Fine-Grained Multitask Allocation for Participatory Sensing With a Shared Budget

Wang, Jiangtao; Wang, Yasha; Zhang, Daqing; Wang, Leye; Xiong, Haoyi; Helal, Sumi; He, Yuanduo; Wang, Feng

doi:10.1109/jiot.2016.2608141

Cited by 64 publications

(60 citation statements)

References 21 publications

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“…For example, the authors studied worker recruitment for a single MCS task, and they proposed different recruitment strategies to select a predefined number of workers so as to maximize the task's sensing quality [21], [22], [23], [24], or select a minimum number of workers to ensure a certain level of sensing quality [25], [29]. Another group of studies attempted to optimize the overall utility of multiple concurrent sensing tasks in a multi-task-oriented MCS platform, where tasks share the limited resources [30], [31], [32], [53]. For example, both [30] and [31] proposed multi-task allocation algorithms to maximize overall system utility when the tasks share a limited incentive budget.…”

Section: Related Workmentioning

confidence: 99%

“…Another group of studies attempted to optimize the overall utility of multiple concurrent sensing tasks in a multi-task-oriented MCS platform, where tasks share the limited resources [30], [31], [32], [53]. For example, both [30] and [31] proposed multi-task allocation algorithms to maximize overall system utility when the tasks share a limited incentive budget. The multi-task allocation strategy proposed in [32] aims to optimize the overall utility when multiple tasks share a pool of workers with a sensing bandwidth constraint.…”

Section: Related Workmentioning

confidence: 99%

“…Different from existing MCS worker selection approaches, we develop a novel opportunistic worker selection algorithm with two unique mechanisms. First, instead of selecting workers who can only complete more tasks or cover larger areas [22], [25], [31], [30], HyTasker assigns higher priority to those who can complete more tasks in participatory-worker-sparse areas. Second, while existing worker selection approaches commonly end when the total budget has been used up and output the final set of selected workers [22], [25], [31], [30], HyTasker further records, estimates, and selects the best set of workers by simultaneously considering the predicted task assignments of the participatory workers.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

HyTasker: Hybrid Task Allocation in Mobile Crowd Sensing

Wang

et al. 2020

IEEE Trans. on Mobile Comput.

Self Cite

103

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Task allocation is a major challenge in Mobile Crowd Sensing (MCS). While previous task allocation approaches follow either the opportunistic or participatory mode, this paper proposes to integrate these two complementary modes in a two-phased hybrid framework called HyTasker. In the offline phase, a group of workers (called opportunistic workers) are selected, and they complete MCS tasks during their daily routines (i.e., opportunistic mode). In the online phase, we assign another set of workers (called participatory workers) and require them to move specifically to perform tasks that are not completed by the opportunistic workers (i.e., participatory mode). Instead of considering these two phases separately, HyTasker jointly optimizes them with a total incentive budget constraint. In particular, when selecting opportunistic workers in the offline phase of HyTasker, we propose a novel algorithm that simultaneously considers the predicted task assignment for the participatory workers, in which the density and mobility of participatory workers are taken into account. Experiments on two real-world mobility datasets demonstrate that HyTasker outperforms other methods with more completed tasks under the same budget constraint.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

HyTasker: Hybrid Task Allocation in Mobile Crowd Sensing

Wang

et al. 2020

IEEE Trans. on Mobile Comput.

Self Cite

103

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“…In [51], Messaoud et al proposed a mobile sensing scheme that reduces the sensing time required by participants, and increases the fairness of sensing tasks assignment to ensure participants' commitment to sensing while maintaining same data quality as in non-fair schemes. In an attempt to maximize the overall data quality, in [52], Wang et al proposed a multitask allocation framework (MTPS) which pays participants a compensation from a shared budget for each sensing task, with additional compensation if a participant is assigned more than one task. This greedy framework allows the allocation of multiple tasks to participants.…”

Section: Social Computingmentioning

confidence: 99%

Big Data Meet Cyber-Physical Systems: A Panoramic Survey

et al. 2018

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The world is witnessing an unprecedented growth of cyber-physical systems (CPS), which are foreseen to revolutionize our world via creating new services and applications in a variety of sectors such as environmental monitoring, mobilehealth systems, intelligent transportation systems and so on. The information and communication technology (ICT) sector is experiencing a significant growth in data traffic, driven by the widespread usage of smartphones, tablets and video streaming, along with the significant growth of sensors deployments that are anticipated in the near future. It is expected to outstandingly increase the growth rate of raw sensed data. In this paper, we present the CPS taxonomy via providing a broad overview of data collection, storage, access, processing and analysis. Compared with other survey papers, this is the first panoramic survey on big data for CPS, where our objective is to provide a panoramic summary of different CPS aspects. Furthermore, CPS require cybersecurity to protect them against malicious attacks and unauthorized intrusion, which become a challenge with the enormous amount of data that is continuously being generated in the network. Thus, we also provide an overview of the different security solutions proposed for CPS big data storage, access and analytics. We also discuss big data meeting green challenges in the contexts of CPS.

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“…For this reason, a number of incentive mechanisms have been proposed to increase not only the collected amount of data, but also its quality. Furthermore, a number of frameworks aimed to recruit participants in order to maximize the coverage of the sensing area have been recently proposed [30,61,85,86,104,182,183,208], including detailed reviews of the related literature [58,81,147]. Therefore, in this paper we focus on the QoI Estimation and Enforcement steps of the QoI loop, which has received relatively less attention.…”

Section: A Comprehensive Framework For Qoi In Mobile Crowdsensingmentioning

confidence: 99%

Quality of Information in Mobile Crowdsensing

Restuccia

Ghosh

Bhattacharjee

et al. 2017

ACM Trans. Sen. Netw.

152

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Smartphones have become the most pervasive devices in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work. F. Restuccia et al.Not only are today's smartphones ubiquitous devices, they are also equipped with a plethora of embedded multi-modal sensors; integrate wireless communication technologies such as 4G/WiFi Internet connectivity, and possess complex processing capabilities. For example, the cameras on smartphones can be used as video and image sensors [29], the microphone can be used as an acoustic sensor [39,154,206], and the embedded global positioning system (GPS) receiver can be used to gather accurate location information, while gyroscopes, accelerometers, and proximity sensors can be used to extract contextual information about the users, such as driving or walking states [75,122]. Further, additional sensors can be easily interfaced with smartphones via Bluetooth or wired connections, such as temperature, air quality, and humidity [69,168].These technological features, combined with the advanced sensing capability of humans, has spurred a significant amount of research from both academia and industry, which together have proposed over the last 10 years a myriad of applications based on the emerging mobile crowdsensing paradigm 1 . Mobile crowdsensing empowers ordinary citizens (or users 2 ) with the capability to actively monitor various phenomena pertaining to themselves (e.g., health, social connections) or their community (e.g., environment). This rich information or inference about themselves or the community may also be sent back to the participating or other concerned users to improve their life experiences, thus influencing their choices. Real-life applications, which can take advantage of both low-level sensor data and high-level user activities, range from real-time traffic monitoring applications, to environmental pollution monitoring, crime monitoring, and social networking, just to name a few. For a survey on mobile crowdsensin...

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Fine-Grained Multitask Allocation for Participatory Sensing With a Shared Budget

Cited by 64 publications

References 21 publications

HyTasker: Hybrid Task Allocation in Mobile Crowd Sensing

HyTasker: Hybrid Task Allocation in Mobile Crowd Sensing

Big Data Meet Cyber-Physical Systems: A Panoramic Survey

Quality of Information in Mobile Crowdsensing

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