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Today, Smart Assistants (SAs) are supported by significantly improved Natural Language Processing (NLP) and Natural Language Understanding (NLU) engines as well as AI-enabled decision support, enabling efficient information communication, easy appliance/device control, and seamless access to entertainment services, among others. In fact, an increasing number of modern households are being equipped with SAs, which promise to enhance user experience in the context of smart environments through verbal interaction. Currently, the market in SAs is dominated by products manufactured by technology giants that provide well designed off-the-shelf solutions. However, their simple setup and ease of use come with trade-offs, as these SAs abide by proprietary and/or closed-source architectures and offer limited functionality. Their enforced vendor lock-in does not provide (power) users with the ability to build custom conversational applications through their SAs. On the other hand, employing an open-source approach for building and deploying an SA (which comes with a significant overhead) necessitates expertise in multiple domains and fluency in the multimodal technologies used to build the envisioned applications. In this context, this paper proposes a methodology for developing and deploying conversational applications on the edge on top of an open-source software and hardware infrastructure via a multilayer architecture that simplifies low-level complexity and reduces learning overhead. The proposed approach facilitates the rapid development of applications by third-party developers, thereby enabling the establishment of a marketplace of customized applications aimed at the smart assisted living domain, among others. The supporting framework supports application developers, device owners, and ecosystem administrators in building, testing, uploading, and deploying applications, remotely controlling devices, and monitoring device performance. A demonstration of this methodology is presented and discussed focusing on health and assisted living applications for the elderly. [ABSTRACT FROM AUTHOR]

Recently, great attention has been given to the idea of a smart environment. It often involves the use of reconfigurable intelligent surfaces (RIS) for the management of electromagnetic wave reflections as the world awaits the emergence of 6G. Changeable intelligent surfaces may enhance the creation of wireless communication. The design and analysis of several unit cell reflections are presented in this work. The first design relies on the Switching Technique which involves switching on and off to acquire the phase as well as the coefficient of reflection to accommodate 6G standards. The unit cells design is configured to operate in the millimeter band and X band. In the second design, the radius of the circular patch was changed to adjustment of the phase and reflection coefficient. The use of Floquet technique is employed in investigating the scattering characteristics of a unit cell's constituent elements based on the assumption that every element consists of an extremely iterating periodic structure. To determine the optimal force reflection and the transformation phase, the return loss alongside reflection phase graphs of each resonant component were examined. The simulation results indicate that the first design exhibits a reflection phase shift range of -180 to 90 and a reflection magnitude over 0.93 at a frequency of 11GHz. In contrast, the second design demonstrates a reflection phase shift range of -135 to 135 and a reflection magnitude surpassing 0.9 at a frequency of 28GHz. The analysis and simulation of the design models were carried out using the CST model. [ABSTRACT FROM AUTHOR]

Nowadays, modern Internet of Things (IoT) applications are enabling smart cities across the world. They provide remote device monitoring, management, and control, and even the extraction of new perspectives and actionable data from massive amounts of real-time data. A high degree of information technology integration and extensive utilization of resources are two biggest features of smart cities. Due to the obvious increasing amount and mobility of such distributed interconnected objects, attackers are becoming increasingly interested in them. Hence, a set of approaches have been developed to improve IoT Security. Intrusion detection systems (IDS) have previously gotten a lot of attention in the research field and industry. Therefore, several intrusion detection systems (IDSs) relies on approaches of machine learning (ML) and deep learning (DL) have been suggested to detect malicious intrusions. This study describes a revolutionary intrusion detection methodology for IoT-based smart environments that uses Ensemble Learning. The approach typically presented an optimum anomaly detection model which is based on AdaBoost and the Boruta feature selection technique based on the Xgboost algorithm. Furthermore, the suggested model metrics have been evaluated utilizing the NSL-KDD and BoT-IoT datasets. When compared to existing IDS, the results demonstrate that the proposed method produces excellent performance metrics in high accuracy (ACC), recall, and F1-score. It gives 99.9% on record detection and computation time. [ABSTRACT FROM AUTHOR]

