Enhancing Cellular IoT Access with Multi-Hop Uplink NOMA, and Mobile Edge Computing for Smart Cities

An Internet of Things (IoT) network is made up of multiple connected devices to collect and exchange information over the Internet for remote monitoring and control. IoT has drawbacks such as data security vulnerabilities posed by networking devices, data protection issues associated with data collection and sharing, and interoperability challenges among different IOT platforms and devices. A Global Channel Matching (GCM) algorithm is integrated into the Enhancing Cellular IoT Access with Multi-Hop Uplink NOMA, and Mobile Edge Computing for Smart Cities (ECMMS) in this article, which aims to optimize relay transmission links for use in IoT networks. In a smart city environment, the GCM algorithm is designed to mimic Firefly behavior and dynamically adjust relay placement for network efficiency and interference reduction. The network model includes IoT devices connected to a Virtual Mobile Edge Computing (MEC) fabric to facilitate task offloading and distributed computing. The proposed framework aims to improve computational efficiency by creating virtual servers that use networked IoT devices based on task requirements. ECMMS has demonstrated through detailed simulations and evaluations that it is more effective in optimizing relay selection, reducing interference, and achieving optimal data rates, which supports the scalability and reliability of IoT applications in smart city infrastructure. Using these parameters with discussed the overcomes of our ECMMS model when compared to MNMD, SHBT, and IHAG models. Such as packet loss, average throughput, average end-to-end delay, energy efficiency, and data success rate where calculate. Here packet loss and data success rates are high rates of 99 % and 95.74 %. Were average throughput, energy efficiency, and average end-to-end delay archives low efficiency of 71.02, 254.06, and 254.06 are calculated.