Toward Energy-Efficient and Cost-Effective Task Offloading in Mobile Edge Computing for Intelligent Surveillance Systems Manash Kumar Mondal, Sourav Banerjee, Debashis Das, Uttam Ghosh, Mohammed S. Al-Numay, et al. IEEE Transactions on Consumer Electronics, 2024 In the era of pervasive digital connectivity, intelligent surveillance systems (ISS) have become essential tools for ensuring public safety, protecting critical infrastructure, and deterring security threats in various environments. The current state of these systems heavily relies on the computational capabilities of mobile devices for tasks such as real-time video analysis, object detection, and tracking. However, the limited processing power and energy constraints of these devices hinder their ability to perform these tasks efficiently and effectively. The dynamic nature of the surveillance environment also adds complexity to the task-offloading process. To address this issue, mobile edge computing (MEC) comes into play by offering edge servers with higher computational capabilities and proximity to mobile devices. It enables ISS by offloading computationally intensive tasks from resource-constrained mobile devices to nearby MEC servers. Therefore, in this paper, we propose and implement an energy-efficient and cost-effective task-offloading framework in the MEC environment. The amalgamation of binary and partial task-offloading strategies is used to achieve a cost-effective and energy-efficient system. We also compare the proposed framework in MEC with mobile cloud computing (MCC) environments. The proposed framework addresses the challenge of achieving energy-efficient and cost-effective solutions in the context of MEC for ISS. The iFogSim simulator is used for implementation and simulation purposes. The simulation results show that the proposed framework reduces latency, cost, execution time, network usage, and energy consumption.
Big IoT Data Analytics in Fog Computing Fog Computing for Intelligent Cloud Iot Systems, 2024
Toward Fast Reliable Intelligent Industry 5.0—A Comprehensive Study Manash Kumar Mondal, Sourav Banerjee, Yudong Zhang Secure and Smart Cyber Physical Systems, 2024 Industry 5.0 represents the next phase of industrial evolution, promising unprecedented levels of efficiency, reliability, and intelligence in manufacturing and production processes. This book chapter provides a comprehensive exploration of the key principles, methodologies, and emerging technologies essential to realizing the vision of Industry 5.0. In the context of Industry 5.0, this chapter delves into the convergence of various cutting-edge technologies, including the Internet of Things (IoT), artificial intelligence (AI), big data analytics, and advanced robotics, to create an ecosystem of interconnected, intelligent, and adaptive industrial systems. It highlights the pivotal role of real-time data acquisition, processing, and decision-making in optimizing industrial operations. The chapter further investigates the challenges and opportunities associated with Industry 5.0, addressing concerns related to data security, privacy, and scalability. It emphasizes the need for robust, fast, and reliable communication networks, such as 5G, as a critical enabler for the seamless integration of cyber-physical systems., This study provides insights into the adoption of digital twins, predictive maintenance, and autonomous manufacturing in Industry 5.0, illustrating how these concepts enhance reliability and performance while reducing downtime and operational costs. It also explores the concept of human-machine collaboration and the evolving role of the workforce in an intelligent manufacturing environment. This book chapter serves as a comprehensive guide for researchers, engineers, and industrial practitioners interested in the transformational journey towards Fast, Reliable, Intelligent Industry 5.0, offering insights, best practices, and a vision for the future of manufacturing and production.
A Deep Learning-Based Car Accident Detection Framework Using Edge and Cloud Computing Sourav Banerjee, Manash Kumar Mondal, Moumita Roy, Waleed S. Alnumay, Utpal Biswas IEEE Access, 2024 The ever-changing technology landscape has seen a significant breakthrough with the introduction of edge computing. This innovation has revolutionized various domains, and one of its critical applications is in the domain of accident detection. Edge computing can help enhance road safety and emergency response by enabling real-time processing and analysis of sensory information from onboard sensors, cameras, and other connected devices. By integrating edge computing into accident detection systems, we can overcome the limitations of conventional centralized cloud-based methods and create a safer transportation network. In this article, we have proposed an accident detection framework using Deep Learning (DL) in the edge cloud environment. For accident detection, we have used a Convolutional Neural Network (CNN)- based DL model. The DL model detects the accident in the edge node which is near the data source. The proposed framework provides low latency, minimal network usage, and lower execution time as compared to only cloud-based deployment. Additionally, the proposed accident detection model is accurate up to 95.91% with Precision 0.9574, Recall 0.9574 and F1 score 0.9574 in the cloud-edge environment.
