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The proliferation of sensors and smart Internet of Things (IoT) devices in our everyday environments is reshaping our interactions with everyday objects. This change underlines the need to empower non-expert users to easily configure the behaviour of these devices to align with their preferences and habits. At the same time, recent advances in generative transformers, such as ChatGPT, have opened up new possibilities in a variety of natural language processing tasks, enhancing reasoning capabilities and conversational interactions. This paper presents RuleBot + + , a conversational agent that exploits GPT-4 to assist the user in the creation and modification of trigger-action automations through natural language. After an introduction to motivations and related work, we present the design and implementation of RuleBot + + and report the results of the user test in which users interacted with our solution and Home Assistant, one of the most used open-source tools for managing smart environments. [ABSTRACT FROM AUTHOR]

Blockchain is a developing technology that promises advancements when it is applied to other fields. Applying blockchain to other systems requires a customized blockchain model to satisfy the requirements of different application fields. One important area is to integrate blockchain with smart spaces and the Internet of Things to process, manage, and store data. Actually, smart spaces and Internet of Things systems include various types of transactions in terms of sensitivity. The sensitivity can be considered as correctness sensitivity, time sensitivity, and specialization sensitivity. Correctness sensitivity means that the systems should accept precise or approximated data in some cases, whereas time sensitivity means that there are time bounds for each type of transaction, and specialization sensitivity applies when some transactions are processed only by specialized people. Therefore, this work introduces the smart partitioned blockchain model, where we use machine learning and deep learning models to classify transactions into different pools according to their sensitivity levels. Then, each pool is mapped to a specific part of the smart partitioned blockchain model. The parts can be permissioned or permissionless. The permissioned parts can have different sub-parts if needed. Consequently, the smart partitioned blockchain can be customized to meet application-field requirements. In the experimental results, we use bank and medical datasets with a predefined sensitivity threshold for classification accuracy in each system. The bank transactions are critical, whereas the classification of the medical dataset is speculative and less critical. The Random Forest model is used for bank-dataset classification, and its accuracy reaches 100%, whereas Sequential Deep Learning is used for the medical dataset, which reaches 91%. This means that all bank transactions are correctly mapped to the corresponding parts of the blockchain, whereas accuracy is lower for the medical dataset. However, acceptability is determined based on the predefined sensitivity threshold. [ABSTRACT FROM AUTHOR]

Nowadays, research works into the dynamic and static human activities on Smart spaces abounds. Artificial Intelligence (AI) and low cost non-privacy invasive ambient sensors have made this ubiquitous. This review presents a state-of-the-art analysis, performance evaluation, and future research direction. One of the aims of activity recognition (especially that of humans) systems using thermal sensors and AI is the safety of persons in Smart spaces. In a Smart home, human activity detection systems are put in place to ensure the safety of persons in such an environment. This system should have the ability to monitor issues like fall detection, a common home-related accident. In this work, a review of trends in thermal sensor deployment, an appraisal of the popular datasets, AI algorithms, testbeds, and critical challenges of the recent works was provided to direct the research focus. In addition, a summary of AI models and their performance under various sensor resolutions was presented.

The expanding computer landscape leads us toward ubiquitous computing, in which smart gadgets seamlessly provide intelligent services anytime, anywhere. Smartphones and other smart devices with multiple sensors are at the vanguard of this paradigm, enabling context-aware computing. Similar setups are also known as smart spaces. Context-aware systems, primarily deployed on mobile and other resource-constrained wearable devices, use a variety of implementation approaches. Rule-based reasoning, noted for its simplicity, is based on a collection of assertions in working memory and a set of rules that regulate decision-making. However, controlling working memory capacity efficiently is a key challenge, particularly in the context of resource-constrained systems. The paper’s main focus lies in addressing the dynamic working memory challenge in memory-constrained devices by introducing a systematic method for content removal. The initiative intends to improve the creation of intelligent systems for resource-constrained devices, optimize memory utilization, and enhance context-aware computing. [ABSTRACT FROM AUTHOR]

Abstract—Trends in the development of smart spaces are described. Modern tools for designing the information support of smart spaces are presented, and a brief description of them, their applied methods, and their standards are given. It is concluded that there is a growing need for specialist designers of this field of knowledge. [ABSTRACT FROM AUTHOR]

