Edge Intelligence: The Convergence of Humans, Things, and AI
Abstract
Edge AI and Human Augmentation are two major technology trends, driven by recent advancements in edge computing, IoT, and AI accelerators. As humans, things, and AI continue to grow closer together, systems engineers and researchers are faced with new and unique challenges. In this paper, we analyze the role of edge computing and AI in the cyber-human evolution, and identify challenges that edge computing systems will consequently be faced with. We take a closer look at how a cyber-physical fabric will be complemented by AI operationalization to enable seamless end-to-end edge intelligence systems.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Runtime monitoring is important for operationalized ML in order to increase trust and detect performance degradations.
“Runtime monitoring is important for operationalized ML in order to increase trust and detect performance degradations [28,42].”
From Operationalizing machine learning models: a systematic literature review (2022) · cites this paper as [42]
Containerization is a commonly reported method of packaging ML models.
“Containerization is a commonly reported method of packaging ML models [39,42,45]”
From Operationalizing machine learning models: a systematic literature review (2022) · cites this paper as [42]
Some metrics, such as concept drift and model drift, require continuous access to the original training set.
“First, some metrics, such as concept drift and model drift, require continuous access to the original training set.”
From Operationalizing machine learning models: a systematic literature review (2022) · cites this paper as [42]
There is a lack of generic solutions for deploying ML models to embedded and edge devices.
“[7] and [42] reported a lack of generic solutions for deploying ML models to embedded and edge devices.”
From Operationalizing machine learning models: a systematic literature review (2022) · cites this paper as [42]
Conventional IoT provisioning frameworks are not suited for deploying ML models.
“[42] reported that conventional IoT provisioning frameworks are not suited for deploying ML models”
From Operationalizing machine learning models: a systematic literature review (2022) · cites this paper as [42]
Edge devices may not have access to the training set for multiple reasons, including storage space constraints and data privacy concerns.
“However, edge devices may not have access to the training set for multiple reasons, including storage space constraints and data privacy concerns.”
From Operationalizing machine learning models: a systematic literature review (2022) · cites this paper as [42]
Cites
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387 · see citing works
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017) · arXiv · doi:10.48550/arxiv.1704.04861 · Cited by 19,015 · see citing works
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612 · see citing works
A survey on concept drift adaptation
Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., & Bouchachia, A. (2014) · ACM Computing Surveys · doi:10.1145/2523813 · Cited by 3,735 · see citing works
The case for vm-based cloudlets in mobile computing
Satyanarayanan, M., Bahl, P., Caceres, R., & Davies, N. (2009) · IEEE Pervasive Computing · doi:10.1109/mprv.2009.82 · Cited by 3,660 · see citing works
Large Scale Distributed Deep Networks
Dean, J. B., Corrado, G. S., Monga, R., Chen, K., Devin, M., Mao, M., Ranzato, M., Senior, A., Tucker, P. A., Yang, K., et al. (2012) · Cited by 3,189 · see citing works
The emergence of edge computing
Satyanarayanan, M. (2017) · Computer · doi:10.1109/mc.2017.9 · Cited by 2,832 · see citing works
