Robust Mobile Crowd Sensing: When Deep Learning Meets Edge Computing

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474

Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing

Li, H., Ota, K., & Dong, M. (2018) · IEEE Network · doi:10.1109/mnet.2018.1700202 · Cited by 1,566

Nericell

Mohan, P., Padmanabhan, V. N., & Ramjee, R. (2008) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/1460412.1460444 · Cited by 1,271

State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems

Fadlullah, Z. M., Tang, F., Mao, B., Kato, N., Akashi, O., Inoue, T., & Mizutani, K. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2707140 · Cited by 810

DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing

Yao, S., Hu, S., Zhao, Y., Zhang, A., & Abdelzaher, T. (2017) · Proceedings of the 26th International Conference on World Wide Web · doi:10.1145/3038912.3052577 · Cited by 667

Can Deep Learning Revolutionize Mobile Sensing?

Lane, N. D., & Georgiev, P. (2015) · Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications · doi:10.1145/2699343.2699349 · Cited by 282

A Privacy-Preserving Vehicular Crowdsensing-Based Road Surface Condition Monitoring System Using Fog Computing

Basudan, S., Lin, X., & Sankaranarayanan, K. (2017) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2666783 · Cited by 265

Exploiting Massive D2D Collaboration for Energy-Efficient Mobile Edge Computing

Chen, X., Pu, L., Gao, L., Wu, W., & Wu, D. (2017) · IEEE Wireless Communications · doi:10.1109/mwc.2017.1600321 · Cited by 239

A Novel Deployment Scheme for Green Internet of Things

Huang, J., Meng, Y. S., Gong, X., Liu, Y., & Duan, Q. (2014) · IEEE Internet of Things Journal · doi:10.1109/jiot.2014.2301819 · Cited by 227

A Secure Mobile Crowdsensing Game With Deep Reinforcement Learning

Xiao, L., Li, Y., Han, G., Dai, H., & Poor, H. V. (2017) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2017.2737968 · Cited by 180

Green Internet of Things Application of a Medical Massage Robot With System Interruption

Si, W., Srivastava, G., Zhang, Y., & Jiang, L. (2019) · IEEE Access · doi:10.1109/access.2019.2939502 · Cited by 29

Design and Performance Evaluation of an AI-Based W-Band Suspicious Object Detection System for Moving Persons in the IoT Paradigm

Yu, K., Qi, X., Sato, T., Myint, S. H., Wen, Z., Katsuyama, Y., Tokuda, K., Kameyama, W., & Sato, T. (2020) · IEEE Access · doi:10.1109/access.2020.2991225 · Cited by 29

Human–Machine Interaction-Oriented Image Coding for Resource-Constrained Visual Monitoring in IoT

Wang, Z., Li, F., Xu, J., & Cosman, P. C. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3150417 · Cited by 30

Biologically Inspired Machine Learning-Based Trajectory Analysis in Intelligent Dispatching Energy Storage System

Mou, J., Duan, P., Gao, L., Pan, Q.-K., Gao, K., & Singh, A. K. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3154750 · Cited by 28

Distributed artificial intelligence: Taxonomy, review, framework, and reference architecture

Janbi, N. F., Katib, I. A., & Mehmood, R. (2023) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2023.200231 · Cited by 30

Fusion of Infrared-Visible Images in UE-IoT for Fault Point Detection Based on GAN

Liao, B., Du, Y., & Yin, X. (2020) · IEEE Access · doi:10.1109/access.2020.2990539 · Cited by 28

Task Offloading for Vehicular Fog Computing under Information Uncertainty: A Matching-Learning Approach

Liao, H., Zhou, Z., Zhao, X., Ai, B., & Mumtaz, S. (2019) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc.2019.8766579 · Cited by 28

A Novel Content Popularity Prediction Algorithm Based on Auto Regressive Model in Information-Centric IoT

Liu, Y., Zhi, T., Xi, H., Duan, X., & Zhang, H. (2019) · IEEE Access · doi:10.1109/access.2019.2901525 · Cited by 27

