Indoor Localization for Personalized Ambient Assisted Living of Multiple Users in Multi-Floor Smart Environments

This paper presents a multifunctional interdisciplinary framework that makes four scientific contributions towards the development of personalized ambient assisted living, with a specific focus to address the different and dynamic needs of the diverse aging population in the future of smart living environments. First, it presents a probabilistic reasoning-based mathematical approach to model all possible forms of user interactions for any activity arising from the user diversity of multiple users in such environments. Second, it presents a system that uses this approach with a machine learning method to model individual user profiles and user-specific user interactions for detecting the dynamic indoor location of each specific user. Third, to address the need to develop highly accurate indoor localization systems for increased trust, reliance, and seamless user acceptance, the framework introduces a novel methodology where two boosting approaches Gradient Boosting and the AdaBoost algorithm are integrated and used on a decision tree-based learning model to perform indoor localization. Fourth, the framework introduces two novel functionalities to provide semantic context to indoor localization in terms of detecting each user's floor-specific location as well as tracking whether a specific user was located inside or outside a given spatial region in a multi-floor-based indoor setting. These novel functionalities of the proposed framework were tested on a dataset of localization-related Big Data collected from 18 different users who navigated in 3 buildings consisting of 5 floors and 254 indoor spatial regions. The results show that this approach of indoor localization for personalized AAL that models each specific user always achieves higher accuracy as compared to the traditional approach of modeling an average user

Human-Computer/Device Interaction

Any interaction refers to the communication between two or more entities (be it abstract/conceptual or physical entity). Successful interaction is equated from the properties of each entity involved in the interaction as well as the capabilities of the interacting entities. With the diversified use and application of computers and specialized devices for specific tasks, such as biomechanical and biomedical devices, interaction design needs to further study the context of the tasks as well. Moreover, with the inclusion of embedded systems and smart devices, instead of focusing only on the hardware performance, the computer architecture needs to consider the opportunities. Especially, HCI can be improved as the current technologies are giving an opportunity for building smart interaction where the user interacts with devices implicitly and in less obtrusive way. In light of this, the design and architecture of an engineered product need to strive for making the product usable and used while making it useful to the user. And this can be achieved if interaction design is dictated by scrutinizing the user model with respects to the usability attributes in view of the context of its task as well as the platform capabilities and constraints as discussed in this chapter

The impact of security and privacy issues on data management in fog Computing

With the increased growth of the application domains of IoT and the associated volumes of data generation, IoT systems are complicated and have small storage and recycling capacity. The cloud, a primary IoT storage medium with countless benefits, is not ideal for processing real time IoT data without delays. Capacity of data generated by IoTs keep increasing rampantly with associated security risks and privacy-preserving problems. Therefore, privacy maintenance, confidentiality and integrity of user’s data, improved latency and bandwidth restrictions are some of the major respective challenges of cloud computing. Fog computing is therefore a novel paradigm and an extension of the cloud. Which aims to improve cloud efficiency by enabling IoTs to locally process data before cloud transmission. However, some of the issues present in cloud such as the establishment of connection between edge devices often raise security and privacy concerns are also inherent in fog. The goal of this study, however, is to look at the state of data management security and privacy in a fog computing environment by reviewing existing security frameworks and data privacy procedures. This study lays bare the security vulnerabilities that exist inside the fog environment, creating hazards to user data privacy and security, and in lieu of that, this study incorporates features of data in addition to the acquired facts and statistics. Privacy-preservation is key to the continued use of services within the context of internet usage, as a result respondents indicated that they were experienced internet users who have been using the internet and its associated resources for various purposes, however respondents neither agreed nor disagreed with the possibility of the tracking or monitoring of their usage of the internet. The perception of respondents influenced the usage of the internet and various computing devices

Computer Architecture in Industrial, Biomechanical and Biomedical Engineering

This book aims to provide state-of-the-art information on computer architecture and simulation in industry, engineering, and clinical scenarios. Accepted submissions are high in scientific value and provide a significant contribution to computer architecture. Each submission expands upon novel and innovative research where the methods, analysis, and conclusions are robust and of the highest standard. This book is a valuable resource for researchers, students, non-governmental organizations, and key decision-makers involved in earthquake disaster management systems at the national, regional, and local levels