Jason W. P. Ng

An overview of localization techniques for Wireless Sensor Networks

Localization of sensor nodes is an important aspect in Wireless Sensor Networks (WSNs). This paper presents an overview of the major localization techniques for WSNs. These techniques are classified into centralized and distributed depending on where the computational effort is carried out. The paper concentrates on the factors that need to be considered when selecting a localization technique. The advantages and limitation of various techniques are also discussed. Finally, future research directions and challenges are highlighted.

Indoor Localization and Guidance Using Portable Smartphones

Indoor guidance is becoming a significant issue with the increasing number of buildings. This paper describes an Android based indoor map guidance system that assists and guides visitors inside public buildings (e.g. schools, shopping malls, airports, museums, exhibition centers). It utilizes NFC (Near Field Communication) technology and QR (Quick Response) Codes, which are low cost, to determine the location as well as to provide navigation within the buildings. Also, it provides a variety of helpful features such as finding destination, calculating shortest path, storing car parking location, giving feedback to building management, entering surveys for restaurants and coffee shops, finding nearest toilet and making donation. In addition, the system is bilingual and available in both English and Arabic versions. The developed system relies on a server that contains its web server, map server and spatial database. For wide accessibility, the whole system is developed using open source and freely-available software. For example, (a) the Android SDK is used to develop the client interface, (b) the Apache server is used for the web server, (c) Google sketch up and Quantum GIS are used to draw the floor plans, (d) Postgre SQL/PostGIS is used for spatial database to store the drawn floor plans, and (e) MapServer (MS4W) is used for map server to retrieve and draw the stored floor plans from the spatial database. Thus far, the developed mobile application has been fully evaluated and validated for use in a smart campus environment, which has been encapsulated in a test case study delineated herein.

The Intelligent Campus (iCampus): End-to-End Learning Lifecycle of a Knowledge Ecosystem

A new paradigm of thinking pertaining to a novel holistic intelligent campus (iCampus) environment is proposed, in this paper, in order to enrich and enhance, as well as to transform, the end-to-end learning lifecycle of a knowledge ecosystem. Analogous to the different functions of a biological brain, the central digital nervous system of the campus is comprised of various different interconnected functional intelligences. Each of these intelligent areas is set to perform its specified functional role in a dynamic and coherent inter- and intra-integrative manner within the environment itself. A generalized roadmap has also been devised to encapsulate the concept from within an existing or a new campus setting. Note that the nature of the iCampus proposition is inherently multi-disciplinary and has multi-applicability to other forms of intelligent environment. To capture part of the essence of the concept, some of the key challenges pertinent to the iCampus ecosystem have also been highlighted within the campus value proposition framework.

Intelligent Mobile Cloud Education: Smart Anytime-Anywhere Learning for the Next Generation Campus Environment

Learning has evolved significantly, creating several challenges for the traditional educational system. This paradigm shift in education is imminent and has since gathered a great deal of interest in recent years, as an attempt to bridge the technological gap in the educational sector. Under EBTICs international iCampus (intelligent campus) initiative [1], this paper examines mobile cloud-education -- a novel cutting-edge research in the area of intelligent learning, based on the design, development and testing of a mobile cloud learning system. This system can provide smart anytime-anywhere learning that is customised and adapted to individuals, and delivered via personal portables devices. A preliminary testing of the system reveals its effectiveness in supporting teaching and learning in an intelligent campus environment.

Quantitative approach to collaborative learning: performance prediction, individual assessment, and group composition

The benefits of collaborative learning, although widely reported, lack the quantitative rigor and detailed insight into the dynamics of interactions within the group, while individual contributions and their impacts on group members and their collaborative work remain hidden behind joint group assessment. To bridge this gap we intend to address three important aspects of collaborative learning focused on quantitative evaluation and prediction of group performance. First, we use machine learning techniques to predict group performance based on the data of member interactions and thereby identify whether, and to what extent, the group’s performance is driven by specific patterns of learning and interaction. Specifically, we explore the application of Extreme Learning Machine and Classification and Regression Trees to assess the predictability of group academic performance from live interaction data. Second, we propose a comparative model to unscramble individual student performances within the group. These performances are then used further in a generative mixture model of group grading as an explicit combination of isolated individual student grade expectations and compared against the actual group performances to define what we coined as collaboration synergy - directly measuring the improvements of collaborative learning. Finally the impact of group composition of gender and skills on learning performance and collaboration synergy is evaluated. The analysis indicates a high level of predictability of group performance based solely on the style and mechanics of collaboration and quantitatively supports the claim that heterogeneous groups with the diversity of skills and genders benefit more from collaborative learning than homogeneous groups.

