AUXILIARY CHANNEL STRUCTURING USING MESH SIZE

Abstract

CSPR formalizes the sparse path representation and enables accurate and efficient per-packet path renovation. By viewing the entire network like a path representation space, a random routing path could be symbolized with a path vector within the space. CSPR is invulnerable to network dynamics and loss links because of its distinct design. Evaluation results reveal that CSPR achieves high path recovery precision and outperforms the condition-of-the-art approaches in a variety of network settings. This paper presents CSPR, a compressive-sensing based method for path renovation in wireless sensor systems. As path length is generally much smaller sized compared to network size, such path vectors are sparse, i.e., nearly all elements are zeros. We evaluate CSPR both in tested-based experiments and enormous-scale trace-driven simulations. By encoding sparse path representation into packets, the road vector (and therefore the symbolized routing path) could be retrieved from a tiny bit of packets using compressive sensing technique. Some optimization techniques are further suggested to enhance the look.