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CEDAR: A Low-Latency and Distributed Strategy for Packet Recovery in Wireless Networks

CEDAR: A Low-Latency and Distributed Strategy for Packet Recovery in Wireless Networks

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CEDAR: A Low-Latency and Distributed Strategy for Packet Recovery in Wireless Networks

 Underlying link-layer protocols of well-established wireless networks that use the conventional “store-and-forward” design paradigm cannot provide highly sustainable reliability and stability in wireless communication, which introduce significant barriers and setbacks in scalability and deployments of wireless networks. In this paper, we propose a Code Embedded Distributed Adaptive and Reliable (CEDAR) link-layer framework that targets low latency and balancing en/decoding load among nodes. CEDAR is the first comprehensive theoretical framework for analyzing and designing distributed and adaptive error recovery for wireless networks. It employs a theoretically sound framework for embedding channel codes in each packet and performs the error correcting process in selected intermediate nodes in a packet’s route. To identify the intermediate nodes for the decoding, we mathematically calculate the average packet delay and formalize the problem as a nonlinear integer programming problem. By minimizing the delays, we derive three propositions that: 1) can identify the intermediate nodes that minimize the propagation and transmission delay of a packet; and 2) and 3) can identify the intermediate nodes that simultaneously minimize the queuing delay and maximize the fairness of en/decoding load of all the nodes. Guided by the propositions, we then propose a scalable and distributed scheme in CEDAR to choose the intermediate en/de-coding nodes in a route to achieve its objective. The results from real-world testbed “NESTbed” and simulation with MATLAB prove that CEDAR is superior to schemes using hop-by-hop decoding and destination decoding not only in packet delay and throughput but also in energy-consumption and load distribution balance.


 


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  • Model: PROJ5163
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This product was added to our catalog on Monday 12 September, 2016.

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