Line Monitoring and Identification Based on Roadmap Towards Edge Computing


In recent years, with the rapid growth of Internet of Things (IoT) and cloud services having received special attention from the research community across the world. IoT provides a platform of creating a world connected through internet. The implementation of smart devices collects information from our surroundings and works as per our needs. The implementation of IoT is very challenging as it requires the use of different new technologies like the emergence of fog and edge computing. The growth of fog and edge computing introduces many new requirements that needs to be investigated. The line monitoring system requirements for edge computing scenarios are not yet fully accomplished. The prime focus behind this study is to identify the challenges in the field of line monitoring within the application based on edge computing and to present the requirements of line monitoring for adaptive applications depending on edge computing frameworks. In this article we describes the architecture of fog and edge computing and presented their benefits among each other. The objective behind this study is to study the evolution of edge computing and present their benefits. The main contribution of this article is to present the layered architecture study for fog and edge computing. Moreover this article presents the key differences of fog and edge computing. On comparison with cloud computing, the edge computing performs the processing and storage on network edge closer to the user. The latest advances in the edge computing states that edge technology is an optimal solution for issues like latency, data privacy and bandwidth requirements.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10


  1. 1.

    Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., & Zaharia, M. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50–58.

    Article  Google Scholar 

  2. 2.

    Dillon, T., Wu, C., & Chang, E. (2010). Cloud computing: issues and challenges. In 2010 24th IEEE international conference on advanced information networking and applications (pp. 27–33).

  3. 3.

    Li, H., Dong, M., Ota, K., & Guo, M. (2016). Pricing and repurchasing for big data processing in multi-clouds. IEEE Transactions on Emerging Topics in Computing, 4(2), 266–277.

    Article  Google Scholar 

  4. 4.

    Stojmenovic, I., & Wen, S. (2014). The fog computing paradigm: Scenarios and security issues. In 2014 federated conference on computer science and information systems (pp. 1–8). IEEE.

  5. 5.

    Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5), 637–646.

    Article  Google Scholar 

  6. 6.

    Mahmud, R., Kotagiri, R., & Buyya, R. (2018). Fog computing: A taxonomy, survey and future directions. In B. Di Martino, K.-C. Li, L. T. Yang, & A. Esposito (Eds.), Internet of everything (pp. 103–130). Singapore: Springer.

    Google Scholar 

  7. 7.

    Lee, I., & Lee, K. (2015). The Internet of Things (IoT): Applications, investments, and challenges for enterprises. Business Horizons, 58(4), 431–440.

    Article  Google Scholar 

  8. 8.

    Hu, Y. C., Patel, M., Sabella, D., Sprecher, N., & Young, V. (2015). Mobile edge computing—A key technology towards 5G. ETSI white paper, 11(11), 1–16.

    Google Scholar 

  9. 9.

    Marston, S., Li, Z., Bandyopadhyay, S., Zhang, J., & Ghalsasi, A. (2011). Cloud computing—The business perspective. Decision Support Systems, 51(1), 176–189.

    Article  Google Scholar 

  10. 10.

    Benazzouz, Y., Munilla, C., Günalp, O., Gallissot, M., & Gürgen, L. (2014). Sharing user IoT devices in the cloud. In 2014 IEEE world forum on internet of things (WF-IoT) (pp. 373–374). IEEE.

  11. 11.

    Khan, M. A., & Salah, K. (2018). IoT security: Review, blockchain solutions, and open challenges. Future Generation Computer Systems, 82, 395–411.

    Article  Google Scholar 

  12. 12.

    Mao, Y., You, C., Zhang, J., Huang, K., & Letaief, K. B. (2017). A survey on mobile edge computing: The communication perspective. IEEE Communications Surveys and Tutorials, 19(4), 2322–2358.

    Article  Google Scholar 

  13. 13.

    Mach, P., & Becvar, Z. (2017). Mobile edge computing: A survey on architecture and computation offloading. IEEE Communications Surveys and Tutorials, 19(3), 1628–1656.

    Article  Google Scholar 

  14. 14.

