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Audio encryption needs critical attention due to an insecure channel in the wireless medium. This paper presents the first- of- its- kind work addressing a Discrete Wavelet Transform (DWT) and Elliptic Curve Cryptography (ECC) influenced Single Carrier Frequency Division Multiple Access (SC-FDMA) for secure audio data communication. The Percent Res...
Citations
... The research framework is to include the IoT-based encryption process in SC-FDMA communication systems [34,36]. Earlier work only checks the encryption algorithms designed on hardware and software frameworks. ...
... It plays an essential part in the transmission of wireless systems of data (text, image, and video). The advantages of SC-FDMA are lower complexity, high flexibility, and increased channel capacity [34,36]. Several common surveillance industrial scenarios illustrate an interest in a new era of operating and monitoring various applications, as depicted in Fig. 2. The university and school detection techniques and industrial sectors were monitoring surveillance. ...
... Cloud Service Provider (SCP) is a semi-trusted party in cloud storage that protects data from disclosure; user data is encrypted before being uploaded to a cloud server. The proposed work addresses Elliptic Curve Cryptography (ECC) to encrypt and decrypt the image accordingly and enhance industrial security surveillance amid transmission utilising 4G Long Term Evolution (LTE) uplink SC-FDMA [34][35][36] strategies in IoTs. A hardware architecture for a secure web camera integrated with the Atmega-328 microcontroller, suitable for IoT applications, is proposed in this work. ...
This research allows the secure surveillance approach for the Internet of Things (IoT) methodology to be developed by integrating wireless signalling and image encryption strategy. Since the Cloud Service Telco (CST) is a semi-trusted body in cloud services, user data is encrypted before uploading to a cloud server for data protection from disclosure. The flexibility of encrypted data sharing is essential for cloud storage users. This study investigates the Discrete Wavelet Transform (DWT) technique with modified Huffman compression and Elliptic Curve Cryptography (ECC). It encrypts and decrypts the data and enhances industrial security surveillance in transmission. It uses the wireless network’s next generation (5G or 6G) as uplink Single Carrier Frequency Division Multiple Access (SC-FDMA) strategies via the IoT. This study presented a novel approach to proposing hardware architecture for a secure web camera integrated with the Atmel in the mega AVR family (ATMEGA) microcontroller, suitable for IoT applications. The experimental results confirm the proposed model’s efficacy compared with existing robustness and security analysis algorithms. These systems are also used by implementing industry-standard protocols using IoTs to monitor industrial applications. The proposed framework can also minimise bandwidth, transmission cost, storage space, tracking data, and decisions about abnormal events such as potential fraud and extinguisher detection in surveilling applications.
Cognitive Radio Networks (CRNs) have emerged as a promising paradigm for addressing the spectrum scarcity challenge by enabling opportunistic access to underutilized frequency bands. Spectrum sensing, a fundamental function of CRNs, plays a pivotal role in identifying available spectral opportunities. Among various sensing techniques, energy detection stands out as a simple yet effective method for detecting the presence of primary users. This abstract explores the integration of energy detection with cognitive radio networks to enhance spectrum sensing efficiency. Firstly, the abstract elucidates the principles of energy detection and its suitability for spectrum sensing in CRNs. Unlike other sensing techniques that require prior knowledge of primary user signals, energy detection operates solely based on the received energy levels, making it robust and versatile across different environmental conditions. Secondly, the abstract discusses the challenges associated with energy detection in CRNs, including the trade-off between sensing accuracy and sensing duration, the impact of noise uncertainty, and the vulnerability to signal fading. Addressing these challenges requires innovative solutions such as adaptive thresholding, cooperative sensing, and advanced signal processing algorithms. Furthermore, the abstract delves into the benefits of integrating energy detection with cognitive radio networks. By leveraging the cognitive capabilities of CRNs, such as spectrum awareness and learning, energy detection can be enhanced to achieve higher sensing accuracy, lower false alarm rates, and improved spectrum utilization efficiency. Finally, the abstract highlights ongoing research directions and future prospects in the integration of energy detection with cognitive radio networks. These include the development of machine learning-based sensing techniques, optimization of spectrum sensing parameters, and integration with emerging technologies such as 5G and beyond. In conclusion, the integration of energy detection with cognitive radio networks holds significant promise for enhancing spectrum sensing efficiency and enabling more dynamic and efficient spectrum access in future wireless communication systems.
