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.
This work is partially supported by the National Science Foundation, USA, under grant number CCF-0430061 and CNS-0627028.
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Pathak, A., Prasanna, V.K. (2008). Energy-Efficient Task Mapping for Data-Driven Sensor Network Macroprogramming. In: Nikoletseas, S.E., Chlebus, B.S., Johnson, D.B., Krishnamachari, B. (eds) Distributed Computing in Sensor Systems. DCOSS 2008. Lecture Notes in Computer Science, vol 5067. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69170-9_38
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DOI: https://doi.org/10.1007/978-3-540-69170-9_38
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