Context-aware Solution Research Articles

The Application Status and Technological Progress of Service Robots in Home Automation

This paper examines the application status and technological progress of service robots in home automation, with a particular focus on floor cleaning robots and companion robots for Alzheimer's patients. The evolution of service robots has been driven by advancements in artificial intelligence, sensor technology, and autonomous navigation, transforming them into indispensable tools in modern households. Floor cleaning robots, equipped with advanced sensors and efficient algorithms, have revolutionized routine household chores by autonomously maintaining clean floors. Companion robots, such as Pepper and those developed in projects like RAMCIP, have shown promise in assisting Alzheimer's patients, offering both practical and therapeutic support. The article also delves into the key technologies enabling these robots, including sensor systems, obstacle avoidance algorithms, and human-computer interaction. Despite the significant progress, challenges remain, particularly in optimizing obstacle avoidance, enhancing HCI, and addressing ethical concerns in caregiving roles. The article concludes by highlighting the future potential of service robots to further integrate into smart homes, offering personalized and context-aware solutions to meet the unique needs of individual households.

Universally Designed Augmented Reality as Interface for Artificial Intelligence Assisted Decision-Making in Everyday Life Scenarios.

This paper presents a conceptual prototype that integrates Artificial Intelligence (AI) and Augmented Reality (AR) with the principles of Universal Design (UD) to enhance decision-making in everyday scenarios for a diverse user base, eliminating the need for conventional text or voice AI interfaces. The study employed a mixed-method approach, including surveys, user testing, and interviews with eight participants from various age groups. The focus was on user interaction styles (head-mounted, handheld) within three everyday scenarios: 1) medication assistance, 2) food and beverage assistance, and 3) sustainability advocacy. Findings revealed that AR as an interface for AI was well-received for its intuitiveness and practical utility. However, users expressed concerns about privacy, the discomfort of wearable technology, and potential over-reliance on AI. This study demonstrates the potential of integrating AR as an interface for AI, combined with UD principles, to create inclusive, context-aware solutions, adaptable to users with diverse skills and abilities.

Content-Adaptive Bitrate Ladder Estimation in High-Efficiency Video Coding Utilizing Spatiotemporal Resolutions

The constant increase in multimedia Internet traffic in the form of video streaming requires new solutions for efficient video coding to save bandwidth and network resources. HTTP adaptive streaming (HAS), the most widely used solution for video streaming, allows the client to adaptively select the bitrate according to the transmission conditions. For this purpose, multiple presentations of the same video content are generated on the video server, which contains video sequences encoded at different bitrates with resolution adjustment to achieve the best Quality of Experience (QoE). This set of bitrate–resolution pairs is called a bitrate ladder. In addition to the traditional one-size-fits-all scheme for the bitrate ladder, context-aware solutions have recently been proposed that enable optimum bitrate–resolution pairs for video sequences of different complexity. However, these solutions use only spatial resolution for optimization, while the selection of the optimal combination of spatial and temporal resolution for a given bitrate has not been sufficiently investigated. This paper proposes bit-ladder optimization considering spatiotemporal features of video sequences and usage of optimal spatial and temporal resolution related to video content complexity. Optimization along two dimensions of resolution significantly increases the complexity of the problem and the approach of intensive encoding for all spatial and temporal resolutions in a wide range of bitrates, for each video sequence, is not feasible in real time. In order to reduce the level of complexity, we propose a data augmentation using a neural network (NN)-based model. To train the NN model, we used seven video sequences of different content complexity, encoded with the HEVC encoder at five different spatial resolutions (SR) up to 4K. Also, all video sequences were encoded using four frame rates up to 120 fps, presenting different temporal resolutions (TR). The Structural Similarity Index Measure (SSIM) is used as an objective video quality metric. After data augmentation, we propose NN models that estimate optimal TR and bitrate values as switching points to a higher SR. These results can be further used as input parameters for the bitrate ladder construction for video sequences of a certain complexity.

