Sushil Kumar Singh
@seoultech.ac.kr
Computer Science and Engineering
Seoul National University of Science and Technology, Seoul, South Korea
RESEARCH INTERESTS
IoT, Blockchain, Smart City, Machine Learning
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Sushil Kumar Singh, Manish Kumar, Sudeep Tanwar, and Jong Hyuk Park
Elsevier BV
Ankur Sapariya, Ravikumar R N, Urvi Bhatt, Suraj Prakash Singh, Soram Wanglen, and Sushil Kumar Singh
IEEE
In response to the evolving landscape of modern military operations, where drone technology now constitutes an estimated 70% of Indian military missions, as evidenced by recent statistics, our proposed AI-based Visual Attention Scenario Identification Model takes center stage. This groundbreaking model is meticulously designed to augment the safety and efficiency of military endeavors in challenging terrains. Our model harnesses advanced deep-learning CNN techniques by processing real-time images from various sources, including drones, strategically focusing on critical details within these visuals. This approach enables the timely detection of potential dangers, even in complex environments, with the primary objective of significantly enhancing situational awareness for military personnel. Positioned as a valuable asset in the military's toolkit, our model, with 85% accuracy, proves particularly effective in identifying potential threats in forested and mountainous regions, ultimately minimizing risks faced by soldiers on the ground.
Radha Raman Chandan, Sushil Kumar, Sushil Kumar Singh, Abdul Aleem, and Basu Dev Shivahare
CRC Press
Neeraj Joshi, Sheshikala Martha, Shivam Chaudhary, Prakhar Consul, and Sushil Kumar Singh
Springer Nature Switzerland
Sushil Kumar Singh, Laurence T. Yang, and Jong Hyuk Park
Elsevier BV
Radha Raman Chandan, Sushil Kumar, Sushil Kumar Singh, Abdul Aleem, and Basu Dev Shivahare
CRC Press
Sushil Kumar Singh and Jong Hyuk Park
Institute of Electrical and Electronics Engineers (IEEE)
Sushil Kumar Singh, Pradip Kumar Sharma, Yi Pan, and Jong Hyuk Park
Institute of Electrical and Electronics Engineers (IEEE)
Sushil Kumar Singh, Changhoon Lee, and Jong Hyuk Park
Elsevier BV
Jin Ho Park Jin Ho Park, Sushil Kumar Singh Jin Ho Park, Mikail Mohammed Salim Sushil Kumar Singh, Abir EL Azzaoui Mikail Mohammed Salim, and Jong Hyuk Park Abir EL Azzaoui
Angle Publishing Co., Ltd.
<p>Internet of Things (IoT) and sensor devices have been connected due to the development of the IoT and Information Communication Technology (ICT). It offers automatic environments in smart city and IoT scenarios and describes investments in advanced resources in futuristic human lives as sustainable growth of quality-wise life with intelligent infrastructure. Nowadays, IoT devices are continuously increasing and utilized in advanced IoT applications, including Smart Homes, Smart Farming, Smart Enterprises, and others. However, security and privacy are significant challenges with Ransomware-based Cyber-attack detection in IoT due to the lack of security design and heterogeneity of IoT devices. In the last few years, various advanced paradigms and technologies have been utilized to mitigate the security issues with Ransomware attack detection in IoT devices and data. This paper comprehensively surveys Ransomware-based Cyber Attacks and discusses solutions based on advanced technologies such as Artificial Intelligence (AI), Blockchain, and Software Defined Networks (SDN). Then, we design service scenarios for ransomware-based cyber-attack detection. Finally, we summarize the open research challenges and future directions for ransomware in IoT.</p> <p>&nbsp;</p>
Sushil Kumar Singh, Laihyuk Park, and Jong Hyuk Park
IEEE
Over the last few years, smart vehicles have continuously grown and connected to the Internet of Things (IoT), sensors, and advanced communication technologies. Then, it creates a cluster of distributed networks known as IoT-enabled Smart Vehicular Networks. Integrating smart vehicular networks, IoT, and the Internet of Vehicles (IoV) provide interactive solutions such as traffic efficiency, driving safety, autonomous driving, and robust information exchange in the smart city infrastructure. Still, Smart vehicular networks have challenges, such as privacy preservation, security, data authentication, communication bandwidth, and centralization due to vehicles and networks-related data directly stored in the traditional cloud. Motivated by advanced technologies, including Blockchain and Federated Learning, we propose an approach for Privacy-Preserved IoT-enabled Smart Vehicular Networks to address these challenges. The concept of Blockchain and Federated Learning is leveraged in the middle layer of the proposed work for privacy preservation and smart vehicle data authentication, stored at the cloud layer. Furthermore, we show the technological flow of the proposed approach for the IoT-enabled smart vehicular networks in the smart city.
