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Optimizing energy and latency trade-offs in mobile ultra-dense IoT networks within futuristic smart vertical networks

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Abstract

As the Internet of Things (IoT) evolves and is integrated into cutting-edge Smart Vertical Networks-based IoT, a plethora of IoT mobile devices (IMD) must contend with the increasing processing demands of time-critical tasks. The dynamic nature of the environment raises novel challenges for networks that use mobile edge computing. As a proactive response to these issues, the concept of ultra-dense IoT with Mobile Edge Computing has emerged. Within this architecture, Integrated Mobile Devices (IMDs) can save power and preserve their internal processing resources by offloading compute-intensive tasks to servers located at the network’s periphery (the “edge”). Nevertheless, the increased efficiency comes at the cost of greater transmission overhead, leading to an elevated delay. To achieve an ideal equilibrium between energy preservation and latency reduction, we propose a new optimization problem that focuses on minimizing both energy utilization and latency in ultra-dense IoT networks with multiple users and tasks. This issue entails the complex optimization of concurrent user (IMD) associations, computation offloading decisions, and resource allocations. To achieve a fair distribution of network load and maximize the utilization of computational resources, we integrate multi-step computation offloading methodologies into the issue formulation. Finally, the Adaptive Particle Swarm Optimization (PSO) technique is utilized as an intelligent way of solving the problem. Significantly, our methodology exhibits a noteworthy improvement over traditional Particle Swarm Optimization (PSO) techniques, resulting in a substantial decrease in overall expenses, encompassing reductions that span from 20 to 65%.

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References

  1. Huseien, G.F., Shah, K.W.: A review of 5G technology for smart energy management and smart buildings in Singapore. Energy AI 7, 100116 (2022). https://doi.org/10.1016/j.egyai.2021.100116

    Article  Google Scholar 

  2. Zhang, Y., Wang, W., Wu, X., Lei, Y., Cao, J., Bowen, C., Bader, S., Yang, B.: A comprehensive review of self-powered smart bearings. Renew. Sustain. Energy Rev. 183, 113446 (2023). https://doi.org/10.1016/j.rser.2023.113446

    Article  Google Scholar 

  3. Biazi, V., Marques, C.: Industry 40-based smart systems in aquaculture: a comprehensive review. Aquacult. Eng. (2023). https://doi.org/10.1016/j.aquaeng.2023.102360

    Article  Google Scholar 

  4. Pliatsios, A., Kotis, K., Goumopoulos, C.: A systematic review on semantic interoperability in the IoE-enabled smart cities. Internet Things 22, 100754 (2023). https://doi.org/10.1016/j.iot.2023.100754

    Article  Google Scholar 

  5. Kumar, A., de Jesus, A., Pacheco, D., Kaushik, K., Rodrigues, J.J.: Futuristic view of the internet of quantum drones: review, challenges and research agenda. Veh. Commun. 36, 100487 (2022). https://doi.org/10.1016/j.vehcom.2022.100487

    Article  Google Scholar 

  6. Mehta, P., Gupta, R., Tanwar, S.: Blockchain envisioned UAV networks: challenges, solutions, and comparisons. Comput. Commun. 151, 518–538 (2020). https://doi.org/10.1016/j.comcom.2020.01.023

    Article  Google Scholar 

  7. Yapa, C., De Alwis, C., Liyanage, M., Ekanayake, J.: Survey on blockchain for future smart grids: technical aspects, applications, integration challenges, and future research. Energy Rep. 7, 6530–6564 (2021). https://doi.org/10.1016/j.egyr.2021.09.112

    Article  Google Scholar 

  8. Nain, G., Pattanaik, K., Sharma, G.: Toward edge computing in intelligent manufacturing: past, present and future. J. Manuf. Syst. 62, 588–611 (2022). https://doi.org/10.1016/j.jmsy.2022.01.010

    Article  Google Scholar 

  9. Kumar, A., Ahuja, N.J., Thapliyal, M., Dutt, S., Kumar, T., De Jesus Pacheco, D.A., Konstantinou, C., Raymond Choo, K.: Blockchain for unmanned underwater drones: research issues, challenges, trends and future directions. J. Netw. Comput. Appl. 215, 103649 (2023). https://doi.org/10.1016/j.jnca.2023.103649

