The emergence of multi-sensor Internet of Things (IoT) deployments has led to an immediate demand to have unified intelligence models that can take advantage of heterogeneous data modalities, such as time-series sensor readings, visual data, audio streams as well as contextual metadata, to form incoherent understanding and decision-making. The conventional methods treat each of the modalities separately, which does not reflect the deep cross-modal associations that are necessary to understand a scene holistically and act appropriately in a context. The proposed paper suggests a new cross-modal transformer architecture network operating on the principle of joint representation learning of various sensor modalities in order to achieve the unification of intelligence in multi-sensor IoT systems. The framework presented is based on modality-specific encoders and then a set of cross-modal attention mechanisms which allow two way flow of information between sensor streams to capture the highly complex inter-modal dependencies without paired training data. The new hierarchical fusion approach is a combination of the local cross-modal interactions and global contextual reasoning where the model can adaptively weigh the contributions of the sensors depending on the environmental conditions and tasks. The architecture requires built-in adaptive modality gating mechanisms, which ensures that performance does not suffer even in case of a failure of individual sensors or in case of poor quality. Cross-modal evaluation on three multi- sensor IoT benchmark datasets indicates that the proposed cross-modal transformer is 96.8% accurate in unified perception tasks, which is 18.4% and 11.2% better than single-modality evaluation and other traditional fusion methods, respectively. The framework exhibits high levels of robustness to sensor dropouts of up to 40 percent and is also able to generalize to unobservable sensor layouts. The results have made cross-modal transformer networks a disruptive paradigm of unified intelligence in heterogeneous multi-sensor IoT systems.
Selvin Pradeep Kumar S, Lourdu Mahimai Doss, L. S et al.· 2026 7th International Confe...· 0 citations
The ubiquitous use of Internet of Things (IoT) system in dynamic and real-life contexts presents a high level of challenges because of the constant changes in the data distributions, otherwise known as concept drift. The conventional machine learning models that are implemented in IoT systems are usually trained in static mode and cannot respond to changing trends in data, which causes deterioration in performance as time goes by. The drawback restricts the reliability and independence of intelligent IoT applications in the long run. In order to overcome these challenges, this paper will present a omprehensive lifelong learning model that can be used to enable drift-robust and autonomous IoT intelligence. The framework proposed combines the real-time drift detection, incremental model adaptation with memory-based knowledge retention into a single framework. It allows the IoT systems to continuously learn stream data and retain the past knowledge, thus eliminating catastrophic forgetting. The architecture is edge deportable, which means that it is low-latency inference and less reliant on centralized retraining. This is proven by on-the-job evidence of the proposed approach; it has been shown to be able to sustain stable performance even when data distribution changes, it converts to concept drift faster and it has a higher level of robustness than the traditional batch and online approaches to learning. The framework offers a scalable and effective way of facilitating self-adaptive, resilient, and autonomous IoT systems in the new generation smart environments.
T.Muthumanickam, L. S, D. Jayalakshmi et al.· 2026 6th International Confe...· 0 citations
The intersection of sixth-generation (6G) communication networks, edge computing, and federated learning offers a novel occasion regarding empowering trustful and scalable collaboration throughout distributed Internet of Things (IoT) ecosystems. Conventional centralized machine learning technology is plagued by severe constraints in IoT scenes, such as privacy issues, network congestion, and network bottlenecks. The paper has presented a new 6G-integrated federated learning system which builds on the native intelligence of 6G networks to support secure, efficient, and scalable cooperative learning among heterogeneous edge-IoT devices. The suggested architecture combines terahertz frequencies to synchronize model communication in real-time at ultra-low latency, reconfigurable intelligent surfaces to improve the quality of communication, and network slicing to provide differentiated quality-of-service assurances. Another new hierarchical federated learning system integrates intra-edge aggregation and inter-edge cooperation that can reduce communication overhead by 85-percent and achieve the same accuracy in models. This framework integrates blockchain-based trust management involving zero-knowledge proofs of verifiable model update, to provide integrity and accountability without impacting on privacy. Experimental analysis of massive scale edge-IoT applications has revealed that the suggested scheme attains 97.2% model precision and lowers communication expenses by 87 percent and convergence rate by 3.4 times that of traditional federated learning techniques. The framework has high-uniform performance in adversarial environments where 99.6 percent of malicious model updates are identified with a small false positive. The results define the 6G-integrated federated learning as a framework of reliable and scalable edge-IoT cooperation.
T.Muthumanickam, D. Jayalakshmi, Sathiyamoorthy M et al.· 2026 6th International Confe...· 0 citations