Abstract
Upon the ubiquitous sensor network capable of deciding control actions by a sophisticated inference engine, this paper designs an actuator controller which coordinates the actuator operations over the multiple farms. Basically, not beginning tasks as soon as they get triggered, local schedulers determine the operation plan according to genetic algorithms, for the sake of reducing peak power consumption for the given scheduling window. For global scheduling, each local scheduler retrieves the current load information maintained in the coordinator, runs its own schedule, and finally sends to the coordinator. The fitness function gives penalty to the allocation which assigns much power to the heavily loaded time slots. The procedure reduces the peak load by up to 22.8 % for the given task set. Moreover, all schedules are not necessarily run with tight concurrency control. Our simulation shows that 40 % of schedulers can run in parallel just with negligible performance loss.
This research was supported by the MKE (The Ministry of Knowledge Economy), through the project of Region technical renovation, Republic of Korea.
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Lee, J., Park, GL., Kwak, HY., Han, J. (2012). Power-Constrained Actuator Coordination for Agricultural Sensor Networks. In: Li, R., Cao, J., Bourgeois, J. (eds) Advances in Grid and Pervasive Computing. GPC 2012. Lecture Notes in Computer Science, vol 7296. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30767-6_14
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DOI: https://doi.org/10.1007/978-3-642-30767-6_14
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