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"Ubiquitous computing."
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Privacy in mobile and pervasive computing
It is easy to imagine that a future populated with an ever-increasing number of mobile and pervasive devices that record our minute goings and doings will significantly expand the amount of information that will be collected, stored, processed, and shared about us by both corporations and governments. The vast majority of this data is likely to benefit us greatly--making our lives more convenient, efficient, and safer through custom-tailored and context-aware services that anticipate what we need, where we need it, and when we need it. But beneath all this convenience, efficiency, and safety lurks the risk of losing control and awareness of what is known about us in the many different contexts of our lives. Eventually, we may find ourselves in a situation where something we said or did will be misinterpreted and held against us, even if the activities were perfectly innocuous at the time. Even more concerning, privacy implications rarely manifest as an explicit, tangible harm. Instead, most privacy harms manifest as an absence of opportunity, which may go unnoticed even though it may substantially impact our lives. In this Synthesis Lecture, we dissect and discuss the privacy implications of mobile and pervasive computing technology. For this purpose, we not only look at how mobile and pervasive computing technology affects our expectations of--and ability to enjoy--privacy, but also look at what constitutes \"privacy\" in the first place, and why we should care about maintaining it. We describe key characteristics of mobile and pervasive computing technology and how those characteristics lead to privacy implications. We discuss seven approaches that can help support end-user privacy in the design of mobile and pervasive computing technologies, and set forward six challenges that will need to be addressed by future research. The prime target audience of this lecture is researchers and practitioners working in mobile and pervasive computing who want to better understand and account for the nuanced privacy implications of the technologies they are creating. Those new to either mobile and pervasive computing or privacy may also benefit from reading this book to gain an overview and deeper understanding of this highly interdisciplinary and dynamic field.
Book
Energy Optimization Using a Case-Based Reasoning Strategy
At present, the domotization of homes and public buildings is becoming increasingly popular. Domotization is most commonly applied to the field of energy management, since it gives the possibility of managing the consumption of the devices connected to the electric network, the way in which the users interact with these devices, as well as other external factors that influence consumption. In buildings, Heating, Ventilation and Air Conditioning (HVAC) systems have the highest consumption rates. The systems proposed so far have not succeeded in optimizing the energy consumption associated with a HVAC system because they do not monitor all the variables involved in electricity consumption. For this reason, this article presents an agent approach that benefits from the advantages provided by a Multi-Agent architecture (MAS) deployed in a Cloud environment with a wireless sensor network (WSN) in order to achieve energy savings. The agents of the MAS learn social behavior thanks to the collection of data and the use of an artificial neural network (ANN). The proposed system has been assessed in an office building achieving an average energy savings of 41% in the experimental group offices.
Journal Article
Biometric User Identification Based on Human Activity Recognition Using Wearable Sensors: An Experiment Using Deep Learning Models
Currently, a significant amount of interest is focused on research in the field of Human Activity Recognition (HAR) as a result of the wide variety of its practical uses in real-world applications, such as biometric user identification, health monitoring of the elderly, and surveillance by authorities. The widespread use of wearable sensor devices and the Internet of Things (IoT) has led the topic of HAR to become a significant subject in areas of mobile and ubiquitous computing. In recent years, the most widely-used inference and problem-solving approach in the HAR system has been deep learning. Nevertheless, major challenges exist with regard to the application of HAR for problems in biometric user identification in which various human behaviors can be regarded as types of biometric qualities and used for identifying people. In this research study, a novel framework for multi-class wearable user identification, with a basis in the recognition of human behavior through the use of deep learning models, is presented. In order to obtain advanced information regarding users during the performance of various activities, sensory data from tri-axial gyroscopes and tri-axial accelerometers of the wearable devices are applied. Additionally, a set of experiments were shown to validate this work, and the proposed framework’s effectiveness was demonstrated. The results for the two basic models, namely, the Convolutional Neural Network (CNN) and the Long Short-Term Memory (LSTM) deep learning, showed that the highest accuracy for all users was 91.77% and 92.43%, respectively. With regard to the biometric user identification, these are both acceptable levels.