Intelligent defense strategies: Comprehensive attack detection in VANET with deep reinforcement learning

Vehicular Ad Hoc Network (VANET) facilitates the exchange of vehicular information through Vehicle-to-Vehicle (V2V) communication, contributing to Cooperative Intelligent Transportation Systems (C-ITS). The transmitted messages among vehicles are vulnerable to various security threats executed by malicious insider nodes. The dynamic VANET necessitates context-aware solutions for detecting various security attacks. Existing learning and deterministic mechanisms showed high detection accuracy for attacks on which they were trained explicitly for large datasets. Therefore, we propose an intelligent framework utilizing Deep Reinforcement Learning (DRL) for attack detection in evolving scenarios and mitigate the need for extensive training datasets. Our approach employs a Deep Q Network (DQN) trained on a compact dataset encompassing multiple attacks. The trained model is then applied to an unknown and extensive dataset, detecting various attacks with high accuracy. Notably, the model autonomously updates itself upon observing changes in the network context. This framework represents a promising security solution that is effective and adaptable for V2V communication in VANET.

An innovative algorithm based on weighted fuzzy soft multisets and its application in selecting optimal construction materials

<p>Effective decision-making is critical across various domains, including technology, medicine, and engineering. To address the complexities of decision-making, particularly in scenarios involving both positive and negative parameters, this paper introduces an innovative algorithm based on weighted fuzzy soft multisets. This algorithm mitigates the issue of counterintuitive results often encountered in existing methods. By incorporating the concept of uniform fuzzy soft multisets and considering the conditional structure of these sets, our approach advances the theoretical framework of decision-making while providing a practical tool for complex scenarios. To demonstrate its practical applicability, we conduct a case study focused on selecting optimal construction materials for a building project, utilizing data from established engineering standards and a comprehensive wood properties database. The key findings of our sensitivity analysis highlight the algorithm's robustness to weight changes and adaptability to different decision sequences. These findings highlight the algorithm's potential to enhance decision support systems across various fields, such as engineering, healthcare, and environmental management. This potential is particularly valuable in complex, multi-criteria scenarios that demand nuanced, context-aware solutions.</p>

STRUCTURAL FEATURES OF TWO-WAY TRANSLATION OF COMPLEX AGRICULTURAL TERMS

This research delves into the structural intricacies of complex agricultural terms in Ukrainian and English, scrutinizing linguistic nuances, cultural connotations, and technical dimensions. Complex terms, examined in terms of their morphological variations and syntactic differences, were identified as key challenges in bilateral translation. Cultural specificity and regional variations in Ukrainian terms added layers of complexity. The findings underscore the significance of understanding the structural features for accurate translation in the agricultural domain, emphasizing the interplay of linguistic precision, cultural sensitivity, and technical expertise. Translation examples highlight variations and propose context-aware solutions, emphasizing the need for a holistic approach. These examples reveal challenges in conveying the holistic nature of Ukrainian terms in English, emphasizing the need for context-specific solutions. Recommendations for translators include creating glossaries, collaborating with experts since the translation is not a solitary activity. It involves communication and interaction with other translators, clients, agronomists, reviewers, and experts. Also, it is extremely significant to utilize translation technologies and tools wisely. Looking into the future, areas of further research include the study of translation technologies impact on agricultural translation, consideration of regional variations, the dynamic nature of agricultural language, and interdisciplinary collaboration. This research contributes to the evolving landscape of agricultural translation, providing insights into complexities and offering practical guidance for translators.

Augmented Reality and Deep Learning Integration for Enhanced Design and Maintenance in Mechanical Engineering.

The convergence of augmented reality (AR) and deep learning has emerged as a transformative paradigm in the field of mechanical engineering, revolutionizing the design and maintenance processes. This paper explores the integration of AR and deep learning technologies to create a synergistic framework that enhances efficiency, precision, and overall performance in mechanical engineering applications. The fusion of AR, a technology that overlays digital information onto the physical world, and deep learning, a subset of artificial intelligence (AI) characterized by intricate neural networks, presents a groundbreaking approach to address the challenges faced in the design and maintenance phases of mechanical systems. In the design realm, this integration facilitates a more immersive and collaborative environment for engineers and designers. AR provides real-time visualization of complex 3D models and simulations, allowing engineers to interact with virtual prototypes overlaid onto physical objects. [1] Deep learning algorithms enable predictive modeling and analysis, enhancing the understanding of system behavior under various conditions. The combination empowers designers to iterate rapidly, make informed decisions, and optimize designs, ultimately reducing time-to-market and improving overall product quality. In the maintenance domain, the AR and deep learning integration transforms traditional approaches by offering intelligent and context-aware solutions. AR-enabled smart glasses or devices equipped with computer vision capabilities allow technicians to access real-time data, such as equipment specifications, maintenance procedures, and live diagnostics. Deep learning algorithms enhance fault detection and predictive maintenance by analyzing historical data and identifying patterns indicative of potential issues. This proactive approach minimizes downtime, reduces maintenance costs, and extends the lifespan of mechanical systems. The paper illuminates the potential of their integration in reshaping the landscape of mechanical engineering. The presented framework not only fosters innovation in design and maintenance but also sets the stage for a new era in which intelligent technologies work in tandem to propel the field forward.