Sushil Kumar Singh, Yi Pan, and Jong Hyuk Park
Elsevier BV
Mikail Mohammed Salim, Sushil Kumar Singh, and Jong Hyuk Park
Elsevier BV
Abir EL Azzaoui, Sushil Kumar Singh, and Jong Hyuk Park
Elsevier BV
Nowadays, the world is experiencing a pandemic crisis due to the spread of COVID-19, a novel coronavirus disease. The contamination rate and death cases are expeditiously increasing. Simultaneously, people are no longer relying on traditional news channels to enlighten themselves about the epidemic situation. Alternately, smart cities citizens are relying more on Social Network Service (SNS) to follow the latest news and information regarding the outbreak, share their opinions, and express their feelings and symptoms. In this paper, we propose an SNS Big Data Analysis Framework for COVID-19 Outbreak Prediction in Smart Sustainable Healthy City, where Twitter platform is adopted. Over 1000 Tweets were collected during two months, 38% of users aged between 18 and 29, while 26% are between 30 and 49 years old. 56% of them are males and 44% are females. The geospatial location is USA, and the used language is English. Natural Language Processing (NLP) is deployed to filter the tweets. Results demonstrated an outbreak cluster predicted seven days earlier than the confirmed cases with an indicator of 0.989. Analyzing data from SNS platforms enabled predicting future outbreaks several days earlier, and scientifically reduce the infection rate in a smart sustainable healthy city environment.
Jeonghun Cha, Sushil Kumar Singh, Tae Woo Kim, and Jong Hyuk Park
Elsevier BV
Sushil Singh, Jeonghun Cha, Tae Kim, and Jong Park
National Library of Serbia
For the advancement of the Internet of Things (IoT) and Next Generation Web, various applications have emerged to process structured or unstructured data. Latency, accuracy, load balancing, centralization, and others are issues on the cloud layer of transferring the IoT data. Machine learning is an emerging technology for big data analytics in IoT applications. Traditional data analyzing and processing techniques have several limitations, such as centralization and load managing in a massive amount of data. This paper introduces a Machine Learning Based Distributed Big Data Analysis Framework for Next Generation Web in IoT. We are utilizing feature extraction and data scaling at the edge layer paradigm for processing the data. Extreme Learning Machine (ELM) is adopting in the cloud layer for classification and big data analysis in IoT. The experimental evaluation demonstrates that the proposed distributed framework has a more reliable performance than the traditional framework.
Sushil Kumar Singh, Yi Pan, and Jong Hyuk Park
Computers, Materials and Continua (Tech Science Press)
Sushil Kumar Singh, Young-Sik Jeong, and Jong Hyuk Park
Elsevier BV
Abstract In recent years, the Internet of Things (IoT) infrastructures are developing in various industrial applications in sustainable smart cities and societies such as smart manufacturing, smart industries. The Cyber-Physical System (CPS) is also part of IoT-oriented infrastructure. CPS has gained considerable success in industrial applications and critical infrastructure with a distributed environment. This system aims to integrate the physical world to computational facilities as cyberspace. However, there are many challenges, such as security and privacy, centralization, communication latency, scalability in such an environment. To mitigate these challenges, we propose a Deep Learning-based IoT-oriented infrastructure for a secure smart city where Blockchain provides a distributed environment at the communication phase of CPS, and Software-Defined Networking (SDN) establishes the protocols for data forwarding in the network. A deep learning-based cloud is utilized at the application layer of the proposed infrastructure to resolve communication latency and centralization, scalability. It enables cost-effective, high-performance computing resources for smart city applications such as the smart industry, smart transportation. Finally, we evaluated the performance of our proposed infrastructure. We compared it with existing methods using quantitative analysis and security and privacy analysis with different measures such as scalability and latency. The evaluation of our implementation results shows that performance is improved.
Sushil Kumar Singh, Shailendra Rathore, and Jong Hyuk Park
Elsevier BV
Abstract In the recent year, Internet of Things (IoT) is industrializing in several real-world applications such as smart transportation, smart city to make human life reliable. With the increasing industrialization in IoT, an excessive amount of sensing data is producing from various sensors devices in the Industrial IoT. To analyzes of big data, Artificial Intelligence (AI) plays a significant role as a strong analytic tool and delivers a scalable and accurate analysis of data in real-time. However, the design and development of a useful big data analysis tool using AI have some challenges, such as centralized architecture, security, and privacy, resource constraints, lack of enough training data. Conversely, as an emerging technology, Blockchain supports a decentralized architecture. It provides a secure sharing of data and resources to the various nodes of the IoT network is encouraged to remove centralized control and can overcome the existing challenges in AI. The main goal of our research is to design and develop an IoT architecture with blockchain and AI to support an effective big data analysis. In this paper, we propose a Blockchain-enabled Intelligent IoT Architecture with Artificial Intelligence that provides an efficient way of converging blockchain and AI for IoT with current state-of-the-art techniques and applications. We evaluate the proposed architecture and categorized into two parts: qualitative analysis and quantitative analysis. In qualitative evaluation, we describe how to use AI and Blockchain in IoT applications with “AI-driven Blockchain” and “Blockchain-driven AI.” In quantitative analysis, we present a performance evaluation of the BlockIoTIntelligence architecture to compare existing researches on device, fog, edge and cloud intelligence according to some parameters such as accuracy, latency, security and privacy, computational complexity and energy cost in IoT applications. The evaluation results show that the proposed architecture performance over the existing IoT architectures and mitigate the current challenges.
