American Journal of Applied Science and Technology

Artificial Intelligence-Based Test Automation Frameworks for Next-Generation Software Quality Engineering

2026-08-17T09:13:33+00:00

The increasing complexity, scale, and dynamic behavior of contemporary software systems have created substantial challenges for conventional test automation approaches. Traditional automation frameworks generally depend on predefined scripts, deterministic rules, and manually maintained test artifacts, which limits their adaptability when applications, interfaces, requirements, and execution environments change continuously. This research and review paper examines the conceptual foundations of Artificial Intelligence (AI)-based test automation frameworks for next-generation software quality engineering by integrating perspectives from human-like computing, functionalism, semantic representation, conceptual spaces, distributed intelligence, and machine-intelligence measurement. The study develops a conceptual framework in which AI-supported test automation is organized around five interconnected capabilities: intelligent requirement interpretation, semantic test modeling, adaptive test generation, autonomous execution and maintenance, and predictive quality-risk analysis. The theoretical synthesis indicates that human-like and symbolic approaches can provide interpretability and structured reasoning, whereas conceptual-space and semantic approaches can support contextual representation of software behavior. Distributed intelligence perspectives further suggest opportunities for scalable quality engineering across heterogeneous testing environments. The analysis also incorporates AI-driven project-risk prediction as a complementary decision-support mechanism for prioritizing testing resources and identifying high-risk software components. Findings indicate that the most promising next-generation architecture is not a completely autonomous testing system but a human-centered, adaptive framework combining machine intelligence with explainable representations and continuous feedback. The paper identifies important limitations involving semantic ambiguity, model reliability, maintenance complexity, and the absence of universal evaluation criteria. It concludes that AI-based test automation can substantially strengthen software quality engineering when intelligence is integrated as an adaptive reasoning layer rather than treated merely as an alternative mechanism for script generation.

Intelligent Evaluation Model for Determining Multiple Intelligence Profiles of Junior High Students Through Digital Likert Scale Architecture

2026-08-01T05:17:24+00:00

The identification of students’ multiple intelligence profiles has become an important component in developing personalized learning strategies, particularly at the junior high school level where students demonstrate diverse cognitive abilities, learning preferences, and academic potentials. Conventional evaluation approaches frequently emphasize limited academic indicators and provide insufficient representation of broader intelligence dimensions. This research proposes an Intelligent Evaluation Model for Determining Multiple Intelligence Profiles of Junior High Students Through Digital Likert Scale Architecture. The study conceptualizes a technology-supported assessment framework that integrates multiple intelligence indicators, digital questionnaire mechanisms, and structured evaluation processes using a Likert scale approach. The proposed model focuses on improving the accuracy, efficiency, and accessibility of intelligence profile identification through web-based assessment architecture.

The research adopts a conceptual development approach by analyzing previous studies related to multiple intelligence-based learning materials, digital learning systems, educational assessment models, and technology-supported evaluation methods. The model consists of several functional components, including intelligence indicator formulation, digital data acquisition, response weighting mechanisms, profile classification, and interpretation of evaluation outcomes. The framework is designed to support teachers in understanding student characteristics and enabling more adaptive instructional planning. Previous research regarding contextual learning approaches and guided discovery methods demonstrates the importance of aligning educational strategies with students’ cognitive and emotional characteristics (Agustyarini and Jailani, 2015). Similarly, studies on multiple intelligence-based educational resources highlight the potential of intelligence-oriented approaches for improving student engagement and learning effectiveness (Lestari and Nisa, 2018).

The proposed architecture contributes to educational technology research by providing a systematic approach for transforming qualitative intelligence assessment into a measurable digital evaluation process. The model also provides opportunities for data-driven educational decision-making while acknowledging limitations related to indicator selection, respondent subjectivity, and contextual differences among learners. This research provides a foundation for developing intelligent educational assessment systems that promote personalized learning environments in junior high schools.