    Article  Google Scholar 

  10. Borgia, E.: The Internet of Things vision: key features, applications, and open issues. Comput. Commun. 54, 1–31 (2014). https://doi.org/10.1016/j.comcom.2014.09.008

    Article  Google Scholar 

  11. Yang, H., Zhao, J., Lam, K.-Y., Xiong, Z., Wu, Q., Xiao, L.: Distributed deep reinforcement learning-based spectrum and power allocation for heterogeneous networks. IEEE Trans. Wireless Commun. 21(9), 6935–6948 (2022). https://doi.org/10.1109/TWC.2022.3153175

    Article  Google Scholar 

  12. Muñoz, P., Adamuz-Hinojosa, Ñ., Navarro-Ortiz, J., Sallent, O., Pérez-Romero, J.: Radio access network slicing strategies at spectrum planning level in 5G and beyond. IEEE Access 8, 79604–79618 (2020). https://doi.org/10.1109/ACCESS.2020.2990802

    Article  Google Scholar 

  13. Wang, B., Sun, Y., Xu, X.: A scalable and energy-efficient anomaly detection scheme in wireless SDN-based mMTC networks for IoT. IEEE Internet Things J. 8(3), 1388–1405 (2021). https://doi.org/10.1109/JIOT.2020.3011521

    Article  Google Scholar 

  14. Duo, B., Wu, Q., Yuan, X., Zhang, R.: Anti-jamming 3D trajectory design for UAV-enabled wireless sensor networks under probabilistic LoS channel. IEEE Trans. Veh. Technol. 69(12), 16288–16293 (2020). https://doi.org/10.1109/TVT.2020.3040334

    Article  Google Scholar 

  15. Kumar, S., Sharma, A.: Switched beam array antenna optimized for microwave powering of 3-D distributed nodes in clustered wireless sensor network. IEEE Trans. Antennas Propag. 70(12), 11734–11742 (2022). https://doi.org/10.1109/TAP.2022.3209744

    Article  Google Scholar 

  16. Cui, Q., Zhang, Z., Shi, Y., Ni, W., Zeng, M., Zhou, M.: Dynamic multichannel access based on deep reinforcement learning in distributed wireless networks. IEEE Syst. J. 16(4), 5831–5834 (2022). https://doi.org/10.1109/JSYST.2021.3134820

    Article  Google Scholar 

  17. Chu, H., Wang, P.-J., Zhu, X.-H., Hong, H.: Antenna-in-package design and robust test for the link between wireless ingestible capsule and smart phone. IEEE Access 7, 35231–35241 (2019). https://doi.org/10.1109/ACCESS.2019.2891880

    Article  Google Scholar 

  18. Wang, S., Ouyang, J., Li, D., Liu, C.: An integrated industrial ethernet solution for the implementation of smart factory. IEEE Access 5, 25455–25462 (2017). https://doi.org/10.1109/ACCESS.2017.2770180

    Article  Google Scholar 

  19. Docquier, T., Song, Y., Chevrier, V., Pontnau, L., Ahmed-Nacer, A.: Performance evaluation methodologies for smart grid substation communication networks: a survey. Comput. Commun. 198, 228–246 (2023). https://doi.org/10.1016/j.comcom.2022.11.005

    Article  Google Scholar 

  20. Shi, W., Zhang, J., Zhang, R.: Share-based edge computing paradigm with mobile-to-wired offloading computing. IEEE Commun. Lett. 23(11), 1953–1957 (2019). https://doi.org/10.1109/LCOMM.2019.2934411

    Article  Google Scholar 

  21. Cui, G., He, Q., Chen, F., Zhang, Y., Jin, H., Yang, Y.: Interference-aware game-theoretic device allocation for mobile edge computing. IEEE Trans. Mobile Comput. 21(11), 4001–4012 (2022). https://doi.org/10.1109/TMC.2021.3064063

    Article  Google Scholar 

  22. Li, Q., Ma, X., Zhou, A., Luo, X., Yang, F., Wang, S.: User-oriented edge node grouping in mobile edge computing. IEEE Trans. Mobile Comput. 22(6), 3691–3705 (2023). https://doi.org/10.1109/TMC.2021.3139362

