Networked Systems Laboratory

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The worldwide education landscape has transformed due to the COVID-19 pandemic, providing an opportunity to enhance virtual learning. Nevertheless, this transition has disadvantages, including less face-to-face interaction and the possibility of student seclusion. The metaverse, a digital realm, provides an optimal substitute for conventional virtual education. Nevertheless, its immersive nature poses issues for conventional digital learning management systems. This article proposes using blockchain technology and non-fungible tokens (NFTs) as unique identifiers to handle credentials and transactions in the metaverse while ensuring security effectively. This approach tackles difficulties associated with the administration of certificates, including issuing, maintaining records, ensuring traceability, and preserving integrity. Additionally, it establishes a fairly secure setting that effectively prevents data manipulation. The proposed approach entails the implementation of a smart contract that integrates a credential's transactions and an attribute-based access control mechanism. The research further addresses the challenge of limited storage resources caused by the dynamic nature of the metaverse and the requirement to manage huge quantities of data. The implementation results and applicability assessment confirm the feasibility of the proposed approach. Eventually, this study contributes to the ongoing discussion on the convergence of blockchain, NFTs, and smart contracts with its new perspective on digital learning management in the metaverse platform.

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Unmanned aerial vehicles (UAVs) have recently sparked attention in the logistics supply chain throughout the period. However, using UAVs in logistics has challenges related to security, data integrity, and trustworthiness in the healthcare industry. To address these issues, this paper introduces a blockchain-integrated scheme to improve the security and trustworthiness of medical supply delivery by leveraging UAVs. Blockchain, a distributed ledger system, enables transparent and reliable participant interactions. The proposed approach employs a sophisticated two-phase verification mechanism to tackle security concerns. The initial phase employs $\alpha-hash$ bloom filter to verify the integrity of data and identify any unauthorized senders. In the second step, digital signature methods authenticate the sender's identity, guaranteeing the data's integrity and prohibiting unauthorized entry. Smart contracts ensure transactions maintain trustworthiness, data integrity, and security without alteration. The InterPlanetary File System (IPFS) protocol addresses storage cost issues. Introducing the 5G-enabled Tactile Internet (TI) enhances communication by being more responsive and efficient. The proposed approach creates a credit scoring mechanism for UAV role selection to improve the delivery process swiftly. The scheme surpasses traditional systems in key performance metrics, and comparing the model's validity with several existing schemes illustrates the proposed scheme's efficacy and feasibility. This scheme could efficiently tackle challenges in providing unmanned medical supplies in smart healthcare systems using blockchain, smart contracts, IPFS, and TI.

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With the annual rise in the number of creative startup enterprises in the Manufacturing Execution System (MES), there is
a rising need for research on the job-shop scheduling problem(JSSP). JSSP, a complex NP-hard issue, aims to optimize
allocating scarce resources to improve production efficiency. JSS is employed in several sectors, such as warehouse order
packaging and manufacturing on production lines. During practical circumstances, the operating context might become
Complicated owing to dynamic events such as the delivery of tasks at different times, delays in job completion, machine
malfunctions, or unexpected incidents. A lot of scheduling heuristics, including dispatching rules, have been used to
make good schedules by effectively prioritizing candidates, like manufacturing machines. Recently, Artificial Intelligence
(AI), specifically Deep Reinforcement Learning (DRL), has proven advantageous in autonomously acquiring scheduling
heuristics for JSS, mostly because of its versatility, effectiveness, and efficiency. Regrettably, there is an insufficient
number of surveys accessible to analyze and evaluate the benefits and drawbacks of these active initiatives. This work
seeks to address this deficiency by undertaking a thorough investigation of Bio-inspired heuristic algorithms and different
types of AI algorithms for JSS. This article has provided a concise overview of the utilized learning models, primary
objectives, datasets, and performance metrics. The outcomes are presented in tabular format, which enables a clear and
concise exhibition of the discoveries. In addition, this article analyzes certain issues and hurdles to recommend specific
areas that may be targeted for the future advancement of autonomous scheduling heuristic design.