An AI-Robotics Framework for Operational Efficiency and Sustainability in Construction Management

2026-08-15T04:51:09+00:00

The construction industry is increasingly characterized by complex resource coordination, fragmented workflows, schedule uncertainty, safety constraints, and growing sustainability requirements. Artificial intelligence (AI), robotics, cloud manufacturing principles, reinforcement learning, and intelligent resource-allocation mechanisms provide an emerging technological basis for addressing these challenges. This research develops an AI-Robotics Framework for Operational Efficiency and Sustainability in Construction Management by synthesizing concepts from cloud manufacturing, resource-service composition, intelligent scheduling, reinforcement learning, multi-objective optimization, and automated negotiation. The study adopts a conceptual research and review methodology based exclusively on the supplied literature and translates its manufacturing-oriented principles into a construction-management context. The proposed framework consists of five interconnected layers: data and sensing, intelligent resource orchestration, AI decision optimization, robotic execution, and sustainability-performance feedback. The analysis indicates that operational efficiency can be improved when AI-based decision mechanisms and robotic systems are integrated rather than deployed as isolated technologies. Cloud-oriented resource coordination provides scalability, reinforcement learning enables adaptive allocation, multi-objective optimization supports simultaneous consideration of productivity and sustainability, and automated negotiation can facilitate coordination among multiple project stakeholders. The framework further emphasizes continuous feedback between construction-site operations and management decisions. Its principal contribution is a theoretically grounded architecture for integrating AI and robotics with construction-resource management while maintaining sustainability as an explicit optimization objective. The study also identifies limitations associated with interoperability, data quality, organizational readiness, computational complexity, and the transferability of manufacturing-oriented models to construction environments.

Adaptive Multi-Agent AI Framework for Real-Time Data Streaming with Enhanced Scalability and Resilience

2026-08-20T08:36:39+00:00

Real-time data streaming systems increasingly operate under highly variable workloads, heterogeneous data sources, latency constraints, and frequent service disruptions. Conventional stream-processing architectures generally depend on predefined routing, static resource allocation, and centralized coordination, which can limit their ability to adapt when event rates, computational requirements, or infrastructure conditions change rapidly. This paper proposes an Adaptive Multi-Agent AI Framework for Real-Time Data Streaming with Enhanced Scalability and Resilience, in which autonomous AI agents collaboratively perform stream monitoring, workload classification, task allocation, resource adaptation, anomaly detection, and recovery. The theoretical foundation combines multi-agent coordination with contextual representation, long-document processing, memory management, and adaptive decision-making. Prior work on aspect-controllable summarization demonstrates the value of controlling computational objectives according to task requirements, while studies of coreference, lexical chains, and entity-based coherence emphasize the importance of preserving relationships across distributed information units (Amplayo, Angelidis, & Lapata, 2021; Baldwin & Morton, 1998; Barzilay & Elhadad, 1997; Barzilay & Lapata, 2005). Long-context language modeling further motivates mechanisms capable of retaining relevant information over extended streaming windows (Beltagy, Peters, & Cohan, 2020). The proposed framework extends these principles to adaptive streaming environments and aligns with recent multi-agent event-streaming research emphasizing resiliency and scalability (Reddy et al., 2026). Analytical findings indicate that decentralized agent specialization, shared contextual state, adaptive workload redistribution, and failure-aware coordination can provide a stronger basis for resilient streaming than static pipelines. The paper also identifies trade-offs involving coordination overhead, state consistency, model complexity, and resource consumption.

Predictive Modeling of Research Productivity in Higher Educational Institutes Using Regression and Deep Learning

2026-08-12T08:44:09+00:00

Research productivity has become a fundamental indicator for evaluating the academic performance, institutional reputation, and global competitiveness of higher educational institutes. Universities increasingly rely on quantitative performance indicators to allocate research funding, assess faculty achievements, and formulate strategic development policies. However, research productivity is influenced by numerous interrelated institutional, professional, and individual factors, making accurate prediction a complex analytical challenge. Conventional statistical approaches have provided valuable insights into linear relationships among productivity determinants, yet they often fail to capture complex nonlinear interactions present in large educational datasets. Recent developments in machine learning, particularly deep learning, offer new opportunities for developing predictive models capable of identifying hidden relationships and improving forecasting accuracy.

This research and review article develops a predictive modeling framework integrating regression analysis with deep learning techniques to estimate research productivity in higher educational institutes. The study synthesizes previous investigations concerning faculty motivation, institutional performance, research evaluation indicators, scientometric analysis, organizational risk, and productivity assessment to establish a comprehensive analytical foundation. Regression analysis is employed to quantify statistically significant predictors, while deep learning models are proposed to capture multidimensional nonlinear relationships among institutional and academic variables. The proposed framework demonstrates how hybrid predictive approaches can improve institutional decision-making, faculty development strategies, and research policy formulation.

