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Exploring IoT Communication Technologies and Data-Driven Solutions

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Learning Techniques for the Internet of Things

Abstract

Over the past decade, Internet of Things (IoT) networks have been the subject of active research due to their wide range of potential applications. The successful implementation and effective performance of IoT networks depend on the communication protocols used to connect spatially distributed devices or sensors. However, existing communication technologies face several challenges, including security, interoperability, scalability, and energy optimization. Therefore, researchers are currently exploring novel IoT communication protocols and embracing data-driven approaches along with other solutions to overcome these challenges. This chapter comprehensively explores emerging trends in IoT communication technologies and the integration of data-driven solutions. Additionally, we study the potential role of data-driven technologies, such as artificial intelligence (AI), machine learning (ML), and deep learning (DL), focusing on their integration with IoT technologies. We have also briefly discussed the benefits of using data-driven technologies in various IoT applications. Furthermore, we have outlined several potential challenges and how data-driven technologies can address them, emphasizing recent innovations.

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References

  • A survey on bluetooth multi-hop networks. 2019. In Ad Hoc Networks.

    Google Scholar 

  • Adhikari, Mainak, et al. 2021. A roadmap of next-generation wireless technology for 6G-enabled vehicular networks. IEEE Internet of Things Magazine 4 (4): 79–85. https://doi.org/10.1109/IOTM.001.2100075.

    Article  Google Scholar 

  • Aguilar, Sergio, et al. 2022. Energy consumption model of SCHC packet fragmentation over Sigfox LPWAN. Sensors 22 (6). ISSN: 1424–8220. https://www.mdpi.com/1424-8220/22/6/2120.

  • Aihara, Naoki, et al. 2019. Q-learning aided resource allocation and environment recognition in LoRaWAN with CSMA/CA. IEEE Access 7: 152126–152137. https://doi.org/10.1109/ACCESS.2019.2948111.

    Article  Google Scholar 

  • Ajorlou, Amir, and Aliazam Abbasfar. 2020. An optimized structure of state channel network to improve scalability of blockchain algorithms. In 2020 17th International ISC Conference on Information Security and Cryptology (IS- CISC), 73–76. IEEE.

    Google Scholar 

  • Al-Qaseemi, Sarah A., et al. 2016. IoT architecture challenges and issues: Lack of standardization. In 2016 Future Technologies Conference (FTC), 731–738. https://doi.org/10.1109/FTC.2016.7821686.

  • Ali, Zainab H., et al. 2015. Internet of Things (IoT): definitions, challenges and recent research directions. International Journal of Computer Applications 128 (1), 37–47.

    Article  Google Scholar 

  • Alizadeh, Faezeh, and Amir Jalaly Bidgoly. 2023. Bit flipping attack detection in low power wide area networks using a deep learning approach. In Peer-to-Peer Networking and Applications, 1–11.

    Google Scholar 

  • Aruna, K., and G. Pradeep. 2020. Performance and scalability improvement using IoT-based edge computing container technologies. SN Computer Science 1: 1–7.

    Article  Google Scholar 

  • Barua, Arup, et al. 2022. Security and privacy threats for bluetooth low energy in IoT and wearable devices: a comprehensive survey. IEEE Open Journal of the Communications Society 3: 251–281.

    Article  Google Scholar 

  • Ben Saad, Sabra, et al. 2022. A trust and explainable federated deep learning framework in zero touch B5G Networks. In GLOBECOM 2022–2022 IEEE Global Communications Conference, 1037–1042. https://doi.org/10.1109/GLOBECOM48099.2022.10001371.

  • Benites, Fernando, and Elena Sapozhnikova. 2017. Improving scalability of ART neural networks. Neurocomputing 230: 219–229. ISSN: 0925–2312. https://doi.org/10.1016/j.neucom.2016.12.022. https://www.sciencedirect.com/science/article/pii/S0925231216314977.

  • Bhat, Showkat Ahmad, et al. 2022. Agriculture-food supply chain management based on blockchain and IoT: A narrative on enterprise blockchain interoperability. Agriculture 12 (1). ISSN: 2077-0472. https://www.mdpi.com/2077-0472/12/1/40.

