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  • Raboaca, Maria Simona  (29)
  • English  (29)
  • 1
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    Blockchain and IoT-Driven Optimized Consensus Mechanism for Electric Vehicle Scheduling at Charging Stations
    MDPI AG ; 2022
    In:  Sustainability Vol. 14, No. 19 ( 2022-10-07), p. 12800-
    Details
    In: Sustainability, MDPI AG, Vol. 14, No. 19 ( 2022-10-07), p. 12800-
    Abstract: The emerging demand for electric vehicles in urban cities leads to the need to install a huge number of charging stations. With this requirement, electric vehicle coordination and scheduling at charging stations in real-time becomes highly tedious. Thus, there is a need for an efficient scheduling mechanism for electric vehicle charging at charging stations. This paper proposes a novel blockchain and Internet of Things-based consensus mechanism called COME for secure and trustable electric vehicle scheduling at charging stations. The proposed mechanism is intending to resolve conflicts at charging stations. The integrated InterPlanetary File System protocol facilitates a cost-efficient mechanism with minimized bandwidth for electric vehicle scheduling. The proposed mechanism ensures that there is no loss for either the electric vehicle or the charging station. We formulate different scenarios for electric vehicle charging and apply different scheduling algorithms, including first-come first-served, longest remaining time first, and coalition game theory. The performance of the proposed COME consensus mechanism is estimated by comparing it with the practical Byzantine Fault Tolerance consensus protocol and traditional systems based on the charging demand, wait time, conflict resolution, scalability, and InterPlanetary File System bandwidth parameters. The performance results show that the proposed COME consensus mechanism ensures that electric vehicles can have their vehicle charged without any conflict and that the charging station can be satisfied in terms of profit. Moreover, the proposed COME consensus mechanism outperforms the both practical Byzantine Fault Tolerance consensus protocol and the traditional system in terms of scalability and conflict resolution along with additional parameters such as wait time, charging demand, and bandwidth analysis.
    Type of Medium: Online Resource
    ISSN: 2071-1050
    URL: Article
    DOI: 10.3390/su141912800
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2518383-7
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  • 2
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    An Overview of Fog Data Analytics for IoT Applications
    MDPI AG ; 2022
    In:  Sensors Vol. 23, No. 1 ( 2022-12-24), p. 199-
    Details
    In: Sensors, MDPI AG, Vol. 23, No. 1 ( 2022-12-24), p. 199-
    Abstract: With the rapid growth in the data and processing over the cloud, it has become easier to access those data. On the other hand, it poses many technical and security challenges to the users of those provisions. Fog computing makes these technical issues manageable to some extent. Fog computing is one of the promising solutions for handling the big data produced by the IoT, which are often security-critical and time-sensitive. Massive IoT data analytics by a fog computing structure is emerging and requires extensive research for more proficient knowledge and smart decisions. Though an advancement in big data analytics is taking place, it does not consider fog data analytics. However, there are many challenges, including heterogeneity, security, accessibility, resource sharing, network communication overhead, the real-time data processing of complex data, etc. This paper explores various research challenges and their solution using the next-generation fog data analytics and IoT networks. We also performed an experimental analysis based on fog computing and cloud architecture. The result shows that fog computing outperforms the cloud in terms of network utilization and latency. Finally, the paper is concluded with future trends.
    Type of Medium: Online Resource
    ISSN: 1424-8220
    URL: Article
    DOI: 10.3390/s23010199
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2052857-7
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  • 3
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    Blockchain-Based Peer-to-Peer Transactive Energy Management Scheme for Smart Grid System
    MDPI AG ; 2022
    In:  Sensors Vol. 22, No. 13 ( 2022-06-26), p. 4826-
    Details
    In: Sensors, MDPI AG, Vol. 22, No. 13 ( 2022-06-26), p. 4826-
    Abstract: In Smart Grid (SG), Transactive Energy Management (TEM) is one of the most promising approaches to boost consumer participation in energy generation, energy management, and establishing decentralized energy market models using Peer-to-Peer (P2P). In P2P, a prosumer produces electric energy at their place using Renewable Energy Resources (RES) such as solar energy, wind energy, etc. Then, this generated energy is traded with consumers (who need the energy) in a nearby locality. P2P facilitates energy exchange in localized micro-energy markets of the TEM system. Such decentralized P2P energy management could cater to diverse prosumers and utility business models. However, the existing P2P approaches suffer from several issues such as single-point-of-failure, network bandwidth, scalability, trust, and security issues. To handle the aforementioned issues, this paper proposes a Decentralized and Transparent P2P Energy Trading (DT-P2PET) scheme using blockchain. The proposed DT-P2PET scheme aims to reduce the grid’s energy generation and management burden while also increasing profit for both consumers and prosumers through a dynamic pricing mechanism. The DT-P2PET scheme uses Ethereum-blockchain-based Smart Contracts (SCs) and InterPlanetary File System (IPFS) for the P2P energy trading. Furthermore, a recommender mechanism is also introduced in this study to increase the number of prosumers. The Ethereum SCs are designed and deployed to perform P2P in real time in the proposed DT-P2PET scheme. The DT-P2PET scheme is evaluated based on the various parameters such as profit generation (for prosumer and consumer both), data storage cost, network bandwidth, and data transfer rate in contrast to the existing approaches.