    Article  Google Scholar 

  23. Guim, F., et al.: Autonomous lifecycle management for resource-efficient workload orchestration for green edge computing. IEEE Trans. Green Commun. Netw. 6(1), 571–582 (2022). https://doi.org/10.1109/TGCN.2021.3127531

    Article  Google Scholar 

  24. Marjanović, M., Antonić, A., Žarko, I.P.: Edge computing architecture for mobile crowdsensing. IEEE Access 6, 10662–10674 (2018). https://doi.org/10.1109/ACCESS.2018.2799707

    Article  Google Scholar 

  25. Luo, R., Jin, H., He, Q., Wu, S., Xia, X.: Cost-effective edge server network design in mobile edge computing environment. IEEE Trans. Sustain. Comput. 7(4), 839–850 (2022). https://doi.org/10.1109/TSUSC.2022.3178661

    Article  Google Scholar 

  26. Wang, S., et al.: A cloud-guided feature extraction approach for image retrieval in mobile edge computing. IEEE Trans. Mobile Comput. 20(2), 292–305 (2021). https://doi.org/10.1109/TMC.2019.2944371

    Article  Google Scholar 

  27. Cui, G., et al.: OL-EUA: online user allocation for NOMA-based mobile edge computing. IEEE Trans. Mobile Comput. 22(4), 2295–2306 (2023). https://doi.org/10.1109/TMC.2021.3112941

    Article  Google Scholar 

  28. Luo, R., Jin, H., He, Q., Wu, S., Xia, X.: Enabling balanced data deduplication in mobile edge computing. IEEE Trans. Parallel Distrib. Syst. 34(5), 1420–1431 (2023). https://doi.org/10.1109/TPDS.2023.3247061

    Article  Google Scholar 

  29. Wu, D., Huang, X., Xie, X., Nie, X., Bao, L., Qin, Z.: LEDGE: leveraging edge computing for resilient access management of mobile IoT. IEEE Trans. Mobile Comput. 20(3), 1110–1125 (2021). https://doi.org/10.1109/TMC.2019.2954872

    Article  Google Scholar 

  30. Cui, G., et al.: Demand response in NOMA-based mobile edge computing: a two-phase game-theoretical approach. IEEE Trans. Mobile Comput. 22(3), 1449–1463 (2023). https://doi.org/10.1109/TMC.2021.3108581

    Article  Google Scholar 

  31. Bozorgchenani, A., Mashhadi, F., Tarchi, D., Salinas Monroy, S.A.: Multi-objective computation sharing in energy and delay constrained mobile edge computing environments. IEEE Trans. Mobile Comput. 20(10), 2992–3005 (2021). https://doi.org/10.1109/TMC.2020.2994232

    Article  Google Scholar 

  32. Masoudi, M., Cavdar, C.: Device versus edge computing for mobile services: delay-aware decision making to minimize power consumption. IEEE Trans. Mobile Comput. 20(12), 3324–3337 (2021). https://doi.org/10.1109/TMC.2020.2999784

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, Indiana State University, Terre Haute, USA

    Venkata Chunduri

  2. Dr. D. Y. Patil B-School, Pune, India

    Atul Kumar

  3. Mechanical Engineering, Marathwada Mitra Mandal’s Institute of Technology, Lohagaon, Pune, India

    Anjali Joshi

  4. Department of Computer Science and Engineering, Mahindra University, Hyderabad, Telangana, 500043, India

    Soumya Ranjan Jena

  5. The Department of Statistics and Econometrics, Tashkent Institute of Finance, Tashkent, Uzbekistan

    Akbar Jumaev

  6. Department of Computer Engineering, Trinity College of Engineering and Research, Pune, India

    Sujeet More

Authors
  1. Venkata Chunduri
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  2. Atul Kumar
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  4. Soumya Ranjan Jena
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  5. Akbar Jumaev
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Contributions

VC and AK wrote the manuscript text and provided the methodology. AJ and SRJ prepared the figures and provided the methodology. AJ and SM validated the research. All authors have reviewed the manuscript.

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Correspondence to Venkata Chunduri.

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Chunduri, V., Kumar, A., Joshi, A. et al. Optimizing energy and latency trade-offs in mobile ultra-dense IoT networks within futuristic smart vertical networks. Int J Data Sci Anal (2023). https://doi.org/10.1007/s41060-023-00477-7

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