Search Results (112)

NetworkGuard is a modular edge-based virtual network sensing framework designed for residential smart home security. The system interprets network telemetry—such as DNS queries, firewall events, VPN latency, and connection establishment delay—as structured sensing signals for gateway-level monitoring. Implemented on a Raspberry Pi 4 and managed via an Android interface, NetworkGuard integrates DNS filtering (Pi-hole), firewall enforcement (UFW), encrypted VPN tunneling (WireGuard), and an AI-assisted advisory layer for contextual log interpretation. During a six-week residential deployment, DNS blocking efficiency improved from 81.2% to 97.0% following blocklist refinement, while VPN connection establishment time decreased from approximately 3012 ms to 2410 ms after configuration tuning. ICMP-based measurements indicated a stable tunnel latency under moderate traffic conditions. Controlled validation scenarios—including DNS manipulation attempts, port scanning, and VPN interruption testing—confirmed consistent firewall enforcement and tunnel containment. The results demonstrate that layered security principles can be adapted into a lightweight, reproducible edge architecture suitable for small-scale residential IoT environments without a reliance on enterprise infrastructure. Full article

This contribution outlines a completely new, fully local approach for secure web-based device control on the basis of browser inter-window messaging. Modern smart home IoT (Internet of Things) devices are commonly controlled with proprietary mobile applications via remote servers, which can have significant adverse implications for the end user. Given that many IoT devices in use today are limited in both available memory and processing speed, standard approaches such as HTTPS-based transport security are not always feasible and a need for more suitable alternatives for such constrained devices arises. The proposed local method for lightweight and secure web-based device control using inter-window messaging leverages existing standard web technologies to enable a maximum degree of privacy, choice, and sustainability within the smart home ecosystem. The implemented proof-of-concept shows that it is feasible to meet essential security objectives in a local web IoT control context while utilizing less than a kilobyte of additional memory compared to an unsecured solution, thereby promoting sustainability through hardening of the control protocols used by existing devices with too few resources for implementing standard web cryptography. In this way, the present work contributes to achieving the vision of a fully open and secure local smart home. Full article

Cloud-centric IoT frameworks remain dominant; however, they introduce major challenges related to data privacy, latency, and system resilience. Existing open-source solutions often lack standardized principles for scalable, local-first deployment and do not adequately integrate fault tolerance with hybrid automation logic. This study presents a practical and extensible local-first IoT architecture designed for full operational autonomy using open-source components. The proposed system features a modular, layered design that includes device, communication, data, management, service, security, and presentation layers. It integrates MQTT, Zigbee, REST, and WebSocket protocols to enable reliable publish–subscribe and request–response communication among heterogeneous devices. A hybrid automation model combines rule-based logic with lightweight data-driven routines for context-aware decision-making. The implementation uses Proxmox-based virtualization with Home Assistant as the core automation engine and operates entirely offline, ensuring privacy and continuity without cloud dependency. The architecture was deployed in a real-world office environment and evaluated under workload and fault-injection scenarios. Results demonstrate stable operation with MQTT throughput exceeding 360,000 messages without packet loss, automatic recovery from simulated failures within three minutes, and energy savings of approximately 28% compared to baseline manual control. Compared to established frameworks such as FIWARE and IoT-A, the proposed approach achieves enhanced modularity, local autonomy, and hybrid control capabilities, offering a reproducible model for privacy-sensitive smart environments. Full article

The fast expansion of the Internet of Things (IoT) has accelerated the advancement of smart home technologies. However, secure communication and access control remain significant challenges. This paper presents a fully implemented FPGA-based IoT gateway that utilizes the Zynq-7000 SoC, integrating sensing, processing, wireless communication, and hardware-level authentication. Analog temperature data from an LM35 sensor is digitized via a 12-bit XADC and transmitted over Wi-Fi using an ESP8266-01 module. An SPI-based OLED provides real-time feedback. To ensure device-level trust, an XOR-based Physically Unclonable Function (PUF) enables lightweight challenge–response authentication with over good uniqueness and a latency of under 10 ms. The system demonstrates ±0.5 °C sensing accuracy, <50 ms transmission delay, and low power consumption. It offers a scalable and secure platform suitable for real-time smart home and facility automation. Full article