Building Watson: An Overview of the DeepQA Project
Ferrucci, D. A., Brown, E. W., Chu‐Carroll, J., Fan, J., Gondek, D. C., Kalyanpur, A. A., Lally, A., Murdock, J. W., Nyberg, E., Prager, J. M., et al. (2010) · AI Magazine · doi:10.1609/aimag.v31i3.2303 · Cited by 1,523 · see citing works
(2015) · neural information processing systems · Cited by 1,305 · see citing works
Shi, W., & Dustdar, S. (2016) · Computer · doi:10.1109/mc.2016.145 · Cited by 1,241 · see citing works
Self-citationSamek, W., Wiegand, T., & Müller, K.-R. (2017) · arXiv · doi:10.48550/arxiv.1708.08296 · Cited by 1,080 · see citing works
Edge-centric Computing: Vision and Challenges
Garcia Lopez, P., Montresor, A., Epema, D., Datta, A., Higashino, T., Iamnitchi, A., Barcellos, M., Felber, P., & Riviere, E. (2015) · ACM SIGCOMM Computer Communication Review · doi:10.1145/2831347.2831354 · Cited by 934 · see citing works
Federated Learning of Deep Networks using Model AveragingPreprint
McMahan, H. B., Moore, E., Ramage, D., & y Arcas, B. A. (2016) · arXiv (Cornell University) · Cited by 923 · see citing works
Explainable Artificial Intelligence (XAI)
Hossain, M. (2021) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.6371435 · Cited by 544 · see citing works
Towards wearable cognitive assistance
Ha, K., Chen, Z., Hu, W., Richter, W., Pillai, P., & Satyanarayanan, M. (2014) · Proceedings of the 12th annual international conference on Mobile systems, applications, and services · doi:10.1145/2594368.2594383 · Cited by 476 · see citing works
Edge analytics in the internet of things
Satyanarayanan, M., Simoens, P., Xiao, Y., Pillai, P., Chen, Z., Ha, K., Hu, W., & Amos, B. (2015) · IEEE Pervasive Computing · doi:10.1109/mprv.2015.32 · Cited by 430 · see citing works
Madhavapeddy, A., Mortier, R., Rotsos, C., Scott, D., Singh, B., Gazagnaire, T., Smith, S., Hand, S., & Crowcroft, J. (2013) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/2451116.2451167 · Cited by 400 · see citing works
TFX: A TensorFlow-Based Production-Scale Machine Learning Platform
Baylor, D., Breck, E., Cheng, H.-T., Fiedel, N., Foo, C. Y., Haque, Z., Haykal, S., Ispir, M., Jain, V., Koc, L., et al. (2017) · Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/3097983.3098021 · Cited by 350 · see citing works
Understanding collective crowd behaviors: Learning a Mixture model of Dynamic pedestrian-Agents
Zhou, B., Wang, X., & Tang, X. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6248013 · Cited by 327 · see citing works
Evaluating the Robustness of Neural Networks: An Extreme Value Theory ApproachPreprint
Weng, T.-W., Zhang, H., Chen, P., Yi, J., Su, D., Gao, Y., Hsieh, C., & Daniel, L. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.10578 · Cited by 307 · see citing works
Morabito, R. (2017) · IEEE Access · doi:10.1109/access.2017.2704444 · Cited by 248 · see citing works
A Serverless Real-Time Data Analytics Platform for Edge Computing
Nastić, S., Rausch, T., Šćekić, O., Dustdar, S., Gušev, M., Koteska, B., Kostoska, M., Jakimovski, B., Ristov, S., & Prodan, R. (2017) · IEEE Internet Computing · doi:10.1109/mic.2017.2911430 · Cited by 224 · see citing works
Self-citationDistributed, decentralized, and democratized artificial intelligence
Montes, G. A., & Goertzel, B. (2018) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2018.11.010 · Cited by 185 · see citing works
Efficient Online Evaluation of Big Data Stream Classifiers
Bifet, A., Morales, G. D. F., Read, J., Holmes, G., & Pfahringer, B. (2015) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2783258.2783372 · Cited by 174 · see citing works
Explainable Artificial Intelligence (XAI)
