Wildfires are environmental hazards with severe ecological, social, and economic impacts. Wildfires devastate ecosystems, communities, and economies worldwide, with rising frequency and intensity driven by climate change, human activity, and environmental shifts. Analyzing wildfire insights such as detection, predictive patterns, and risk assessment enables proactive response and long-term prevention. However, most of the existing approaches have been focused on isolated processing of data, making it challenging to orchestrate cross-modal reasoning and transparency. This study proposed a novel orchestrator-based multi-agent system (MAS), with the aim of transforming multimodal environmental data into actionable intelligence for decision making. We designed a framework to utilize Large Multimodal Models (LMMs) augmented by structured prompt engineering and specialized Retrieval-Augmented Generation (RAG) pipelines to enable transparent and context-aware reasoning, providing a cutting-edge Visual Question Answering (VQA) system. It ingests diverse inputs like satellite imagery, sensor readings, weather data, and ground footage and then answers user queries. Validated by several public datasets, the system achieved a precision of 0.797 and an F1-score of 0.736. Thus, powered by Agentic AI, the proposed, human-centric solution for wildfire management, empowers firefighters, governments, and researchers to mitigate threats effectively.

We present SenseLess, a hybrid anomaly detection framework for smart homes that, during the training phase, automatically labels images without manual annotation by combining sensor-guided detection, self-supervised visual clustering, and unsupervised multi-sensor delay estimation for precise alignment. During operation, the system relies primarily on non-vision sensors and activates a confidence-aware vision model only under low-confidence, thereby preserving privacy while maintaining adaptability. Evaluated in real home monitoring, SenseLess achieved an average label coverage of 97.65% with 94.9% accuracy and reduced vision usage to less than 4% of wall-clock operating time. Calibration mechanisms and minimal configuration requirements support scalability and deployment across diverse residential environments.

Limitations on the availability of Dynamic Vision Sensors (DVS) present a fundamental challenge to researchers of neuromorphic computer vision applications. In response, datasets have been created by the research community, but often contain a limited number of samples or scenarios. To address the lack of a comprehensive simulator of neuromorphic vision datasets, we introduce the Anomalous Neuromorphic Tool for Shapes (ANTShapes), a novel dataset simulation framework. Built in the Unity engine, ANTShapes simulates abstract, configurable 3D scenes populated by objects displaying randomly-generated behaviours describing attributes such as motion and rotation. The sampling of object behaviours, and the labelling of anomalously-acting objects, is a statistical process following central limit theorem principles. Datasets containing an arbitrary number of samples can be created and exported from ANTShapes, along with accompanying label and frame data, through the adjustment of a limited number of parameters within the software. ANTShapes addresses the limitations of data availability to researchers of event-based computer vision by allowing for the simulation of bespoke datasets to suit purposes including object recognition and localisation alongside anomaly detection.

Spiking Neural Networks (SNNs) executed on neuromorphic hardware promise energyefficient, low-latency inference well-suited to edge deployment in size, weight, and powerconstrained environments such as autonomous vehicles, wearable devices, and unmanned aerial platforms. However, a coherent research pathway to deployment of neuromorphic devices remains elusive. This paper presents a structured review and position on the state of SNN-based vision across four interconnected dimensions: network architectures, training methodologies, event-based datasets and simulation techniques, and neuromorphic computing hardware. We survey the evolution from shallow convolutional SNNs to spiking Transformers and hybrid designs which leverage the advantages of SNNs and conventional artificial neural networks. We also examine surrogate gradient training and ANN-to-SNN conversion approaches, catalogue real-world and simulated event-based datasets, and assess the landscape of neuromorphic platforms ranging from rigid mixed-signal architectures to fully-configurable digital systems. Our analysis reveals that while each area has matured considerably in isolation, critical integration challenges persist. In particular, event-based datasets remain scarce and lack standardisation, training methodologies introduce systematic gaps relative to deployment hardware, and access to neuromorphic platforms is restricted by proprietary toolchains and limited development kit availability. We conclude that bridging these integration gaps, rather than advancing individual components alone, represents the most important and least addressed work required to realise the potential of SNN-based vision at the edge.

