Efficient Location Training Protocols for Localization in Heterogeneous Sensor and Actor Networks

International audienceAbstract--In this work we consider a large-scale geographic area populated by tiny sensors and some more powerful devices called actors, authorized to organize the sensors in their vicinity into short-lived, actor-centric sensor networks. The tiny sensors run on miniature non-rechargeable batteries, are anonymous and are unaware of their location. The sensors differ in their ability to dynamically alter their sleep times. Indeed, the periodic sensors have sleep periods of predefined lengths, established at fabrication time; by contrast, the free sensors can dynamically alter their sleep periods, under program control. The main contribution of this work is to propose an energy-efficient location training protocol for heterogeneous actor-centric sensor networks where the sensors acquire coarse-grain location awareness with respect to the actor in their vicinity. Our analytical analysis, confirmed by experimental evaluation, show that the proposed protocol outperforms the best previously-known location training protocols in terms of the number of sleep/awake transitions, overall sensor awake time, and energy consumption

The Development of eServices in an Enlarged EU: eLearning in Slovakia

In 2005, IPTS launched a project which aimed to assess the developments in eGoverment, eHealth and eLearning in the 10 New Member States at national, and at cross-country level. At that time, the 10 New Member States were Cyprus, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovenia and Slovakia. A report for each country was produced, describing its educational system and the role played by eLearning within both the formal education system and other aspects of lifelong learning. Each report then analyzes, on the basis of desk research and expert interviews, the major achievements, shortcomings, drivers and barriers in the development of eLearning in one of the countries in question. This analysis provides the basis for the identification and discussion of national policy options to address the major challenges and to suggest R&D issues relevant to the needs of each country - in this case, Slovakia.JRC.J.4-Information Societ

Ubiquitous Computing

The aim of this book is to give a treatment of the actively developed domain of Ubiquitous computing. Originally proposed by Mark D. Weiser, the concept of Ubiquitous computing enables a real-time global sensing, context-aware informational retrieval, multi-modal interaction with the user and enhanced visualization capabilities. In effect, Ubiquitous computing environments give extremely new and futuristic abilities to look at and interact with our habitat at any time and from anywhere. In that domain, researchers are confronted with many foundational, technological and engineering issues which were not known before. Detailed cross-disciplinary coverage of these issues is really needed today for further progress and widening of application range. This book collects twelve original works of researchers from eleven countries, which are clustered into four sections: Foundations, Security and Privacy, Integration and Middleware, Practical Applications

A Highly Accurate Deep Learning Based Approach For Developing Wireless Sensor Network Middleware

Despite the popularity of wireless sensor networks (WSNs) in a wide range of applications, the security problems associated with WSNs have not been completely resolved. Since these applications deal with the transfer of sensitive data, protection from various attacks and intrusions is essential. From the current literature, we observed that existing security algorithms are not suitable for large-scale WSNs due to limitations in energy consumption, throughput, and overhead. Middleware is generally introduced as an intermediate layer between WSNs and the end user to address security challenges. However, literature suggests that most existing middleware only cater to intrusions and malicious attacks at the application level rather than during data transmission. This results in loss of nodes during data transmission, increased energy consumption, and increased overhead. In this research, we introduce an intelligent middleware based on an unsupervised learning technique called the Generative Adversarial Networks (GANs) algorithm. GANs contain two networks: a generator (G) network and a discriminator (D) network. The G network generates fake data that is identical to the data from the sensor nodes; it combines fake and real data to confuse the adversary and stop them from differentiating between the two. This technique completely eliminates the need for fake sensor nodes, which consume more power and reduce both throughput and the lifetime of the network. The D network contains multiple layers that have the ability to differentiate between real and fake data. The output intended for this algorithm shows an actual interpretation of the data that is securely communicated through the WSN. The framework is implemented in Python with experiments performed using Keras. The results illustrate that the suggested algorithm not only improves the accuracy of the data but also enhances its security by protecting it from attacks. Data transmission from the WSN to the end user then becomes much more secure and accurate compared to conventional techniques. Simulation results show that the proposed technique provides higher throughput and increases successful data rates while keeping the energy consumption low

Hierarchical Bayesian Data Fusion Using Autoencoders

In this thesis, a novel method for tracker fusion is proposed and evaluated for vision-based tracking. This work combines three distinct popular techniques into a recursive Bayesian estimation algorithm. First, semi supervised learning approaches are used to partition data and to train a deep neural network that is capable of capturing normal visual tracking operation and is able to detect anomalous data. We compare various methods by examining their respective receiver operating conditions (ROC) curves, which represent the trade off between specificity and sensitivity for various detection threshold levels. Next, we incorporate the trained neural networks into an existing data fusion algorithm to replace its observation weighing mechanism, which is based on the Mahalanobis distance. We evaluate different semi-supervised learning architectures to determine which is the best for our problem. We evaluated the proposed algorithm on the OTB-50 benchmark dataset and compared its performance to the performance of the constituent trackers as well as with previous fusion. Future work involving this proposed method is to be incorporated into an autonomous following unmanned aerial vehicle (UAV)

Applying an integrated theoretical lens to evaluate the perceived effectiveness of a computer-based reading development course in higher education

South African higher education institutions have implemented different means of developing first-year students’ English language abilities. In 2007, a university of technology introduced a compulsory computer-based reading development course to help first-year students improve their reading ability. A decade after implementation, an investigation into the effectiveness of the course, from the students’ perspectives, became imperative. This article reports on an evaluation of the perceived effectiveness of the course that was undertaken in 2018. A questionnaire survey of 269 Bachelor of Education students, followed by focus group interviews, were used to gather data. The design of the study was informed by a theoretical lens that highlights a set of directives that are underpinned by theory on student engagement and creating motivational conditions. This theory is integrated with theory emanating from studies on technology acceptance and use of the computer as medium. The findings indicate that the course was generally perceived as easy to use, useful and engaging, and that a good level of inclusion had been established. A few aspects needed attention, however, and pointed to the institution’s obligation to ensure that all conditions are adhered to for the creation of a motivational environment for culturally diverse communities. The article not only touches upon the practical implementation of reading development in higher education, but also describes in detail an integrated theoretical lens that can be customised to evaluate the perceived effectiveness of many technologically enhanced teaching and learning applications in higher education. &nbsp

Early Detection and Intervention in Audiology

"Early hearing detection and intervention (EHDI) is the gold standard for any practising audiologist, and for families of infants and children with hearing impairment. Yet EHDI remains a significant challenge for Africa, and various initiatives are in place to address this gap in transferring policy into practice within the southern African context. Early Detection and Intervention in Audiology: An African Perspective aims to address the diversity of factors in southern Africa that presents unique challenges to teaching and research in this field. The South African government’s heightened focus on increasing access to health care, which includes ongoing Early Childhood Development (ECD) programmes, makes this an opportune time for establishing and documenting evidence-based research for current undergraduate and postgraduate students. Detailed case studies pay careful attention to contextual relevance and responsiveness to both identification and intervention in hearing impairment. With diverse contributions from local and international experts, but always with an African perspective, this textbook will be an essential resource for students, researchers and practitioners.