Rashid Mehmood

Mobile Cloud Computing Model and Big Data Analysis for Healthcare Applications

Mobile devices are increasingly becoming an indispensable part of peoples daily life, facilitating to perform a variety of useful tasks. Mobile cloud computing integrates mobile and cloud computing to expand their capabilities and benefits and overcomes their limitations, such as limited memory, CPU power, and battery life. Big data analytics technologies enable extracting value from data having four Vs: volume, variety, velocity, and veracity. This paper discusses networked healthcare and the role of mobile cloud computing and big data analytics in its enablement. The motivation and development of networked healthcare applications and systems is presented along with the adoption of cloud computing in healthcare. A cloudlet-based mobile cloud-computing infrastructure to be used for healthcare big data applications is described. The techniques, tools, and applications of big data analytics are reviewed. Conclusions are drawn concerning the design of networked healthcare systems using big data and mobile cloud-computing technologies. An outlook on networked healthcare is given.

Data Fusion and IoT for Smart Ubiquitous Environments: A Survey

The Internet of Things (IoT) is set to become one of the key technological developments of our times provided we are able to realize its full potential. The number of objects connected to IoT is expected to reach 50 billion by 2020 due to the massive influx of diverse objects emerging progressively. IoT, hence, is expected to be a major producer of big data. Sharing and collaboration of data and other resources would be the key for enabling sustainable ubiquitous environments, such as smart cities and societies. A timely fusion and analysis of big data, acquired from IoT and other sources, to enable highly efficient, reliable, and accurate decision making and management of ubiquitous environments would be a grand future challenge. Computational intelligence would play a key role in this challenge. A number of surveys exist on data fusion. However, these are mainly focused on specific application areas or classifications. The aim of this paper is to review literature on data fusion for IoT with a particular focus on mathematical methods (including probabilistic methods, artificial intelligence, and theory of belief) and specific IoT environments (distributed, heterogeneous, nonlinear, and object tracking environments). The opportunities and challenges for each of the mathematical methods and environments are given. Future developments, including emerging areas that would intrinsically benefit from data fusion and IoT, autonomous vehicles, deep learning for data fusion, and smart cities, are discussed.

Analysis of Eight Data Mining Algorithms for Smarter Internet of Things (IoT)

Internet of Things (IoT) is set to revolutionize all aspects of our lives. The number of objects connected to IoT is expected to reach 50 billion by 2020, giving rise to an enormous amounts of valuable data. The data collected from the IoT devices will be used to understand and control complex environments around us, enabling better decision making, greater automation, higher efficiencies, productivity, accuracy, and wealth generation. Data mining and other artificial intelligence methods would play a critical role in creating smarter IoTs, albeit with many challenges. In this paper, we examine the applicability of eight well-known data mining algorithms for IoT data. These include, among others, the deep learning artificial neural networks (DLANNs), which build a feed forward multi-layer artificial neural network (ANN) for modelling high-level data abstractions. Our preliminary results on three real IoT datasets show that C4.5 and C5.0 have better accuracy, are memory efficient and have relatively higher processing speeds. ANNs and DLANNs can provide highly accurate results but are computationally expensive.

Greener and Smarter Phones for Future Cities: Characterizing the Impact of GPS Signal Strength on Power Consumption

Smart cities appear as the next stage of urbanization aiming to not only exploit physical and digital infrastructure for urban development but also the intellectual and social capital as its core ingredient for urbanization. Smart cities harness the power of data from sensors in order to understand and manage city systems. The most important of these sensing devices are smartphones as they provide the most important means to connect the smart city systems with its citizens, allowing personalization n and cocreation. The battery lifetime of smartphones is one of the most important parameters in achieving good user experience for the device. Therefore, the management and the optimization of handheld device applications in relation to their power consumption are an important area of research. This paper investigates the relationship between the energy consumption of a localization application and the strength of the global positioning system (GPS) signal. This is an important focus, because location-based applications are among the top power-hungry applications. We conduct experiments on two android location-based applications, one developed by us, and the other one, off the shelf. We use the results from the measurements of the two applications to derive a mathematical model that describes the power consumption in smartphones in terms of SNR and the time to first fix. The results from this study show that higher SNR values of GPS signals do consume less energy, while low GPS signals causing faster battery drain (38% as compared with 13%). To the best of our knowledge, this is the first study that provides a quantitative understanding of how the poor strength (SNR) of satellite signals will cause relatively higher power drain from a smartphones battery.

UTiLearn: A Personalised Ubiquitous Teaching and Learning System for Smart Societies

The education industry around the globe is undergoing major transformations. Organizations, such as Coursera are advancing new business models for education. A number of major industries have dropped degrees from the job requirements. While the economics of higher education institutions are under threat in a continuing gloomy global economy, digital and lifelong learners are increasingly demanding new teaching and learning paradigms from educational institutions. There is an urgent need to transform teaching and learning landscape in order to drive global economic growth. The use of distance eTeaching and eLearning (DTL) is on the rise among digital natives alongside our evolution toward smart societies. However, the DTL systems today lack the necessary sophistication due to several challenges including data analysis and management, learner-system interactivity, system cognition, resource planning, agility, and scalability. This paper proposes a personalised Ubiquitous eTeaching & eLearning (UTiLearn) framework that leverages Internet of Things, big data, supercomputing, and deep learning to provide enhanced development, management, and delivery of teaching and learning in smart society settings. A proof of concept UTiLearn system has been developed based on the framework. A detailed design, implementation, and evaluation of the UTiLearn system, including its five components, are provided using 11 widely used datasets.