Design and Development of a Fog-Assisted Elephant Corridor over a Railway Track Manash Kumar Mondal, Riman Mandal, Sourav Banerjee, Utpal Biswas, Jerry Chun-Wei Lin, Osama Alfarraj, Amr Tolba Sustainability Switzerland, 2023 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.
Fog Assisted Tiger Alarming Framework for Saving Endangered Wild Life Manash Kumar Mondal, Riman Mandal, Sourav Banerjee, Monali Sanyal, Uttam Ghosh, Utpal Biswas Conference Proceedings IEEE SOUTHEASTCON, 2023 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.
Fog assisted Visitor Identification Framework with improved Latency and Network Usage Manash Kumar Mondal, Riman Mandal, Sourav Banerjee, Pushpita Chatterjee, Wathiq Mansoor, Utpal Biswas 2023 Advances in Science and Engineering Technology International Conferences Aset 2023, 2023 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.
Intelligent Water Management: Deep Learning in Edge and Fog Computing for Smart Irrigation MK Mondal, S Banerjee, M Roy, U Biswas, NC Debnath International Conference on Advanced Intelligent Systems and Informatics … , 2025 2025 Citations: 1
A Deep Learning-based Car Accident Detection Framework using Edge and Cloud Computing S Banerjee, MK Mondal, M Roy, WS Alnumay, U Biswas IEEE Access 12, 130207-130115 , 2024 2024 Citations: 11
Toward fast reliable intelligent industry 5.0—A comprehensive study MK Mondal, S Banerjee, Y Zhang Secure and Smart Cyber-Physical Systems, 56-80 , 2024 2024 Citations: 5
Big IoT data analytics in fog computing MK Mondal, R Mandal, U Biswas Fog Computing for Intelligent Cloud IoT Systems, 279-307 , 2024 2024 Citations: 2
Toward Energy-Efficient and Cost-Effective Task Offloading in Mobile Edge Computing for Intelligent Surveillance Systems MK Mondal, S Banerjee, D Das, U Ghosh, MS Al-Numay, U Biswas IEEE Transactions on Consumer Electronics 70 (1), 4087-4094 , 2024 2024 Citations: 38
Fog assisted tiger alarming framework for saving endangered wild life MK Mondal, R Mandal, S Banerjee, M Sanyal, U Ghosh, U Biswas SoutheastCon 2023, 798-803 , 2023 2023 Citations: 4
Design and development of a fog-assisted elephant corridor over a railway track MK Mondal, R Mandal, S Banerjee, U Biswas, JCW Lin, O Alfarraj, ... Sustainability 15 (7), 5944 , 2023 2023 Citations: 15
Fog assisted visitor identification framework with improved latency and network usage MK Mondal, R Mandal, S Banerjee, P Chatterjee, W Mansoor, U Biswas 2023 Advances in Science and Engineering Technology International … , 2023 2023 Citations: 2
MECpVmS: an SLA aware energy-efficient virtual machine selection policy for green cloud computing R Mandal, MK Mondal, S Banerjee, G Srivastava, W Alnumay, U Ghosh, ... Cluster Computing 26 (1), 651-665 , 2023 2023 Citations: 51
PbV mSp: A priority-based VM selection policy for VM consolidation in green cloud computing R Mandal, MK Mondal, S Banerjee, P Chatterjee, W Mansoor, U Biswas 2022 5th International Conference on Signal Processing and Information … , 2022 2022 Citations: 9
Design and implementation of an SLA and energy-aware VM placement policy in green cloud computing R Mandal, MK Mondal, S Banerjee, P Chatterjee, W Mansoor, U Biswas 2022 IEEE Globecom Workshops (GC Wkshps), 777-782 , 2022 2022 Citations: 11
A CPS based social distancing measuring model using edge and fog computing MK Mondal, R Mandal, S Banerjee, U Biswas, P Chatterjee, W Alnumay Computer Communications 194, 378-386 , 2022 2022 Citations: 13
Various Security issues and Its Solutions in the Domain of Fog Computing MK Mondal, M Bandyopadhyay 2nd International Conference on the Emerging Technologies in Computing … , 2022 2022
A comparative study between cloud computing and fog computing MK Mondal, M Bandyopadhyay Brainwave: A Multidisciplinary Journal 2 (1), 36-42 , 2021 2021 Citations: 5
