Dr.G.Rajesh works as an Assistant professor, Department of Information Technology of Anna University, Chennai, India. He completed his PhD in 2016 from Anna University, India, in wireless sensor networks. He did his Post graduation in Computer science and engineering from Government college of engineering in 2007 and his undergraduate from AVC college of engineering in 2005, both under Anna university, India. He has around 12 years of teaching and research experience. His area of research interest includes wireless sensor networks and its IoT applications, machine Learning, Data analytics software engineering and computational optimization. He published more than 20 research papers in reputed journals and conferences. He authored two books and he is the guest editor and reviewer of several journals and book chapters. Phone: +919788856412 Address: India
On-demand cloud computing is one of the rapidly evolving technologies that is being widely used i... more On-demand cloud computing is one of the rapidly evolving technologies that is being widely used in the industries now. With the increase in IoT devices and real-time business analytics requirements, enterprises that ought to scale up and scale down their services have started coming towards on-demand cloud computing service providers. In a cloud data center, a high volume of continuous incoming task requests to physical hosts makes an imbalance in the cloud data center load. Most existing works balance the load by optimizing the algorithm in selecting the optimal host and achieves instantaneous load balancing but with execution inefficiency for tasks when carried out in the long run. Considering the long-term perspective of load balancing, the research paper proposes Stackelberg (leader-follower) game-theoretical model reinforced with the satisfaction factor for selecting the optimal physical host for deploying the tasks arriving at the data center in a balanced way. Stackelberg Game Theoretical Model for Load Balancing (SGMLB) algorithm deploys the tasks on the host in the data center by considering the utilization factor of every individual host, which helps in achieving high resource utilization on an average of 60%. Experimental results show that the Stackelberg equilibrium incorporated with a satisfaction index has been very useful in balancing the loading across the cluster by choosing the optimal hosts. The results show better execution efficiency in terms of the reduced number of task failures by 47%, decreased 'makespan' value by 17%, increased throughput by 6%, and a decreased front-end error rate as compared to the traditional random allocation algorithms and flow-shop scheduling algorithm.
Over the past few years, unmanned aerial vehicles (UAV) or drones have been used for many applica... more Over the past few years, unmanned aerial vehicles (UAV) or drones have been used for many applications. In certain applications like surveillance and emergency rescue operations, multiple drones work as a network to achieve the target in which any one of the drones will act as the master or coordinator to communicate, monitor, and control other drones. Hence, drones are energy-constrained; there is a need for effective coordination among them in terms of decision making and communication between drones and base stations during these critical situations. This paper focuses on providing an efficient approach for the election of the cluster head dynamically, which heads the other drones in the network. The main objective of the paper is to provide an effective solution to elect the cluster head among multi drones at different periods based on the various physical constraints of drones. The elected cluster head acts as the decision-maker and assigns tasks to other drones. In a case where the cluster head fails, then the next eligible drone is re-elected as the leader. Hence, an optimally distributed solution proposed is called Bio-Inspired Optimized Leader Election for Multiple Drones (BOLD), which is based on two AI-based optimization techniques. The simulation results of BOLD compared with the existing Particle Swarm Optimization-Cluster head election (PSO-C) in terms of network lifetime and energy consumption, and from the results, it has been proven that the lifetime of drones with the BOLD algorithm is 15% higher than the drones with PSO-C algorithm.
Wireless Sensor Network (WSN) is an anthology of distributed sensor nodes that constantly monitor... more Wireless Sensor Network (WSN) is an anthology of distributed sensor nodes that constantly monitors physical and ecological conditions. Depending on the application, node count in WSN ranges from few hundreds to thousands. A node in Sensor Network constantly monitors and communally passes their data through the network to a Sink Node. Based on literatures, a trivial issue in densely deployed sensor network, the data collected from adjacent nodes has higher level of similarity and data redundancy. To overcome the redundancy issue, the proposed method called "Numerical Integration Technique" includes-Simpson's 3/8 rule to reduce data redundancy. On performance analysis, the proposed method achieves higher rate of data aggregation compared to Kalman Filter and minimizes energy utilization caused by not transmitting the redundant data.
On-demand cloud computing is one of the rapidly evolving technologies that is being widely used i... more On-demand cloud computing is one of the rapidly evolving technologies that is being widely used in the industries now. With the increase in IoT devices and real-time business analytics requirements, enterprises that ought to scale up and scale down their services have started coming towards on-demand cloud computing service providers. In a cloud data center, a high volume of continuous incoming task requests to physical hosts makes an imbalance in the cloud data center load. Most existing works balance the load by optimizing the algorithm in selecting the optimal host and achieves instantaneous load balancing but with execution inefficiency for tasks when carried out in the long run. Considering the long-term perspective of load balancing, the research paper proposes Stackelberg (leader-follower) game-theoretical model reinforced with the satisfaction factor for selecting the optimal physical host for deploying the tasks arriving at the data center in a balanced way. Stackelberg Game Theoretical Model for Load Balancing (SGMLB) algorithm deploys the tasks on the host in the data center by considering the utilization factor of every individual host, which helps in achieving high resource utilization on an average of 60%. Experimental results show that the Stackelberg equilibrium incorporated with a satisfaction index has been very useful in balancing the loading across the cluster by choosing the optimal hosts. The results show better execution efficiency in terms of the reduced number of task failures by 47%, decreased 'makespan' value by 17%, increased throughput by 6%, and a decreased front-end error rate as compared to the traditional random allocation algorithms and flow-shop scheduling algorithm.
Over the past few years, unmanned aerial vehicles (UAV) or drones have been used for many applica... more Over the past few years, unmanned aerial vehicles (UAV) or drones have been used for many applications. In certain applications like surveillance and emergency rescue operations, multiple drones work as a network to achieve the target in which any one of the drones will act as the master or coordinator to communicate, monitor, and control other drones. Hence, drones are energy-constrained; there is a need for effective coordination among them in terms of decision making and communication between drones and base stations during these critical situations. This paper focuses on providing an efficient approach for the election of the cluster head dynamically, which heads the other drones in the network. The main objective of the paper is to provide an effective solution to elect the cluster head among multi drones at different periods based on the various physical constraints of drones. The elected cluster head acts as the decision-maker and assigns tasks to other drones. In a case where the cluster head fails, then the next eligible drone is re-elected as the leader. Hence, an optimally distributed solution proposed is called Bio-Inspired Optimized Leader Election for Multiple Drones (BOLD), which is based on two AI-based optimization techniques. The simulation results of BOLD compared with the existing Particle Swarm Optimization-Cluster head election (PSO-C) in terms of network lifetime and energy consumption, and from the results, it has been proven that the lifetime of drones with the BOLD algorithm is 15% higher than the drones with PSO-C algorithm.
Wireless Sensor Network (WSN) is an anthology of distributed sensor nodes that constantly monitor... more Wireless Sensor Network (WSN) is an anthology of distributed sensor nodes that constantly monitors physical and ecological conditions. Depending on the application, node count in WSN ranges from few hundreds to thousands. A node in Sensor Network constantly monitors and communally passes their data through the network to a Sink Node. Based on literatures, a trivial issue in densely deployed sensor network, the data collected from adjacent nodes has higher level of similarity and data redundancy. To overcome the redundancy issue, the proposed method called "Numerical Integration Technique" includes-Simpson's 3/8 rule to reduce data redundancy. On performance analysis, the proposed method achieves higher rate of data aggregation compared to Kalman Filter and minimizes energy utilization caused by not transmitting the redundant data.
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