    Type of Medium: Online Resource
    ISSN: 1424-8220
    URL: Article
    DOI: 10.3390/s22134826
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
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  • 4
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    Blockchain and Double Auction-Based Trustful EVs Energy Trading Scheme for Optimum Pricing
    MDPI AG ; 2022
    In:  Mathematics Vol. 10, No. 15 ( 2022-08-03), p. 2748-
    Details
    In: Mathematics, MDPI AG, Vol. 10, No. 15 ( 2022-08-03), p. 2748-
    Abstract: Electric vehicles (EVs) have gained prominence in smart transportation due to their unparalleled benefits of reduced carbon footprints, improved performance, and intelligent energy trading mechanisms. These potential benefits have increased EV adoption at massive scales, but energy management in EVs is a critical study problem. The problem is further intensified due to the scarcity of charging stations (CSs) in near EV proximity. Moreover, as energy transactions occur over open channels, it presents critical security, privacy, and trust issues among decentralized channels. To address the open limitations of trusted energy management and optimize the pricing control among EV entities (i.e., prosumers and consumers), the paper proposes a scheme that integrates blockchain and a truthful double auction strategy for trustful EV trading. To address the transaction scalability, we integrate an Interplanetary File System (IPFS) with a double auction mechanism handled through the Remix Smart Contract environment. The double auction leverages an optimal payoff condition between peer EVs. To address the communication latency, we present the scheme at the backdrop of Fifth Generation (5G) networks that minimizes the optimal payoff response time. The scheme is simulated against parameters such as convergence, profit for consumers, computation time, and blockchain analysis regarding node commit latency, collusion attacks, and EV energy consumption. The results indicate the scheme’s viability against traditional (non-blockchain) approaches with high reliability, scalability, and improved cost-efficiency.
    Type of Medium: Online Resource
    ISSN: 2227-7390
    URL: Article
    DOI: 10.3390/math10152748
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
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  • 5
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    EISM-CPS: An Enhanced Intelligent Security Methodology for Cyber-Physical Systems through Hyper-Parameter Optimization
    MDPI AG ; 2022
    In:  Mathematics Vol. 11, No. 1 ( 2022-12-29), p. 189-
    Details
    In: Mathematics, MDPI AG, Vol. 11, No. 1 ( 2022-12-29), p. 189-
    Abstract: The increased usage of cyber-physical systems (CPS) has gained the focus of cybercriminals, particularly with the involvement of the internet, provoking an increased attack surface. The increased usage of these systems generates heavy data flows, which must be analyzed to ensure security. In particular, machine learning (ML) and deep learning (DL) algorithms have shown feasibility and promising results to fulfill the security requirement through the adoption of intelligence. However, the performance of these models strongly depends on the model structure, hyper-parameters, dataset, and application. So, the developers only possess control over defining the model structure and its hyper-parameters for diversified applications. Generally, not all models perform well in default hyper-parameter settings. Their specification is a challenging and complex task and requires significant expertise. This problem can be mitigated by utilizing hyper-parameter optimization (HPO) techniques, which intend to automatically find efficient learning model hyper-parameters in specific applications or datasets. This paper proposes an enhanced intelligent security mechanism for CPS by utilizing HPO. Specifically, exhaustive HPO techniques have been considered for performance evaluation and evaluation of computational requirements to analyze their capabilities to build an effective intelligent security model to cope with security infringements in CPS. Moreover, we analyze the capabilities of various HPO techniques, normalization, and feature selection. To ensure the HPO, we evaluated the effectiveness of a DL-based artificial neural network (ANN) on a standard CPS dataset under manual hyper-parameter settings and exhaustive HPO techniques, such as random search, directed grid search, and Bayesian optimization. We utilized the min-max algorithm for normalization and SelectKBest for feature selection. The HPO techniques performed better than the manual hyper-parameter settings. They achieved an accuracy, precision, recall, and F1 score of more than 98%. The results highlight the importance of HPO for performance enhancement and reduction of computational requirements, human efforts, and expertise.
