Dr. Riman Mandal
@krmangalam.edu.in
Assistant Professor, School of Engineering and Technology
K R Manglam University
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Engineering, Computer Science, Computer Science Applications, Human-Computer Interaction
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Manash Kumar Mondal, Riman Mandal, Sourav Banerjee, Utpal Biswas, Jerry Chun-Wei Lin, Osama Alfarraj, and Amr Tolba
MDPI AG
Elephants are one of the largest animals on earth and are found in forests, grasslands and savannahs in the tropical and subtropical regions of Asia and Africa. A country like India, especially the northeastern region, is covered by deep forests and is home to many elephants. Railroads are an effective and inexpensive means of transporting goods and passengers in this region. Due to poor visibility in the forests, collisions between trains and elephants are increasing day by day. In the last ten years, more than 190 elephants died due to train accidents. The most effective solution to this collision problem is to stop the train immediately. To address this sensitive issue, a solution is needed to detect and monitor elephants near railroad tracks and analyze data from the camera trap near the intersection of elephant corridors and railroad tracks. In this paper, we have developed a fog computing-based framework that not only detects and monitors the elephants but also improves the latency, network utilization and execution time. The fog-enabled elephant monitoring system informs the train control system of the existence of elephants in the corridor and a warning light LED flashes near the train tracks. This system is deployed and simulated in the iFogSim simulator and shows improvements in latency, network utilization, and execution time compared to cloud-based infrastructures.
Riman Mandal, Manash Kumar Mondal, Sourav Banerjee, Gautam Srivastava, Waleed Alnumay, Uttam Ghosh, and Utpal Biswas
Springer Science and Business Media LLC
Manash Kumar Mondal, Riman Mandal, Sourav Banerjee, Pushpita Chatterjee, Wathiq Mansoor, and Utpal Biswas
IEEE
Visitor identification is one of the vital problems of society. The visitor identification process consists of an intelligent security camera that monitors the entrance of the door of a residence, taking pictures of the human when they enter, automated techniques extract the human face from the image and identify them with the help of a database in real-time. However, there is no such existing research that can deal with visitor identification using fog computing. In this paper, a visitor identification framework is proposed that is entirely deployed in the fog computing environment. The proposed prototype is deployed in the Java-based iFogSim simulator. Each simulation uses a different number of cameras for video stream generations. The human object part is trimmed and selected from the raw data for processing. The trimmed data is transferred to the fog node instead of the cloud for processing. This paper deals with how the generated data pass through each module and takes how much amount of time to process the data in distinct modules. Generally, the fog-based framework reduces latency, network usage, and energy consumption. The research finds an improvement in overall latency and network usage of fog computing environments over the cloud environment.
Manash Kumar Mondal, Riman Mandal, Sourav Banerjee, Monali Sanyal, Uttam Ghosh, and Utpal Biswas
IEEE
Real-time monitoring is necessary for saving endangered wildlife. The camera-trapping technology is used to monitor wild animals like tigers, lions, bears etc. in forests. Due to changes in the forest echo system and the expansion of human civilization near the forest, tigers often enter the villages. As a consequence, the Tiger-Human conflict occurs more frequently. Typically, cloud computing technologies are used for storing and processing the image data generated from trap cameras. A wildlife monitoring system is a time-sensitive application, and processing and decision-making using cloud computing are relatively slow. Timeliness and quick response are essential for these types of applications. Highlighting this issue, this article focuses on the design and development of a fog-assisted tiger alarming framework that detects tigers in the corridor. The application also delivers systematic alerts to the villagers. Therefore, the conflict between humans and tigers will reduce. For comparison, we have deployed the same model in a cloud computing environment. The proposed framework is simulated in the iFogSim simulator. The outcome exhibits that the proposed fog-based model successfully reduces latency and network usage compared to the traditional cloud-based model. The comparative analysis also indicates a significant improvement in the execution time over the cloud system.
