fully integrated architecture of both digital and analog components (including local oscillator) that offers significant reduction in cost, size, and overall power consumption of the node. Even though such a radical architecture cannot offer the reliable tuning of standard designs, it is shown that by using random network coding, a dense network of such nodes can achieve throughput linear in the number of channels available for communication. Moreover, the ratio of the achievable throughput of the untuned network to the throughput of a tuned network with perfect coordination is shown to be close to 1/e. This work uses network coding to leverage the fact that throughput equal to the max-flow in a graph is achievable even if the topology is not know a priori. However, the challenge here is finding the max-flow of the random graph corresponding to the network.