HyTasker: Hybrid Task Allocation in Mobile Crowd Sensing

Wang, Jiangtao; Wang, Feng; Wang, Yasha; Qiu, Zhaopeng; Zhang, Daqing; Guo, Bin; Lv, Qin

doi:10.1109/tmc.2019.2898950

Cited by 101 publications

(15 citation statements)

References 47 publications

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“…J. Wang et al [25] study multi-task allocation problem and propose a novel multi-task allocation framework named MTasker to maximize the overall system utility. In [26],J. Wang et al propose a two-phased hybrid framework called HyTasker, which jointly optimizes two phases with a total incentive budget constraint.…”

Section: Related Workmentioning

confidence: 99%

Reinforcement Learning Based Advertising Strategy Using Crowdsensing Vehicular Data

Lou

Yang

Wang

et al. 2021

IEEE Trans. Intell. Transport. Syst.

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The commercial profit depends on the number of attracted customers, hence the advertiser needs to adopt an effective advertising strategy to determine the advertisement switching policy for each digital billboard to attract as many potential customers as possible. Whether a customer could be attracted is influenced by numerous factors, such as the probability that the customer could see the billboard and the degree of his/her interests in the advertisement. Besides, cooperation and competition among all digital billboards will also affect the commercial profit. Taking the above factors into consideration, we formulate the dynamic advertising problem to maximize the commercial profit for the advertiser. To address the problem, we first extract potential customers' implicit information by using the vehicular data collected by Mobile CrowdSensing (MCS), such as their vehicular trajectories and their preferences. With this information, we then propose an advertising strategy based on multi-agent deep reinforcement learning. By using the proposed advertising strategy, the advertiser could determine the advertising policy for each digital billboard and maximize the commercial profit. Extensive experiments on three realworld datasets have been conducted to verify that our proposed advertising strategy could achieve the superior commercial profit compared with the state-of-the-art strategies.

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Section: Related Workmentioning

confidence: 99%

Reinforcement Learning Based Advertising Strategy Using Crowdsensing Vehicular Data

Lou

Yang

Wang

et al. 2021

IEEE Trans. Intell. Transport. Syst.

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show abstract

“…Due to the rapid improvement of the computing, storage, and perception capabilities of IoT devices, IoT devices are widely deployed in various applications [44][45][46]. Although the storage and computing power of a single IoT device is relatively small [47], the massive amount of IoT devices deployed to the edge of the network have huge computing and storage capabilities.…”

Section: Related Workmentioning

confidence: 99%

An active and verifiable trust evaluation approach for edge computing

Wang

Zhang

et al. 2020

J Cloud Comp

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Billions of Internet of Thing (IoT) devices are deployed in edge network. They are used to monitor specific event, process and to collect huge data to control center with smart decision based on the collected data. However, some malicious IoT devices may interrupt and interfere with normal nodes in data collection, causing damage to edge network. Due to the open character of the edge network, how to identify the credibility of these nodes, thereby identifying malicious IoT devices, and ensure reliable data collection in the edge network is a great challenge. In this paper, an Active and Verifiable Trust Evaluation (AVTE) approach is proposed to identify the credibility of IoT devices, so to ensure reliable data collection for Edge Computing with low cost. The main innovations of the AVTE approach compared with the existing work are as follows: (1) In AVTE approach, the trust of the device is obtained by an actively initiated trusted detection routing method. It is fast, accurate and targeted. (2) The acquisition of trust in the AVTE approach is based on a verifiable method and it ensures that the trust degree has higher reliability. (3) The trust acquisition method proposed in this paper is low-cost. An encoding returned verification method is applied to obtain verification messages at a very low cost. This paper proposes an encoding returned verification method, which can obtain verification messages at a very low cost. In addition, the strategy of this paper adopts initiation and verification of adaptive active trust detection according to the different energy consumption of IoT devices, so as to reliably obtain the trust of device under the premise of ensuring network lifetime. Theoretical analysis shows that AVTE approach can improve the data collection rate by 0.5 ~ 23.16% while ensuring long network lifetime compared with the existing scheme.

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“…With the development of the Internet of Things (IoT) [15], more and more sensor devices are deployed in various applications, such as vehicles network [18,42], traffic monitoring, healthcare [43], security monitoring [44][45][46], and industrial networks [47][48][49]). Numerous sensor devices are used to sense and collect data, providing rich data for various applications [50].…”

Section: Related Workmentioning

confidence: 99%

Energy-aware MAC protocol for data differentiated services in sensor-cloud computing

et al. 2020

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The energy-aware MAC protocol is the basic communication protocol for device-to-device communication in sensor-cloud computing and facilitates data sensing, computing, and sharing for applications. Transmitting highpriority data to control centers quickly to manage emergencies is essential for sensor-cloud applications. In this paper, a Dynamic Wait time-based MAC (DWT-MAC) protocol is proposed for data differentiated services of a sensor-cloud system (SCS). The DWT-MAC protocol is novel in that it changes the receiver wait time, which has been fixed in previous MAC protocols, and it reduces the delay in data transmission by dynamically adjusting the wait time. In the DWT-MAC protocol, the wait time changes according to the number of senders, which can ensure that it always approaches the optimal value. A dynamic time adjustment algorithm, which causes the wait time to always trail the optimal value, is proposed to reduce delays. While the DWT-MAC protocol cannot ensure wait times of the optimal value, it can ensure that it quickly comes close to the optimal value, which is suitable for dynamically changing networks. Extensive experiments show that the DWT-MAC protocol reduces the average delay in the transmission of data of the highest priority by 49.3%.

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HyTasker: Hybrid Task Allocation in Mobile Crowd Sensing

Cited by 101 publications

References 47 publications

Reinforcement Learning Based Advertising Strategy Using Crowdsensing Vehicular Data

Reinforcement Learning Based Advertising Strategy Using Crowdsensing Vehicular Data

An active and verifiable trust evaluation approach for edge computing

Energy-aware MAC protocol for data differentiated services in sensor-cloud computing

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