Efficient allocation of data mining tasks in mobile environments

Mobile data mining can be a significant added service for nomadic users, enterprises, and organizations that need to perform analysis of data generated either from a mobile device or from remote sources. 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 scheduling strategy that assigns data mining tasks over a network of mobile devices optimizing the energy usage. The main design principle of the energy-aware strategy is finding a task allocation that prolongs the lifetime of the entire network of mobile devices by balancing the energy load among the devices. The energy-aware strategy has been evaluated through discrete-event simulation. The experimental results show that significant energy savings can be achieved by using the energy-aware scheduler in a mobile data mining scenario, compared to classical time-based schedulers.