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Deep learning (DL)-based automatic modulation classification (AMC) is a primary research field for identifying modulation types. However, traditional DL-based AMC approaches rely on hand-crafted features, which can be time consuming and may not capture all relevant information in the signal. Additionally, they are centralized solutions that are trained on large amounts of data acquired from local clients and stored on a server, leading to weak performance in terms of correct classification probability. To address these issues, a federated learning (FL)-based AMC approach is proposed, called FL-MP-CNN-AMC, which takes into account the effects of multipath channels (reflected and scattered paths) and considers the use of a modified loss function for solving the class imbalance problem caused by these channels. In addition, hyper parameter tuning and optimization of the loss function are discussed and analyzed to improve the performance of the proposed approach. The classification performance is investigated by considering the effects of interference level, delay spread, scattered and reflected paths, phase offset, and frequency offset. The simulation results show that the proposed approach provides  excellent performance in terms of correct classification probability, confusion matrix, convergence and communication overhead when compared to contemporary methods.

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The rapid integration of the Internet of Artificial Intelligence and Internet of Things (AI-IoT) technologies has given rise to a pivotal element of the upcoming digital era, the Metaverse. This confluence has significantly impacted virtual learning platforms by introducing enhanced, immersive, and interactive environments for learners, educators, and institutions. However, the growing reliance on Metaverse necessitates robust cybersecurity measures to detect and mitigate cyber threats and ensure the safety of users. This paper proposes an explainable deep neural network (DNN) designed to identify and address network intrusion attacks within Metaverse learning environments. By leveraging the latest cybersecurity IoT datasets and employing an effective feature selection method, this study provides a visually interpretable, trustworthy, and quantitative explanation of the network intrusion detection system (NIDS) model using Shapley Additive exPlanations  (SHAP) and local interpretable model-agnostic explanation (LIME) explainability methods. The adopted explainable DNN, capable of processing network traffic features from interconnected Metaverse devices and IoT sensors, can facilitate accurate and comprehensible remediation of anomalous from benign activities within Metaverse. The NIDS model yields a high performing accuracy of 99.9%  for establishing a more secure and trustworthy metaverse learning environment. https://www.sciencedirect.com/science/article/pii/S2542660523003694

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Over the last few decades, technology has been improving dramatically and consequently transformed the standard of living and socio-economic conditions. The entire process will revolutionize when the next advanced technologies will be fully functional. Advanced technologies like the metaverse, Web 3.0, and others necessitate high computing power, invincible security, and ultra-fast internet. Despite increasing demand, traditional computing methods have limitations and are not capable of satisfying the requirements. To solve these tribulations, quantum computing is shining a light of hope. This survey aims to analyze the methodology, constraints, and potential of integrating quantum computing with the metaverse. We begin with an overview of quantum computing and related terms. We then investigate the feasibility of applying quantum-enabled technologies to enhance the metaverse. Furthermore, this survey also considers middleware for seamless conversion between traditional and quantum computing with the metaverse. In the subsequent phase of this survey, our objective is to discern and delineate the prospective application domains for the quantum-enabled metaverse. In essence, the difficulties of integrating quantum computing with the metaverse, present research approaches, and open research issues with consequences for additional in-depth investigations are highlighted.

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The heterogeneous connections in metaverse environments pose vulnerabilities to cyber-attacks. To prevent and mitigate malicious network activities in a distributed metaverse, conventional intrusion detection systems (IDS) have communication overhead and privacy concerns. Federated learning (FL) techniques are widely employed to develop IDS frameworks and enable privacy-preserving collaborative learning schemes in decentralized ecosystems. However, the vanilla FL system utilizes a centralized FL aggregation technique, which introduces a single point of failure (SPoF) and potential unauthorized aggregators, allowing malicious clients to inject false data parameters, known as poisoning attacks. Furthermore, low-quality clients in the FL system can result in degraded model performance and hinder convergence. This study proposes a secure and reliable blockchain-aided federated learning (BFL)-based IDS framework using a lightweight model for securing metaverse. An authorized federated IDS is proposed to establish a trustworthy decentralized aggregation mechanism, utilizing proof-of-authority (PoA) consensus. The proposed federated IDS implemented a hybrid client selection (HCS) technique, considering the accuracy and reputation of client histories, to select high-quality metaverse edge devices. Additionally, a fairness ERC-20 token-based incentive mechanism was developed to reward selected FL clients as a token of appreciation for their contribution to the FL training processes. According to the IDS framework measurements, the proposed model performs better than the existing approaches for detecting cyber-attacks in metaverse environments, achieving an accuracy of 99.28% with trainable parameters of 1.8K and mega floating-point operations (MFLOPs) of 0.0016.