Sewada, R., Jangid, A., Kumar, P., & Mishra, N. (2023) · Journal of Nonlinear Analysis and Optimization Theory & Applications (JNAO) · doi:10.36893/jnao.2022.v13i02.041-047 · Cited by 173 · see citing works
Contemporary Approaches to Artificial General Intelligence
Pennachin, C. L., & Goertzel, B. (2007) · Cognitive technologies · doi:10.1007/978-3-540-68677-4_1 · Cited by 117 · see citing works
ParaDrop: Enabling Lightweight Multi-tenancy at the Network’s Extreme Edge
Liu, P., Willis, D., & Banerjee, S. (2016) · IEEE/ACM Symposium on Edge Computing (SEC) · doi:10.1109/sec.2016.39 · Cited by 115 · see citing works
ModelOps: Cloud-Based Lifecycle Management for Reliable and Trusted AI
Hummer, W., Muthusamy, V., Rausch, T., Dube, P., El Maghraoui, K., Murthi, A., & Oum, P. (2019) · 2019 IEEE International Conference on Cloud Engineering (IC2E) · doi:10.1109/ic2e.2019.00025 · Cited by 105 · see citing works
Self-citationMadhavapeddy, A., Mortier, R., Rotsos, C., Scott, D., Singh, B., Gazagnaire, T., Smith, S., Hand, S., & Crowcroft, J. (2013) · ACM SIGPLAN Notices · doi:10.1145/2499368.2451167 · Cited by 94 · see citing works
EMMA: Distributed QoS-Aware MQTT Middleware for Edge Computing Applications
Rausch, T., Nastić, S., & Dustdar, S. (2018) · IEEE International Conference on Cloud Engineering · doi:10.1109/ic2e.2018.00043 · Cited by 93 · see citing works
Self-citationModelHub: Deep Learning Lifecycle Management
Miao, H., Li, A., Davis, L. S., & Deshpande, A. (2017) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2017.192 · Cited by 61 · see citing works
LEONORE -- Large-Scale Provisioning of Resource-Constrained IoT Deployments
Vögler, M., Schleicher, J. M., Inzinger, C., Nastić, S., Sehic, S., & Dustdar, S. (2015) · IEEE International Symposium on Service-Oriented System Engineering (SOSE) · doi:10.1109/sose.2015.23 · Cited by 54 · see citing works
Self-citationEnabling a Smart City Application Ecosystem: Requirements and Architectural Aspects
Schleicher, J. M., Vögler, M., Dustdar, S., & Inzinger, C. (2016) · IEEE Internet Computing · doi:10.1109/mic.2016.39 · Cited by 53 · see citing works
Self-citationBhattacharjee, B., Boag, S., Doshi, C., Dube, P., Herta, B., Ishakian, V., Jayaram, K. R., Khalaf, R. Y., Krishna, A., Li, Y. B., et al. (2017) · IBM Journal of Research and Development · doi:10.1147/jrd.2017.2716578 · Cited by 46 · see citing works
Li, T., Zhong, J. P., Liu, J., Wu, W., & Zhang, C. (2018) · Proceedings of the VLDB Endowment · doi:10.1145/3187009.3177737 · Cited by 38 · see citing works
Zhang, J., Iannucci, B., Hennessy, M., Gopal, K., Xiao, S., Kumar, S., Pfeffer, D., Aljedia, B., Ren, Y., Griss, M., et al. (2013) · IEEE International Conference on Services Computing · doi:10.1109/scc.2013.34 · Cited by 34 · see citing works
Smart Cities: The Internet of Things, People and Systems
Dustdar, S., Nastić, S., & Šćekić, O. (2017) · Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)) · doi:10.1007/978-3-319-60030-7 · Cited by 33 · see citing works
Self-citationA Middleware Infrastructure for Utility-Based Provisioning of IoT Cloud Systems
Nastić, S., Truong, H., & Dustdar, S. (2016) · IEEE/ACM Symposium on Edge Computing (SEC) · doi:10.1109/sec.2016.35 · Cited by 31 · see citing works
Self-citationOsmotic Message-Oriented Middleware for the Internet of Things
Rausch, T., Dustdar, S., & Ranjan, R. K. (2018) · IEEE Cloud Computing · doi:10.1109/mcc.2018.022171663 · Cited by 30 · see citing works
Self-citationFrom the Editor in Chief: Augmenting Cognition
Satyanarayanan, M. (2004) · IEEE Pervasive Computing · doi:10.1109/mprv.2004.1316809 · Cited by 22 · see citing works
Portable Energy-Aware Cluster-Based Edge Computers
