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  • 1
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    Massive MIMO NOMA: Double-Mode Model towards Green 5G Networks
    MDPI AG ; 2023
    In:  Sensors Vol. 23, No. 14 ( 2023-07-15), p. 6425-
    Details
    In: Sensors, MDPI AG, Vol. 23, No. 14 ( 2023-07-15), p. 6425-
    Abstract: With the development of the Internet of Things (IoT), the number of devices will also increase tremendously. However, we need more wireless communication resources. It has been shown in the literature that non-orthogonal multiple access (NOMA) offers high multiplexing gains due to the simultaneous transfer of signals, and massive multiple-input–multiple-outputs (mMIMOs) offer high spectrum efficiency due to the high antenna gain and high multiplexing gains. Therefore, a downlink mMIMO NOMA cooperative system is considered in this paper. The users at the cell edge in 5G cellular system generally suffer from poor signal quality as they are far away from the BS and expend high battery power to decode the signals superimposed through NOMA. Thus, this paper uses a cooperative relay system and proposes the mMIMO NOMA double-mode model to reduce battery expenditure and increase the cell edge user’s energy efficiency and sum rate. In the mMIMO NOMA double-mode model, two modes of operation are defined. Depending on the relay’s battery level, these modes are chosen to utilize the system’s energy efficiency. Comprehensive numerical results show the improvement in the proposed system’s average sum rate and average energy efficiency compared with a conventional system. In a cooperative NOMA system, the base station (BS) transmits a signal to a relay, and the relay forwards the signal to a cluster of users. This cluster formation depends on the user positions and geographical restrictions concerning the relay equipment. Therefore, it is vital to form user clusters for efficient and simultaneous transmission. This paper also presents a novel method for efficient cluster formation.
    Type of Medium: Online Resource
    ISSN: 1424-8220
    URL: Article
    DOI: 10.3390/s23146425
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2052857-7
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  • 2
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    Modeling Topics in DFA-Based Lemmatized Gujarati Text
    MDPI AG ; 2023
    In:  Sensors Vol. 23, No. 5 ( 2023-03-01), p. 2708-
    Details
    In: Sensors, MDPI AG, Vol. 23, No. 5 ( 2023-03-01), p. 2708-
    Abstract: Topic modeling is a machine learning algorithm based on statistics that follows unsupervised machine learning techniques for mapping a high-dimensional corpus to a low-dimensional topical subspace, but it could be better. A topic model’s topic is expected to be interpretable as a concept, i.e., correspond to human understanding of a topic occurring in texts. While discovering corpus themes, inference constantly uses vocabulary that impacts topic quality due to its size. Inflectional forms are in the corpus. Since words frequently appear in the same sentence and are likely to have a latent topic, practically all topic models rely on co-occurrence signals between various terms in the corpus. The topics get weaker because of the abundance of distinct tokens in languages with extensive inflectional morphology. Lemmatization is often used to preempt this problem. Gujarati is one of the morphologically rich languages, as a word may have several inflectional forms. This paper proposes a deterministic finite automaton (DFA) based lemmatization technique for the Gujarati language to transform lemmas into their root words. The set of topics is then inferred from this lemmatized corpus of Gujarati text. We employ statistical divergence measurements to identify semantically less coherent (overly general) topics. The result shows that the lemmatized Gujarati corpus learns more interpretable and meaningful subjects than unlemmatized text. Finally, results show that lemmatization curtails the size of vocabulary decreases by 16% and the semantic coherence for all three measurements—Log Conditional Probability, Pointwise Mutual Information, and Normalized Pointwise Mutual Information—from −9.39 to −7.49, −6.79 to −5.18, and −0.23 to −0.17, respectively.
