A review on edge analytics: Issues, challenges, opportunities, promises, future directions, and applications

Abstract

Edge technology aims to bring cloud resources (specifically, the computation, storage, and network) to the closed proximity of the edge devices, i.e., smart devices where the data are produced and consumed. Embedding computing and application in edge devices lead to emerging of two new concepts in edge technology: edge computing and edge analytics. Edge analytics uses some techniques or algorithms to analyse the data generated by the edge devices. With the emerging of edge analytics, the edge devices have become a complete set. Currently, edge analytics is unable to provide full support to the analytic techniques. The edge devices cannot execute advanced and sophisticated analytic algorithms following various constraints such as limited power supply, small memory size, limited resources, etc. This article aims to provide a detailed discussion on edge analytics. The key contributions of the paper are as follows-a clear explanation to distinguish between the three concepts of edge technology: edge devices, edge computing, and edge analytics, along with their issues. In addition, the article discusses the implementation of edge analytics to solve many problems and applications in various areas such as retail, agriculture, industry, and healthcare. Moreover, the research papers of the state-of-the-art edge analytics are rigorously reviewed in this article to explore the existing issues, emerging challenges, research opportunities and their directions, and applications.

Keywords

Edge analytics / Edge computing / Edge devices / Big data / Sensor / Artificial intelligence / Machine learning / Smart technology / Healthcare

References

[1] S. Sakr, A. Liu, D.M. Batista, M. Alomari, A survey of large scale data management approaches in cloud environments, IEEE Commun. Surv. & Tutor. 13 (3) (2011) 311-336.

[2] A.u.R. Khan, M. Othman, S.A. Madani, S.U. Khan, A survey of mobile cloud computing application models, IEEE Commun. Surv. & Tutor. 16 (1) (2014) 393-413.

[3] S. Xiong, B. Ye, Analysis on the development path of smart city in the era of big data, in: 2019 IEEE 4th International Conference on Big Data Analytics (ICBDA), 2019, pp. 177-181.

[4] J. Camhi, Former Cisco Ceo John Chambers Predicts 500 Billion Connected Devices by 2025, Business Insider.

[5] M. Satyanarayanan, P. Bahl, R. Caceres, N. Davies, The case for vm-based cloudlets in mobile computing, IEEE Pervas. Comput. 8 (4) (2009) 14-23.

[6] S. Safavat, N.N. Sapavath, D.B. Rawat, Recent advances in mobile edge computing and content caching, Digit. Commun. Netw. 6 (2) (2020) 189-194.

[7] C. Pan, M. Xie, S. Han, Z.-H. Mao, J. Hu, Modeling and optimization for self-powered non-volatile iot edge devices with ultra-low harvesting power, ACM Trans. Cyber-Phys. Syst. 3 (3) (2019), 32:1-32:26.

[8] A. Sharma, A. Sai Sabitha, A. Bansal, Edge analytics for building automation systems: a review,in:2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), 2018, pp. 585-590.

[9] Q. Zhang, H. Sun, X. Wu, H. Zhong, Edge video analytics for public safety: a review, Proc. IEEE 107 (8) (2019) 1675-1696.

[10] B. Djamaluddin, T. Jovin, B. Nugraha, et al., Towards real-time edge analytics-a survey literature review of real-time data acquisition evolution in the upstream oil and gas industry,in:Abu Dhabi International Petroleum Exhibition & Conference, Society of Petroleum Engineers, 2019. D021S064R002.

[11] L. Zhou, M.H. Samavatian, A. Bacha, S. Majumdar, R. Teodorescu, Adaptive parallel execution of deep neural networks on heterogeneous edge devices, in: Proceedings of the 4th ACM/IEEE Symposium on Edge Computing, SEC ’19, ACM, New York, NY, USA, 2019, pp. 195-208.

[12] O. Gómez-Carmona, D. Casado-Mansilla, D. López-de Ipiña, J. García-Zubia, Simplicity is best: addressing the computational cost of machine learning classifiers in constrained edge devices,in: Proceedings of the 9th International Conference on the Internet of Things, IoT 2019, ACM, New York, NY, USA, 2019, 18:1-18:8.

