scholarly journals Design of a Local Information Incentive Mechanism for Mobile Crowdsensing

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2532 ◽  
Author(s):  
Jose Mauricio Nava Auza ◽  
Jose Roberto Boisson de Marca ◽  
Glaucio Lima Siqueira
Keyword(s):  
Large Scale ◽  
Incentive Mechanism ◽  
Network Connectivity ◽  
Physical World ◽  
Local Information ◽  
Mobile Crowd Sensing ◽  
Great Opportunity ◽  
Crowd Sensing ◽  
The Incentive Mechanism ◽  
Mobile Crowd

The world of telecommunications has seen the growing popularity of mobile devices and its massive technological advancements and innovations (e.g., smartphones, smart watches, among others). One critical particularity is that these devices have a series of built-in sensors and continuous network connectivity. Therefore, they present a great opportunity to perform large-scale sensing of different activities in the physical world. This new sensor application, better known as Mobile crowd-sensing (MCS), has lately become a focus of research. One of the challenges when developing a MCS-based network is to attract and convince users to participate. In this paper, we present a framework for MCS that includes a model to represent the behavior of the users and a novel incentive mechanism. The model aims to characterize the behavior of users considering the availability of their resources and the non-homogeneity of their responses. The incentive mechanism proposed assigns different values of incentives and in it the users only consider their local information to decide their participation in the framework. The performance of the proposed framework is evaluated through simulations. The results allow us to prove the uncertainty of participation of the users and that they react in different ways to the incentives offered. They also prove that the incentive mechanism estimates satisfactorily the type of users and the incentive that will be offered to each user. In addition, we show the advantages of an incentive mechanism that considers different values of payments.

Dynamic Participant Selection for Large-Scale Mobile Crowd Sensing

2019 ◽  
Vol 18 (12) ◽  
pp. 2842-2855 ◽  
Author(s):  
Hanshang Li ◽  
Ting Li ◽  
Weichao Wang ◽  
Yu Wang
Keyword(s):  
Large Scale ◽  
Mobile Crowd Sensing ◽  
Crowd Sensing ◽  
Participant Selection ◽  
Selection For ◽  
Mobile Crowd

scholarly journals Staged Incentive and Punishment Mechanism for Mobile Crowd Sensing

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2391 ◽  
Author(s):  
Dan Tao ◽  
Shan Zhong ◽  
Hong Luo
Keyword(s):  
Incentive Mechanism ◽  
Extensive Study ◽  
Mobile Crowd Sensing ◽  
Space Correlation ◽  
Data Utility ◽  
Sensing Data ◽  
Crowd Sensing ◽  
Time Space ◽  
Punishment Mechanism ◽  
Mobile Crowd

Having an incentive mechanism is crucial for the recruitment of mobile users to participate in a sensing task and to ensure that participants provide high-quality sensing data. In this paper, we investigate a staged incentive and punishment mechanism for mobile crowd sensing. We first divide the incentive process into two stages: the recruiting stage and the sensing stage. In the recruiting stage, we introduce the payment incentive coefficient and design a Stackelberg-based game method. The participants can be recruited via game interaction. In the sensing stage, we propose a sensing data utility algorithm in the interaction. After the sensing task, the winners can be filtered out using data utility, which is affected by time–space correlation. In particular, the participants’ reputation accumulation can be carried out based on data utility, and a punishment mechanism is presented to reduce the waste of payment costs caused by malicious participants. Finally, we conduct an extensive study of our solution based on realistic data. Extensive experiments show that compared to the existing positive auction incentive mechanism (PAIM) and reverse auction incentive mechanism (RAIM), our proposed staged incentive mechanism (SIM) can effectively extend the incentive behavior from the recruiting stage to the sensing stage. It not only achieves being a real-time incentive in both the recruiting and sensing stages but also improves the utility of sensing data.

scholarly journals A Blockchain-Based Location Privacy Protection Incentive Mechanism in Crowd Sensing Networks

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3894 ◽  
Author(s):  
Bing Jia ◽  
Tao Zhou ◽  
Wuyungerile Li ◽  
Zhenchang Liu ◽  
Jiantao Zhang
Keyword(s):  
Cloud Computing ◽  
Privacy Protection ◽  
Large Scale ◽  
Location Privacy ◽  
Incentive Mechanism ◽  
User Participation ◽  
Crowd Sensing ◽  
The Incentive Mechanism ◽  
Crowd Sensing Networks ◽  
Location Privacy Protection

