Musaed Alhussein

Green data center with IoT sensing and cloud-assisted smart temperature control system

With the growing shortage of energy around the world, energy efficiency is one of the most important considerations for a data center. In this paper, we propose a green data center air conditioning system assisted by cloud techniques, which consists of two subsystems: a data center air conditioning system and a cloud management platform. The data center air conditioning system includes environment monitoring, air conditioning, ventilation and temperature control, whereas the cloud platform provides data storage and analysis to support upper-layer applications. Moreover, the detailed design and implementation are presented, including the dispatch algorithm for the temperature control, topological structure of the sensor network, and framework for the environment monitoring node. A feasibility evaluation is used to verify that the proposed system can significantly reduce the data center energy consumption without degradation in the cooling performance.

McMAC: Towards a MAC Protocol with Multi-Constrained QoS Provisioning for Diverse Traffic in Wireless Body Area Networks

The emergence of heterogeneous applications with diverse requirements for resource-constrained Wireless Body Area Networks (WBANs) poses significant challenges for provisioning Quality of Service (QoS) with multi-constraints (delay and reliability) while preserving energy efficiency. To address such challenges, this paper proposes McMAC, a MAC protocol with multi-constrained QoS provisioning for diverse traffic classes in WBANs. McMAC classifies traffic based on their multi-constrained QoS demands and introduces a novel superframe structure based on the “transmit-whenever-appropriate” principle, which allows diverse periods for diverse traffic classes according to their respective QoS requirements. Furthermore, a novel emergency packet handling mechanism is proposed to ensure packet delivery with the least possible delay and the highest reliability. McMAC is also modeled analytically, and extensive simulations were performed to evaluate its performance. The results reveal that McMAC achieves the desired delay and reliability guarantee according to the requirements of a particular traffic class while achieving energy efficiency.

Cloud based framework for Parkinson’s disease diagnosis and monitoring system for remote healthcare applications

Abstract Speech signal processing and its recognition system have gained a lot of attention from last few years due to its widespread application. In this paper, a novel approach is proposed for diagnosis and monitoring the Parkinson’s Disease (PD) which is the second most severe neurological disease in the world. PD is a neurodegenerative disease which impairs person’s balance, motor skills, speech, and other characteristics such as decision making process, emotions, and sensation. Here, we proposed a cloud based framework for detecting and monitoring Parkinson patients that will enable healthcare service in low resource setting. In the developing countries, where most of the people do not get proper healthcare services and are not well aware of Parkinson’s disease, let alone detecting and getting healthcare for PD, this system can be very practical and useful. For this system, the patients of rural areas, patients from the regions where doctors are not available, can communicate to the doctors only if they have internet connections in their smart phones to access the cloud. Doctors can check and detect patient’s PD by checking their voice disorders or Dysphonia over cloud. With this system, a PD patient can be easily detected and diagnosed by giving their voice samples through their phones, regardless of their location. Based on the evaluation, our proposed systems are avail to achieve 96.6% accuracy in the cloud environment for detecting PD. It is expected that the proposed framework will have great potential to enable healthcare service for PD patients, who live in remote areas, especially in developing countries.

Image Tampering Detection Based on Local Texture Descriptor and Extreme Learning Machine

This paper proposed a new image tampering detection method based on local texture descriptor and extreme learning machine (ELM). The image tampering includes both splicing and copy-move forgery. First, the image was decomposed into three color channels (one luminance and two Chroma), and each channel was divided into non-overlapping blocks. Local textures in the form of local binary pattern (LBP) were extracted from each block. The histograms of the patterns of all the blocks were concatenated to form a feature vector. The feature vector was then fed to an ELM for classification. The ELM is a powerful and fast classification approach. The experiments was performed using two publicly available databases. The experimental results showed that the proposed method achieved a high detection accuracy in both the databases.

Watermarking of Parkinson disease speech in cloud-based healthcare framework

Mobile healthcare in a cloud-based system increases the easiness and the ubiquitous nature of patient-doctor relationship. One of the major issues of this healthcare is secure transmission and data authenticity. If the data is not transmitted securely or not authenticated, the clients may face embarrassment. In this paper, we propose a cloud-based healthcare framework that will authenticate speech data from a patient suspected to have Parkinsons disease. The patient sends his or her speech signal recorded via a smart phone through Internet to the cloud. A discrete wavelet transform- (DWT-) singular value decomposition (SVD) based speech watermarking module is run in the cloud to embed watermark to the signal. In case of authentication, watermark is extracted from the questioned signal and matched with the stored watermark. Experimental results indicate that the proposed DWT-SVD based watermarking system achieves imperceptibility and is robust against attacks such as additive white Gaussian noise and filtering.

