Prediction-based redundant data elimination with content overhearing in wireless networks
Haiying Shen, Shenghua He, Lei Yu, Ankur Sarker · 2017
This paper aims to improve wireless network throughput by suppressing duplicate data transmissions from network links. It has been demonstrated that IP-layer Redundancy Elimination (RE) with content overhearing can significantly improve the goodput and utilization of wireless channels in wireless environment. However, the integration of IP-layer RE and wireless overhearing introduces a challenge. That is, probabilistic wireless overhearing and the possibility of a receiver overhearing from multiple transmitters cause the caches of a sender and a receiver far from synchronization, which can disrupt IP-layer RE's correctness and degrade its performance. The previous work deals with this challenge by the overhearing probability estimation, which however is not efficient or scalable. In this paper, we propose a Prediction-based Redundancy Elimination with Content Overhearing method (PRECO) to address this challenge. By exploiting prediction-based RE, PRECO does not require cache synchronization and overhearing probability estimation, which enables its efficient and scalable deployment. Based on PRECO, we exploit the benefits of deploying sub-packet level RE as a primitive IP-layer service on all nodes in wireless mesh networks by proposing a redundancy-aware routing protocol. Trace-driven performance evaluation shows the effectiveness and efficiency of PRECO compared with other RE methods.