Comprehensive tempo-spatial data collection in crowd sensing using a heterogeneous sensing vehicle selection method. Issue 3 (June 2016)

• Record Type: Journal Article
• Authors: Liu, Yazhi; Niu, Jianwei; Liu, Xiting
• Is Part Of: Personal and ubiquitous computing. Volume 20:Issue 3(2016)
• Page Start: 397
• Page End: 411
• Publication Date: 2016-06
• Subjects: Vehicular sensing -- Mobile crowd sensing -- Comprehensive tempo-spatial data coverage; Mobile computing -- Periodicals; Portable computers -- Periodicals; Human-computer interaction -- Periodicals; 004.16
• DOI: 10.1007/s00779-016-0932-x
• Languages: English
• ISSNs: 1617-4909

Abstract

Abstract The wide distribution of mobile vehicles installed with various sensing devices and wireless communication interfaces has made vehicular mobile crowd sensing possible in practice. However, owing to the heterogeneity of vehicles in terms of sensing interfaces and mobilities, collecting comprehensive tempo-spatial sensing data with only one sensing vehicle is impossible. Moreover, the sensing data collected may expire in the future; as a result, sensing vehicles may have to continuously collect sensing data to ensure the relevance of such data. Although including more sensing vehicles can improve the quality of collected sensing data, this step also requires additional cost. Thus, how to continuously collect comprehensive tempo-spatial sensing data with a limited number of heterogeneous sensing vehicles is a critical issue in vehicular mobile crowd sensing systems. In this work, a heterogeneous sensing vehicle selection (HVS) method for the collection of comprehensive tempo-spatial sensing data is proposed. On the basis of the spatial distribution and sensing interfaces of sensing vehicles and the tempo-spatial coverage of collected sensing data, a utility function is designed in HVS to estimate the sensing capacity of sensing vehicles. Then, according to the utilities of sensing vehicles and the restriction on the number of recruited sensing vehicles, sensing vehicle selection is modeled as a knapsack problem. Finally, a greedy optimal sensing vehicle selection algorithm is designed. Real trace-driven simulations show that the HVS algorithm can collect sensing data with a higher coverage ratio in a more uniform and continuous manner than existing mobile crowd sensing methods.