Browsing by Author "Daraghmi, Yousef-Awwad"

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Forwarding Methods in Data Dissemination and Routing Protocols for Vehicular Ad Hoc Networks
(2013-11-01)
Daraghmi, Yousef-Awwad
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Yi, Chih-Wei
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Stojmenovic, Ivan
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資訊工程學系
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Department of Computer Science
Intermittent connectivity, abrupt changes in network topology and low reception rate are the most important properties that distinguish VANET (vehicular ad hoc networks) from other types of ad hoc networks. To optimize reliability and time criticality metrics in data communication protocols for VANET, novel ideas are needed. In this article, we present a tutorial on methods (at the network layer), encountered in recent literature, for small and large scale routing protocols, and geocasting (broadcasting, data dissemination, and warning delivery) protocols.
MedChain: A Design of Blockchain-Based System for Medical Records Access and Permissions Management
(2019-01-01)
Daraghmi, Eman-Yasser
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Daraghmi, Yousef-Awwad
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Yuan, Shyan-Ming
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資訊工程學系
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Department of Computer Science
Although the blockchain technology was first introduced through Bitcoin, extending its usage to non-financial applications, such as managing electronic medical records, is an attractive mission for recent research to balance the needs for increasing data privacy and the regular interaction among patients and health providers. Various systems that adopts the blockchain in managing medical records have been proposed. However, there is a need for more work to better characterize, understand and evaluate the employment of blockchain technology in the healthcare industry. In this paper, a design of blockchain based system, namely MedChain, for managing medical records is proposed. MedChain is designed to improve the current systems as it provides interoperable, secure, and effective access for medical records by patients, health care providers, and other third parties, while keeping the patients' privacy. MedChain employs timed-based smart contracts for governing transactions and controlling accesses to electronic medical records. It adopts advanced encryption techniques for providing further security. This work proposes a new incentive mechanism that leverages the degree of health providers regarding their efforts on maintaining medical records and creating new blocks. Extensive experiments are conducted to evaluate the MedChain performance, and results indicate the efficiency of our proposal in handling a large dataset at low latency.
Mining Overdispersed and Autocorrelated Vehicular Traffic Volume
(2013)
Daraghmi, Yousef-Awwad
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Yi, Chih-Wei
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Chiang, Tsun-Chieh
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資訊工程學系
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Department of Computer Science
Vehicular congestion is a major problem in urban cities and is managed by real time control of traffic that requires accurate modeling and forecasting of traffic volumes. Traffic volume is a time series that has complex characteristics such as autocorrelation, trend, seasonality and overdispersion. Several data mining methods have been proposed to model and forecast traffic volume for the support of congestion control strategies. However, these methods focus on some of the characteristics and ignore others. Some methods address the autocorrelation and ignore the overdispersion and vice versa. In this research, we propose a data mining method that can consider all characteristics by capturing the volume autocorrelation, trend, and seasonality and by handling the overdispersion. The proposed method adopts the Holt-Winters-Taylor (HWT) count data method. Data from Taipei city are used to evaluate the proposed method which outperforms other methods by achieving a lower root mean square error.
Negative Binomial Additive Models for Short-Term Traffic Flow Forecasting in Urban Areas
(2014-04-01)
Daraghmi, Yousef-Awwad
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Yi, Chih-Wei
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Chiang, Tsun-Chieh
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資訊工程學系
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Department of Computer Science
Parallel, coordinated, and network-wide traffic management requires accurate and efficient traffic forecasting models to support online, real-time, and proactive dynamic control. Forecast accuracy is impacted by a critical characteristic of traffic flow, i.e., overdispersion. Efficiency depends on the time complexity of forecasting algorithms. Therefore, this paper proposes a novel spatiotemporal multivariate forecasting model that is based on the negative binomial additive models (NBAMs). Negative binomial is utilized to handle overdispersion, and additive models are used to efficiently smooth nonlinear spatial and temporal variables. To evaluate the model, it is applied to real-world data collected from Taipei City and compared with other forecasting models. The results indicate that the proposed model is an accurate and efficient approach in forecasting traffic flow in urban context where flow is overdispersed, autocorrelated, and influenced by upstream and downstream roads as well as the daily seasonal patterns, namely, low-, moderate-, and high-traffic seasons.
Smart Shopping Carts Based on Mobile Computing and Deep Learning Cloud Services
(2020-01-01)
Sarwart, Muhammad Atif
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Daraghmi, Yousef-Awwad
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Liu, Kuan-Wen
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Chi, Hong-Chuan
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Ik, Tsi-Ui
Self-checkout systems enable retailers to reduce costs and customers to process their purchases quickly without waiting in queues. However, existing self-checkout systems suffer from design problems as they require large hardware consisting of a camera, sensors, RFID and other IoT technologies which increases the cost of such systems. Therefore, we propose a smart shopping cart with self-checkout, called iCart, to improve customer's experience at retail stores by enabling just walk out checkout and overcome the aforementioned problems. iCart is based on mobile cloud computing and deep learning cloud services. In iCart, a checkout event video is captured and sent to the cloud server for classification and segmentation where an item is identified and added to the shopping list. The Linux based cloud server contained the yolov2 deep learning network. iCart is a lightweight system of low cost solution which is suitable for the small-scale retail stores. The system is evaluated using real-world checkout video, and the accuracy of the shopping event detection and item recognition is about 97%.
Space-time Multivariate Negative Binomial Regression for Urban Short-Term Traffic Volume Prediction
(2012-01-01)
Daraghmi, Yousef-Awwad
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Yi, Chih-Wei
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Chiang, Tsun-Chieh
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資訊工程學系
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Department of Computer Science
The accuracy of short-term traffic volume prediction in urban areas depends on the traffic volume characteristics and how prediction models address these characteristics. In this paper, we propose a space-time multivariate Negative Binomial (NB) regression for short-term traffic volume prediction in urban areas. The NB regression spatially correlates multiple overdispersed traffic volumes on multiple roads. We add the temporal correlation of volumes by allowing each volume to correlate with its values at previous time segments. Data consisting of traffic volumes collected in Taipei city are used to verify the model. The root mean square error is used to compare the proposed model with the Holt-Winters (HW) and Multivariate Structural Time series (MST) models. The results show that the proposed model is more accurate than the HW and MST models in all traffic conditions. The proposed model also determines causal interactions among spatial variables which assists in identifying roads affecting the prediction accuracy. Upstream roads are always significant, distant roads are always insignificant and downstream roads are significant during rush hours only.
UniChain: A Design of Blockchain-Based System for Electronic Academic Records Access and Permissions Management
(2019-11-02)
Daraghmi, Eman-Yasser
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Daraghmi, Yousef-Awwad
;
Yuan, Shyan-Ming
;
資訊工程學系
;
Department of Computer Science
Although blockchain technology was first introduced through Bitcoin, extending its usage to non-financial applications, such as managing academic records, is a new mission for recent research to balance the needs for increasing data privacy and the regular interaction among students and universities. In this paper, a design for a blockchain-based system, namely UniChain, for managing Electronic Academic Records (EARs) is proposed. UniChain is designed to improve the current management systems as it provides interoperable, secure, and effective access to EARs by students, universities, and other third parties, while keeping the students' privacy. UniChain employs timed-based smart contracts for governing transactions and controlling access to EARs. It adopts advanced encryption techniques for providing further security. This work proposes a new incentive mechanism that leverages the degree of universities regarding their efforts on maintaining academic records and creating new blocks. Extensive experiments were conducted to evaluate the UniChain performance, and the results indicate the efficiency of the proposal in handling a large dataset at low latency.