Mid-gesture interfaces have become popular for specific scenarios, such as interactions with augmented reality via head-mounted displays, specific controls over smartphones, or gaming platforms. This article explores the use of a location-aware mid-air gesture-based command triplet syntax to interact with a smart space. The syntax, inspired by human language, is built as a vocative case with an imperative structure. In a sentence like "Light, please switch on!", the object being activated is invoked via making a gesture that mimics its initial letter/acronym (vocative, coincident with the sentence's elliptical subject). A geometrical or directional gesture then identifies the action (imperative verb) and may include an object feature or a second object with which to network (complement), which also represented by the initial or acronym letter. Technically, an interpreter relying on a trainable multidevice gesture recognition layer makes the pair/triplet syntax decoding possible. The recognition layer works on acceleration and position input signals from graspable (smartphone) and free-hand devices (smartwatch and external depth cameras), as well as a specific compiler. On a specific deployment at a Living Lab facility, the syntax has been instantiated via the use of a lexicon derived from English (with respect to the initial letters and acronyms). A within-subject analysis with twelve users has enabled the analysis of the syntax acceptance (in terms of usability, gesture agreement for actions over objects, and social acceptance) and technology preference of the gesture syntax within its three device implementations (graspable, wearable, and device-free ones). Participants express consensus regarding the simplicity of learning the syntax and its potential effectiveness in managing smart resources. Socially, participants favoured the Watch for outdoor activities and the Phone for home and work settings, underscoring the importance of social context in technology design. The Phone emerged as the preferred option for gesture recognition due to its efficiency and familiarity. The system, which can be adapted to different sensing technologies, addresses the scalability concerns (as it can be easily extended for new objects and actions) and allows for personalised interaction. [ABSTRACT FROM AUTHOR]

This paper presents a physical space design method for mixed reality-based immersive environments by improving the color rendering of optically visible head-mounted displays. In OST-HMD, the phenomenon that the light of the external environment is combined with the enhanced image occurs, which reduces the visibility of the content. Moreover, previous studies to solve this problem required additional optical components or performed only simple verification. Proposed physical space was designed using black curtains, blinds, low-reflection structures, and adjustable lighting. The physical space can be adapted to the intended use of the physical space through physical transitions for multiple users without additional optics. To verify the usefulness of this environment, we measured the color gamut and intensity spectrum in three experimental environments. Also, we analyzed the factors that change the color gamut according to the components and the surrounding environment. As a result, the color gamut improved by up to 4.7 times from 19.92% to 93.53% based on the environment and improved by up to 4.1 times from 21% to 86.29% depending on the location. The experimental results revealed that the presented method could reduce the factors that degrade the color gamut within all measurement locations and significantly improve the color gamut.

The expanding computer landscape leads us toward ubiquitous computing, in which smart gadgets seamlessly provide intelligent services anytime, anywhere. Smartphones and other smart devices with multiple sensors are at the vanguard of this paradigm, enabling context-aware computing. Similar setups are also known as smart spaces. Context-aware systems, primarily deployed on mobile and other resource-constrained wearable devices, use a variety of implementation approaches. Rule-based reasoning, noted for its simplicity, is based on a collection of assertions in working memory and a set of rules that regulate decision-making. However, controlling working memory capacity efficiently is a key challenge, particularly in the context of resource-constrained systems. The paper’s main focus lies in addressing the dynamic working memory challenge in memory-constrained devices by introducing a systematic method for content removal. The initiative intends to improve the creation of intelligent systems for resource-constrained devices, optimize memory utilization, and enhance context-aware computing.

