IECE Transactions on Sensing, Communication, and Control
ISSN: 3065-7431 (Online) | ISSN: 3065-7423 (Print)
Email: [email protected]
[1] Chin, J., Callaghan, V., & Allouch, S. B. (2019). The Internet-of-Things: Reflections on the past, present and future from a user-centered and smart environment perspective. Journal of Ambient Intelligence and Smart Environments, 11(1), 45-69.
[2] Mohanty, S. P., Choppali, U., & Kougianos, E. (2016). Everything you wanted to know about smart cities: The Internet of things is the backbone. IEEE consumer electronics magazine, 5(3), 60-70.
[3] Pantano, E., & Timmermans, H. (2014). What is smart for retailing?. Procedia Environmental Sciences, 22, 101-107.
[4] Ikrissi, G., & Mazri, T. (2021). IOT-based Smart Environments: State of the art, security threats and solutions. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 46, 279-286.
[5] Popescul, D., & Genete, L. D. (2016). Data security in smart cities: challenges and solutions. Informatica Economică, 20(1).
[6] Patrono, L., Atzori, L., Šolić, P., Mongiello, M., & Almeida, A. (2020). Challenges to be addressed to realize Internet of Things solutions for smart environments. Future generation computer systems, 111, 873-878.
[7] Reyna, A., Martín, C., Chen, J., Soler, E., & Díaz, M. (2018). On blockchain and its integration with IoT. Challenges and opportunities. Future generation computer systems, 88, 173-190.
[8] Theodorou, S., & Sklavos, N. (2019). Blockchain-based security and privacy in smart cities. In Smart cities cybersecurity and privacy (pp. 21-37). Elsevier.
[9] Majeed, U., Khan, L. U., Yaqoob, I., Kazmi, S. A., Salah, K., & Hong, C. S. (2021). Blockchain for IoT-based smart cities: Recent advances, requirements, and future challenges. Journal of Network and Computer Applications, 181, 103007.
[10] Michelin, R. A., Dorri, A., Steger, M., Lunardi, R. C., Kanhere, S. S., Jurdak, R., & Zorzo, A. F. (2018, November). SpeedyChain: A framework for decoupling data from blockchain for smart cities. In Proceedings of the 15th EAI international conference on mobile and ubiquitous systems: Computing, networking and services (pp. 145-154).
[11] Makhdoom, I., Zhou, I., Abolhasan, M., Lipman, J., & Ni, W. (2020). PrivySharing: A blockchain-based framework for privacy-preserving and secure data sharing in smart cities. Computers & Security, 88, 101653.
[12] Pandya, S., Srivastava, G., Jhaveri, R., Babu, M. R., Bhattacharya, S., Maddikunta, P. K. R., ... & Gadekallu, T. R. (2023). Federated learning for smart cities: A comprehensive survey. Sustainable Energy Technologies and Assessments, 55, 102987.
[13] Li, Q., Wen, Z., Wu, Z., Hu, S., Wang, N., Li, Y., ... & He, B. (2021). A survey on federated learning systems: Vision, hype and reality for data privacy and protection. IEEE Transactions on Knowledge and Data Engineering, 35(4), 3347-3366.
[14] Konečný, J. (2016). Federated Learning: Strategies for Improving Communication Efficiency. arXiv preprint arXiv:1610.05492.
[15] Zhang, R., Xue, R., & Liu, L. (2021). Security and privacy for healthcare blockchains. IEEE Transactions on Services Computing, 15(6), 3668-3686.
[16] Orecchini, F., Santiangeli, A., Zuccari, F., Pieroni, A., & Suppa, T. (2019). Blockchain technology in smart city: A new opportunity for smart environment and smart mobility. In Intelligent Computing & Optimization 1 (pp. 346-354). Springer International Publishing.
[17] Mukherjee, P., Barik, R. K., & Pradhan, C. (2021). A comprehensive proposal for blockchain-oriented smart city. Security and Privacy Applications for Smart City Development, 55-87.