  • Buhalis, Dimitrios, and Rosanna Leung. 2018. Smart hospitality-Interconnectivity and interoperability towards an ecosystem. International Journal of Hospitality Management 71: 41–50. ISSN: 0278-4319. https://doi.org/10.1016/j.ijhm.2017.11.011. https://www.sciencedirect.com/science/article/pii/S0278431917301974.

  • Carvalho, Rodrigo, et al. 2021. Q-learning ADR agent for LoRaWAN optimization. In 2021 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT), 104–108. https://doi.org/10.1109/IAICT52856.2021.9532518.

    Google Scholar 

  • Caso, Giuseppe, et al. 2021. NB-IoT random access: data-driven analysis and ML-based enhancements. IEEE Internet of Things Journal 8 (14), 11384–11399. https://doi.org/10.1109/JIOT.2021.3051755.

    Article  Google Scholar 

  • Chauhan, Chetan, and Manoj Kumar Ramaiya. 2022. Advanced model for improving iot security using blockchain technology. In 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT), 83–89. IEEE.

    Google Scholar 

  • Chen, Mi, et al. 2023. Dynamic parameter allocation with reinforcement learning for LoRaWAN. IEEE Internet of Things Journal 10(12): 10250–10265. https://doi.org/10.1109/JIOT.2023.3239301.

    Article  Google Scholar 

  • El Soussi, Mohieddine, et al. 2018. Evaluating the performance of eMTC and NB-IoT for smart city applications. In 2018 IEEE International Conference on Communications (ICC), 1–7. IEEE.

    Google Scholar 

  • Farhad, Arshad, Dae-Ho Kim, et al. 2022. Deep learning-based channel adaptive resource allocation in LoRaWAN. In 2022 International Conference on Electronics, Information, and Communication (ICEIC), 1–5. https://doi.org/10.1109/ICEIC54506.2022.9748580.

    Google Scholar 

  • Farhad, Arshad, and Jae-Young Pyun. 2023. AI-ERA: Artificial intelligence-empowered resource allocation for LoRa-enabled IoT applications. IEEE Transactions on Industrial Informatics, 1–13. https://doi.org/10.1109/TII.2023.3248074.

  • Fu, Hua, et al. 2023. Deep learning based RF fingerprint identification with channel effects mitigation. IEEE Open Journal of the Communications Society, 1668–1681.

    Google Scholar 

  • Hasan, Ayesha, and Bilal Muhammad Khan. 2023. Deep learning aided wireless interference identification for coexistence management in the ISM bands. Wireless Networks, 1–21.

    Google Scholar 

  • Hazra, Abhishek, Mainak Adhikari, et al. Nov. 2021a. A comprehensive survey on interoperability for IIoT: taxonomy, standards, and future directions. ACM Computing Surveys 55 (1). ISSN: 0360-0300. https://doi.org/10.1145/3485130.

  • Hazra, Abhishek, Prakash Choudhary, et al. 2021b. Recent advances in deep learning techniques and its applications: an overview. In Advances in Biomedical Engineering and Technology: Select Proceedings of ICBEST 2018, 103–122.

    Google Scholar 

  • Hazra, Abhishek, et al. (2022). Fog computing for energy-efficient data offloading of IoT applications in industrial sensor networks. IEEE Sensors Journal 22 (9): 8663–8671. https://doi.org/10.1109/JSEN.2022.3157863.

    Article  Google Scholar 

  • Hazra, Abhishek, Pradeep Rana, et al. 2023. Fog computing for next-generation Internet of Things: Fundamental, state-of-the-art and research challenges. Computer Science Review 48: 100549.

    Article  Google Scholar 

  • Huang, Yiwei, and Kwan-Wu Chin. 2023a. A hierarchical deep learning approach for optimizing CCA threshold and transmit power in WiFi networks. IEEE Transactions on Cognitive Communications and Networking, 1–1. https://doi.org/10.1109/TCCN.2023.3282984.

  • Huang, Yiwei, and Kwan-Wu Chin. 2023b. A three-tier deep learning based channel access method for WiFi networks. IEEE Transactions on Machine Learning in Communications and Networking, 90–106.

    Google Scholar 

  • Iannizzotto, Giancarlo, et al. 2023. Improving BLE-based passive human sensing with deep learning. Sensors 23 (5): 2581.

    Article  Google Scholar 

  • IoT in 2023 and beyond (2023). Report. https://techinformed.com/iot-in-2023-and-beyond/.