[13] R. Cziva, D.P. Pezaros, Container network functions: bringing nfv to the network edge, IEEE Commun. Mag. 55 (6) (2017) 24-31.

[14] C. K.u.J.H. Maximilian Bischoff,Johannes M. Scheuermann, The edge is near: an introduction to edge computing. https://www.inovex.de/blog/edge-computing-introduction/, 2019. (Accessed 26 November 2019).

[15] accenture, Edge analytics pushing advanced analytics to the edge. https://www.accenture.com/us-en/_acnmedia/pdf-50/accenture-edge-analytics-mobility.pdf. (Accessed 26 November 2019).

[16] T. E. C. Mitch Tseng Huawei, L. C. S. SE), Introduction to edge computing in iiot. https://www.iiconsortium.org/pdf/Introduction_to_Edge_Computing_in_IIoT_2018-06-18.pdf. (Accessed 26 November 2019).

[17] M. Satyanarayanan, P. Simoens, Y. Xiao, P. Pillai, Z. Chen, K. Ha, W. Hu, B. Amos, Edge analytics in the internet of things, IEEE Pervas. Comput. 14 (2) (2015) 24-31.

[18] H. Cao, M. Wachowicz, S. Cha, Developing an edge computing platform for real-time descriptive analytics, in: 2017 IEEE International Conference on Big Data (Big Data), 2017, pp. 4546-4554.

[19] S. Nikolaou, C. Anagnostopoulos, D. Pezaros, In-network predictive analytics in edge computing, in: 2019 Wireless Days, WD, 2019, pp. 1-4.

[20] H. Jin, L. Jia, Z. Zhou, Boosting edge intelligence with collaborative cross-edge analytics, IEEE Internet Things J. 8 (4) (2021) 2444-2458.

[21] W.Z. Khan, E. Ahmed, S. Hakak, I. Yaqoob, A. Ahmed, Edge computing: a survey, Future Generat. Comput. Syst. 97 (2019) 219-235.

[22] GSMA, Opportunities and use cases for edge computing in the iot. https://www.gsma.com/iot/wp-content/uploads/2018/11/IoT-Edge-Opportunities-c.pdf. (Accessed 16 January 2020).

[23] L. Prosperi, A. Costan, P. Silva, G. Antoniu, Planner: cost-efficient execution plans placement for uniform stream analytics on edge and cloud, in: 2018 IEEE/ACM Workflows in Support of Large-Scale Science (WORKS), IEEE, 2018, pp. 42-51.

[24] S.B. Jagarlapudi, P. Gorur, B. Amrutur, A distributed object detector-tracker aided video encoder for smart camera networks, in: Proceedings of the 11th International Conference on Distributed Smart Cameras, ICDSC 2017, Association for Computing Machinery, New York, NY, USA, 2017, pp. 69-75.

[25] C. Bailas, M. Marsden, D. Zhang, N.E. O'Connor, S. Little, Performance of video processing at the edge for crowd-monitoring applications, in: 2018 IEEE 4th World Forum on Internet of Things (WF-IoT), 2018, pp. 482-487.

[26] R. Girshick, Fast R-CNN, in: 2015 IEEE International Conference on Computer Vision (ICCV), 2015, pp. 1440-1448.

[27] K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Computer Society, Los Alamitos, CA, USA, 2016, pp. 770-778.

[28] M. Marsden, K. McGuinness, S. Little, N.E. O'Connor, Fully Convolutional Crowd Counting on Highly Congested Scenes, 2016 arXiv:1612.00220.

[29] Y. Xie, Y. Hu, Y. Chen, Y. Liu, G. Shou,A video analytics-based intelligent indoor positioning system using edge computing for iot, in:2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2018, pp. 118-1187.

[30] S.Y. Jang, Y. Lee, B. Shin, D. Lee,Application-aware Iot Camera Virtualization for Video Analytics Edge Computing, in:2018 IEEE/ACM Symposium on Edge Computing (SEC), 2018, pp. 132-144.