[18] Chen, J., Gan, W., Hu, M., & Chen, C. M. (2021). On the construction of a post-quantum blockchain for smart city. Journal of information security and applications, 58, 102780.
[19] Paul, R., Ghosh, N., Sau, S., Chakrabarti, A., & Mohapatra, P. (2021). Blockchain based secure smart city architecture using low resource IoTs. Computer Networks, 196, 108234.
[20] Wong, P. F., Chia, F. C., Kiu, M. S., & Lou, E. C. (2022). Potential integration of blockchain technology into smart sustainable city (SSC) developments: a systematic review. Smart and Sustainable Built Environment, 11(3), 559-574.
[21] Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019). Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST), 10(2), 1-19.
[22] Zheng, Z., Zhou, Y., Sun, Y., Wang, Z., Liu, B., & Li, K. (2022). Applications of federated learning in smart cities: recent advances, taxonomy, and open challenges. Connection Science, 34(1), 1-28.
[23] Sater, R. A., & Hamza, A. B. (2021). A federated learning approach to anomaly detection in smart buildings. ACM Transactions on Internet of Things, 2(4), 1-23.
[24] Yang, Z., Chen, M., Wong, K. K., Poor, H. V., & Cui, S. (2022). Federated learning for 6G: Applications, challenges, and opportunities. Engineering, 8, 33-41.
[25] Huang, X., Li, P., Yu, R., Wu, Y., Xie, K., & Xie, S. (2021). FedParking: A federated learning based parking space estimation with parked vehicle assisted edge computing. IEEE Transactions on Vehicular Technology, 70(9), 9355-9368.
[26] Li, D., Luo, Z., & Cao, B. (2022). Blockchain-based federated learning methodologies in smart environments. Cluster Computing, 25(4), 2585-2599.
[27] Li, L., Fan, Y., Tse, M., & Lin, K. Y. (2020). A review of applications in federated learning. Computers & Industrial Engineering, 149, 106854.
[28] Chai, H., Leng, S., Chen, Y., & Zhang, K. (2020). A hierarchical blockchain-enabled federated learning algorithm for knowledge sharing in internet of vehicles. IEEE Transactions on Intelligent Transportation Systems, 22(7), 3975-3986.
[29] Kuang, Z., & Chen, C. (2023). Research on smart city data encryption and communication efficiency improvement under federated learning framework. Egyptian Informatics Journal, 24(2), 217-227.
[30] Zhao, Y., Zhao, J., Jiang, L., Tan, R., Niyato, D., Li, Z., ... & Liu, Y. (2020). Privacy-preserving blockchain-based federated learning for IoT devices. IEEE Internet of Things Journal, 8(3), 1817-1829.
[31] Albaseer, A., Ciftler, B. S., Abdallah, M., & Al-Fuqaha, A. (2020, June). Exploiting unlabeled data in smart cities using federated edge learning. In 2020 International Wireless Communications and Mobile Computing (IWCMC) (pp. 1666-1671). IEEE.
[32] Otoum, S., Al Ridhawi, I., & Mouftah, H. (2021). Securing critical IoT infrastructures with blockchain-supported federated learning. IEEE Internet of Things Journal, 9(4), 2592-2601.
[33] Jie, W., Qiu, W., Koe, A. S. V., Li, J., Wang, Y., Wu, Y., & Li, J. (2023). A Secure and Flexible Blockchain-Based Offline Payment Protocol. IEEE Transactions on Computers.
[34] Demertzis, K. (2021). Blockchained federated learning for threat defense. arXiv preprint arXiv:2102.12746.
[35] Yuan, X., Chen, J., Yang, J., Zhang, N., Yang, T., Han, T., & Taherkordi, A. (2022). Fedstn: Graph representation driven federated learning for edge computing enabled urban traffic flow prediction. IEEE Transactions on Intelligent Transportation Systems, 24(8), 8738-8748.