    Type of Medium: Online Resource
    ISSN: 1424-8220
    URL: Article
    DOI: 10.3390/s23052708
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
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  • 3
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    Investigating UAV-Based Applications in Indoor–Outdoor Sports Stadiums and Open-Air Gatherings for Different Interference Conditions beyond 5G Networks
    MDPI AG ; 2023
    In:  Sensors Vol. 23, No. 15 ( 2023-07-27), p. 6721-
    Details
    In: Sensors, MDPI AG, Vol. 23, No. 15 ( 2023-07-27), p. 6721-
    Abstract: With the onset of 5G technology, the number of users is increasing drastically. These increased numbers of users demand better service on the network. This study examines the millimeter wave bands working frequencies. Working in the millimeter wave band has the disadvantage of interference. This study aims to analyze the impact of different interference conditions on unmanned aerial vehicle use scenarios, such as open-air gatherings and indoor-outdoor sports stadiums. Performance analysis was carried out in terms of received power and path loss readings.
    Type of Medium: Online Resource
    ISSN: 1424-8220
    URL: Article
    DOI: 10.3390/s23156721
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2052857-7
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  • 4
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    A Review on Standardizing Electric Vehicles Community Charging Service Operator Infrastructure
    MDPI AG ; 2022
    In:  Applied Sciences Vol. 12, No. 23 ( 2022-11-26), p. 12096-
    Details
    In: Applied Sciences, MDPI AG, Vol. 12, No. 23 ( 2022-11-26), p. 12096-
    Abstract: The deployment of charging infrastructure is one of the main challenges that need to be tackled due to the increasing demand for electric vehicles (EVs). Moreover, EVs associated with different charging standards can face compatibility issues while charging via public or private infrastructure. Many solutions were surveyed by researchers on EVs, but they were not focused on addressing the issue of charging infrastructure standardization. Motivated by this, we present a comprehensive survey on standardizing EV charging infrastructure. We also present a taxonomy on various aspects such as charging levels, charging modes, charging standards, charging technologies (based on the different charging types such as conductive charging and wireless charging), and types of vehicle (i.e., 2-wheeler (2W), 3-wheeler (3W), and 4-wheeler (4W)). Furthermore, we target the benefits associated with community EV charging operated by the community charging service operator. Furthermore, we propose an architecture for standardized EV community charging infrastructure to provide adaptability for EVs with different charging standards. Finally, the research challenges and opportunities of the proposed survey have been discussed for efficient EV charging.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    URL: Article
    DOI: 10.3390/app122312096
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2704225-X
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  • 5
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    Stochastic Neural Networks-Based Algorithmic Trading for the Cryptocurrency Market
    MDPI AG ; 2022
    In:  Mathematics Vol. 10, No. 9 ( 2022-04-26), p. 1456-
    Details
    In: Mathematics, MDPI AG, Vol. 10, No. 9 ( 2022-04-26), p. 1456-
    Abstract: Throughout the history of modern finance, very few financial instruments have been as strikingly volatile as cryptocurrencies. The long-term prospects of cryptocurrencies remain uncertain; however, taking advantage of recent advances in neural networks and volatility, we show that the trading algorithms reinforced by short-term price predictions are bankable. Traditional trading algorithms and indicators are often based on mean reversal strategies that do not advantage price predictions. Furthermore, deterministic models cannot capture market volatility even after incorporating price predictions. Thus motivated by these issues, we integrate randomness in the price prediction models to simulate stochastic behavior. This paper proposes hybrid trading strategies that take advantage of the traditional mean reversal strategies alongside robust price predictions from stochastic neural networks. We trained stochastic neural networks to predict prices based on market data and social sentiment. The backtesting was conducted on three cryptocurrencies: Bitcoin, Ethereum, and Litecoin, for over 600 days from August 2017 to December 2019. We show that the proposed trading algorithms are better when compared to the traditional buy and hold strategy in terms of both stability and returns.
