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@article{203338,
        author = {P.Deepak and R Carol Praveen and M Ismail sait and S.chandru},
        title = {IoT-Linked Collective Human Brain (Cognitive Mesh)},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {12370-12378},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=203338},
        abstract = {The rapid advancement of the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) has introduced new possibilities for creating interconnected intelligent environments capable of collective decision-making and adaptive learning. Traditional intelligent systems generally operate independently and lack mechanisms for continuous cognitive collaboration across distributed devices. This limitation creates challenges in achieving synchronized knowledge sharing, large-scale contextual awareness, and real-time collective intelligence. This research proposes an IoT-Linked Collective Human Brain framework, referred to as a Cognitive Mesh, which integrates IoT sensing infrastructure with machine learning techniques to simulate collaborative cognitive behavior among interconnected nodes. The proposed architecture enables distributed devices to collect environmental and behavioral data, preprocess information locally, and exchange learned representations through a mesh-based communication structure. Machine learning models are employed to identify patterns, generate predictive insights, and support adaptive decision processes across the network. The framework uses a dataset-driven methodology where heterogeneous sensor information and contextual attributes are processed through multiple stages including acquisition, cleaning, feature extraction, model training, inference generation, and collective response formation. Rather than replicating biological neural activity directly, the system emulates selected cognitive principles such as information aggregation, distributed reasoning, memory retention, and cooperative learning. Experimental evaluation demonstrates that the proposed approach improves responsiveness, scalability, and intelligent coordination compared with conventional isolated IoT architectures. The integration of machine learning contributes to improved prediction capability and dynamic adaptation under changing conditions. The results indicate that the Cognitive Mesh architecture can support future applications including smart healthcare, collaborative robotics, intelligent transportation, smart cities, and human-cantered cyber-physical systems.
The study highlights the feasibility of constructing interconnected cognitive environments capable of transforming traditional IoT networks into intelligent collective ecosystems.},
        keywords = {Cognitive Mesh, Internet of Things (IoT), Machine Learning, Collective Intelligence, Distributed Decision Systems, Cognitive Computing, Intelligent Sensor Networks},
        month = {May},
        }