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The Internet of Things (IoT) has significantly transformed various aspects of people's lives, employing multiple sensors and devices for data collection to enhance control decisions and user experiences. Among these technologies, the Kinect sensor has emerged as a prominent tool in IoT applications, facilitating motion sensing and gesture recognition. However, existing systems often face challenges in recognizing new movements, requiring time-consuming retraining processes. To address this issue, a new multi-stage machine learning algorithm framework with knowledge transfer, leveraging Kinect skeleton node position data to characterize user movements is proposed. Our framework employs three parallel backpropagation Artificial Neural Network (ANN) models to identify hand, leg, and body movements, extracting five distinct movements from each. These movements are then inputted into a secondary neural network model to recognize complex movements. Importantly, our framework enables seamless incorporation of new movements without extensive retraining, utilizing the knowledge transfer mechanisms. The framework has been validated using a single Xbox Kinect V2 sensor, recording video data of 3 individuals performing various movements. Our results show that simple movement of different joints groups of the body can be categorized with accuracy up to 98%. Subsequently, complex movements are categorized with accuracy of approximately 92%. Finally, the knowledge transfer reduced the retraining complexity and an accuracy of 89.8% has been recorded for the new complex movements. [ABSTRACT FROM AUTHOR]

The Electric vehicles (EVs) are progressively supplanting gasoline vehicles in the automotive industry as a result of their environmental and economic advantages. Jordan’s investment in the electric vehicle (EV) market is projected to grow by 35% by 2025. In addition, the Jordan National Vision 2025 initiative provides backing for environmentally friendly energy projects, specifically those related to electric vehicles (EVs) and the necessary infrastructure, including charging stations. This paper presents the proposal, design, and implementation of an affordable intelligent EV charging management system based on the Internet of Things (IoT). This system streamlines the charging process while simultaneously ensuring robust security measures and enhancing customer convenience. Given that the entire university campus is reliant on solar energy, the management system at Al-Zaytoonah University of Jordan (ZUJ) is being introduced to regulate the charging process of electric vehicles (EVs) belonging to ZUJ staff, students, visitors, and the local community [ABSTRACT FROM AUTHOR]

This work carried out a measurement study of the Ethereum Peer-to-Peer (P2P) network to gain a better understanding of the underlying nodes. Ethereum was applied because it pioneered distributed applications, smart contracts, and Web3. Moreover, its application layer language "Solidity" is widely used in smart contracts across different public and private blockchains. To this end, we wrote a new Ethereum client based on Geth to collect Ethereum node information. Moreover, various web scrapers have been written to collect nodes' historical data fromthe Internet Archive and theWaybackMachine project. The collected data has been compared with two other services that harvest the number of Ethereumnodes. Ourmethod has collectedmore than 30%more than the other services. The data trained a neural network model regarding time series to predict the number of online nodes in the future.Our findings show that there are less than 20%of the same nodes daily, indicating thatmost nodes in the network change frequently. It poses a question of the stability of the network. Furthermore, historical data shows that the top ten countries with Ethereum clients have not changed since 2016. The popular operating system of the underlying nodes has shifted from Windows to Linux over time, increasing node security. The results have also shown that the number of Middle East and North Africa (MENA) Ethereum nodes is neglected compared with nodes recorded from other regions. It opens the door for developing new mechanisms to encourage users from these regions to contribute to this technology. Finally, the model has been trained and demonstrated an accuracy of 92% in predicting the future number of nodes in the Ethereum network. [ABSTRACT FROM AUTHOR]

IEEE802.11, known as WiFi has proliferated in the last decade. It can be found in smartphones, laptops, smart TVs and surveillance cameras. This popularity has revealed many issues in health, data privacy and security. In this work, a WiFi measurement study has been conducted in Amman, the capital city of Jordan. An Android App has been written to harvest WiFi information of the transmitted frames of any surrounding Access points (APs). More than 240,000 APs information has been harvested in this work. The harvested data have been analyzed to find statistics ofWiFi devices in this city. Moreover, three power distribution models have been derived from the data for three different areas, closed, open and hybrid areas. In addition, the collected data revealed that the SSID can be leveraged as a landmark for the access points (APs). To this end, SSIDtrack algorithm is proposed to track shoppers/walkers in closed areas, such as malls to find their walking route utilizing only the SSID information collected from the surrounding area. The algorithm has been tested in two different malls that consist of four different floors. The accuracy recorded for the algorithm acceded 95%. [ABSTRACT FROM AUTHOR]

In this work we attempt to study the turnover aspect in an organization located in a country in the Middle East, namely, Jordan. This organization consists of 1000 employees and it works in the service sector. A questionnairee was harvested from 280 employees in this organization throughout an entire year. Afterwards, a statistical study has been conducted. In addition, a soft clustering (SC) algorithm has been leveraged to construct a turnover prediction model. The model divides the employees into three clusters depending on the survey results. At the end of the year, the predicted clusters and the actual results from the company's records have been compared. 84% of the employees clustered in the mobile cluster voluntarily turned over. Moreover, our results showed that public sector and abroad contracts are the main criteria of turnover. This statistical study, also, indicated that the HR department should take serious steps to improve the quality of the work environment in order to reduce the turnover rate in this company. These steps vary from small management gratitude to employees to financial incentives. [ABSTRACT FROM AUTHOR]