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#	Title	Status	Type	By
50	Leveraging Quantum Blockchain for Secure Multiparty Space Sharing and Authentication on Specialized Metaverse Platform Nature Scientific Reports	Under Review	International	Esmot Ara
49	Meta-Learning: A Digital Learning Management Framework using Blockchain for Metaverses IEEE Access	Minor Revision	International	Mohtasin
48	BUS-Health: Blockchain and UAV Integrated Smart Medical Supplies Blockchain: Research and Applications	Submitted	International	Mohtasin
47	AI-Based Job Shop Scheduling in Manufacturing Execution System: A Review Perspective Elsevier computers & industrial engineering	Submitted	International	Md Javed Ahmed
46	Enhancing Security and Accountability in Autonomous Vehicles through Robust Speaker Identification and Blockchain-Based Event Recording MDPI Electronics	Published	International	Judith
45	Federated Learning Based Modulation Classification for Multipath Channels Parallel Computing	Published	International	Sanjay
44	RF-RSCV: Robust Security Framework Against Intrusions in Internet of Drone Communication Networks %Securing the Drone Network Based On Hierarchical Intrusion Detection System Using Efficient and Optimized Random-Forest Model Journal Of Communications and Networks	Minor Revision	International	Vivian
43	ASR-Fed: Agnostic Straggler-Resilient Semi-Asynchronous Federated Learning Technique for Secured Drone Network International Journal of Machine Learning and Cybernetics	Minor Revision	International	Vivian
42	MetaWatch: Trends, Challenges, and Future Directions for Intrusion Detection in the Metaverse IEEE Internet of Things	Under Review	International	Ebuka
41	Zero-Trust Marine Cyberdefense for IoT-Based Communications: An Explainable Approach Electronics	Published	International	Ebuka
40	Detecting Cyberthreats in Metaverse Learning Platforms using an Explainable DNN Internet of Things	Published	International	Ebuka
39	Integration of Quantum Technologies into Metaverse: Applications, Potentials, and Challenges IEEE Access	Published	International
38	Whale optimization-based PTS scheme for PAPR reduction in UFMC systems IET Communications	Published	International
37	Tamper-Resistant Collaborative Attack Detection using Efficient Blockchain for Securing IIoT IEEE Transactions on Industrial Informatics	Under Review	International	Ahmad
36	Dragonfly Interaction Algorithm for Optimization of Queuing Delay in Industrial Wireless Networks, 10.1007/s42235-023-00462-7 Bionic Engineering	Published	International	Sanjay
35	IoMT-Net: Blockchain Integrated Unauthorized UAV Localization Using Lightweight Convolution Neural Network for Internet of Military Things IEEE Internet Things of Journal	Accepted	International
34	Droneilliance and detection dynamics: A review of radar techniques and trends None	Preparation	International
33	Sanjay Bhardwaj, Da-Hye Kim and Dong-Seong Kim,” Federated Learning Based Resource Allocation for V2X Communications”. Transactions on Intelligent Transportation Systems	Minor Revision	International	Sanjay
32	Enhancing Reliability and Energy Efficiency for Industrial IoT using IRSA-PSO The Journal of Korean Institute of Communications and Information Sciences (J-KICS)	Published	Domestic	Love Allen Chijioke
