The idea of social participatory sensing provides a substrate to benefit from friendship relations in recruiting a critical mass of participants willing to attend in a sensing campaign. However, the selection of suitable participants who are trustable and provide high quality contributions is challenging. In this paper, we propose a recruitment framework for social participatory sensing. Our framework leverages multi-hop friendship relations to identify and select suitable and trustworthy participants among friends or friends of friends, and finds the most trustable paths to them. The framework also includes a suggestion component which provides a cluster of suggested friends along with the path to them, which can be further used for recruitment or friendship establishment. Simulation results demonstrate the efficacy of our proposed recruitment framework in terms of selecting a large number of well-suited participants and providing contributions with high overall trust, in comparison with one-hop recruitment architecture.
For most participatory sensing applications, availability of participants in terms of geographic and temporal coverage of the sensing area is important. For example, in a noise map sensing campaign, it is desirable to recruit participants who regularly pass through the sensing region and cover as much of the area as possible. @cite_4 proposed a recruitment framework for participatory sensing systems which aims at identifying suitable participants based on parameters such as geographical and temporal availability of participant. Participants have previously collected location traces for a period of time that represent their typical behaviour. The location traces are used by the qualifier component to select participants who have the task minimum location requirements. Once participants are selected, the assessment component identifies which subset of participants maximizes coverage over task specific area. These participants are then recruited and begin producing sensor data. During the campaign, the progress review component periodically monitors the coverage and availability of participants to see whether they remain consistent with task requirements.
@cite_4
2005406065 2131848448 1553085258 2058210123
Participatory sensing has emerged as a novel paradigm for data collection and collective knowledge formation about a state or condition of interest, sometimes linked to a geographic area. In this paper, we address the problem of incentive mechanism design for data contributors for participatory sensing applications. The service provider receives service queries in an area from service requesters and initiates an auction for user participation. Upon request, each user reports its perceived cost per unit of amount of participation, which essentially maps to a requested amount of compensation for participation. The participation cost quantifies the dissatisfaction caused to user due to participation. This cost is considered to be private information for each device, as it strongly depends on various factors inherent to it, such as the energy cost for sensing, data processing and transmission to the closest point of wireless access, the residual battery level, the number of concurrent jobs at the device processor, the required bandwidth to transmit data and the related charges of the mobile network operator, or even the user discomfort due to manual effort to submit data. Hence, participants have strong motive to mis-report their cost, i.e. declare a higher cost that the actual one, so as to obtain higher payment. We seek a mechanism for user participation level determination and payment allocation which is most viable for the provider, that is, it minimizes the total cost of compensating participants, while delivering a certain quality of experience to service requesters. We cast the problem in the context of optimal reverse auction design, and we show how the different quality of submitted information by participants can be tracked by the service provider and used in the participation level and payment selection procedures. We derive a mechanism that optimally solves the problem above, and at the same time it is individually rational (i.e., it motivates users to participate) and incentive-compatible (i.e. it motivates truthful cost reporting by participants). Finally, a representative participatory sensing case study involving parameter estimation is presented, which exemplifies the incentive mechanism above. Because participatory sensing – targeted campaigns where people harness mobile phones as tools for data collection – involves large and distributed groups of people, participatory sensing systems benefit from tools to measure and evaluate the contributions of individual participants. This paper develops a set of metrics to help participatory sensing organizers determine individual participants’ fit with any given sensing project, and describes experiments evaluating the resulting reputation system. I. INTRODUCTION The rapid adoption of mobile phones over the last decade and an increasing ability to capture, classify, and transmit a wide variety of data (image, audio, and location) have enabled a new sensing paradigm – participatory urban sensing – where humans carrying mobile phones act as, and contribute to, sensing systems [1], [2], [3]. In this paper, we discuss an important factor in participatory sensing systems: measurement and evaluation of participation and performance during sensing projects. In participatory sensing, mobile phone-based data gathering is coordinated across a potentially large number of participants over wide spans of space and time. We draw from three pilot projects to illustrate participatory sensing and describe the unique challenges to measurement and evaluation provided by “campaigns”: distributed and targeted efforts to collect data. Project Budburst [4], Personal Environmental Impact Report (PEIR) [5], and Walkability all situate “humans in the loop”, but have critical differences in their goals and challenges (Table I). Mobile phones have evolved from devices that are just used for voice and text communication to platforms that are able to capture and transmit a range of data types (image, audio, and location). The adoption of these increasingly capable devices by society has enabled a potentially pervasive sensing paradigm - participatory sensing. A coordinated participatory sensing system engages individuals carrying mobile phones to explore phenomena of interest using in situ data collection. For participatory sensing to succeed, several technical challenges need to be solved. In this paper, we discuss one particular issue: developing a recruitment framework to enable organizers to identify well-suited participants for data collections based on geographic and temporal availability as well as participation habits. This recruitment system is evaluated through a series of pilot data collections where volunteers explored sustainable processes on a university campus. The idea of social participatory sensing provides a substrate to benefit from friendship relations in recruiting a critical mass of participants willing to attend in a sensing campaign. However, the selection of suitable participants who are trustable and provide high quality contributions is challenging. In this paper, we propose a recruitment framework for social participatory sensing. Our framework leverages multi-hop friendship relations to identify and select suitable and trustworthy participants among friends or friends of friends, and finds the most trustable paths to them. The framework also includes a suggestion component which provides a cluster of suggested friends along with the path to them, which can be further used for recruitment or friendship establishment. Simulation results demonstrate the efficacy of our proposed recruitment framework in terms of selecting a large number of well-suited participants and providing contributions with high overall trust, in comparison with one-hop recruitment architecture.