Context-aware Big Data Quality Assessment: A Scoping Review

The term data quality refers to measuring the fitness of data regarding the intended usage. Poor data quality leads to inadequate, inconsistent, and erroneous decisions that could escalate the computational cost, cause a decline in profits, and cause customer churn. Thus, data quality is crucial for researchers and industry practitioners. Different factors drive the assessment of data quality. Data context is deemed one of the key factors due to the contextual diversity of real-world use cases of various entities such as people and organizations. Data used in a specific context (e.g., an organization policy) may need to be more efficacious for another context. Hence, implementing a data quality assessment solution in different contexts is challenging. Traditional technologies for data quality assessment reached the pinnacle of maturity. Existing solutions can solve most of the quality issues. The data context in these solutions is defined as validation rules applied within the ETL (extract, transform, load) process, i.e., the data warehousing process. In contrast to traditional data quality management, it is impossible to specify all the data semantics beforehand for big data. We need context-aware data quality rules to detect semantic errors in a massive amount of heterogeneous data generated at high speed. While many researchers tackle the quality issues of big data, they define the data context from a specific standpoint. Although data quality is a longstanding research issue in academia and industries, it remains an open issue, especially with the advent of big data, which has fostered the challenge of data quality assessment more than ever. This article provides a scoping review to study the existing context-aware data quality assessment solutions, starting with the existing big data quality solutions in general and then covering context-aware solutions. The strength and weaknesses of such solutions are outlined and discussed. The survey showed that none of the existing data quality assessment solutions could guarantee context awareness with the ability to handle big data. Notably, each solution dealt only with a partial view of the context. We compared the existing quality models and solutions to reach a comprehensive view covering the aspects of context awareness when assessing data quality. This led us to a set of recommendations framed in a methodological framework shaping the design and implementation of any context-aware data quality service for big data. Open challenges are then identified and discussed.

Context-Aware Wireless Connectivity and Processing Unit Optimization for IoT Networks

A novel approach is presented in this work for context-aware connectivity and processing optimization of Internet of Things (IoT) networks. Different from the state-of-the-art approaches, the proposed approach simultaneously selects the best connectivity and processing unit (e.g., device, fog, and cloud) along with the percentage of data to be offloaded by jointly optimizing energy consumption, response time, security, and monetary cost. The proposed scheme employs a reinforcement learning algorithm and manages to achieve significant gains compared to deterministic solutions. In particular, the requirements of IoT devices in terms of response time and security are taken as inputs along with the remaining battery level of the devices, and the developed algorithm returns an optimized policy. The results obtained show that only our method is able to meet the holistic multiobjective optimization criteria, albeit, the benchmark approaches may achieve better results on a particular metric at the cost of failing to reach the other targets. Thus, the proposed approach is a device-centric and context-aware solution that accounts for the monetary and battery constraints.

