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Coordinated Workload Scheduling in Hierarchical Sensor Networks for Data Fusion Applications

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Abstract

To minimize the execution time of a sensing task over a multi-hop hierarchical sensor network, we present a coordinated scheduling method following the divisible load scheduling paradigm. The proposed scheduling strategy builds on eliminating transmission collisions and idle gaps between two successive data transmissions. We consider a sensor network consisting of several clusters. In a cluster, after related raw data measured by source nodes are collected at the fusion node, in-network data aggregation is further considered. 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. Finally, numerical examples are presented to demonstrate the impacts of different system parameters such as the number of sensor nodes, measurement, ommunication, and processing speed, on the finish time and energy consumption.

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Authors and Affiliations

  1. Scalable Software Systems Lab, Department of Computer Science, Oklahoma State University, Stillwater, OK, 74078, U.S.A.

    Xiao-Lin Li

  2. Internet and Mobile Computing Lab, Department of Computing, Hong Kong Polytechnic University, Hung Hom Kowloon, Hong Kong, China

    Jian-Nong Cao

Authors
  1. Xiao-Lin Li
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  2. Jian-Nong Cao
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Correspondence to Xiao-Lin Li.

Additional information

This work is supported in part by the National Science Foundation of USA under Grant No. CNS-0709329, Hong Kong Polytechnic University via the ICRG Grant No.G-YE57, Hong Kong RGC via the grant of a Research Center for Ubiquitous Computing, and the National Hi-Tech Research and Development 863 Program of China under Grant No. 2006AA01Z231.

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Li, XL., Cao, JN. Coordinated Workload Scheduling in Hierarchical Sensor Networks for Data Fusion Applications. J. Comput. Sci. Technol. 23, 355–364 (2008). https://doi.org/10.1007/s11390-008-9138-7

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