Introduction: The rapid expansion of the Internet of Things (IoT) as one of the most transformative technologies of the digital age has led to the production and processing of vast volumes of personal and contextual data. While this transformation offers significant opportunities to improve quality of life, it also generates profound ethical challenges, particularly in the areas of privacy and data management. The invisible, automated, and pervasive nature of data collection within the IoT ecosystem calls into question traditional concepts of informed consent, individual control over data, and ethical accountability, and intensifies risks such as pervasive surveillance, data misuse, information insecurity, and the erosion of individual autonomy. Material and Methods: This article adopts a review–analytical approach to examine the scientific literature related to data ethics and privacy in the Internet of Things. Based on existing studies, it explores the foundational aspects of the topic and ultimately draws conclusions from the information reviewed. Conclusion: The study demonstrates that privacy in this context requires a contextual and relational redefinition. The findings indicate that ethical responsibility within the IoT data cycle is distributed in nature, and that multiple actors-from designers and companies to governments-play a fundamental role in upholding principles of data ethics. Ultimately, it can be argued that adherence to data ethics and privacy principles is a prerequisite for the formation of social trust and the sustainable acceptance of the Internet of Things. Without simultaneous attention to technical, ethical, and social dimensions, the development of this technology will face serious challenges.

Smart buildings remain heterogeneous across sensing infrastructure, metadata quality, legacy protocols, and analytics requirements, hindering reusable human–building natural language interfaces. We present OntoSage, a modular framework for ontologically grounded question answering (QA) and fulfillment of analytic intents over smart building data. The framework (i) leverages Brick Schema-based RDF model with reasoning capabilities, (ii) translates natural language (NL) questions into executable SPARQL via a fine-tuned seq2seq model (T5-Base), and (iii) orchestrates portable analytics microservices that operate on time-series sensor data referenced through ontology-linked UUIDs. A summarization component (open-weights Mistral-7B, zero-shot) converts structured SPARQL/SQL/analytic outputs into concise stakeholder-aware responses without requiring task-specific fine-tuning. We categorize QA complexity into four reasoning classes and report component-level execution metrics supporting these categories. To address portability, we formalize a lightweight adaptation workflow (ontology ingestion → entity enrichment for NLU → NL2SPARQL validity checks → analytics binding) designed to minimize per-building retraining. Reproducibility is enabled through public source code, synthetic and ontology-derived datasets, Docker/Compose service descriptors, and documented supporting scripts “( https://github.com/suhasdevmane/OntoBot )”. The developers’ documentation is publicly accessible “( https://ontosage-docs.github.io )”.

Human-Building Interaction (HBI) is an emerging field that enhances building design, construction, and operation by facilitating interactions between occupants and buildings. HBI supports managers and occupants in achieving energy efficiency, sustainability, and improved livability, driving the evolution of smart buildings. The Internet of Things (IoT) integrates diverse building systems into networks of connected devices, generating data that informs adaptive responses to occupant needs and promotes sustainable operations. This survey reviews the role of IoT sensors in HBI, emphasizing their potential to improve communication between buildings and occupants to achieve sustainability objectives. It examines how sensors can be used to generate actionable insights, helping stakeholders meet sustainability goals. To provide a focused analysis, this review is constrained to the two most prominent sustainability objectives identified in global standards: energy efficiency and health and well-being. We identify key factors, sensor types, and benefits shaping sustainable environments. Furthermore, we describe HBI advancements supporting sustainability, alongside challenges regarding IoT integration, occupant engagement, and system constraints. Finally, this review situates IoT sensors within HBI, linking human-building engagement and sustainability goals to provide a comprehensive understanding of their role in shaping smart, adaptive, and sustainable buildings.

Efficient communication between building occupants and facility managers is essential for optimizing space usage, improving user satisfaction, and promoting sustainable practices. This study investigates the effectiveness of the Internet of Things (IoT)-enabled EcoCube device in addressing communication challenges within open design study spaces in a university setting, aiming to create more responsive and adaptive built environments. By integrating principles of social computing and IoT-based sensing and feedback mechanisms, the EcoCube enables students to report issues, share feedback on space preferences, and actively engage in building management processes. Findings reveal differences in satisfaction levels across various study spaces and environmental conditions, emphasizing the need for adaptable solutions to accommodate diverse user needs. Key challenges include improving communication channels and enhancing space efficiency to support sustainability goals. Integrating direct communication features and leveraging occupancy data for real-time management are recommended to advance building sustainability and user satisfaction. As a contribution to IoT research in the context of HBI, this study highlights the EcoCube’s potential to foster collaborative building management, enhance user experience, and support sustainable operations through innovative technology and user-centered design.