[36] Ahmed, S. T., & Jeong, J. (2024). Heterogeneous Workload based Consumer Resource Recommendation Model for Smart Cities: eHealth Edge-Cloud Connectivity Using Federated Split Learning. IEEE Transactions on Consumer Electronics.
[37] Qi, Y., Hossain, M. S., Nie, J., & Li, X. (2021). Privacy-preserving blockchain-based federated learning for traffic flow prediction. Future Generation Computer Systems, 117, 328-337.
[38] Farooq, K., Syed, H. J., Alqahtani, S. O., Nagmeldin, W., Ibrahim, A. O., & Gani, A. (2022). Blockchain federated learning for in-home health monitoring. Electronics, 12(1), 136.
[39] Ayaz, F., Sheng, Z., Tian, D., & Guan, Y. L. (2021). A blockchain based federated learning for message dissemination in vehicular networks. IEEE Transactions on Vehicular Technology, 71(2), 1927-1940.
[40] Yang, Z., Shi, Y., Zhou, Y., Wang, Z., & Yang, K. (2022). Trustworthy federated learning via blockchain. IEEE Internet of Things Journal, 10(1), 92-109.
[41] Liu, H., Zhang, S., Zhang, P., Zhou, X., Shao, X., Pu, G., & Zhang, Y. (2021). Blockchain and federated learning for collaborative intrusion detection in vehicular edge computing. IEEE Transactions on Vehicular Technology, 70(6), 6073-6084.
[42] Wang, R., & Tsai, W. T. (2022). Asynchronous federated learning system based on permissioned blockchains. Sensors, 22(4), 1672.
[43] Li, Y., Chen, C., Liu, N., Huang, H., Zheng, Z., & Yan, Q. (2020). A blockchain-based decentralized federated learning framework with committee consensus. IEEE Network, 35(1), 234-241.
[44] Lu, Y., Huang, X., Zhang, K., Maharjan, S., & Zhang, Y. (2020). Blockchain and federated learning for 5G beyond. IEEE Network, 35(1), 219-225.
[45] Zhang, W., Lu, Q., Yu, Q., Li, Z., Liu, Y., Lo, S. K., ... & Zhu, L. (2020). Blockchain-based federated learning for device failure detection in industrial IoT. IEEE Internet of Things Journal, 8(7), 5926-5937.
[46] Awan, S., Li, F., Luo, B., & Liu, M. (2019, November). Poster: A reliable and accountable privacy-preserving federated learning framework using the blockchain. In Proceedings of the 2019 ACM SIGSAC conference on computer and communications security (pp. 2561-2563).
[47] Hua, G., Zhu, L., Wu, J., Shen, C., Zhou, L., & Lin, Q. (2020). Blockchain-based federated learning for intelligent control in heavy haul railway. IEEE Access, 8, 176830-176839.
[48] Jain, A. K., Sahoo, S. R., & Kaubiyal, J. (2021). Online social networks security and privacy: comprehensive review and analysis. Complex & Intelligent Systems, 7(5), 2157-2177.
[49] Etuh, E., & Bakpo, F. S. (2022). Social Media Networks Attacks and their Preventive Mechanisms: A Review. arXiv preprint arXiv:2201.03330.
[50] Salim, S., Turnbull, B., & Moustafa, N. (2021). A blockchain-enabled explainable federated learning for securing internet-of-things-based social media 3.0 networks. IEEE Transactions on Computational Social Systems.
[51] Dubai Blockchain Policy. (2022). DIGITAL DUBAI. https://www.digitaldubai.ae/docs/defaultsource/publications/ dubaiblockchainpolicy.pdf?sfvrsn=4a4bb396_4
[52] Faruk, M. J. H., Shahriar, H., Valero, M., Barsha, F. L., Sobhan, S., Khan, M. A., ... & Wu, F. (2021, December). Malware detection and prevention using artificial intelligence techniques. In 2021 IEEE international conference on big data (big data) (pp. 5369-5377). IEEE.