    Zhang, K., Mao, Y., Leng, S., Zhao, Q., Li, L., Peng, X., & Zhang, Y. (2016). Energy-efficient offloading for mobile edge computing in 5G heterogeneous networks. IEEE Access, 4, 5896–5907.

    Article  Google Scholar 

  15. 15.

    Pellicer, S., Santa, G., Bleda, A. L., Maestre, R., Jara, A. J., & Skarmeta, A. G. (2013). A global perspective of smart cities: A survey. In 2013 Seventh International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (pp. 439–444). IEEE.

  16. 16.

    Akcin, M., Kaygusuz, A., Karabiber, A., Alagoz, S., Alagoz, B. B., & Keles, C. (2016). Opportunities for energy efficiency in smart cities. In 2016 4th International Istanbul Smart Grid Congress and Fair (ICSG) (pp. 1–5). IEEE.

  17. 17.

    Polianytsia, A., Starkova, O., & Herasymenko, K. (2016). Survey of hardware IoT platforms. In 2016 Third International Scientific-Practical Conference Problems of Infocommunications Science and Technology (PIC S&T) (pp. 152–153). IEEE.

  18. 18.

    Kaur, N., & Sood, S. K. (2015). An energy-efficient architecture for the Internet of Things (IoT). IEEE Systems Journal, 11(2), 796–805.

    Article  Google Scholar 

  19. 19.

    Kaur, K., Garg, S., Aujla, G. S., Kumar, N., Rodrigues, J. J., & Guizani, M. (2018). Edge computing in the industrial internet of things environment: Software-defined-networks-based edge-cloud interplay. IEEE Communications Magazine, 56(2), 44–51.

    Article  Google Scholar 

  20. 20.

    Beck, M. T., Werner, M., Feld, S., & Schimper, S. (2014). Mobile edge computing: A taxonomy. In Proc. of the Sixth International Conference on Advances in Future Internet (pp. 48–55). Citeseer.

  21. 21.

    Dolui, K., & Datta, S. K. (2017). Comparison of edge computing implementations: Fog computing, cloudlet and mobile edge computing. In 2017 Global Internet of Things Summit (GIoTS) (pp. 1–6). IEEE.

  22. 22.

    Li, H., Ota, K., & Dong, M. (2018). Learning IoT in edge: Deep learning for the Internet of Things with edge computing. IEEE Network, 32(1), 96–101.

    Article  Google Scholar 

  23. 23.

    Varghese, B., Wang, N., Barbhuiya, S., Kilpatrick, P., & Nikolopoulos, D. S. (2016). Challenges and opportunities in edge computing. In 2016 IEEE International Conference on Smart Cloud (SmartCloud) (pp. 20–26). IEEE.

  24. 24.

    Tran, T. X., Hajisami, A., Pandey, P., & Pompili, D. (2017). Collaborative mobile edge computing in 5G networks: New paradigms, scenarios, and challenges. IEEE Communications Magazine, 55(4), 54–61.

    Article  Google Scholar 

  25. 25.

    Samie, F., Tsoutsouras, V., Bauer, L., Xydis, S., Soudris, D. & Henkel, J. (2016). Computation offloading and resource allocation for low-power IoT edge devices. In 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT) (pp. 7–12). IEEE.

  26. 26.

    Al-Turjman, F. (2019). 5G-enabled devices and smart-spaces in social-IoT: An overview. Future Generation Computer Systems, 92, 732–744.

    Article  Google Scholar 

  27. 27.

    Elijah, O., Rahman, T. A., Orikumhi, I., Leow, C. Y., & Hindia, M. N. (2018). An overview of Internet of Things (IoT) and data analytics in agriculture: Benefits and challenges. IEEE Internet of Things Journal, 5(5), 3758–3773.

    Article  Google Scholar 

  28. 28.

    Hwang, Y. H. (2015). IoT security and privacy: Threats and challenges. In Proceedings of the 1st ACM workshop on IoT privacy, trust, and security (pp. 1–1).

  29. 29.

    Nastic, S., Rausch, T., Scekic, O., Dustdar, S., Gusev, M., Koteska, B., & Prodan, R. (2017). A serverless real-time data analytics platform for edge computing. IEEE Internet Computing, 21(4), 64–71.

