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Limitations encountered with the models developed in the previous studies had occurrences of global minima; due to which this study developed a new intelligent ubiquitous computational model that learns with gradient descent learning rule and operates with auto-encoders and decoders to attain better energy optimization. Ubiquitous machine learning computational model process performs training in a better way than regular supervised learning or unsupervised learning computational models with deep learning techniques, resulting in better learning and optimization for the considered problem domain of cloud-based internet-of-things (IOTs). This study aims to improve the network quality and improve the data accuracy rate during the network transmission process using the developed ubiquitous deep learning computational model.

In this research study, a novel intelligent ubiquitous machine learning computational model is designed and modelled to maintain the optimal energy level of cloud IOTs in sensor network domains. A new intelligent ubiquitous computational model that learns with gradient descent learning rule and operates with auto-encoders and decoders to attain better energy optimization is developed. A new unified deterministic sine-cosine algorithm has been developed in this study for parameter optimization of weight factors in the ubiquitous machine learning model.

The newly developed ubiquitous model is used for finding network energy and performing its optimization in the considered sensor network model. At the time of progressive simulation, residual energy, network overhead, end-to-end delay, network lifetime and a number of live nodes are evaluated. It is elucidated from the results attained, that the ubiquitous deep learning model resulted in better metrics based on its appropriate cluster selection and minimized route selection mechanism.

In this research study, a novel ubiquitous computing model derived from a new optimization algorithm called a unified deterministic sine-cosine algorithm and deep learning technique was derived and applied for maintaining the optimal energy level of cloud IOTs in sensor networks. The deterministic levy flight concept is applied for developing the new optimization technique and this tends to determine the parametric weight values for the deep learning model. The ubiquitous deep learning model is designed with auto-encoders and decoders and their corresponding layers weights are determined for optimal values with the optimization algorithm. The modelled ubiquitous deep learning approach was applied in this study to determine the network energy consumption rate and thereby optimize the energy level by increasing the lifetime of the sensor network model considered. For all the considered network metrics, the ubiquitous computing model has proved to be effective and versatile than previous approaches from early research studies.

The developed ubiquitous computing model with deep learning techniques can be applied for any type of cloud-assisted IOTs in respect of wireless sensor networks, ad hoc networks, radio access technology networks, heterogeneous networks, etc. Practically, the developed model facilitates computing the optimal energy level of the cloud IOTs for any considered network models and this helps in maintaining a better network lifetime and reducing the end-to-end delay of the networks.

The social implication of the proposed research study is that it helps in reducing energy consumption and increases the network lifetime of the cloud IOT based sensor network models. This approach helps the people in large to have a better transmission rate with minimized energy consumption and also reduces the delay in transmission.

In this research study, the network optimization of cloud-assisted IOTs of sensor network models is modelled and analysed using machine learning models as a kind of ubiquitous computing system. Ubiquitous computing models with machine learning techniques develop intelligent systems and enhances the users to make better and faster decisions. In the communication domain, the use of predictive and optimization models created with machine learning accelerates new ways to determine solutions to problems. Considering the importance of learning techniques, the ubiquitous computing model is designed based on a deep learning strategy and the learning mechanism adapts itself to attain a better network optimization model.

A cyber world (CW) is a digitized world created on cyberspaces inside computers interconnected by networks including the Internet. Following ubiquitous computers, sensors, e‐tags, networks, information, services, etc., is a road towards a smart world (SW) created on both cyberspaces and real spaces. It is mainly characterized by ubiquitous intelligence or computational intelligence pervasion in the physical world filled with smart things. In recent years, many novel and imaginative researches have been conducted to try and experiment a variety of smart things including characteristic smart objects and specific smart spaces or environments as well as smart systems. The next research phase to emerge, we believe, is to coordinate these diverse smart objects and integrate these isolated smart spaces together into a higher level of spaces known as smart hyperspace or hyper‐environments, and eventually create the smart world. In this paper, we discuss the potential trends and related challenges toward the smart world and ubiquitous intelligence from smart things to smart spaces and then to smart hyperspaces. Likewise, we show our efforts in developing a smart hyperspace of ubiquitous care for kids, called UbicKids.

This paper theorizes ubiquitous computing as a novel configuration of the archive. Such a configuration is characterized by shifts in agency underlying archival mechanics and a pronounced rhythmic diminution of such mechanics in which the user's experiential present tense is rendered fundamentally historical. In doing so, this paper troubles the relationship between: archival mechanics such as appraisal, accession and access; the archive as a site of historical knowledge production and the pervasiveness of data-driven daily life.

