In various industries, including Agriculture, the application of the Fifth Generation (5G) of wireless technology has led to significant advancement. One of the most intriguing aspects of 5G technology is the potential to reduce latency for Internet of Things (IoT) applications, especially for latency-sensitive smart farming applications such as fire detection. The data generated by IoT devices such as sensors, cameras, and actuators in intelligent farming applications are growing exponentially. Traditional methods of processing and storing IoT data usually include cloud data centres, which are often far from data sources, resulting in multiple network hops that increase latency. To this end, the existing network infrastructures struggle to cope with increasing traffic and meet the stringent low-latency requirements of various real-time environmental monitoring IoT applications. To solve this problem, Edge Computing (EC) emerged as a solution. Edge technology allows the deployment of 5G Core (5GC) network functions close to the data-generating IoT sensors. This method allows data processing to be performed near the sensor, thereby reducing latency. As a result, this paper proposed an efficient architecture to minimise latency in 5G networks by moving the User Plane Function (UPF) node to the edge of the network closer to users using the Control and User Plane Separation (CUPS) strategy. Furthermore, this paper further proposed Software-Defined Networking (SDN) based backhaul. This backhaul was configured to use the Open Network Operating System (ONOS) controller, which has been customised with a distributed core to improve throughput, latency, and scalability. Using SDN in the 5G backhaul network allows operators to create dynamic, scalable, and efficient networks capable of serving a diverse variety of services and applications with varying performance needs. The results of the experiment conducted on the Third Generation Partnership Project (3GPP) compliant 5G testbed demonstrated that the proposed architecture reduced the average Round Time Trip (RTT) by 60.7% while improving the throughput by approximately 40.48%. This significant reduction in latency and improvement in throughput paves the way for the implementation of real-world 5G applications, particularly in latency-sensitive sectors like smart agriculture. Agricultural operations will benefit from faster data processing and communication, enabling real-time monitoring, and precision irrigation, ultimately leading to increased efficiency.

The rapid development of smart sensors has enabled the integration of wireless sensor networks with the Internet. The emergence of the Internet of Things represents a significant paradigm shift, with wireless sensor networks remaining essential to its framework. Most smart sensor nodes will serve as foundational components, which can cause network congestion. Congestion is a critical factor affecting the performance of Internet of Things networks. The constrained application protocol, developed by the Internet Engineering Task Force, serves as a lightweight RESTful protocol designed for resource-constrained nodes. Resource-constrained networks often face limitations in hardware and link capabilities, leading to challenges such as packet loss and increased delay. This review examines the constrained application protocol, highlighting its distinct fundamental features that facilitate deployment in constrained Internet of Things environments. This paper provides an overview of wireless sensor networks as foundational elements for Internet of Things applications, followed by a discussion on low power wide area networks and their standardisation as promising technologies for these applications. This paper discusses prominent state-of-the-art methods for congestion control in the constrained application protocol, highlighting several research challenges and future directions. It proposes a three-state Markov model and machine learning-based strategies for congestion control in resource-constrained Internet of Things networks.

Over the past decades, we have seen an increasing number of users with the need and desire to be connected and reachable anywhere at any time. As a result, we have seen a growing number of wireless technologies and services providers in order to fulfill users' needs. Wireless networks enable users to roam seamless in areas that are covered by network providers with which the user does not have any prior arrangements. While users are roaming between different network providers there is a handoff process that takes place, but ensuring the security and efficiency of this process is challenging. Mainly, because during the handoff process, a user has to wait for some time before it can communicate with an Access Point (AP) of the network provider with which it has no preceding arrangement. As a result, during this period of delay, certain applications and services are interrupted. Therefore, it would be beneficial to provide desirable Quality of Services (QoS) to users for interactive VoIP, real time gaming and streaming traffic by reducing the handoff delay and packet loss during the roaming process. In this paper, an Enhanced Secure Handoff (ESH) algorithm is designed by integrating Protocol for Carrying Authentication for Network Access (PANA) and fast roaming algorithm in order to optimize inter-domain and inter-technology handoff and provide secured roaming. In ESH algorithm the number of handoff procedures between lots of APs and users can be replaced by only one handoff procedure between the inter-domain and inter-technology. The ESH algorithm first creates and maintains a table of APs based on the signal strength thereafter security associations between users and APs are prepared. Experimental results showed that the proposed model can achieve a significant reduction in handover delays and provide secured roaming when compared with Media-Independent Handover (MIH) and Media-Independent Handover Security (MIHSec) algorithms.

The building sector accounts for a significant portion of the global energy consumption and carbon dioxide (CO₂) emissions, making it a critical area for improving energy efficiency. In Africa, the rapid energy demand and costs have further emphasized the urgency of developing effective solutions for reducing building energy use. This paper presents a comprehensive review of data-driven and physics-based modeling approaches for forecasting and optimizing energy performance in non-domestic buildings. The review highlights the evolution of statistical models, classical machine learning methods, deep learning, and hybrid approaches across various application scenarios. Emphasis is placed on the role of data pre-processing techniques, including data fusion and transfer learning, as strategies to address data limitations and improve model generalization. Furthermore, the study evaluates the strengths and limitations of different modeling methods in terms of accuracy, scalability, and applicability in real-world contexts. By integrating insights from recent literature, this paper identifies key research gaps such as the need for standard datasets, physics-informed hybrid modeling, and policy-oriented frameworks. The findings aim to guide building managers, policymakers, and researchers toward adopting robust data-driven solutions that enhance energy resilience, reduce operational costs, and support environmental sustainability in the built environment. The review also justifies the importance of these models for practical applications like energy benchmarking, retrofit planning, and CO₂ reduction, providing a clear link between research and industry implementation.