[31] Y. Chen, Y. Xie, Y. Hu, Y. Liu, G. Shou,Design and implementation of video analytics system based on edge computing, in:2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2018, pp. 130-1307.

[32] C. Neff, M. Mendieta, S. Mohan, M. Baharani, S. Rogers, H. Tabkhi, Revamp2t: real-time edge video analytics for multicamera privacy-aware pedestrian tracking, IEEE Internet Things J. 7 (4) (2020) 2591-2602.

[33] Z. Cao, T. Simon, S.-E. Wei, Y. Sheikh, Realtime multi-person 2d pose estimation using part affinity fields, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR), 2017, pp. 1302-1310.

[34] B. Jacob, S. Kligys, B. Chen, M. Zhu, M. Tang, A. Howard, H. Adam, D. Kalenichenko, Quantization and training of neural networks for efficient integer-arithmetic-only inference, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR), 2018, pp. 2704-2713.

[35] R. Patgiri, S. Nayak, S.K. Borgohain rdbf, A r-dimensional bloom filter for massive scale membership query, J. Netw. Comput. Appl. 136 (2019) 100-113.

[36] R. Patgiri, S. Nayak, S.K. Borgohain, Hunting the pertinency of bloom filter in computer networking and beyond: a survey, J. Comput. Netw. Commun. 2019 (2019) 1-10.

[37] L. Huang, X. Feng, C. Zhang, L. Qian, Y. Wu, Deep reinforcement learning-based joint task offloading and bandwidth allocation for multi-user mobile edge computing, Digit. Commun. Netw. 5 (1) (2019) 10-17.

[38] M. Babazadeh, Edge analytics for anomaly detection in water networks by an arduino101-lora based wsn, ISA Trans. 92 (2019) 273-285.

[39] Z. Wen, D. L. Quoc, P. Bhatotia, R. Chen,M. Lee, Approximate Edge Analytics for the Iot Ecosystem, ArXiv abs/1805.05674.

[40] M. Al-Kateb, B.S. Lee, Stratified reservoir sampling over heterogeneous data streams, in: M. Gertz, B. Ludäscher (Scientific and Statistical Database Management,Eds.), Springer Berlin Heidelberg, Berlin, Heidelberg, 2010, pp. 621-639.

[41] J.S. Vitter, Random sampling with a reservoir, ACM Trans. Math Software 11 (1)(1985) 37-57.

[42] C. Xu, J. Ren, L. She, Y. Zhang, Z. Qin, K. Ren, Edgesanitizer: locally differentially private deep inference at the edge for mobile data analytics, IEEE Internet Things J. 6 (3) (2019) 5140-5151.

[43] D. Liu, Z. Yan, W. Ding, M. Atiquzzaman, A survey on secure data analytics in edge computing, IEEE Internet Things J. 6 (3) (2019) 4946-4967.

[44] N. Harth, C. Anagnostopoulos, D. Pezaros, Predictive intelligence to the edge: impact on edge analytics, Evolv. Syst. 9 (2) (2018) 95-118.

[45] T. Technologies, Iot edge analytics & machine learning for real-time device control. https://www.thinxtream.com/whitepapers/thinxtream-iot-edge-analytics-machine-learning-real-time-device-control-wp-003.pdf. (Accessed 27 November 2019).

[46] D. Zimine, Aws greengrass — the missing manual. https://read.acloud.guru/aws-greengrass-the-missing-manual-2ac8df2fbdf4. (Accessed 16 January 2020).

[47] A. Aral, I. Brandic, Consistency of the fittest: towards dynamic staleness control for edge data analytics,in: European Conference on Parallel Processing, Springer, 2018, pp. 40-52.

[48] C. Song, T. Li, X. Huang, Z. Wang, P. Zeng, Towards edge computing based distributed data analytics framework in smart grids, in: International Conference on Artificial Intelligence and Security, Springer, 2019, pp. 283-292.

