Location-Aware Crowdsensing: Dynamic Task Assignment and Truth Inference

Wang, Xiong; Jia, Riheng; Tian, Xiaohua; Gan, Xiaoying; Fu, Luoyi; Wang, Xinbing

doi:10.1109/tmc.2018.2878821

Cited by 41 publications

(11 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results in Fig.10 and Fig.11 illustrate the comparison between non-optimal profits and the optimal Stackelberg profit of the platform. In the simulation, the platform announces a fixed price ranging from 0.3 to 0.5 and each smartphone responds with the service rate according to (19)- (20). We can see that the optimal Stackelberg strategy always outperforms other strategies.…”

Section: ) Stage I: Profit Maximization Of the Platformmentioning

confidence: 96%

“…In [19], the authors propose a dynamic task bundling mechanism that minimizes movement cost and the variance of participation, while maximizing the number of potential smartphones. The authors in [20]- [22] study the profit-maximizing dynamic mobile crowdsensing system, where the state of smartphones changes over time and sensing tasks arrive stochastically.…”

Section: Related Workmentioning

confidence: 99%

“…The platform calculates the price p j (t) according to (38)- (39) and offers it to each smartphone. Upon receiving the price, each smartphone responds with the service rate to maximize its own profit according to (19)- (20). Finally, the platform updates queue backlogs according to (2).…”

Section: ) Stage I: Admission Control and Price Determinationmentioning

confidence: 99%

See 2 more Smart Citations

Stackelberg Game Based Dynamic Admission and Scheduling in Mobile Crowdsensing

Wang

Zhou

Zhao³

et al. 2019

IEEE Access

View full text Add to dashboard Cite

Mobile crowdsensing is a crowdsourcing-based paradigm, where the platform executes the sensing requests with the help of many common peoples' handheld devices (typically smartphones). In this paper, we mainly address the dynamic sensing request admission and smartphone scheduling problem to maximize the long-term profit, taking into account the competitive interaction procedure between the platform and smartphones, the queue backlog, and the location of sensing requests and smartphones. First, formulate this problem as a discrete time model and the interaction procedure between the platform and smartphones as a Stackelberg game. Then, we introduce the Lyapunov optimization technique and design a Stackelberg game based dynamic Admission and Scheduling algorithm(SAS). In SAS, all control decisions are made only based on the currently available information and none of the stakeholders, including the platform and smartphones, can improve his utility by unilaterally changing its current strategy. Next, we design an online Cooperative dynamic Admission and Scheduling algorithm(CAS) for the situation where the platform and smartphones work in a cooperative way. Theoretical analysis shows that under any control parameter V > 0, both SAS and CAS algorithm can achieve O(1/V)-optimal average profit while the sensing request backlog is bounded by O(V). The extensive numerical results based on both synthetic and real trace demonstrate the Stackelberg equilibrium of the SAS. The CAS always outperforms SAS, and in some certain situations, the profit of SAS is very close to that of the CAS. INDEX TERMS Dynamic admission and scheduling, Lyapunov optimization, mobile crowdsensing, Stackelberg game.

show abstract

Section: ) Stage I: Profit Maximization Of the Platformmentioning

confidence: 96%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Stackelberg Game Based Dynamic Admission and Scheduling in Mobile Crowdsensing

Wang

Zhou

Zhao³

et al. 2019

IEEE Access

View full text Add to dashboard Cite

show abstract

“…In this paper, we consider task allocation process as a dynamic combinational optimization problem, while most methods regarded task allocation as a static allocation problem. In the dynamic special scenario, through a large number of literature review, such as [ 11 , 17 , 29 ], we found that most methods are improved on the basis of random. Therefore, we believe that the random method is widely representative in this scenario, and used the random method as the comparison method for experimental comparison.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…A common challenge in crowdsening is to find a suitable allocation from tasks to users, in order to achieve an optimal task completion. To this end, most of the existing researches [ 11 , 12 , 13 , 14 , 15 , 16 , 17 ] regard task allocation as a static matching problem between users and tasks. In most cases, they first measure the contribution of a user to all the tasks, then the ranking of contributions is regarded as important references to select suitable users.…”

Section: Introductionmentioning

confidence: 99%

A Personalized Task Allocation Strategy in Mobile Crowdsensing for Minimizing Total Cost

Gao

Zhao

2022

Sensors

View full text Add to dashboard Cite

Mobile crowdsensing utilizes the devices of a group of users to cooperatively perform some sensing tasks, where finding the perfect allocation from tasks to users is commonly crucial to guarantee task completion efficiency. However, existing works usually assume a static task allocation by sorting the cost of users to complete the tasks, where the cost is measured by the expense of time or distance. In this paper, we argue that the task allocation process is actually a dynamic combinational optimization problem because the previous allocated task will influence the initial state of the user to finish the next task, and the user’s preference will also influence the actual cost. To this end, we propose a personalized task allocation strategy for minimizing total cost, where the cost for a user to finish a task is measured by both the moving distance and the user’s preference for the task, then instead of statically allocating the tasks, the allocation problem is formulated as a heterogeneous, asymmetric, multiple traveling salesman problem (TSP). Furthermore, we transform the multiple-TSP to the single-TSP by proving the equivalency, and two solutions are presented to solve the single-TSP. One is a greedy algorithm, which is proved to have a bound to the optimal solution. The other is a genetic algorithm, which spends more calculation time while achieving a lower total cost. Finally, we have conducted a number of simulations based on three widely-used real-world traces: roma/taxi, epfl, and geolife. The simulation results could match the results of theoretical analysis.

show abstract