Secure-XPAPF: A Cyber-Secure and Explainable Deep Learning Framework for Automated Pronunciation Assessment and Personalized Feedback in Distance Language Learning

2026-08-19T10:30:16+00:00

Automated pronunciation assessment has become a core technology for distance foreign language learning, yet existing systems frequently behave as opaque scoring engines and rarely address the cybersecurity risks created by collecting speech, storing learner profiles, generating AI feedback and operating learning analytics at scale. This article proposes Secure-XPAPF, a cyber-secure explainable deep learning framework for automated pronunciation assessment and personalized corrective feedback. The framework integrates automatic speech recognition, pronunciation error detection, pronunciation quality scoring, explainable AI, personalized feedback recommendation, learning analytics, longitudinal progress modeling and a cross-cutting cybersecurity layer. Technically, Secure-XPAPF combines self-supervised speech representations, Transformer-based multi-task pronunciation modeling, phoneme-level error diagnosis, SHAP-style acoustic attribution, attention and saliency visualization, evidence-bound large-language-model feedback, privacy-preserving learning analytics, differential-privacy-aware federated training, adversarial audio robustness testing and zero-trust access governance. Pedagogically, it transforms numerical scores into learner-facing explanations, targeted corrective exercises and longitudinal dashboards for teachers and learners. From a cybersecurity perspective, the framework maps the full attack surface of distance pronunciation learning, including speech data leakage, metadata re-identification, model inversion, membership inference, data poisoning, adversarial audio, prompt injection, insecure LLM output handling and analytics dashboard abuse. The evaluation design combines regression, classification, explanation fidelity, recommendation ranking, learning gain, trust, privacy leakage and security resilience metrics. Illustrative, simulation-calibrated results are provided only as a reproducible reporting template and indicate how a deployed system could compare against GOP, CNN-BiLSTM, Whisper-based and wav2vec2-based baselines. The contribution is a unified, auditable and security-by-design framework that connects speech AI, explainable pedagogy and cyber-resilient learning analytics for high-stakes distance language learning environments.

Natural Geography as a Determinant of Urban Form: Topography, Hydrology, and Climate in Contemporary City Planning

2026-08-10T11:00:25+00:00

Urban form has never developed independently of the physical landscape beneath it. Relief, hydrology, coastal position, climate, and geology set the boundary conditions within which every subsequent planning decision — street layout, building height, land use, and infrastructure routing — must operate. This article examines the interaction between natural geography and urban planning through three lenses: topographic constraint and ventilation, hydrological exposure and flood risk, and climate-responsive bioclimatic design. Drawing on quantitative data from developed-country research institutions and international case studies — including Rotterdam's water-sensitive urban design programme, Stuttgart's topography-based ventilation-corridor policy, and global assessments of coastal flood exposure — the article argues that cities which formally embed physical-geographic analysis into statutory planning instruments achieve measurably better resilience outcomes than cities that treat geography as a site constraint to be engineered around after the fact.

Artificial Intelligence-Based Regression Testing Frameworks for Agile Software Development

2026-08-17T19:37:18+00:00

Agile software development emphasizes short release cycles, continuous integration, incremental delivery, and rapid adaptation to changing requirements. Although these practices improve responsiveness, they substantially increase the frequency and complexity of regression testing because previously validated functionality must repeatedly be reassessed after code modifications. Conventional regression-testing strategies frequently rely on static test suites, manually defined prioritization rules, and deterministic execution policies, which can become inefficient as software systems evolve. This research presents a conceptual artificial intelligence-based regression testing framework for Agile software development that integrates machine-learning-assisted test selection, clustering, prioritization, and adaptive execution. The framework is theoretically positioned around unsupervised learning, clustering, data-driven validation, and risk-oriented decision making. The provided literature demonstrates the applicability of unsupervised machine learning to complex pattern discovery, clustering validation, and data-science workflows, while research on artificial intelligence emphasizes the importance of computational and ethical considerations when AI is introduced into sensitive decision processes. The proposed framework consequently treats regression testing as a dynamic decision problem rather than a fixed execution task. A structured methodology is developed covering test-data preparation, feature extraction, test-case clustering, risk prediction, prioritization, execution feedback, and continuous model refinement. The analytical findings indicate that AI can improve the scalability and adaptability of regression testing when test selection is driven by historical behavior, code-change characteristics, dependency information, and execution outcomes. However, model uncertainty, training-data quality, explainability, computational overhead, and inappropriate clustering assumptions remain significant limitations. The study concludes that AI-based regression testing is most effective when implemented as a human-supervised, feedback-oriented component of the Agile quality-engineering pipeline rather than as a fully autonomous replacement for established testing practices.

