A distributed algorithm for maximizing utility of data collection in a crowd sensing system

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.

ADAPT: Adaptive distributed optimization approach for uploading data with redundancy in cooperative mobile cloud

“…However, this approach is designed based on a centric decision scheme that is impracticable in many scenarios as discussed in Section 1. Works conducted by Han et al 24 and Chen et al 25 are similar to our proposed approach in this paper. They also take the information redundancy into consideration when collecting sensed data from sensors.…”

“…The assumption is practicable in reality and can be implemented by the piggybacking technology easily. 24,25,31 We start the design with transforming the energy cost constraints into a queue stability problem. For every mobile device, we define a virtual queue Q i (t), and Q(t) = (Q 1 (t), … , Q N (t)).…”

“…We assume that the feedback information about channel states ω ( t ), storage states d ( t ) and decisions α ( t ) is delivered to all mobile devices at the end of ( t + D )th upload procedure, where D ≥ 0 is the system feedback delay. The assumption is practicable in reality and can be implemented by the piggybacking technology easily 24,25,31 …”