[53] Wen, H., Fang, J., Wu, J., & Zheng, Z. (2022). Hide and seek: An adversarial hiding approach against phishing detection on ethereum. IEEE Transactions on Computational Social Systems, 10(6), 3512-3523.
[54] Basit, A., Zafar, M., Liu, X., Javed, A. R., Jalil, Z., & Kifayat, K. (2021). A comprehensive survey of AI-enabled phishing attacks detection techniques. Telecommunication Systems, 76, 139-154.
[55] Zheng, H., Ma, M., Ma, H., Chen, J., Xiong, H., & Yang, Z. (2023). Tegdetector: a phishing detector that knows evolving transaction behaviors. IEEE Transactions on Computational Social Systems.
[56] Huang, H., Zhang, X., Wang, J., Gao, C., Li, X., Zhu, R., & Ma, Q. (2024). PEAE-GNN: Phishing Detection on Ethereum via Augmentation Ego-Graph Based on Graph Neural Network. IEEE Transactions on Computational Social Systems.
[57] Valecha, R., Mandaokar, P., & Rao, H. R. (2021). Phishing email detection using persuasion cues. IEEE transactions on Dependable and secure computing, 19(2), 747-756.
[58] Duman, S., Büchler, M., Egele, M., & Kirda, E. (2023). PellucidAttachment: Protecting users from attacks via e-mail attachments. IEEE Transactions on Dependable and Secure Computing, 21(3), 1342-1354.
[59] Rao, S., Verma, A. K., & Bhatia, T. (2021). A review on social spam detection: Challenges, open issues, and future directions. Expert Systems with Applications, 186, 115742.
[60] Haber, M. J., & Rolls, D. (2019). Identity attack vectors: implementing an effective identity and access management solution. Apress.
[61] Punkamol, D., & Marukatat, R. (2020, March). Detection of account cloning in online social networks. In 2020 8th International Electrical Engineering Congress (iEECON) (pp. 1-4). IEEE.
[62] Alsaffar, M., Aljaloud, S., Mohammed, B. A., Al-Mekhlafi, Z. G., Almurayziq, T. S., Alshammari, G., & Alshammari, A. (2022). Detection of Web Cross-Site Scripting (XSS) Attacks. Electronics, 11(14), 2212.
[63] Muñoz, F., Isaza, G., & Castillo, L. (2020, June). Smartsec4cop: smart cyber-grooming detection using natural language processing and convolutional neural networks. In International Symposium on Distributed Computing and Artificial Intelligence (pp. 11-20). Cham: Springer International Publishing.
[64] Ojha, R. P., Srivastava, P. K., Sanyal, G., & Gupta, N. (2021). Improved model for the stability analysis of wireless sensor network against malware attacks. Wireless Personal Communications, 116(3), 2525-2548.
[65] Zhao, K., Zhou, H., Zhu, Y., Zhan, X., Zhou, K., Li, J., ... & Luo, X. (2021, November). Structural attack against graph based android malware detection. In Proceedings of the 2021 ACM SIGSAC conference on computer and communications security (pp. 3218-3235).
[66] Wang, M., & Song, L. (2021). Efficient defense strategy against spam and phishing email: An evolutionary game model. Journal of Information Security and Applications, 61, 102947.
[67] Saad, M., Kim, J., Nyang, D., & Mohaisen, D. (2021). Contra-∗: Mechanisms for countering spam attacks on blockchain’s memory pools. Journal of Network and Computer Applications, 179, 102971.
[68] Bilge, L., Strufe, T., Balzarotti, D., & Kirda, E. (2009, April). All your contacts are belong to us: automated identity theft attacks on social networks. In Proceedings of the 18th international conference on World wide web (pp. 551-560).
