<![CDATA[Internet of Things (IoT) facilitates communication between machines and devices which plays a crucial role in the conservation of energy. In largescale multidomain environments securing the data exchange among various IoT devices and key sharing creates a significant challenge. However, the message queuing telemetry transport (MQTT) lacks functional security mechanisms as well as mutual authentication between brokers and clients. To address these issues, a novel Cognitive IoT in Teroperability Recognition USing deep learning (CITRUS) framework is developed for real-time decision-making and sharing information among multiple IoT systems. Initially, the healthcare and weather data are collected remotely by using interoperable sensors which are then fed to the deep learning (DL) module for efficient decision-making. The MQTT module makes an energy-efficient IoT data communication over a resource-constrained network and the QoS1 introduces an acknowledgment and retransmission mechanism to ensure message delivery. The efficacy of the CITRUS model has been analyzed in terms of accuracy (AC), recall (RC), F1-score (F1S), sensitivity, packet delivery ratio (PDR), transmission speed, communication overhead, packet loss ratio (PLR) and delay. The experimental result shows that the CITRUS method achieves 89.89% of delay whereas, the IHPEC, SemBox, and DynoIoT methods achieve 161.63%, 128.99%, and 111.70% respectively for efficient data transmission.]]>
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