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Energy Inefficacy in IoT Networks: Causes, Solutions and Enabling Techniques

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IoT as a Service (IoTaaS 2021)

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Abstract

The Internet of things (IoT) concept can be generally described as the ability of machines to communicate via the Internet to perform tasks. In addition to the communication between devices, humans can remotely control IoT devices via controllers such as smartphones. The main aim of introducing IoT technologies is to make our lives easier and more convenient. Due to the massive increase in both IoT devices and research on enhancing the security and the speed of these devices, there is a strong demand to work in parallel to promote IoT networks’ energy efficiency to make IoT systems scalable. This paper outlines the causes of energy inefficiency in IoT systems and proposes some key tools to prolong the network lifetime of these devices.

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References

  1. Khan, M.A., Salah, K.: IoT security: review, blockchain solutions, and open challenges. Future Gener. Comput. Syst. 82, 395–411 (2018)

    Article  Google Scholar 

  2. Nižetić, S., Šolić, P., González-de, D.L.-D.-I., Patrono, L.: Internet of Things (IoT): opportunities, issues and challenges towards a smart and sustainable future. J. Cleaner Prod. 274, 122877 (2020)

    Google Scholar 

  3. Sen, S., Koo, J., Bagchi, S.: TRIFECTA: security, energy efficiency, and communication capacity comparison for wireless IoT devices. IEEE Internet Comput. 22, 74–81 (2018)

    Article  Google Scholar 

  4. Liu, L., Guo, X., Lee, C.: Promoting smart cities into the 5G era with multi-field Internet of Things (IoT) applications powered with advanced mechanical energy harvesters. Nano Energy, 106304 (2021)

    Google Scholar 

  5. Tsai, C.-W.: SEIRA: an effective algorithm for IoT resource allocation problem. Comput. Commun. 119, 156–166 (2018)

    Article  Google Scholar 

  6. Bilgen, S.: Structure and environmental impact of global energy consumption. Renew. Sustain. Energy Rev. 38, 890–902 (2014)

    Article  Google Scholar 

  7. Yousefpour, A., Ishigaki, G., Gour, R., Jue, J.P.: On reducing IoT service delay via fog offloading. IEEE Internet Things J. 5, 998–1010 (2018)

    Article  Google Scholar 

  8. Jiang, J., Li, Z., Tian, Y., Al-Nabhan, N.: A review of techniques and methods for IoT applications in collaborative cloud-fog environment. Secur. Commun. Networks 2020 (2020)

    Google Scholar 

  9. Aburukba, R.O., AliKarrar, M., Landolsi, T., El-Fakih, K.: Scheduling Internet of Things requests to minimize latency in hybrid Fog–Cloud​ computing. Future Gener. Comput. Syst. 111, 539–551 (2020)

    Article  Google Scholar 

  10. Ma, Z., Xiao, M., Xiao, Y., Pang, Z., Poor, H.V., Vucetic, B.: High-reliability and low-latency wireless communication for internet of things: challenges, fundamentals, and enabling technologies. IEEE Internet Things J. 6, 7946–7970 (2019)

    Article  Google Scholar 

  11. Marietta, J., Mohan, B.C.: A review on routing in internet of things. Wireless Pers. Commun. 111, 209–233 (2020)

    Article  Google Scholar 

  12. Tseng, C.H.: Multipath load balancing routing for Internet of things. J. Sens. 2016 (2016)

    Google Scholar 

  13. Kaur, M., Aron, R.: A systematic study of load balancing approaches in the fog computing environment. J. Supercomput., 1–46 (2021)

    Google Scholar 

  14. Mahmud, R., Kotagiri, R., Buyya, R.: Fog computing: a taxonomy, survey and future directions. In: Di Martino, B., Li, K.-C., Yang, L.T., Esposito, A. (eds.) Internet of everything. IT, pp. 103–130. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-5861-5_5

    Chapter  Google Scholar 

  15. Zahmatkesh, H., Al-Turjman, F.: Fog computing for sustainable smart cities in the IoT era: caching techniques and enabling technologies-an overview. Sustain. Cities Soc. 59, 102139 (2020)

    Google Scholar 

  16. Musaddiq, A., Zikria, Y.B., Hahm, O., Yu, H., Bashir, A.K., Kim, S.W.: A survey on resource management in IoT operating systems. IEEE Access 6, 8459–8482 (2018)

    Google Scholar 

  17. Bhandari, K.S., Hosen, A., Cho, G.H.: CoAR: congestion-aware routing protocol for low power and lossy networks for IoT applications. Sensors 18(11), 3838 (2018)

    Google Scholar 

  18. Verma, L.P., Kumar, M.: An IoT based congestion control algorithm. Internet Things 9, 100157 (2020)

    Article  Google Scholar 

  19. Bhandari, K.S., Hosen, A., Cho, G.H.: CoAR: congestion-aware routing protocol for low power and lossy networks for IoT applications. Sensors 18, 3838 (2018)