    Type of Medium: Online Resource
    ISSN: 2227-7390
    URL: Article
    DOI: 10.3390/math11010189
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
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  • 6
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    Towards Future Internet: The Metaverse Perspective for Diverse Industrial Applications
    MDPI AG ; 2023
    In:  Mathematics Vol. 11, No. 4 ( 2023-02-13), p. 941-
    Details
    In: Mathematics, MDPI AG, Vol. 11, No. 4 ( 2023-02-13), p. 941-
    Abstract: The Metaverse allows the integration of physical and digital versions of users, processes, and environments where entities communicate, transact, and socialize. With the shift towards Extended Reality (XR) technologies, the Metaverse is envisioned to support a wide range of applicative verticals. It will support a seamless mix of physical and virtual worlds (realities) and, thus, will be a game changer for the Future Internet, built on the Semantic Web framework. The Metaverse will be ably assisted by the convergence of emerging wireless communication networks (such as Fifth-Generation and Beyond networks) or Sixth-Generation (6G) networks, Blockchain (BC), Web 3.0, Artificial Intelligence (AI), and Non-Fungible Tokens (NFTs). It has the potential for convergence in diverse industrial applications such as digital twins, telehealth care, connected vehicles, virtual education, social networks, and financial applications. Recent studies on the Metaverse have focused on explaining its key components, but a systematic study of the Metaverse in terms of industrial applications has not yet been performed. Owing to this gap, this survey presents the salient features and assistive Metaverse technologies. We discuss a high-level and generic Metaverse framework for modern industrial cyberspace and discuss the potential challenges and future directions of the Metaverse’s realization. A case study on Metaverse-assisted Real Estate Management (REM) is presented, where the Metaverse governs a Buyer–Broker–Seller (BBS) architecture for land registrations. We discuss the performance evaluation of the current land registration ecosystem in terms of cost evaluation, trust probability, and mining cost on the BC network. The obtained results show the viability of the Metaverse in REM setups.
    Type of Medium: Online Resource
    ISSN: 2227-7390
    URL: Article
    DOI: 10.3390/math11040941
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
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  • 7
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    A Novel Context-Aware Reliable Routing Protocol and SVM Implementation in Vehicular Area Networks
    MDPI AG ; 2023
    In:  Mathematics Vol. 11, No. 3 ( 2023-01-18), p. 514-
    Details
    In: Mathematics, MDPI AG, Vol. 11, No. 3 ( 2023-01-18), p. 514-
    Abstract: The Vehicular Ad-hoc Network (VANET) is an innovative technology that allows vehicles to connect with neighboring roadside structures to deliver intelligent transportation applications. To deliver safe communication among vehicles, a reliable routing approach is required. Due to the excessive mobility and frequent variation in network topology, establishing a reliable routing for VANETs takes a lot of work. In VANETs, transmission links are extremely susceptible to interruption; as a result, the routing efficiency of these constantly evolving networks requires special attention. To promote reliable routing in VANETs, we propose a novel context-aware reliable routing protocol that integrates k-means clustering and support vector machine (SVM) in this paper. The k-means clustering divides the routes into two clusters named GOOD and BAD. The cluster with high mean square error (MSE) is labelled as BAD, and the cluster with low MSE is labelled as GOOD. After training the routing data with SVM, the performance of each route from source to target is improved in terms of Packet Delivery Ratio (PDR), throughput, and End to End Delay (E2E). The proposed protocol will achieve improved routing efficiency with these changes.
    Type of Medium: Online Resource
    ISSN: 2227-7390
    URL: Article
    DOI: 10.3390/math11030514
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
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  • 8
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    AI-Empowered Attack Detection and Prevention Scheme for Smart Grid System
    MDPI AG ; 2022
    In:  Mathematics Vol. 10, No. 16 ( 2022-08-10), p. 2852-
    Details
    In: Mathematics, MDPI AG, Vol. 10, No. 16 ( 2022-08-10), p. 2852-
    Abstract: The existing grid infrastructure has already begun transforming into the next-generation cyber-physical smart grid (SG) system. This transformation has improved the grid’s reliability and efficiency but has exposed severe vulnerabilities due to growing cyberattacks and threats. For example, malicious actors may be able to tamper with system readings, parameters, and energy prices and penetrate to get direct access to the data. Several works exist to handle the aforementioned issues, but they have not been fully explored. Consequently, this paper proposes an AI-ADP scheme for the SG system, which is an artificial intelligence (AI)-based attack-detection and prevention (ADP) mechanism by using a cryptography-driven recommender system to ensure data security and integrity. The proposed AI-ADP scheme is divided into two phases: (i) attack detection and (ii) attack prevention. We employed the extreme gradient-boosting (XGBoost) mechanism for attack detection and classification. It is a new ensemble learning methodology that offers many advantages over similar methods, including built-in features, etc. Then, SHA-512 is used to secure the communication that employs faster performance, allowing the transmission of more data with the same security level. The performance of the proposed AI-ADP scheme is evaluated based on various parameters, such as attack-detection accuracy, cycles used per byte, and total cycles used. The proposed AI-ADP scheme outperformed the existing approaches and obtained 99.12% accuracy, which is relatively high compared to the pre-existing methods.