Rausch, T., Avasalcai, C., & Dustdar, S. (2018) · IEEE/ACM Symposium on Edge Computing (SEC) · doi:10.1109/sec.2018.00026 · Cited by 22 · see citing works
Self-citationRecent Advances in Mobile Agent‐Oriented Applications
Hurson, A. R., Jean, E., Ongtang, M., Gao, X., Jiao, Y., & Potok, T. E. (2010) · doi:10.1002/9780470579398.ch6 · Cited by 9 · see citing works
Multi-device, Multi-tenant Model Selection with GP-EI.Preprint
Chen, Y., Karlaš, B., Zhong, J., Zhang, C., & Ji, L. (2018) · arXiv (Cornell University) · Cited by 3 · see citing works
Mobility in Publish/Subscribe Systems
Muthusamy, V., & Jacobsen, H. (2010) · doi:10.1002/9780470579398.ch4 · Cited by 1 · see citing works
Flagged first, then by citations
2 of the 46 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Fraga‐Lamas, P., Lopes, S. I., & Fernández‐Caramés, T. M. (2021) · Sensors · doi:10.3390/s21175745 · Cited by 434 · see citing works
Huo, R., Zeng, S., Wang, Z., Shang, J., Chen, W., Huang, T., Wang, S., Yu, F. R., & Liu, Y. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3141490 · Cited by 277 · see citing works
Optimized container scheduling for data-intensive serverless edge computing
Rausch, T., Rashed, A., & Dustdar, S. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2020.07.017 · Cited by 190 · see citing works
Self-citationIntelligent Cooperative Edge Computing in Internet of Things
Gong, C., Lin, F., Gong, X., & Lu, Y. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2986015 · Cited by 158 · see citing works
On the Edge of the Deployment: A Survey on Multi-access Edge Computing
Cruz, P., Achir, N., & Viana, A. C. (2022) · ACM Computing Surveys · doi:10.1145/3529758 · Cited by 135 · see citing works
Steidl, M., Felderer, M., & Ramler, R. (2023) · Journal of Systems and Software · doi:10.1016/j.jss.2023.111615 · Cited by 126 · see citing works
6G White Paper on Edge Intelligence
Peltonen, E., Bennis, M., Capobianco, M., Debbah, M., Ding, A., Gil-Castiñeira, F., Jurmu, M., Karvonen, T., Kelanti, M., Kliks, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2004.14850 · Cited by 118 · see citing works
Badidi, E., Moumane, K., & Ghazi, F. E. (2023) · IEEE Access · doi:10.1109/access.2023.3300658 · Cited by 115 · see citing works
Rahman, A., Hasan, K., Kundu, D., Islam, M. J., Debnath, T., Band, S. S., & Kumar, N. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.08.004 · Cited by 111 · see citing works
Semantic Communication: A Survey on Research Landscape, Challenges, and Future Directions
Getu, T. M., Kaddoum, G., & Bennis, M. (2024) · Proceedings of the IEEE · doi:10.1109/jproc.2024.3520707 · Cited by 110 · see citing works
Distributed Artificial Intelligence-as-a-Service (DAIaaS) for Smarter IoE and 6G Environments
Janbi, N. F., Katib, I. A., Albeshri, A. A., & Mehmood, R. (2020) · Sensors · doi:10.3390/s20205796 · Cited by 102 · see citing works
A Systematic Literature Review on Distributed Machine Learning in Edge Computing
Filho, C. P., Marques, E. L., Chang, V., dos Santos, L. A., Bernardini, F., Pires, P. F., Ochi, L., & Delicato, F. C. (2022) · Italian National Conference on Sensors · doi:10.3390/s22072665 · Cited by 92 · see citing works
Information fusion for edge intelligence: A survey
Zhang⋆, Y., Jiang, C., Yue, B., Wan, J., & Guizani, M. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.11.018 · Cited by 91 · see citing works
Edge intelligence-driven digital twin of CNC system: Architecture and deployment
Yu, H., Yu, D. I., Wang, C., Hu, Y., & Li, Y. (2022) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2022.102418 · Cited by 72 · see citing works
(2019) · Cited by 62 · see citing works
Self-citationDisclosing Edge Intelligence: A Systematic Meta-Survey