  • Kherani, Arzad Alam, and Poonam Maurya. 2019. Improved packet detection in LoRa-like chirp spread spectrum systems. In 2019 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), 1–4. https://doi.org/10.1109/ANTS47819.2019.9118076.

    Google Scholar 

  • Kurniawan, Agus, and Marcel Kyas. 2022. Machine learning models for LoRa Wan IoT anomaly detection. In 2022 International Conference on Advanced Computer Science and Information Systems (ICACSIS), 193–198. https://doi.org/10.1109/ICACSIS56558.2022.9923439.

    Google Scholar 

  • Lee, Junhee, et al. 2018. A scheduling algorithm for improving scalability of LoRaWAN. In 2018 International Conference on Information and Communication Technology Convergence (ICTC), 1383–1388. IEEE.

    Google Scholar 

  • Levchenko, Polina, et al. 2022. Performance comparison of NB-Fi, Sigfox, and LoRaWAN. Sensors 22 (24). ISSN: 1424-8220. https://www.mdpi.com/1424-8220/22/24/9633.

  • Li, Ang, et al. 2023. Secure UHF RFID authentication with smart devices. IEEE Transactions on Wireless Communications 22 (7), 4520–4533. https://doi.org/10.1109/TWC.2022.3226753.

    Article  Google Scholar 

  • Li, Aohan. 2022. Deep reinforcement learning based resource allocation for LoRaWAN. In 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall), 1–4. https://doi.org/10.1109/VTC2022-Fall57202.2022.10012698.

  • LoRa and LoRaWAN: A Technical Overview (Dec. 2019). en. Technical Paper. https://lora-developers.semtech.com/documentation/tech-papers-and-guides/lora-and-lorawan/.

  • LoRAWAN Regional Parameters (Sept. 2022). en. Specification RP002-1.0.4. https://resources.lora-alliance.org/technical-specifications/rp002-1-0-4-regional-parameters. Fremont, United States.

  • Magaia, Naercio, et al. 2020. Industrial internet-of-things security enhanced with deep learning approaches for smart cities. IEEE Internet of Things Journal 8 (8): 6393–6405.

    Article  Google Scholar 

  • Mao, Wenliang, et al. 2021. Energy-efficient industrial internet of things: overview and open issues. IEEE Transactions on Industrial Informatics 17 (11): 7225–7237. https://doi.org/10.1109/TII.2021.3067026.

    Article  Google Scholar 

  • Maurya, Poonam, and Arzad Alam Kherani. 2020. Tracking performance in LoRaWAN-like systems and equivalence of a class of distributed learning algorithms. IEEE Communications Letters 24 (11): 2584–2588. https://doi.org/10.1109/LCOMM.2020.3012569.

    Article  Google Scholar 

  • Maurya, Poonam, Aatmjeet Singh, et al. 2022a. A review: spreading factor allocation schemes for LoRaWAN. Telecommunication Systems 80 (3): 449–468.

    Article  Google Scholar 

  • Maurya, Poonam, Aatmjeet Singh, et al. 2022b. Design LoRaWAN network for unbiased communication between nodes and gateway. In 2022 14th International Conference on COMmunication Systems & NETworkS (COMSNETS), 581–589. https://doi.org/10.1109/COMSNETS53615.2022.9668447.

    Google Scholar 

  • Mayer, Philipp, et al. 2019. ZeroPowerTouch: zero-power smart receiver for touch communication and sensing in wearable applications. In 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), 944–947. https://doi.org/10.23919/DATE.2019.8715062.

  • Minhaj, Syed Usama, et al. 2023. Intelligent resource allocation in LoRaWAN using machine learning techniques. IEEE Access 11: 10092–10106. https://doi.org/10.1109/ACCESS.2023.3240308.

    Article  Google Scholar 

  • Misra, Sudip, et al. 2021. Introduction to IoT. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Mocanu, Elena, et al. (2019). On-line building energy optimization using deep reinforcement learning. IEEE Transactions on Smart Grid 10 (4): 3698–3708. https://doi.org/10.1109/TSG.2018.2834219.

    Article  Google Scholar 

  • Mohammed, Chand Pasha, and Shakti Raj Chopra. 2023. Blockchain security implementation using Python with NB-IoT deployment in food supply chain. In 2023 International Conference on Emerging Smart Computing and Informatics (ESCI), 1–5. IEEE.