Sensor technology in buildings aims to reduce costs and enhance resource efficiency. This study employed a mixed-methods approach, utilizing both qualitative and quantitative data, to investigate how people use various open-design spaces in a new university building. Initial workshops and interviews with students and facility managers clarified their preferences and operational needs. The sensor data provided valuable insights into student space usage, improving communication between students and facility managers by offering a clearer understanding of student behaviors and needs, which enhances the ability of facility managers to make more informed decisions. Follow-up interviews with facility managers provided additional perspectives on the effectiveness of sensor-based solutions in educational settings. The study identified patterns in space usage and discovered opportunities for improving educational facilities. It emphasizes the importance of monitoring both space usage and environmental conditions and highlights the potential to support more informed decision-making in facility management. Future research will explore predictive modeling to better manage space and expand environmental metrics to further enhance educational environments, promote sustainability, and contribute to sustainable societies.

People are increasingly bringing Internet of Things (IoT) devices into their homes without understanding how their data is gathered, processed, and used. We describe PrivacyCube, a novel data physicalization designed to increase privacy awareness within smart home environments. PrivacyCube visualizes IoT data consumption by displaying privacy-related notices. PrivacyCube aims at assisting smart home occupants to (i) understand their data privacy better and (ii) have conversations around data management practices of IoT devices used within their homes. Using PrivacyCube, households can learn and make informed privacy decisions collectively. To evaluate PrivacyCube, we used multiple research methods throughout the different stages of design. We first conducted a focus group study in two stages with six participants to compare PrivacyCube to text and state-of-the-art privacy policies. We then deployed PrivacyCube in a 14-day-long in-home field study with eight households. Lastly, we conducted an event-based field study comparing PrivacyCube with a mobile application, engaging 26 participants with diverse demographics. Our results show that PrivacyCube helps home occupants comprehend IoT privacy better with significantly increased privacy awareness at p < .05 (p = 0.00041, t = -5.57). Participants preferred PrivacyCube over text privacy policies because it was comprehensive and easier to use. PrivacyCube, Privacy Label, and the mobile application, all received positive reviews from participants, with PrivacyCube being preferred for its interactivity and ability to encourage conversations. PrivacyCube was also considered by home occupants as a piece of home furniture, encouraging them to socialize and discuss IoT privacy implications using this device. Watch the demo (Demo Video) (Source Code).

Smart-home technologies are becoming increasingly pervasive, automating lighting, heating, security, and other vital household functions to enhance the comfort, efficiency, and convenience of residents. However, the growing complexity and interconnectivity of these systems expose them to advanced cyber threats, putting residents' privacy and safety at risk. In this study, we investigated the design of future smart home environments to support collaborative anomaly exploration, enabling occupants and devices to jointly identify and address emerging threats. Using a mixed-methods approach---an initial questionnaire (N=40) followed by interactive focus groups (N=36)---we gathered in-depth perspectives on smart home device configurations, user workflows, and potential security vulnerabilities. Our findings include: (i) a taxonomy of realistic security threats, (ii) illustrative layouts and scenarios that highlight how anomalies emerge in everyday household routines, and (iii) concrete examples of how these anomalies can be detected collaboratively. Building on these insights, we propose a comprehensive set of design criteria to guide the development of user-centered, resilient anomaly exploration capabilities in smart homes. Our results offer recommendations for researchers, system designers, and technology practitioners seeking to balance the benefits of automation with robust user-driven security in next-generation ubiquitous home environments.

The integration of Internet of Things (IoT) devices in industrial applications has become viable due to advancements in ubiquitous computing that enable complex machine learning (ML) tasks on resource-constrained devices. Unlike prior approaches that rely on built-in sensors, our system utilizes externally gathered inertial measurement units (IMU) data for anomaly detection. In this article, we show that simple 1D-CNN and LSTM models on an ultralow-power device (Nicla Sense ME) optimized for edge-based industrial anomaly detection can achieve approximately 98% accuracy and F1 score in detecting movement-based anomalies (e.g., collisions and joint velocity deviations) in industrial robotic arms. We analyzed an advanced manufacturing scenario where the robotic arm performs three consecutive, distinct tasks (pick-and-place, painting, and screwdriving) and demonstrated that the proposed anomaly detection system is task-independent. We implemented these models on-device by designing a minimal model architecture and modifying source code to minimize RAM usage and Bluetooth low energy (BLE) overhead. Additionally, we examined the challenges of deploying ML models in resource-constrained environments by analyzing various quantization methods and the impact of hyperparameter choices on inference time, accuracy, and memory consumption. Our approach focuses on detecting anomalies directly at the data source which enables true real-time detection with a complete edge computing framework that achieves a 10-Hz data frequency and a 250-ms inference time when BLE is active. Furthermore, we generated a comprehensive dataset capturing quaternion and IMU data from an industrial robotic arm over 26 h, including various anomaly scenarios, and made the source code available on GitHub for replicability.