A Hybrid Predictive Learning Model for Red Wine Quality Assessment Using Classification and Visual Analytics

2026-08-08T12:13:41+00:00

The assessment of wine quality has become an important research area due to the increasing demand for objective, consistent, and data-driven evaluation methods within the food and beverage industry. Traditional sensory evaluation performed by expert tasters is inherently subjective and influenced by individual perception, making automated predictive systems an attractive alternative. This study proposes a hybrid predictive learning model that integrates machine learning-based classification with visual analytics to improve the assessment of red wine quality. The proposed framework combines systematic data preprocessing, feature optimization, supervised classification, and interactive visualization to support both accurate prediction and meaningful interpretation of quality-related characteristics. The study synthesizes existing research on wine informatics, probabilistic classifiers, regression analysis, recommendation systems, and clustering techniques to establish a comprehensive theoretical foundation for intelligent wine quality prediction. Unlike conventional predictive approaches that primarily emphasize classification accuracy, the proposed model incorporates visual analytical techniques to enhance model transparency and facilitate informed decision-making. The framework also adopts an integrated project management perspective for systematic model planning, implementation, validation, and continuous optimization, following principles highlighted by Philip (2026). Comparative analysis indicates that hybrid learning architectures can effectively manage nonlinear relationships among physicochemical attributes while simultaneously improving classification robustness and interpretability. The proposed methodology demonstrates how data visualization can reveal hidden quality patterns, identify influential variables, and support practical applications in quality assurance, winery management, and recommendation systems. The research contributes a structured conceptual framework that integrates predictive analytics and visualization into a unified decision-support architecture suitable for future intelligent wine quality management systems.

An Integrated AI-Robotics Framework for Construction Efficiency, Sustainability, and Digital Innovation

2026-08-15T17:04:01+00:00

The construction sector is increasingly characterized by fragmented information flows, variable operational conditions, resource-intensive processes, and complex decision-making requirements. Artificial intelligence (AI) and robotics provide opportunities to address these challenges through predictive analytics, adaptive decision support, automated execution, and continuous performance monitoring. However, the value of these technologies depends on their integration into a coherent socio-technical framework rather than their isolated deployment. This research develops an integrated conceptual framework connecting AI-based analytics, robotics-enabled execution, digital learning, operational feedback, and sustainability-oriented decision-making in construction. The methodology synthesizes the conceptual and empirical implications of the seven provided studies, particularly their findings concerning machine learning prediction, learning analytics, behavioral modeling, uninterrupted task engagement, active learning, and blended learning. Although the references originate primarily from educational and learning environments, their methodological principles provide transferable foundations for designing intelligent construction systems in which data are converted into predictions, predictions inform decisions, and decisions guide human or robotic action. The proposed framework contains five interconnected layers: data acquisition, AI intelligence, decision orchestration, robotic execution, and feedback-based learning. The analysis indicates that integration can improve operational visibility, resource allocation, adaptive planning, workforce learning, and sustainability monitoring. Nevertheless, limitations associated with contextual transferability, data quality, interoperability, human acceptance, and the absence of construction-specific empirical validation remain significant. The study therefore positions the framework as a research-oriented conceptual architecture that requires field experimentation and longitudinal evaluation before claims of measurable construction performance improvement can be generalized.

Species Composition and Bioecological Characteristics of Mammals in The Lower Amudarya State Biosphere Reserve

2026-08-20T10:56:32+00:00

This article analyzes the species composition, ecological distribution, and major bioecological characteristics of mammals inhabiting the Lower Amudarya State Biosphere Reserve. The analysis demonstrates that mammalian distribution and population dynamics are determined primarily by vegetation structure, water availability, food resources, seasonal climatic conditions, habitat connectivity, and anthropogenic pressure. Special consideration is given to the rapid recovery of the Bukhara deer population and the ecological consequences of increasing population density. It is concluded that effective mammal conservation in the Lower Amudarya requires an ecosystem-based approach integrating population monitoring, protection and restoration of tugai vegetation, maintenance of suitable hydrological conditions, preservation of ecological corridors, and scientifically based management of anthropogenic impacts.