This paper presents a physical space design method for mixed reality-based immersive environments by improving the color rendering of optically visible head-mounted displays. In OST-HMD, the phenomenon that the light of the external environment is combined with the enhanced image occurs, which reduces the visibility of the content. Moreover, previous studies to solve this problem required additional optical components or performed only simple verification. Proposed physical space was designed using black curtains, blinds, low-reflection structures, and adjustable lighting. The physical space can be adapted to the intended use of the physical space through physical transitions for multiple users without additional optics. To verify the usefulness of this environment, we measured the color gamut and intensity spectrum in three experimental environments. Also, we analyzed the factors that change the color gamut according to the components and the surrounding environment. As a result, the color gamut improved by up to 4.7 times from 19.92% to 93.53% based on the environment and improved by up to 4.1 times from 21% to 86.29% depending on the location. The experimental results revealed that the presented method could reduce the factors that degrade the color gamut within all measurement locations and significantly improve the color gamut. [ABSTRACT FROM AUTHOR]

This paper presents GAVT, a highly accurate audiovisual 3D tracking system based on particle filters and a probabilistic framework, employing a single camera and a microphone array. Our first contribution is a complex visual appearance model that accurately locates the speaker's mouth. It transforms a Viola & Jones face detector classifier kernel into a likelihood estimator, leveraging knowledge from multiple classifiers trained for different face poses. Additionally, we propose a mechanism to handle occlusions based on the new likelihood's dispersion. The audio localization proposal utilizes a probabilistic steered response power, representing cross-correlation functions as Gaussian mixture models. Moreover, to prevent tracker interference, we introduce a novel mechanism for associating Gaussians with speakers. The evaluation is carried out using the AV16.3 and CAV3D databases for Single- and Multiple-Object Tracking tasks (SOT and MOT, respectively). GAVT significantly improves the localization performance over audio-only and video-only modalities, with up to 50.3 % average relative improvement in 3D when compared with the video-only modality. When compared to the state of the art, our audiovisual system achieves up to 69.7 % average relative improvement for the SOT and MOT tasks in the AV16.3 dataset (2D comparison), and up to 18.1 % average relative improvement in the MOT task for the CAV3D dataset (3D comparison). [ABSTRACT FROM AUTHOR]

This literature review highlights the emergence of the Internet of Things (IoT) and the proliferation of connected devices as the driving force behind the adoption of smart spaces. This review also discusses the various applications of smart spaces, including smart homes, smart cities, and smart healthcare: (1) Background: the aim of this research is to provide a comprehensive overview of the concept of smart spaces, including their key features, technologies, and applications in built environments and urban areas; (2) Methods: The study adopts a qualitative approach, drawing on secondary sources, such as academic journals, reports, and online sources; (3) Results: The findings suggest that smart spaces have the potential to transform the way people interact with their environment and each other. They could improve efficiency, safety, and quality of life. However, there are also concerns about privacy and security in relation to the collection and use of personal data; (4) Conclusions: The study concludes that smart spaces have significant theoretical and practical implications for various fields, including architecture, urban planning, and healthcare. The theoretical implications include the need for new models and frameworks to understand the complex relationships between technology, space, and society. The practical implications involve the development of new standards and regulations to ensure the responsible and ethical use of smart spaces. [ABSTRACT FROM AUTHOR]

Mid-gesture interfaces have become popular for specific scenarios, such as interactions with augmented reality via head-mounted displays, specific controls over smartphones, or gaming platforms. This article explores the use of a location-aware mid-air gesture-based command triplet syntax to interact with a smart space. The syntax, inspired by human language, is built as a vocative case with an imperative structure. In a sentence like “Light, please switch on!”, the object being activated is invoked via making a gesture that mimics its initial letter/acronym (vocative, coincident with the sentence’s elliptical subject). A geometrical or directional gesture then identifies the action (imperative verb) and may include an object feature or a second object with which to network (complement), which also represented by the initial or acronym letter. Technically, an interpreter relying on a trainable multidevice gesture recognition layer makes the pair/triplet syntax decoding possible. The recognition layer works on acceleration and position input signals from graspable (smartphone) and free-hand devices (smartwatch and external depth cameras), as well as a specific compiler. On a specific deployment at a Living Lab facility, the syntax has been instantiated via the use of a lexicon derived from English (with respect to the initial letters and acronyms). A within-subject analysis with twelve users has enabled the analysis of the syntax acceptance (in terms of usability, gesture agreement for actions over objects, and social acceptance) and technology preference of the gesture syntax within its three device implementations (graspable, wearable, and device-free ones). Participants express consensus regarding the simplicity of learning the syntax and its potential effectiveness in managing smart resources. Socially, participants favoured the Watch for outdoor activities and the Phone for home and work settings, underscoring the importance of social context in technology design. The Phone emerged as the preferred option for gesture recognition due to its efficiency and familiarity. The system, which can be adapted to different sensing technologies, addresses the scalability concerns (as it can be easily extended for new objects and actions) and allows for personalised interaction.