Triboelectric nanogenerators (TENGs) have emerged as efficient mechanical-energy harvesters with advantages—simple architectures, broad material compatibility, low cost, and strong environmental tolerance—positioning them as key enablers of self-powered systems. This review synthesizes recent progress in energy-storage interfaces, power management, and system-level integration for TENGs. We analyze how intrinsic source characteristics—high output voltage, low current, large internal impedance, and pulsed waveforms—complicate efficient charge extraction and utilization. Accordingly, this work highlights a variety of power-conditioning approaches, including advanced rectification, multistage buffering, impedance transformation/matching, and voltage regulation. Moreover, recent developments in the integration of TENGs with storage elements, cover hybrid topologies and flexible architectures. Application case studies in wearable electronics, environmental monitoring, smart-home security, and human–machine interfaces illustrate the dual roles of TENGs as power sources and self-driven sensors. Finally, we outline research priorities: miniaturized and integrated power-management circuits, AI-assisted adaptive control, multimodal hybrid storage platforms, load-adaptive power delivery, and flexible, biocompatible encapsulation. Overall, this review provides a consolidated view of state-of-the-art TENG-based self-powered systems and practical guidance toward real-world deployment. Full article

This paper provides a perspective on the use of RGB-D cameras and non-invasive brain–computer interfaces (BCIs) for human activity recognition (HAR). Then, it explores the potential of integrating both the technologies for active and assisted living. RGB-D cameras can offer monitoring of users in their living environments, preserving their privacy in human activity recognition through depth images and skeleton tracking. Concurrently, non-invasive BCIs can provide access to intent and control of users by decoding neural signals. The synergy between these technologies may allow holistic understanding of both physical context and cognitive state of users, to enhance personalized assistance inside smart homes. The successful deployment in integrating the two technologies needs addressing critical technical hurdles, including computational demands for real-time multi-modal data processing, and user acceptance challenges related to data privacy, security, and BCI illiteracy. Continued interdisciplinary research is essential to realize the full potential of RGB-D cameras and BCIs as AAL solutions, in order to improve the quality of life for independent or impaired people. Full article

The complexity of smart home technologies and the need for personalized energy efficiency solutions highlight the importance of user-oriented decision-support tools. This study presents a Smart Home Management System (SHMS) selection solution that combines a web-based dashboard, a mobile application, and a relational database. A 54-question structured questionnaire was designed to capture user requirements, and four alternatives—KNX, JUNG Home, LB Management, and eNet Smart Home—were compared using the Simple Additive Weighting (SAW) method. Evaluation criteria included installation complexity, communication technology, integration and control capabilities, and user experience. The system was implemented with Next.js, React Native, and Post-greSQL, ensuring flexibility, scalability, and secure data management. Preliminary evaluation with specialists (system integrators, architects, designers) and students confirmed the coherence of the questionnaire, the adequacy of criteria, and the clarity of recommendations. Results showed that the tool improves user engagement, reduces decision-making uncertainty, and supports the adoption of energy-efficient residential solutions. The study’s main limitation is the small test sample, which will be expanded in future large-scale validation. Planned improvements include interactive product comparisons, cost estimation, adaptive questionnaire logic, and 3D visualizations. Overall, the system bridges the gap between technical SHMS solutions and user-oriented decision-making, offering practical and academic value. Full article

Improving energy efficiency in buildings is critical for supporting sustainable growth in the construction sector. In this context, the implementation of passive solar solutions in the building envelope plays an important role. Trombe wall is a passive solar system that presents great potential for passive solar heating purposes. However, its performance can be enhanced when the Internet of Things is applied. This study employs a multi-domain smart system based on Matter-enabled IoT technology for maximizing Trombe wall functionality using appropriate 3D-printed ventilation grids. The system includes ESP32-C6 microcontrollers with temperature sensors and ventilation grids controlled by actuated servo motors. The system is automated with a Raspberry Pi 5 running Home Assistant OS with Matter Server. The integration of the Matter protocol provides end-to-end interoperability and secure communication, avoiding traditional systems based on MQTT. This work demonstrates the technical feasibility of implementing smart ventilation control for Trombe walls using a Matter-enabled infrastructure. The system proves to be capable of executing real-time vent management based on predefined temperature thresholds. This setup lays the foundation for scalable and interoperable thermal automation in passive solar systems, paving the way for future optimizations and addicional implementations, namely in order to improve indoor thermal comfort in smart and more efficient buildings. Full article