Performance analysis of Centralized, Distributed and Hybrid Demand Load Control architecture for Smart power Grid

Demand Response (DR) technology makes it possible to have two-way communication between service providers and home appliances inside the customer premise. This state-of-art technology equipped with smart meters, load control devices (smart devices), smart thermostats, smart switches and home energy consoles. DR technology can help us to modernize our electricity system and provide customers with new information and options for managing their electricity use. Customers can reduce or shift their power usage during peak demand periods in response to timebased rates or other forms of financial incentives. Demand response solutions play a key role in several areas: pricing, emergency response, grid reliability, infrastructure planning and design, operations, and deferral. In previous studies Demand Load for smart power grid is a mostly centralized approach. The worth of this study is we are proposing DR as a fully distributed and hybrid approach. We are investigating performance of DR in context of fully centralize, Fully Distributed and Hybrid fashion.

An implementation of normal distribution based segmentation and entropy controlled features selection for skin lesion detection and classification

BackgroundMelanoma is the deadliest type of skin cancer with highest mortality rate. However, the annihilation in its early stage implies a high survival rate therefore, it demands early diagnosis. The accustomed diagnosis methods are costly and cumbersome due to the involvement of experienced experts as well as the requirements for the highly equipped environment. The recent advancements in computerized solutions for this diagnosis are highly promising with improved accuracy and efficiency.MethodsIn this article, a method for the identification and classification of the lesion based on probabilistic distribution and best features selection is proposed. The probabilistic distribution such as normal distribution and uniform distribution are implemented for segmentation of lesion in the dermoscopic images. Then multi-level features are extracted and parallel strategy is performed for fusion. A novel entropy-based method with the combination of Bhattacharyya distance and variance are calculated for the selection of best features. Only selected features are classified using multi-class support vector machine, which is selected as a base classifier.ResultsThe proposed method is validated on three publicly available datasets such as PH2, ISIC (i.e. ISIC MSK-2 and ISIC UDA), and Combined (ISBI 2016 and ISBI 2017), including multi-resolution RGB images and achieved accuracy of 97.5%, 97.75%, and 93.2%, respectively.ConclusionThe base classifier performs significantly better on proposed features fusion and selection method as compared to other methods in terms of sensitivity, specificity, and accuracy. Furthermore, the presented method achieved satisfactory segmentation results on selected datasets.

LTE-based humanoid robotics system

Although the robots integrated with communication module can provide various functions, there is intrinsic limitation because of the instable wireless connection, restricted bandwidth and limited coverage of network. Fortunately, assisted by LTE (Long Term Evolution) techniques, the robots can be deployed more widely to support bandwidth-intensive applications. Hence, this paper proposes a LTE-based robotics system integrated with cloud computing to enhance the capability of data transmissions and intelligence for providing higher quality and more friendly services. Furthermore, we develop a robot with emotional recognition and feedback for improving Quality of Service (QoS) and Quality of Experience (QoE), and design a testbed for verifying systems feasibility and performance.

Monitoring Parkinson’s Disease in Smart Cities

Parkinson’s Disease (PD) is one of the most severe neurological diseases prevalent in the world. A neurodegenerative disease, it impairs the body’s balance, damages motor skills, and leads to disorder in speech production. These problems also affect decision-making processes and the expression of emotions. In this paper, we propose a PD monitoring framework for use in smart cities. Using this framework, city residents will have their health constantly monitored and get feedback on their PD situation. Early PD symptoms can, therefore, be detected and the proper medication provided. In this framework, we use speech signals from clients captured from various sensors and transmitted to the cloud for processing. In the cloud, decisions are made using a support vector machine-based classifier. Decisions, along with the signal features, are sent to registered doctors, who then prescribe certain medications to the client. Several experiments were performed, with the results demonstrating that the proposed framework can achieve 97.2% accuracy in detecting PD.

Modeling Learners’ Readiness to Adopt Mobile Learning: A Perspective from a GCC Higher Education Institution

Mobile learning (M-learning) has gained significant popularity in recent past due to the explosion of portable devices and the availability of the Internet. The use of this specific technology in learning and training has enriched the success stories of next generation mobile information systems. While M-learning is being widely used in developed countries such as the USA, South Korea, Japan, UK, Singapore, Taiwan, and European Union, most of the Gulf Cooperation Council (GCC) countries are lagging behind and facing diversified challenges in adopting M-learning. Thus, investigating learners’ readiness to adopt M-learning in higher education institution in the context of GCC is the focus of this paper. To this end, we introduce a hypothesized model to investigate learners’ readiness to adopt M-learning. The empirical study is conducted by analyzing data collected from participants from a GCC university using a survey questionnaire with the help of statistical tools. The results of the study will be valuable for policy-makers in designing comprehensive M-learning systems in the context of GCC. The implication of the study results on the next generation mobile information system is also discussed with future research directions.