In the last decade, the need for smart-space design has been on the rise. Various data collected from Internet-of-Things (IoT) and sensors are used to optimize the operation of smart spaces, which, in urban areas, are evolving into smart cities. How can smart spaces provide value to citizens? There is a need to develop smart services that leverage emerging technologies while taking an inclusive and empowering approach to the inhabitants. To address this need, we present a framework for designing smart spaces and we use a bottom-up (inclusive) approach to instantiate a smart kiosk (SK). The SK prototype provides a practical approach for transforming a traditional building into a smart space utilizing IoT and artificial intelligence technologies. The design science research (DSR) methodology was followed for designing and evaluating the prototype. An iterative process that involves occupant feedback and brainstorming sessions coupled with a literature review was carried out to identify the issues and services related to a smart building. The SK prototype implements three intelligent services that were prioritized by the citizens of the building. The results show that the SK has a high usage and acceptance rate and it can transform a lobby into a highly engaged and smart building space. The prototyping process suggests important factors to ideate and assess smart services and shows that small-scale projects can be successful to enable smart buildings. The framework provides a theoretical contribution while the design and development process assists practitioners in identifying and developing intelligent services based on IoT technology. [ABSTRACT FROM AUTHOR]

Smart technology is already present in many areas of everyday life. People rely on algorithms in crucial life domains such as finance and healthcare, and the smart car promises a more relaxed driving experience—all the while, the technology recedes further into the background. The smarter the technology, the more intransparent it tends to become. Users no longer understand how the technology works, what its limits are, and what consequences regarding autonomy and privacy emerge. Both extremes, total invisibility and total transparency, come with specific challenges and do not form reasonable design goals. This research explores the potential tension between smart and invisible versus transparent and understandable technology. We discuss related theories from the fields of explainable AI (XAI) as well as trust psychology, and then introduce transparency in smart spaces as a special field of application. A case study explores specific challenges and design approaches through the example of a so-called room intelligence (RI), i.e., a special kind of smart living room. We conclude with research perspectives for more general design approaches and implications for future research. [ABSTRACT FROM AUTHOR]

The progress of Internet of Things (IoT) leads to appearance of diverse networked environments where specific class of service-oriented applicationssmart spacesare becoming developed and deployed. The deployment is based on certain software platforms, widely accepted as smart spaces middleware. This paper overviews this type of middleware with focus on the requirements that the middleware supports in IoT environments. We analyze and systemize the middleware requirements. The systemized requirements are mapped to the particular existing middleware solutions, leading to identification and comparison of basic approaches in the smart spaces development problem.

In the last decade, smart spaces and automatic systems have gained significant popularity and importance. Moreover, as the COVID-19 pandemic continues, the world is seeking remote intervention applications with autonomous and intelligent capabilities. Context-aware computing (CAC) is a key paradigm that can satisfy this need. A CAC-enabled system recognizes humans' status and situation and provides proper services without requiring manual participation or extra control by humans. However, CAC is insufficient to achieve full automaticity since it needs manual modeling and configuration of context. To achieve full automation, a method is needed to automate the modeling and reasoning of contexts in smart spaces. In this paper, we propose a method that consists of two phases: the first is to instantiate and generate a context model based on data that were previously observed in the smart space, and the second is to discern a present context and predict the next context based on dynamic changes (e.g., user behavior and interaction with the smart space). In our previous work, we defined "context" as a meaningful and descriptive state of a smart space, in which relevant activities and movements of human residents are consecutively performed. The methods proposed in this paper, which is based on stochastic analysis, utilize the same definition, and enable us to infer context from sensor datasets collected from a smart space. By utilizing three statistical techniques, including a conditional probability table (CPT), K-means clustering, and principal component analysis (PCA), we are able to automatically infer the sequence of context transitions that matches the space–state changes (the dynamic changes) in the smart space. Once the contexts are obtained, they are used as references when the present context needs to discover the next context. This will provide the piece missing in traditional CAC, which will enable the creation of fully automated smart-space applications. To this end, we developed a method to reason the current state space by applying Euclidean distance and cosine similarity. In this paper, we first reconsolidate our context models, and then we introduce the proposed modeling and reasoning methods. Through experimental validation in a real-world smart space, we show how consistently the approach can correctly reason contexts. [ABSTRACT FROM AUTHOR]

