Iyad Katib

Spectrum management techniques for elastic optical networks: A survey☆

In recent years, OFDM has been the focus of extensive research efforts in optical transmission and networking, initially as a means to overcome physical impairments in optical communications. However, unlike, say, in wireless LANs or xDSL systems where OFDM is deployed as a transmission technology in a single link, in optical networks it is being considered as the technology underlying the novel elastic network paradigm. Consequently, network-wide spectrum management arises as the key challenge to be addressed in network design and control. In this work, we review and classify a range of spectrum management techniques for elastic optical networks, including offline and online routing and spectrum assignment (RSA), distance-adaptive RSA, fragmentation-aware RSA, traffic grooming, and survivability.

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.

Spectrum Assignment in Optical Networks: A Multiprocessor Scheduling Perspective

The routing and spectrum assignment problem has emerged as the key design and control problem in elastic optical networks. In this work, we show that the spectrum assignment (SA) problem in mesh networks transforms to the problem of scheduling multiprocessor tasks on dedicated processors. Based on this new perspective, we show that the SA problem in chain (linear) networks is NP-hard for four or more links, but is solvable in polynomial time for three links. We also develop new constant-ratio approximation algorithms for the SA problem in chains when the number of links is fixed. Finally, we present several list scheduling algorithms that are computationally efficient and simple to implement, yet produce solutions that, on average, are within 1%-5% of the lower bound.

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.

Saudi-KAU Coupled Global Climate Model: Description and Performance

BackgroundA new coupled global climate model (CGCM) has been developed at the Center of Excellence for Climate Change Research (CECCR), King Abdulaziz University (KAU), known as Saudi-KAU CGCM.PurposeThe main aim of the model development is to generate seasonal to subseasonal forecasting and long-term climate simulations.MethodsThe Saudi-KAU CGCM currently includes two atmospheric dynamical cores, two land components, three ocean components, and multiple physical parameterization options. The component modules and parameterization schemes have been adopted from different sources, and some have undergone modifications at CECCR. The model is characterized by its versatility, ease of use, and the physical fidelity of its climate simulations, in both idealized and realistic configurations. A description of the model, its component packages, and parameterizations is provided.ResultsResults from selected configurations demonstrate the model’s ability to reasonably simulate the climate on different time scales. The coupled model simulates El Niño-Southern Oscillation (ENSO) variability, which is fundamental for seasonal forecasting. It also simulates Madden-Julian Oscillation (MJO)-like disturbances with features similar to observations, although slightly weaker.ConclusionsThe Saudi-KAU CGCM ability to simulate the ENSO and the MJO suggests that it is capable of making useful predictions on subseasonal to seasonal timescales.

On Routing and Spectrum Assignment in Rings

We present a theoretical study of the routing and spectrum assignment (RSA) problem in ring networks. We first show that the RSA problem with fixed-alternate routing in general-topology (mesh) networks (and, hence, in rings as well) is a special case of a multiprocessor scheduling problem. We then consider bidirectional ring networks and investigate two problems: 1) the spectrum assignment problem under the assumption that each demand is routed along a single fixed path (e.g., the shortest path), and 2) the general case of the RSA problem whereby a routing decision along the clockwise and counter-clockwise directions must be made jointly with spectrum allocation. Based on insights from multiprocessor scheduling theory, we derive the complexity of the two problems and develop new constant-ratio approximation algorithms with a ratio that is strictly smaller than the best known ratio to date.

Network protection design models, a heuristic, and a study for concurrent single-link per layer failures in three-layer networks

Multilayer network design has received significant attention in current literature. However, the explicit modeling of IP/MPLS over OTN over DWDM in which the OTN layers technological constraints are specifically considered has not been investigated before. In this paper, we present an optimization design model for protecting an IP/MPLS over OTN over DWDM three-layer network. While considering the technological constraints of each layer, we provide a protection mechanism at each layer that guarantees the multilayer network survivability when three links fail simultaneously where each layer suffers a single failure. We present a heuristic approach to reduce the complexity of the problem and present a study based on varying several network parameters to understand their impacts on the protection capacity and the overall network cost. In addition, we present and solve three variations of our original model where we exclude each layer protection in each one of them to compare the cost performance of all models. We observe that generally the DWDM layer protection is the most expensive capacity component. The IP/MPLS layer protection becomes more expensive only when the IP/MPLS unit cost is high.