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18 pages, 1300 KiB

Open AccessArticle

XAI-Based Accurate Anomaly Detector That Is Robust Against Black-Box Evasion Attacks for the Smart Grid

by Islam Elgarhy, Mahmoud M. Badr, Mohamed Mahmoud, Maazen Alsabaan, Tariq Alshawi and Muteb Alsaqhan

Appl. Sci. 2024, 14(21), 9897; https://doi.org/10.3390/app14219897 - 29 Oct 2024

Viewed by 826

Abstract

In the realm of smart grids, machine learning (ML) detectors—both binary (or supervised) and anomaly (or unsupervised)—have proven effective in detecting electricity theft (ET). However, binary detectors are designed for specific attacks, making their performance unpredictable against new attacks. Anomaly detectors, conversely, are [...] Read more.

In the realm of smart grids, machine learning (ML) detectors—both binary (or supervised) and anomaly (or unsupervised)—have proven effective in detecting electricity theft (ET). However, binary detectors are designed for specific attacks, making their performance unpredictable against new attacks. Anomaly detectors, conversely, are trained on benign data and identify deviations from benign patterns as anomalies, but their performance is highly sensitive to the selected threshold values. Additionally, ML detectors are vulnerable to evasion attacks, where attackers make minimal changes to malicious samples to evade detection. To address these limitations, we introduce a hybrid anomaly detector that combines a Deep Auto-Encoder (DAE) with a One-Class Support Vector Machine (OCSVM). This detector not only enhances classification performance but also mitigates the threshold sensitivity of the DAE. Furthermore, we evaluate the vulnerability of this detector to benchmark evasion attacks. Lastly, we propose an accurate and robust cluster-based DAE+OCSVM ET anomaly detector, trained using Explainable Artificial Intelligence (XAI) explanations generated by the Shapley Additive Explanations (SHAP) method on consumption readings. Our experimental results demonstrate that the proposed XAI-based detector achieves superior classification performance and exhibits enhanced robustness against various evasion attacks, including gradient-based and optimization-based methods, under a black-box threat model. Full article

(This article belongs to the Special Issue IoT in Smart Cities and Homes, 2nd Edition)

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18 pages, 14570 KiB

Open AccessArticle

AI-Aided Proximity Detection and Location-Dependent Authentication on Mobile-Based Digital Twin Networks: A Case Study of Door Materials

by Woojin Park, Hyeyoung An, Yongbin Yim and Soochang Park

Appl. Sci. 2024, 14(20), 9402; https://doi.org/10.3390/app14209402 - 15 Oct 2024

Viewed by 718

Abstract

Nowadays, mobile–mobile interaction is becoming a fundamental methodology for human–human networking services since mobile devices are the most common interfacing equipment for recent smart services such as food delivery, e-commerce, ride-hailing, etc. Unlike legacy ways of human interaction, on-site and in-person mutual recognition [...] Read more.

Nowadays, mobile–mobile interaction is becoming a fundamental methodology for human–human networking services since mobile devices are the most common interfacing equipment for recent smart services such as food delivery, e-commerce, ride-hailing, etc. Unlike legacy ways of human interaction, on-site and in-person mutual recognition between a service provider and a client in mobile–mobile interaction is not trivial. This is because of not only the avoidance of face-to-face communication due to safety and health concerns but also the difficulty of matching up the online user using mobiles with the real person in the physical world. So, a novel mutual recognition scheme for mobile–mobile interaction is highly necessary. This paper comes up with a novel cyber-physical secure communication scheme relying on the digital twin paradigm. The proposed scheme designs the digital twin networking architecture on which real-world users form digital twins as their own online abstraction, and the digital twins authenticate each other for a smart service interaction. Thus, inter-twin communication (ITC) could support secure mutual recognition in mobile–mobile interaction. Such cyber-physical authentication (CPA) with the ITC is built on the dynamic BLE beaconing scheme with accurate proximity detection and dynamic identifier (ID) allocation. To achieve high accuracy in proximity detection, the proposed scheme is conducted using a wide variety of data pre-processing algorithms, machine learning technologies, and ensemble techniques. A location-dependent ID exploited in the CPA is dynamically generated by the physical user for their own digital twin per each mobile service. Full article