Hybrid Precoding for an Adaptive Interference Decoding SWIPT System With Full-Duplex IoT Devices

Zhao, X., Zhang, Y., Geng, S., Du, F., Zhou, Z., & Yang, L. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2953312 · Cited by 27

Towards the Applicability of Measuring the Electrodermal Activity in the Context of Process Model Comprehension: Feasibility Study

Winter, M. R., Pryss, R. C., Probst, T., & Reichert, M. (2020) · Sensors · doi:10.3390/s20164561 · Cited by 27

Two-Timescale Resource Allocation for Automated Networks in IIoT

He, Y., Ren, Y., Zhou, Z., Mumtaz, S., Al–Rubaye, S., Tsourdos, A., & Dobre, O. A. (2022) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2022.3162722 · Cited by 27

Dynamic User Recruitment in Edge-Aided Mobile Crowdsensing

Zhang, Y., Li, P., Zhang, T., Liu, J., Huang, W., & Nie, L. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2023.3238172 · Cited by 27

A Comprehensive Survey of 6G Wireless CommunicationsPreprint

Zhao, Y., Zhai, W., Zhao, J., Zhang, T., Sun, S., Niyato, D. T., & Lam, K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.03889 · Cited by 44

CUE: An Intelligent Edge Computing Framework

Yang, B., Wu, D., & Wang, R. (2019) · IEEE Network · doi:10.1109/mnet.2019.1800316 · Cited by 25

Green Fog Planning for Optimal Internet-of-Thing Task Scheduling

He, Z., Zhang⋆, Y., Tak, B., & Peng, L. (2019) · IEEE Access · doi:10.1109/access.2019.2961952 · Cited by 25

Machine Learning-Based 5G RAN Slicing for Broadcasting Services

Mu, J., Jing, X., Zhang, Y., Gong, Y., Zhang, R., & Zhang, F. (2021) · IEEE Transactions on Broadcasting · doi:10.1109/tbc.2021.3122353 · Cited by 25

A Packet-Length-Adjustable Attention Model Based on Bytes Embedding Using Flow-WGAN for Smart Cybersecurity

Han, L., Sheng, Y., & Zeng, X. (2019) · IEEE Access · doi:10.1109/access.2019.2924492 · Cited by 26

Combined Forecasting Model of Cloud Computing Resource Load for Energy-Efficient IoT System

Li, H.-A., Zhang, M., Yu, K., Zhang, J., Hua, Q., Wu, B., & Yu, Z. (2019) · IEEE Access · doi:10.1109/access.2019.2945046 · Cited by 23

A 3.5-tier container-based edge computing architecture

Chen, C.-H., & Liu, C.-T. (2021) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2021.107227 · Cited by 24

Cooperative Computing for Mobile Crowdsensing: Design and Optimization

Xie, X., Bai, T., Guo, W., Wang, Z., & Nallanathan, A. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3323350 · Cited by 22

Edge Deployment of Deep Networks for Visual Detection: A Review

Zhai, H., Du, J., Ai, Y., & Hu, T. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3502539 · Cited by 28

D2D Computation Offloading Optimization for Precedence-Constrained Tasks in Information-Centric IoT

Xie, J., Jia, Y., Chen, Z., Nan, Z., & Liang, L. (2019) · IEEE Access · doi:10.1109/access.2019.2928891 · Cited by 22

An Efficient SCMA Codebook Design Based on Lattice Theory for Information-Centric IoT

Zhang, X., Han, G., Zhang, D., Zhang, D., & Yang, L. (2019) · IEEE Access · doi:10.1109/access.2019.2938637 · Cited by 22

Federated Learning Based Mobile Crowd Sensing with Unreliable User Data

Jiang, Y., Cong, R., Shu, C., Yang, A., Zhao, Z., & Min, G. (2020) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc-smartcity-dss50907.2020.00039 · Cited by 22

Age Estimation Using Aging/Rejuvenation Features With Device-Edge Synergy

Duan, M., Ouyang, A., Tan, G., & Tian, Q. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2020.2981117 · Cited by 21

