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.

In this paper, we consider a sensory data gathering application of a vehicular ad hoc network (VANET) in which vehicles produce sensory data, which should be gathered for data analysis and making decisions. Data delivery is particularly challenging because of the unique characteristics of VANETs, such as fast topology change, frequent disruptions, and rare contact opportunities. Through empirical study based on real vehicular traces, we find an important observation that a noticeable percentage of data packets cannot be delivered within timeto-live. In this paper, we explore the problem of 3G-assisted data delivery in a VANET with a budget constraint of 3G traffic. A packet can either be delivered via multihop transmissions in the VANET or via 3G. The main challenge for solving the problem is twofold. On the one hand, there is an intrinsic tradeoff between delivery ratio and delivery delay when using the 3G. On the other hand, it is difficult to decide which set of packets should be selected for 3G transmissions and when to deliver them via 3G. In this paper, we propose an approach called 3GDD for 3G-assisted data delivery in a VANET. We construct a utility function to explore the tradeoff between delivery ratio and delivery delay, which provides a unified framework to reflect the two factors. We formulate the 3G-assisted data delivery as an optimization problem in which the objective is to maximize the overall utility under the 3G budget constraint. To circumvent the high complexity of this optimization problem, we further transition the original optimization problem as an integer linear programming problem (ILP). Solving this ILP, we derive the 3G allocation over different time stages. Given the 3G budget at each time stage, those packets that are most unlikely delivered via the VANET are selected for 3G transmissions. We comprehensively evaluate our 3GDD using both synthetic vehicular traces and real Manuscript

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