Recently, the number of Internet of Things (IoT) networks has been grown exponentially, which results in more data sharing between devices without appropriate security mechanisms. Since huge data management is involved, maintaining the time constraints between the devices in IoT networks is another significant issue. To address these issues, an intelligent intrusion detection system has been adapted to recognize or predict a cyber-attack using Elite Machine Learning algorithms (EML), and a lightweight protocol is used to manage the time-constrained issue. The experimental analysis of work is done on a testbed setup with the hardware and sensors connected using a lightweight Message Queue Telemetry Transport (MQTT) protocol. This comprises three parts: (i) collection of data with the help of a sensor for three different scenarios called SEN-MQTTSET; (ii) multi-context feature generation using an ensemble statistical multi-view cascade feature generation algorithm from the SEN-MQTTSET dataset; and (iii) evaluating the dataset using ML algorithms. The SEN-MQTTSET dataset has been created from the three scenarios, such as normal, attack on a subscriber, and attack on a broker. The multi-context feature is generated from the raw dataset using an ensemble statistical multi-view cascade feature generation algorithm. The EML is proposed to select the best model for intrusion detection among ML algorithms such as Logistic Regression, K-Nearest Neighbour, Random Forest, Naive Bias, Support Vector Machine, Gradient Boosting, and Decision Tree by the performance metrics such as accuracy, prediction time, F1-score, and others. The proposed dataset is validated and the accuracy is found to be above 99considered system model. Different quality parameters have been carried out for legitimate and attack traffic features to calculate the delay between the IoT-MQTT network.

This survey provides a comprehensive overview of Cooperative non orthogonal multiple access (C-NOMA) systems, where cooperative relaying enhances reliability, extends coverage, and strengthens system robustness. The study begins by classifying the fundamental structures of C-NOMA into two key categories: user-assisted relaying, where end-user devices forward information to other users, and dedicated relaying, where separate relay nodes assist transmission. Beyond its basic principles, the survey explores the integration of C-NOMA with advanced technologies such as cognitive radio (CR), full-duplex (FD) transmission, simultaneous wireless information and power transfer (SWIPT), power line communication (PLC), multiple-input multiple-output (MIMO), heterogeneous networks (HetNets), rate-splitting multiple access (RSMA), visible light communication (VLC), and intelligent reflecting surfaces (IRS). These integrations significantly improve energy efficiency, spectral utilization, and coverage reliability. Moreover, artificial intelligence (AI), including deep reinforcement learning (DRL) and federated learning (FL), is identified as a vital enabler for intelligent power allocation, relay selection, and user clustering, addressing the limitations of traditional optimization approaches. This survey elaborates in detail the integration of C-NOMA with blockchain, edge computing, quantum computing, extended reality (XR) i.e., virtual reality/augmented reality (VR/AR), and unmanned aerial vehicles (UAV). Furthermore, this survey identifies quantum-enabled C-NOMA as a transformative paradigm, where quantum key distribution (QKD) ensures physical layer security (PLS), and quantum machine learning (QML) enhances interference mitigation and large-scale optimization. Performance evaluation in terms of spectral efficiency (SE), energy efficiency, and quality-of-service (QoS) requirements is critically analyzed, with special emphasis on challenges such as accurate channel state information (CSI) acquisition, robust successive interference cancellation (SIC), scalable relay selection, and security in heterogeneous environments. In summary, this survey consolidates the state-of-the-art developments in C-NOMA and outlines future research directions, including AI-driven optimization, quantum-assisted communication, UAV and satellite integration, reconfigurable intelligent surface (RIS)-enabled designs, and hybrid OMA-NOMA switching. By addressing these open challenges, C-NOMA is expected to serve as a cornerstone for secure, energy-efficient, and ultra-reliable connectivity in 6G and beyond, enabling massive IoT, autonomous systems, and next-generation smart city infrastructures.

Orthogonal Frequency Division Multiplexing (OFDM) is an efficient transmission technique for high data rate communication systems. The major problem of OFDM system is Peak to Average Power Ratio (PAPR) which reduces the efficiency of the system and increases the system complexity. In this paper, logarithmic based companding (i.e, mu law) and Square rooting companding (SQRT) have been suggested as simple and effective PAPR reduction techniques for OFDM. This paper analyzes mu law and SQRT companding OFDM signal on M ary QAM modulation technique. Simulation results show that the SQRT OFDM system is an effective technique in reducing PAPR and improved BER performance than OFDM system with mu-law companding transform. Comparison among mu law and SQRT companding techniques is made for the best suited companding technique for PAPR reduction in OFDM system. The Simulation result also shows that by modulating a signal at a fixed subcarriers for different modulation technique, the error in the transmitted data increases rapidly whereas in case of fixed modulation techniques, increasing the number of subcarriers the PAPR increases at the transmit, the bit error rate (BER) in the transmitted data is constant and varies by a small fraction.