Journal of ICT Systems

The SDN Based WLAN Resilience Enhancement for Smart Grid

Yona Andegelile — Wed, 28 Feb 2024 00:00:00 +0000

A resilient wireless network for smart grid communication is essential for ensuring continuous network availability while maintaining an acceptable quality of service (QoS). The primary challenge is developing wireless networks that not only meet the stringent availability requirements of smart grids but also deliver satisfactory QoS. Current Wireless Local Area Networks (WLANs) struggle with seamless handover between Access Points (APs) when the serving AP encounters issues. In this study, we propose a Software Defined Networking (SDN)-based WLAN utilizing the Odin framework to enhance WLAN resilience against network challenges, thereby ensuring smart grid availability with acceptable bandwidth and latency. Our solution was evaluated in a physical laboratory setup employing off-the-shelf network components, including APs, routers, and switches. Our results demonstrate that in the event of an AP failure, the SDN-based WLAN effectively redirects users to a nearby AP, achieving 100% availability, a throughput of 8.93 Mbps, and a latency of 20ms metrics that fulfill the resilience requirements of smart grids. Notably, this approach employs standard APs and necessitates no modifications to end stations.

Systematic review on challenges for integration of health management information systems in enhancing HIV and AIDS estimation and monitoring across Sub-Saharan African countries

Mohamed Turay, Dr. Ruthbetha Kateule, Dr. Honest Kimaro — Wed, 16 Apr 2025 00:00:00 +0000

Sub-Saharan African (SSA) countries suffer from high disease burdens, including HIV and AIDS. Data from the national Health Management Information System (HMIS) to inform HIV and AIDS estimates are relatively poor, negatively impacting monitoring of the epidemic. The challenge necessitates a need for increased integration and functionality of the national HMIS to deliver timely and reliable information to healthcare workers and managers. This study investigates how the challenges to effective integration of HMIS in SSA countries impact the availability of good quality HIV estimation and monitoring. We conducted a systematic review of relevant publications from 2008 to 2024. The findings show that, despite the efforts by SSA countries, challenges continue to impede the meaningful utilization of HMIS to produce good quality HIV estimates. The challenges are attributed to technical, organizational and behavioural factors, such as limited integration of HIV programme components, poor data quality, inadequate resources, weak leadership, lack of accountability, and limited enforcement of HMIS regulatory frameworks. Hence, the systems hardly respond timely to the expanding data requirements of the HIV estimation tools and monitoring requirements. The study recommends SSA governments and partners to support HMIS development and on job training in data collection, cleaning, and analysis.

Effects and Optimal Integration of Electric Vehicle Charging Systems in the Tanzania Electrical Distribution Networks using Metaheuristic Algorithms

Daudi Mnyanghwalo, Shamte Kawambwa — Wed, 30 Apr 2025 00:00:00 +0000

Electric vehicles (EVs) present a viable solution for reducing carbon emissions, environmental pollution, and the effects of climate change. The EV utilizes energy stored in its battery banks, which are charged by electric vehicle charging systems (EVCS), primarily integrated with the power grid. However, integrating EVCS into grid poses significant challenges, including increased power losses, voltage deviations, harmonic injection, and grid instability. This study examines the impacts of connecting EVCS to Tanzania's electrical distribution networks and proposes an optimization approach using metaheuristic algorithms to mitigate power loss and voltage deviation challenges. The study reveals that adding one EVCS raises power loss from 13.0357 kW to 17.1963 kW, while voltage deviation increases from 0.47 V to 0.63 V, with further deterioration in system performance as more EVCS units are introduced. An enhanced Symbiotic Organism Search algorithm was employed to determine the optimal allocation and size of EVCS and PV systems. The results show that integrating 1 PV in the power system with 3 EVCSs reduced power loss to 5.26 kW from 61.42 kW. This research reveals the effectiveness of optimal PV system placement in improving the stability of the electrical network and the feasibility of an efficient EV penetration in Tanzania.

Roadmap to Systematic Understanding of Artificial Intelligence Technology Development and Implementation: A Narrative Review of Frameworks, Platforms, Scenarios of Use and Empirical Guide

Matendo Didas, Shuubi Alphonce Mutajwaa — Wed, 21 May 2025 00:00:00 +0000

One of the most significant inventions that have influenced both our daily lives and industrial operations is artificial intelligence (AI). Its quick development portends revolutionary effects in several areas, from cutting-edge businesses to regular people's lives. The current literature on technical understanding of what is required for AI development and implementation is fragmented across the existing studies. Nonetheless, there is still a shortage of systematic knowledge and comprehension regarding the AI implementation frameworks, techniques, tools, and scenarios of use (SoU). To contribute to the ongoing research efforts on the subject, the study conducted a narrative review using published resources from scholarly databases, such as IEEE, ACM, and Science Direct. 115 publications were found in well-chosen academic databases by the search to be eligible for further analysis intended for the study. The paper proposed an empirical guide towards implementing AI projects by AI stakeholders. The overall impact of the paper is that it emerges as a powerful tool for directing the whole AI continuum by both novice and experienced AI development schemes and stakeholders to approach the roadmap of developing and implementing AI initiatives. The paper offers systematic provision of direction towards any thought aiming at developing and implementing AI solutions.

Hybrid Dehazing Algorithm for Enhancing Quality of Homogeneous and Non-Homogeneous Hazy Images

Easter Christopher, Josiah Nombo, Nassor Ally — Wed, 21 May 2025 00:00:00 +0000

Restoring high-quality images from hazy environments presents a significant challenge, particularly when dealing with both homogeneous and non-homogeneous haze images. Homogeneous haze is uniformly distributed, while non-homogeneous haze varies across the image, making it difficult for existing dehazing methods to balance image clarity, preserve fine details, and minimize artifacts, such as color distortion. To address these challenges, this study proposes a hybrid dehazing algorithm that integrates fusion based techniques with Dark Channel Prior (DCP) and guided filtering to enhance atmospheric light estimation and refine the transmission map. A multi-scale fusion process is then applied to recover scene radiance, enhancing visual quality. Performance tests on standard datasets, including RESIDE and NH-HAZE, demonstrate the algorithm’s effectiveness, outperforming other state-of-the-art methods, achieving an average Peak Signal-to Noise Ratio (PSNR) of 26.70 dB and an average Structural Similarity Index Measure (SSIM) of 0.8843. These results underscore the algorithm's effectiveness in improving image quality while maintaining computational efficiency.