Energy-Efficient Task Mapping for Data-Driven Sensor Network Macroprogramming

Authors:
Animesh Pathak;Viktor K. Prasanna
Affiliations:
Ming Hsieh Department of Electrical Engineering, University of Southern California, USA;Ming Hsieh Department of Electrical Engineering, University of Southern California, USA
Venue:
DCOSS '08 Proceedings of the 4th IEEE international conference on Distributed Computing in Sensor Systems
Year:
2008

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Abstract

Data-driven macroprogramming of wireless sensor networks (WSNs) provides an easy to use high-level task graph representation to the application developer. However, determining an energy-efficient initial placement of these tasks onto the nodes of the target network poses a set of interesting problems. We present a framework to model this task-mapping problem arising in WSN macroprogramming. Our model can capture task placement constraints, and supports easy specification of energy-based optimization goals. Using our framework, we provide mathematical formulations for the task-mapping problem for two different metrics -- energy balance and total energy spent. Due to the complex nature of the problems, these formulations are not linear. We provide linearization heuristics for the same, resulting in mixed-integer programming (MIP) formulations. We also provide efficient heuristics for the above. Our experiments show that the our heuristics give the same results as the MIP for real-world sensor network macroprograms, and show a speedup of up to several orders of magnitude.