(This article belongs to the Special Issue IoT in Smart Cities and Homes, 2nd Edition)

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18 pages, 2511 KiB

Open AccessArticle

Smart City Aquaculture: AI-Driven Fry Sorting and Identification Model

by Chang-Yi Kao and I-Chih Chen

Appl. Sci. 2024, 14(19), 8803; https://doi.org/10.3390/app14198803 - 30 Sep 2024

Viewed by 742

Abstract

The development of smart agriculture has become a critical issue for the future of smart cities, with large-scale management of aquaculture posing numerous challenges. Particularly in the fish farming industry, producing single-sex fingerlings (especially male fingerlings) is crucial for enhancing rearing efficiency and [...] Read more.

The development of smart agriculture has become a critical issue for the future of smart cities, with large-scale management of aquaculture posing numerous challenges. Particularly in the fish farming industry, producing single-sex fingerlings (especially male fingerlings) is crucial for enhancing rearing efficiency and could even provide key support in addressing future global food demands. However, traditional methods of manually selecting the gender of broodfish rely heavily on experienced technicians, are labor-intensive and time-consuming, and present significant bottlenecks in improving production efficiency, thus limiting the capacity and sustainable development potential of fish farms. In response to this situation, this study has developed an intelligent identification system based on the You Only Look Once (YOLO) artificial intelligence (AI) model, specifically designed for analyzing secondary sexual characteristics and gender screening in farmed fish. Through this system, farmers can quickly photograph the fish’s cloaca using a mobile phone, and AI technology is then used to perform real-time gender identification. The study involved two phases of training with different sample sets: in the first phase, the AI model was trained on a single batch of images with varying parameter conditions. In the second phase, additional sample data were introduced to improve generalization. The results of the study show that the system achieved an identification accuracy of over 95% even in complex farming environments, significantly reducing the labor costs and physical strain associated with traditional screening operations and greatly improving the production efficiency of breeding facilities. This research not only has the potential to overcome existing technological bottlenecks but also may become an essential tool for smart aquaculture. As the system continues to be refined, it is expected to be applicable across the entire life cycle management of fish, including gender screening during the growth phase, thereby enabling a more efficient production and management model. This not only provides an opportunity for technological upgrades in the aquaculture industry but also promotes the sustainable development of aquaculture. The smart aquaculture solution proposed in this study demonstrates the immense potential of applying AI technology to the aquaculture industry and offers strong support for global food security and the construction of smart cities. Full article

(This article belongs to the Special Issue IoT in Smart Cities and Homes, 2nd Edition)

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19 pages, 6704 KiB

Open AccessArticle

An IoT Healthcare System Based on Fog Computing and Data Mining: A Diabetic Use Case

by Azin Karimi, Nazila Razi and Javad Rezazadeh

Appl. Sci. 2024, 14(17), 7924; https://doi.org/10.3390/app14177924 - 5 Sep 2024

Viewed by 808

Abstract

The advent of the Internet of Things (IoT) has revolutionized numerous sectors, with healthcare being particularly significant. Despite extensive studies addressing healthcare challenges, two persist: (1) the need for the swift detection of abnormalities in patients under medical care and timely notifications to [...] Read more.

The advent of the Internet of Things (IoT) has revolutionized numerous sectors, with healthcare being particularly significant. Despite extensive studies addressing healthcare challenges, two persist: (1) the need for the swift detection of abnormalities in patients under medical care and timely notifications to patients or caregivers and (2) the accurate diagnosis of abnormalities tailored to the patient’s condition. Addressing these challenges, numerous studies have focused on developing healthcare systems, leveraging technologies like edge computing, which plays a pivotal role in enhancing system efficiency. Fog computing, situated at the edge of network hierarchies, leverages multiple nodes to expedite system processes. Furthermore, the wealth of data generated by sensors connected to patients presents invaluable insights for optimizing medical care. Data mining techniques, in this context, offer a means to enhance healthcare system performance by refining abnormality notifications and disease analysis. In this study, we present a system utilizing the K-Nearest Neighbor (KNN) algorithm and Raspberry Pi microcomputer within the fog layer for a diabetic patient data analysis. The KNN algorithm, trained on historical patient data, facilitates the real-time assessment of patient conditions based on past vital signs. A simulation using an IBM SPSS dataset and real-world testing on a diabetic patient demonstrate the system’s efficacy. The results manifest in prompt alerts or normal notifications, illustrating the system’s potential for enhancing patient care in healthcare settings. Full article