An Advanced Graph-Based Deep Learning Approach for Cyberattack Detection in Cloud Computing Networks

2026-08-13T08:40:14+00:00

The increasing structural complexity of cloud computing networks has created a need for cyberattack detection techniques capable of representing relationships among users, workloads, services, virtual machines, communication flows, and security events. Conventional detection approaches frequently treat network observations as independent records, limiting their ability to capture relational dependencies that may characterize coordinated or multi-stage attacks. This paper proposes an advanced graph-based deep learning approach in which cloud-network entities are represented as nodes, their interactions as edges, and security-relevant observations as graph attributes. The methodological foundation integrates graph representation, learned heuristic reasoning, structured state-space analysis, and neural learning principles derived from the supplied literature. The approach is conceptually positioned between classical graph-search methods and modern neural architectures, enabling contextual threat identification while preserving structural information. The study develops a graph construction mechanism, graph-based representation learning process, cyberattack classification layer, and adaptive threat-prioritization mechanism. Theoretical analysis indicates that graph-based modeling can improve contextual interpretation of attacks because suspicious behavior is evaluated not only from individual events but also from their surrounding relational structure. The proposed framework further incorporates heuristic concepts from planning research to support efficient exploration of large and complex attack states. Findings suggest that combining graph structure with deep representation learning provides a stronger foundation for cloud cyberattack detection than isolated event classification, although computational complexity, graph construction quality, adversarial manipulation, and model interpretability remain important limitations. The framework extends the graph-based cybersecurity direction established by Marri et al. (2025) by emphasizing an integrated architectural and analytical perspective.

Scale Core: An LLM-Based Combinatorial Architecture for Scalable Constraint Management

2026-08-19T14:15:21+00:00

The rapid evolution of large language models (LLMs) has created new opportunities for managing complex computational constraints across heterogeneous software, information, and decision environments. However, the practical deployment of LLM-based systems remains constrained by competing requirements involving scalability, consistency, computational cost, contextual complexity, and reliability. This paper proposes ScaleCore, an LLM-based combinatorial architecture designed to transform natural-language and system-level constraints into structured, prioritized, and computationally manageable constraint configurations. The architecture integrates constraint extraction, normalization, dependency modeling, combinatorial composition, conflict detection, adaptive allocation, and validation into a coordinated processing pipeline. The methodological foundation is informed by the rapidly developing LLM ecosystem and the observed industry transition toward increasingly capable conversational and code-oriented AI systems. Prior work has demonstrated both the accelerating adoption of LLM technologies and the operational challenges associated with reliability, competitive deployment, and AI-generated outputs. The proposed architecture extends these observations by treating scalability as a constraint-management problem rather than solely a model-capacity problem. ScaleCore uses combinatorial constraint representations to identify compatible and conflicting requirements before resource-intensive generation or execution. The framework is conceptually aligned with recent work on combinatorial LLM architectures for scalability constraints, particularly the ScalePulse framework, which motivates systematic treatment of scalability through compositional constraint reasoning (Ramamurthy, Bellamkonda and Amanmadov, 2026). The resulting architecture provides a structured foundation for scalable LLM deployment while exposing important trade-offs involving computational overhead, constraint completeness, interpretability, and model dependence. The paper concludes that scalable LLM systems require an intermediate constraint-management layer capable of translating ambiguous requirements into machine-operable representations before downstream execution.

Developing a National Cybersecurity Strategy in the Age of Artificial Intelligence in Palestine

2026-08-11T05:14:34+00:00

The rapid advancement of artificial intelligence (AI) has significantly transformed the cybersecurity landscape, introducing both enhanced defensive mechanisms and increasingly sophisticated cyber threats. Nations worldwide are integrating AI into their cybersecurity strategies to improve threat detection, automate responses, and strengthen resilience against cyberattacks. However, the dual-use nature of AI also enables adversaries to develop advanced attack techniques, including intelligent malware, automated phishing, and deepfake-based disinformation campaigns [1], [2].

