Crowdsensing Under (Soft) Control

Rula, John P.; Bustamante, Fabián E.

doi:10.1109/infocom.2015.7218610

Cited by 14 publications

(6 citation statements)

References 34 publications

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“…Mechanisms may be either platform-centric or user-centric for which theoretical properties have been studied in [46]. A complementary pathway to attract sufficiently meaningful participation is to leverage the built-in incentives of location-based gaming and social applications by pairing crowd-sensing with such applications [39]. Obviously, privacy guarantees should be offered to participants, which may in particular be handled at the time of data collection and aggregation [9,43,17,29].…”

Section: Background and Motivationmentioning

confidence: 99%

Dos and Don'ts in Mobile Phone Sensing Middleware

Issarny¹,

Mallet²,

Nguyen³

et al. 2016

Proceedings of the 17th International Middleware Conference

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Mobile phone sensing contributes to changing the way we approach science: massive amount of data is being contributed across places and time, and paves the way for advanced analyses of numerous phenomena at an unprecedented scale. Still, despite the extensive research work on enabling resource-efficient mobile phone sensing with a verylarge crowd, key challenges remain. One challenge is facing the introduction of a new heterogeneity dimension in the traditional middleware research landscape. The middleware must deal with the heterogeneity of the contributing crowd in addition to the system's technical heterogeneities. In order to tackle these two heterogeneity dimensions together, we have been conducting a large-scale empirical study in cooperation with the city of Paris. Our experiment revolves around the public release of a mobile app for urban pollution monitoring that builds upon a dedicated mobile crowdsensing middleware. In this paper, we report on the empirical analysis of the resulting mobile phone sensing efficiency from both technical and social perspectives, in face of a large and highly heterogeneous population of participants. We concentrate on the data originating from the 20 most popular phone models of our user base, which represent contributions from over 2,000 users with 23 million observations collected over 10 months. Following our analysis, we introduce a few recommendations to overcome-technical and crowd-heterogeneities in the implementation of mobile phone sensing applications and supporting middleware. CCS Concepts •Information systems → Mobile information processing systems; Sensor networks; •Human-centered computing → Ubiquitous and mobile computing systems and tools; Empirical studies in ubiquitous and mo

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Section: Background and Motivationmentioning

confidence: 99%

Dos and Don'ts in Mobile Phone Sensing Middleware

Issarny¹,

Mallet²,

Nguyen³

et al. 2016

Proceedings of the 17th International Middleware Conference

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show abstract

“…Zhou et al [31] propose an urban traffic monitoring system by using bus riders' mobile phones which can turn buses into probes for monitoring buses' travel situations and inferring instant traffic map of the city. Rula et al [32] propose an approach which leverages the built-in incentives of location-based gaming and social applications to exert limited control over the actions of participators. Siahaan et al [33] study the influence of different experimental settings and rating scales on the reliability and repeatability of aesthetic appeal assessments in the process of collecting picture aesthetic appeal ground truth data by a systematic way.…”

Section: Photo Crowdsourcingmentioning

confidence: 99%

Storage Management Strategy in Mobile Phones for Photo Crowdsensing

Wang

Liang

et al. 2020

Sensors

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In mobile crowdsensing, some users jointly finish a sensing task through the sensors equipped in their intelligent terminals. In particular, the photo crowdsensing based on Mobile Edge Computing (MEC) collects pictures for some specific targets or events and uploads them to nearby edge servers, which leads to richer data content and more efficient data storage compared with the common mobile crowdsensing; hence, it has attracted an important amount of attention recently. However, the mobile users prefer uploading the photos through Wifi APs (PoIs) rather than cellular networks. Therefore, photos stored in mobile phones are exchanged among users, in order to quickly upload them to the PoIs, which are actually the edge services. In this paper, we propose a utility-based Storage Management strategy in mobile phones for Photo Crowdsensing (SMPC), which makes a sending/deleting decision on a user's device for either maximizing photo delivery ratio (SMPC-R) or minimizing average delay (SMPC-D). The decision is made according to the photo's utility, which is calculated by measuring the impact of reproducing or deleting a photo on the above performance goals. We have done simulations based on the random-waypoint model and three real traces: roma/taxi, epfl, and geolife. The results show that, compared with other storage management strategies, SMPC-R gets the highest delivery ratio and SMPC-D achieves the lowest average delay.

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“…An overview of the role of motivation in crowd sensing is given in [10], [11], and [12], where different motivational factors have been discussed, such as self-efficacy, sense of community, use of contextual clues, and so on. There are several crowdsourcing platforms that provide workers with non-monetary incentives such as entertainments, social and educational opportunities [13] and [14].…”

Section: Related Workmentioning

confidence: 99%

Interval Tree-Based Task Scheduling Method for Mobile Crowd Sensing Systems

Ahmed

Alsharkawy

2018

JCOMSS

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Nowadays there is an increasing demand to provide a real-time environmental information. So, the growing number of mobile devices carried by users establish a new and fastgrowing sensing paradigm to satisfy this need, which is called Mobile Crowd Sensing (MCS). The MCS uses different sensing abilities to acquire local knowledge through enhanced mobile devices. In MCS, it is very important to collect high-quality sensory data that satisfies the needs of all assigned tasks and the task organizers with a minimum cost for the participants. One of the most important factors which affect the MCS cost is how to schedule different sensing tasks which must be assigned to a smartphone with the objective of minimizing sensing energy consumption while ensuring high-quality sensory data. In this paper, the problem of scheduling the tasks which have mutual sensor is formulated and a scheduling method to minimize the energy consumption by reducing the sensor utilization is proposed. The proposed method will incentive the users to participate in multiple tasks at the same time, which minimizes the total cost of the performed tasks and increases his rewards. The experimental results by using synthetic and real data show that the proposed scheduling method can minimize the energy consumption and preserve the task requirements compared to existing algorithms.

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Crowdsensing Under (Soft) Control

Cited by 14 publications

References 34 publications

Dos and Don'ts in Mobile Phone Sensing Middleware

Dos and Don'ts in Mobile Phone Sensing Middleware

Storage Management Strategy in Mobile Phones for Photo Crowdsensing

Interval Tree-Based Task Scheduling Method for Mobile Crowd Sensing Systems

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