Toward Collaborative Mobile Crowdsourcing

Hamrouni, A., Alelyani, T., Ghazzai, H., & Massoud, Y. (2021) · IEEE Internet of Things Magazine · doi:10.1109/iotm.0001.2000185 · Cited by 21

Data sharing and exchanging with incentive and optimization: a survey

Liu, L., & Han, M. (2024) · Discover Data · doi:10.1007/s44248-024-00006-2 · Cited by 22

Reputation Based Attacker Identification Policy for Multi-Access Edge Computing in Internet of Things

Sandal, Y. S., Pusane, A. E., Kurt, G. K., & Benedetto, F. (2020) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2020.3040105 · Cited by 21

Can We Enhance the Quality of Mobile Crowdsensing Data Without Ground Truth?

Li, J., Gu, B., Gong, S., Su, Z., & Guizani, M. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3526277 · Cited by 20

Design and Performance Evaluation of Content-Oriented Communication System for IoT Network: A Case Study of Named Node Networking for Real-Time Video Streaming System

Qi, X., Su, Y., Yu, K., Li, J., Hua, Q., Wen, Z., Lopez, J. E., & Sato, T. (2019) · IEEE Access · doi:10.1109/access.2019.2925885 · Cited by 20

QoE-Oriented Rate Control and Resource Allocation for Cognitive M2M Communication in Spectrum-Sharing OFDM Networks

Yin, J., Chen, Y., Sang, G., Liao, B. Y., & Wang, X. (2019) · IEEE Access · doi:10.1109/access.2019.2908681 · Cited by 20

SVM-BiLSTM: A Fault Detection Method for the Gas Station IoT System Based on Deep Learning

Jiahao, Y., Jiang, X., Wang, S., Jiang, K., & Yu, X. (2020) · IEEE Access · doi:10.1109/access.2020.3034939 · Cited by 22

How Artificial Intelligence and Mobile Crowd Sourcing are Inextricably Intertwined

Abououf, M., Otrok, H., Mizouni, R., Singh, S., & Damiani, E. (2020) · IEEE Network · doi:10.1109/mnet.011.2000516 · Cited by 19

Hybrid-Learning-Based Operational Visual Quality Inspection for Edge-Computing-Enabled IoT System

Chu, Y., Feng, D., Liu, Z., Zhao, Z., Wang, Z., Xia, X., & Quek, T. Q. S. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3107902 · Cited by 20

FRNet: An MCS Framework for Efficient and Secure Data Sensing and Privacy Protection in IoVs

Wang, Z., Li, Z., Li, Z., Xu, Y., Yang, X., Qi, F. Z., & Jia, H. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3267782 · Cited by 18

IOTA‐Based Mobile Crowd Sensing: Detection of Fake Sensing Using Logit‐Boosted Machine Learning Algorithms

Hameed, M., Yang, F., Ghafoor, M. I., Jaskani, F. H., Islam, U., Fayaz, M., & Mehmood, G. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/6274114 · Cited by 18

Towards Artificial Intelligence Empowered Security and Privacy Issues in 6G Communications

Bandi, A., & Yalamarthi, S. (2022) · International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) · doi:10.1109/icscds53736.2022.9760857 · Cited by 17

A Study on Mobile Crowd Sensing Systems for Healthcare Scenarios

Zhang, E., Trujillo, R., Templeton, J. M., & Poellabauer, C. (2023) · IEEE Access · doi:10.1109/access.2023.3342158 · Cited by 20

An Efficient Privacy Preserving Spectrum Sharing Framework for Internet of Things

Wang, X., Umehira, M., Han, B., Zhou, H., Li, P., & Wu, C. (2020) · IEEE Access · doi:10.1109/access.2020.2974227 · Cited by 17

A Link-Based Variable Probability Learning Approach for Partially Overlapping Channels Assignment on Multi-Radio Multi-Channel Wireless Mesh Information-Centric IoT Networks

Zhao, X., Li, L., Geng, S., Zhang, H., & Ma, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2908872 · Cited by 16

3D Remote Healthcare for Noisy CT Images in the Internet of Things Using Edge Computing