31	Paul Michael Custodio, Made Adi Paramartha Putra, Jae-Min Lee, and Dong-Seong Kim, "TLFed: Secure Smart Grid Transmission Line Fault Detection and Classification using Federated Learning", IEEE Transactions on Power Delivery, 2023 (Under Review) None	Preparation	International
30	PureRx: An NFT-Prescription Management for Efficient and Secure Healthcare System IEEE Transactions on Big Data Journal	Under Review	International	Gifar Arif
29	Collaborative Edge Learning-Assisted Interactive Avatar Generation for Metaverse in B5G Networks IEEE Internet of Things Journal	Under Review	International	Ahmad
28	XAI with Adversarial Defence for Zero-Trust Metaverse DDoS Attack Detection IEEE Transactions on Dependable and Secure Computing	Under Review	International	Love Allen Chijioke
27	PureFed: An Efficient Collaborative and Trustworthy Blockchain-Assisted Federated Learning Framework with Dynamic Aggregation Internet of Things, Elsevier	Under Review	International	Ahmad
26	Bandit Learning based Distributed Computation Offloading in Fog Computing Networks: A survey IEEE Access	Accepted	International	Love Allen Chijioke
25	Blockchain-Inspired Collaborative Cyber-Attacks Detection for Securing Metaverse IEEE Internet of Things Journal	Published	International	Ahmad
24	AI Model Stability in Industrial IoT Intrusion Detection: Leveraging the Characteristics Stability The Journal of Korean Institute of Communications and Information Sciences (J-KICS)	Published	Domestic	Love Allen Chijioke
23	Federated Learning Inspired Low-Complexity Intrusion Detection and Classification Technique for SDN-based Industrial CPS IEEE Transactions on Network and Service Management	Published	International	Ahmad
22	Goodness Oluchi Anyanwu, Cosmas Ifeanyi Nwakanma, Jae-Min Lee, Dong-Seong Kim, Optimization of RBF-SVM Kernel Using Grid Search Algorithm for DDoS Attack Detection in SDN-Based VANET," in IEEE Internet of Things Journal, vol. 10, no. 10, pp. 8477-8490, 15 May15, 2023, doi: 10.1109/JIOT.2022.3199712. None	Accepted	International
21	TSFed: A Three-Stage Optimization Mechanism for Secure and Efficient Federated Learning in Industrial IoT Networks IEEE Transactions on Industrial Informatics	Under Review	International	Ahmad
20	Blockchain-Empowered Federated Human Activity Recognition for Creating Interactive Consumer Avatar in Metaverse Platform IEEE Transactions on Customer Electronics	Preparation	International	Ahmad
19	MAD-DDS: Memory-efficient Automatic Discovery Data Distribution Service for Large-Scale Distributed Control Network IET Communications	Published	International	Love Allen Chijioke
18	Machine Learning Explainability for Intrusion Detection in the Industrial Internet of Things IEEE Internet of Things Magazine	Under Review	International	Love Allen Chijioke
17	Campus-based Test-Bed Implementation of 5G+ Networks at the 28 GHz Band: Challenges and Opportunities The Journal of Korean Institute of Communications and Information Sciences (J-KICS)	Published	Domestic	Love Allen Chijioke
16	Sanjay Bhardwaj, Dong-Seong Kim,” Deep Q-Learning Based Sparse Code Multiple Access for Ultra Reliable Low Latency Communication in Industrial Wireless Networks”, Telecommunication Systems, volume:83, number:4, pages:409–421, Aug-2023: DOI: 10.1007/s11235-023-01034-0 None	Preparation	International
15	Made Adi Paramartha Putra, Ade Pitra Hermawan, Dong-Seong Kim and Jae-Min Lee, "Data Prediction-Based Energy-Efficient Architecture in Industrial IoT", IEEE Sensors Journal, vol. 23, no. 14, pp. 15856-15866, July 15, 2023, , doi: 10.1109/JSEN.2023.3280485 None	Accepted	International