A survey and critical analysis on energy generation from datacenter R Mandal, MK Mondal, S Banerjee, C Chakraborty, U Biswas Data Deduplication Approaches, 203-230 , 2021 2021 Citations: 19
An approach toward design and development of an energy-aware VM selection policy with improved SLA violation in the domain of green cloud computing: R. Mandal et al. R Mandal, MK Mondal, S Banerjee, U Biswas The Journal of Supercomputing 76 (9), 7374-7393 , 2020 2020 Citations: 80
MOST CITED SCHOLAR PUBLICATIONS
An approach toward design and development of an energy-aware VM selection policy with improved SLA violation in the domain of green cloud computing: R. Mandal et al. R Mandal, MK Mondal, S Banerjee, U Biswas The Journal of Supercomputing 76 (9), 7374-7393 , 2020 2020 Citations: 80
MECpVmS: an SLA aware energy-efficient virtual machine selection policy for green cloud computing R Mandal, MK Mondal, S Banerjee, G Srivastava, W Alnumay, U Ghosh, ... Cluster Computing 26 (1), 651-665 , 2023 2023 Citations: 51
Toward Energy-Efficient and Cost-Effective Task Offloading in Mobile Edge Computing for Intelligent Surveillance Systems MK Mondal, S Banerjee, D Das, U Ghosh, MS Al-Numay, U Biswas IEEE Transactions on Consumer Electronics 70 (1), 4087-4094 , 2024 2024 Citations: 38
A survey and critical analysis on energy generation from datacenter R Mandal, MK Mondal, S Banerjee, C Chakraborty, U Biswas Data Deduplication Approaches, 203-230 , 2021 2021 Citations: 19
Design and development of a fog-assisted elephant corridor over a railway track MK Mondal, R Mandal, S Banerjee, U Biswas, JCW Lin, O Alfarraj, ... Sustainability 15 (7), 5944 , 2023 2023 Citations: 15
A CPS based social distancing measuring model using edge and fog computing MK Mondal, R Mandal, S Banerjee, U Biswas, P Chatterjee, W Alnumay Computer Communications 194, 378-386 , 2022 2022 Citations: 13
A Deep Learning-based Car Accident Detection Framework using Edge and Cloud Computing S Banerjee, MK Mondal, M Roy, WS Alnumay, U Biswas IEEE Access 12, 130207-130115 , 2024 2024 Citations: 11
Design and implementation of an SLA and energy-aware VM placement policy in green cloud computing R Mandal, MK Mondal, S Banerjee, P Chatterjee, W Mansoor, U Biswas 2022 IEEE Globecom Workshops (GC Wkshps), 777-782 , 2022 2022 Citations: 11
PbV mSp: A priority-based VM selection policy for VM consolidation in green cloud computing R Mandal, MK Mondal, S Banerjee, P Chatterjee, W Mansoor, U Biswas 2022 5th International Conference on Signal Processing and Information … , 2022 2022 Citations: 9
Toward fast reliable intelligent industry 5.0—A comprehensive study MK Mondal, S Banerjee, Y Zhang Secure and Smart Cyber-Physical Systems, 56-80 , 2024 2024 Citations: 5
A comparative study between cloud computing and fog computing MK Mondal, M Bandyopadhyay Brainwave: A Multidisciplinary Journal 2 (1), 36-42 , 2021 2021 Citations: 5
Fog assisted tiger alarming framework for saving endangered wild life MK Mondal, R Mandal, S Banerjee, M Sanyal, U Ghosh, U Biswas SoutheastCon 2023, 798-803 , 2023 2023 Citations: 4
Big IoT data analytics in fog computing MK Mondal, R Mandal, U Biswas Fog Computing for Intelligent Cloud IoT Systems, 279-307 , 2024 2024 Citations: 2
Fog assisted visitor identification framework with improved latency and network usage MK Mondal, R Mandal, S Banerjee, P Chatterjee, W Mansoor, U Biswas 2023 Advances in Science and Engineering Technology International … , 2023 2023 Citations: 2
Intelligent Water Management: Deep Learning in Edge and Fog Computing for Smart Irrigation MK Mondal, S Banerjee, M Roy, U Biswas, NC Debnath International Conference on Advanced Intelligent Systems and Informatics … , 2025 2025 Citations: 1
Various Security issues and Its Solutions in the Domain of Fog Computing MK Mondal, M Bandyopadhyay 2nd International Conference on the Emerging Technologies in Computing … , 2022 2022