    Article  Google Scholar 

  30. 30.

    Al-Ali, A. R., Zualkernan, I. A., Rashid, M., Gupta, R., & AliKarar, M. (2017). A smart home energy management system using IoT and big data analytics approach. IEEE Transactions on Consumer Electronics, 63(4), 426–434.

    Article  Google Scholar 

  31. 31.

    Mao, Y., Zhang, J., & Letaief, K. B. (2016). Dynamic computation offloading for mobile-edge computing with energy harvesting devices. IEEE Journal on Selected Areas in Communications, 34(12), 3590–3605.

    Article  Google Scholar 

  32. 32.

    Guo, M., Li, L., & Guan, Q. (2019). Energy-efficient and delay-guaranteed workload allocation in IoT-edge-cloud computing systems. IEEE Access, 7, 78685–78697.

    Article  Google Scholar 

  33. 33.

    Cao, K., Li, L., Cui, Y., Wei, T., & Hu, S. (2020). Exploring placement of heterogeneous edge servers for response time minimization in mobile edge-cloud computing. IEEE Transactions on Industrial Informatics.

    Article  Google Scholar 

  34. 34.

    Zhu, Z., Peng, J., Gu, X., Li, H., Liu, K., Zhou, Z., & Liu, W. (2018). Fair resource allocation for system throughput maximization in mobile edge computing. IEEE Access, 6, 5332–5340.

    Article  Google Scholar 

  35. 35.

    Wang, T., Zhang, G., Liu, A., Bhuiyan, M. Z. A., & Jin, Q. (2018). A secure IoT service architecture with an efficient balance dynamics based on cloud and edge computing. IEEE Internet of Things Journal, 6(3), 4831–4843.

    Article  Google Scholar 

  36. 36.

    Mao, S., Leng, S., Yang, K., Zhao, Q. & Liu, M. (2017). Energy efficiency and delay tradeoff in multi-user wireless powered mobile-edge computing systems. In GLOBECOM 2017–2017 IEEE Global Communications Conference (pp. 1–6). IEEE.

  37. 37.

    Lee, Y. C., & Zomaya, A. Y. (2012). Energy efficient utilization of resources in cloud computing systems. The Journal of Supercomputing, 60(2), 268–280.

    Article  Google Scholar 

  38. 38.

    Sardellitti, S., Scutari, G., & Barbarossa, S. (2015). Joint optimization of radio and computational resources for multicell mobile-edge computing. IEEE Transactions on Signal and Information Processing over Networks, 1(2), 89–103.

    MathSciNet  Article  Google Scholar 

  39. 39.

    Li, X., Li, D., Wan, J., Liu, C., & Imran, M. (2018). Adaptive transmission optimization in SDN-based industrial Internet of Things with edge computing. IEEE Internet of Things Journal, 5(3), 1351–1360.

    Article  Google Scholar 

  40. 40.

    Long, C., Cao, Y., Jiang, T., & Zhang, Q. (2017). Edge computing framework for cooperative video processing in multimedia IoT systems. IEEE Transactions on Multimedia, 20(5), 1126–1139.

    Article  Google Scholar 

  41. 41.

    Casado-Vara, R., de la Prieta, F., Prieto, J., & Corchado, J. M. (2018). Blockchain framework for IoT data quality via edge computing. In Proceedings of the 1st Workshop on Blockchain-enabled Networked Sensor Systems (pp. 19–24).

  42. 42.

    Vimal, S., Kalaivani, L., Kaliappan, M., Suresh, A., Gao, X. Z., & Varatharajan, R. (2020). Development of secured data transmission using machine learning-based discrete-time partially observed Markov model and energy optimization in cognitive radio networks. Neural Computing and Applications, 32(1), 151–161.

    Article  Google Scholar 

  43. 43.

    Vimal, S., Kalaivani, L., & Kaliappan, M. (2019). Collaborative approach on mitigating spectrum sensing data hijack attack and dynamic spectrum allocation based on CASG modeling in wireless cognitive radio networks. Cluster Computing, 22(5), 10491–10501.

    Article  Google Scholar 

  44. 44.

