Competition-Based Participant Recruitment for Delay-Sensitive Crowdsourcing Applications in D2D Networks

Mobile crowdsourcing takes advantage of mobile devices such as smart phones and tablets to process data for a lot of applications (e.g., geotagging for mobile touring guiding monitoring and spectrum sensing). In this paper, we propose a mobile crowdsourcing paradigm to make a task requester exploit encountered mobile workers for high-quality results. Since a task may be too complex for a single worker, it is necessary for a task requester to divide a complex task into several parts so that a mobile worker can nish a part of the task easily. We describe the task crowdsourcing process and propose the worker arrival model and task model. Furthermore, the probability that all parts of the complicated task are executed by mobile workers is introduced to evaluate the result of task crowdsourcing. Based on these models, considering computing capacity and rewards for mobile workers, we formulate a task partition problem to maximize the introduced probability which is used to evaluate the result of task crowdsourcing. en, using a Markov chain, a task partition policy is designed for the task requester to realize high-quality mobile crowdsourcing. With this task partition policy, the task requester is able to divide the complicated task into precise number of parts based on mobile workers' arrival, and the probability that the total parts are executed by mobile workers is maximized. Also, the invalid number of task assignment attempts is analyzed accurately, which is helpful to evaluate the resource consumption of requesters due to probing potential workers. Simulations show that our task partition policy improves the results of task crowdsourcing.

Section: Related Workmentioning

confidence: 99%

Optimal Task Partition with Delay Requirement in Mobile Crowdsourcing

Zhai

Wang

2019

“…Reddy et al [32] discuss the issue that developing a recruitment framework to enable organizers to identify well-suited participants for data collections based on geographic and temporal availability and their habits. Han et al [38] studied a participant recruitment problem where the initial set of recruited nodes needs to make an optimal decision on what other nodes to recruit to perform the crowdsourcing task. They also [39] study how to efficiently distribute a crowdsourcing task and recruit participants based on the future 5G networks with the feature of D2D communications.…”

Section: Related Workmentioning

confidence: 99%

SMF-GA: Optimized Multitask Allocation Algorithm in Urban Crowdsourced Transportation

Wang

2019

Urban crowdsourced transportation, which can solve traffic problem within city, is a new scenario where citizens share vehicles to take passengers and packages while driving. Differing from the traditional location based crowdsourcing system (e.g., crowdsensing system), the task has to be completed with visiting two different locations (i.e., start and end points), so task allocation algorithms in crowdsensing cannot be leveraged in urban crowdsourced transportation directly. To solve this problem, we first prove that maximizing the crowdsourcing system’s profit (i.e., maximizing the total saved distance) is an NP-hard problem. We propose a heuristic greedy algorithm called Saving Most First (SMF) which is simple and effective in assigning tasks. Then, an optimized SMF based genetic algorithm (SMF-GA) is devised to jump out of the local optimal result. Finally, we demonstrate the performance of SMF and SMF-GA with extensive evaluations, based on a large scale real vehicle traces. The evaluation with large scale real dataset indicates that both SMF and SMF-GA algorithms outperform other benchmark algorithms in terms of saved distance, participant profits, etc.

“…With the help of ubiquitous mobile users who can share the information between each other, the large-scale crowdsourcing tasks can be facilitated [2]. The D2D communications can realize the high-speed data transmission among mobile users with lower power and shorter delay, which largely eases collaborations among participants [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…The D2D communications can realize the high-speed data transmission among mobile users with lower power and shorter delay, which largely eases collaborations among participants [3][4][5][6]. Therefore, the D2D cellular network is more preferable for mobile crowdsourcing than conventional cellular network [2].…”

Section: Introductionmentioning

confidence: 99%

Channel Access and Power Control for Mobile Crowdsourcing in Device‐to‐Device Underlaid Cellular Networks

Zhou

et al. 2018

With the access of a myriad of smart handheld devices in cellular networks, mobile crowdsourcing becomes increasingly popular, which can leverage omnipresent mobile devices to promote the complicated crowdsourcing tasks. Device-to-device (D2D) communication is highly desired in mobile crowdsourcing when cellular communications are costly. The D2D cellular network is more preferable for mobile crowdsourcing than conventional cellular network. Therefore, this paper addresses the channel access and power control problem in the D2D underlaid cellular networks. We propose a novel semidistributed network-assisted power and a channel access control scheme for D2D user equipment (DUE) pieces. It can control the interference from DUE pieces to the cellular user accurately and has low information feedback overhead. For the proposed scheme, the stochastic geometry tool is employed and analytic expressions are derived for the coverage probabilities of both the cellular link and D2D links. We analyze the impact of key system parameters on the proposed scheme. The Pareto optimal access threshold maximizing the total area spectral efficiency is obtained. Unlike the existing works, the performances of the cellular link and D2D links are both considered. Simulation results show that the proposed method can improve the total area spectral efficiency significantly compared to existing schemes.