    Type of Medium: Online Resource
    ISSN: 2227-7390
    URL: Article
    DOI: 10.3390/math10091456
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2704244-3
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  • 6
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    Blockchain and Deep Learning-Based Fault Detection Framework for Electric Vehicles
    MDPI AG ; 2022
    In:  Mathematics Vol. 10, No. 19 ( 2022-10-04), p. 3626-
    Details
    In: Mathematics, MDPI AG, Vol. 10, No. 19 ( 2022-10-04), p. 3626-
    Abstract: The gradual transition from a traditional transportation system to an intelligent transportation system (ITS) has paved the way to preserve green environments in metro cities. Moreover, electric vehicles (EVs) seem to be beneficial choices for traveling purposes due to their low charging costs, low energy consumption, and reduced greenhouse gas emission. However, a single failure in an EV’s intrinsic components can worsen travel experiences due to poor charging infrastructure. As a result, we propose a deep learning and blockchain-based EV fault detection framework to identify various types of faults, such as air tire pressure, temperature, and battery faults in vehicles. Furthermore, we employed a 5G wireless network with an interplanetary file system (IPFS) protocol to execute the fault detection data transactions with high scalability and reliability for EVs. Initially, we utilized a convolutional neural network (CNN) and a long-short term memory (LSTM) model to deal with air tire pressure fault, anomaly detection for temperature fault, and battery fault detection for EVs to predict the presence of faulty data, which ensure safer journeys for users. Furthermore, the incorporated IPFS and blockchain network ensure highly secure, cost-efficient, and reliable EV fault detection. Finally, the performance evaluation for EV fault detection has been simulated, considering several performance metrics, such as accuracy, loss, and the state-of-health (SoH) prediction curve for various types of identified faults. The simulation results of EV fault detection have been estimated at an accuracy of 70% for air tire pressure fault, anomaly detection of the temperature fault, and battery fault detection, with R2 scores of 0.874 and 0.9375.
    Type of Medium: Online Resource
    ISSN: 2227-7390
    URL: Article
    DOI: 10.3390/math10193626
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2704244-3
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  • 7
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    Blockchain-Driven Real-Time Incentive Approach for Energy Management System
    MDPI AG ; 2023
    In:  Mathematics Vol. 11, No. 4 ( 2023-02-12), p. 928-
    Details
    In: Mathematics, MDPI AG, Vol. 11, No. 4 ( 2023-02-12), p. 928-
    Abstract: In the current era, the skyrocketing demand for energy necessitates a powerful mechanism to mitigate the supply–demand gap in intelligent energy infrastructure, i.e., the smart grid. To handle this issue, an intelligent and secure energy management system (EMS) could benefit end-consumers participating in the Demand–Response (DR) program. Therefore, in this paper, we proposed a real-time and secure incentive-based EMS for smart grid, i.e., RI-EMS approach using Reinforcement Learning (RL) and blockchain technology. In the RI-EMS approach, we proposed a novel reward mechanism for better convergence of the RL-based model using a Q-learning approach based on the greedy policy that guides the RL-agent for faster convergence. Then, the proposed RI-EMS approach designed a real-time incentive mechanism to minimize energy consumption in peak hours and reduce end-consumers’ energy bills to provide incentives to the end-consumers. Experimental results show that the proposed RI-EMS approach induces end-consumer participation and increases customer profitabilities compared to existing approaches considering the different performance evaluation metrics such as energy consumption for end-consumers, energy consumption reduction, and total cost comparison to end-consumers. Furthermore, blockchain-based results are simulated and analyzed with the help of deployed smart contracts in a Remix Integrated Development Environment (IDE) with the parameters such as transaction efficiency and data storage cost.