The SOSAc-Reasoner is a commonsense reasoning engine, implemented using Answer Set Programming. It is designed to automatically generate IoT context knowledge, representing the capabilities of system devices, from a simple smart scenario description. The inference engine is fed with knowledge about device types and generates knowledge according to two ontologies derived from the SOSA (Sensor, Observation, Sample, and Actuator) ontology. The SOSAc-Reasoner comprises two ASP rule modules: the basic and advanced inference modules, which perform reasoning with different objectives. Implemented with Potassco, the SOSAc-Reasoner effectively generates context knowledge within a reasonable timeframe. This significantly facilitates the task of modeling a highly valuable type of knowledge in intelligent environments, a task that traditionally involves manual efforts, is prone to errors, and consumes a significant amount of time.

This comprehensive review investigates the transformative potential of sensor-driven digital twin technology in enhancing healthcare delivery within smart environments. We explore the integration of smart environments with sensor technologies, digital health capabilities, and location-based services, focusing on their impacts on healthcare objectives and outcomes. This work analyzes the foundational technologies, encompassing the Internet of Things (IoT), Internet of Medical Things (IoMT), machine learning (ML), and artificial intelligence (AI), that underpin the functionalities within smart environments. We also examine the unique characteristics of smart homes and smart hospitals, highlighting their potential to revolutionize healthcare delivery through remote patient monitoring, telemedicine, and real-time data sharing. The review presents a novel solution framework leveraging sensor-driven digital twins to address both healthcare needs and user requirements. This framework incorporates wearable health devices, AI-driven health analytics, and a proof-of-concept digital twin application. Furthermore, we explore the role of location-based services (LBS) in smart environments, emphasizing their potential to enhance personalized healthcare interventions and emergency response capabilities. By analyzing the technical advancements in sensor technologies and digital twin applications, this review contributes valuable insights to the evolving landscape of smart environments for healthcare. We identify the opportunities and challenges associated with this emerging field and highlight the need for further research to fully realize its potential to improve healthcare delivery and patient well-being. [ABSTRACT FROM AUTHOR]

The ubiquity of sensors in smart-homes facilitates the support of independent living for older adults and enables cognitive assessment. Notably, there has been a growing interest in utilizing movement traces for identifying signs of cognitive impairment in recent years. In this study, we introduce an innovative approach to identify abnormal indoor movement patterns that may signal cognitive decline. This is achieved through the non-intrusive integration of smart-home sensors, including passive infrared sensors and sensors embedded in everyday objects. The methodology involves visualizing user locomotion traces and discerning interactions with objects on a floor plan representation of the smart-home, and employing different image descriptor features designed for image analysis tasks and synthetic minority oversampling techniques to enhance the methodology. This approach distinguishes itself by its flexibility in effortlessly incorporating additional features through sensor data. A comprehensive analysis, conducted with a substantial dataset obtained from a real smart-home, involving 99 seniors, including those with cognitive diseases, reveals the effectiveness of the proposed functional prototype of the system architecture. The results validate the system's efficacy in accurately discerning the cognitive status of seniors, achieving a macro-averaged F1-score of 72.22% for the two targeted categories: cognitively healthy and people with dementia. Furthermore, through experimental comparison, our system demonstrates superior performance compared with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Recently, great attention has been given to the idea of a smart environment. It often involves the use of reconfigurable intelligent surfaces (RIS) for the management of electromagnetic wave reflections as the world awaits the emergence of 6G. Changeable intelligent surfaces may enhance the creation of wireless communication. The design and analysis of several unit cell reflections are presented in this work. The first design relies on the Switching Technique which involves switching on and off to acquire the phase as well as the coefficient of reflection to accommodate 6G standards. The unit cells design is configured to operate in the millimeter band and X band. In the second design, the radius of the circular patch was changed to adjustment of the phase and reflection coefficient. The use of Floquet technique is employed in investigating the scattering characteristics of a unit cell's constituent elements based on the assumption that every element consists of an extremely iterating periodic structure. To determine the optimal force reflection and the transformation phase, the return loss alongside reflection phase graphs of each resonant component were examined. The simulation results indicate that the first design exhibits a reflection phase shift range of -180 to 90 and a reflection magnitude over 0.93 at a frequency of 11GHz. In contrast, the second design demonstrates a reflection phase shift range of -135 to 135 and a reflection magnitude surpassing 0.9 at a frequency of 28GHz. The analysis and simulation of the design models were carried out using the CST model.