The ever-growing demand for wireless communication services has led to the exploration of spectrum sensing techniques to efficiently utilize the limited available spectrum resources. However, spectrum sensing is often hindered by the presence of colored noise, which arises from various sources such as co-channel interference and environmental factors. Traditional signal processing methods struggle to effectively mitigate colored noise, prompting the exploration of machine learning (ML) approaches for enhanced noise mitigation. This paper presents a comprehensive review of machine learning techniques employed for mitigating colored noise in spectrum sensing applications. We begin by discussing the challenges posed by colored noise in spectrum sensing and its detrimental effects on detection performance. Subsequently, we delve into various ML algorithms, including deep learning, support vector machines, and neural networks, that have been adapted to address the colored noise mitigation problem. Furthermore, we examine the methodologies for training ML models using synthetic and real-world colored noise data, highlighting the importance of robust training datasets for achieving optimal performance. Additionally, we discuss the integration of ML-based noise mitigation techniques into existing spectrum sensing frameworks, emphasizing the potential benefits in terms of detection accuracy and spectral efficiency. Through a comparative analysis of different ML approaches, we identify their strengths, limitations, and potential avenues for future research. Moreover, we discuss practical considerations such as computational complexity and real-time implementation challenges associated with deploying ML-based noise mitigation solutions in spectrum sensing systems. Overall, this paper provides valuable insights into the emerging field of machine learning approaches for colored noise mitigation in spectrum sensing, offering guidance for researchers and practitioners seeking to enhance the reliability and efficiency of wireless communication systems in noisy environments.
Function as a Service (FaaS) has garnered significant attention for its approach to deploying computations to serverlessbackends across various cloud environments. It simplifies the complexity of provisioning and managing resources for applications by leveraging cloud providers' capabilities, offering users an illusion of perpetual resource availability. Among these providers, the AWS serverless platform stands out, offering a novel paradigm for cloud application development, abstracting away concerns about underlying hardware infrastructure while ensuring scalability, security, and cost-effectiveness.However, due to the absence of standardized benchmarks, serverless functions often rely on ad-hoc solutions for building cost-efficient and scalable applications. The development of the SeBS framework has addressed this gap, enabling comprehensive testing, evaluation, and performance analysis across different cloud providers. While previous research has explored serverless platforms among various providers, little attention has been given to AWS Lambda service's performance within the ARM64 architecture and its comparison with traditional x86 architectures.
In today's digital era, the significance of data cannot be overstated. It embodies the factual and numerical essence of our everyday transactions, arriving not just statically but dynamically, in the form of data streams. These streams constitute an influx of limitless, continuous, and swift information, particularly prominent in sectors like healthcare. However, navigating this torrent of data presents formidable challenges. The sheer volume, pace, and variety make processing data streams exceedingly complex. Moreover, the task of classifying data streams is compounded by the phenomenon of concept drift, where the underlying statistical characteristics of the target variable undergo unexpected changes, especially noticeable in supervised learning scenarios.Addressing these challenges head-on, our research delves into various manifestations of concept drift within healthcare data streams. We offer an overview of established statistical and machine learning techniques tailored to tackle concept drift. Furthermore, we underscore the efficacy of deep learning algorithms in detecting concept drift and elucidate the diverse healthcare datasets employed in this endeavor.
Artificial Intelligence (AI) is a pivotal domain within computer science, profoundly influencing the software development lifecycle, particularly during the implementation phase. Here, developers grapple with the task of translating software requirements and designs into executable code. Automated Code Generation (ACG) leveraging AI emerges as a promising solution in this context. The automation of code generation processes is gaining traction as a means to tackle diverse software development challenges while boosting productivity. This paper presents a thorough review and discourse on both traditional and AI-driven techniques employed in ACG, highlighting their respective challenges and constraints. Through an examination of pertinent literature, we identify various AI methodologies and algorithms utilized in ACG, extracting evaluation metrics such as Accuracy, Efficiency, Scalability, Correctness, Generalization, among others. These metrics serve as the basis for a comparative analysis of AI-driven ACG methods, delving into their applications, strengths, weaknesses, performance metrics, and future prospects.
This paper explores the integration of chaos theory principles to bolster the security of communication systems, specifically focusing on Single Carrier Frequency Division Multiple Access (SC-FDMA) networks. By harnessing chaos for audio enhancement, this research presents a novel approach to fortify the confidentiality and integrity of data transmission over SC-FDMA systems. The proposed methodology leverages the inherent randomness and unpredictability of chaotic signals to encrypt audio data, thereby enhancing the resilience against eavesdropping and malicious interference. Through a comprehensive examination of the integration process, encryption techniques, and security enhancements, this study elucidates the potential of chaos-assisted audio to augment the security posture of SC-FDMA communication systems. Evaluation through simulations demonstrates promising results, underscoring the effectiveness and viability of the proposed approach. The findings not only contribute to advancing the understanding of secure communication methodologies but also open avenues for future research and practical applications in the realm of telecommunications security.