[69] Daraghmi, E., Jayousi, S., Daraghmi, Y., Daraghmi, R., & Fouchal, H. (2024). Smart Contracts for Managing the Agricultural Supply Chain: A Practical Case Study. IEEE Access.
[70] Alterkavı, S., & Erbay, H. (2021). Design and analysis of a novel authorship verification framework for hijacked social media accounts compromised by a human. Security and Communication Networks, 2021(1), 8869681.
[71] Ayeni, B. K., Sahalu, J. B., & Adeyanju, K. R. (2018). Detecting Cross-Site Scripting in Web Applications Using Fuzzy Inference System. Journal of Computer Networks and Communications, 2018(1), 8159548.
[72] Marashdih, A. W., Zaaba, Z. F., Suwais, K., & Mohd, N. A. (2019). Web application security: An investigation on static analysis with other algorithms to detect cross site scripting. Procedia Computer Science, 161, 1173-1181.
[73] Rivera, R., Pazmiño, L., Becerra, F., & Barriga, J. (2022). An analysis of cyber espionage process. In Developments and Advances in Defense and Security: Proceedings of MICRADS 2021 (pp. 3-14). Springer Singapore.
[74] Bederna, Z., & Szadeczky, T. (2020). Cyber espionage through Botnets. Security Journal, 33(1), 43-62.
[75] Shrivastava, P., Jamal, M. S., & Kataoka, K. (2020). EvilScout: Detection and mitigation of evil twin attack in SDN enabled WiFi. IEEE Transactions on Network and Service Management, 17(1), 89-102.
[76] Agarwal, M., Biswas, S., & Nandi, S. (2018). An efficient scheme to detect evil twin rogue access point attack in 802.11 Wi-Fi networks. International Journal of Wireless Information Networks, 25, 130-145.
[77] Kopecký, K., & Szotkowski, R. (2017). Cyberbullying, cyber aggression and their impact on the victim–The teacher. Telematics and informatics, 34(2), 506-517.
[78] al-Khateeb, H. M., & Epiphaniou, G. (2016). How technology can mitigate and counteract cyber-stalking and online grooming. Computer Fraud & Security, 2016(1), 14-18.
[79] Rybnicek, M., Poisel, R., & Tjoa, S. (2013, October). Facebook watchdog: a research agenda for detecting online grooming and bullying activities. In 2013 IEEE International Conference on Systems, Man, and Cybernetics (pp. 2854-2859). IEEE.
[80] Young, A., & Verhulst, S. (2020). Zug Digital ID: Blockchain Case Study for Government Issued Identity. Consensys. https://consensys.io/blockchain-use-cases/ government-and-the-public-sector/zug
[81] Zambrano, P., Torres, J., Tello-Oquendo, L., Jácome, R., Benalcazar, M. E., Andrade, R., & Fuertes, W. (2019). Technical mapping of the grooming anatomy using machine learning paradigms: An information security approach. IEEE Access, 7, 142129-142146.
[82] Zhan, Y., Xiong, Y., & Xing, X. (2023). A conceptual model and case study of blockchain-enabled social media platform. Technovation, 119, 102610.
[83] Basem, O., Ullah, A., & Hassen, H. R. (2022). Stick: an end-to-end encryption protocol tailored for social network platforms. IEEE Transactions on Dependable and Secure Computing, 20(2), 1258-1269.
[84] Zhou, X., Liang, W., Ma, J., Yan, Z., Kevin, I., & Wang, K. (2022). 2D federated learning for personalized human activity recognition in cyber-physical-social systems. IEEE Transactions on Network Science and Engineering, 9(6), 3934-3944.
[85] Krishnan, P., Jain, K., Jose, P. G., Achuthan, K., & Buyya, R. (2021). SDN enabled QoE and security framework for multimedia applications in 5G networks. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 17(2), 1-29.
[86] Wati, V., Kusrini, K., Al Fatta, H., & Kapoor, N. (2021). Security of facial biometric authentication for attendance system. Multimedia Tools and Applications, 80(15), 23625-23646.
