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Cell Selection with Deep Reinforcement Learning in Sparse Mobile Crowdsensing [article]

Leye Wang, Wenbin Liu, Daqing Zhang, Yasha Wang, En Wang, Yongjian Yang
2018 arXiv   pre-print
To address this issue, this paper proposes a Deep Reinforcement learning based Cell selection mechanism for Sparse MCS, called DR-Cell.  ...  Experiments on various real-life sensing datasets verify the effectiveness of DR-Cell over the state-of-the-art cell selection mechanisms in Sparse MCS by reducing up to 15% of sensed cells with the same  ...  To this end, in this paper, we design a new cell selection framework for Sparse MCS, called DR-Cell, with Deep Reinforcement learning.  ... 
arXiv:1804.07047v2 fatcat:chqxohovxbbqpgkbtwq6zuotx4

Active crowd sensing

Zhiyong Yu, Jiangtao Wang, Jordán Pascual Espada
2021 Personal and Ubiquitous Computing  
deep/transfer learning in MCS, etc.  ...  In the scheme, the Micro Mobile Data Centers are designed and later selected to connect the huge number of intelligent sensing devices. By using reinforcement learning, Zhao et al.  ... 
doi:10.1007/s00779-021-01564-x fatcat:iwdlqfwzergszgsklzzalxslbm

Multi-dimensional Urban Sensing in Sparse Mobile CrowdSensing

Wenbin Liu, Yongjian Yang, En Wang, Leye Wang, Djamal Zeghlache, Daqing Zhang
2019 IEEE Access  
INDEX TERMS Sparse mobile crowdsensing, reinforcement learning, compressive sensing, urban sensing.  ...  Sparse mobile crowdsensing (MCS) is a promising paradigm for the large-scale urban sensing, which allows us to collect data from only a few areas (cell selection) and infer the data of other areas (data  ...  REINFORCEMENT LEARNING-ASSISTED MULTI-TASK SPARSE MOBILE CROWDSENSING In this section, we present the reinforcement learning-assisted multi-task Sparse MCS to address the data inference and cell selection  ... 
doi:10.1109/access.2019.2924184 fatcat:btnuao3hfjdvrd7rofr5jheg6i

A Survey of Sparse Mobile Crowdsensing: Developments and Opportunities

Shiting Zhao, Guozi Qi, Tengjiao He, Jinpeng Chen, Zhiquan Liu, Kaimin Wei
2022 IEEE Open Journal of the Computer Society  
SMCS confronts numerous challenges, such as sensing cell selection and missing data inference, when compared to mobile crowdsensing.  ...  Our objective is to provide researchers with a comprehensive understanding of SMCS. INDEX TERMS Sparse mobile crowdsensing, participant recruitment, privacy protection, incentive mechanism.  ...  Moreover, reinforcement learning and deep learning have been exploited to choose those sensing regions with distinct properties [62] .  ... 
doi:10.1109/ojcs.2022.3177290 fatcat:gs5npdb3dne57ob2lksjzd7wni

Spider: Deep Learning-driven Sparse Mobile Traffic Measurement Collection and Reconstruction

Yini Fang, Alec F. Diallo, Chaoyun Zhang, Paul Patras
2021 2021 IEEE Global Communications Conference (GLOBECOM)  
Spider harnesses Reinforcement Learning and tackles large action spaces to train a policy network that selectively samples a minimal number of cells where data should be collected.  ...  retaining state-of-the-art accuracy in inferring mobile traffic consumption with fine geographic granularity.  ...  RL for Cell Selection with Large Action Spaces To collect measurements with minimum sampling overhead, we first leverage Reinforcement Learning (RL) and train an agent that learns the likelihood of selecting  ... 
doi:10.1109/globecom46510.2021.9685804 fatcat:fqkghupeejc6rktdybw43l3h44

Table of contents

2021 IEEE Internet of Things Journal  
Buyya 3822 A Cost-Quality Beneficial Cell Selection Approach for Sparse Mobile Crowdsensing With Diverse Sensing Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Seneviratne 3567 Cloud Resource Scheduling With Deep Reinforcement Learning and Imitation Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/jiot.2021.3057194 fatcat:zzozuzqaazbcjmjegs2rh5n734

Table of contents

2021 IEEE Transactions on Network and Service Management  
Management of Mobile and Wireless NetworksSelf-Imitation Learning-Based Inter-Cell Interference Coordination in Autonomous HetNets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Mostafa Ibrahim, Umair Sajid Hashmi, Muhammad Nabeel, Ali Imran, and Sabit Ekin 4042 Harnessing UAVs for Fair 5G Bandwidth Allocation in Vehicular Communication via Deep Reinforcement Learning . . . .  ... 
doi:10.1109/tnsm.2021.3106439 fatcat:ze67mhvuinejfg6olbdsxclpga

