Abstract
Mobile crowd sensing harnesses the data sensing capability of individual smartphones, underpinning a variety of valuable knowledge discovery, environment monitoring, and decision-making applications. It is a central issue for a mobile crowd sensing system to maximize the utility of sensing data collection at a given cost of resource consumption at each smartphone. However, it is particularly challenging. On the one hand, the utility of sensing data from a smartphone is usually dependent on its context which is random and varies over time. On the other hand, because of the marginal effect, the sensing decision of a smartphone is also dependent on decisions of other smartphones. Little work has explored the utility maximization problem of sensing data collection. This article proposes a distributed algorithm for maximizing the utility of sensing data collection when the smartphone cost is constrained. The design of the algorithm is inspired by stochastic network optimization technique and distributed correlated scheduling. It does not require any prior knowledge of smartphone contexts in the future, and hence sensing decisions can be made by individual smartphone. Rigorous theoretical analysis shows that the proposed algorithm can achieve a time average utility that is within O(1/ V) of the theoretical optimum.
Highlights
Over the past decades, mobile phones have become an indispensable part of the daily life of almost everyone
To tackle the aforementioned challenges, we take advantage of the stochastic network optimization technique developed in Neely[9] and the idea of distributed correlated scheduling[10] to design a distributed online scheduling algorithm. It does not require any prior knowledge of smartphone contexts in the future, and sensing decisions can be made by individual smartphones
We perform rigorous theoretical analysis to show that our algorithm can achieve a time average utility that is within O(1=V ) of the optimum with tradeoffs on the time required to converge to the cost constraints, for any V .0 and can adapt to the mobility of mobile smartphones very well
Summary
Mobile phones have become an indispensable part of the daily life of almost everyone. We propose a distributed algorithm for maximizing the utility of sensing data collection in a mobile crowd sensing system. Our major contributions are summarized as follows: It is the first attempt, to the best of our knowledge, to explore the crucial problem of utility maximization of sensing data collection in a mobile crowd sensing system when the cost of smartphones is constrained. In Zhu et al.,[15] the authors develop a novel smartphone-based vehicular crowd sensing system that achieves efficient utilization of limited 3G budgets to improve system performance They propose heuristic algorithm based on the statistic data to estimate whether a WiFi encounter is approaching so as to make decisions. Our optimal online scheduling algorithm does not require any prior knowledge of the future patterns and can achieve a time average utility that could be arbitrarily close to the optimum, in a distributed manner
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