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All Journal International Journal of Electrical and Computer Engineering
Rao, Malode Vishwanatha Panduranga
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Computer Science & IT Electrical & Electronics Engineering

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A novel secured open standard framework for internet of things applications integrating elliptic curve cryptography and fog computing Ravindra, Krishnapura Srinivasa; Rao, Malode Vishwanatha Panduranga
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp7224-7235

Abstract

The internet of things (IoT) has revolutionized various fields by enabling seamless connectivity and data exchange among numerous devices. However, this interconnectivity introduces significant security challenges, particularly in ensuring data confidentiality, integrity, and authenticity. This study proposes a novel secure open standard framework for IoT applications, addressing these challenges through the integration of elliptic curve cryptography (ECC) and fog computing. The framework consists of three core components: secure device registration, data encryption within the fog gateway, and a robust mechanism for detecting man-in-the-middle (MITM) attacks. The unique aspect of the proposed method lies in its comprehensive approach to IoT security. Utilizing ECC, the framework ensures secure communication among resource constrained IoT devices, balancing encryption strength and efficiency. The integration of fog computing reduces latency and enhances processing efficiency by offloading intensive tasks from IoT devices to the fog layer. The MITM attack detection mechanism continuously monitors cryptographic keys and communication patterns, providing an additional layer of security against advanced cyber threats. The system was implemented and evaluated using the NS-3.26 network simulator and Python for data visualization. The experimental setup included 100 IoT devices, 25 users, a fog gateway, a datacenter, and a cloud server. Results demonstrate the framework's scalability and efficiency, with consistent throughput increases and balanced power consumption across varying IoT device numbers.
Enhanced global navigation satellite system signal processing using field programmable gate array and system-on-chip based software receivers Kh, Chetna Devi; Rao, Malode Vishwanatha Panduranga
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp480-492

Abstract

This paper presents a new approach to improving global navigation satellite system (GNSS) signal processing by using baseband processing techniques on field-programmable gate array (FPGA) platforms and a system-on-chip (SoC)-based GNSS software receiver. By leveraging the flexibility and computational power of FPGAs and the integration capabilities of SoC platforms, the method significantly enhances signal acquisition, tracking accuracy, and overall system performance. The integration of the ADFMCOMMS3-EBZ RF front end with the Zynq 7000 SoC board, along with high-speed parallel I/O and serial peripheral interface (SPI) for data management and configuration, enables efficient processing of high-speed signals. The study also explores wavelet transform techniques, such as the discrete wavelet transform (DWT), to improve filtering and noise reduction in GNSS signals. The results show that the proposed baseband processing algorithm for GNSS software-defined radio (SDR) reduces acquisition time and enhances tracking accuracy compared to traditional personal computer (PC)-based systems. Additionally, the SoC-based receiver is more energy-efficient and uses fewer resources. Comparative analysis shows that the proposed method provides more received samples, fewer dropped samples, and a lower data loss rate, confirming its effectiveness in boosting GNSS signal processing reliability and efficiency.
Energy-efficient secure software-defined networking with reinforcement learning and Weierstrass cryptography Andanaiah, Nagaraju Tumakuru; Rao, Malode Vishwanatha Panduranga
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp4227-4238

Abstract

In the age of rapidly advancing 5G connectivity, artificial intelligence (AI), and the internet of things (IoT), network data has grown enormously, demanding more efficient and secure management solutions. Traditional networking systems, limited by manual controls and static environments, are unable to fulfill the dynamic demands of modern internet services. This paper proposes an innovative software-defined networking (SDN) framework that utilizes exponential spline regression reinforcement learning (ESR-RL) with genus Weierstrass curve cryptography (GWCC) to boost energy efficiency and data security. The ESR-RL algorithm reliably anticipates network traffic patterns, optimizing path selection to enhance routing efficiency while minimizing consumption of energy. GWCC also enables strong encryption and decryption, considerably increasing data security without impacting system performance. To further improve network reliability, the Skellam distributed Siberian TIGER optimization algorithm (SDSTOA) is used to dynamically acquire features and balance loads, resulting in optimal network performance. Extensive simulations show that the proposed framework performs better than existing models in terms of accuracy, precision, recall, F-measure, sensitivity, and specificity. Improvements in latency, turnaround time, and network throughput demonstrate the framework's success. This scalable and adaptive technology establishes a new standard for SDN systems by providing a safe, energy-efficient, and performance-optimized strategy for future network infrastructures.
Co-Authors Andanaiah, Nagaraju Tumakuru Kh, Chetna Devi Ravindra, Krishnapura Srinivasa