Sensing workload scheduling in hierarchical sensor networks for data fusion applications

We consider a sensing task scheduling problem in two-level hierarchical sensor networks. To minimize the execution time of a given task, we propose efficient scheduling strategies following the divisible load scheduling paradigm. The proposed scheduling strategies minimize the finish time by eliminating transmission collisions and idle gaps between two successive data transmissions. In-network data aggregation for sensor data is further considered at data fusion nodes. Fused data are produced by some fusion functions on original data from local clusters. The scheduling strategies consist of two phases: intra-cluster scheduling and inter-cluster scheduling. Intra-cluster scheduling deals with assigning different fractions of a sensing workload among source nodes in each cluster; inter-cluster scheduling involves the distribution of fused data among all fusion nodes. Closed-form solutions to the problem of task scheduling are derived. Energy model is described for each kind of sensor nodes, considering data acquisition, communication, and processing. Finally, simulation results are presented to demonstrate the impacts of different system parameters such as the number of sensor nodes, measurement, communication, and processing speed, on the finish time and energy consumption.