Crowd sensing is a perception mode that recruits mobile device users to complete tasks such as data collection and cloud computing. For the cloud computing platform, crowd sensing can not only enable users to collaborate to complete large-scale awareness tasks but also provide users for types, social attributes, and other information for the cloud platform. In order to improve the effectiveness of crowd sensing, many incentive mechanisms have been proposed. Common incentives are monetary reward, entertainment & gamification, social relation, and virtual credit. However, there are rare incentives based on privacy protection basically. In this paper, we proposed a mixed incentive mechanism which combined privacy protection and virtual credit called a blockchain-based location privacy protection incentive mechanism in crowd sensing networks. Its network structure can be divided into three parts which are intelligence crowd sensing networks, confusion mechanism, and blockchain. We conducted the experiments in the campus environment and the results shows that the incentive mechanism proposed in this paper has the efficacious effect in stimulating user participation.

scholarly journals Privacy protection in mobile crowd sensing: a survey

2019 ◽  
Vol 23 (1) ◽  
pp. 421-452 ◽  
Author(s):  
Yongfeng Wang ◽  
Zheng Yan ◽  
Wei Feng ◽  
Shushu Liu
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Large Scale ◽  
System Structure ◽  
Smart Devices ◽  
Future Research ◽  
Sensitive Information ◽  
Mobile Crowd Sensing ◽  
Crowd Sensing ◽  
Mobile Crowd

AbstractThe unprecedented proliferation of mobile smart devices has propelled a promising computing paradigm, Mobile Crowd Sensing (MCS), where people share surrounding insight or personal data with others. As a fast, easy, and cost-effective way to address large-scale societal problems, MCS is widely applied into many fields, e.g., environment monitoring, map construction, public safety, etc. Despite the popularity, the risk of sensitive information disclosure in MCS poses a serious threat to the participants and limits its further development in privacy-sensitive fields. Thus, the research on privacy protection in MCS becomes important and urgent. This paper targets the privacy issues of MCS and conducts a comprehensive literature research on it by providing a thorough survey. We first introduce a typical system structure of MCS, summarize its characteristics, propose essential requirements on privacy on the basis of a threat model. Then, we survey existing solutions on privacy protection and evaluate their performances by employing the proposed requirements. In essence, we classify the privacy protection schemes into four categories with regard to identity privacy, data privacy, attribute privacy, and task privacy. Besides, we review the achievements on privacy-preserving incentives in MCS from four viewpoints of incentive measures: credit incentive, auction incentive, currency incentive, and reputation incentive. Finally, we point out some open issues and propose future research directions based on the findings from our survey.

scholarly journals Crowd-Based Cognitive Perception of the Physical World: Towards the Internet of Senses

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2437 ◽  
Author(s):  
Gianni Pasolini ◽  
Anna Guerra ◽  
Francesco Guidi ◽  
Nicolò Decarli ◽  
Davide Dardari
Keyword(s):  
Antenna Arrays ◽  
Physical World ◽  
Machine Learning Techniques ◽  
The Internet ◽  
User Cooperation ◽  
Learning Mechanisms ◽  
Mobile Crowd Sensing ◽  
Crowd Sensing ◽  
Action Capabilities ◽  
Mobile Crowd

This paper introduces a possible architecture and discusses the research directions for the realization of the Cognitive Perceptual Internet (CPI), which is enabled by the convergence of wired and wireless communications, traditional sensor networks, mobile crowd-sensing, and machine learning techniques. The CPI concept stems from the fact that mobile devices, such as smartphones and wearables, are becoming an outstanding mean for zero-effort world-sensing and digitalization thanks to their pervasive diffusion and the increasing number of embedded sensors. Data collected by such devices provide unprecedented insights into the physical world that can be inferred through cognitive processes, thus originating a digital sixth sense. In this paper, we describe how the Internet can behave like a sensing brain, thus evolving into the Internet of Senses, with network-based cognitive perception and action capabilities built upon mobile crowd-sensing mechanisms. The new concept of hyper-map is envisioned as an efficient geo-referenced repository of knowledge about the physical world. Such knowledge is acquired and augmented through heterogeneous sensors, multi-user cooperation and distributed learning mechanisms. Furthermore, we indicate the possibility to accommodate proactive sensors, in addition to common reactive sensors such as cameras, antennas, thermometers and inertial measurement units, by exploiting massive antenna arrays at millimeter-waves to enhance mobile terminals perception capabilities as well as the range of new applications. Finally, we distillate some insights about the challenges arising in the realization of the CPI, corroborated by preliminary results, and we depict a futuristic scenario where the proposed Internet of Senses becomes true.

scholarly journals The Truthful Evolution and Incentive for Large-Scale Mobile Crowd Sensing Networks

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 51187-51199 ◽  
Author(s):  
Yingjie Wang ◽  
Yingshu Li ◽  
Zhongyang Chi ◽  
Xiangrong Tong
Keyword(s):  
Large Scale ◽  
Mobile Crowd Sensing ◽  
Crowd Sensing ◽  
Crowd Sensing Networks ◽  
Mobile Crowd
Sign in / Sign up

Export Citation Format

Share Document