Abstract
One of the most challenging issues for nowadays Wireless Sensor Networks (WSNs) is represented by the capability of self-powering the network sensor nodes by means of suitable Energy Harvesting (EH) techniques. However, the nature of such energy captured from the environment is often irregular and unpredictable and therefore some intelligence is required to efficiently use it for information processing at the sensor level. In particular in this work the authors address the problem of task scheduling in processors located in WSN nodes powered by EH sources. The authors’ objective consists in employing a conservative scheduling paradigm in order to achieve a more efficient management of energy resources. To prove such a claim, the recently advanced Lazy Scheduling Algorithm (LSA) has been taken as reference and integrated with the automatic ability of foreseeing at runtime the task energy starving, i.e. the impossibility of finalizing a task due to the lack of power. The resulting technique, namely Energy Aware Lazy Scheduling Algorithm (EA-LSA), has then been tested in comparison with the original one and a relevant performance improvement has been registered in terms of number of executable tasks.
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Severini, M., Squartini, S., Piazza, F. (2012). An Energy Aware Approach for Task Scheduling in Energy-Harvesting Sensor Nodes. In: Wang, J., Yen, G.G., Polycarpou, M.M. (eds) Advances in Neural Networks – ISNN 2012. ISNN 2012. Lecture Notes in Computer Science, vol 7368. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31362-2_66
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DOI: https://doi.org/10.1007/978-3-642-31362-2_66
Publisher Name: Springer, Berlin, Heidelberg
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