    Type of Medium: Online Resource
    ISSN: 2227-7390
    URL: Article
    DOI: 10.3390/math10162852
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
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  • 9
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    Log Transformed Coherency Matrix for Differentiating Scattering Behaviour of Oil Spill Emulsions Using SAR Images
    MDPI AG ; 2022
    In:  Mathematics Vol. 10, No. 10 ( 2022-05-16), p. 1697-
    Details
    In: Mathematics, MDPI AG, Vol. 10, No. 10 ( 2022-05-16), p. 1697-
    Abstract: Oil spills on the ocean surface are a serious threat to the marine ecosystem. Automation of oil spill detection through full/dual polarimetric Synthetic Aperture Radar (SAR) images is considered a good aid for oil spill disaster management. This paper uses the power of log transformation to discern the scattering behavior more effectively from the coherency matrix (T3). The proposed coherency matrix is tested on patches of the clean sea surface and four different classes of oil spills, viz. heavy sedimented oil, thick oil, oil-water emulsion, fresh oil; by analyzing the entropy (H), anisotropy (A), and mean scattering angle alpha (α), following the H/A/α decomposition. Experimental results show that not only does the proposed T3 matrix differentiate between Bragg scattering of the clean sea surface from a random scattering of thick oil spills but is also able to distinguish between different emulsions of oil spills with water and sediments. Moreover, unlike classical T3, the proposed method distinguishes concrete-like structures and heavy sedimented oil even though both exhibit similar scattering behavior. The proposed algorithm is developed and validated on the data acquired by the UAVSAR full polarimetric L band SAR sensor over the Gulf of Mexico (GOM) region during the Deepwater Horizon (DWH) oil spill accident in June 2010.
    Type of Medium: Online Resource
    ISSN: 2227-7390
    URL: Article
    DOI: 10.3390/math10101697
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
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  • 10
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    Federated Learning for the Internet-of-Medical-Things: A Survey
    MDPI AG ; 2022
    In:  Mathematics Vol. 11, No. 1 ( 2022-12-28), p. 151-
    Details
    In: Mathematics, MDPI AG, Vol. 11, No. 1 ( 2022-12-28), p. 151-
    Abstract: Recently, in healthcare organizations, real-time data have been collected from connected or implantable sensors, layered protocol stacks, lightweight communication frameworks, and end devices, named the Internet-of-Medical-Things (IoMT) ecosystems. IoMT is vital in driving healthcare analytics (HA) toward extracting meaningful data-driven insights. Recently, concerns have been raised over data sharing over IoMT, and stored electronic health records (EHRs) forms due to privacy regulations. Thus, with less data, the analytics model is deemed inaccurate. Thus, a transformative shift has started in HA from centralized learning paradigms towards distributed or edge-learning paradigms. In distributed learning, federated learning (FL) allows for training on local data without explicit data-sharing requirements. However, FL suffers from a high degree of statistical heterogeneity of learning models, level of data partitions, and fragmentation, which jeopardizes its accuracy during the learning and updating process. Recent surveys of FL in healthcare have yet to discuss the challenges of massive distributed datasets, sparsification, and scalability concerns. Because of this gap, the survey highlights the potential integration of FL in IoMT, the FL aggregation policies, reference architecture, and the use of distributed learning models to support FL in IoMT ecosystems. A case study of a trusted cross-cluster-based FL, named Cross-FL, is presented, highlighting the gradient aggregation policy over remotely connected and networked hospitals. Performance analysis is conducted regarding system latency, model accuracy, and the trust of consensus mechanism. The distributed FL outperforms the centralized FL approaches by a potential margin, which makes it viable for real-IoMT prototypes. As potential outcomes, the proposed survey addresses key solutions and the potential of FL in IoMT to support distributed networked healthcare organizations.
    Type of Medium: Online Resource
    ISSN: 2227-7390
    URL: Article
    DOI: 10.3390/math11010151
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
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