Manash Kumar Mondal, Riman Mandal, Sourav Banerjee, U. Biswas, Pushpita Chatterjee and Waleed S. Alnumay
Riman Mandal, Manash Kumar Mondal, Sourav Banerjee, Pushpita Chatterjee, Wathiq Mansoor, and Utpal Biswas
IEEE
Cloud computing forms the backbone of the era of automation and the Internet of Things (IoT). It offers computing and storage-based services on consumption-based pricing. Large-scale datacenters are used to provide these service and consumes enormous electricity. Datacenters contribute a large portion of the carbon footprint in the environment. Through virtual machine (VM) consolidation, datacenter energy consumption can be reduced via efficient resource management. VM selection policy is used to choose the VM that needs migration. In this research, we have proposed PbV mSp: A priority-based VM selection policy for VM consolidation. The PbV mSp is implemented in cloudsim and evaluated compared with well-known VM selection policies like gpa, gpammt, mimt, mums, and mxu. The results show that the proposed PbV mSp selection policy has outperformed the exisitng policies in terms of energy consumption and other metrics.
Riman Mandal, Manash Kumar Mondal, Sourav Banerjee, Pushpita Chatterjee, Wathiq Mansoor, and Utpal Biswas
IEEE
The pay-per-use model of cloud computing is booming in every industry. Power-hungry large heterogeneous data centers are providing these cloud services for 24× 7. Green cloud computing offers an energy-efficient and environment-friendly mechanism to offer cloud services while maintaining the quality of service (QoS). QoS is defined in terms of service-level agreement (SLA). VM Consolidation is one of the widely used frameworks that involve Virtual Machine (VM) migration and VM Placement to reduce energy consumption and maintain better QoS. Using the VM Placement policy VMs are allocated to suitable physical machines (PM). Efficient VM placement with better resource management can conserve electricity and provide better QoS. A new SLA and energy-aware VM placement policy have been developed in this research. The VMs are placed in a PM having the least energy consumption and SLA violation (SLAV). Further, the proposed VM placement policy has been implemented using CloudSim and it has been shown that the proposed VM placement policy outperforms state-of-art VM placement policies like PEBFD, MBFD, MFPED, and PEFED.
Riman Mandal, Sourav Banerjee, Md Bagbul Islam, Pushpita Chatterjee, and Utpal Biswas
Springer International Publishing
Riman Mandal, Manash Kumar Mondal, Sourav Banerjee, and Utpal Biswas
Springer Science and Business Media LLC
With the rapid demand for service-oriented computing in association with the growth of cloud computing technologies, large-scale virtualized data centers have been established throughout the globe. These huge data centers consume power at a large scale that results in a high operational cost. The massive carbon footprint from the energy generators is another great issue to deal global warming. It is essential to lower the rate of carbon emission and energy consumption as much as possible. The live-migration-enabled dynamic virtual machine consolidation results in high energy saving. But it also incurs the violation of service level agreement (SLA). Excessive migration may lead to performance degradation and SLA violation. The process of VM selection for migration plays a vital role in the domain of energy-aware cloud computing. Using VM selection policies, VMs are selected for migration. A new power-aware VM selection policy has been proposed in this research that helps in VM selection for migration. The proposed power-aware VM selection policy has been further evaluated using trace-based simulation environment.
Riman Mandal, Manash Kumar Mondal, Sourav Banerjee, Chinmay Chakraborty, and Utpal Biswas
Elsevier
Sourav Banerjee, Aritra Roy, A. Chowdhury, Ranit Mutsuddy, R. Mandal and U. Biswas
Cloud computing is a varied computing archetype uniting the benefits of service-oriented architecture and utility computing. In cloud computing, resource allocation and its proper utilization, to achieve a higher throughput and quality of service (QoS), has become a great research issue. This paper highlights a new cloudlet allocation strategy that utilizes all available resources efficiently and enhances the QoS by applying deadline-based workload distribution. It is believed that this paper would benefit both cloud users and researchers in various aspects. The entire experiment is done in Cloudsim Toolkit-3.0.3, by modifying the required classes.
Sourav Banerjee, R. Mandal and U. Biswas
Cloud computing is on the horizon of the domain of information technology over the recent few years, giving different remotely accessible services to the cloud users. The quality-of-service (QoS) maintaining of a cloud service provider is the most dominating research issue today. The QoS embraces with different issues like virtual machine (VM) allocation, optimization of response time and throughput, utilizing processing capability, load balancing etc. VM allocation policy deals with the allocation of VMs to the hosts in different datacenters. This paper highlights a new VM allocation policy that distributes the load of VMs among hosts which improves the utilization of hosts’ processing capability as well as makespan and throughput of cloud system. The experimental results are obtained by utilizing trace based simulation in CloudSim 3.0.3 and compared with existing VM allocation policies.