Barbuto, V., Savaglio, C., Chen, M., & Fortino, G. (2023) · Big Data and Cognitive Computing · doi:10.3390/bdcc7010044 · Cited by 53 · see citing works
A Survey on Goal-Oriented Semantic Communication: Techniques, Challenges, and Future Directions
Getu, T. M., Kaddoum, G., & Bennis, M. (2024) · IEEE Access · doi:10.1109/access.2024.3381967 · Cited by 61 · see citing works
Tam, P., Corrado, R., Eang, C., & Kim, S. (2023) · Applied Sciences · doi:10.3390/app13053083 · Cited by 53 · see citing works
Towards cost-effective and robust AI microservice deployment in edge computing environments
Wu, C., Peng, Q., Xia, Y., Jin, Y. J., & Hu, Z. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.10.015 · Cited by 41 · see citing works
Low-Power Audio Keyword Spotting Using Tsetlin Machines
Lei, J., Rahman, T., Shafik, R., Yakovlev, A., Yakovlev, A., Granmo, O., Kawsar, F., & Mathur, A. (2021) · Journal of Low Power Electronics and Applications · doi:10.3390/jlpea11020018 · Cited by 40 · see citing works
Janbi, N. F., Mehmood, R., Katib, I. A., Albeshri, A. A., Corchado, J. M., & Yiğitcanlar, T. (2022) · Sensors · doi:10.3390/s22051854 · Cited by 39 · see citing works
Edge intelligence based Economic Dispatch for Virtual Power Plant in 5G Internet of Energy
Fang, D., Guan, X., Lin, L., Yu, P., Sun, D., & Hassan, M. M. (2019) · Computer Communications · doi:10.1016/j.comcom.2019.12.021 · Cited by 38 · see citing works
E-Tree Learning: A Novel Decentralized Model Learning Framework for Edge AI
Yang, L., Lu, Y., Cao, J., Huang, J., & Zhang, M. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3052195 · Cited by 36 · see citing works
Operationalizing machine learning models: a systematic literature review
Kolltveit, A. B., & Li, J. (2022) · Proceedings of the 1st Workshop on Software Engineering for Responsible AI · doi:10.1145/3526073.3527584 · Cited by 34 · see citing works
When machine learning meets Network Management and Orchestration in Edge-based networking paradigms
Shahraki, A., Ohlenforst, T., & Kreyß, F. (2022) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2022.103558 · Cited by 34 · see citing works
Virtualizing AI at the Distributed Edge towards Intelligent IoT Applications
Campolo, C., Genovese, G., Iera, A., & Molinaro, A. (2021) · Journal of Sensor and Actuator Networks · doi:10.3390/jsan10010013 · Cited by 33 · see citing works
Securing Clustered Edge Intelligence With Blockchain
Dehury, C. K., Srirama, S. N., Donta, P. K., & Dustdar, S. (2022) · IEEE Consumer Electronics Magazine · doi:10.1109/mce.2022.3164529 · Cited by 33 · see citing works
Self-citationPang, Y., Wang, D., Wang, D., Guan, L., Zhang, C., & Zhang, M. (2020) · IEEE World Congress on Services (SERVICES) · doi:10.1109/services48979.2020.00060 · Cited by 32 · see citing works
Edge-AI-Based Real-Time Automated License Plate Recognition System
Lin, C., Chuang, C., & Lin, H.-Y. (2022) · Applied Sciences · doi:10.3390/app12031445 · Cited by 29 · see citing works
Venkatachary, S. K., Alagappan, A., & Andrews, L. J. B. (2021) · Energy Informatics · doi:10.1186/s42162-021-00139-7 · Cited by 30 · see citing works
Resource-efficient Edge AI solution for predictive maintenance
Artiushenko, V., Lang, S., Lerez, C., Reggelin, T., & Hackert‐Oschätzchen, M. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.01.034 · Cited by 24 · see citing works
Federated Edge Intelligence and Edge Caching Mechanisms
Karras, A., Karras, C., Giotopoulos, K. C., Tsolis, D. K., Οικονόμου, Κ., & Sioutas, S. (2023) · Information · doi:10.3390/info14070414 · Cited by 22 · see citing works
Rojek, I., Prokopowicz, P., Piechowiak, M., Kotlarz, P., Náprstková, N., & Mikołajewski, D. (2025) · Applied Sciences · doi:10.3390/app16010225 · Cited by 20 · see citing works