[87] Mbarek, B., Ge, M., & Pitner, T. (2020). An efficient mutual authentication scheme for internet of things. Internet of things, 9, 100160.
[88] El Sibai, R., Gemayel, N., Bou Abdo, J., & Demerjian, J. (2020). A survey on access control mechanisms for cloud computing. Transactions on Emerging Telecommunications Technologies, 31(2), e3720.
[89] Kempe, E., & Massey, A. (2021, September). Perspectives on regulatory compliance in software engineering. In 2021 IEEE 29th International Requirements Engineering Conference (RE) (pp. 46-57). IEEE.
[90] Fraga-Lamas, P., & Fernandez-Carames, T. M. (2020). Fake news, disinformation, and deepfakes: Leveraging distributed ledger technologies and blockchain to combat digital deception and counterfeit reality. IT professional, 22(2), 53-59.
[91] Cohn, J. M., Finn, P. G., Nair, S. P., Panikkar, S. B., & Pureswaran, V. S. (2019). US Patent No. 10,257,270.
[92] Sengan, S., Subramaniyaswamy, V., Nair, S. K., Indragandhi, V., Manikandan, J., & Ravi, L. (2020). Enhancing cyber–physical systems with hybrid smart city cyber security architecture for secure public data-smart network. Future generation computer systems, 112, 724-737.
[93] Singh, S. K., Jeong, Y. S., & Park, J. H. (2020). A deep learning-based IoT-oriented infrastructure for secure smart city. Sustainable Cities and Society, 60, 102252.
[94] Ali, Z., Chaudhry, S. A., Ramzan, M. S., & Al-Turjman, F. (2020). Securing smart city surveillance: A lightweight authentication mechanism for unmanned vehicles. IEEE Access, 8, 43711-43724.
[95] Park, J. H., Yotxay, S., Singh, S. K., & Park, J. H. (2024). PoAh-Enabled Federated Learning Architecture for DDoS Attack Detection in IoT Networks. Human-Centric Computing And Information Sciences, 14.
[96] Singh, S. K., Azzaoui, A. E., Choo, K. K. R., Yang, L. T., & Park, J. H. (2023). Articles A Comprehensive Survey on Blockchain for Secure IoT-enabled Smart City beyond 5G: Approaches, Processes, Challenges, and Opportunities. Hum.-Centric Comput. Inf. Sci, 13, 51.
[97] Al Dakheel, J., Del Pero, C., Aste, N., & Leonforte, F. (2020). Smart buildings features and key performance indicators: A review. Sustainable Cities and Society, 61, 102328.
[98] Fantin Irudaya Raj, E., & Appadurai, M. (2022). Internet of things-based smart transportation system for smart cities. In Intelligent Systems for Social Good: Theory and Practice (pp. 39-50). Singapore: Springer Nature Singapore.
[99] Sinha, B. B., & Dhanalakshmi, R. (2022). Recent advancements and challenges of Internet of Things in smart agriculture: A survey. Future Generation Computer Systems, 126, 169-184.
[100] Bourg, L., Chatzidimitris, T., Chatzigiannakis, I., Gavalas, D., Giannakopoulou, K., Kasapakis, V., ... & Zaroliagis, C. (2023). Enhancing shopping experiences in smart retailing. Journal of Ambient Intelligence and Humanized Computing, 1-19.
[101] Mohamed, G., Visumathi, J., Mahdal, M., Anand, J., & Elangovan, M. (2022). An effective and secure mechanism for phishing attacks using a machine learning approach. Processes, 10(7), 1356.
[102] Aljaidi, M., Alsarhan, A., Samara, G., Alazaidah, R., Almatarneh, S., Khalid, M., & Al-Gumaei, Y. A. (2022, November). NHS WannaCry ransomware attack: technical explanation of the vulnerability, exploitation, and countermeasures. In 2022 International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI) (pp. 1-6). IEEE.
