Performance Analysis of Flow-Based Traffic Splitting Strategy on Cluster-Mesh Sensor Networks

Abstract

Performance analysis is crucial for designing predictable and cost-efficient sensor networks. Based on the network calculus theory, we propose a flow-based traffic splitting strategy and its analytical method for worst-case performance analysis on cluster-mesh sensor networks. The traffic splitting strategy can be used to alleviate the problem of uneven network traffic load. The analytical method is able to derive close-form formulas for the worst-case performance in terms of the end-to-end least upper delay bounds for individual flows, the least upper backlog bounds, and power consumptions for individual nodes. Numerical results and simulations are conducted to show benefits of the splitting strategy as well as validate the analytical method. The numerical results show that the splitting strategy enables much better balance on network traffic load and power consumption. Moreover, the simulation results verify that the theoretic bounds are fairly tight.