280 research outputs found

    Internet of Nano-Things, Things and Everything: Future Growth Trends

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    The current statuses and future promises of the Internet of Things (IoT), Internet of Everything (IoE) and Internet of Nano-Things (IoNT) are extensively reviewed and a summarized survey is presented. The analysis clearly distinguishes between IoT and IoE, which are wrongly considered to be the same by many commentators. After evaluating the current trends of advancement in the fields of IoT, IoE and IoNT, this paper identifies the 21 most significant current and future challenges as well as scenarios for the possible future expansion of their applications. Despite possible negative aspects of these developments, there are grounds for general optimism about the coming technologies. Certainly, many tedious tasks can be taken over by IoT devices. However, the dangers of criminal and other nefarious activities, plus those of hardware and software errors, pose major challenges that are a priority for further research. Major specific priority issues for research are identified

    A Cognitive IoE (Internet of Everything) Approach to Ambient-Intelligent Smart Space

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    At present, the United Nations figures claim that the current world population would rise from 7.6 billion to 8.5 billion in 2030 and 9.7 billion in 2050. Therefore by the 2050, 65 percent of world’s population would be living in urban mega-cities and each megacity would be accommodating around 10 million inhabitants. Such massive urbanization of growing population would be known as 21st century’s ’Urban Age’. On the other side, by 2020 the growing population of elderly people above 65 years old would be increasing by 25 percent in EU countries and by 30 percent in other developing nations including Asia and North America. As a result, the growth of massive population and elderly inhabitants in urban cities would require an assisted living environment for independent and comfortable living experiences. As can be expected, a persuasive demand of assisted living environment would be vital to the humankind. The goal of an assisted living environment is to support the aging population and inhabitants to live independently in their own home and communities with the support of trained services and personal digital assistants. Therefore, the continuous growing demand of assisted living environment targets to improve the inhabitants comfort level and efficiency to do their ADL (Activity Daily Living) routine tasks by enabling the cooperation among various IoT smart objects and sensors which will understand the environmental surroundings and the inhabitant’s contextual needs in a proactive manner.In this work, a Cognitive IoE (Internet of Everything) framework with ambient intelligence capability is proposed to observe the inhabitant activities with heterogeneous IoT network objects and sensors in a time series manner to perceive the inhabitant intentions and situations in the environment. The predictive regression model forecasts the inhabitant’s activity patterns with regressive machine learning algorithms. The interconnected network objects (sensors and actuators) behave as agents to learn, think and adapt to contextual situations in the dynamic environment with no or minimum human intervention. Therefore, the first research challenge is to recognize the inhabitant’s intentional-situation in the environment, and it is achieved by the Ambient Cognition Model(ACM). The ACM not only performs IoT data-fusion but also applies a statistical model for threshold and weight scheme to extract contextual information in a more systematic manner. The second research challenge of automating the predictive regression model to forecast the time series activity patterns of inhabitants is addressed within the Ambient-Expert Model(AEM). The hidden activity state patterns are identified, trained and tested with the supervised machine learning method of Hidden Markov Model, Recurrent-Neural Network, and Naive Bayes classifier. In addition, a recursive training mechanism of DATAWELL is integrated with the architecture to train(re-train) the model over new datasets and perform predictive analysis in a proactive manner.Furthermore, the unified framework CAiSH (Cognitive Ambient Intelligent Smart Home), built upon the integration of ACMand AEM architectures to a provide an intelligent IoT framework for the ambient intelligence smart home environment. The trained model uses maximum likelihood posterior probabilities to forecast the inhabitant’s intentional activity states. The CAiSH works as a proactive digital assistant to the inhabitant provide a development platform for autonomous and enhanced assisted living services in the cognitive IoE environment. The research has been carried out on time-series data sets, deploying IoT lab to generate and collect time series data for the training and testing purpose and providing hands-on research experience on IoT prototype deployment. Overall, 5499 datasets of 30 SA (Spot-Activities) and 9 IA (Intention- Activities) data sets have been engaged for the training and evaluation. The result outputs are evaluated with MAE (Mean-Square Error), MAPE (Mean Absolute Percentage Error) and MAE (Mean Absolute Error) metrics for the prediction accuracy measures

    Challenges and Opportunities in Applied System Innovation

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    This book introduces and provides solutions to a variety of problems faced by society, companies and individuals in a quickly changing and technology-dependent world. The wide acceptance of artificial intelligence, the upcoming fourth industrial revolution and newly designed 6G technologies are seen as the main enablers and game changers in this environment. The book considers these issues not only from a technological viewpoint but also on how society, labor and the economy are affected, leading to a circular economy that affects the way people design, function and deploy complex systems

    Survey on 6G Frontiers: Trends, Applications, Requirements, Technologies and Future Research

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    Emerging applications such as Internet of Everything, Holographic Telepresence, collaborative robots, and space and deep-sea tourism are already highlighting the limitations of existing fifth-generation (5G) mobile networks. These limitations are in terms of data-rate, latency, reliability, availability, processing, connection density and global coverage, spanning over ground, underwater and space. The sixth-generation (6G) of mobile networks are expected to burgeon in the coming decade to address these limitations. The development of 6G vision, applications, technologies and standards has already become a popular research theme in academia and the industry. In this paper, we provide a comprehensive survey of the current developments towards 6G. We highlight the societal and technological trends that initiate the drive towards 6G. Emerging applications to realize the demands raised by 6G driving trends are discussed subsequently. We also elaborate the requirements that are necessary to realize the 6G applications. Then we present the key enabling technologies in detail. We also outline current research projects and activities including standardization efforts towards the development of 6G. Finally, we summarize lessons learned from state-of-the-art research and discuss technical challenges that would shed a new light on future research directions towards 6G

    Towards 6G-Enabled Internet of Things with IRS-Empowered Backscatter-Assisted WPCNs

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    Wireless powered communication networks (WPCNs) are expected to play a key role in the forthcoming 6G systems. However, they have not yet found their way to large-scale practical implementations due to their inherent shortcomings such as the low efficiency of energy transfer and information transmission. In this thesis, we aim to study the integration of WPCNs with other novel technologies of backscatter communication and intelligent reflecting surface (IRS) to enhance the performance and improve the efficiency of these networks so as to prepare them for being seamlessly fitted into the 6G ecosystem. We first study the incorporation of backscatter communication into conventional WPCNs and investigate the performance of backscatter-assisted WPCNs (BS-WPCNs). We then study the inclusion of IRS into the WPCN environment, where an IRS is used for improving the performance of energy transfer and information transmission in WPCNs. After that, the simultaneous integration of backscatter communication and IRS technologies into WPCNs is investigated, where the analyses show the significant performance gains that can be achieved by this integration
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