    Subbulakshmi, P., & Vimal, S. (2016). Secure data packet transmission in manet using enhanced identity-based cryptography (EIBC). International Journal of New Technologies in Science and Engineering, 3(12), 35–42.

    Google Scholar 

  45. 45.

    Vimal, S., Khari, M., Dey, N., Crespo, R. G., & Robinson, Y. H. (2020). Enhanced resource allocation in mobile edge computing using reinforcement learning based MOACO algorithm for IIOT. Computer Communications, 151, 355–364.

    Article  Google Scholar 

  46. 46.

    Vimal, S., Khari, M., Crespo, R. G., Kalaivani, L., Dey, N., & Kaliappan, M. (2020). Energy enhancement using multiobjective ant colony optimization with Double Q learning algorithm for IoT based cognitive radio networks. Computer Communications, 154, 481–490.

    Article  Google Scholar 

  47. 47.

    Robinson, Y. H., Vimal, S., Julie, E. G., Khari, M., Expósito-Izquierdo, C., & Martínez, J. (2020). Hybrid optimization routing management for autonomous underwater vehicle in the internet of underwater things. Earth Science Informatics.

    Article  Google Scholar 

  48. 48

    Pasupathi, S., Vimal, S., Harold-Robinson, Y., Khari, M., Verdú, E., & Crespo, R. G. (2020). Energy efficiency maximization algorithm for underwater Mobile sensor networks. Earth Science Informatics.

    Article  Google Scholar 

  49. 49.

    Annamalai, S., Udendhran, R., & Vimal, S. (2019). An intelligent grid network based on cloud computing infrastructures. In Novel Practices and Trends in Grid and Cloud Computing (pp. 59–73). IGI Global.

  50. 50.

    Annamalai, S., Udendhran, R., & Vimal, S. (2019). Cloud-based predictive maintenance and machine monitoring for intelligent manufacturing for automobile industry. In Novel Practices and Trends in Grid and Cloud Computing (pp. 74–89). IGI Global.

  51. 51.

    Vimal, S., Suresh, A., Subbulakshmi, P., Pradeepa, S., & Kaliappan, M. (2020). Edge Computing-Based Intrusion Detection System for Smart Cities Development Using IoT in Urban Areas. In Internet of Things in Smart Technologies for Sustainable Urban Development (pp. 219–237). Springer, Cham.

  52. 52

    Kanagachidambaresan, G. R., Maheswar, R., Manikandan, V., & Ramakrishnan, K. (Eds.). (2020). Internet of Things in Smart Technologies for Sustainable Urban Development. Cham: Springer.

    Google Scholar 

  53. 53.

    Gopikumar, S., Raja, S., Robinson, Y. H., Shanmuganathan, V., Chang, H., & Rho, S. (2020). A method of landfill leachate management using internet of things for sustainable smart city development. Sustainable Cities and Society, 2020, 102521.

    Google Scholar 

  54. 54.

    Sharma, A., Singh, P. K., Sharma, A., & Kumar, R. (2019). An efficient architecture for the accurate detection and monitoring of an event through the sky. Computer Communications, 148, 115–128.

    Article  Google Scholar 

  55. 55.

    Sharma, A., Singh, P. K., & Kumar, Y. (2020). An integrated fire detection system using IoT and image processing technique for smart cities. Sustainable Cities and Society, 61, 102332.

    Article  Google Scholar 

  56. 56.

    Kumar, D., Sharma, A., Kumar, R., & Sharma, N. (2019). Restoration of the Network for Next Generation (5G) Optical Communication Network. In 2019 International Conference on Signal Processing and Communication (ICSC) (pp. 64–68). IEEE.

  57. 57.

    Sharma, A., Tomar, R., Chilamkurti, N., & Kim, B. G. (2020). Blockchain based smart contracts for internet of medical things in e-healthcare. Electronics, 9(10), 1609.

    Article  Google Scholar 

  58. 58.

    Sharma, A., & Kumar, R. (2019). Computation of the reliable and quickest data path for healthcare services by using service-level agreements and energy constraints. Arabian Journal for Science and Engineering, 44(11), 9087–9104.

