Scopus Research — alyaa kadhim mohammed

Wireless Sensor Networks (WSNs) play a vital role in applications ranging from smart cities to environmental monitoring, yet their performance is often limited by inefficient cluster head (CH) selection. This paper introduces OCHSAT, a novel clustering framework that integrates Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to achieve robust and adaptive CH selection. Unlike prior Multi-Attribute Decision-Making (MADM)-based approaches, OCHSAT dynamically considers residual energy, spatial centrality, and distance to the base station, ensuring balanced energy consumption and scalability. Extensive simulations demonstrate that OCHSAT significantly improves network performance, extending lifetime by up to 68%, reducing delay by 42%, and enhancing throughput and reliability by up to 58% and 79%, respectively, compared to state-of-the-art protocols. These results are statistically validated (p<0.05), underscoring OCHSAT’s robustness. By enabling more sustainable and scalable WSN operations, OCHSAT contributes to applications aligned with global goals, including smart cities, clean water monitoring, and climate action. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026.

The neurological disorder known as epilepsy has an ongoing negative impact on the brain. Identification of seizures is essential to the clinical care of individuals with epilepsy. Expert doctors frequently use visual electroencephalography (EEG) data analysis to detect epileptic seizures which is a method for observing the nonlinear electrical activity of the brain’s nerve cells. It is an epilepsy detection diagnostic tool. In this paper, we suggest an Internet of Things (IoT) framework for precise and effective seizure detection and monitoring for epileptic patients utilizing machine learning techniques. Three layers make up the proposed IoT framework: the things/devices, fog, and cloud tiers. The proposed method is summarized in transmitting the collected data from the thing layer to the FoG layer where a number of critical steps are carried out starting from segmenting the EEG data by converting it into 2-D table format and creating a Weighted Visibility Graph (WVG) from EEG data. Our suggested method extracts nine features from the WVG and an additional ten statistical features from the original EEG dataset. All these features are fed to the machine learning methods to classify the obtained signal as normal or abnormal. Two actions will be taken depending on the classification state either sending a notification to any predetermined caretaker in case of the occurrence of a seizure or reducing the data by using the threshold-based method in case of the absence of the seizure. As a result, in both cases, the data is uploaded to the cloud layer to be reviewed later by a specialized medical team. Four scenarios were used to evaluate our proposed method using performance evaluation metrics. The power of the provided methods is demonstrated by the proposed strategy, which yields a percentage of 100% in the fourth scenario which uses ML models with hyper-parameters, balanced EEG data, and extracted features. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.