Enterprise systems and performance of future city logistics

Abstract Future cities are driven by the developments in Information and Communication Technology to support the triple bottom line requirements of sustainability. Logistics will play a critical role in future cities due to the increasingly micro-dynamic nature of socio-economics of these cities and globalised production and consumption patterns. Enterprise systems (ES), the founding applications to automate and integrate different business processes, will be the key enablers for providing the necessary support required by the future city logistics. However, the implementation of ES and realisation of proposed benefits within the estimated time frame is challenging due to the huge resource requirements in terms of manpower, budget and time. This study takes the antecedent (critical success factors) approach for the implementation process to establish a success predictive model for the realisation of ES benefits. The partial least square regression has been used to test the model. Moreover, this study explores the impact of ES benefits on the logistics performance indicators to explore the synergies between them. This is fundamentally important because future city logistics will rely heavily on appropriately designed and implemented (enterprise) information systems.

Enabling Next Generation Logistics and Planning for Smarter Societies

Abstract: Social media has revolutionized our societies. It has made fundamental impact on the way we work and live. More importantly, social media is gradually becoming a key pulse of smart societies by sensing the information about the people and their spatio-temporal experiences around the living spaces. Big data and computational intelligence technologies are helping us to manage and analyze large amounts of data generated by the social media, such as twitter, and make informed decisions about us and the living spaces. This paper reports our preliminary work on the use of social media for the detection of spatio-temporal events related to logistics and planning. Specifically, we use big data and AI platforms including Hadoop, Spark, and Tableau, to study twitter data about London. Moreover, we use the Google Maps Geocoding API to locate the tweeters and make additional analysis. We find and locate congestion around the London city. We also discover that, during a certain period, top third tweeted words were about job and hiring, leading us to locate the source of the tweets which happened to be originating from around the Canary Wharf area, UKs major financial center. The results presented in the paper have been obtained using 500,000 tweets.

Enabling Smarter Societies through Mobile Big Data Fogs and Clouds

Abstract: Smart societies require next generation mobility platforms and applications to enable the needed quality and pace of life. This paper proposes a mobile computing system that enables smarter cities with enhanced mobility information through big data technologies, fogs and clouds. The system includes a mobile application, a backend cloud-based big data analysis system, and a middleware platform based on fog computing. The system architecture and its component technologies are described in addition to a mobile application use case. The technologies used in this paper have been used in the literature in the past. However, we have not found any work where all these technologies have been brought together to develop a mobile application that provides uniquely focused information on user mobility. Google Maps notifications could provide information about nearby road closure or other events where relevant. However, we propose to pull in and provide information to the users about their travel locally, nationally, and internationally. More importantly, relevant information is pulled in from multiple news media and other sources and provided to the user in multimedia formats including text, voice and video.

Loop Block Profiling with Performance Prediction

With increase in the complexity of High Performance Computing systems, the complexity of applications has increased as well. To achieve better performance by effectively exploiting parallelism from High Performance Computing architectures, we need to analyze/identify various parameters such as, the code hotspot (kernel), execution time, etc of the program. Statistics say that a program usually spends 90% of the time in executing less than 10% of the code. If we could optimize even some small portion of the 10% of the code that takes 90% of the execution time we have a high probability of getting better performance. So we must find the bottleneck, that is the part of the code which takes a long time to run which is usually called the hotspot. Profiling provides a solution to the question: which portions of the code should be optimized/parallelized, for achieving better performance. In this research work we develop a light-weight profiler that gives information about which portions of the code is the hotspot and estimates the maximum speedup that could be achieved, if the hotspot is parallelized. Keywords—Profiling, Loop Block Profile, Code Analysis, Performance Prediction, Speedup Estimation

Analysis of Tweets in Arabic Language for Detection of Road Traffic Conditions

Traffic congestion is a worldwide problem, resulting in massive delays, increased fuel wastage, and damages to human wealth, health, and lives. Various social media e.g. Twitter have emerged as an important source of information on various topics including real-time road traffic. Particularly, social media can provide information about certain future events, the causes behind the certain behavior, anomalies, and accidents, as well as the public feelings on a matter. In this paper, we aim to analyze tweets (in the Arabic language) related to the road traffic in Jeddah city to detect the most congested roads. Using the SAP HANA platform for Twitter data extraction, storage, and analysis, we discover that Al-Madinah, King AbdulAziz, and Alharamain are the most congested roads in the city, the tweets related to the road traffic are posted mostly in the rush hours, and the highest traffic tweeting time is 1 pm.