Journal Article
IoT Based Smart and Portable System for Remote Patient Monitoring and Drug Delivery
In recent, there has been significant development in the healthcare technology particularly due to Internet of Things (IoT). The ubiquitous computing has brought a progressive change in the field of healthcare. In this way, the wellbeing experts are serving the society in a superior manner by utilizing such IoT based gadgets. This paper presents an IoT based model “Smart Portable Intensive Care Unit” for real-time patient monitoring with an additional feature of drug delivery from the remote location. The proposed model assisting the healthcare professionals and the relatives of the patient to monitor the physiological data of the patient from the remote location. The patient’s physiological data is transmitted to the cloud. An android based mobile application is developed to fetch the patient’s data from the cloud database in real-time. The authenticity of the user is maintained throughout the data communication process. The patient’s data is transmitted on the mobile application of the doctor both in analog and digital form. The doctor can also set the flow of drug infusion from a remote location. The proposed model can be effectively used for the patients admitted in the hospital or inside the ambulance.
Journal Article
Managing and designing online courses in ubiquitous learning environments
\"This book examines the effective construction of ubiquitous learning environments and projections for the future of managing and designing ubiquitous learning environments\"-- Provided by publisher.
Book
Identity Management for Internet of Things
The Internet of Things is a wide-reaching network of devices, and these devices can intercommunicate and collaborate with each other to produce variety of services at any time, any place, and in any way. Maintaining access control, authentication and managing the identity of devices while they interact with other devices, services and people is an important challenge for identity management. The identity management presents significant challenges in the current Internet communication. These challenges are exacerbated in the internet of things by the unbound number of devices and expected limitations in constrained resources. Current identity management solutions are mainly concerned with identities that are used by end users, and services to identify themselves in the networked world. However, these identity management solutions are designed by considering that significant resources are available and applicability of these identity management solutions to the resource constrained internet of things needs a thorough analysis. Technical topics discussed in the book include: • Internet of Things; • Identity Management; • Identity models in Internet of Things; • Identity management and trust in the Internet of Things context; • Authentication and access control; Identitymanagement for Internet of Things contributes to the area of identity management for ubiquitous devices in the Internet of Things. It initially presents the motivational factors together with the identity management problems in the context of Internet of Things and proposes an identity management framework. Following this, it refers to the major challenges for Identitymanagement and presents different identity management models. This book also presents relationship between identity and trust, different approaches for trust management, authentication and access control.Key milestones identified for Identitymanagement are clustering with hierarchical addressing, trust management, mutual authentication and access control. Identitymanagement for Internet of Things is ideal forpersonnel in computer/communication industries as well as academic staff and master/research students in wireless communication, computer science, operational research, electrical engineering andtelecommunication systems Internet, and cloud computing.
eBook
Designing for gesture and tangible interaction
\"Interactive technology is increasingly integrated with physical objects that do not have a traditional keyboard and mouse style of interaction, and many do not even have a display. These objects require new approaches to interaction design, referred to as post-WIMP (Windows, Icons, Menus, and Pointer) or as embodied interaction design.
Book
Fuzzy inference rule based task offloading model (FI-RBTOM) for edge computing
The key objective of edge computing is to reduce delays and provide consumers with high-quality services. However, there are certain challenges, such as high user mobility and the dynamic environments created by IoT devices. Additionally, the limitations of constrained device resources impede effective task completion. The challenge of task offloading plays a crucial role as one of the key challenges for edge computing, which is addressed in this research. An efficient rule-based task-offloading model (FI-RBTOM) is proposed in this context. The key decision of the proposed model is to choose either the task to be offloaded over an edge server or the cloud server or it can be processed over a local node. The four important input parameters are bandwidth, CPU utilization, task length, and task size. The proposed (FI-RBTOM), simulation is carried out using MATLAB (fuzzy logic) tool with 75% training and 25% testing with an overall error rate of 0.39875 is achieved.
Journal Article