Abstract
Traditional IoT setups are cloud-centric and typically focused around a centralized IoT platform to which data is uploaded for further processing. Next generation IoT applications are incorporating technologies such as artificial intelligence, augmented reality, and distributed ledgers to realize semi-autonomous behaviour of vehicles, guidance for human users, and machine-to-machine interactions in a trustworthy manner. Such applications require more dynamic IoT environments, which can operate locally without the necessity to communicate with the Cloud. In this paper, we describe three use cases of next generation IoT applications and highlight associated challenges for future research. We further present the IntellIoT framework that comprises the required components to address the identified challenges.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Ansari, R.I., et al:. 5G D2D networks: techniques, challenges, and future prospects. IEEE Syst. J. 12(4), 3970–3984 (2017)
Di Martino, B., Li, K.-C., Yang, L.T., Esposito, A. (eds.): Internet of Everything. IT, Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-5861-5
Bennis, M., Debbah, M., Poor, H.V.: Ultrareliable and low-latency wireless communication: tail, risk, and scale. Proc. IEEE 106(10), 1834–1853 (2018)
Bröring, A., Seeger, J., Papoutsakis, M., Fysarakis, K., Caracalli, A.: Networking-aware IoT application development. Sensors 20(3), 897 (2020)
Bu, F., Wang, X.: A smart agriculture IoT system based on deep reinforcement learning. Futur. Gener. Comput. Syst. 99, 500–507 (2019)
Cardellini, V., Grassi, V., Presti, F.L., Nardelli, M.: Optimal operator placement for distributed stream processing applications. In: Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems, pp. 69–80. ACM Press (2016)
Chen, M., Yang, Z., Saad, W., Yin, C., Poor, H.V, Cui, S.: A joint learning and communications framework for federated learning over wireless networks. IEEE Trans. Wireless Commun. PP, 1–1 (2020)
Ciortea, A., Mayer, S., Gandon, F., Boissier, O., Ricci, A., Zimmermann, A.: A decade in Hindsight: the missing bridge between multi-agent systems and the world wide web. In: Proceedings of the 18th International Conference on Autonomous Agents and Multi Agent Systems, pp. 1659–1663. International Foundation for Autonomous Agents and Multiagent Systems (2019)
IntellIoT consortium. Deliverable D2.3 - High level architecture (first version). https://intelliot.eu/wp-content/uploads/2021/10/D2.3-High-level-architecture-first-version.pdf
Conti, M., Dehghantanha, A., Franke, K., Watson, S.: Challenges and opportunities, Internet of Things security and forensics (2018)
Danzi, P., et al.: Communication aspects of the integration of wireless IoT devices with distributed ledger technology. IEEE Netw. 34(1), 47–53 (2020)
Giordani, M., Polese, M., Roy, A., Castor, D., Zorzi, M.: Initial access frameworks for 3GPP NR at mmWave frequencies. In: 2018 17th Annual Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net), pp. 1–8. IEEE (2018)
Ha, K., Chen, Z., Hu,W., Richter, W., Pillai, P., Satyanarayanan, M.: Towards wearable cognitive assistance. In: Proceedings of the 12th Annual International Conference on Mobile Systems, Applications, and Services, pp. 68–81. ACM (2014)
Holzinger, A., et al.: Interactive machine learning: experimental evidence for the human in the algorithmic loop. App. Intell. 49(7), 2401–2414 (2019)
Hsieh, B.Z., Chao,Y.H, Cheng, R.G., Nikaein, N.: Design of a UE-specific uplink scheduler for narrowband Internet-of-Things (NB-IoT) systems. In: 2018 3rd International Conference on Intelligent Green Building and Smart Grid (IGBSG), pp. 1–5. IEEE (2018)
Islam, M.M, Morshed, S., Goswami. P.: Cloud computing: a survey on its limitations and potential solutions. Int. J. Comput. Sci. Iss. (IJCSI) 10(4), 159 (2013)
Kaltenberger, F., Souza, G.D., Knopp, R., Wang, H.:The OpenAirInterface 5G new radio implementation: current status and roadmap. In: WSA 2019; 23rd International ITG Workshop on Smart Antennas, pp. 1–5. VDE (2019)
Khan, M.A., Salah, K.: IoT security: review, blockchain solutions, and open challenges. Futur. Gener. Comput. Syst. 82, 395–411 (2018)
Konečnỳ J., McMahan, B.H., Ramage, D., Richtárik, P.: Federated optimization: distributed machine learning for on-device intelligence. arXiv preprint arXiv:1610.02527 (2016)
Kovatsch, M., Matsukura, R., Lagally, M., Kawagucchi, T., Toumura, K., Kajimoto, K.: Web of Things (WoT) Architecture (2019). https://w3c.github.io/wot-architecture
Mohan, N., Kangasharju, J.: Edge-Fog cloud: a distributed cloud for Internet of Things computations. In: 2016 Cloudification of the Internet of Things (CIoT), pp. 1–6. IEEE (2016)
Park, J., Samarakoon, S., Bennis, M., Debbah, M.: Wireless network intelligence at the edge. Proc. IEEE 107(11), 2204–2239 (2019)
Seeger, J., Bröring, A., Carle, G.: Optimally self-healing IoT choreographies. ACM Trans. Internet Technol. (TOIT) 20(3), 1–20 (2020)
Sun, L., Peng, C., Zhan, W., Tomizuka, M.: A fast integrated planning and control framework for autonomous driving via imitation learning. In: Dynamic Systems and Control Conference, vol. 51913, p. V003T37A012. American Society of Mechanical Engineers (2018)
Xiong, X.-L., Yang, L., Zhao, G.-S.: Effectiveness evaluation model of moving target defense based on system attack surface. IEEE Access 7, 9998–10014 (2019)
Yang, H.H., Liu, Z., Quek, T.Q., Poor, H.V.: Scheduling policies for federated learning in wireless networks. IEEE Trans. Commun. 68(1), 317–333 (2019)
Yi, S., Hao, Z., Qin, Z., Li, Q.: Fog computing: platform and applications. In: 2015 Third IEEE Workshop on Hot Topics in Web Systems and Technologies (HotWeb), pp. 73–78. IEEE (2015)
Yousefpour, A., et al.: All one needs to know about fog computing and related edge computing paradigms: a complete survey. J. Syst. Architect. 98, 289–330 (2019)
Acknowledgment
This work has received funding from the European Union’s Horizon 2020 research and innovation programme H2020-ICT-56–2020, under grant agreement No. 957218 (Project IntellIoT).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Bröring, A. et al. (2022). IntellIoT: Intelligent IoT Environments. In: González-Vidal, A., Mohamed Abdelgawad, A., Sabir, E., Ziegler, S., Ladid, L. (eds) Internet of Things. GIoTS 2022. Lecture Notes in Computer Science, vol 13533. Springer, Cham. https://doi.org/10.1007/978-3-031-20936-9_5
Download citation
DOI: https://doi.org/10.1007/978-3-031-20936-9_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-20935-2
Online ISBN: 978-3-031-20936-9
eBook Packages: Computer ScienceComputer Science (R0)