Summary: In distributed systems, tuple space is one of the coordination models that significantly maximizes system performance against failure due to its space and time decoupling features. With the growing popularity of distributed computing and increasing complexity in the network, host and link failure occurs frequently, resulting in poor system performance. This article proposes a fault‐tolerant model named Tuple Space Replication (TSR) for tuple space coordination in distributed environments. The model introduces a multi‐agent system that consists of multiple hosts. Each host in a multi‐agent system comprises an agent space with a tuple space for coordination. In this model, we introduce three novel fault‐tolerant algorithms for tuple space primitives to provide coordination among hosts with tolerance to multiple links and hosts failure. The first algorithm is given for out() operation to insert tuples in the tuple space. The second algorithm is presented for rdp() operation to read any tuple from the tuple space. The third algorithm is given for inp() operation to delete or withdraw tuples from the tuple space. These algorithms use less number of messages to ensure consistency in the system. The message complexity of the proposed algorithms is analyzed and found O(n) for out(), O(1) for rdp(), and O(n) for inp() operations which is comparable and better than existing works, where n is the number of hosts. The testbed experiment reveals that the proposed TSR model gives performance improvement up to 88%, 70.94%, and 63.80% for out(), rdp(), and inp() operations compared to existing models such as FT‐SHE, LBTS, DEPSPACE, and E‐DEPSPACE. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is fragmented into many different smart environments, each requiring specific technical solutions for their use cases. This is a problem that has worsened over the years as new standards and technologies are constantly being developed. The term "horizontal integration" is used in literature to describe the attempt to reunite this fragmented IoT landscape. Abstract (in the sense of the OSI model) solutions are often proposed for this. However, the limitations of the underlying technologies are rarely sufficiently pointed out. This paper examines the integration of two smart environments as examples - the Smart Home and the Smart City. More specifically, it takes a theoretical look at how technologies can be connected between two Smart Home systems with the help of a Smart City technology and the problems that arise with it. ZigBee and LoRa are used for this purpose as exemplary technologies. Various possible application scenarios are shown to carry out a qualitative assessment of the feasibility of such a solution. The resulting problems of such an integration are shown and possible solutions for these are discussed. [ABSTRACT FROM AUTHOR]

Multi-agent systems (MASs) have gained a lot of interest recently, due to their ability to solve problems that are difficult or even impossible for an individual agent. However, an important procedure that needs attention in designing multi-agent systems, and consequently applications that utilize MASs, is achieving a fair agreement between the involved agents. Researchers try to prevent agreement manipulation by utilizing decentralized control and strategic voting. Moreover, emphasis is given to local decision making and perception of events occurring locally. This manuscript presents a novel secure decision-support algorithm in a multi-agent system that aims to ensure the system's robustness and credibility. The proposed consensus-based model can be applied to production planning and control, supply chain management, and product design and development. The algorithm considers an open system; i.e., the number of agents present can be variable in each procedure. While a group of agents can make different decisions during a task, the algorithm chooses one of these decisions in a way that is logical, safe, efficient, fast, and is not influenced by factors that might affect production. [ABSTRACT FROM AUTHOR]

