The working of the Internet of Things (IoT) ecosystem indeed depends extensively on the mechanisms of real-time data collection, sharing, and automatic operation. Among these fundamentals, wireless sensor networks (WSNs) are important for maintaining a countenance with their many distributed Sensor Nodes (SNs), which can sense and transmit environmental data wirelessly. Because WSNs possess advantages for remote data collection, they are severely hampered by constraints imposed by the limited energy capacity of SNs; hence, energy-efficient routing is a pertinent challenge. Therefore, in the case of clustering and routing mechanisms, these two play important roles where clustering is performed to reduce energy consumption and prolong the lifetime of the network, while routing refers to the actual paths for transmission of data. Addressing the limitations witnessed in the conventional IoT-based routing of data, this proposal presents an FL-oriented framework that presents a new energy-efficient routing scheme. Such routing is facilitated by the ADDQL model, which creates smart high-speed routing across changing scenarios in WSNs. The proposed ADDQL-IRHO model has been compared to other existing state-of-the-art algorithms according to multiple performance metrics such as energy consumption, communication delay, temporal complexity, data sum rate, message overhead, and scalability, with extensive experimental evaluation reporting superior performance. This also substantiates the applicability and competitiveness of the framework in variable-serviced IoT-oriented WSNs for next-gen intelligent routing solutions.

Emotion identification is important for human–computer interaction, and its applications include medical and customer service provision. In the past, emotion recognition systems have been based on one modality, such as text, image, audio, or video, each with advantages. However, these single-mode approaches are often less accurate due to their failure to fully capture the complexity of human emotions. Several limitations characterize the existing methods, such as isolated analysis per modality, loss of contextual information, and sub-optimal performance. Usually, these approaches cannot accurately detect emotions in real-life situations where multiple channels exist through which people express themselves in terms of experiencing feelings. To overcome this situation,a multi-modal emotional recognition using a cross-modal fusion (MER-CMF) system is proposed that makes different data sources work together to understand emotional states more completely and holistically. The fusion process synergizes the strengths of each modality, thus enabling the system to capture subtle nuances in emotional expressions. Consequently, the MER-CMF model enhances emotion recognition accuracy significantly compared to existing methods. Our experimental results show that it outperforms unimodal baselines in terms of accuracy and F1 score, achieving an impressive accuracy of 98.32%. These results demonstrate that cross-modality fusion can improve the reliability and efficiency of emotion recognition systems so that they can become more intuitive and responsive for their users.

In the current days with the growth of communication systems, the Internet of Things (IoT) has become a famous mechanism that allows large systems to be allowed with connectivity with heterogeneous frameworks. Nevertheless, it exists with technical complexity in the existing networks to manage certain massive systems in an effective way. Nowadays, the Software Defined Network (SDN) method with its elasticity and agility has been integrated with IoT to face the powerful flexibility and scale demands and create a novel IoT framework. Effective routing models with high security and low latency are needed, as the SDN-IoT architecture’s size is enhanced. However, the existing SDN routing models are still suspicious of flow control’s dynamic change, more importantly when the network is under threat. The IoT systems are normally performed in unattended and hostile environments. In addition, the routing in the present IoT framework becomes ineffective because of the existence of unauthenticated and malicious nodes, insecure routing, minimum network lifespan, and so on. In order to manage these problems, this work designs an effective SDN routing strategy-enabled IoT system with a blockchain mechanism to prevent malicious threats during data transmission. The deep learning strategy is supportive for recognizing suspicious IoT devices based on each node’s energy features. This article performs two significant tasks including the identification of malicious nodes and the selection of the optimal path. At first, the data of the IoT node is stored in the blockchain since the nodes in the IoT have a constrained lifetime. In addition, the nodes are validated to verify the authentication by applying a smart contract. The Cascaded Dilated Recurrent Neural Network (CD-RNN) is employed for recognizing the malicious and trusted nodes of the network. After recognizing the malicious node, the selection of the optimal route is carried out. In this, the routes are chosen optimally by the Transitive Phase of Pelican Optimization (TPPO). Lastly, the estimation is conducted by considering some factors including security, Packet Delivery Ratio (PDR), delay, and throughput. Hence, the suggested system offers better functionality than the previous approaches. The suggested scheme presents a hybrid mechanism, which combines a CD-RNN-based malicious node detection with TPPO-based routing optimization and blockchain-based trust management that guarantee a high level of security and performance in SDN-IoT settings.