    Google Scholar 

  • Najm, Ihab Ahmed, et al. 2019. Machine learning prediction approach to enhance congestion control in 5G IoT environment. Electronics 8 (6): 607.

    Article  Google Scholar 

  • Natarajan, Yuvaraj, et al. 2022. An IoT and machine learning-based routing protocol for reconfigurable engineering application. IET Communications 16 (5): 464–475.

    Article  Google Scholar 

  • Nilsson, Jacob, and Fredrik Sandin. 2018. Semantic interoperability in industry 4.0: survey of recent developments and outlook. In 2018 IEEE 16th International Conference on Industrial Informatics (INDIN), 127–132. https://doi.org/10.1109/INDIN.2018.8471971.

    Google Scholar 

  • Omar, Hassan Aboubakr, et al. 2016. A survey on high efficiency wireless local area networks: Next generation WiFi. IEEE Communications Surveys & Tutorials 18 (4): 2315–2344.

    Article  MathSciNet  Google Scholar 

  • Praveen Kumar, Donta, et al. 2023. Exploring the potential of distributed computing continuum systems. Computers 12: 198.

    Google Scholar 

  • Rajab, Husam, et al. 2021. Reducing power requirement of LPWA networks via machine learning. Pollack Periodica 16 (2): 86–91.

    Article  Google Scholar 

  • Rajawat, Anand Singh, et al. 2021. Blockchain-based model for expanding IoT device data security. In Advances in Applications of Data-Driven Computing, 61–71.

    Google Scholar 

  • Ramezanpour, Keyvan, et al. 2023. Security and privacy vulnerabilities of 5G/6G and WiFi 6: Survey and research directions from a coexistence perspective. Computer Networks 221: 109515.

    Article  Google Scholar 

  • Rana, Bharti, et al. 2021. A systematic survey on internet of things: Energy efficiency and interoperability perspective. Transactions on Emerging Telecommunications Technologies 32 (8): e4166.

    Article  Google Scholar 

  • Raval, Maulin, et al. 2021. Smart energy optimization for massive IoT using artificial intelligence. Internet of Things 13: 100354. ISSN: 2542-6605. https://doi.org/10.1016/j.iot.2020.100354. https://www.sciencedirect.com/science/article/pii/S2542660520301852.

  • Recommendation ITU-T Y.4480 (Nov. 2021). Low Power Protocol for Wide Area Wireless Networks. en. Recommendation ITU-T Y.4480. https://www.itu.int/rec/T-REC-Y.4480/. Geneva, Switcherland: Telecommunication Standardization Sector of ITU.

  • Reddy, Gogulamudi Pradeep, et al. 2022. Communication technologies for interoperable smart microgrids in urban energy community: a broad review of the state of the art, challenges, and research perspectives. Sensors 22 (15). https://www.mdpi.com/1424-8220/22/15/5881.

  • Ren, Rong, et al. 2023. Deep reinforcement learning for connection density maximization in NOMA-based NB-IoT networks. In 2023 8th International Conference on Computer and Communication Systems (ICCCS), 357–361. IEEE.

    Google Scholar 

  • Sanjoyo, Danu Dwi, and Masahiro Mambo. 2022. Accountable bootstrapping based on attack resilient public key infrastructure and secure zero touch provisioning. IEEE Access 10: 134086–134112. https://doi.org/10.1109/ACCESS.2022.3231015.

    Article  Google Scholar 

  • Self-evolving intelligent algorithms for facilitating data interoperability in IoT environments (2018). Future Generation Computer Systems 86: 421–432. ISSN: 0167-739X.

    Google Scholar 

  • Shahjalal, Md. et al. 2022. Implementation of a secure LoRaWAN system for industrial internet of things integrated with IPFS and blockchain. IEEE Systems Journal 16 (4): 5455–5464. https://doi.org/10.1109/JSYST.2022.3174157.

    Article  Google Scholar 

  • Sivaganesan, Dr. D. 2021. A data driven trust mechanism based on blockchain in IoT sensor networks for detection and mitigation of attacks. Journal of Trends in Computer Science and Smart Technology 3 (1): 59–69.

    Article  Google Scholar 

  • Sivanandam, Nishanth, and T. Ananthan. 2022. Intrusion detection system for bluetooth mesh networks using machine learning. In 2022 International Conference on Industry 4.0 Technology (I4Tech), 1–6. https://doi.org/10.1109/I4Tech55392.2022.9952758.