    Type of Medium: Online Resource
    ISSN: 2227-7390
    URL: Article
    DOI: 10.3390/math11040928
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
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  • 8
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    RaKShA: A Trusted Explainable LSTM Model to Classify Fraud Patterns on Credit Card Transactions
    MDPI AG ; 2023
    In:  Mathematics Vol. 11, No. 8 ( 2023-04-17), p. 1901-
    Details
    In: Mathematics, MDPI AG, Vol. 11, No. 8 ( 2023-04-17), p. 1901-
    Abstract: Credit card (CC) fraud has been a persistent problem and has affected financial organizations. Traditional machine learning (ML) algorithms are ineffective owing to the increased attack space, and techniques such as long short-term memory (LSTM) have shown promising results in detecting CC fraud patterns. However, owing to the black box nature of the LSTM model, the decision-making process could be improved. Thus, in this paper, we propose a scheme, RaKShA, which presents explainable artificial intelligence (XAI) to help understand and interpret the behavior of black box models. XAI is formally used to interpret these black box models; however, we used XAI to extract essential features from the CC fraud dataset, consequently improving the performance of the LSTM model. The XAI was integrated with LSTM to form an explainable LSTM (X-LSTM) model. The proposed approach takes preprocessed data and feeds it to the XAI model, which computes the variable importance plot for the dataset, which simplifies the feature selection. Then, the data are presented to the LSTM model, and the output classification is stored in a smart contract (SC), ensuring no tampering with the results. The final data are stored on the blockchain (BC), which forms trusted and chronological ledger entries. We have considered two open-source CC datasets. We obtain an accuracy of 99.8% with our proposed X-LSTM model over 50 epochs compared to 85% without XAI (simple LSTM model). We present the gas fee requirements, IPFS bandwidth, and the fraud detection contract specification in blockchain metrics. The proposed results indicate the practical viability of our scheme in real-financial CC spending and lending setups.
    Type of Medium: Online Resource
    ISSN: 2227-7390
    URL: Article
    DOI: 10.3390/math11081901
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2704244-3
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  • 9
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    Deep Learning-Based Malicious Smart Contract and Intrusion Detection System for IoT Environment
    MDPI AG ; 2023
    In:  Mathematics Vol. 11, No. 2 ( 2023-01-12), p. 418-
    Details
    In: Mathematics, MDPI AG, Vol. 11, No. 2 ( 2023-01-12), p. 418-
    Abstract: The Internet of Things (IoT) is a key enabler technology that recently received significant attention from the scientific community across the globe. It helps transform everyone’s life by connecting physical and virtual devices with each other to offer staggering benefits, such as automation and control, higher productivity, real-time information access, and improved efficiency. However, IoT devices and their accumulated data are susceptible to various security threats and vulnerabilities, such as data integrity, denial-of-service, interception, and information disclosure attacks. In recent years, the IoT with blockchain technology has seen rapid growth, where smart contracts play an essential role in validating IoT data. However, these smart contracts can be vulnerable and degrade the performance of IoT applications. Hence, besides offering indispensable features to ease human lives, there is also a need to confront IoT environment security attacks, especially data integrity attacks. Toward this aim, this paper proposed an artificial intelligence-based system model with a dual objective. It first detects the malicious user trying to compromise the IoT environment using a binary classification problem. Further, blockchain technology is utilized to offer tamper-proof storage to store non-malicious IoT data. However, a malicious user can exploit the blockchain-based smart contract to deteriorate the performance IoT environment. For that, this paper utilizes deep learning algorithms to classify malicious and non-malicious smart contracts. The proposed system model offers an end-to-end security pipeline through which the IoT data are disseminated to the recipient. Lastly, the proposed system model is evaluated by considering different assessment measures that comprise the training accuracy, training loss, classification measures (precision, recall, and F1 score), and receiver operating characteristic (ROC) curve.
    Type of Medium: Online Resource
    ISSN: 2227-7390
    URL: Article
    DOI: 10.3390/math11020418
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2704244-3
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  • 10
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    Blockchain‐based electric vehicle charging reservation scheme for optimum pricing
    Hindawi Limited ; 2022
    In:  International Journal of Energy Research Vol. 46, No. 11 ( 2022-09), p. 14994-15007
    Details
    In: International Journal of Energy Research, Hindawi Limited, Vol. 46, No. 11 ( 2022-09), p. 14994-15007
    Type of Medium: Online Resource
    ISSN: 0363-907X , 1099-114X
    URL: Issue
    URL: Article
    DOI: 10.1002/er.v46.11
    DOI: 10.1002/er.8199
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 1480879-1
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