Abstract
The increasing computing power of mobile devices has opened the way to perform analysis and mining of data in many real-life mobile scenarios, such as body-health monitoring, vehicle control, and wireless security systems. A key aspect to enable data analysis and mining over mobile devices is ensuring energy efficiency, as mobile devices are battery-power operated. We worked in this direction by defining a distributed architecture in which mobile devices cooperate in a peer-to-peer style to perform a data mining process, tackling the problem of energy capacity shortage by distributing the energy consumption among the available devices. Within this framework, we propose an energy-aware (EA) scheduling strategy that assigns data mining tasks over a network of mobile devices optimizing the energy usage. The main design principle of the EA strategy is finding a task allocation that prolongs network lifetime by balancing the energy load among the devices. The EA strategy has been evaluated through discrete-event simulation. The experimental results show that significant energy savings can be achieved by using the EA scheduler in a mobile data mining scenario, compared to classical time-based schedulers.
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© 2013 Springer-Verlag Berlin Heidelberg
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Comito, C., Falcone, D., Talia, D., Trunfio, P. (2013). A Distributed Allocation Strategy for Data Mining Tasks in Mobile Environments. In: Fortino, G., Badica, C., Malgeri, M., Unland, R. (eds) Intelligent Distributed Computing VI. Studies in Computational Intelligence, vol 446. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32524-3_29
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DOI: https://doi.org/10.1007/978-3-642-32524-3_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32523-6
Online ISBN: 978-3-642-32524-3
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