WBroximity: Mobile participatory sensing for WLAN- and Bluetooth-based positioning

Zaid, Farid; Costantini, Diego; Parag, S. Mogre; Reinhardt, Andreas; Schmitt, Jens; Steinmetz, Ralf

doi:10.1109/lcn.2010.5735829

Cited by 2 publications

(2 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Define I 1 I 2 as the union of two sensing intervals or interval sets. For example, [2,5] [3,6] = [2,6], { [2,4], [5,7]} [3,6] Definition 4 (Aggregate sensing time). The aggregate sensing time i of a smartphone i is the overall sensing time that i should spend on completing the allocated tasks.…”

Section: A System Modelmentioning

confidence: 99%

See 1 more Smart Citation

Fair energy-efficient sensing task allocation in participatory sensing with smartphones

Zhao

Zhu

et al. 2014

IEEE INFOCOM 2014 - IEEE Conference on Computer Communications

View full text Add to dashboard Cite

With the proliferation of smartphones, participatory sensing using smartphones provides unprecedented opportunities for collecting enormous sensing data. There are two crucial requirements in participatory sensing, fair task allocation and energy efficiency, which are particularly challenging given high combinatorial complexity, tradeoff between energy efficiency and fairness, and dynamic and unpredictable task arrivals. In this paper, we present a novel fair energy-efficient allocation framework whose objective is characterized by min-max aggregate sensing time. We rigorously prove that optimizing the min-max aggregate sensing time is NP hard even when the tasks are assumed as a priori. We consider two allocation models: offline allocation and online allocation. For the offline allocation model, we design an efficient approximation algorithm with the approximation ratio of 2 − 1 m , where m is the number of member smartphones in the system. For the online allocation model, we propose a greedy online algorithm which achieves a competitive ratio of at most m. The results demonstrate that the approximation algorithm reduces over 81% total sensing time, the greedy online algorithm reduces more than 73% total sensing time, and both algorithms achieve over 3x better min-max fairness.

show abstract

Section: A System Modelmentioning

confidence: 99%

“…A lot of collaborative, crowdsourcing based applications spring up. Example applications of participatory sensing include intelligent transportation [1] [2] [3], localization [4] [5] [6], environmental monitoring [7] [8], and crowding counting [9].…”

Section: Introductionmentioning

confidence: 99%