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    Bandwidth-deadline IoT task scheduling in fog–cloud computing environment based on the task bandwidth
    (Springer, 2023) Alsamarai, Naseem Adnan
    Given the huge increase of internet of things (IoT) devices and the enormous amounts of data are generated every minute. Some IoT applications needs real-time services and bit latency to meet the term 'Data Never Sleeps.' Therefore, Cisco proposed the fog computing concept as an expansion of cloud computing to optimize the benefits of providing processing, storage, and services with minimum delays. In this paper, we submitted a novel task scheduling algorithm called the bandwidth-deadline algorithm that focuses on the makespan and tasks' deadline satisfaction time. In our algorithm, we consider the deadline of a task by giving priority to the task with the earliest deadline and assigning it to the corresponding resource that can execute it in minimum completion time to achieve the minimum makespan. The first part of our algorithm is called Fog Max–Cloud Min, and the second part is Ant Colony Optimization. We evaluated the performance of our proposed algorithm approaches concerning deadline satisfaction and the system’s makespan. Comparatively to current algorithms. The study findings demonstratethat our algorithm is better by achieving the lowest makespan and best-satisfied deadline tasks.
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    Improved performance and cost algorithm for scheduling IoT tasks in fog-cloud environment using gray wolf optimization algorithm
    (2024) Alsamarai, Naseem Adnan; Uçan, Osman Nuri
    Today, the IoT has become a vital part of our lives because it has entered into the precise details of human life, like smart homes, healthcare, eldercare, vehicles, augmented reality, and industrial robotics. Cloud computing and fog computing give us services to process IoT tasks, and we are seeing a growth in the number of IoT devices every day. This massive increase needs huge amounts of resources to process it, and these vast resources need a lot of power to work because the fog and cloud are based on the term pay-per-use. We make to improve the performance and cost (PC) algorithm to give priority to the high-profit cost and to reduce energy consumption and Makespan; in this paper, we propose the performance and cost-gray wolf optimization (PC-GWO) algorithm, which is the combination of the PCA and GWO algorithms. The results of the trial reveal that the PC-GWO algorithm reduces the average overall energy usage by 12.17%, 11.57%, and 7.19%, and reduces the Makespan by 16.72%, 16.38%, and 14.107%, with the best average resource utilization enhanced by 13.2%, 12.05%, and 10.9% compared with the gray wolf optimization (GWO) algorithm, performance and cost algorithm (PCA), and Particle Swarm Optimization (PSO) algorithm.
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    Wind farm sites selection using a machine learning approach and geographical information systems in Türkiye
    (Springer Science and Business Media B.V., 2025) Khalaf, Oras Fadhil; Uçan, Osman Nuri; Alsamarai, Naseem Adnan
    This research highlights the importance of integrating machine learning algorithms with Geographical Information Systems (GIS) applications in the field of renewable energy by finding a suitable site for wind farms due to their importance in preserving the environment to achieve efficiency and cost-effectiveness and reduce the environmental impact of fossil fuel energy sources. Using GIS various factors affecting wind energy localization were processed and analyzed including natural, socio-economic and environmental criteria. Ensemble learning of four supervised machine learning algorithms (Random Forest, K-Nearest Neighbor, Support Vector Machines, Naive Bayes) was used to classify suitable and unsuitable data representing geo-referenced points on the ground with three criteria for each site (wind speed, elevation and slope). The results of the algorithms varied in terms of accuracy and variance, then the results were collected, and the intersection between them was found so that the location classification would be agreed upon in the results of the algorithms used. The aim of using this technique is to reduce the error, increase the accuracy and avoid the bias or variance present in individual models. Accuracy of the algorithms result was respectively (K-Nearest Neighbor, Random Forest, Support Vector Machines, Naive Bayes) (93.022%, 93.018%, 95.095%, 89.553%). The final result is a map using GIS showing the suitable and unsuitable sites of wind farms in the study area (Türkiye) has been chosen as a study area in the research due to several factors that make it suitable for wind energy projects, including its geographical location, which gives it great climatic and terrain diversity, as it is surrounded by seas (Black Sea, Aegean Sea, and Mediterranean Sea), which leads to the activity of seasonal and continuous winds, which contributes to the activity of seasonal and permanent winds. Its drive to develop investment in renewable energy due to economic and population growth has increased the demand for energy and consequently the development of renewable and sustainable energy sources. This research contributes to supporting the global transition to sustainable energy by providing a new methodology for integrating multiple technologies to support a sustainable energy future.