The Internet of Robotic Things is a concept that is rapidly gaining traction in the robotics industry. As the field of robotics advances, one of the obstacles to its widespread adoption remains the high cost of purchasing and maintaining robot. Although the gap is continuously closing, robots these days cannot make decisions with the efficiency that humans can. People are spatially aware, and we can perceive and understand changes in the environment that robots are not capable of. This paper attempts to propose how a smart space can be implemented to increase the spatial awareness of a robot by providing more data to make better informed decisions. We focus on using the Robot Operating System (ROS) as a framework to integrate Smart Space and a mobile robot to expand the robots sensory information and make collision avoidance decisions.

NGSI-LD is an open specification released by ETSI which proposes an information model and an API for an easy to use and standard management of context information. The NGSI- LD information model is framed within an ontology and adopts JSON-LD as serialization format for context information. This paper presents an approach to the implementation of the NGSI- LD speci?cation over a SPARQL Event Processing Architecture. This work is being developed within the European-Brasilian H2020 SWAMP project focused on implementing an Internet of Things platform providing services for smart water management in agriculture.

Performance fluctuation on the semantic information broker side due to critical growth of the request queue length can cause its complete failure or substantial degradation of its services.Since semantic broker is one of the key element of the smart space architecture its performance is a vital issue for the whole environment. We consider two possible sources of the performance degradation.One of them is the growth of the request arrival rate beyond the limits of its capacity. Another source ?nds its origin in the request synchronization which happens due to the presence of the regular patterns in the activities of the smart space participants. Rather often implementation of the relevant approaches at SIB size poses substantial dif?culties. We propose several algorithms that can be implemented on the KPs side. The algorithms form an additional mechanism that can reduce SIB fault rate and avoid performance degradation by regulating the KPs activity and hence balancing the load of SIB. The mechanism presumes additional timeout to the persistent request. The timeout value is selected according to the current system state and its history in the nearest past. The timeout consists of two components. The ?rst one is determined be the active control with the adaptive strategy and the second one is defined by the random backoff algorithm.

Smart technology is already present in many areas of everyday life. People rely on algorithms in crucial life domains such as finance and healthcare, and the smart car promises a more relaxed driving experience—all the while, the technology recedes further into the background. The smarter the technology, the more intransparent it tends to become. Users no longer understand how the technology works, what its limits are, and what consequences regarding autonomy and privacy emerge. Both extremes, total invisibility and total transparency, come with specific challenges and do not form reasonable design goals. This research explores the potential tension between smart and invisible versus transparent and understandable technology. We discuss related theories from the fields of explainable AI (XAI) as well as trust psychology, and then introduce transparency in smart spaces as a special field of application. A case study explores specific challenges and design approaches through the example of a so-called room intelligence (RI), i.e., a special kind of smart living room. We conclude with research perspectives for more general design approaches and implications for future research.

The proliferation of smart spaces, such as smart buildings, is increasing opportunities for offenders to exploit the interplay between cyber and physical components, in order to trigger security incidents. Organizations are obliged to report security incidents to comply with recent data protection regulations. Organizations can also use incident reports to improve security of the smart spaces where they operate. Incident reporting is often documented in structured natural language. However, reports often do not capture relevant information about cyber and physical vulnerabilities present in a smart space that are exploited during an incident. Moreover, sharing information about security incidents can be difficult, or even impossible, since a report may contain sensitive information about an organization. In previous work, we provided a meta-model to represent security incidents in smart spaces. We also developed an automated approach to share incident knowledge across different organizations. In this paper we focus on incident reporting. We provide a System Editor to represent smart buildings where incidents can occur. Our editor allows us to represent cyber and physical components within a smart building and their interplay. We also propose an Incident Editor to represent the activities of an incident, including -for each activity- the target and the resources exploited, the location where the activity occurred, and the activity initiator. Building on our previous work, incidents represented using our editor can be shared across various organizations, and instantiated in different smart spaces to assess how they can re-occur. We also propose an Incident Filter component that allows viewing and prioritizing the most relevant incident instantiations, for example, involving a minimum number of activities. We assess the feasibility of our approach in assisting incident reporting using an example of a security incident that occurred in a research center.