(This article belongs to the Special Issue IoT in Smart Cities and Homes, 2nd Edition)

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42 pages, 6747 KiB

Open AccessArticle

Integrated Home Energy Management with Hybrid Backup Storage and Vehicle-to-Home Systems for Enhanced Resilience, Efficiency, and Energy Independence in Green Buildings

by Liu Pai, Tomonobu Senjyu and M. H. Elkholy

Appl. Sci. 2024, 14(17), 7747; https://doi.org/10.3390/app14177747 - 2 Sep 2024

Viewed by 1152

Abstract

This study presents an innovative home energy management system (HEMS) that incorporates PV, WTs, and hybrid backup storage systems, including a hydrogen storage system (HSS), a battery energy storage system (BESS), and electric vehicles (EVs) with vehicle-to-home (V2H) technology. The research, conducted in [...] Read more.

This study presents an innovative home energy management system (HEMS) that incorporates PV, WTs, and hybrid backup storage systems, including a hydrogen storage system (HSS), a battery energy storage system (BESS), and electric vehicles (EVs) with vehicle-to-home (V2H) technology. The research, conducted in Liaoning Province, China, evaluates the performance of the HEMS under various demand response (DR) scenarios, aiming to enhance resilience, efficiency, and energy independence in green buildings. Four DR scenarios were analyzed: No DR, 20% DR, 30% DR, and 40% DR. The findings indicate that implementing DR programs significantly reduces peak load and operating costs. The 40% DR scenario achieved the lowest cumulative operating cost of $749.09, reflecting a 2.34% reduction compared with the $767.07 cost in the No DR scenario. The integration of backup systems, particularly batteries and fuel cells (FCs), effectively managed energy supply, ensuring continuous power availability. The system maintained a low loss of power supply probability (LPSP), indicating high reliability. Advanced optimization techniques, particularly the reptile search algorithm (RSA), are crucial in enhancing system performance and efficiency. These results underscore the potential of hybrid backup storage systems with V2H technology to enhance energy independence and sustainability in residential energy management. Full article

(This article belongs to the Special Issue IoT in Smart Cities and Homes, 2nd Edition)

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19 pages, 4954 KiB

Open AccessArticle

Virtual Reality and Internet of Things Based Digital Twin for Smart City Cross-Domain Interoperability

by Guillermo del Campo, Edgar Saavedra, Luca Piovano, Francisco Luque and Asuncion Santamaria

Appl. Sci. 2024, 14(7), 2747; https://doi.org/10.3390/app14072747 - 25 Mar 2024

Cited by 6 | Viewed by 2302

Abstract

The fusion of Internet of Things (IoT), Digital Twins, and Virtual Reality (VR) technologies marks a pivotal advancement in urban development, offering new services to citizens and municipalities in urban environments. This integration promises enhanced urban planning, management, and engagement by providing a [...] Read more.

The fusion of Internet of Things (IoT), Digital Twins, and Virtual Reality (VR) technologies marks a pivotal advancement in urban development, offering new services to citizens and municipalities in urban environments. This integration promises enhanced urban planning, management, and engagement by providing a comprehensive, real-time digital reflection of the city, enriched with immersive experiences and interactive capabilities. It enables smarter decision-making, efficient resource management, and personalized citizen services, transforming the urban landscape into a more sustainable, livable, and responsive environment. The research presented herein focuses on the practical implementation of a DT concept for managing cross-domain smart city services, leveraging VR technology to create a virtual replica of the urban environment and IoT devices. Imperative for cross-domain city services is interoperability, which is crucial not only for the seamless operation of these advanced tools but also for unlocking the potential of cross-service applications. Through the deployment of our model at the IoTMADLab facilities, we showcase the integration of IoT devices within varied urban infrastructures. The outcomes demonstrate the efficacy of VR interfaces in simplifying complex interactions, offering pivotal insights into device functionality, and enabling informed decision-making processes. Full article