Zhang, J., Li, D., Hua, Q., Qi, X., Wen, Z., & Myint, S. H. (2021) · IEEE Access · doi:10.1109/access.2021.3052469 · Cited by 16

Blockchain-Based Hybrid Reliable User Selection Scheme for Task Allocation in Mobile Crowd Sensing

Zhang, S., Li, Z., Liang, W., Li, K., & Bhuiyan, M. Z. A. (2024) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2024.3449146 · Cited by 16

A Game-Enabled Blockchain Consensus and Incentive Mechanism for Secure and Efficient Mobile Crowdsensing

Tang, C., Bao, Z., Cai, X., Lin, F., Zhang, Z., & Chen, G. (2023) · IEEE Systems Journal · doi:10.1109/jsyst.2023.3333399 · Cited by 14

Efficient resource allocation for 5G/6G cognitive radio networks using probabilistic interference models

Zaheer, O., Ali, M., Imran, M., Khan, H. Z., & Naeem, M. (2024) · Physical Communication · doi:10.1016/j.phycom.2024.102335 · Cited by 14

The Optimal Control Policy for Point-to-Point Wireless Body Area Network Based on Simultaneous Time-Ratio and Transmission Power Allocation

Ling, Z., Hu, F., & Shao, M. (2019) · IEEE Access · doi:10.1109/access.2019.2901969 · Cited by 14

Efficient Data Uploading for Mobile Crowdsensing via Team Collaborating and Matching

Xu, C., & Song, W. (2021) · IEEE Transactions on Green Communications and Networking · doi:10.1109/tgcn.2021.3109740 · Cited by 14

Incentivizing for Truth Discovery in Edge-assisted Large-scale Mobile Crowdsensing

Xu, J. H., Yang, S., Lu, W., Xu, L., & Yang, D. (2020) · Sensors · doi:10.3390/s20030805 · Cited by 14

Modeling cooperative behavior for resilience in cyber-physical systems using SDN and NFV

Moura, J., & Hutchison, D. (2020) · SN Applied Sciences · doi:10.1007/s42452-020-03335-4 · Cited by 13

Robust Task Offloading for IoT Fog Computing Under Information Asymmetry and Information Uncertainty

Liao, H., Zhou, Z., Mumtaz, S., & Rodrı́guez, J. (2019) · International Conference on Communications · doi:10.1109/icc.2019.8761969 · Cited by 13

Deploying Network Functions for Multiaccess Edge-IoT With Deep Reinforcement Learning

Shu, C., Zhao, Z., Min, G., Hu, J. W., & Zhang, J. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2987011 · Cited by 13

Resource Efficient Authentication and Session Key Establishment Procedure for Low-Resource IoT Devices

Khan, S., Alzahrani, A. I., Alfarraj, O., Alalwan, N., & Al-Bayatti, A. H. (2019) · IEEE Access · doi:10.1109/access.2019.2955604 · Cited by 12

A security integration model for private data of intelligent mobile communication based on edge computing

Chen, X. (2020) · Computer Communications · doi:10.1016/j.comcom.2020.08.026 · Cited by 12

Fast RFID Tag Sorting at the Edge for Internet of Things

Yang, Y., & Wang, X. (2021) · IEEE Access · doi:10.1109/access.2021.3090438 · Cited by 12

Quality-Aware Task Allocation for Mobile Crowd Sensing Based on Edge Computing

Li, Z., Li, Z., & Zhang, W. (2023) · Electronics · doi:10.3390/electronics12040960 · Cited by 11

Intelligent acceptance systems for distribution automation terminals: an overview of edge computing technologies and applications

Zhu, M., Liang, M., Li, H., Lu, Y., & Pang, M. (2023) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-023-00529-0 · Cited by 11

A Pricing Approach Toward Incentive Mechanisms for Participant Mobile Crowdsensing in Edge Computing

Chen, X., Tang, C., Li, Z., Qi, L., Chen, Y., & Chen, S. (2020) · Mobile Networks and Applications · doi:10.1007/s11036-020-01538-y · Cited by 10