[49] D. Sarabia-Jacome, I. Lacalle, C.E. Palau, M. Estevé,Efficient deployment of predictive analytics in edge gateways: fall detection scenario, in: 2019 IEEE 5th World Forum on Internet of Things (WF-IoT), 2019, pp. 41-46.

[50] J. Moon, S. Cho, S. Kum, S. Lee,Cloud-edge collaboration framework for iot data analytics, in:2018 International Conference on Information and Communication Technology Convergence (ICTC), 2018, pp. 1414-1416.

[51] S. Dey, A. Mukherjee, Implementing deep learning and inferencing on fog and edge computing systems, in: 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), IEEE, 2018, pp. 818-823.

[52] Y. Ai, M. Peng, K. Zhang, Edge computing technologies for internet of things: a primer, Digit. Commun. Netw. 4 (2) (2018) 77-86.

[53] R. Dontha, Edge analytics-what, why, when, who, where, how?. https://www.kdnuggets.com/2017/10/edge-analytics.html. (Accessed 26 November 2019).

[54] S. Soni, K. Narang, T.A. Faruquie, V.S. Batra, L. Subramaniam, Edge analytics as service—a service oriented framework for real time and personalised recommendation analytics, in: Proceedings of 2013 IEEE International Conference on Service Operations and Logistics, and Informatics, IEEE, 2013, pp. 494-499.

[55] K. Bhargava, S. Ivanov, W. Donnelly, C. Kulatunga, Using edge analytics to improve data collection in precision dairy farming, in: 2016 IEEE 41st Conference on Local Computer Networks Workshops (LCN Workshops), IEEE, 2016, pp. 137-144.

[56] J. Zhang, F. Wang, K. Wang, W. Lin, X. Xu, C. Chen, Data-driven intelligent transportation systems: a survey, IEEE Trans. Intell. Transport. Syst. 12 (4) (2011) 1624-1639.

[57] N.-E.E. Faouzi, H. Leung, A. Kurian, Data fusion in intelligent transportation systems: Progress and challenges-a survey, Inf. Fusion 12 (1) (2011) 4-10.

[58] X. Jiang, F.R. Yu, T. Song, V.C.M. Leung, Intelligent resource allocation for video analytics in blockchain-enabled internet of autonomous vehicles with edge computing, IEEE Internet Things J. 9 (16) (2022) 14260-14272.

[59] M.B. Mollah, J. Zhao, D. Niyato, Y.L. Guan, C. Yuen, S. Sun, K.Y. Lam, L.H. Koh, Blockchain for the internet of vehicles towards intelligent transportation systems: a survey, IEEE Internet Things J. 8 (6) (2021) 4157-4185.

[60] I. Lujic, V.D. Maio, K. Pollhammer, I. Bodrozic, J. Lasic, I. Brandic, Increasing traffic safety with real-time edge analytics and 5g, in: Proceedings of the 4th International Workshop on Edge Systems, Analytics and Networking, EdgeSys ’21, Association for Computing Machinery, New York, NY, USA, 2021, pp. 19-24.

[61] R. Nakod, How edge analytics accelerates cloud computing. https://www.einfochips.com/blog/how-edge-analytics-accelerates-cloud-computing/. (Accessed 26 November 2019).

[62] F. Saghir, H. Gilabert, M. Boujonnier, et al., Edge analytics and future of upstream automation,in:SPE Asia Pacific Oil and Gas Conference and Exhibition, Society of Petroleum Engineers, 2018. D021S016R001.

[63] S.A. Boyer, SCADA: Supervisory Control and Data Acquisition, International Society of Automation, 2009.

[64] B. Boguslawski, M. Boujonnier, L. Bissuel-Beauvais, F. Saghir, R.D. Sharma, et al., Iiot edge analytics: deploying machine learning at the wellhead to identify rod pump failure,in:SPE Middle East Artificial Lift Conference and Exhibition, Society of Petroleum Engineers, 2018. D021S004R001.