A Fuzzy-Based Context-Aware Misbehavior Detecting Scheme for Detecting Rogue Nodes in Vehicular Ad Hoc Network

A vehicular ad hoc network (VANET) is an emerging technology that improves road safety, traffic efficiency, and passenger comfort. VANETs’ applications rely on co-operativeness among vehicles by periodically sharing their context information, such as position speed and acceleration, among others, at a high rate due to high vehicles mobility. However, rogue nodes, which exploit the co-operativeness feature and share false messages, can disrupt the fundamental operations of any potential application and cause the loss of people’s lives and properties. Unfortunately, most of the current solutions cannot effectively detect rogue nodes due to the continuous context change and the inconsideration of dynamic data uncertainty during the identification. Although there are few context-aware solutions proposed for VANET, most of these solutions are data-centric. A vehicle is considered malicious if it shares false or inaccurate messages. Such a rule is fuzzy and not consistently accurate due to the dynamic uncertainty of the vehicular context, which leads to a poor detection rate. To this end, this study proposed a fuzzy-based context-aware detection model to improve the overall detection performance. A fuzzy inference system is constructed to evaluate the vehicles based on their generated information. The output of the proposed fuzzy inference system is used to build a dynamic context reference based on the proposed fuzzy inference system. Vehicles are classified into either honest or rogue nodes based on the deviation of their evaluation scores calculated using the proposed fuzzy inference system from the context reference. Extensive experiments were carried out to evaluate the proposed model. Results show that the proposed model outperforms the state-of-the-art models. It achieves a 7.88% improvement in the overall performance, while a 16.46% improvement is attained for detection rate compared to the state-of-the-art model. The proposed model can be used to evict the rogue nodes, and thus improve the safety and traffic efficiency of crewed or uncrewed vehicles designed for different environments, land, naval, or air.

Real-Time Stress Level Feedback from Raw Ecg Signals for Personalised, Context-Aware Applications Using Lightweight Convolutional Neural Network Architectures.

Human stress is intricately linked with mental processes such as decision making. Public protection practitioners, including Law Enforcement Agents (LEAs), are forced to make difficult decisions during high-pressure operations, under strenuous circumstances. In this respect, systems and applications that assist such practitioners to take decisions, are increasingly incorporating user stress level information for their development, adaptation, and evaluation. To that end, our goal is to accurately detect and classify the level of acute, short-term stress, in real time, for the development of personalized, context-aware solutions for LEAs. Deep Neural Networks (DNNs), and in particular Convolutional Neural Networks (CNNs), have been gaining traction in the field of stress analysis, exhibiting promising results. Furthermore, the electrocardiogram (ECG) signals, have also been widely adopted for estimating levels of stress. In this work, we propose two CNN architectures for the stress detection and 3-level (low, moderate, high) stress classification tasks, using ultra short-term raw ECG signals (3 s). One architecture is simple and with a low memory footprint, suitable for running in wearable edge-computing nodes, and the other is able to learn more complex features, having more trainable parameters. The models were trained on the two publicly available stress classification datasets, after applying pre-processing techniques, such as data pruning, down-sampling, and data augmentation, using a sliding window approach. After hyperparameter tuning, using 4-fold cross-validation, the evaluation on the test set demonstrated state-of-the-art accuracy both on the 3- and 2-level stress classification task using the DriveDB dataset, reporting an accuracy of 83.55% and 98.77% respectively.

Enabling Context-Aware Data Analytics in Smart Environments: An Open Source Reference Implementation.

In recent years, many proposals of context-aware systems applied to IoT-based smart environments have been presented in the literature. Most previous works provide a generic high-level structure of how a context-aware system can be operationalized, but do not offer clues on how to implement it. On the other hand, there are many implementations of context-aware systems applied to specific IoT-based smart environments that are context-specific: it is not clear how they can be extended to other use cases. In this article, we aim to provide an open-source reference implementation for providing context-aware data analytics capabilities to IoT-based smart environments. We rely on the building blocks of the FIWARE ecosystem and the NGSI data standard, providing an agnostic end-to-end solution that considers the complete data lifecycle, covering from data acquisition and modeling, to data reasoning and dissemination. In other words, our reference implementation can be readily operationalized in any IoT-based smart environment regardless of its field of application, providing a context-aware solution that is not context-specific. Furthermore, we provide two example use cases that showcase how our reference implementation can be used in a variety of fields.