Smart cities are being enabled all around the world by Internet of Things (IoT) applications. A smart city idea necessitates the integration of information and communication technologies and devices throughout a network in order to provide improved services to consumers. Because of their increasing amount and mobility, they are increasingly appealing to attackers. Therefore, several solutions, including as encryptions, authentication, availability, and data integrity, have been combined to protect IoT. Intrusion detection systems (IDSs) are a powerful security tool that may be improved by incorporating machine learning (ML) and deep learning (DP) techniques. This paper presents a novel intrusion detection framework for IoT-based smart environments with Ensemble Learning called IDS-SIoEL. Typically, the framework proposed an optimal anomaly detection model that uses AdaBoost, and combining different feature selection techniques Boruta, mutual information and correlation furthermore. The proposed model was evaluated on IoT-23, BoT-IoT, and Edge-IIoT datasets using the GPU. When compared to existing IDS, our approach provides good rating performance features of ACC, recall, and precision, with around 99.9% on record detection and calculation time of 33.68 s for learning and 0.02156 s for detection. [ABSTRACT FROM AUTHOR]

In this article, we describe and evaluate a crowdsensing approach that entails local cooperation between crowdsensing participants in smart environments, utilizing an underlying fog computing-enabled Internet of Things. A fog computing-based Internet-of-Things architecture involves a layer of computing nodes residing closer to the sensing devices, with this layer of fog nodes lying in between mobile and sensing devices at the network edge and the cloud. This motivates us to propose a model for crowdsensing in smart environments that involves both competition and cooperation between nearby crowdsensing participants at the edge network. Comprehensive simulations are presented to evaluate the performance of the proposed approach. The work shows desirable characteristics in terms of number of active participants, number of samples collected within a given budget and coverage, resulting from localized cooperation by crowdsensing participants at the edge layer that can support various smart environment applications. [ABSTRACT FROM AUTHOR]

Usually, approaches driven by data proposed in literature for sensor-based activity recognition use the begin label and the end label of each activity in the dataset, fixing a temporal window with sensor data events to identify the activity carried out in this window. This type of approach cannot be carried out in real time because it is not possible to predict the start time of an activity, i.e., the class of the future activity that an inhabitant will perform, neither when he/she will begin to carry out this activity. However, an activity can be marked as finished in real time only with the previous observations. Therefore, there is a need of online activity recognition approaches that classify activities using only the end label of the activity. In this paper, we propose and evaluate a new approach for online activity recognition with three temporal sub-windows that uses only the end label of the activity. The advantage of our approach is that the temporal sub-windows keep a partial order in the sensor data stream from the end time of the activity in a short-term, medium-term, long-term. The experiments conducted to evaluate our approach suggest the importance of the use of temporal sub-windows versus a single temporal window in terms of accuracy, using only the end time of the activity. The use of temporal sub-windows has improved the accuracy in the 98.95% of experiments carried out. [ABSTRACT FROM AUTHOR]

Today, Smart Assistants (SAs) are supported by significantly improved Natural Language Processing (NLP) and Natural Language Understanding (NLU) engines as well as AI-enabled decision support, enabling efficient information communication, easy appliance/device control, and seamless access to entertainment services, among others. In fact, an increasing number of modern households are being equipped with SAs, which promise to enhance user experience in the context of smart environments through verbal interaction. Currently, the market in SAs is dominated by products manufactured by technology giants that provide well designed off-the-shelf solutions. However, their simple setup and ease of use come with trade-offs, as these SAs abide by proprietary and/or closed-source architectures and offer limited functionality. Their enforced vendor lock-in does not provide (power) users with the ability to build custom conversational applications through their SAs. On the other hand, employing an open-source approach for building and deploying an SA (which comes with a significant overhead) necessitates expertise in multiple domains and fluency in the multimodal technologies used to build the envisioned applications. In this context, this paper proposes a methodology for developing and deploying conversational applications on the edge on top of an open-source software and hardware infrastructure via a multilayer architecture that simplifies low-level complexity and reduces learning overhead. The proposed approach facilitates the rapid development of applications by third-party developers, thereby enabling the establishment of a marketplace of customized applications aimed at the smart assisted living domain, among others. The supporting framework supports application developers, device owners, and ecosystem administrators in building, testing, uploading, and deploying applications, remotely controlling devices, and monitoring device performance. A demonstration of this methodology is presented and discussed focusing on health and assisted living applications for the elderly.