Mobility-Aware Task Parallel Offloading for Vehicle Fog Computing

Xie, J., Jia, Y., Chen, Z., & Liang, L. (2019) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-22971-9_32 · Cited by 10

Parameter Identification and State-of-Charge Estimation for Li-Ion Batteries Using an Improved Tree Seed Algorithm

Chen, W., Cai, M., Tan, X., & Wei, B. (2019) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2019edp7015 · Cited by 10

A Novel Mobile CrowdSensing Architecture for Road Safety

Abou-Zbiba, W., Gadi, H. E., Bakkali, H. E., Benbrahim, H., & Benhaddou, D. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-66840-2_24 · Cited by 10

Joint rate control and power allocation for low-latency reliable D2D-based relay network

Wang, Y., He, Y., Xu, C. G., Zhou, Z., Mumtaz, S., Rodrı́guez, J., & Pervaiz, H. (2019) · EURASIP Journal on Wireless Communications and Networking · doi:10.1186/s13638-019-1418-0 · Cited by 9

Resource Allocation for Energy Harvesting Based Cognitive Machine-to-Machine Communications

Zhang, C., Zhou, Z., Liu, P., & Gu, B. (2019) · International Conference on Communications · doi:10.1109/icc.2019.8761936 · Cited by 9

Design of English Diagnostic Practice Sentence Repetition Recognition System Based on Matching Tree and Edge Computing

Xi, T. (2021) · Wireless Communications and Mobile Computing · doi:10.1155/2021/6651145 · Cited by 9

Pushing Participatory Sensing Further to the Edge

Song, Z., Cheng, J., Chauhan, A., & Tilevich, E. (2019) · IEEE International Conference on Edge Computing and Communications (EDGE) · doi:10.1109/edge.2019.00019 · Cited by 9

Delay and Energy-Efficiency-Balanced Task Offloading for Electric Internet of Things

Wei, Y., Yang, H., Wang, J., Chen, X., Li, J., Zhang, S., & Huang, B. (2022) · Electronics · doi:10.3390/electronics11060839 · Cited by 9

Crowd anomaly estimation and detection: A review

Hussein, A., Raed, M. W., Al‐Shaikhi, A., Mohandes, M. A., & Liu, B. (2024) · Franklin Open · doi:10.1016/j.fraope.2024.100169 · Cited by 8

Energy-Efficient Offloading and User Association in UAV-assisted Vehicular Ad Hoc Network

Aung, P. S., Tun, Y. K., Nway, N., & Hong, C. S. (2020) · Asia-Pacific Network Operations and Management Symposium (APNOMS) · doi:10.23919/apnoms50412.2020.9237040 · Cited by 8

Evaluation of Logistics Service Quality: Sentiment Analysis of Comment Text Based on Multi-Level Graph Neural Network

Chen, W., Zheng, X., Zhou, H., & Li, Z. (2021) · Traitement du signal · doi:10.18280/ts.380630 · Cited by 9

Privacy-Preserving Pedestrian Detection for Smart City with Edge Computing

Yuan, D., Zhu, X., Mao, Y., Zheng, B., & Wu, T. (2019) · International Conference on Wireless Communications and Signal Processing (WCSP) · doi:10.1109/wcsp.2019.8927923 · Cited by 8

Learning-Based Energy-Efficient Channel Selection for Edge Computing-Empowered Cognitive Machine-to-Machine Communications

Liao, H., Zhou, Z., Ai, B., & Guizani, M. (2020) · IEEE Vehicular Technology Conference · doi:10.1109/vtc2020-spring48590.2020.9128780 · Cited by 8

Using efficient deep learning techniques for mobile crowd sensing detection in an IOTA-based framework

Alatawi, M. N. (2024) · Discover Computing · doi:10.1007/s10791-024-09493-y · Cited by 7

Ensemble Learning Against Adversarial AI-driven Fake Task Submission in Mobile Crowdsensing

Zhang, Y., Şimşek, M., & Kantarcı, B. (2020) · International Conference on Communications · doi:10.1109/icc40277.2020.9149145 · Cited by 7

