Coordinated Workload Scheduling in Hierarchical Sensor Networks for Data Fusion Applications

To minimize the execution time of a sensing task over a multi-hophierarchical sensor network, we present a coordinated scheduling methodfollowing the divisible load scheduling paradigm. The proposedscheduling strategy builds on eliminating transmission collisions andidle gaps between two successive data transmissions. We consider asensor network consisting of several clusters. In a cluster, afterrelated raw data measured by source nodes are collected at the fusionnode, in-network data aggregation is further considered. The schedulingstrategies consist of two phases: intra-cluster scheduling andinter-cluster scheduling. Intra-cluster scheduling deals with assigningdifferent fractions of a sensing workload among source nodes in eachcluster; inter-cluster scheduling involves the distribution of fuseddata among all fusion nodes. Closed-form solutions to the problem oftask scheduling are derived. Finally, numerical examples are presentedto demonstrate the impacts of different system parameters such as thenumber of sensor nodes, measurement, communication, and processingspeed, on the finish time and energy consumption.