The rise or fall of the stock markets directly affects investors' interest and loyalty. Therefore, it is necessary to measure the performance of stocks in the market in advance to prevent our assets from suffering significant losses. In our proposed study, six supervised machine learning (ML) strategies and deep learning (DL) models with long short-term memory (LSTM) of data science was deployed for thorough analysis and measurement of the performance of the technology stocks. Under discussion are Apple Inc. (AAPL), Microsoft Corporation (MSFT), Broadcom Inc., Taiwan Semiconductor Manufacturing Company Limited (TSM), NVIDIA Corporation (NVDA), and Avigilon Corporation (AVGO). The datasets were taken from the Yahoo Finance API from 06-05-2005 to 06-05-2022 (seventeen years) with 4280 samples. As already noted, multiple studies have been performed to resolve this problem using linear regression, support vectormachines, deep long short-termmemory (LSTM), and many other models. In this research, the Hidden Markov Model (HMM) outperformed other employed machine learning ensembles, tree-basedmodels, the ARIMA (Auto Regressive IntegratedMoving Average)model, and long short-term memory with a robust mean accuracy score of 99.98. Other statistical analyses and measurements for machine learning ensemble algorithms, the Long Short-TermModel, and ARIMA were also carried out for further investigation of the performance of advanced models for forecasting time series data. Thus, the proposed research found the best model to be HMM, and LSTM was the second-best model that performed well in all aspects. A developedmodel will be highly recommended and helpful for earlymeasurement of technology stock performance for investment or withdrawal based on the future stock rise or fall for creating smart environments. [ABSTRACT FROM AUTHOR]

Recently, a world-wide trend has been observed that there is widespread adoption across all fields to embrace smart environments and automation. Smart environments include a wide variety of Internet-of-Things (IoT) devices, so many challenges face conventional digital forensic investigation (DFI) in such environments. These challenges include data heterogeneity, data distribution, and massive amounts of data, which exceed digital forensic (DF) investigators' human capabilities to deal with all of these challenges within a short period of time. Furthermore, they significantly slow down or even incapacitate the conventional DFI process. With the increasing frequency of digital crimes, better and more sophisticated DFI procedures are desperately needed, particularly in such environments. Since machine-learning (ML) techniques might be a viable option in smart environments, this paper presents the integration of ML into DF, through reviewing the most recent papers concerned with the applications of ML in DF, specifically within smart environments. It also explores the potential further use of ML techniques in DF in smart environments to reduce the hard work of human beings, as well what to expect from future ML applications to the conventional DFI process. [ABSTRACT FROM AUTHOR]

This comprehensive review investigates the transformative potential of sensor-driven digital twin technology in enhancing healthcare delivery within smart environments. We explore the integration of smart environments with sensor technologies, digital health capabilities, and location-based services, focusing on their impacts on healthcare objectives and outcomes. This work analyzes the foundational technologies, encompassing the Internet of Things (IoT), Internet of Medical Things (IoMT), machine learning (ML), and artificial intelligence (AI), that underpin the functionalities within smart environments. We also examine the unique characteristics of smart homes and smart hospitals, highlighting their potential to revolutionize healthcare delivery through remote patient monitoring, telemedicine, and real-time data sharing. The review presents a novel solution framework leveraging sensor-driven digital twins to address both healthcare needs and user requirements. This framework incorporates wearable health devices, AI-driven health analytics, and a proof-of-concept digital twin application. Furthermore, we explore the role of location-based services (LBS) in smart environments, emphasizing their potential to enhance personalized healthcare interventions and emergency response capabilities. By analyzing the technical advancements in sensor technologies and digital twin applications, this review contributes valuable insights to the evolving landscape of smart environments for healthcare. We identify the opportunities and challenges associated with this emerging field and highlight the need for further research to fully realize its potential to improve healthcare delivery and patient well-being.