    Article  Google Scholar 

  59. 59

    Barshandeh, S., Piri, F., & Sangani, S. R. (2020). HMPA: An innovative hybrid multi-population algorithm based on artificial ecosystem-based and Harris Hawks optimization algorithms for engineering problems. Engineering with Computers.

    Article  Google Scholar 

  60. 60

    Barshandeh, S., & Haghzadeh, M. (2020). A new hybrid chaotic atom search optimization based on tree-seed algorithm and Levy flight for solving optimization problems. Engineering with Computers.

    Article  Google Scholar 

  61. 61.

    Dhiman, G., & Kumar, V. (2017). Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications. Advances in Engineering Software, 114, 48–70.

    Article  Google Scholar 

  62. 62.

    Dhiman, G., & Kumar, V. (2018). Emperor penguin optimizer: A bio-inspired algorithm for engineering problems. Knowledge-Based Systems, 159, 20–50.

    Article  Google Scholar 

  63. 63.

    Dhiman, G., & Kumar, V. (2018). Multi-objective spotted hyena optimizer: A multi-objective optimization algorithm for engineering problems. Knowledge-Based Systems, 150, 175–197.

    Article  Google Scholar 

  64. 64.

    Dhiman, G., & Kumar, V. (2019). Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems. Knowledge-Based Systems, 165, 169–196.

    Article  Google Scholar 

  65. 65.

    Dhiman, G., & Kaur, A. (2019). STOA: a bio-inspired based optimization algorithm for industrial engineering problems. Engineering Applications of Artificial Intelligence, 82, 148–174.

    Article  Google Scholar 

  66. 66.

    Kaur, S., Awasthi, L. K., Sangal, A. L., & Dhiman, G. (2020). Tunicate swarm algorithm: A new bio-inspired based metaheuristic paradigm for global optimization. Engineering Applications of Artificial Intelligence, 90, 103541.

    Article  Google Scholar 

  67. 67

    Dhiman, G. (2019). ESA: a hybrid bio-inspired metaheuristic optimization approach for engineering problems. Engineering with Computers.

    Article  Google Scholar 

  68. 68

    Dhiman, G., & Garg, M. (2020). MoSSE: A novel hybrid multi-objective meta-heuristic algorithm for engineering design problems. Soft Computing.

    Article  Google Scholar 

  69. 69

    Dhiman, G., Singh, K. K., Slowik, A., Chang, V., Yildiz, A. R., Kaur, A., & Garg, M. (2020). EMoSOA: a new evolutionary multi-objective seagull optimization algorithm for global optimization. International Journal of Machine Learning and Cybernetics.

    Article  Google Scholar 

  70. 70

    Dhiman, G., Oliva, D., Kaur, A., Singh, K. K., Vimal, S., Sharma, A., & Cengiz, K. (2021). BEPO: A novel binary emperor penguin optimizer for automatic feature selection. Knowledge-Based Systems, 211, 106560.

    Article  Google Scholar 

  71. 71.

    Dhiman, G., Singh, K. K., Soni, M., Nagar, A., Dehghani, M., Slowik, A., et al. (2020). MOSOA: A new multi-objective seagull optimization algorithm. Expert Systems with Applications, 20, 114150.

    Google Scholar 

  72. 72.

    Kaur, H., Rai, A., Bhatia, S. S., & Dhiman, G. (2020). MOEPO: A novel Multi-objective Emperor Penguin Optimizer for global optimization: Special application in ranking of cloud service providers. Engineering Applications of Artificial Intelligence, 96, 104008.

    Article  Google Scholar 

  73. 73.

    Dehghani, M., Montazeri, Z., Dehghani, A., Samet, H., Sotelo, C., Sotelo, D., et al. (2020). DM: Dehghani method for modifying optimization algorithms. Applied Sciences, 10(21), 7683.

    Article  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Gaurav Dhiman.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Liu, Y., Sun, Q., Sharma, A. et al. Line Monitoring and Identification Based on Roadmap Towards Edge Computing. Wireless Pers Commun (2021).

Download citation


  • Cloud systems
  • Fog computing
  • Edge computing
  • Internet of things (IoT)