An Efficient Allocation Mechanism for Crowdsourcing Tasks with Minimum Execution Time

Wu, Xiaocan; Huang, Danlei; Sun, Yu-E; Bu, Xiaofei; Xin, Yu; Huang, He

doi:10.1007/978-3-319-63315-2_14

Cited by 6 publications

(5 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the MET algorithm, tasks are executed on a machine, which leads to the shortest execution times. Although this algorithm pays great attention to the load‐balancing issue, it does not consider the availability of resources when assigning tasks 45–47 . In the MCT algorithm, the task is assigned to a machine that provides it with the shortest completion time among all the available machines 48,49 …”

Section: Organization Of the Task Schedulingmentioning

confidence: 99%

Task scheduling approaches in fog computing: A systematic review

Alizadeh

Khajehvand

Rahmani

et al. 2020

Int J Communication

View full text Add to dashboard Cite

SummaryThe Internet of Things (IoT) interconnects billions of physical objects to collect and exchange information and makes available various applications. Despite all the advantages of the IoT, some of its applications are not feasible because of the existing restrictions in the IoT sensors. To overcome these restrictions, cloud computing has been developed in recent years. Although cloud computing has removed some of these restrictions, the cloud itself has faced some other challenges. One of these challenges is the distance between the cloud and the end‐devices, which is not ideal for delay‐sensitive applications and also leads to high communication costs and security problems. To handle these challenges, the fog computing has been introduced in literature, which places the resources and services at the edge of the network, close to the end‐devices. On the other hand, fog nodes suffer from heterogeneity, uncertainty, and dispersion of resources, and also processing, storage, and memory limitations. Thus, there is need for a proper task scheduling approach to use these resources optimally. In this study, the task scheduling algorithms proposed by different researchers for the cloud‐fog environment, their advantages and disadvantages, and also various tools and issues regarding the scheduling methods and their restrictions were discussed. The results indicated that about 58% of the scheduling algorithms use static scheduling and that the other 42% use dynamic scheduling, and the delay metric is the most noteworthy parameter considered in most studies with a share of nearly 17%. Finally, we identified the open issues related to the task scheduling based on cloud‐fog computing and provided some directions for future works.

show abstract

Section: Organization Of the Task Schedulingmentioning

confidence: 99%

Task scheduling approaches in fog computing: A systematic review

Alizadeh

Khajehvand

Rahmani

et al. 2020

Int J Communication

View full text Add to dashboard Cite

show abstract

“…Task management and assignment are the main components that affect the efficiency of MCS significantly [54,55]. In this part, we summarize the characteristics of tasks, the process of task management and assignment, as well as their enabling technologies.…”

Section: A Task Management and Assignmentmentioning

confidence: 99%

Mobile Crowdsourcing in Smart Cities: Technologies, Applications, and Future Challenges

Kong

Liu

Jedari

et al. 2019

IEEE Internet Things J.

105

View full text Add to dashboard Cite

Local administrations and governments aim at leveraging wireless communications and Internet of Things (IoT) technologies to manage the city infrastructures and enhance the public services in an efficient and sustainable manner. Furthermore, they strive to adopt smart and cost-effective mobile applications to deal with major urbanization problems, such as natural disasters, pollution, and traffic congestion. Mobile crowdsourcing (MCS) is known as a key emerging paradigm for enabling smart cities, which integrates the wisdom of dynamic crowds with mobile devices to provide decentralized ubiquitous services and applications. Using MCS solutions, residents (i.e., mobile carriers) play the role of active workers who generate a wealth of crowdsourced data to significantly promote the development of smart cities. In this article, we present an overview of state-of-the-art technologies and applications of MCS in smart cities. First, we provide an overview of MCS in smart cities and highlight its major characteristics. Second, we introduce the general architecture of MCS and its enabling technologies. Third, we study novel applications of MCS in smart cities. Finally, we discuss several open problems and future research challenges in the context of MCS in smart cities.

show abstract

“…A number of research works [17]- [19] focused on task allocations with timeliness restrictions. An efficient and reliable mechanism [17] was proposed to meet the individual rationality, which offered the guideline for a platform to recruit suitable mobile contributors.…”

Section: Related Workmentioning

confidence: 99%

“…The distributed task selection algorithm [18] provided the task selection plan for contributors, however the algorithm was based on the fixed mobile contributor set ignoring the dynamic changes of contributors. Wu et al [19] proposed an approximation algorithm which minimized the total execution time of all tasks, and defined approximate optimal ratios as the ratio of algorithm execution time to the optimal value. The proposed algorithm had good approximate optimal ratios under different parameter sets.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

How Mobile Contributors Will Interact With Each Other in Mobile Crowdsourcing With Word of Mouth Mode

Zeng

Wang

2019

IEEE Access

View full text Add to dashboard Cite

Mobile crowdsourcing is a promising paradigm for collecting sensing data by leveraging contributions of numerous mobile smart phones. It works efficiently with Word of Mouth Mode (WoM), especially for sensing tasks with time and location constraints, since the sensing task can be spread quickly among mobile contributors in the WoM mode. In this paper, we first investigate the behaviors of contributors, categorize all contributors into four types according to their different behaviors, and propose an inviting algorithm for contributors to recruit cooperators through social closeness. Then, we design a reward mechanism for crowdsourcing platform to evaluate the budget and pay the reward to contributors, meanwhile stimulate contributors to make the maximum contribution. Furthermore, considering two different scenarios, we model the interactions among contributors as two Stackelberg games, and backward induction approach is used to analyze each game. We propose an algorithm to compute the best response of every contributor, and we theoretically prove that this proposed algorithm may converge a unique Stackelberg equilibrium. The proposed approach can be applied to task formulation and task budget evaluations for crowdsourcing platforms. INDEX TERMS Mobile crowdsourcing, word of mouth, game theory, Stackelberg game.

show abstract

An Efficient Allocation Mechanism for Crowdsourcing Tasks with Minimum Execution Time

Cited by 6 publications

References 12 publications

Task scheduling approaches in fog computing: A systematic review

Task scheduling approaches in fog computing: A systematic review

Mobile Crowdsourcing in Smart Cities: Technologies, Applications, and Future Challenges

How Mobile Contributors Will Interact With Each Other in Mobile Crowdsourcing With Word of Mouth Mode

Contact Info

Product

Resources

About