Hierarchical kernel mixture models for the prediction of AIDS disease progression using HIV structural gp120 profiles

Changes to the glycosylation profile on HIV gp120 can influence viral pathogenesis and alter AIDS disease progression. The characterization of glycosylation differences at the sequence level is inadequate as the placement of carbohydrates is structurally complex. However, no structural framework is available to date for the study of HIV disease progression. In this study, we propose a novel machine-learning based framework for the prediction of AIDS disease progression in three stages (RP, SP, and LTNP) using the HIV structural gp120 profile. This new intelligent framework proves to be accurate and provides an important benchmark for predicting AIDS disease progression computationally. The model is trained using a novel HIV gp120 glycosylation structural profile to detect possible stages of AIDS disease progression for the target sequences of HIV+ individuals. The performance of the proposed model was compared to seven existing different machine-learning models on newly proposed gp120-Benchmark_1 dataset in terms of error-rate (MSE), accuracy (CCI), stability (STD), and complexity (TBM). The novel framework showed better predictive performance with 67.82% CCI, 30.21 MSE, 0.8 STD, and 2.62 TBM on the three stages of AIDS disease progression of 50 HIV+ individuals. This framework is an invaluable bioinformatics tool that will be useful to the clinical assessment of viral pathogenesis.

MOOCs: Is There an App for That? Expanding Mobilegogy through an Analysis of MOOCS and iTunes University

As part of the iCampus initiative launched at Etisalat BT Innovation Center (EBTIC), this study evaluates the instructional design elements and mobile learning (mLearning) experiences of iTunes University (iTU) and three emerging Massive Open Online Courses (MOOCs). Findings are used to generate a Mobilegogy model, which is based on Wangs Location, Culture, Technology and Satisfaction Model [1], and consists of theories and guidelines for designing engaging mobile learning material. The discussion and analysis of the findings focuses specifically on the instructional design elements of iTU as they compare to three MOOCs: MITx, Coursera and Udacity. We give suggestions for enhancing the iTU design tools for course designers and also discuss the implications for the evolution of mLearning design theory.

iARBook: An Immersive Augmented Reality system for education

The advancement in technology nowadays has improved learning methods that are beginning to override the traditional methods. Augmented Reality (AR) is one such technology that has seen many applications in education. This paper describes how an Immersive Augmented Reality (iAR) application in conjunction with a book, can act as a new smart learning method by engaging as many of the users senses and human functions as possible. In addition, a survey was conducted on students and educators who have tested the application. The purpose of the survey is to study the effectiveness of the application in enhancing the users learning experience and help to devise plans to improve the system.

Indoor localization and navigation using smartphones augmented reality and inertial tracking

Over the last years, indoor localization and navigation is becoming a hot topic. With the increasing number of buildings, indoor positioning and navigation has turned out to be more important than outdoors. In the literature, many papers discuss wireless based indoor positioning systems. Essentially based on Wireless Fidelity (Wi-Fi), Bluetooth, Radio Frequency Identification (RFID) or existing solutions that imply the measurement of radio signals. In this paper, we evaluate an indoor image-based positioning system that takes advantage of smartphones augmented reality (AR) and inertial tracking. The excellent computing capabilities in todays highend phones or smartphones in combination with its resources of sensors, such as Global Positioning System (GPS), inertial sensors, camera, wireless receivers, are powering the mobile application sector to the extent of becoming the fastest growing one in data communication technologies. AR as an emerging technology has the potential of creating new types of indoor location based services for the near future. Here, we show some of the AR capabilities combined with inertial tracking for localization and navigation.

Investigation of a hybrid localization technique using Received Signal Strength and Direction of arrival

This paper investigates a hybrid technique that uses a combined version of Received Signal Strength (RSS) and Direction of arrival (DOA) localization techniques. Simulation results prove that using the hybrid technique in one node increases the localization accuracy and provides the node with the capability of localizing other nodes by itself without any assistance from other nodes. The performance of the hybrid technique has been evaluated under both Additive White Gaussian Noise (AWGN) and Rayleigh channel conditions. Moreover, the study shows when to use the hybrid node, and where to place such node in a given environment to get the optimum results.