Rajkumar Kalimuthu
@gnithyd.ac.in
Assistant Professor/ Department of Information Technology
Guru Nanak Institute of Technology
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Engineering, Computer Science, Computer Networks and Communications, Software
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Rajkumar Kalimuthu and Brindha Thomas
Springer Nature Singapore
Rajkumar Kalimuthu, Limbika Zangazanga, S. Jayanthi, and Ignatius A. Herman
Springer Nature Singapore
Raja Babu Bandi, Venkata Kishore Kothapudi, Lakshman Pappula, Rajkumar Kalimuthu, and Srinivasa Kiran Gottapu
Hindawi Limited
New frequencies can be supported with very effective space use by using the shared aperture antenna This work presents on designing a dual-banddual-polarized (DBDP) S/X-band shared aperture antenna (SAA) for synthetic aperture radar (SAR) applications operating at S-band frequency (3.2 GHz) and X-band frequency (9.65 GHz). The single-layer SAA DBDP S-band antenna is designed in a square-shaped patch with coaxial feeding in both vertical and horizontal polarization. The X-band antenna design is in 1 × 3 vertical series with microstrip feeding and arranged at four corners of the proposed antenna. The S-band antenna is mainly used for airborne applications such as air traffic control and surface ship radar. In contrast, the X-band antenna application is maritime vessel traffic control, defense tracking, and vehicle speed detection for law enforcement. To verify the antenna, a prototype is fabricated and measured with s-parameters. The proposed design exhibits that the gain of the S-band is 7.2 dB and for the X-band is 12.4 dB, and the isolation is achieved more than −35 dB, and for this antenna, we achieved a bandwidth of 0.12 GHz for S-band and 0.27 GHz for X-band. However, the X-band antenna is a multi-input and multioutput antenna that is to be validated by using MIMO characteristic parameters such as envelope correlation coefficient (ECC), diversity gain (DG), channel capacity loss (CCL), mean effective gain, and mutual coupling. The MIMO characteristic parameter of X-band antenna values is found to be in a similar manner to both simulated and measured values. For this X-Band antenna, ECC, DG, CCL, and mutual coupling were achieved as below 0.05, 9.5 dB, 0.5 bps/Hz, and −30 dB to −55 dB, respectively. The total size of the antenna is 100 mm × 100 mm × 1.6 mm.
Raj Kumar Kalimuthu and Brindha Thomas
IOS Press
In today’s world, cloud computing plays a significant role in the development of an effective computing paradigm that adds more benefits to the modern Internet of Things (IoT) frameworks. However, cloud resources are considered to be dynamic and the demands necessitated for resource allocation for a certain task are different. These diverse factors may cause load and power imbalance which also affect the resource utilization and task scheduling in the cloud-based IoT environment. Recently, a bio-inspired algorithm can work effectually to solve task scheduling problems in the cloud-based IoT system. Therefore, this work focuses on efficient task scheduling and resource allocation through a novel Hybrid Bio-Inspired algorithm with the hybridized of Improvised Particle Swarm Optimization and Ant Colony Optimization. The vital objective of hybridizing these two approaches is to determine the nearest multiple sources to attain discrete and continuous solutions. Here, the task has been allocated to the virtual machine through a particle swarm and continuous resource management can be carried out by an ant colony. The performance of the proposed approach has been evaluated using the CloudSim simulator. The simulation results manifest that the proposed Hybridized algorithm efficiently scheduling the task in the cloud-based IoT environment with a lesser average response time of 2.18 sec and average waiting time of 3.6 sec as compared with existing state-of-the-art algorithms.
Rajkumar Kalimuthu and Brindha Thomas
ENGG Journals Publications
Cloud computing (CC) is an emergent and revolutionary technological paradigm to facilitate data generation over the networking environment. It is determined as the computing edge where it provides cloud services over the networking point. It assists in handling the delay issues during the task scheduling process. With the advent analysis over the existing approaches, inappropriate task scheduling in cloud computing outcomes in huge delay than the other computing models. Therefore, the actual advantages of cloud computing are attained with the adoption of appropriate task scheduling strategies. However, it is an NP-hard issue and needs effectual and optimal approaches to dealing with resource utilization, response time, and latency at the networking model. This research concentrates on modelling an efficient hybrid optimization approach to overcome the drawbacks of single standard optimization approaches. This work hybridizes the resistive-based Particle swarm Optimization (cid:4666)ℎ𝑟𝑃𝑆𝑂 ) approach with Improved ranking-based Grey wolf optimization ( 𝑖𝑟 (cid:3398) 𝐺𝑊𝑂 ) approach to handle the exploration and exploitation issues over the standard methods. Here, ℎ𝑟 (cid:3398) 𝑃𝑆𝑂 is used to schedule the task among the connected devices, and 𝑖𝑟 (cid:3398) 𝐺𝑊𝑂 helps manage the resources at the device level. The resources are allocated and managed based on the demand generated with the incoming requests in the anticipated model. The ultimate target of this work is to diminish the delay, average response time, and optimal resource utilization by efficiently scheduling the tasks by managing the available resources. The simulation is done with the MATLAB 2016b simulator. The evaluation results show that the anticipated model provides promising outcomes with energy consumption, average response time, execution time, etc. The proposed model shows a better trade-off in contrast to prevailing approaches.