Palliative treatments for back pain usually include exercise, analgesics, physiotherapy, prostheses, and surgery in severe cases. Technologies for postural monitoring are growing, and they are important in preventing back pain and mitigating permanent damage. Remote work, especially after the COVID-19 pandemic, made people spend more time than usual in chairs and environments not certified by the health aspects of work. This research investigated through a Systematic Mapping Study (SMS) contributions in posture monitoring for healthcare in smart environments, including the different methods to obtain the posture, the limitations, and the target audience of the proposed models. The SMS was conducted in eight databases, including articles from January 2012 to March 2022. The initial search yielded 3161 articles, of which 34 were selected after applying the filtering criteria. Moreover, this study presents the challenges related to posture behavior monitoring, identifying studies and implementations that apply assistive technology for postural monitoring and improving the health and life of remote workers. In addition, three commercial postural devices are presented, and what challenges they currently face. Regarding healthcare, results showed a prevalence of using the Internet of Things (IoT) devices such as wireless sensor networks and inertial measurement unit (IMU) sensors. This article also proposes a taxonomy, showing the most used technologies and algorithms for improving posture, besides the posture-monitoring hierarchy classifying into three important branches: (a) Data Collect; (b) Data Transmission; and (c) Data Analysis. [ABSTRACT FROM AUTHOR]

We examine radar-based gesture input for interactive computer systems, a technology that has recently grown in terms of commercial availability, affordability, and popularity among researchers and practitioners, where radar sensors are leveraged to detect user input performed in mid-air, on the body, and around physical objects and digital devices. We analyze forty-five academic papers published on this topic between 2010 and 2021, and report results regarding gesture recognition techniques, application types, and evaluation approaches for radar-based gesture input. Our findings reveal that (1) deep learning techniques, such as Convolutional Neural Networks, have been the most popular approach for radar-based gesture recognition, (2) application opportunities for implementing radar gestures have been diverse, but without any clear contender for a game changer in this area, and (3) the gesture sets employed in prior work have been small with a median of just six gesture types. Based on these findings, we draw ten implications for integrating radar-based gesture sensing in ambient intelligence environments. [ABSTRACT FROM AUTHOR]

The sustainable management of smart environments enabled by the Internet of Things (IoT) requires new methodologies and tools to suitably handle potentially many users and their objectives on cyber-physical systems, e.g. smart lighting, smart A/C. In this article, we propose a declarative framework to model IoT-enabled smart environments. Our methodology permits (i) expressing user roles and hierarchical environments, (ii) declaring customized policies to mediate user objectives into a target state and (iii) determining valid settings for IoT actuators to achieve such a target also reducing energy consumption. An open-source Prolog prototype of the framework is showcased over two lifelike motivating examples and its scalability is assessed at increasing sizes of the managed smart environment. [ABSTRACT FROM AUTHOR]

In this modern age of technology, the Internet of Things has covered all aspects of life including smart situations, smart homes, and smart spaces. Smart homes have a large number of IoT objects that are working continuously without any interruption. Better security and authentication of these smart devices can provide peaceful environments to live in such spaces. It is important to monitor the activities of smart IoT devices to make them work fault-free. Such devices are small, consume relatively less power and resources, and are easily attackable by attackers. It is crucial to protect the integrity and characteristics of the smart home environment from external attacks. Machine Learning played a vital role in recognizing such malicious activities and attempts. Several Machine Learning approaches are available to detect the normal and abnormal behavior of IoT device traffic. This study proposed a machine learning-based anomaly detection approach for smart homes using different classifiers. Testing and evaluation are performed using the University of New South Wales (UNSW) BoT IoT dataset. Machine learning models based on four classifiers are built using an IoT devices dataset. For the Test dataset, the Weighted Precision, Recall, and F1 score of Random forest, decision tree, and AdaBoost is 1 as compared to ANN which has 0.98, 0.96, and 0.96 respectively Results show that high performance, precision, and robustness can be achieved using the proposed methodology. In this way, smart homes’ security and identity of devices can be monitored and anomalies can be detected with high accuracy. Attack categories include binary class, multiclass class, and subclasses. Results show Random Forest algorithm outperforms enough to use this methodology in smart environments.