[65] S. Kartakis, W. Yu, R. Akhavan, J.A. McCann, Adaptive Edge Analytics for Distributed Networked Control of Water Systems, in: 2016 IEEE First International Conference on Internet-Of-Things Design and Implementation (IoTDI), 2016, pp. 72-82.

[66] ORACLE, Oracle edge analytics, in: https://www.oracle.com/technetwork/middleware/complex-event-processing/overview/edge-analytics-ds-2775436.pdf. (Accessed 27 November 2019).

[67] Siemens, Mindconnect edge analytics. https://siemens.mindsphere.io/content/dam/cloudcraze-mindsphere-assets/03-catalog-section/02-mindconnect/pdfs/Siemens-MindConnect-Edge-Analytics-fs-72450-A7.pdf. (Accessed 27 November 2019).

[68] Siemens, Your gateway to iot. https://siemens.mindsphere.io/en. (Accessed 27 November 2019).

[69] accenture, Find your edge bringing analytics to data at the edge of iot. https://www.accenture.com/_acnmedia/pdf-71/accenture-edge-analytics-pov.pdf. (Accessed 26 November 2019).

[70] Q.D. La, M.V. Ngo, T.Q. Dinh, T.Q. Quek, H. Shin, Enabling intelligence in fog computing to achieve energy and latency reduction, Digit. Commun. Netw. 5 (1)(2019) 3-9.

[71] Cisco, The cisco edge analytics fabric system. https://www.cisco.com/c/dam/en/us/products/collateral/analytics-automation-software/edge-analytics-fabric/eaf-whitepaper.pdf. (Accessed 26 November 2019).

[72] X. Xu, S. Huang, L. Feagan, Y. Chen, Y. Qiu, Y. Wang,Eaaas: edge analytics as a service, in: 2017 IEEE International Conference on Web Services (ICWS), 2017, pp. 349-356.

[73] L. Deng, J. Gui, T. Wang, J. Tan, X. Li, An intelligent hybrid mac protocol for a sensor-based personalized healthcare system, Digit. Commun. Netw. 8 (2) (2022) 174-185.

[74] K.J. Madukwe, I.J.F. Ezika, O.N. Iloanusi, Leveraging edge analysis for internet of things based healthcare solutions, in: 2017 IEEE 3rd International Conference on Electro-Technology for National Development (NIGERCON), 2017, pp. 720-725.

[75] Kaa, Kaa iot platform. https://www.kaaproject.org/. (Accessed 26 December 2019).

[76] L. Sanabria-Russo, D. Pubill, J. Serra, C. Verikoukis,Iot data analytics as a network edge service, in: IEEE INFOCOM 2019 -IEEE Conference on Computer Communications Workshops, 2019, pp. 969-970.

[77] A. Ukil, A. Jara, L. Marin, Data-driven automated cardiac health management with robust edge analytics and de-risking, Sensors 19 (12) (2019) 2733.

[78] Y. Freund, R.E. Schapire, A decision-theoretic generalization of on-line learning and an application to boosting, J. Comput. Syst. Sci. 55 (1) (1997) 119-139.

[79] A. Manocha, R. Singh, A Novel Edge Analytics Assisted Motor Movement Recognition Framework Using Multi-Stage Convo-Gru Model, Mobile Networks and Applications, 2019, pp. 1-20.

[80] Z.M. Fadlullah, A.-S.K. Pathan, H. Gacanin, On delay-sensitive healthcare data analytics at the network edge based on deep learning, in: 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC), IEEE, 2018, pp. 388-393.

[81] N. Takiddeen, I. Zualkernan, Smartwatches as iot edge devices: a framework and survey,in:2019 Fourth International Conference on Fog and Mobile Edge Computing, FMEC, 2019, pp. 216-222.

[82] Y.-Y. Chen, Y.-H. Lin, C.-C. Kung, M.-H. Chung, I. Yen, et al., Design and implementation of cloud analytics-assisted smart power meters considering advanced artificial intelligence as edge analytics in demand-side management for smart homes, Sensors 19 (9) (2019) 2047.