Recent advancements in technology have led to profusion of personal computing devices, such as smart phone, tablet, watch, glasses, and many more. This has contributed to the realization of a digital world where important daily tasks can be performed over the Internet from any place and at any time and using any device. At the same time, advances in pervasive computing technologies have brought to fruition the concept of smart spaces that target the automated provision of customized services to the inhabitants effortlessly. User authentication, i.e., a procedure to verify the identity of the user, is essential in the digital world so as to protect the user's personal data stored online (e.g., online bank accounts) and on personal devices (e.g., smart phones) and to also enable customized services in smart spaces (e.g., adjusting room temperature and so on). Recently, traditional authentication mechanisms (e.g., passwords or fingerprints) have been repeatedly shown to be vulnerable to subversion. Researchers thus have proposed numerous new mechanisms to authenticate the users in the aforementioned scenarios. This paper presents an overview of these novel systems, so as to guide the future research efforts in these domains.

The massive introduction of video capturing devices in Internet of Things (IoT) environments leads to development of various video data based services. In this paper, we consider the need and background on the video data based services in IoT environments. Based on the smart spaces approach, we introduce the architecture and distributed configurations to construct such services using primarily local devices and to deliver such services using smartphones. We discuss possible data models that can be used on such mediatory components as a local video server and a semantic information broker.

Semantic Web technologies are increasingly used in the Internet of Things due to their intrinsic propensity to foster interoperability among heterogenous devices and services. However, some of the IoT application domains have strict requirements in terms of timeliness of the exchanged messages, latency and support for constrained devices. An example of these domains is represented by the emerging area of the Internet of Musical Things. In this paper we propose C Minor, a CoAP-based semantic publish/subscribe broker specifically designed to meet the requirements of Internet of Musical Things applications, but relevant for any IoT scenario. We assess its validity through a practical use case.

The most recent advances in Internet of Things (IoT) are aimed at the development of software platforms defined as middleware. A smart space makes fusion of the physical and information worlds enhancing a IoT environment by enabling information sharing for a variety of local digital devices and global resources from the Internet. The growing number of application smart spaces requires a middleware that provides opportunities for the development of context-aware applications based on multi-agent approach. The paper presents a perspective on how to design a software infrastructure of semantic-oriented middleware for smart spaces deployment in IoT environments. This paper introduces the middleware infrastructure multilayer model. The paper also involves an analysis of middleware requirements for development and deployment of smart spaces in IoT environments and the implementation of the requirements in the proposed model. As a reference case study the CuteSIB semantic information broker implementation of the Smart-M3 platform is considered.

Nowadays there is a need to receive and process large video streams in shortest time. This can be realized by interaction of video cameras with additional components of the environment (IoT-devices). Thus, third-party devices will be involved, allowing to simultaneously calculate large amounts of data. Applied methods and calculations will be intelligent due to the increase effectiveness of video information management and the use of other peripheral devices for processing. Intellectualization technology can be used in many applied areas. So, in medicine, you can monitor the patient at home using reflective elements (Parkinson's disease). Vision systems allow you to recognize different information based on video data. In the industry, monitoring tasks arise production equipment directly by the employee (for example, identification of deviations in the work).

Performance degradation on broker side and the subsequent possible failure is an important issue in smart spaces. One of the reasons for this problem is the increase in the number of requests to the broker beyond what it can handle. The broker can have own management tools to solve such problematic situations. More often, it is more difficult to implement relevant approaches to broker itself. Our suggestion is to implement additional mechanism on agents sides to reduce fault events. The mechanism is to add a timeout to the persistent request, the value of which is selected based on the current system state. For timeout determining we suggest active control with adaptive strategy and improve it by adding backoff algorithm.