Achieving Private and Fair Truth Discovery in Crowdsourcing Systems

Wang, Z., Cheng, X., Su, S., & Wang, L. (2022) · Security and Communication Networks · doi:10.1155/2022/9281729 · Cited by 6

EPC-Based Efficient Tag Selection in RFID Systems

Zhu, L., Wang, X., Yang, Y., Xu, S., Wu, X., Zhao, W., & Feng, H. (2020) · IEEE Access · doi:10.1109/access.2020.2969188 · Cited by 7

Distributed Machine Learning for Predictive Analytics in Mobile Edge Computing Based IoT Environments

Abeysekara, P., Dong, H., & Qin, A. K. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9206867 · Cited by 7

A Case for Machine Learning in Edge-Oriented Computing to Enhance Mobility as a Service

Carvalho, G., Cabral, B., Pereira, V., & Bernardino, J. (2019) · International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT) · doi:10.1109/dcoss.2019.00101 · Cited by 7

Incentivizing WiFi-Based Multilateration Location Verification

Li, M., Wang, W., Zhang, J., & Zhang, Q. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3096820 · Cited by 6

FAHP‐Based Reliability Evaluation of Distributed IoT Devices in a Distribution Power Grid

You, X., Zhang, P., Li, S., Wang, F., Su, G., & Zhang, S. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/6772467 · Cited by 6

Electrical fire monitoring IoT framework for ancient architectural complex leveraging edge computing

Dai, H., Shi, P., Liu, H., Chen, R., & Chen, Z. (2022) · International Journal of Communication Systems · doi:10.1002/dac.5329 · Cited by 6

Optimal Sensing Task Distribution Algorithm for Mobile Sensor Networks With Agent Cooperation Relationship

Zhang, Y., Ye, T., Zhang, S., Zhang, L., & Long, H. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3045256 · Cited by 5

Performance Evaluation of Hybrid Crowdsensing and Fixed Sensor Systems for Event Detection in Urban Environments

Hirth, M., Seufert, M., Lange, S., Meixner, M., & Tran‐Gia, P. (2021) · Sensors · doi:10.3390/s21175880 · Cited by 5

Channel-Adaptive Quantization for Wireless Federated Learning

Lin, X., Liu, Y., & Chen, F. (2021) · IEEE/CIC International Conference on Communications in China (ICCC) · doi:10.1109/iccc52777.2021.9580257 · Cited by 5

A Review on Concepts and Technologies of 6G Cellular Network and Future ScopePreprint

Sulaiman, R. B., & Kareem, A. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3731869 · Cited by 5

A 6G network survey: state-of-the-art, issues, and future technologies

Lingaraj, K., Malghan, R. L., Rao, M. C., & Garg, L. (2025) · Engineering Research Express · doi:10.1088/2631-8695/ade02b · Cited by 6

Scalable Multi-Clustering Aggregation Scheme in WSN Using Machine Learning

Ramesh, D., & Jaya, T. (2025) · Turkish Journal of Engineering · doi:10.31127/tuje.1488192 · Cited by 4

FairShare: An Incentive-Based Fairness-Aware Data Sharing Framework for Federated Learning

Liu, L., Kong, Y., Li, G., & Han, M. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-6486-4_10 · Cited by 4

Duopoly Price Competition in Wireless Sensor Network-Based Service Provision

Li, X., Zhao, L., Zhou, Z., Gu, B., Chen, G., Cheng, F., & Zhang, H. (2018) · Sensors · doi:10.3390/s18124422 · Cited by 4

Boundary Node Identification in Three Dimensional Wireless Sensor Networks for Surface Coverage

Wei, L., Song, X., Zheng, X., Wu, X., & Gui, G. (2019) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2018edp7355 · Cited by 4

Device Type Identification via Network Traffic and Lightweight Convolutional Neural Network for Internet of Things

Qing, G., Wang, H., Guo, L., & Yang, J. (2020) · IEEE Access · doi:10.1109/access.2020.3032469 · Cited by 4

Lightweight Convolutional Neural Network-Based Method for Crane Safety Inspection

