Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Reema Goyal
Chitkara University
Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
A hybrid edge–cloud computing framework for low-latency, energy-efficient, and sustainable smart city applications Kamal Saluja; Tanya Khaneja; Sunil Gupta; Reema Goyal; Wai Yie Leong
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 2: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i2.pp791-799

Abstract

Smart-city applications demand ultra-low latency, high reliability, and sustainable operation, which are difficult to achieve using cloud-only or edge-only computing paradigms. This study suggests a carbon-conscious architecture for managing smart cities’ intelligent job offloading between the edge and the cloud. This is made possible by the Internet of Things and driven by reinforcement learning (RL). A deep Q-network (DQN) is used to dynamically assign tasks to cloud servers and edge nodes based on how much energy they use, how long it takes to send data over the network, and how much bandwidth they have. A lightweight permissioned blockchain layer makes sure that data is correct across all of its parts, and carbon-aware scheduling puts low-carbon resources first. EdgeCloudSim is used to test the system with real-world smart city workloads. When compared to systems that simply use the cloud, the proposed solution showed a 64.6% drop in average latency, a 24.2% drop in energy use, and a 15% drop in carbon emissions. Combining artificial intelligence (AI)-driven orchestration with scheduling that takes sustainability into account in a hybrid edge-cloud environment yields positive outcomes.
Machine learning for adaptive intrusion detection system towards secure internet of things enabled healthcare systems Omar Isam Al Mrayat; Malik Jawarneh; D. Vetrithangam; Pallavi Sunil Bangare; Khaled Abdulwahab Zaid Alyamani; Reema Goyal; Sugumar Rajendran; Shubham Kumar; Yuvaraj Kunnathur Periyasamy; Abhishek Raghuvanshi
Bulletin of Electrical Engineering and Informatics Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i5.10512

Abstract

Internet of medical things (IoMT) ties medical sensors, actuators, wearable, and communication technologies to provide an improved healthcare delivery process by means of constant patient monitoring and decision-making. The trend has resulted in an increase in its use because of the increasing prevalence of chronic diseases and aging, and the need to have cost-effective healthcare services. Nonetheless, the networks provided by the IoMT are prone to cyberattacks under the considerable threat as both process huge volumes of sensitive health data, and do not have the resources to do it. In this paper, the author suggests a new adaptive intrusion detection system (IDS) as a way to enhance the security of IoMT-enabled healthcare environments. The proposed model the pre-treats NSL-KDD dataset, filling gaps and transmitting nominal values. Particle swarm optimization (PSO) is used in the optimal selection of features and artificial neural network (ANN), K-nearest neighbors (KNN), and random forest (RF) classifiers are used in intrusion detection. Experimental performance proves that the PSO-ANN model can perform better with a high accuracy of 99.54%, sensitivity of 99.31% and specificity of 97.32% on average with high protection of intelligent healthcare systems.
Co-Authors Abhishek Raghuvanshi D. Vetrithangam Kamal Saluja Khaled Abdulwahab Zaid Alyamani Malik Jawarneh Omar Isam Al Mrayat Pallavi Sunil Bangare Shubham Kumar Sugumar Rajendran Sunil Gupta Tanya Khaneja Wai Yie Leong Yuvaraj Kunnathur Periyasamy