Jose Costa Sapalo Sicato, S. Singh, Shailendra Rathore and J. Park
Nowadays, the Internet of Things (IoT) network, is increasingly becoming a ubiquitous connectivity between different advanced applications such as smart cities, smart homes, smart grids, and many others. The emerging network of smart devices and objects enables people to make smart decisions through machine to machine (M2M) communication. Most real-world security and IoT-related challenges are vulnerable to various attacks that pose numerous security and privacy challenges. Therefore, IoT offers efficient and effective solutions. intrusion detection system (IDS) is a solution to address security and privacy challenges with detecting different IoT attacks. To develop an attack detection and a stable network, this paper’s main objective is to provide a comprehensive overview of existing intrusion detections system for IoT environment, cyber-security threats challenges, and transparent problems and concerns are analyzed and discussed. In this paper, we propose software-defined IDS based distributed cloud architecture, that provides a secure IoT environment. Experimental evaluation of proposed architecture shows that it has better detection and accuracy than traditional methods.
Danlami Gabi, Abdul Samad Ismail, Anazida Zainal, Zalmiyah Zakaria, and Ahmad Al-Khasawneh
UUM Press, Universiti Utara Malaysia
The unpredictable number of task arriving at cloud datacentre and the rescaling of virtual processing elements can affect the provisioning of better Quality of Service expectations during task scheduling in cloud computing. Existing researchers have contributed several task scheduling algorithms to provide better QoS expectations but are characterized with entrapment at the local search and high dimensional breakdown due to slow convergence speed and imbalance between global and local search, resulting from lack of scalability. Dynamic task scheduling algorithms that can adjust to long-time changes and continue facilitating the provisioning of better QoS are necessary for cloud computing environment. In this study, a Cloud Scalable Multi-Objective Cat Swarm Optimization-based Simulated Annealing algorithm is proposed. In the proposed method, the orthogonal Taguchi approach is applied to enhance the SA which is incorporated into the local search of the proposed CSMCSOSA algorithm for scalability performance. A multi-objective QoS model based on execution time and execution cost criteria is presented to evaluate the efficiency of the proposed algorithm on CloudSim tool with two different datasets. Quantitative analysis of the algorithm is carried out with metrics of execution time, execution cost, QoS and performance improvement rate percentage. Meanwhile, the scalability analysis of the proposed algorithm using Isospeed-efficiency scalability metric is also reported. The results of the experiment show that the proposed CSM-CSOSA has outperformed Multi-Objective Genetic Algorithm, Multi-Objective Ant Colony and Multi-Objective Particle Swarm Optimization by returning minimum execution time and execution cost as well as better scalability acceptance rate of 0.4811−0.8990 respectively. The proposed solution when implemented in real cloud computing environment could possibly meet customers QoS expectations as well as that of the service providers.
Sushil Kumar Singh, Mikail Mohammed Salim, Jeonghun Cha, Yi Pan, and Jong Hyuk Park
MDPI AG
Nowadays, 5G network infrastructures are being developed for various industrial IoT (Internet of Things) applications worldwide, emerging with the IoT. As such, it is possible to deploy power-optimized technology in a way that promotes the long-term sustainability of networks. Network slicing is a fundamental technology that is implemented to handle load balancing issues within a multi-tenant network system. Separate network slices are formed to process applications having different requirements, such as low latency, high reliability, and high spectral efficiency. Modern IoT applications have dynamic needs, and various systems prioritize assorted types of network resources accordingly. In this paper, we present a new framework for the optimum performance of device applications with optimized network slice resources. Specifically, we propose a Machine Learning-based Network Sub-slicing Framework in a Sustainable 5G Environment in order to optimize network load balancing problems, where each logical slice is divided into a virtualized sub-slice of resources. Each sub-slice provides the application system with different prioritized resources as necessary. One sub-slice focuses on spectral efficiency, whereas the other focuses on providing low latency with reduced power consumption. We identify different connected device application requirements through feature selection using the Support Vector Machine (SVM) algorithm. The K-means algorithm is used to create clusters of sub-slices for the similar grouping of types of application services such as application-based, platform-based, and infrastructure-based services. Latency, load balancing, heterogeneity, and power efficiency are the four primary key considerations for the proposed framework. We evaluate and present a comparative analysis of the proposed framework, which outperforms existing studies based on experimental evaluation.