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In Wireless Sensor Network (i.e. WSN) based resource constrained Internet of Things (i.e. IoT) environments, efficient data forwarding is achieved through cluster based mechanisms, where cluster heads facilitate communication among themselves and with the sink node. Data collected by each cluster head is temporarily buffered before being transmitted to the sink via multi-hop communication. The integration of advanced wireless technologies, such as 5th Generation (i.e. 5G) networks, offers significant benefits, including reduced latency, extensive coverage, improved spectral efficiency, and higher data transmission rates. Incorporating Device-to-Device (i.e. D2D) communication further enhances energy efficiency and offloads data traffic, addressing critical IoT requirements such as low latency, increased network capacity, and improved spectral and energy efficiency. Software Defined Networking (i.e. SDN) addresses diverse IoT network needs across domains like smart grids, healthcare, traffic signaling, agriculture, and smart homes by enabling efficient communication, network management, and innovative control procedures. However, SDN’s application for anomaly detection and primary defense against security threats in IoT systems remains underexplored. This research investigates the potential of the design of an intelligent mechanism for energy efficient, privacy preserving, and secure communication in WSN based resource constrained IoT systems. The proposed approach leverages advanced technologies such as SDN, Machine Learning (i.e. ML), Deep Learning (i.e. DL), D2D communication, Computer Vision, and Network Function Virtualization (i.e. NFV). Additionally, it emphasizes assessing and offloading specific IoT application functions onto the network’s edge to enhance performance. Moreover, the development of lightweight security mechanisms for secure communication in resource constrained IoT environments is also identified as a crucial research domain. |
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