D2D-Enabled Reliable Data Collection for Mobile Crowd Sensing

Wang, Pengfei; Yu, Zhenwei; Lin, Chi; Yang, Leyou; Hou, Yaqing; Zhang, Qiang

doi:10.1109/icpads51040.2020.00033

Cited by 7 publications

(5 citation statements)

References 24 publications

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“…Wang et al [23] have presented a solution for collecting crowdsensing data to identify and collect reliable data. They measure data reliability on the client side by formulating data collection as a probability problem.…”

Section: Related Workmentioning

confidence: 99%

An Incentive Mechanism for Data Delivery in Mobile Crowdsensing Based on D2D Communications

Esmaeilyfard,

Gharechahi

2023

Journal of Sensors

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Mobile crowdsensing systems employ crowd smartphones to collect data using these device-embedded sensors. The increase in the number of users and the data volume has limited the possibility of using cellular networks. In this regard, collector users need to be able to send their data to the publishers with less delay using device-to-device (D2D) communication. This paper proposes an incentive mechanism for improving the transmission rate to multiple publishers in mobile crowdsensing networks based on device-to-device communication. Users exchange their data for delivering it to interested publishers in this mechanism. Users receive credits for their contributions to data delivery from collector users. Also, the cheque concept has been introduced to facilitate collector users’ payment of this credit. Communication between users is expressed as a cooperative two-player game. A heuristic approach finds the solution to this game. A cheque and credit exchange process is also planned as an online auction to increase credit circulation in the network. Finally, the performance of the proposed mechanism is evaluated using the simulator. The results show that this mechanism performs better than other methods in average delay and delivery rate and has a stable traffic overhead.

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Section: Related Workmentioning

confidence: 99%

An Incentive Mechanism for Data Delivery in Mobile Crowdsensing Based on D2D Communications

Esmaeilyfard,

Gharechahi

2023

Journal of Sensors

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“…In [19,20], authors study the computation, communication, and the storage resources problems in both IoV and MEC networks. Wang et al in [21] consider using D2D technology in MEC system to collect larger and better quality sensing data.…”

Section: Related Workmentioning

confidence: 99%

“…(i) Constraint (18) indicates that the decision variable is a Boolean value 5 Wireless Communications and Mobile Computing (ii) Constraint (19) guarantees that tasks need to be completed within the maximum time delay (iii) Constraint (20) guarantees that the computation resources allocated by each RSU do not exceed its current available computation resources (iv) Constraint (21) guarantees that the bandwidth allocated by each RSU does not exceed its current available bandwidth…”

Section: Problem Formulationmentioning

confidence: 99%

Deep Reinforcement Learning for Collaborative Computation Offloading on Internet of Vehicles

2021

Wireless Communications and Mobile Computing

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With the increase of Internet of vehicles (IoVs) traffic, the contradiction between a large number of computing tasks and limited computing resources has become increasingly prominent. Although many existing studies have been proposed to solve this problem, their main consideration is to achieve different optimization goals in the case of edge offloading in static scenarios. Since realistic scenarios are complicated and generally time-varying, these studies in static scenes are imperfect. In this paper, we consider a collaborative computation offloading in a time-varying edge-cloud network, and we formulate an optimization problem with considering both delay constraints and resource constraints, aiming to minimize the long-term system cost. Since the set of feasible solutions to the problem is nonconvex, and the complexity of the problem is very large, we propose a Q-learning-based approach to solve the optimization problem. In addition, due to the dimensional catastrophes, we further propose a DQN-based approach to solve the optimization problem. Finally, by comparing our two proposed algorithms with typical algorithms, the simulation results show that our proposed approaches achieve better performance. Under the same conditions, by comparing our two proposed algorithms with typical algorithms, the simulation results show that our proposed Q-learning-based method reduces the system cost by about 49% and 42% compared to typical algorithms. And in the same case, compared with the classical two schemes, our proposed DQN-based scheme reduces the system cost by 62% and 57%.

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“…Motivation. Crowdsourcing has been proven to be effective in task delivering applications including Amazon Mechanical Turks (AMT) [21][22][23]. The requirements for communication resources of devices can be regarded as a task, and NIB is responsible for fulfilling the task.…”

Section: Introductionmentioning

confidence: 99%

CrowdBox: Crowdsourced Network‐in‐Box Recruitment for Edge Computing‐Enabled Industrial Internet of Things

Wang

Lin

et al. 2021

Wireless Communications and Mobile Computing

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The rapidly increasing number of smart devices deployed in the Industrial Internet of Things (IIoT) environment has been witnessed. To improve communication efficiency, edge computing-enabled Industrial Internet of Things (E-IIoT) has gained attention recently. Nevertheless, E-IIoT still cannot conquer the rapidly increasing communication demands when hundreds of millions of IIoT devices are connected at the same time. Considering the future 6G environment where smart network-in-box (NIB) nodes are everywhere (e.g., deployed in vehicles, buses, backpacks, etc.), we propose a crowdsourcing-based recruitment framework, leveraging the power of the crowd to provide extra communication resources and enhance the communication capabilities. We creatively treat NIB nodes as edge layer devices, and CrowdBox is devised using a Stackelberg game where the E-IIoT system is the leader, and the NIB nodes are the followers. CrowdBox can calculate the optimal reward to reach the unique Stackelberg equilibrium where the utility of E-IIoT can be maximized while none of the NIB nodes can improve its utility by deviating from its strategy. Finally, we evaluate the performance of CrowdBox with extensive simulations with various settings, and it shows that CrowdBox outperforms the compared algorithms in improving system utility and attracting more NIB nodes.

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D2D-Enabled Reliable Data Collection for Mobile Crowd Sensing

Cited by 7 publications

References 24 publications

An Incentive Mechanism for Data Delivery in Mobile Crowdsensing Based on D2D Communications

An Incentive Mechanism for Data Delivery in Mobile Crowdsensing Based on D2D Communications

Deep Reinforcement Learning for Collaborative Computation Offloading on Internet of Vehicles

CrowdBox: Crowdsourced Network‐in‐Box Recruitment for Edge Computing‐Enabled Industrial Internet of Things

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