Smart city applications demand lightweight, efficient and dependable communication protocols to facilitate the functioning of resource-limited Internet of Things (IoT) devices. This work performs an extensive empirical study of the three most prominent IoT standards; Message Queuing Telemetry Transport (MQTT), the Constrained Application Protocol (CoAP) and Hypertext Transfer Protocol (HTTP) by emulating real-world smart city use cases using a Raspberry Pi based testbed. The primary metrics based on which the protocols are analyzed are latency, message overhead, delivery rate and energy consumption. ANOVA and Tukey's HSD tests are used to determine the statistical significance of experimental data. The test results indicate that CoAP under (QoS-1 reliability) shows the least latency and energy consumption and MQTT due to its support for Quality of Service (QoS) is the most reliable. Among the others, HTTP is in general performance terms certainly at the bottom of all metrics mainly for its verbosity and synchronous nature. The paper then also suggests a decision flowchart for developers to choose the suitable protocol according to application requirements. The results are more than just numbers on a graph, and the research can be deployed for advice for protocol selection in practice, where this study helps identify issues with encryption overhead (over 75\%) while showcasing multi-hop network scalability and adaptive switch mechanisms as areas that remain to be resolved. Such findings can be used as a basis for design approaches to construct secure, efficient and scalable communication protocols in urban IoT settings.
This paper presents a simulation-based framework to optimize 5G/6G mmWave network deployments in enterprise environments. Using ns-3 and NYUSIM, it evaluates next-generation Node B (gNB) placement, beamforming, and handover strategies across factory, office, and campus settings. Leveraging the inherent high bandwidth and low latency capabilities of mmWave technology, this study systematically addresses critical challenges such as severe signal attenuation, dynamic blockage, and efficient beam management in complex indoor and outdoor enterprise settings, including large-scale industrial complexes, multi-floor smart offices, and expansive university campuses. Utilizing established open-source network simulators, specifically ns-3, and integrating publicly available, industry-standard channel models such as 3GPP TR 38.901 and NYUSIM, the research proposes and rigorously evaluates novel deployment strategies, advanced beamforming techniques, and intelligent handover mechanisms. The anticipated outcomes include validated guidelines for optimal base station placement, robust performance benchmarks for key enterprise applications (e.g., Ultra-Reliable Low-Latency Communication (URLLC), enhanced Mobile Broadband (eMBB), massive Machine-Type Communication (mMTC)), and a robust, extensible simulation framework. This work aims to provide critical, data-driven insights for telecommunication providers and network planners, enabling them to design and implement superior, reliable, and future-proof 5G/6G connectivity solutions, thereby accelerating digital transformation across various industrial and commercial sectors.
This research introduces a deep learning-based framework for anomaly detection in wireless communication networks using Channel State Information (CSI)—a fine-grained physical-layer signal that captures wireless channel dynamics. Traditional detection methods often fall short in identifying subtle or evolving threats, whereas CSI provides a rich, underutilized source for context-aware monitoring. Inspired by its use in human activity recognition, we apply and compare deep learning architectures such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTMs), and Transformers to learn normal network behavior and detect anomalies, including spoofing, jamming, rogue access points, environmental disruptions, and Quality of Service (QoS) degradation. The system supports supervised, semi-supervised, and unsupervised settings, accommodating scenarios with limited labeled data. CSI data is collected using tools like the Intel 5300 NIC and Nexmon CSI under both controlled and realistic conditions. We benchmark our models against traditional techniques (e.g., Isolation Forests, Support Vector Machines (SVMs), Principal Component Analysis (PCA)), evaluating accuracy, false positives, latency, and robustness. To enhance transparency, we employ interpretability methods such as Gradient-weighted Class Activation Mapping (Grad-CAM) and t-distributed Stochastic Neighbor Embedding (t-SNE). Experimental results show that deep learning models outperform classical baselines by up to 30% in detection accuracy. The Transformer architecture achieved 96.2% accuracy with a false positive rate of 3.9%, while the CNN-LSTM hybrid achieved the best latency–performance tradeoff (5.1ms inference). Compared to Isolation Forest and One-Class SVM, our framework reduced false positives by over 10–14%.
Harsh industrial environments such as oilfields present unique challenges to electronic systems, including extreme temperatures, limited connectivity, power constraints, and operational unpredictability. Traditional Internet of Things (IoT) deployments often fail to adapt in real-time, exposing systems to risks such as data loss, late anomaly detection, or critical failure. This paper proposes a lightweight, Artificial Intelligence (AI)-driven eSystem architecture tailored for such conditions, integrating edge intelligence, secure communication, and self-adaptive mechanisms. We demonstrate the framework's viability through simulating a case study of real-time sensor data from pipeline infrastructure, applying a Long Short-Term Memory (LSTM)-based anomaly detection model deployed at the edge. Results show significant improvements in detection latency, bandwidth efficiency, and system resilience. The framework offers a modular blueprint for deploying AI-enhanced eSystems across energy, mining, and remote critical infrastructure domains.