Fusion of smartphone sensor data for classification of daily user activities

New mobile applications need to estimate user activities by using sensor data provided by smart wearable devices and deliver context-aware solutions to users living in smart environments. We propose a novel hybrid data fusion method to estimate three types of daily user activities (being in a meeting, walking, and driving with a motorized vehicle) using the accelerometer and gyroscope data acquired from a smart watch using a mobile phone. The approach is based on the matrix time series method for feature fusion, and the modified Better-than-the-Best Fusion (BB-Fus) method with a stochastic gradient descent algorithm for construction of optimal decision trees for classification. For the estimation of user activities, we adopted a statistical pattern recognition approach and used the k-Nearest Neighbor (kNN) and Support Vector Machine (SVM) classifiers. We acquired and used our own dataset of 354 min of data from 20 subjects for this study. We report a classification performance of 98.32 % for SVM and 97.42 % for kNN.

ADAPT: Approach to Develop context-Aware solutions for Personalised asthma managemenT.

People with asthma have heterogeneous triggers and symptoms, which they need to be aware of in order to implement the strategies to manage their condition. Context-aware reasoning has the potential to provide the personalisation that is required to address the heterogeneity of asthma by helping people to define the information that is relevant considering the characteristics of their condition and delivering services based on this information. This research work proposes the Approach to Develop context-Aware solutions for Personalised asthma managemenT (ADAPT), whose aim is to facilitate the creation of solutions allowing the required customisation to address the heterogeneity of asthma. ADAPT is the result of the constant interaction with people affected by asthma throughout the research project, which was possible to achieve thanks to the collaboration formed with the Centre for Applied Research of Asthma UK. ADAPT context dimensions facilitate the development of preventive and reactive features that can be configured depending on the characteristics of the person with asthma. The approach also provides support to people not knowing their triggers through case-based reasoning and includes virtual assistant as a complementing technology supporting asthma management. ADAPT is validated by people with asthma, carers and experts in respiratory conditions, who evaluated a mobile application that was built based on the approach.

SmarterROUTES—A Data-driven Context-aware Solution for Personalized Dynamic Routing and Navigation

SmarterROUTES contributes to personalised routing and navigation by data-driven route ranking and an environmentally aware road scene complexity-estimation mechanism. Traditional routing algorithms provide the fastest, shortest, or most ecological route by calculating the lowest-cost path from A to B using an underlying network of weighted connections. Our implementation goes a step further and ranks a set of such routes. The ranking is the result of additional weighing based on governmental data sets, extracts of the OpenStreetMap (OSM) database, or periodically adapted extracts of web services (e.g., Representational state transfer APIs). The selected data sets and their relative contributions to the overall ranking mechanism can be dynamically adapted by the end-user. Another major contribution toward a fully personalised navigation experience is the implementation of a road scene complexity scoring mechanism. Road complexity is estimated based on the combined input from geospatial data sources, traffic data, sensor analysis, and Street View–based complexity analysis. The latter input source uses Street View images as input for a Densenet Convolutional Neural Network (CNN), pre-trained on buildings using Bag-of-Words and Structure-from-motion features, outputting an image descriptor. The current version uses an adapted version of this network to predict road complexity scores. The predicted values correspond with the subjective complexity judgements of the end-users.

Deep Sensing: Inertial and Ambient Sensing for Activity Context Recognition Using Deep Convolutional Neural Networks.

With the widespread use of embedded sensing capabilities of mobile devices, there has been unprecedented development of context-aware solutions. This allows the proliferation of various intelligent applications, such as those for remote health and lifestyle monitoring, intelligent personalized services, etc. However, activity context recognition based on multivariate time series signals obtained from mobile devices in unconstrained conditions is naturally prone to imbalance class problems. This means that recognition models tend to predict classes with the majority number of samples whilst ignoring classes with the least number of samples, resulting in poor generalization. To address this problem, we propose augmentation of the time series signals from inertial sensors with signals from ambient sensing to train Deep Convolutional Neural Network (DCNNs) models. DCNNs provide the characteristics that capture local dependency and scale invariance of these combined sensor signals. Consequently, we developed a DCNN model using only inertial sensor signals and then developed another model that combined signals from both inertial and ambient sensors aiming to investigate the class imbalance problem by improving the performance of the recognition model. Evaluation and analysis of the proposed system using data with imbalanced classes show that the system achieved better recognition accuracy when data from inertial sensors are combined with those from ambient sensors, such as environmental noise level and illumination, with an overall improvement of 5.3% accuracy.

