Radhwan M. Abdullah

In the era of pervasive mobile and heterogeneous networks, maintaining seamless connectivity during handover events while minimizing energy consumption is paramount. Traditional handover mechanisms prioritize metrics such as signal strength, user mobility, and network load, often neglecting the critical aspect of energy consumption. This study presents a novel approach to handover decision-making in mobile networks by incorporating energy-related metrics, such as battery level, energy consumption rate, and environmental context, to make informed handover decisions that balance connectivity quality and energy efficiency. Unlike traditional methods that primarily focus on signal strength and network load, our approach addresses the critical need for energy efficiency, particularly in high-mobility scenarios. This innovative framework not only enhances connectivity but also significantly improves power consumption management, offering a more sustainable solution for modern mobile networks. Through extensive simulations, we demonstrate the effectiveness of our proposed solution in reducing energy usage without compromising network performance. The results reveal significant improvements in energy savings for mobile devices, especially under high-mobility scenarios and varying network conditions. By prioritizing energy-efficient handovers, our approach not only extends the battery life of mobile devices but also contributes to the overall sustainability of mobile networks. This paper underscores the importance of incorporating energy metrics into handover decisions and sets the stage for future research in energy-aware network management.

The industry of e-commerce (EC) has become more popular and creates tremendous business opportunities for many firms. Modern societies are gradually shifting towards convenient online shopping as a result of the emergence of EC. The rapid growth in the volume of the data puts users in a big challenge when purchasing products that best meet their preferences. The reason for this is that people will be overwhelmed with many similar products with different brands, prices, and ratings. Consequently, they will be unable to make the best decision about what to purchase. Various studies on recommendation systems have been reported in the literature, concentrating on the issues of cold-start and data sparsity, which are among the most common challenges in recommendation systems. This study attempts to examine a new clustering technique named the Ordered Clustering-based Algorithm (OCA), with the aim of reducing the impact of the cold-start and the data sparsity problems in EC recommendation systems. A comprehensive review of data clustering techniques has been conducted, to discuss and examine these data clustering techniques. The OCA attempts to exploit the collaborative filtering strategy for e-commerce recommendation systems to cluster users based on their similarities in preferences. Several experiments have been conducted over a real-world e-commerce data set to evaluate the efficiency and the effectiveness of the proposed solution. The results of the experiments confirmed that OCA outperforms the previous approaches, achieving higher percentages of Precision (P), Recall (R), and F-measure (F).