Wildlife research activities generate data on ecosystems and species interactions from varied independent projects. Forest Observatories are online platforms that curate, integrate, and analyze wildlife research data for forest monitoring. However, integrating data from disparate sources can be challenging due to data heterogeneity. This study, in collaboration with a research facility in the forest of Sabah, Malaysian Borneo, proposes a novel approach to integrate heterogeneous wildlife data for Forest Observatories. We used the Forest Observatory Ontology (FOO) to standardize wildlife data entities generated by sensors. Four semantically modeled wildlife datasets populated FOO, resulting in an ontology-based knowledge graph named FooDS (Forest Observatory Ontology Data Store). We evaluated FOO and FooDS using specialized open-source ontology scanners, domain experts’ feedback, and applied use cases. This study contributes FooDS, the first ontology-based knowledge graph for Forest Observatories, which provides accurate query responses, reasoning about data, and granular data acquisition from diverse datasets. FOO in turtle format, FOO’s documentation and FooDS in turtle format and their resource website are published at https://w3id.org/def/foo, https://w3id.org/def/fooDocs, https://w3id.org/def/fooDS, and https://ontology.forest-observatory.org.

Biodiversity conservation in fragmented and remote ecosystems often requires labour-intensive, time-consuming fieldwork, placing staff at risk and limiting the scope of long-term monitoring. To address these challenges in a sustainable, locally adaptable manner, we present BearWave, a novel, place-based HF communication framework attuned to local infrastructure constraints and designed to support low-power sensing networks under severe radio-frequency (RF) conditions. By leveraging Near Vertical Incidence Skywave (NVIS) propagation and the FT8 digital modulation technique, BearWave achieves reliable, bidirectional data transfer-even in dense tropical rainforest conditions-while keeping costs under 200 pound per node and enabling extended battery-powered operation. A case study in UK woodlands, chosen as an environmental analogue to Borneo's rainforest, demonstrated BearWave's robustness and adaptability: despite dense vegetation and non-line-of-sight paths, the system maintained over 90% message reliability at distances of up to 25 km using only 1 W of RF power. Notably, the strongest signal propagation and best reception rates occurred during nighttime, reflecting diurnal ionospheric variations. These empirical results confirm that BearWave outperforms conventional technologies such as LoRaWAN or satellite systems in harsh, attenuating environments, offering a scalable, energy-efficient approach that lowers both ecological footprints and staff labor risks. This research advances conservation-focused communication by providing a universal, scientifically validated framework capable of supporting long-term ecological monitoring, poacher detection, and improved animal welfare. Crucially, BearWave's low-cost, low-impact design broadens access for under-resourced organisations and communitydriven conservation programs, where local knowledge and stakeholder insights help shape technology decisions on the ground. By embracing a socio-technical innovation model, BearWave exemplifies how computing can be sustainably embedded in remote ecosystems worldwide.

Smart-home systems represent the future of modern building infrastructure as they integrate numerous devices and applications to improve the overall quality of life. These systems establish connectivity among smart devices, leveraging network technologies and algorithmic controls to monitor and manage physical environments. However, ensuring robust security in smart homes, along with securing smart devices, presents a formidable challenge. A substantial number of security solutions for smart homes rely on data-driven approaches (e.g., machine/deep learning) to identify and mitigate potential threats. These approaches involve training models on extensive datasets, which distinguishes them from knowledge-driven methods. In this review, we examine the role of knowledge within smart homes, focusing on understanding and reasoning regarding various events and their utility towards securing smart homes. We propose a taxonomy to characterize the categorization of decision-making approaches. By specifying the most common vulnerabilities, attacks, and threats, we can analyze and assess the countermeasures against them. We also examine how smart homes have been evaluated in the reviewed literature. Furthermore, we explore the challenges inherent in smart homes and investigate existing solutions that aim to overcome these limitations. Finally, we examine the key gaps in smart-home-security research and define future research directions for knowledge-driven schemes.

Elephant sound identification is crucial in wildlife conservation and ecological research. The identification of elephant vocalizations provides insights into the behavior, social dynamics, and emotional expressions, leading to elephant conservation. This study addresses elephant sound classification utilizing raw audio processing. Our focus lies on exploring lightweight models suitable for deployment on resource-costrained edge devices, including MobileNet, YAMNET, and RawNet, alongside introducing a novel model termed ElephantCallerNet. Notably, our investigation reveals that the proposed ElephantCallerNet achieves an impressive accuracy of 89% in classifying raw audio directly without converting it to spectrograms. Leveraging Bayesian optimization techniques, we fine-tuned crucial parameters such as learning rate, dropout, and kernel size, thereby enhancing the model’s performance. Moreover, we scrutinized the efficacy of spectrogram-based training, a prevalent approach in animal sound classification. Through comparative analysis, the raw audio processing outperforms spectrogram-based methods. In contrast to other models in the literature that primarily focus on a single caller type or binary classification that identifies whether a sound is an elephant voice or not, our solution is designed to classify three distinct caller-types namely roar, rumble, and trumpet.