Horizontally Distributed Inference of Deep Neural Networks for AI-Enabled IoT
Rodríguez, I., Campos, C., & Fdez‐Riverola, F. (2023) · Sensors · doi:10.3390/s23041911 · Cited by 22 · see citing works
Seabird surveillance: combining CCTV and artificial intelligence for monitoring and research
Hentati‐Sundberg, J., Olin, A. B., Reddy, S., Berglund, P., SVENSSON, E. I., Reddy, M. J., Kasarareni, S., Carlsen, A. A., Hanes, M., Kad, S., et al. (2023) · Remote Sensing in Ecology and Conservation · doi:10.1002/rse2.329 · Cited by 18 · see citing works
Unogwu, O. J., Doshi, R., Hiran, K. K., Mijwil, M. M., Catherine, A. T., & Abotaleb, M. S. A. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-2440-0.ch009 · Cited by 18 · see citing works
CCEI-IoT: Clustered and Cohesive Edge Intelligence in Internet of Things
Dehury, C., Donta, P. K., Dustdar, S., & Srirama, S. (2022) · International Conference on Edge Computing [Services Society] · doi:10.1109/edge55608.2022.00017 · Cited by 16 · see citing works
Self-citationTowards a Platform for Smart City-Scale Cognitive Assistance Applications
Rausch, T., Hummer, W., Stippel, C., Vasiljevic, S., Elvezio, C., Dustdar, S., & Krösl, K. (2021) · IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW) · doi:10.1109/vrw52623.2021.00066 · Cited by 14 · see citing works
Self-citationEdge Intelligence as a Service
Raith, P., & Dustdar, S. (2021) · IEEE International Conference on Services Computing · doi:10.1109/scc53864.2021.00038 · Cited by 12 · see citing works
Self-citationMohanty, L., Kumar, A., Mehta, V., Agarwal, M., & Suri, J. S. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19192-x · Cited by 11 · see citing works
A Privacy Preserving System for AI-assisted Video Analytics
Lachner, C., Rausch, T., & Dustdar, S. (2021) · IEEE International Conference on Fog and Edge Computing (ICFEC) · doi:10.1109/icfec51620.2021.00018 · Cited by 11 · see citing works
Self-citationJadaun, P., Cui, C., Liu, S., & Incorvia, J. A. C. (2022) · PNAS Nexus · doi:10.1093/pnasnexus/pgac206 · Cited by 11 · see citing works
Liao, Z., Zhang, X., He, S., & Tang, Q. (2023) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2023.103720 · Cited by 9 · see citing works
Trustworthy and Sustainable Edge AI: A Research Agenda
Ding, A. Y., Janssen, M., & Crowcroft, J. (2021) · IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA) · doi:10.1109/tpsisa52974.2021.00019 · Cited by 9 · see citing works
Alomari, M. K., Khan, H. U., Khan, S., Al-Maadid, A. A., Abushawish, Z., & Hammami, H. (2021) · Security and Communication Networks · doi:10.1155/2021/8686469 · Cited by 8 · see citing works
Virtual Power Plants Security Challenges, Solutions, and Emerging Trends: A Review
Rao, S. P., Tiruvalluru, R. S., Balaji, S. R. A., Tomomewo, O. S., & Ranganathan, P. (2024) · Cyber Awareness and Research Symposium (CARS) · doi:10.1109/cars61786.2024.10778817 · Cited by 6 · see citing works
Tam, P., Math, S., & Kim, S. (2022) · Computer Systems Science and Engineering · doi:10.32604/csse.2023.030984 · Cited by 6 · see citing works
An Edge Deployment Framework to Scale AI in Industrial Applications
Kemnitz, J., Weißenfeld, A., Schoeffl, L., Stiftinger, A., Rechberger, D., Prangl, B., Kaufmann, T., Hießl, T., Holly, S., Heistracher, C., et al. (2023) · IEEE International Conference on Fog and Edge Computing (ICFEC) · doi:10.1109/icfec57925.2023.00012 · Cited by 5 · see citing works
Edge Computing in the Internet of Things: A SurveyPreprint
Elmassik, Z. (2023) · doi:10.36227/techrxiv.22121660 · Cited by 3 · see citing works
Edge Intelligence in Autonomous Vehicle Navigation