The proliferation of smart environments and Internet of Things (IoT) applications has intensified the demand for efficient, privacy-preserving multi-face recognition systems. Conventional centralized systems suffer from latency, scalability, and security vulnerabilities. This paper presents a practical hybrid multi-face recognition framework designed for decentralized IoT deployments. Our approach leverages a pre-trained Convolutional Neural Network (VGG16) for robust feature extraction and a Support Vector Machine (SVM) for lightweight classification, enabling real-time recognition on resource-constrained devices such as IoT cameras and Raspberry Pi boards. The purpose of this work is to demonstrate the feasibility and effectiveness of a lightweight hybrid system for decentralized multi-face recognition, specifically tailored to the constraints and requirements of IoT applications. The system is validated on a custom dataset of 20 subjects collected under varied lighting conditions and facial expressions, achieving an average accuracy exceeding 95% while simultaneously recognizing multiple faces. Experimental results demonstrate the system’s potential for real-world applications in surveillance, access control, and smart home environments. The proposed architecture minimizes computational load, reduces dependency on centralized servers, and enhances privacy, offering a promising step toward scalable edge AI solutions. Full article

The Internet of Things (IoT) has revolutionized industries by enabling seamless data exchange between billions of connected devices. However, the rapid proliferation of IoT devices has introduced significant security challenges, as many of these devices lack robust protection against cyber threats such as data breaches and denial-of-service attacks. Addressing these vulnerabilities is critical to maintaining the integrity and trust of IoT ecosystems. Traditional cybersecurity solutions often fail in dynamic, heterogeneous IoT environments due to device diversity, limited computational resources, and inconsistent communication protocols, which hinder the deployment of uniform and scalable security mechanisms. Moreover, there is a notable lack of realistic, high-quality datasets for training and evaluating machine learning (ML) models for IoT security, limiting their effectiveness in detecting complex and evolving threats. This paper presents the development and implementation of a novel physical smart office/home testbed designed to evaluate ML algorithms for detecting and mitigating IoT security vulnerabilities. The testbed replicates a real-world office environment, integrating a variety of IoT devices, such as different types of sensors, cameras, smart plugs, and workstations, within a network generating authentic traffic patterns. By simulating diverse attack scenarios including unauthorized access and network intrusions, the testbed provides a controlled platform to train, test, and validate ML-based anomaly detection systems. Experimental results show that the XGBoost model achieved a balanced accuracy of up to 99.977% on testbed-generated data, comparable to 99.985% on the benchmark IoT-23 dataset. Notably, the SVM model achieved up to 96.71% accuracy using our testbed data, outperforming its results on IoT-23, which peaked at 94.572%. The findings demonstrate the testbed’s effectiveness in enabling realistic security evaluations and ability to generate real-world datasets, highlighting its potential as a valuable tool for advancing IoT security research. This work contributes to the development of more resilient and adaptive security frameworks, offering valuable insights for safeguarding critical IoT infrastructures against evolving threats. Full article

The rapid expansion of the Internet of Things (IoT) demands scalable, efficient, and user-friendly gateway solutions that seamlessly connect resource-constrained edge devices to cloud services. Low-cost, widely available microcontrollers, such as the ESP32 and its ecosystem peers, offer integrated Wi-Fi/Bluetooth connectivity, low power consumption, and a mature developer toolchain at a bill of materials cost of only a few dollars. For smart-home deployments where budgets, energy consumption, and maintainability are critical, these characteristics make MCU-class gateways a pragmatic alternative to single-board computers, enabling always-on local control with minimal overhead. This paper presents the design and implementation of an embedded IoT gateway powered by the ESP32 microcontroller. By using lightweight communication protocols such as Message Queuing Telemetry Transport (MQTT) and REST APIs, the proposed architecture supports local control, distributed intelligence, and secure on-site data storage, all while minimizing dependence on cloud infrastructure. A real-world deployment in an educational building demonstrates the gateway’s capability to monitor energy consumption, execute control commands, and provide an intuitive web-based dashboard with minimal resource overhead. Experimental results confirm that the solution offers strong performance, with RAM usage ranging between 3.6% and 6.8% of available memory (approximately 8.92 KB to 16.9 KB). The initial loading of the single-page application (SPA) results in a temporary RAM spike to 52.4%, which later stabilizes at 50.8%. These findings highlight the ESP32’s ability to serve as a functional IoT gateway with minimal resource demands. Areas for future optimization include improved device discovery mechanisms and enhanced resource management to prolong device longevity. Overall, the gateway represents a cost-effective and vendor-agnostic platform for building resilient and scalable IoT ecosystems. Full article