By employing conceptual analysis, I analyze a classic vision of ubiquitous computing to describe the historicization of the present tense in an increasingly computerized world. The conceptual analysis employed here draws on an interdisciplinary set of literature from library and information science, philosophy and computing fields such as human-computer interaction (HCI) and ubiquitous computing.

I present the concept of the data perfect tense, which is derived from the future perfect tense: the “will have had” construction. It refers to a historicized, data-driven and fundamentally archival present tense characterizing the user's lived world in which the goal of action is to have had created data for future unspecified use. The data perfect reifies ubiquitous computing as an archive, or a site of historical knowledge production predicated on sets of potential statements derived from data generated, appraised, acquisitioned and made accessible through and by means of pervasive “smart” objects.

This paper provides foundational consideration of ubiquitous computing as a configuration of the archive through the analysis of its temporalities: a rhythmic diminution that renders users' experiential present tenses as fundamentally historical, constructed through the agency of smart devices. In doing so, it: contributes to ongoing work within HCI seeking to understand the relationship between HCI and history; introduces concepts relevant to the analysis of novel technological ecologies in terms of archival theory; and constitutes preliminary interdisciplinary steps towards highlighting the relevance of theories of the archive and archival mechanics for critiquing sociotechnical concerns such as surveillance capitalism.

This paper aims to present a methodology for defining and modeling context-awareness and describing efficiently the interactions between systems, applications and their context. Also, the relation of modern context-aware systems with distributed computation is investigated.

On this purpose, definitions of context and context-awareness are developed based on the theory of computation and especially on a computational model for interactive computation which extends the classical Turing Machine model. The computational model proposed here encloses interaction and networking capabilities for computational machines.

The definition of context presented here develops a mathematical framework for working with context. Also, the modeling approach of distributed computing enables us to build robust, scalable and detailed models for systems and application with context-aware capabilities. Also, it enables us to map the procedures that support context-aware operations providing detailed descriptions about the interactions of applications with their context and other external sources.

A case study of a cloud-based context-aware application is examined using the modeling methodology described in the paper so as to demonstrate the practical usage of the theoretical framework that is presented.

The originality on the framework presented here relies on the connection of context-awareness with the theory of computation and distributed computing.

The impact of Ubiquitous Computing on Learning is not confined within technical dimension. Besides its technical facilitation, this new computing paradigm also challenges human’s belief on learning, and compels us to rethink on the design of learning resources and environments. The paper explores the concept of Ubiquitous Learning, and proposes a conceptual framework for a Ubiquitous Learning Environment (ULE) design and implementation. A ULE is established on the combination between Real World and Virtual Space, Personal Space and Shared Space. Learning in a ULE is conducted in the interactions among three essential communicative elements: Social Human, Object in real world, and Artifact in virtual space. A learning process is a social transfer process between tacit and explicit knowledge. Context‐Awareness is indispensable to all kinds of interactions in a ULE. In particular, this paper gives a discussion to context‐awareness supported Interoperability and Adaptability in a ULE, and suggests a five‐dimensional (Who, What, How, When, Where) representation approach for modeling context and providing context‐awareness information. In the practical dimension, this paper presents a design framework for a ULE implementation by integrating the applications of present affordable learning devices, such as networked PCs (Personal Computer), PDAs (Personal Digital Assistant), mobile phones, sensors, and RFIDs. A basic learning system architecture in a ULE and a prototype ubiquitous language learning system are also addressed in this paper.

The event‐driven paradigm is appropriate for context aware, distributed applications, yet basic events may be too low level to be meaningful to users. The authors aim to argue that this bottom‐up approach is insufficient to handle very low‐level sensor data or to express all the queries users might wish to make.

The authors propose an alternative model for querying and subscribing transparently to distributed state in a real‐time, ubiquitous, sensor‐driven environment such as is found in Sentient Computing.

The framework consists of four components: a state‐based, temporal first‐order logic (TFOL) model that represents the concrete state of the world, as perceived by sensors; an expressive TFOL‐based language, the Abstract Event Specification Language (AESL) for creating abstract event definitions, subscriptions and queries; a higherorder service (Abstract Event Detection Service) that accepts a subscription containing an abstract event definition as an argument and in return publishes an interface to a further service, an abstract event detector; and a satisfiability service that applies classical, logical satisfiability in order to check the satisfiability of the AESL definitions against the world model, in a manner similar to a constraint‐satisfaction problem.

The paper develops a model‐based approach, appropriate for distributed, heterogeneous environments.