    Article  Google Scholar 

  20. Srivastava, V., Tripathi, S., Singh, K., Son, L.H.: Energy efficient optimized rate based congestion control routing in wireless sensor network. J. Ambient Intell. Humanized Comput. 11, 1325–1338 (2020)

    Article  Google Scholar 

  21. Radhika, S., Rangarajan, P.: On improving the lifespan of wireless sensor networks with fuzzy based clustering and machine learning based data reduction. Appl. Soft Comput. 83, 105610 (2019)

    Article  Google Scholar 

  22. Raj, J.S., Basar, A.: QoS optimization of energy efficient routing in IoT wireless sensor networks. J. ISMAC 1, 12–23 (2019)

    Article  Google Scholar 

  23. Zikria, Y.B., Yu, H., Afzal, M.K., Rehmani, M.H., Hahm, O.: Internet of Things (IoT): operating system, applications and protocols design, and validation techniques. Elsevier (2018)

    Google Scholar 

  24. Aswale, P., Shukla, A., Bharati, P., Bharambe, S., Palve, S.: An overview of Internet of Things: architecture, protocols and challenges. In: Satapathy, S.C., Joshi, A. (eds.) Information and Communication Technology for Intelligent Systems. SIST, vol. 106, pp. 299–308. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-1742-2_29

    Chapter  Google Scholar 

  25. Liu, G., Quan, W., Cheng, N., Zhang, H., Shen, X.: VLI: variable-length identifier for interconnecting heterogeneous IoT networks. IEEE Wireless Commun. Lett. 9, 1146–1149 (2020)

    Google Scholar 

  26. Qafzezi, E., Bylykbashi, K., Ikeda, M., Matsuo, K., Barolli, L.: Coordination and management of cloud, fog and edge resources in SDN-VANETs using fuzzy logic: a comparison study for two fuzzy-based systems. Internet Things 11, 100169 (2020)

    Article  Google Scholar 

  27. Li, Y., et al.: Enhancing the internet of things with knowledge-driven software-defined networking technology: future perspectives. Sensors 20, 3459 (2020)

    Article  Google Scholar 

  28. Aruna, K., Pradeep, G.: Performance and scalability improvement using IoT-based edge computing container technologies. SN Comput. Sci. 1, 1–7 (2020)

    Article  Google Scholar 

  29. Singh, H., Bala, M., Bamber, S.S.: Augmenting network lifetime for heterogenous WSN assisted IoT using mobile agent. Wireless Netw. 26, 5965–5979 (2020)

    Article  Google Scholar 

  30. Younan, M., Houssein, E.H., Elhoseny, M., Ali, A.A.: Challenges and recommended technologies for the industrial internet of things: a comprehensive review. Measurement 151, 107198 (2020)

    Article  Google Scholar 

  31. Khattab, A., Youssry, N.: Machine learning for IoT systems. Internet Things (IoT), 105–127 (2020)

    Google Scholar 

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

    Article  Google Scholar 

  33. Mohammadi, M., Al-Fuqaha, A., Sorour, S., Guizani, M.: Deep learning for IoT big data and streaming analytics: a survey. IEEE Commun. Surv. Tutorials 20, 2923–2960 (2018)

    Article  Google Scholar 

  34. Darwish, A.: Bio-inspired computing: algorithms review, deep analysis, and the scope of applications. Future Comput. Inf. J. 3, 231–246 (2018)

    Article  Google Scholar 

  35. Hamidouche, R., Aliouat, Z., Gueroui, A.M., Ari, A.A.A., Louail, L.: Classical and bio-inspired mobility in sensor networks for IoT applications. J. Network Comput. Appl. 121, 70–88 (2018)

    Article  Google Scholar 

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Authors and Affiliations

  1. Hail University, Hail, Saudi Arabia

    Ziyad Almudayni

  2. La Trobe University, Melbourne, Australia

    Ben Soh & Alice Li

Authors
  1. Ziyad Almudayni
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  2. Ben Soh
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  3. Alice Li
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Corresponding author

Correspondence to Ziyad Almudayni .

Editor information

Editors and Affiliations

  1. University of Technology Sydney, Sydney, NSW, Australia

    Walayat Hussain

  2. Department of Computer Science, Abdul Wali Khan University, Mardan, Pakistan

    Mian Ahmad Jan

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© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Almudayni, Z., Soh, B., Li, A. (2022). Energy Inefficacy in IoT Networks: Causes, Solutions and Enabling Techniques. In: Hussain, W., Jan, M.A. (eds) IoT as a Service. IoTaaS 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 421. Springer, Cham. https://doi.org/10.1007/978-3-030-95987-6_8

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  • DOI: https://doi.org/10.1007/978-3-030-95987-6_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-95986-9

  • Online ISBN: 978-3-030-95987-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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