Joint source coding rate allocation and flow scheduling for data aggregation in collaborative sensing networks

摘要

In collaborative sensing networks such as WSNs (Wireless Sensor Networks), due to overlapping converge areas among neighbor nodes, they may percept a large number of similar or identical data, which incurs sensing data redundancy and unnecessary energy consumption. Slepian-wolf theorem based source coding is an effective method to reduce redundancy in data aggregation. However, the exponential growth of constraints prevent the method from practical application. Therefore, in this paper, we propose a cross-layer optimization framework to solve data aggregation problem by jointly considering optimal source encoding rate and flow scheduling. By proving the convex of constraints of Spelian-Wolf theorem, we relax original constraints so that the optimal encoding rate scheme can be adopted. The relaxation makes the optimization problem feasible. Furthermore, we employ dual decomposition to separate the original problem into two sub-problems. By solving the two subproblem distributedly, we provide optimal encoding rate allocation for each node and flow scheduling for each link. Simulation results demonstrate that our framework can reduce data redundancy and network traffic significantly compared to the exiting algorithms.