In recent years, the demand for secure communication has intensified with the ubiquitous presence of wireless networks. Subcarrier Frequency Division Multiple Access (SC-FDMA) systems have emerged as a promising technology for efficient data transmission in wireless communication systems. However, ensuring the confidentiality and integrity of transmitted data remains a critical challenge. In this paper, we propose a novel approach to enhance the security of communication over SC-FDMA systems by integrating chaos-assisted audio techniques. Chaos theory has been widely recognized for its potential in enhancing security through its inherent randomness and unpredictability. By embedding chaotic signals into the audio stream, we introduce a layer of complexity that enhances the encryption process, thereby fortifying the security of the communication channel. We leverage the unique properties of chaotic systems to generate cryptographic keys dynamically, further bolstering the security of the transmission. Moreover, the integration of audio signals in the communication process adds an additional layer of obfuscation, making it more challenging for adversaries to intercept or decipher the transmitted data. We evaluate the performance of the proposed system through extensive simulations, considering various metrics such as bit error rate, signal-to-noise ratio, and computational overhead. Our results demonstrate significant improvements in the security and robustness of communication over SC-FDMA systems with the integration of chaos-assisted audio. Furthermore, the proposed approach maintains the efficiency and reliability of SC-FDMA technology while enhancing its resilience against malicious attacks. This research paves the way for the development of more secure and robust wireless communication systems, addressing the growing concerns regarding data privacy and integrity in modern networks.
Sensors and data acquisition play a crucial role in effectively controlling and managing agricultural greenhouse systems. This abstract provides an overview of the significance of sensors and data acquisition in greenhouse control, highlighting their role in optimizing crop growth conditions, resource utilization, and decision-making processes. The abstract explores the various types of sensors commonly employed in greenhouse control systems, including temperature, humidity, light, CO2, and soil moisture sensors, and their functions and benefits. It also examines different methods of data acquisition, such as direct sensor-to-controller communication, wireless sensor networks, data loggers, and Internet of Things (IoT) platforms. The integration of sensors and data acquisition is discussed, emphasizing the importance of data processing, analysis, and real-time monitoring for effective greenhouse control. The abstract further emphasizes the benefits of utilizing sensors and data acquisition in greenhouse systems, including improved crop management, resource optimization, disease and pest prevention, and data-driven decision-making. Several case studies highlight the practical applications of sensors and data acquisition in greenhouse control systems, focusing on temperature and humidity control, lighting optimization, and soil moisture monitoring. The abstract concludes by discussing future trends and challenges in the field, such as emerging sensor technologies, integration with artificial intelligence and machine learning, data privacy and security concerns, and scalability and cost considerations. Overall, sensors and data acquisition offer immense potential for enhancing the efficiency and productivity of agricultural greenhouse systems.
Data processing and analytics play a crucial role in optimizing agricultural greenhouse control systems. With the advent of the Internet of Things (IoT) and advancements in data analytics techniques, farmers and greenhouse managers can now harness the power of data to make informed decisions and maximize crop productivity. This abstract explores the significance of data processing and analytics in agricultural greenhouse control systems. The abstract begins by highlighting the challenges faced in greenhouse agriculture, including the need for precise monitoring of environmental conditions, resource management, disease detection, and yield optimization. It emphasizes the importance of data collection from various sources, such as sensors, weather stations, and crop health monitoring devices, to create a comprehensive dataset. Next, the abstract discusses the role of data processing in agricultural greenhouse control systems. It highlights the need for data cleaning, normalization, and integration to ensure data quality and consistency. Various data processing techniques, such as filtering, aggregation, and feature extraction, are explored to transform raw data into meaningful insights. The abstract then delves into the realm of data analytics. It explores the application of statistical analysis, machine learning, and predictive modeling techniques to extract valuable information from greenhouse data. The abstract emphasizes the use of machine learning algorithms for crop growth prediction, disease detection, and yield optimization. It also discusses the integration of data analytics with decision support systems to provide real-time insights and recommendations to greenhouse managers. Furthermore, the abstract discusses the benefits of data processing and analytics in agricultural greenhouse control systems. It highlights the potential for improved resource utilization, reduced environmental impact, and increased crop yields. It also emphasizes the importance of scalability and adaptability of data processing and analytics frameworks to accommodate the diverse needs of different greenhouse environments. In conclusion, data processing and analytics are integral components of modern agricultural greenhouse control systems. By leveraging IoT technologies and advanced analytics techniques, farmers and greenhouse managers can harness the power of data to optimize crop growth, improve resource management, and make informed decisions. The abstract highlights the potential benefits and challenges associated with data processing and analytics in greenhouse agriculture, paving the way for further research and innovation in this field.