Manivannan, S., Muralidharan, V., Senthil Kumar, G., Sundarambal, B., Kirubakaran, N., & Selvaganesan, C. (2025) · International Conference on Data Science and Business Systems (ICDSBS) · doi:10.1109/icdsbs63635.2025.11031927 · Cited by 2 · see citing works
Realising Edge Analytics for Early Prediction of Readmission: A Case Study
Nan, Y., Li, W., Lu, F., Delicato, F. C., & Zomaya, A. Y. (2020) · IEEE International Conference on Cloud Engineering · doi:10.1109/ic2e48712.2020.00017 · Cited by 2 · see citing works
Fault Diagnosis of Industrial Internet of Things Equipment Based on Edge Intelligent Drive
Sang, H., & Chen, B. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-33-6318-2_82 · Cited by 2 · see citing works
A Design of Edge Distributed Video Analysis System Based on Serverless Computing Service
Wang, Y., Yang, D., Ding, P., Yun, S., Shi, X., & Dai, M. (2023) · IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB) · doi:10.1109/bmsb58369.2023.10211177 · Cited by 2 · see citing works
Fataliyev, T. K., Bayramov, H., & Mikayilova, R. (2023) · International Conference on Problems of Cybernetics and Informatics (PCI) · doi:10.1109/pci60110.2023.10326016 · Cited by 2 · see citing works
Science 4.0 as a Model of Scientific Activity in an Innovative Environment of Industry 4.0
Mehdiyev, S., & Fataliyev, T. K. (2024) · International Journal of Cyber Research and Education · doi:10.4018/ijcre.339191 · Cited by 2 · see citing works
Resource-Aware Split Federated Learning for Edge Intelligence
Arouj, A., Abdelmoniem, A. M., Alhilal, A. Y., You, L., & Wang, C. (2024) · IEEE Workshop on Machine Learning on Edge in Sensor Systems (SenSys-ML) · doi:10.1109/sensys-ml62579.2024.00008 · Cited by 2 · see citing works
Devan, K. (2025) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-7723-9.ch008 · Cited by 2 · see citing works
AI-Driven Privacy Analysis of IoT Services in Edge Computing
Abdulhasan, M. M., Hussain, A., Ali, M. F., Al-Dabagh, M. Z. N., & Al-Assal, J. R. R. (2024) · International Conference on Emerging Research in Computational Science (ICERCS) · doi:10.1109/icercs63125.2024.10895120 · Cited by 2 · see citing works
Partnership-based Collaborative Learning Approach from Decentralized Data
Chen, Y., Dai, J., Su, X., Fan, Y., Bian, W., Ye, P., Chen, G., & Huang, Y. (2021) · doi:10.1109/bigdatase53435.2021.00024 · Cited by 2 · see citing works
Smart Campus Traffic Surveillance with YOLOv8n and IoT-Enabled Monitoring Interface
Shaik, Z. B., Krishnaiah, M., Peddakrishna, S., Kollem, S., Kumar, J., & Kuriti, J. (2025) · IEEE International Conference on Applied Electromagnetics, Signal Processing, & Communication (AESPC) · doi:10.1109/aespc67542.2025.11326868 · Cited by 1 · see citing works
Cyberinfrastructure for Advanced Research with High Performance Edge Computing
Jat, D. S., Jain, A., & Sepo, K. F. (2021) · Studies in autonomic, data-driven and industrial computing · doi:10.1007/978-981-16-3915-9_9 · Cited by 1 · see citing works
Creating Intelligent Computational Edge through Semantic Mediation
Juric, R., Ronchieri, E., & Suh, S. C. (2023) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2023.822 · Cited by 1 · see citing works
Improving edge AI for industrial IoT applications with distributed learning using consensus
Fidelis, S. A., Castro, M., & Siqueira, F. (2024) · Design Automation for Embedded Systems · doi:10.1007/s10617-024-09284-0 · Cited by 1 · see citing works
Lightweight Machine Sound Anomaly Detector Based on Parallel Discrete Wavelet Transform
Choi, E., & Park, H. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3556204 · Cited by 1 · see citing works
Privacy-First Artificial Intelligence: Toward Fair, Transparent, and Accountable Systems