Jiang, W., Liu, T., Chen, Y., Chen, Q., Zhang, X., & Gui, G. (2020) · International Conference on Dependable Systems and Their Applications (DSA) · doi:10.1109/dsa51864.2020.00058 · Cited by 4

BETA: From Behavior Sequentializing to Task Mapping in Mobile Crowdsensing

Zhou, J., Li, D., & Liu, M. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3164672 · Cited by 4

A Simple Deterministic Measurement Matrix Based on GMW Pseudorandom Sequence

Liu, H., Hua, G., Yin, H., Zhu, A., & Cui, R. (2019) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2018edp7324 · Cited by 4

Maximizing Sleeping Capacity Based on QoS Provision for Information-Centric Internet of Things

He, Y., Tang, L., & Ren, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2931443 · Cited by 4

Facility Location Strategy for Minimizing Cost in Edge-Based Mobile Crowdsensing

Wang, E., Luan, D., Yang, Y., & Wu, J. (2019) · IEEE International Conference on Mobile Ad-Hoc and Sensor Systems (MASS) · doi:10.1109/mass.2019.00055 · Cited by 4

Low-Complexity Cross-Layer Resource Allocation for Low-Latency D2D-Based Relay Networks

Wang, Y., Xu, C., He, Y., & Zhou, Z. (2019) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc.2019.8766653 · Cited by 4

Online Resource Allocation for Energy Harvesting Based Large-Scale Multiple Antenna Systems

Yu, H., Zhou, Z., Pan, C., Zhao, X., & Mumtaz, S. (2019) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/gcwkshps45667.2019.9024449 · Cited by 3

Dynamic Distribution of Edge Intelligence at the Node Level for Internet of Things

Mohammed, H., Odetola, T. A., Guo, N., & Hasan, S. R. (2021) · IEEE International Midwest Symposium on Circuits and Systems (MWSCAS) · doi:10.1109/mwscas47672.2021.9531765 · Cited by 4

Dispatching and Control Information Freshness-Aware Federated Learning for Simplified Power IoT

Jia, Z., Yu, Z., Liao, H., Wang, Z., Zhou, Z., Wang, X., He, G., Mumtaz, S., & Guizani, M. (2022) · Globecom · doi:10.1109/globecom48099.2022.10001183 · Cited by 3

A Distributed Deep Learning Approach with Mobile Edge Computing for Next Generation IoT Networks Security

Rathore, S., Sharma, P. K., & Rathore, H. (2023) · World Conference on Communication & Computing (WCONF) · doi:10.1109/wconf58270.2023.10235095 · Cited by 3

Information aggregation and data compression based on PLC for distributed photovoltaic integration

Li, B., Shi, Z., & Wang, X. (2023) · Alexandria Engineering Journal · doi:10.1016/j.aej.2023.11.045 · Cited by 3

Boosting Crowdsourced Annotation Accuracy: Small Loss Filtering and Augmentation-Driven Training

Fu, Y., Han, W., Yang, J., Lu, H., & Yu, X. Y. (2024) · IEEE Access · doi:10.1109/access.2024.3432729 · Cited by 3

Quality-Aware Task Allocation for Mobile Crowd Sensing Based on Edge Computing

Li, Z., Li, Z., & Zhang, W. (2023) · Electronics · doi:10.3390/electronics12040960 · Cited by 11

Sensing Data Aggregation in Mobile Crowd Sensing: A Cloud-Enhanced-Edge–End Framework With DQN-Based Offloading

Yang, G., Sang, J., Zhang, X., He, X., Liu, Y., & Sun, F. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3422083 · Cited by 15

Mobile Crowd Sensing in Edge Computing Environment

Pore, M. (2019)

A crowd sensing data collection framework based on edge computing

Liu, Y., Sun, X., Yang, X., & Zhao, Y. (2022) · ITM Web of Conferences · doi:10.1051/itmconf/20224702050 · Cited by 1

Energy-Efficient Distributed Mobile Crowd Sensing: A Deep Learning Approach

Liu, C. H., Chen, Z., & Zhan, Y. (2019) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2019.2904353 · Cited by 140