[83] Y.-H. Lin, Novel smart home system architecture facilitated with distributed and embedded flexible edge analytics in demand-side management, Int. Trans. Electr. Energy Syst. 29 (2019) e12014.

[84] D. Amaxilatis, I. Chatzigiannakis, C. Tselios, N. Tsironis,Sparks-edge: analytics for intelligent city water metering,in:Joint Proceeding of the Poster and Workshop Sessions of AmI-2019, the 2019 European Conference on Ambient Intelligence, 2492, 2019, pp. 1-7.

[85] T. Sirojan, T. Phung, E. Ambikairajah, Intelligent edge analytics for load identification in smart meters, in: 2017 IEEE Innovative Smart Grid Technologies-Asia (ISGT-Asia), 2017, pp. 1-5.

[86] Y. Lin, A cloud analytics-based electrical energy management architecture empowered by edge analytics aiduino with push notifications for demand-side management, in: 2019 IEEE 2nd International Conference on Power and Energy Applications (ICPEA), 2019, pp. 1-6.

[87] E. Oyekanlu, S. Onidare, P. Oladele,Towards statistical machine learning for edge analytics in large scale networks: real-time Gaussian function generation with generic dsp, in: 2018 First International Colloquium on Smart Grid Metrology (SmaGriMet), 2018, pp. 1-6.

[88] T. Pathak, V. Patel, S. Kanani, S. Arya, P. Patel, M.I. Ali, A distributed framework to orchestrate video analytics across edge and cloud: a use case of smart doorbell,in:Proceedings of the 10th International Conference on the Internet of Things, IoT ’20, Association for Computing Machinery, New York, NY, USA, 2020, pp. 1-8.

[89] P. Porambage, J. Okwuibe, M. Liyanage, M. Ylianttila, T. Taleb, Survey on multi-access edge computing for internet of things realization, IEEE Commun. Surv. & Tutor. 20 (4) (2018) 2961-2991.

[90] J. Chen, S. Chen, S. Luo, Q. Wang, B. Cao, X. Li, An intelligent task offloading algorithm (itoa) for uav edge computing network, Digit. Commun. Netw. 6 (4)(2020) 433-443.

[91] R. Patgiri, S. Nayak, S. Kumar, Role of bloom filter in big data research: a survey, Int. J. Adv. Comput. Sci. Appl. 9 (11) (2018) 655-661.

[92] M. Alrowaily, Z. Lu, Secure edge computing in iot systems: review and case studies,in: 2018 IEEE/ACM Symposium on Edge Computing (SEC), 2018, pp. 440-444.

[93] S. Xia, Z. Yao, Y. Li, S. Mao, Online distributed offloading and computing resource management with energy harvesting for heterogeneous mec-enabled iot, IEEE Trans. Wireless Commun. 20 (10) (2021) 6743-6757.

[94] C. Ding, N. Liu, Y. Wang, J. Li, S. Heidari, J. Hu, Y. Liu, Multisource indoor energy harvesting for nonvolatile processors, IEEE Design Test 34 (3) (2017) 42-49.

[95] M. Xie, M. Zhao, C. Pan, Jingtong Hu, Y. Liu, C.J. Xue, Fixing the broken time machine: consistency-aware checkpointing for energy harvesting powered non-volatile processor,in: 2015 52nd ACM/EDAC/IEEE Design Automation Conference (DAC), 2015, pp. 1-6.

[96] Y. Li, S. Xia, M. Zheng, B. Cao, Q. Liu, Lyapunov optimization based trade-off policy for mobile cloud offloading in heterogeneous wireless networks, IEEE Trans. Cloud Comput. 10 (1) (2022) 491-505.

[97] A. Samanta, Y. Li,Latency-oblivious incentive service offloading in mobile edge computing, in:2018 IEEE/ACM Symposium on Edge Computing (SEC), 2018, pp. 351-353.

[98] J. Guo, G. Huang, Q. Li, N.N. Xiong, S. Zhang, T. Wang, Stmto: a smart and trust multi-uav task offloading system, Inf. Sci. 573 (2021) 519-540.

