Cognitive Computing for Intelligent Internet-of-Things (IIoT) Environment

By: A. Dahiya

IoT and big data analytics have paved the way for modern intelligent services like smart living, sustainable computing, ecological protection, energy saving, supply chain management, etc. Moreover, the support from related communication and networking technologies has revolutionized this even more. Currently, the cognitive aspect of IoT is yet to be explored. However, it doesn’t mean that researchers have not proposed mechanisms like intelligent algorithms and self-organizing networks. There exist some limitations associated with these solutions like complex network management, increased cost of the underlying hardware, and limited adaptability of self-organized networks. Figure 1 shows the architecture of cognitive computing.

Figure 1: Cognitive computing architecture [1]

Big firms are utilizing IoT within their key processes and critical functions. IoT is defined as the huge IT network of devices ranging from smart phone in a user’s hand to the machineries of a critical infrastructure. All these devices are embedded with sensors that tend to collect enormous amounts of data called big data. Organizations are failing to harness this big data to its true potential. It is difficult to integrate IoT and intelligence together to process a big amount of data in real-time.  Further, the intelligent and cognitive approaches help organizations to analyse and process this big data for extracting useful insights from the data that could help in real-time decision making. Efficient decision-making can enhance the performance and productivity of an organization.

According to a study, there will be extensive growth in the number of IoT devices in the coming decade. So, as this network grows exponentially, the sheer amount of data is also increasing, which is only the most valuable thing. Without data, IoT devices will not hold functionalities and cannot perform the way it was conceptualized. Therefore, big data analytics are developed to process and analyse the IoT-generated data. AI adds intelligence to the network as it helps in predictive analysis and maintenance of IoT. For example, the big data collected by IoT sensors in a manufacturing firm empowers AI techniques to take the right decisions on the basis of major issues or maintenance tasks on some faulty machinery. It can keep the owner aware of the technical issues in advance [5-8].

Cognitive computing is emerging as the third era of computing, and it makes IoT more sophisticated, interactive, and intelligent. Cognitive computing has the capability to continuously learn from the interaction with the data, people, and situations with the passage of time. It gets better over tie through self-training and learning. To actually reap benefits from IoT and its generated data, cognitive computing must be an add-on to the IoT. Generally, decision-making devices in IoT networks make the decision on the basis of the pre-programmed model. They cannot make a context-based decision according to a situation. We can induce sense to these devices with the help of cognitive computing as it helps the device to adapt to the context dynamically through interaction and learning.

There is a need to empower researchers to explore the key concepts which can lead to the effective and performance-oriented integration of IoT with big data and AI. However, there exist many challenges which have been discussed below [9-11]:

  • The desire for access to large and heterogeneous IoT data leads to the urgent need to improve the autonomous cognitive potential of IoT, intelligent knowledge transmission, and optimization of these processes.
  • Challenges like complex network management increased cost of the underlying hardware and limited adaptability of self-organized networks are yet to be resolved.
  • Data security and privacy are still one of the major concerns associated with the term IoT.


  1. Chen, M., Herrera, F., & Hwang, K. (2018). Cognitive computing: architecture, technologies and intelligent applications. Ieee Access6, 19774-19783.
  2. Schuetz, S., & Venkatesh, V. (2020). The rise of human machines: How cognitive computing systems challenge assumptions of user-system interactionJournal of the Association for Information Systems21(2), 460-482.
  3. Qu, Y., Pokhrel, S. R., Garg, S., Gao, L., & Xiang, Y. (2020). A blockchained federated learning framework for cognitive computing in industry 4.0 networksIEEE Transactions on Industrial Informatics17(4), 2964-2973.
  4. Siarry, P., Sangaiah, A. K., Lin, Y. B., Mao, S., & Ogiela, M. R. (2020). Guest Editorial: Special Section on Cognitive Big Data Science Over Intelligent IoT Networking Systems in Industrial InformaticsIEEE Transactions on Industrial Informatics17(3), 2112-2115.
  5. Tewari, A., & Gupta, B. B. (2017). A lightweight mutual authentication protocol based on elliptic curve cryptography for IoT devices. International Journal of Advanced Intelligence Paradigms9(2-3), 111-121.
  6. Cvitić, I., Peraković, D., Periša, M., & Gupta, B. (2021). Ensemble machine learning approach for classification of IoT devices in smart home. International Journal of Machine Learning and Cybernetics12(11), 3179-3202.
  7. Sejdiu, B., Ismaili, F., & Ahmedi, L. (2020). Integration of semantics into sensor data for the IoT: A systematic literature review. International Journal on Semantic Web and Information Systems (IJSWIS)16(4), 1-25.
  8. Dowlatshahi, M. B., Rafsanjani, M. K., & Gupta, B. B. (2021). An energy aware grouping memetic algorithm to schedule the sensing activity in WSNs-based IoT for smart cities. Applied Soft Computing108, 107473.
  9. Salhi, D. E., Tari, A., & Kechadi, M. T. (2021). Using clustering for forensics analysis on internet of things. International Journal of Software Science and Computational Intelligence (IJSSCI)13(1), 56-71.
  10. Gupta, B. B., & Agrawal, D. P. (Eds.). (2019). Handbook of research on cloud computing and big data applications in IoT. IGI Global.
  11. Sengupta, J., Ruj, S., & Bit, S. D. (2020). A comprehensive survey on attacks, security issues and blockchain solutions for IoT and IIoT. Journal of Network and Computer Applications149, 102481.
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