Mobile Autonomous Sensing Unit (MASU): a framework that supports distributed pervasive data sensing

Pervasive data sensing is a major issue that transverses various research areas and application domains. It allows identifying people’s behaviour and patterns without overwhelming the monitored persons. Although there are many pervasive data sensing applications, they are typically focused on addressing specific problems in a single application domain, making them difficult to generalize or reuse. On the other hand, the platforms for supporting pervasive data sensing impose restrictions to the devices and operational environments that make them unsuitable for monitoring loosely-coupled or fully distributed work. In order to help address this challenge this paper present a framework that supports distributed pervasive data sensing in a generic way. Developers can use this framework to facilitate the implementations of their applications, thus reducing complexity and effort in such an activity. The framework was evaluated using simulations and also through an empirical test, and the obtained results indicate that it is useful to support such a sensing activity in loosely-coupled or fully distributed work scenarios.Peer ReviewedPostprint (published version

Minimum cost collaborative sensing network with mobile phones

Mobile phones with a rich set of embedded sensors have been applied in various collaborative sensing applications. In some applications, to encourage mobile phone users performing collaborative sensing tasks, the data demanders may pay mobile phone users. However, none of the existing works takes into account it. In this paper, we study the Minimum Cost of Attaining the Required Data with mobile phones (MCARD) problem in collaborative sensing network. Given sensing regions R = {R1, R2,&mellip;, Rm}, the set of requisite data Di for each sensing region Ri and a set of mobile phones M, the MCARD problem is how to select mobile phones to get all the required data such that the total cost on paying mobile phone users is minimized. We first formally define the MCARD problem. Then, we propose an approximation algorithm for the MCARD problem with the determinate trajectories of mobile phones and a heuristic algorithm for that trajectories are unknown respectively. Simulation results demonstrate our algorithms are efficient. © 2013 IEEE.Mobile phones with a rich set of embedded sensors have been applied in various collaborative sensing applications. In some applications, to encourage mobile phone users performing collaborative sensing tasks, the data demanders may pay mobile phone users. However, none of the existing works takes into account it. In this paper, we study the Minimum Cost of Attaining the Required Data with mobile phones (MCARD) problem in collaborative sensing network. Given sensing regions R = {R1, R2,&mellip;, Rm}, the set of requisite data Di for each sensing region Ri and a set of mobile phones M, the MCARD problem is how to select mobile phones to get all the required data such that the total cost on paying mobile phone users is minimized. We first formally define the MCARD problem. Then, we propose an approximation algorithm for the MCARD problem with the determinate trajectories of mobile phones and a heuristic algorithm for that trajectories are unknown respectively. Simulation results demonstrate our algorithms are efficient. © 2013 IEEE