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

Zeng, Feng; Wang, Runhua; Wu, Jinsong

doi:10.1109/access.2019.2893184

Cited by 12 publications

(6 citation statements)

References 32 publications

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“…The limitation is that each vehicle can only belong to one volunteer union. [29] An inviting algorithm for contributors is proposed to recruit cooperators through social closeness.…”

Section: These Papers Have Been Optimizedmentioning

confidence: 99%

Emotion-aware Task Scheduling for Au-tonomous Vehicles in Software-defined Edge Networks

2022

KSII TIIS

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Autonomous vehicles are gradually being regarded as the mainstream trend of future development of the automobile industry. Autonomous driving networks generate many intensive and delay-sensitive computing tasks. The storage space, computing power, and battery capacity of autonomous vehicle terminals cannot meet the resource requirements of the tasks. In this paper, we focus on the task scheduling problem of autonomous driving in software-defined edge networks. By analyzing the intensive and delay-sensitive computing tasks of autonomous vehicles, we propose an emotion model that is related to task urgency and changes with execution time and propose an optimal base station (BS) task scheduling (OBSTS) algorithm. Task sentiment is an important factor that changes with the length of time that computing tasks with different urgency levels remain in the queue. The algorithm uses task sentiment as a performance indicator to measure task scheduling. Experimental results show that the OBSTS algorithm can more effectively meet the intensive and delay-sensitive requirements of vehicle terminals for network resources and improve user service experience.

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“…The limitation is that each vehicle can only belong to one volunteer union. [29] An inviting algorithm for contributors is proposed to recruit cooperators through social closeness.…”

Section: These Papers Have Been Optimizedmentioning

confidence: 99%

Emotion-aware Task Scheduling for Au-tonomous Vehicles in Software-defined Edge Networks

2022

KSII TIIS

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“…For example, the researchers in [98] invited 19,484 users agreed to join the experiment. Sometimes, incentives can improve the performance of collection that contributors who would be rewarded with payment according to their contribution [99], [100]. This method can be costly and requires ethical approvals, or one can use open datasets to reduce this cost.…”

Section: ) Data Collectionmentioning

confidence: 99%

A Survey of Online Data-Driven Proactive 5G Network Optimisation Using Machine Learning

Guo

Zhang

2020

IEEE Access

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In the fifth-generation (5G) mobile networks, proactive network optimisation plays an important role in meeting the exponential traffic growth, more stringent service requirements, and to reduce capital and operational expenditure. Proactive network optimisation is widely acknowledged as one of the most promising ways to transform the 5G network based on big data analysis and cloud-fog-edge computing, but there are many challenges. Proactive algorithms will require accurate forecasting of highly contextualised traffic demand and quantifying the uncertainty to drive decision making with performance guarantees. Context in Cyber-Physical-Social Systems (CPSS) is often challenging to uncover, unfolds over time, and even more difficult to quantify and integrate into decision making. The first part of the review focuses on mining and inferring CPSS context from heterogeneous data sources, such as online user-generated-content. It will examine the state-of-the-art methods currently employed to infer location, social behaviour, and traffic demand through a cloud-edge computing framework; combining them to form the input to proactive algorithms. The second part of the review focuses on exploiting and integrating the demand knowledge for a range of proactive optimisation techniques, including the key aspects of load balancing, mobile edge caching, and interference management. In both parts, appropriate state-of-the-art machine learning techniques (including probabilistic uncertainty cascades in proactive optimisation), complexity-performance trade-offs, and demonstrative examples are presented to inspire readers. This survey couples the potential of online big data analytics, cloud-edge computing, statistical machine learning, and proactive network optimisation in a common cross-layer wireless framework. The wider impact of this survey includes better cross-fertilising the academic fields of data analytics, mobile edge computing, AI, CPSS, and wireless communications, as well as informing the industry of the promising potentials in this area.INDEX TERMS Online data, data analytics, proactive network optimisation, 5G.

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“…Thereafter, smartphones make participation decision to maximize their utility. whereas in [23] and [34]- [37], a smartphone competes for the reward and participates in the task by setting its participation level. All the above Stackelberg game based approaches also assume that there only exists one sensing task or multiple dependent tasks in the system.…”

Section: Related Workmentioning

confidence: 99%

“…with zero. In summary, at the beginning of each time slot t, the platform makes admission control according to (37) and determines the amount of admitted sensing requests for each grid. The platform calculates the price p j (t) according to (38)- (39) and offers it to each smartphone.…”

Section: ) Stage I: Admission Control and Price Determinationmentioning

confidence: 99%

“…Z j (t) is continuous on a closed interval [0, In cooperative online algorithm, the admission control and scheduling control are non-coupled. At each time slot, the platform calculates the admission decision according to (37), and the scheduling decision according to (52)-(53). The cooperative online algorithm is summarized in Algorithm 2.…”

Section: B Cooperative Online Algorithmmentioning

confidence: 99%

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Stackelberg Game Based Dynamic Admission and Scheduling in Mobile Crowdsensing

Wang

Zhou

Zhao³

et al. 2019

IEEE Access

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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.

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

Cited by 12 publications

References 32 publications

Emotion-aware Task Scheduling for Au-tonomous Vehicles in Software-defined Edge Networks

Emotion-aware Task Scheduling for Au-tonomous Vehicles in Software-defined Edge Networks

A Survey of Online Data-Driven Proactive 5G Network Optimisation Using Machine Learning

Stackelberg Game Based Dynamic Admission and Scheduling in Mobile Crowdsensing

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