This paper presents the design and implementation of an Intelligent Home Energy Management System in a smart home. The system is based on an economically decentralized hybrid concept that includes photovoltaic technology, a proton exchange membrane fuel cell, and a hydrogen refueling station, which together provide a reliable, secure, and clean power supply for smart homes. The proposed design enables power transfer between Vehicle-to-Home (V2H) and Home-to-Vehicle (H2V) systems, allowing electric vehicles to function as mobile energy storage devices at the grid level, facilitating a more adaptable and autonomous network. Our approach employs Double Deep Q-networks for adaptive control and forecasting. A Multi-Agent System coordinates actions between home appliances, energy storage systems, electric vehicles, and hydrogen power devices to ensure effective and cost-saving energy distribution for users of the smart grid. The design validation is carried out through MATLAB/Simulink-based simulations using meteorological data from Tunis. Ultimately, the V2H/H2V system enhances the utilization, reliability, and cost-effectiveness of residential energy systems compared with other management systems and conventional networks. Full article

This review examines advancements in smart home security through the integration of home automation technologies. Various security systems, including surveillance cameras, smart locks, and motion sensors, are analyzed, highlighting their effectiveness in enhancing home security. These systems enable users to monitor and control their homes in real-time, providing an additional layer of security. The document also examines how these security systems can enhance the quality of life for users by providing greater convenience and control over their domestic environment. The ability to receive instant alerts and access video recordings from anywhere allows users to respond quickly to unexpected situations, thereby increasing their sense of security and well-being. Additionally, the challenges and future trends in this field are addressed, emphasizing the importance of designing solutions that are intuitive and easy to use. As technology continues to evolve, it is crucial for developers and manufacturers to focus on creating products that seamlessly integrate into users’ daily lives, facilitating their adoption and use. This comprehensive state-of-the-art review, based on the Scopus database, provides a detailed overview of the current status and future potential of smart home security systems. It highlights how ongoing innovation in this field can lead to the development of more advanced and efficient solutions that not only protect homes but also enhance the overall user experience. Full article

There is a growing pressure on industry to secure environments and demonstrate their commitment in taking right steps to secure their products. This is because of the growing number of security compromises in the IT industry, Operational Technology environment, Internet of Things environment and smart home devices. These compromises are not just about data breaches or data exfiltration, but also about unauthorised access to devices that are not configured correctly and vulnerabilities in software components, which usually lead to insecure authentication and authorisation. Incorrect configurations are usually in the form of devices being made available on the Internet (public domain), reusable credentials, access granted without verifying the requestor, and easily available credentials like default credentials. Organisations seeking to address the dual pressure of demonstrating steps in the right direction and addressing unauthorised access to resources can find a viable approach in the form of the zero trust concept. Zero trust principles are about moving security controls closer to the data, applications, assets and services and are based on the principle of “never trust, always verify”. As it stands today, zero trust research has advanced far beyond the concept of “never trust, always verify”. This paper provides the culmination of a literature review of research conducted in the space of smart home devices and IoT and the applicability of the zero trust five-step implementation process to secure them. We discuss the history of zero trust, the tenets of zero trust, the five-step implementation process for zero trust, and its adoption for smart home devices and Internet of Things, and we provide suggestions for future research. Full article

The widespread adoption of the Internet of Things (IoT) raises significant concerns regarding security and energy efficiency, particularly for low-resource devices. To address these IoT issues, we propose a cross-layer IoT architecture employing machine learning (ML) models and lightweight cryptography. Our proposed solution is based on role-based access control (RBAC), ensuring secure authentication in large-scale IoT deployments while preventing unauthorized access attempts. We integrate layer-specific ML models, such as long short-term memory networks for temporal anomaly detection and decision trees for application-layer validation, along with adaptive speck encryption for the dynamic adjustment of cryptographic overheads. We then introduce a granular RBAC system that incorporates energy-aware policies. The novelty of this work is the proposal of a cross-layer IoT architecture that harmonizes ML-driven security with energy-efficient operations. The performance of the proposed cross-layer system is evaluated by extensive simulations. The results obtained show that the proposed system can reduce false positives up to 32% and enhance system security by preventing unauthorized access up to 95%. We also achieve 30% reduction in power consumption using the proposed lightweight Speck encryption method compared to the traditional advanced encryption standard (AES). By leveraging convolutional neural networks and ML, our approach significantly enhances IoT security and energy efficiency in practical scenarios such as smart cities, homes, and schools. Full article