In Palestine, the need for a comprehensive national cybersecurity strategy is becoming increasingly urgent due to ongoing digital transformation across governmental, economic, and social sectors. Despite this progress, the Palestinian cybersecurity ecosystem faces critical challenges, including fragmented institutional governance, limited technical infrastructure, insufficient legal frameworks, and constraints related to digital sovereignty [3].

This research aims to develop a strategic framework for a national cybersecurity strategy in Palestine that integrates AI technologies while addressing local constraints and global best practices. The study adopts a qualitative analytical approach based on literature review, comparative analysis of international models, and evaluation of existing Palestinian policies. The proposed framework—Palestinian Cybersecurity Strategy Framework (PCSF)—is built upon five key pillars: governance, legal and regulatory frameworks, capacity building, technological infrastructure, and international cooperation, with AI integration as a cross-cutting component.

The findings suggest that Palestine can enhance its cybersecurity resilience by adopting a phased and adaptive strategy that leverages human capital, strengthens institutional coordination, and aligns with international cybersecurity standards. The study contributes to both academic research and policy development by providing a context-specific model for cybersecurity strategy in environments characterized by political and technological constraints.

A Combinatorial Large Language Model Framework for Scalability-Aware Constraint Solving

2026-08-18T06:43:55+00:00

Scalability-aware constraint solving requires computational systems to reason over increasingly large combinations of variables, dependencies, constraints, and solution alternatives without allowing computational complexity to grow uncontrollably. Conventional constraint-solving approaches are effective for well-defined optimization and satisfiability problems, but their performance can deteriorate when constraints are heterogeneous, dynamically changing, or expressed through natural language. This research proposes ScaleMind, a combinatorial Large Language Model (LLM) framework designed to integrate natural-language reasoning, constraint decomposition, combinatorial search, and scalability-aware solution selection. The framework conceptualizes an LLM as a semantic reasoning layer rather than a standalone solver and combines it with structured constraint representations and adaptive search mechanisms. The theoretical positioning is informed by research on deep learning, transfer learning, ensemble learning, segmentation, and recognition architectures, which collectively demonstrate the value of decomposition, representation learning, and model combination in complex computational tasks (Acharya et al., 2015; Aneja & Aneja, 2019; Deore & Pravin, 2017). ScaleMind extends this principle toward constraint-solving environments in which the number of possible configurations increases rapidly. Its design is additionally motivated by the combinatorial scalability perspective presented by Ramamurthy et al. (2026). The proposed framework introduces constraint normalization, semantic decomposition, combinatorial candidate generation, adaptive pruning, verification, and scalability-aware ranking. Analytical findings indicate that decomposition and ensemble-style reasoning can reduce unnecessary search, improve interpretability, and provide a more controlled mechanism for handling large constraint spaces. However, LLM-based reasoning introduces risks associated with hallucinated constraints, inconsistent reasoning, computational overhead, and verification requirements. The study therefore positions ScaleMind as a hybrid reasoning architecture rather than a replacement for formal constraint solvers.

Adaptive Spatial Positioning Model for Real-Time Indoor Navigation Using Ultra-Wideband IoT Systems and Unity Engine Optimization

2026-08-10T09:21:39+00:00

The increasing demand for precise indoor navigation has accelerated research into advanced positioning technologies capable of overcoming the limitations of conventional satellite-based localization systems. This research presents an Adaptive Spatial Positioning Model for Real-Time Indoor Navigation using Ultra-Wideband (UWB) IoT systems integrated with the Unity cross-platform development environment. The proposed approach combines high-resolution UWB ranging capabilities, IoT-based spatial data communication, and Unity-driven visualization optimization to develop an interactive and adaptive indoor positioning framework. The model focuses on improving localization reliability through efficient anchor-tag coordination, spatial data processing, and real-time three-dimensional environment representation. Existing UWB localization approaches demonstrate strong accuracy potential; however, challenges related to environmental interference, computational optimization, and seamless cross-platform deployment remain significant. The proposed framework addresses these limitations by integrating adaptive positioning algorithms with Unity-based spatial rendering mechanisms. Experimental analysis indicates that the architecture can enhance indoor navigation performance by improving positioning stability, reducing latency, and supporting scalable IoT applications. The study contributes a unified methodology for intelligent indoor navigation systems applicable to smart buildings, industrial automation, augmented reality environments, and location-aware IoT services.