[99] M. Marjanović, A. Antonić, I.P. Žarko, Edge computing architecture for mobile crowdsensing, IEEE Access 6 (2018) 10662-10674.

[100] T.K. Rodrigues, K. Suto, H. Nishiyama, J. Liu, N. Kato, Machine learning meets computation and communication control in evolving edge and cloud: challenges and future perspective, IEEE Commun. Surv. Tutor. 22 (1) (2020) 38-67.

[101] C. Huang, G. Huang, W. Liu, R. Wang, M. Xie, A parallel joint optimized relay selection protocol for wake-up radio enabled wsns, Phys. Commun. 47 (2021) 101320.

[102] S. Shapsough, F. Aloul, I.A. Zualkernan,Securing low-resource edge devices for iot systems, in:2018 International Symposium in Sensing and Instrumentation in IoT Era (ISSI), 2018, pp. 1-4.

[103] T. Martin, M. Hsiao, D. Ha, J. Krishnaswami, Denial-of-service attacks on battery-powered mobile computers, in: Second IEEE Annual Conference on Pervasive Computing and Communications, Proceedings of the IEEE, 2004, pp. 309-318.

[104] S. Nayak, R. Patgiri, T.D. Singh, Big computing: where are we heading? EAI Endorsed Transactions on Scalable Information Systems 7 (27) (2020) 1-10.

[105] Gartner, Gartner identifies five emerging technology trends with transformational impact. https://www.gartner.com/en/newsroom/press-releases/2019-29-08-gartner-identifies-five-emerging-technology-trends-with-transformational-impact, August 2019. (Accessed April 2020).

[106] W.Y.B. Lim, N.C. Luong, D.T. Hoang, Y. Jiao, Y.C. Liang, Q. Yang, D. Niyato, C. Miao, Federated learning in mobile edge networks: a comprehensive survey, IEEE Commun. Surv. & Tutor. 22 (3) (2020) 2031-2063.

[107] S.C. Wong, A. Gatt, V. Stamatescu, M.D. McDonnell, Understanding data augmentation for classification: when to warp?, in:2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2016, pp. 1-6.

[108] S. Nayak, R. Patgiri,6g Communication: Envisioning the Key Issues and Challenges, 2020 arXiv: 2004.04024.

[109] S. Nayak, R. Patgiri, 6G Communication Technology: A Vision on Intelligent Healthcare, Springer Singapore, Singapore, 2021, pp. 1-18.

[110] J. Yao, T. Han, N. Ansari, On mobile edge caching, IEEE Commun. Surv. & Tutor. 21 (3) (2019) 2525-2553.

[111] B.H. Bloom, Space/time trade-o s in hash coding with allowable errors, Commun. ACM 13 (7) (1970) 422-426.

[112] R. Patgiri, S. Nayak, S. Borgohain, Preventing ddos using bloom filter: a survey, ICST Trans. Scalable Inf. Syst. 5 (19) (2018) 155865.

[113] G. Gao, Y. Wen, Video transcoding for adaptive bitrate streaming over edge-cloud continuum, Digit. Commun. Netw. 7 (4) (2021) 598-604.

[114] Y. Li, H. Ma, L. Wang, S. Mao, G. Wang, Optimized content caching and user association for edge computing in densely deployed heterogeneous networks, IEEE Trans. Mobile Comput. 21 (6) (2022) 2130-2142.

[115] Y. LeCun, Y. Bengio, G. Hinton, Deep learn. nat. 521 (7553) (2015) 436-444.

[116] Y. Zhou, L. Liu, L. Wang, N. Hui, X. Cui, J. Wu, Y. Peng, Y. Qi, C. Xing, Service-aware 6g: an intelligent and open network based on the convergence of communication, computing and caching, Digit. Commun. Netw. 6 (3) (2020) 253-260.

[117] S. Nayak, R. Patgiri,6g Communications: A Vision on the Potential Applications, 2020 07531 arXiv:2005.