The rapid spread of the COVID-19 virus around the world poses a real threat to public safety. Some COVID-19 symptoms are similar to other viral chest diseases, which makes it challenging to develop models for effective detection of COVID-19 infection. This article advocates a model to differentiate between COVID-19 and other four viral chest diseases under uncertainty environment using the viruses primary symptoms and CT scans. The proposed model is based on a plithogenic set, which provides higher accurate evaluation results in an uncertain environment. The proposed model employs the best-worst method (BWM) and the technique in order of preference by similarity to ideal solution (TOPSIS). Besides, this study discusses how smart Internet of Things technology can assist medical staff in monitoring the spread of COVID-19. Experimental evaluation of the proposed model was conducted on five different chest diseases. Evaluation results demonstrate that the proposed model effectiveness in detecting the COVID-19 in all five cases achieving detection accuracy of up to 98%. [ABSTRACT FROM AUTHOR]

Advancements in Internet of Energy (IoE) technologies drive the development of several energy efficient frameworks for better energy optimization, economic savings, safety, and security in smart homes. However, certain challenges such as real-time operational data for each micro-moment, proper application of data fusion techniques, and end-to-end computing and deployment architecture prevent the establishment of an effective energy-efficient framework to provide personalized energy-saving recommendations. This work presents energy management for smart spaces (EMSS), the proposed energy efficiency framework implemented in an edge-cloud computing platform that fuses data from heterogeneous data sources (environmental sensors, camera, plug data, etc.) at appropriate data fusion levels and process them to generate actionable, explainable, personalized, and persuasive recommendations at the right moment. The user response to the generated recommendations triggers the actuators to perform respective energy-saving actions and provide more personalized future recommendations. Further, SMARTHome - a data generation framework based on configurable scenario files and a set of software codes was proposed to generate synthetic data with respect to different building types and micro-moments. The functionalities of the EMSS (device and user registration), user dashboard, analytics, and energy-saving recommendations were made accessible to the user through web and mobile applications. The validation analysis of the EMSS was performed by (i) comparative analysis of the machine learning and deep learning algorithms used by the decision engine to generate energy-saving recommendations and (ii) benchmarking of EMSS based on the taxonomy of data fusion-based energy efficiency frameworks for smart homes. [Display omitted] • EMSS, an end-to-end energy efficiency framework for smart spaces. • SMARTHome, data generation framework to generate synthetic dataset. • Smart data fusion approach to apply different levels of data fusion. • Actionable, explainable, persuasive, and personalized energy-saving recommendations. • Implemented on an edge-cloud computing platform. [ABSTRACT FROM AUTHOR]

In the last decade, smart spaces and automatic systems have gained significant popularity and importance. Moreover, as the COVID-19 pandemic continues, the world is seeking remote intervention applications with autonomous and intelligent capabilities. Context-aware computing (CAC) is a key paradigm that can satisfy this need. A CAC-enabled system recognizes humans’ status and situation and provides proper services without requiring manual participation or extra control by humans. However, CAC is insufficient to achieve full automaticity since it needs manual modeling and configuration of context. To achieve full automation, a method is needed to automate the modeling and reasoning of contexts in smart spaces. In this paper, we propose a method that consists of two phases: the first is to instantiate and generate a context model based on data that were previously observed in the smart space, and the second is to discern a present context and predict the next context based on dynamic changes (e.g., user behavior and interaction with the smart space). In our previous work, we defined “context” as a meaningful and descriptive state of a smart space, in which relevant activities and movements of human residents are consecutively performed. The methods proposed in this paper, which is based on stochastic analysis, utilize the same definition, and enable us to infer context from sensor datasets collected from a smart space. By utilizing three statistical techniques, including a conditional probability table (CPT), K-means clustering, and principal component analysis (PCA), we are able to automatically infer the sequence of context transitions that matches the space–state changes (the dynamic changes) in the smart space. Once the contexts are obtained, they are used as references when the present context needs to discover the next context. This will provide the piece missing in traditional CAC, which will enable the creation of fully automated smart-space applications. To this end, we developed a method to reason the current state space by applying Euclidean distance and cosine similarity. In this paper, we first reconsolidate our context models, and then we introduce the proposed modeling and reasoning methods. Through experimental validation in a real-world smart space, we show how consistently the approach can correctly reason contexts.