14	Vivian Ukamaka Ihekoronye, Cosmas Ifeanyi Nwakanma, Dong-Seong Kim, Jae Min Lee "DATA-FedAVG: Delay-Aware Truncated Accuracy-Based Federated Averaging for Intrusion Detection in UAV Network", The Journal of Korean Institute of Communications and Information Sciences (KICS), vol. 48, no. 6, June 2023. None	Accepted	Domestic
13	Gabriel Avelino R. Sampedro, Syifa Maliah Rachmawati, Dong-Seong Kim, and Jae-Min Lee, "Exploring Machine Learning-Based Fault Monitoring for Polymer-Based Additive Manufacturing: Challenges and Opportunities", Sensors, Vol. 22, no. 23, 9446, 2022, doi: https://doi.org/10.3390/s22239446 None	Accepted	International
12	Simeon Okechukwu Ajakwe, Igboanusi Ikechi Saviour, Vivian Ukamaka Ihekoronye, Odinachi U. Nwankwo, Mohamed Abubakar Dini, Izuazu Urslla Uchechi, Dong-Seong Kim, Jae Min Lee. " Medical IoT Record Security and Blockchain: Survey of Milieu, Milestones, and Momentum" IEEE Communication Surveys and Tutorials, (N8, N12, N13) (Under Review) None	Preparation	International
11	Simeon Okechukwu Ajakwe, Igboanusi Ikechi Saviour, Jung-Hyeon Kim, Dong-Seong Kim, Jae Min Lee. "iBANDA: Incognito Blockchain-Assisted Network for Drone Authentication and Logistics Operations" IEEE Intelligent Transport System”, (N8, N12, N13) (Under Review) None	Preparation	International
10	Allwinnaldo, Revin Naufal Alief, Muhammad Rasyid Redha Ansori, Jae Min Lee, Dong-Seong Kim, "Efficient Blockchain-based Energy Auction Platform for Secured Energy Trading on Industrial Sector," in Journal of KICS 2023 (A) None	Accepted	Domestic
9	Revin Naufal Alief, Syifa Maliah Rachmawati, Jae Min Lee, Dong-Seong Kim, "Malicious Account Classification using CNN for Ethereum Blockchain's Accounts" in Journal of KICS 2023, Vol. 48, No. 7, 15 July 2023. None	Accepted	Domestic
8	Sanjay Bhardwaj, Da-Hye Kim and Dong-Seong Kim,” Backscatter-Enabled CR-NOMA Based Cooperative V2X Communication with Imperfect CSI”, Vehicular Communications, volume:42, pages:100611, 2023: https://doi.org/10.1016/j.vehcom.2023.100611 None	Preparation	International
7	P. T. Daely, J. M. Lee and D.-S. Kim, "Wireless Node Localization Based on Wild Goats Algorithm for Industrial Internet of Things", KICS Journal, vol 48, no. 9, 2023 (In press)) None	Accepted	Domestic
6	김재우,류원재,이재민,김동성 "군용 네트워크 설계 및 분석을 위한 네트워크 시뮬레이션 도구 분석", 정보과학회지, vol.41, no.3, pp.40-50, Mar., 2023 None	Preparation	Domestic
5	차중혁, 김형진, 이재민, 김동성, "스마트 국방 분산 시스템을 위한 메시지 지향 미들웨어 연구 동향", 정보과학회지, vol.41, no.3, pp.32-39, Mar., 2023 None	Preparation	Domestic
4	Ali Aouto, Ali Moallem, and Dong-Seong Kim, "Hybrid Demand Responsive Public Transport System for Conventional Public Transport in City Metropolitan Area" KICS journal (Accepted) None	Accepted	Domestic
3	Philip Tobianto Daely, Jae Min Lee, Dong-Seong Kim, "Vehicle Routing Based Mining for Proof-of-Useful-Work Blockchain", Journal of KICS (S) None	Submitted	Domestic
2	A Multi-MLP Prediction for Inventory Management in Manufacturing Execution System Internet of Things	Published	International	Ahmad
1	Vivian Ukamaka Ihekoronye, Cosmas Ifeanyi Nwakanma, Dong-Seong Kim, Jae Min Lee "Federated Learning-Based Intelligent Intrusion Detection Framework for Edge-Assisted UAV Network". IEEE Access (Rejected) None	Preparation	International

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