Aiiad Albeshri

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.

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.

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.

INTWEEMS: a framework for incremental clustering of tweet streams

Twitter is a popular micro-blogging service for sharing short messages called tweets. Tweets provide public opinion on various topics. Currently twitter presents search results in form of a flat list, sorted either by popularity or by recency. These search results limit the possibility of identifying diverse latent topics covered by the tweets. One way to better understand the tweets is to cluster them where each cluster depicts a latent topic. Suitable clustering algorithms are required to cluster streaming data and map new data into existing clusters. To address this, we propose in this paper a framework called INTWEEMS (INcremental clustering of TWEEt streaMS) which clusters tweets in real-time, adjusts new tweets into existing clusters (incrementally), and provides visualization of clusters that helps in identifying latent topics and sub-topics within the tweets. This paper describes the INTWEEMS framework and its implementation.

A privacy preserving authentication scheme for roaming in ubiquitous networks

Ubiquitous networks enable mobile users to communicate with each other efficiently and independently without the need of inventing agent. This approach is proved to be delay and spectral efficient. Due to the nature of underlying Big data, such networks are prone to several security and privacy challenges. Because such gigantic data is not only difficult to store, maintain and manipulate but Big data’s open architecture makes the security threats inevitable. Therefore, incorporating authentication between mobile node and foreign network during roaming in ubiquitous networks has become a tedious task. Very recently, Farash et al. found a number of weaknesses in the schemes proposed by Wen et al. and Shin et al. Furthermore, Farash et al. proposed an enhanced scheme for roaming user in ubiquitous network. However, after thorough investigation, we show fragilities of Farash et al.’s scheme against (1) mobile user anonymity violation; (2) disclosure of secret parameter of mobile node; (3) session key disclosure; and (4) mobile node impersonation attacks. Therefore, we propose an improved scheme to fix these fragilities. We analyze the security of proposed scheme using popular automated verification tool ProVerif. The analysis confirms that the proposed scheme resists the known attacks while having quite low overhead as compared with Farash et al.’s scheme. Therefore, in order to get better performance proposed scheme is a suitable candidate to be employed along with supercomputing systems for dealing the security challenges of big data in ubiquitous networks.

Security Completeness Problem in Wireless Sensor Networks

With the emergence of wireless sensor networks and its usage in sensitive monitoring and tracking applications, the need of ensuring complete security is gaining more importance than ever before. Complete security can only be ensured by adding privacy, cryptographic-based security and trust management aspects in a security solution. However, integration of all these three aspects in a single solution for resource constraints wireless sensor networks is not trivial. Current research intensively focuses on all these three aspects in an isolated manner. To the best of our knowledge, we have not found any work in the literature that comprehensively discusses: how these various privacy, security and trust solutions work together? In this work, we have made the first step towards this direction and to show how integration of various privacy, security and trust solutions can be performed in a single solution in step-by-step manner.

Toward Exascale Computing Systems: An Energy Efficient Massive Parallel Computational Model

The emerging Exascale supercomputing system expected till 2020 will unravel many scientific mysteries. This extreme computing system will achieve a thousand-fold increase in computing power compared to the current petascale computing system. The forthcoming system will assist system designers and development communities in navigating from traditional homogeneous to the heterogeneous systems that will be incorporated into powerful accelerated GPU devices beside traditional CPUs. For achieving ExaFlops (10^18 calculations per second) performance through the ultrascale and energy-efficient system, the current technologies are facing several challenges. Massive parallelism is one of these challenges, which requires a novel energy-efficient parallel programming (PP) model for providing the massively parallel performance. In the current study, a new parallel programming model has been proposed, which is capable of achieving massively parallel performance through coarse-grained and fine-grained parallelism over inter-node and intra-node architectural-based processing. The suggested model is a tri-level hybrid of MPI, OpenMP and CUDA that is computable over a heterogeneous system with the collaboration of traditional CPUs and energy-efficient GPU devices. Furthermore, the developed model has been demonstrated by implementing dense matrix multiplication (DMM). The proposed model is considered an initial and leading model for obtaining massively parallel performance in an Exascale computing system.

Security Vulnerabilities and Improvements of SPAM: A Secure Password Authentication Mechanism for Seamless Handover in Proxy Mobile IPv6 Networks

The main contribution of this paper is to analyze a secure passwordauthentication mechanism (SPAM), proposed by Chuang et al. in 2013(IEEE Syst J.). The SPAM was used for designing a secure handover inProxy Mobile IPv6 (PMIPv6) networks. Chuang et al. in the originalpaper claimed that SPAM provides high security properties and canresist various attacks. However, in this paper we point out thatSPAM is vulnerable to the critical attacks, such as stolen smartcard and off-line dictionary attack, replay attack and impersonationattack. In addition, we show that the identity of MNs and thesession key between MN and MAG can be disclosed by an insiderattacker; resultantly, anonymity and confidentiality between MNs andMAG will be completely broken in SPAM. As a remedy, we also propose an improved scheme which not only conquers the problems of the previous schemes but also provides a reduction in computational cost. Moreover, the proposed scheme provides the user anonymity and untraceability and secure session key agreement. Finally, the security of the improved protocol is proved in the random oracle model. DOI: http://dx.doi.org/10.5755/j01.itc.46.2.12118