Table of Contents

2021 IEEE Transactions on Vehicular Technology  
Asadi 585 Three-Dimension Trajectory Design for Multi-UAV Wireless Network With Deep Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Hanzo 666 A Deep Learning-Based Low Overhead Beam Selection in mmWave Communications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tvt.2021.3053948 fatcat:cwyk64fuyfgflh33nb24vux7ra

A Semiopportunistic Task Allocation Framework for Mobile Crowdsensing with Deep Learning

Zhenzhen Xie, Liang Hu, Yan Huang, Junjie Pang, Xiao Zhang
2021 Wireless Communications and Mobile Computing  
The Mobile Crowdsensing (MCS) system then emerged.  ...  Then, a reinforcement learning- (RL-) based task assignment is adopted, which can help the MCS system towards better performance improvements while support different utility functions.  ...  To overcome these limitations, recent works use the Sparse Mobile Crowdsensing concept [34] instead to resolve the contradiction between the limited participant resource and the increasing need for sensing  ... 
doi:10.1155/2021/6643229 fatcat:2xi3eavbxrhmjly2k2qer6dvcq

Table of Contents

2021 IEEE Transactions on Vehicular Technology  
Zhang 6094 Trustworthy and Cost-Effective Cell Selection for Sparse Mobile Crowdsensing Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Xu 6122 Deep Reinforcement Learning Based Dynamic Reputation Policy in 5G Based Vehicular Communication Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tvt.2021.3085041 fatcat:a3mnra3v2fb47husbanbnfjo5i

Credibility on Crowdsensing Data Acquisition

Manuel Neto, Danielo Gomes, José Soares
2019 Journal of Communication and Information Systems  
However, one concern with crowdsensing is the information credibility and, over the last few years, we have seen a variety of approaches to leverage credibility on the crowdsensing platforms.  ...  The results show that the absence of standard models in the data capture process and the human factors such as individualism, inattention, and the possibility of errors (whether they are intentional or  ...  ACKNOWLEDGMENTS This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brasil (CAPES) -Finance Code 001.  ... 
doi:10.14209/jcis.2019.26 fatcat:o4cja467jbgenap4akpxmcu3ym

2020 Index IEEE Transactions on Wireless Communications Vol. 19

2020 IEEE Transactions on Wireless Communications  
., TWC Jan. 2020 650-664 Huang, A., see He, H., TWC Dec. 2020 7881-7896 Huang, C., Molisch, A.F., He, R., Wang, R., Tang, P., Ai, B., and Zhong, Z., Machine Learning-Enabled LOS/NLOS Identification  ...  for MIMO Systems in Dynamic Environments; TWC June 2020 3643-3657 Huang, C., see Yang, M., TWC Sept. 2020 5860-5874 Huang, D., Tao, X., Jiang, C., Cui, S., and Lu, J  ...  ., +, TWC April 2020 2601-2612 Offloading and Resource Allocation With General Task Graph in Mobile Edge Computing: A Deep Reinforcement Learning Approach.  ... 
doi:10.1109/twc.2020.3044507 fatcat:ie4rwz4dgvaqbaxf3idysubc54

Table of Contents

2020 IEEE Transactions on Vehicular Technology  
Sagduyu 760 DOA Estimation via Sparse Signal Recovery in 4-D Linear Antenna Arrays With Optimized Time Sequences . State-Aware Rate Adaptation for UAVs by Incorporating On-Board Sensors . S.  ...  Bouscayrol 328 Deterministic Promotion Reinforcement Learning Applied to Longitudinal Velocity Control for Automated Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Chen 971 A Model-Driven Deep Reinforcement Learning Heuristic Algorithm for Resource Allocation in Ultra-Dense Cellular Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tvt.2019.2961921 fatcat:5hwiwrq6hbekpnzaciuur7dvom

2020 Index IEEE Internet of Things Journal Vol. 7

2020 IEEE Internet of Things Journal  
., +, JIoT July 2020 6252-6265 Deep-Reinforcement-Learning-Based Autonomous UAV Navigation With Sparse Rewards.  ...  ., +, JIoT March 2020 1841-1856 User Recruitment for Enhancing Data Inference Accuracy in Sparse Mobile Crowdsensing.  ...  ., +, JIoT Nov. 2020 11223-11237 Land mobile radio Physical-Layer Security in Space Information Networks: A Survey.  ... 
doi:10.1109/jiot.2020.3046055 fatcat:wpyblbhkrbcyxpnajhiz5pj74a

Table of Contents

2020 IEEE Transactions on Vehicular Technology  
Online Rating Protocol Using Endogenous and Incremental Learning Design for Mobile Crowdsensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Ma 2411 Using Reinforcement Learning to Minimize the Probability of Delay Occurrence in Transportation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tvt.2020.2970959 fatcat:rmi2juemdneozo47iagkyagi6i
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