[103] Thangavel, S., & Kannan, S. (2022). Detection and trace back of low and high volume of distributed denial-of-service attack based on statistical measures. Concurrency and Computation: Practice and Experience, 34(8), e5428.
[104] Wang, D., Webb, S., Lee, K., Caverlee, J., & Pu, C. (2023). Granular computing system vulnerabilities: Exploring the dark side of social networking communities. In Granular, Fuzzy, and Soft Computing (pp. 239-250). New York, NY: Springer US.
[105] Aisya, N. R. (2024). Cyberbullying: The Silent Epidemic of The Digital Age. Journal of World Science, 3(6), 691-697.
[106] Khan, S., Kabanov, I., Hua, Y., & Madnick, S. (2022). A systematic analysis of the capital one data breach: Critical lessons learned. ACM Transactions on Privacy and Security, 26(1), 1-29.
[107] Suliman, M., & Leith, D. (2023, September). Two models are better than one: Federated learning is not private for google gboard next word prediction. In European Symposium on Research in Computer Security (pp. 105-122). Cham: Springer Nature Switzerland.
[108] Farahani, B., Tabibian, S., & Ebrahimi, H. (2023). Towards A Personalized Clustered Federated Learning: A Speech Recognition Case Study. IEEE Internet of Things Journal.
[109] Brecko, A., Kajati, E., Koziorek, J., & Zolotova, I. (2022). Federated learning for edge computing: A survey. Applied Sciences, 12(18), 9124.
[110] Singh, S. K., Kumar, M., Khanna, A., & Virdee, B. (2024). Blockchain and FL-based secure architecture for enhanced external Intrusion detection in smart farming. IEEE Internet of Things Journal.
[111] Usman, M. T., Khan, H., Singh, S. K., Lee, M. Y., & Koo, J. (2024). Efficient deepfake detection via layer-frozen assisted dual attention network for consumer imaging devices. IEEE Transactions on Consumer Electronics.
[112] Kumar, M., Singh, S. K., & Kim, S. (2024). Predictive Analytics for Mortality: FSRNCA-FLANN Modeling Using Public Health Inventory Records. IEEE Access.
[113] Singh, S. K., Kumar, M., Tanwar, S., & Park, J. H. (2024). GRU-based digital twin framework for data allocation and storage in IoT-enabled smart home networks. Future Generation Computer Systems, 153, 391-402.
[114] Jeremiah, S. R., Ha, J., Singh, S. K., & Park, J. H. ArticlesPrivacyGuard: Collaborative Edge-Cloud Computing Architecture for Attribute-Preserving Face Anonymization in CCTV Networks.
[115] Khan, H., Ullah, I., Shabaz, M., Omer, M. F., Usman, M. T., Guellil, M. S., & Koo, J. (2024). Visionary vigilance: Optimized YOLOV8 for fallen person detection with large-scale benchmark dataset. Image and Vision Computing, 149, 105195.
[116] Ullah, I., Ali, F., Khan, H., Khan, F., & Bai, X. (2024). Ubiquitous computation in internet of vehicles for human-centric transport systems. Computers in Human Behavior, 161, 108394.
[117] Khan, H., Jan, Z., Ullah, I., Alwabli, A., Alharbi, F., Habib, S., ... & Koo, J. (2024). A deep dive into AI integration and advanced nanobiosensor technologies for enhanced bacterial infection monitoring. Nanotechnology Reviews, 13(1), 20240056.
[118] Singh, S. K., Lee, C., & Park, J. H. (2022). CoVAC: A P2P smart contract-based intelligent smart city architecture for vaccine manufacturing. Computers & Industrial Engineering, 166, 107967.
IECE Transactions on Sensing, Communication, and Control
ISSN: 3065-7431 (Online) | ISSN: 3065-7423 (Print)
Email: [email protected]
Portico
All published articles are preserved here permanently:
https://www.portico.org/publishers/iece/