1
|
Ji J, Guo Y, Gong D, Tang W. MOEA/D-based participant selection method for crowdsensing with social awareness. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2019.105981] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
|
2
|
Chen X, Liu M, Zhou Y, Li Z, Chen S, He X. A Truthful Incentive Mechanism for Online Recruitment in Mobile Crowd Sensing System. SENSORS 2017; 17:s17010079. [PMID: 28045441 PMCID: PMC5298652 DOI: 10.3390/s17010079] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2016] [Accepted: 12/09/2016] [Indexed: 11/24/2022]
Abstract
We investigate emerging mobile crowd sensing (MCS) systems, in which new cloud-based platforms sequentially allocate homogenous sensing jobs to dynamically-arriving users with uncertain service qualities. Given that human beings are selfish in nature, it is crucial yet challenging to design an efficient and truthful incentive mechanism to encourage users to participate. To address the challenge, we propose a novel truthful online auction mechanism that can efficiently learn to make irreversible online decisions on winner selections for new MCS systems without requiring previous knowledge of users. Moreover, we theoretically prove that our incentive possesses truthfulness, individual rationality and computational efficiency. Extensive simulation results under both real and synthetic traces demonstrate that our incentive mechanism can reduce the payment of the platform, increase the utility of the platform and social welfare.
Collapse
Affiliation(s)
- Xiao Chen
- Institute of Computing Technology, Chinese Academy of Sciences, No. 6 Kexueyuan South Road, Haidian District, Beijing 100190, China.
- School of Computer and Control Engineering, University of Chinese Academy of Sciences, No. 19 A Yuquan Road, Shijingshan District, Beijing 100049, China.
| | - Min Liu
- Institute of Computing Technology, Chinese Academy of Sciences, No. 6 Kexueyuan South Road, Haidian District, Beijing 100190, China.
| | - Yaqin Zhou
- Source Clear, 20 Ayer Rajah Crescent, Singapore 139964, Singapore.
| | - Zhongcheng Li
- Institute of Computing Technology, Chinese Academy of Sciences, No. 6 Kexueyuan South Road, Haidian District, Beijing 100190, China.
| | - Shuang Chen
- Institute of Computing Technology, Chinese Academy of Sciences, No. 6 Kexueyuan South Road, Haidian District, Beijing 100190, China.
- School of Computer and Control Engineering, University of Chinese Academy of Sciences, No. 19 A Yuquan Road, Shijingshan District, Beijing 100049, China.
| | - Xiangnan He
- School of Computing, National University of Singapore, Computing 1, Computing Drive, Singapore 117417, Singapore.
| |
Collapse
|
3
|
Khan N, McClean S, Zhang S, Nugent C. Optimal Parameter Exploration for Online Change-Point Detection in Activity Monitoring Using Genetic Algorithms. SENSORS 2016; 16:s16111784. [PMID: 27792177 PMCID: PMC5134443 DOI: 10.3390/s16111784] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/06/2016] [Revised: 10/17/2016] [Accepted: 10/18/2016] [Indexed: 11/16/2022]
Abstract
In recent years, smart phones with inbuilt sensors have become popular devices to facilitate activity recognition. The sensors capture a large amount of data, containing meaningful events, in a short period of time. The change points in this data are used to specify transitions to distinct events and can be used in various scenarios such as identifying change in a patient’s vital signs in the medical domain or requesting activity labels for generating real-world labeled activity datasets. Our work focuses on change-point detection to identify a transition from one activity to another. Within this paper, we extend our previous work on multivariate exponentially weighted moving average (MEWMA) algorithm by using a genetic algorithm (GA) to identify the optimal set of parameters for online change-point detection. The proposed technique finds the maximum accuracy and F_measure by optimizing the different parameters of the MEWMA, which subsequently identifies the exact location of the change point from an existing activity to a new one. Optimal parameter selection facilitates an algorithm to detect accurate change points and minimize false alarms. Results have been evaluated based on two real datasets of accelerometer data collected from a set of different activities from two users, with a high degree of accuracy from 99.4% to 99.8% and F_measure of up to 66.7%.
Collapse
Affiliation(s)
- Naveed Khan
- School of Computing and Information Engineering, Ulster University, Coleraine, Co., Londonderry BTT52 1SA, UK.
| | - Sally McClean
- School of Computing and Information Engineering, Ulster University, Coleraine, Co., Londonderry BTT52 1SA, UK.
| | - Shuai Zhang
- School of Computing and Mathematics, Ulster University, Jordanstown, Co., Antrim BT37 0QB, UK.
| | - Chris Nugent
- School of Computing and Mathematics, Ulster University, Jordanstown, Co., Antrim BT37 0QB, UK.
| |
Collapse
|