Context-aware access control and anonymous authentication in WBAN

The emergence of the Internet of Things (IoT) as the next generation megatrend has paved the way for pervasive, ubiquitous and proficient healthcare monitoring systems. In the diverse kinds of networks, Wireless Body Area Network (WBAN) has been perceived as one of the most promising wireless sensor technologies for improving healthcare services thanks to its potential for continuous and real-time monitoring of health conditions. However, the open nature of wireless communication introduces wide security and privacy concerns as personal health information could be exposed to unauthorized parties or even malicious adversaries. Furthermore, in such a dynamic and heterogeneous environment where the context conditions continuously and frequently change, adaptive and context-aware solutions become mandatory to satisfy burgeoning security and privacy requirements. Therefore, it is indispensable to adaptively secure the extra-body communication between the smart portable device held by the WBAN client and the healthcare providers while considering the dynamic context changes. In this paper, we propose a context-aware access control and anonymous authentication approach based on a secure and efficient Hybrid Certificateless Signcryption (H-CLSC) scheme. Particularly, it incorporates the merits of Ciphertext-Policy Attribute-Based Signcryption (CP-ABSC) and Identity-Based Broadcast Signcryption (IBBSC) in order to meet the security requirements and provide adaptive contextual privacy. From a security perspective, the proposed mechanism achieves confidentiality, integrity, anonymity, context-aware privacy, key escrow resistance, public verifiability, and ciphertext authenticity. Performance analysis proves the efficiency and the effectiveness of the H-CLSC scheme compared to benchmark schemes in terms of functional security, storage, communication, and computational cost.

Context-aware solutions for asthma condition management: a survey

The evolution of information technology has allowed the development of ubiquitous, user-centred, and context-aware solutions. This article considers existing context-aware systems supporting asthma management with the aim of describing their main benefits and opportunities for improvement. To achieve this, the main concepts related to asthma and context awareness are explained before describing and analysing the existing context-aware systems aiding asthma. The survey shows that the concept of personalisation is the key when developing context-aware solutions supporting asthma management because of the high level of heterogeneity of this condition. Hence, the benefits and challenges of context-aware systems supporting asthma management are strongly linked to contextual Just-In-Time information of internal and external factors related to a person and the heterogeneity it represents.

Context-Aware Indoor VLC/RF Heterogeneous Network Selection: Reinforcement Learning With Knowledge Transfer

For the converged use of LTE, WLAN, and visible light communication in indoor scenarios, fine-grained and intelligent network selection is essential for ensuring high user quality of experience. To tackle the challenges associated with dynamic environments and complicated service requirements, we propose a context-aware solution for indoor network selection. Specifically, three-level contextual information is revealed and exploited in both the utility and algorithm designs. In particular, the contextual information about the asymmetric downlink-uplink features of network performance is used to design a fine-grained utility model. A context-aware learning algorithm sensitive to traffic type-location-time information is proposed. The time-location dependent periodic changing rule of load statistical distributions is further used to realize efficient online network selection via knowledge transfer. The simulation results show that the proposed algorithm can achieve much better performance with faster convergence speed than traditional reinforcement learning.

A Transformable Manufacturing Concept for Low-Volume Aerospace Assembly

This paper presents a vision for a transformable manufacturing system based on reconfigurable and adaptive intelligent technologies, anchored in the description of a real automation cell demonstrating the assembly of various aircraft structures. This production environment developed by the University of Nottingham is a new reconfigurable production environment for the complete manufacturing of high-accuracy high-complexity low-volume aerospace products. This agile reconfigurable cell can accommodate multiple product variants and families which ensures that the capital invested in fixturing is not tied to one application. By compressing the assembly of an aircraft wing structure into a single automated cell, the Future Automated Aircraft Assembly Demonstrator features a flexible, holistic, and context-aware solution that includes automated positioning, drilling, and fastening processes. The assembly cell is based on industrial robots for the handling of aircraft components while an intelligent metrology and control system monitors the cell and ensures that the operations carried out are safe and adhere to the tight tolerances required. These modules are integrated into a single, standardised interface requiring only one operator to control the cell. Performance analyses have shown that, using the reconfigurable production environment described hereafter, an assembly part location accuracy better than ±0.1 mm can be achieved.