Information technology (IT) systems are already ubiquitous, and their future growth is expected to drive the global economy for the next several decades. However, energy consumption by these systems is growing rapidly, and their sustained growth requires curbing the energy consumption, and the associated heat removal requirements. Currently, 20-50 percent of the incoming electrical power is used to meet the cooling demands of IT facilities. Careful co-optimization of electrical power and thermal management is essential for reducing energy consumption requirements of IT equipment. Such modeling based co-optimization is complicated by the presence of several decades of spatial and temporal scales. The purpose of this paper is to review recent approaches for handling these challenges.

In this paper, the authors illustrate the challenges and possible modeling approaches by considering three examples. The multi-scale modeling of chip level transient heating using a combination of Progressive Zoom-in, and proper orthogonal decomposition (POD) is an effective approach for chip level electrical/thermal co-design for mitigation of reliability concerns, such as Joule heating driven electromigration. In the second example, the authors will illustrate the optimal microfluidic thermal management of hot spots, and large background heat fluxes associated with future high-performance microprocessors. In the third example, data center facility level energy usage reduction through a transient measurements based POD modeling framework will be illustrated.

Through modeling based electrical/thermal co-design, dramatic savings in energy usage for cooling are possible.

The multi-scale nature of the thermal modeling of IT systems is an important challenge. This paper reviews some of the approaches employed to meet this challenge.

It has been six months from the time the first case was registered, and nations are still working on counter steering regulations. The proposed model in the paper encompasses a novel methodology to equip systems with artificial intelligence and computational audition techniques over voice recognition for detecting the symptoms. Regular and irregular speech/voice patterns are recognized using in-built tools and devices on a hand-held device. Phenomenal patterns can be contextually varied among normal and presence of asymptotic symptoms.

The lives of patients and healthy beings are seriously affected with various precautionary measures and social distancing. The spread of virus infection is mitigated with necessary actions by governments and nations. Resulting in increased death ratio, the novel coronavirus is certainly a serious pandemic which spreads with unhygienic practices and contact with air-borne droplets of infected patients. With minimal measures to detect the symptoms from the early onset and the rise of asymptotic outcomes, coronavirus becomes even difficult for detection and diagnosis.

A number of significant parameters are considered for the analysis, and they are dry cough, wet cough, sneezing, speech under a blocked nose or cold, sleeplessness, pain in chests, eating behaviours and other potential cases of the disease. Risk- and symptom-based measurements are imposed to deliver a symptom subsiding diagnosis plan. Monitoring and tracking down the symptoms inflicted areas, social distancing and its outcomes, treatments, planning and delivery of healthy food intake, immunity improvement measures are other areas of potential guidelines to mitigate the disease.

This paper also lists the challenges in actual scenarios for a solution to work satisfactorily. Emphasizing on the early detection of symptoms, this work highlights the importance of such a mechanism in the absence of medication or vaccine and demand for large-scale screening. A mobile and ubiquitous application is definitely a useful measure of alerting the officials to take necessary actions by eliminating the expensive modes of tests and medical investigations.

Internet has changed radically in the way people interact in the virtual world, in their careers or social relationships. IoT technology has added a new vision to this process by enabling connections between smart objects and humans, and also between smart objects themselves, which leads to anything, anytime, anywhere, and any media communications. IoT allows objects to physically see, hear, think, and perform tasks by making them talk to each other, share information and coordinate decisions. To enable the vision of IoT, it utilizes technologies such as ubiquitous computing, context awareness, RFID, WSN, embedded devices, CPS, communication technologies, and internet protocols. IoT is considered to be the future internet, which is significantly different from the Internet we use today. The purpose of this paper is to provide up-to-date literature on trends of IoT research which is driven by the need for convergence of several interdisciplinary technologies and new applications.

A comprehensive IoT literature review has been performed in this paper as a survey. The survey starts by providing an overview of IoT concepts, visions and evolutions. IoT architectures are also explored. Then, the most important components of IoT are discussed including a thorough discussion of IoT operating systems such as Tiny OS, Contiki OS, FreeRTOS, and RIOT. A review of IoT applications is also presented in this paper and finally, IoT challenges that can be recently encountered by researchers are introduced.

Studies of IoT literature and projects show the disproportionate importance of technology in IoT projects, which are often driven by technological interventions rather than innovation in the business model. There are a number of serious concerns about the dangers of IoT growth, particularly in the areas of privacy and security; hence, industry and government began addressing these concerns. At the end, what makes IoT exciting is that we do not yet know the exact use cases which would have the ability to significantly influence our lives.

This survey provides a comprehensive literature review on IoT techniques, operating systems and trends.