Advances in technology are not only changing the world around us but also driving the wireless industry to develop the next generation of network technology. There is a lot of buzz building over the advent of 5G that will facilitate the entire planet through continuous and ubiquitous communication connecting anybody to anything, anywhere, anytime, and anyhow regardless of the device, service, network or geographical existence. 5G will also prove to be a paradigm shift including high carrier frequencies with massive bandwidths, having large number of antennas, and with extreme base station and device densities. In this paper, we investigate the potential beneficiaries of 5G and identify the use-cases where 5G can make an impact. In particular, we consider three main use-cases: Vehicle-to-Everything (V2X) Communication, Drones, and Healthcare. We explore and highlight the problems and deficiencies of current cellular technologies with respect to these use-cases and identify how 5G will overcome those deficiencies. We also identified the open research problems and provide the possible future directions to cope with those issues.

Cooperative aerial wireless networks composed of small unmanned aerial vehicles(UAVs) are easy and fast to deploy and provide on the fly communication facilities in situations where part of the communication infrastructure is destroyed and the survivors need to be rescued on emergency basis. In this article, we worked on such a cooperative aerial UAV-based wireless network to connect the two participating stations. The proposed method provides on the fly communication facilities to connect the two ground stations through a wireless access point (AP) mounted on a UAV using the IEEE 802.11a/b/g/n. We conducted our experiments both indoor and outdoor to investigate the performance of IEEE 802.11 protocol stack including a/b/g/n. We envisioned two different cases: line of sight (LoS) and non-line of sight (NLoS). In LoS, we consider three different scenarios with respect to UAV altitude and performed the experiments at different altitudes to measure the performance and applicability of the proposed system in catastrophic situations and healthcare applications. Similarly, for NLoS, we performed a single set of experiments in an indoor environment. Based on our observations from the experiments, 802.11n at 2.4 GHz outperforms the other IEEE protocols in terms of data rate followed by 802.11n at 5 GHz band. We also concluded that 802.11n is the more suitable protocol that can be practiced in disastrous situations such as rescue operations and healthcare applications.

With the increasing adoption of Internet of Medical Things (IoMT) devices, modern healthcare systems face persistent challenges related to data privacy, device heterogeneity, communication overhead, and scalability especially in consumer electronics environments. This paper hypothesizes that a hierarchical and privacy-preserving federated learning architecture can address these challenges and enable efficient, secure, and personalized healthcare insights across distributed IoMT networks. To validate this, we present the Adaptive Federated Learning Framework (AFLF), a novel framework tailored for resource-constrained consumer devices such as wearable ECG monitors, glucose trackers, and portable neuro-sensors. AFLF incorporates four core components: (1) a hierarchical edge–fog–cloud model for scalable and latency-aware training, (2) a Secure Data Collaboration Protocol (SDCP) that combines blockchain-based audit trails with differential privacy to ensure integrity and confidentiality, (3) the Adaptive Personalized Federated Learning Algorithm (APFLA) that dynamically tunes learning rates and model weights for personalized inference, and (4) a ternary threshold-based gradient compression technique that reduces communication overhead by 45.3%. The framework is deployed on Raspberry Pi 4 (edge) and NVIDIA Jetson AGX Xavier (fog) platforms, using PyTorch Mobile for implementation. Experimental validation using four real-world datasets, CardioFit (ECG), GlucoWatch (glucose), PulseOx (PPG), and NeuroMotion (EEG), demonstrates that AFLF improves model accuracy by up to 12% reduces training time by 38% and preserves user privacy with a differential privacy budget of ε = 2.1. These results confirm that AFLF offers a robust and scalable privacy-preserving federated learning solution for next-generation consumer-centric smart healthcare applications.

This paper introduces a holistic and scalable framework for optimizing Large Language Models (LLMs) in distributed environments, addressing three critical challenges: computational efficiency, ethical fairness, and governance. As LLMs scale, issues, such as excessive resource consumption, fairness violations, and limited transparency, hinder their broader deployment in real-world applications. We propose a novel three-tier architecture that integrates topology-aware parallelism, communication-efficient gradient aggregation, and memory-aware rematerialization. Our implementation reduces training time by 38% and memory usage by 42% on a 512-GPU A100 cluster, without compromising accuracy. To promote fairness, we incorporate a real-time adversarial debiasing module that reduces demographic AUC gaps by over 60% across gender, ethnicity, and religion. For model interpretability, we introduce a symbolic explainability engine that converts attention weights into transparent rule-based explanations, achieving 89.2% user satisfaction and outperforming Grad-CAM and vanilla attention. Furthermore, a lightweight governance layer aligned with ISO/IEC 27001 and ISO/IEC 23894 standards ensures traceability, audit logging, and policy enforcement throughout the model lifecycle. We validate our framework across diverse datasets, including C4, WikiText-103, RealNews, and BookCorpus, demonstrating low-latency drift and consistent fairness across domains. Comparative benchmarks against DeepSpeed, FairScale, and Megatron-LM show superior throughput, energy efficiency, and transparency. This work advances the foundation for ethical, efficient, and regulation-compliant LLM deployment at scale.