Sustainable forest management (SFM) is essential for preserving biodiversity, maintaining ecosystem services, and mitigating climate change. This systematic review synthesizes global trends and innovations in SFM practices, analyzing peer-reviewed literature from 2015 to 2025 to identify effective strategies and emerging technologies. The review examines a diverse range of approaches, including forest health index, forest health sensing techniques, emphasizing remote sensing, ground-based monitoring, and the application of machine learning (ML) and artificial intelligence (AI). Moreover, the review highlights SFM practices, including ecosystem-based approaches, community and indigenous involvement, carbon sequestration strategies, and local and global policy frameworks. By integrating technological advancements with policy-driven initiatives, this study provides a comprehensive understanding of current trends and innovations in forest management, offering valuable insights for researchers, policymakers, and practitioners.

Efficient management of end-of-life (EoL) products is critical for advancing circularity in supply chains, particularly within the construction industry where EoL strategies are hindered by heterogenous lifecycle data and data silos. Current tools like Environmental Product Declarations (EPDs) and Digital Product Passports (DPPs) are limited by their dependency on seamless data integration and interoperability which remain significant challenges. To address these, we present the Circular Construction Product Ontology (CCPO), an applied framework designed to overcome semantic and data heterogeneity challenges in EoL decision-making for construction products. CCPO standardises vocabulary and facilitates data integration across supply chain stakeholders enabling lifecycle assessments (LCA) and robust decision-making. By aggregating disparate data into a unified product provenance, CCPO enables automated EoL recommendations through customisable SWRL rules aligned with European standards and stakeholder-specific circularity SLAs, demonstrating its scalability and integration capabilities. The adopted circular product scenario depicts CCPO's application while competency question evaluations show its superior performance in generating accurate EoL suggestions highlighting its potential to greatly improve decision-making in circular supply chains and its applicability in real-world construction environments.

The Internet of Things (IoT) has revolutionized built environments by enabling seamless data exchange among devices such as sensors, actuators, and computers. However, IoT devices often lack robust security mechanisms, making them vulnerable to cyberattacks, privacy breaches, and operational anomalies caused by environmental factors or device faults. While anomaly detection techniques are critical for securing IoT systems, the role of testbeds in evaluating these techniques has been largely overlooked. This systematic review addresses this gap by treating testbeds as first-class entities essential for the standardized evaluation and validation of anomaly detection methods in built environments. We analyze testbed characteristics, including infrastructure configurations, device selection, user-interaction models, and methods for anomaly generation. We also examine evaluation frameworks, highlighting key metrics and integrating emerging technologies such as edge computing and 5G networks into testbed design. By providing a structured and comprehensive approach to testbed development and evaluation, this paper offers valuable guidance to researchers and practitioners in enhancing the reliability and effectiveness of anomaly detection systems. Our findings contribute to the development of more secure, adaptable, and scalable IoT systems, ultimately improving the security, resilience, and efficiency of built environments.

Adaptive behavior plays a critical role in how individuals navigate discomfort, influencing their ability to respond to environmental challenges. Addressing thermal discomfort which is a pressing concern in the context of climate adaptation and sustainable living. It requires designers and developers to move beyond traditional Graphical User Interface (GUI) solutions, embracing interactive and physically engaging design approaches. This paper introduces D-FACT (Discomfort Card-Based Toolkit for Facilitating Adaptation), a participatory design tool developed to foster sustainable and inclusive adaptation strategies. Tested in four collaborative workshops with 40 participants, D-FACT enables both designers and non-designers to ideate solutions for adapting to thermal discomfort. Our findings demonstrate the toolkit’s effectiveness in nurturing creative, cooperative approaches that promote adaptive environments while advancing energy efficiency and environmental health. By integrating principles of sustainable design, the toolkit encourages diverse and inclusive participation, resulting in conceptual designs that address localized challenges in thermal adaptation. This work contributes to the discourse on computing and sustainability, offering practical methods for embedding participatory design into efforts to create resilient, equitable, and resource-efficient spaces1.