    Google Scholar 

  • Sodhro, Ali Hassan, et al. 2019. A novel energy optimization approach for artificial intelligence-enabled massive internet of things. In 2019 International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS), 1–6. https://doi.org/10.23919/SPECTS.2019.8823317.

  • Srirama, Satish Narayana. (2023). A decade of research in fog computing: Relevance, challenges, and future directions. Software: Practice and Experience. https://doi.org/10.1002/spe.3243. eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/spe.3243. https://onlinelibrary.wiley.com/doi/abs/10.1002/spe.3243.

  • Strebel, Raphael, and Michele Magno. 2018. Poster abstract: zero-power receiver for touch communication and touch sensing. In 2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), 150–151. https://doi.org/10.1109/IPSN.2018.00038.

    Google Scholar 

  • Sudharsan, Bharath, et al. 2022. RIS-IoT: towards resilient, interoperable, scalable IoT. In 2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS), 296–297. https://doi.org/10.1109/ICCPS54341.2022.00039.

    Chapter  Google Scholar 

  • Suresh, Setti, and Geetha Chakaravarthi. 2022. RFID technology and its diverse applications: A brief exposition with a proposed Machine Learning approach. Measurement 195: 111197. ISSN: 0263-2241. https://doi.org/10.1016/j.measurement.2022.111197. https://www.sciencedirect.com/science/article/pii/S026322412200450X.

  • Tan, Sheng, et al. 2022. Commodity WiFi sensing in ten years: status, challenges, and opportunities. IEEE Internet of Things Journal 9 (18): 17832–17843. https://doi.org/10.1109/JIOT.2022.3164569.

    Article  Google Scholar 

  • Tellache, Amine, et al. 2022. Deep reinforcement learning based resource allocation in dense sliced LoRaWAN networks. In 2022 IEEE International Conference on Consumer Electronics (ICCE), 1–6. https://doi.org/10.1109/ICCE53296.2022.9730234.

    Google Scholar 

  • Tu, Lam-Thanh, et al. (2022). Energy efficiency optimization in LoRa networks—a deep learning approach. IEEE Transactions on Intelligent Transportation Systems, 1–13. https://doi.org/10.1109/TITS.2022.3183073.

  • Wheelus, Charles, and Xingquan Zhu. 2020. IoT network security: Threats, risks, and a data-driven defense framework. IoT 1.2, 259–285.

    Article  Google Scholar 

  • Yoshino, Manabu, et al. 2020. Zero-touch multi-service provisioning with pluggable module-type OLT on access network virtualization testbed. In 2020 Opto-Electronics and Communications Conference (OECC), 1–3. https://doi.org/10.1109/OECC48412.2020.9273446.

  • Zeadally, Sherali, and Michail Tsikerdekis. 2020. Securing Internet of Things (IoT) with machine learning. International Journal of Communication Systems 33 (1): e4169.

    Article  Google Scholar 

  • Zhang, Jiansheng, et al. 2023. Secure blockchain-enabled internet of vehicles scheme with privacy protection. Computers, Materials & Continua 75 (3).

    Google Scholar 

  • Zohourian, Alireza, et al. 2023. IoT Zigbee device security: A comprehensive review. Internet of Things, 100791.

    Google Scholar 

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Authors and Affiliations

  1. Aalborg University, Aalborg, Denmark

    Poonam Maurya

  2. Indian Institute of Information Technology, Sri City, India

    Abhishek Hazra

  3. National Institute of Technology Uttarakhand, Srinagar, India

    Lalit Kumar Awasthi

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  1. Poonam Maurya
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  2. Abhishek Hazra
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Editors and Affiliations

  1. Distributed Systems Group, TU Wein, Vienna, Austria

    Praveen Kumar Donta

  2. Indian Institute of Information Technology, Sri City, India

    Abhishek Hazra

  3. Center for Ubiquitous Computing, University of Oulu, Oulu, Finland

    Lauri Lovén

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Maurya, P., Hazra, A., Awasthi, L.K. (2024). Exploring IoT Communication Technologies and Data-Driven Solutions. In: Donta, P.K., Hazra, A., Lovén, L. (eds) Learning Techniques for the Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-031-50514-0_5

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