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All Journal IAES International Journal of Robotics and Automation (IJRA) International Journal of Reconfigurable and Embedded Systems (IJRES)
Gupta, Prince
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Automotive Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering

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Reconfigurable data intensive service for low latency cyber-physical systems and IoT communication Gupta, Prince; Sharma, Rajeev; Gupta, Sachi
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 3: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i3.pp491-501

Abstract

The fourth industrial revolution is realized through the many developments in cyber-physical systems (CPS) made possible by the widespread use of the internet of things (IoT). CPS sensor networks must enable mobile and wireless CPSs with their specific flexibility and heterogeneity needs without compromising quality of service (QoS). The research article focuses on reconfigurable data communication hardware for numerous IoT-supporting infrastructures and performance estimation using delay, power, throughput, and packet delivery ratio (PDR) for different IoT node configurations. Tree topology-based network configuration from cloud data to sensor fog organizers, sensor network directors, and IoT-embedded sensors is supported. Functional simulation is performed in iFoGSim, Xilinx ISE, and Modelsim 10.0 with a maximum of 64 variable nodes programmed for data communication and interplay verification with a minimum delay of 9.1 ns, maximum frequency of 319 MHz, power of 7.5 mW, throughput of 0.280, and maximum PDR=1. The simulation is applicable for fog computing and CPS processed from different alters in specific topologies.
Analysis and implementation of computation offloading in fog architecture Gupta, Prince; Sharma, Rajeev; Gupta, Sachi; Kumar, Adesh
IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v14i3.pp479-492

Abstract

The fast expansion of connected devices has led to an unparalleled increase in data across sectors like industrial automation, social media, environmental monitoring, and life sciences. The processing of this data presents difficulties owing to its magnitude, temporal urgency, and security stipulations. Computation offloading has arisen as a viable alternative, allowing resource-constrained devices to assign demanding work to more robust platforms, thus improving responsiveness and efficiency. This paper examines decision-making strategies for computing offloading by assessing various algorithms, including a deep neural network with deep reinforcement learning (DNN-DRL), coordinate descent (baseline), AdaBoost, and K-nearest neighbor (KNN). The performance evaluation centers on three primary metrics: system accuracy, training duration, and latency. The computation offloading mitigates these issues by transferring intricate workloads from resource-limited devices to more proficient platforms, thus enhancing efficiency and responsiveness. The evaluation examines accuracy, training duration, and latency as key parameters. The results indicate that KNN attains maximum accuracy and minimal latency, AdaBoost provides a robust balance despite increased training costs, and the baseline underperforms in both efficiency and responsiveness. These findings underscore the trade-offs between computational expense, precision, and real-time application, providing insights for forthcoming IoT and edge-computing systems.
Co-Authors Gupta, Sachi Kumar, Adesh Sharma, Rajeev