(This article belongs to the Special Issue IoT in Smart Cities and Homes, 2nd Edition)

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Review

Jump to: Research

24 pages, 1861 KiB

Open AccessReview

Impact of Virtual Reality on Brain–Computer Interface Performance in IoT Control—Review of Current State of Knowledge

by Adrianna Piszcz, Izabela Rojek and Dariusz Mikołajewski

Appl. Sci. 2024, 14(22), 10541; https://doi.org/10.3390/app142210541 - 15 Nov 2024

Viewed by 580

Abstract

This article examines state-of-the-art research into the impact of virtual reality (VR) on brain–computer interface (BCI) performance: how the use of virtual reality can affect brain activity and neural plasticity in ways that can improve the performance of brain–computer interfaces in IoT control, [...] Read more.

This article examines state-of-the-art research into the impact of virtual reality (VR) on brain–computer interface (BCI) performance: how the use of virtual reality can affect brain activity and neural plasticity in ways that can improve the performance of brain–computer interfaces in IoT control, e.g., for smart home purposes. Integrating BCI with VR improves the performance of brain–computer interfaces in IoT control by providing immersive, adaptive training environments that increase signal accuracy and user control. VR offers real-time feedback and simulations that help users refine their interactions with smart home systems, making the interface more intuitive and responsive. This combination ultimately leads to greater independence, efficiency, and ease of use, especially for users with mobility issues, in managing IoT-connected devices. The integration of BCI and VR shows great potential for transformative applications ranging from neurorehabilitation and human–computer interaction to cognitive assessment and personalized therapeutic interventions for a variety of neurological and cognitive disorders. The literature review highlights the significant advances and multifaceted challenges in this rapidly evolving field. Particularly noteworthy is the emphasis on the importance of adaptive signal processing techniques, which are key to enhancing the overall control and immersion experienced by individuals in virtual environments. The value of multimodal integration, in which BCI technology is combined with complementary biosensors such as gaze tracking and motion capture, is also highlighted. The incorporation of advanced artificial intelligence (AI) techniques will revolutionize the way we approach the diagnosis and treatment of neurodegenerative conditions. Full article

(This article belongs to the Special Issue IoT in Smart Cities and Homes, 2nd Edition)

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55 pages, 12486 KiB

Open AccessReview

Methods and Applications of Space Understanding in Indoor Environment—A Decade Survey

by Sebastian Pokuciński and Dariusz Mrozek

Appl. Sci. 2024, 14(10), 3974; https://doi.org/10.3390/app14103974 - 7 May 2024

Viewed by 1042

Abstract

The demand for digitizing manufacturing and controlling processes has been steadily increasing in recent years. Digitization relies on different techniques and equipment, which produces various data types and further influences the process of space understanding and area recognition. This paper provides an updated [...] Read more.

The demand for digitizing manufacturing and controlling processes has been steadily increasing in recent years. Digitization relies on different techniques and equipment, which produces various data types and further influences the process of space understanding and area recognition. This paper provides an updated view of these data structures and high-level categories of techniques and methods leading to indoor environment segmentation and the discovery of its semantic meaning. To achieve this, we followed the Systematic Literature Review (SLR) methodology and covered a wide range of solutions, from floor plan understanding through 3D model reconstruction and scene recognition to indoor navigation. Based on the obtained SLR results, we identified three different taxonomies (the taxonomy of underlying data type, of performed analysis process, and of accomplished task), which constitute different perspectives we can adopt to study the existing works in the field of space understanding. Our investigations clearly show that the progress of works in this field is accelerating, leading to more sophisticated techniques that rely on multidimensional structures and complex representations, while the processing itself has become focused on artificial intelligence-based methods. Full article

(This article belongs to the Special Issue IoT in Smart Cities and Homes, 2nd Edition)

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