Aunugu, D. R., & Vathsavai, V. G. (2022) · International Journal Of Scientific Advances · doi:10.51542/ijscia.v3i6.26 · Cited by 1 · see citing works
Recursive design for data-driven, self-adaptive IoT services
Frangoudis, P. A., Reisinger, M., & Dustdar, S. (2021) · IEEE International Symposium on Service-Oriented System Engineering (SOSE) · doi:10.1109/sose52839.2021.00009 · Cited by 1 · see citing works
Self-citationRaviKrishna, B., Potluri, S., Jeny, J. R. V., Sajja, G. S., & Rao, K. S. (2022) · doi:10.1002/9781394156887.ch10 · Cited by 1 · see citing works
Edge Computing in the Internet of Things: A SurveyPreprint
Elmassik, Z. (2023) · doi:10.36227/techrxiv.22121660.v1 · Cited by 1 · see citing works
Bi-BLS: A Bidirectional Connection Broad Learning System Model for Traffic Flow Prediction
Yuan, X., Chen, J., Sun, Y., Zhu, Y., Liu, D., & Cao, H. (2020) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/gcwkshps50303.2020.9367455
Edge-Cloud Intelligence in Self-Diagnostic of Land Mobile Radio Systems
Cao, H., Wachowicz, M., & Craig, J. (2021) · IEEE World Forum on Internet of Things (WF-IoT) · doi:10.1109/wf-iot51360.2021.9595618
A Low-Power Approximate Multiplier with Sign-Focus Compressor and Error Compensation
Du, L., Ni, L., Liu, X., Mao, W., & Yu, H. (2022) · IEEE Asia Pacific Conference on Circuits and Systems (APCCAS) · doi:10.1109/apccas55924.2022.10090316
Research on Distributed Edge System Based on Artificial Intelligence Reasoning
Zheng, Z. (2023) · Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS) · doi:10.1109/acctcs58815.2023.00043
RETRACTED ARTICLE: Edge intelligence-based public health influenza pandemic risk prediction
Wang, X., Pan, B., & Cai, C. (2023) · Cognitive Neurodynamics · doi:10.1007/s11571-023-10045-1
RetractedLu, S., Li, D., Zhou, P. Y., You, X., & Wang, Z. (2024) · International Conference on Internet of Things and Cloud Computing Technology (IoTCCT) · doi:10.1145/3702879.3702900
Ding, A. Y., Alwis, C. D., & Liyanage, M. (2024) · Wiley eBooks · doi:10.1002/9781119611646.refs
FCDC: Firefly optimization algorithm based compression of deep CNN model with explainability
Mohanty, L., Agarwal, M., & Mehta, V. (2025) · Cluster Computing · doi:10.1007/s10586-025-05697-z
Development of Optimal Reconfigurable Smart Surfaces for Massive MIMO Wireless Communications
Hussein, Y. (2024) · theses.fr (ABES) · doi:10.70675/b1a6aae5zec56z47b3zb5f1zc1b9827472b3
Ragupathy, V. (2026) · International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) · doi:10.1109/acdsa67686.2026.11468176
Toward adaptive workload scheduling in Kubernetes across the edge-cloud continuum
Bregar, V., & Jurič, M. B. (2026) · Cluster Computing · doi:10.1007/s10586-026-06669-7
Mersni, A., Novikau, A., Kunovac, N., & Parashuram, S. (2026) · World sustainability series · doi:10.1007/978-3-032-34574-5_12
- Authors
- Rausch, T., & Dustdar, S.
- Affiliations
- TU Wien (AT)
- Venue
- 2019 IEEE International Conference on Cloud Engineering (IC2E)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 86-96
- OpenAlex
- W2968416780
- Cited by
- 82
- References
- 46
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 13 · 2023: 17 · 2022: 17 · 2021: 16 · 2020: 8 · 2019: 2
- FWCI
- 9.29
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE International Conference on Cloud Engineering (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Physical Sciences › Engineering › Control and Systems Engineering › Robotics and Automated Systems
- Keywords
- edge computing, edge AI, human augmentation, Internet of Things, AI accelerators, cyber-physical systems, human-AI interaction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
