Academic literature on the topic 'AI-Integrated IoT Networks'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'AI-Integrated IoT Networks.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "AI-Integrated IoT Networks"
1
Thompson, John A. "AI-Integrated IoT Networks for Smart City Traffic Management." International Journal of Innovative Computer Science and IT Research 1, no. 02 (2025): 1–10. https://doi.org/10.63665/ijicsitr.v1i02.01.
Full textAbstract:
The increase in rate of urban population and increasing urban infrastructure complexity have created humongous issues of traffic management. Conventional ways of urban traffic management are not efficient, creating traffic congestion, time delay, and adverse environmental effects. Smart traffic management of a smart city through integration of Artificial Intelligence (AI) and the Internet of Things (IoT) is a paradigm-shift solution to all such issues. This article discusses how AI and IoT networks are converging to design adaptive, dynamic, and intelligent traffic systems. IoT sensors, cameras, and GPS devices capture real-time information, which is analyzed with AI algorithms to forecast traffic flow, optimize traffic flow, and control congestion dynamically. The article describes how AI and IoT technologies work together in making real-time decisions better, improving road safety, and the urban mobility environmental impact. The article is also aware of the largest challenges as infrastructure constraints, data security threats, and scaling solutions to larger numbers in cities. Lastly, the paper examines upcoming trends in smart city traffic management such as the use of autonomous vehicles, predictive traffic, and leveraging 5G networks. The essay ends with a little discussion on the potential value that AI-enabled IoT systems can bring to urban transport system development to become more intelligent, more efficient, and more sustainable.
2
John, A. Thompson. "AI-Integrated IoT Networks for Smart City Traffic Management." International Journal of Innovative Computer Science and IT Research 01, no. 02 (2025): 1–10. https://doi.org/10.5281/zenodo.15147275.
Full textAbstract:
The increase in rate of urban population and increasing urban infrastructure complexity have created humongous issues of traffic management. Conventional ways of urban traffic management are not efficient, creating traffic congestion, time delay, and adverse environmental effects. Smart traffic management of a smart city through integration of Artificial Intelligence (AI) and the Internet of Things (IoT) is a paradigm-shift solution to all such issues. This article discusses how AI and IoT networks are converging to design adaptive, dynamic, and intelligent traffic systems. IoT sensors, cameras, and GPS devices capture real-time information, which is analyzed with AI algorithms to forecast traffic flow, optimize traffic flow, and control congestion dynamically. The article describes how AI and IoT technologies work together in making real-time decisions better, improving road safety, and the urban mobility environmental impact. The article is also aware of the largest challenges as infrastructure constraints, data security threats, and scaling solutions to larger numbers in cities. Lastly, the paper examines upcoming trends in smart city traffic management such as the use of autonomous vehicles, predictive traffic, and leveraging 5G networks. The essay ends with a little discussion on the potential value that AI-enabled IoT systems can bring to urban transport system development to become more intelligent, more efficient, and more sustainable.
3
Kim, Seoyeon, Joonhyouk Jang, Jinman Jung, Bongjae Kim, and Young-Sun Yun. "Automatic Generation Tool for Open Platform-compatible Intelligent IoT Components." Korean Institute of Smart Media 11, no. 11 (2022): 32–39. http://dx.doi.org/10.30693/smj.2022.11.11.32.
Full textAbstract:
As IoT applications that provide AI services increase, various hardware and software that support autonomous learning and inference are being developed. However, as the characteristics and constraints of each hardware increase difficulties in developing IoT applications, the development of an integrated platform is required. In this paper, we propose a tool for automatically generating components based on artificial neural networks and spiking neural networks as well as IoT technologies to be compatible with open platforms. The proposed component automatic generation tool supports the creation of components considering the characteristics of various hardware devices through the virtual component layer of IoT and AI and enables automatic application to open platforms.
4
Petrov, Ivan, and Toni Janevski. "Artificial Intelligence Techniques for Information Security in 5G IoT Environments." European Journal of Engineering Research and Science 5, no. 11 (2020): 1328–33. http://dx.doi.org/10.24018/ejers.2020.5.11.2210.
Full textAbstract:
The development of the telecommunication networks observed in present and future time is impressive. Today we witness rapid implementation of 5G networks. We can say that this actually is the moment when (artificial intelligence) AI enters at small door but in the beyond 5G world it is expected to have the prime role in smart operation, management and maintenance of non-software defined networking (SDN), network function virtualization (NFV) and especially at SDN and NFV aware networks. Number of standardization body’s and work groups are focused in a way to create a framework that will define the future use of AI and security standards necessary to exist in order to create health environment for the next generation telecommunication infrastructure. In the wireless world AI/Machine learning (ML) has great potential to shake the way we operate and to become foundation of the transformation that leads to the next industrial revolution. Network virtualization gives flexibility and freedom of the telco operators to choose the hardware and network topology they need for AI/ML platforms and big data sets. 5G and IoT create positive environment for AI and ML development and usage. As the network requirements are developed and the number of the users raises, gains are expected to grow with the number of variables and the interactions among them so it becomes impossible to relay on humans to control the network for increased number of variables and this is why AI with ML and automation become beneficial and necessity to run the future networks. AI generally is defined as capacity of mind or ability to acquire and apply knowledge and skills while ML is defined as learning that does not require explicit programming. Combined usage of AI and ML can optimize almost any component of the wireless network, this does not mean that it should be used everywhere mainly because at the end of the day the cost benefit analysis of its usage must be positive. Smart operation, management and infrastructure maintenance (SOMM) networks are defined as: Intelligent, data driven, integrated and agile. Today AI is introduced but in future it will represent the network engine. It is interesting to mention that network security must be upgraded because the network will provide services for massive number of IoT devices that will have variety of functions and requests. AI/ML can improve the security services and to be used in order to elevate them at advanced level. In this text we focus our attention at AI/ML and security scenarios defined for IoT in 5G environment.
5
Petrov, Ivan, and Toni Janevski. "Artificial Intelligence Techniques for Information Security in 5G IoT Environments." European Journal of Engineering and Technology Research 5, no. 11 (2020): 1328–33. http://dx.doi.org/10.24018/ejeng.2020.5.11.2210.
Full textAbstract:
The development of the telecommunication networks observed in present and future time is impressive. Today we witness rapid implementation of 5G networks. We can say that this actually is the moment when (artificial intelligence) AI enters at small door but in the beyond 5G world it is expected to have the prime role in smart operation, management and maintenance of non-software defined networking (SDN), network function virtualization (NFV) and especially at SDN and NFV aware networks. Number of standardization body’s and work groups are focused in a way to create a framework that will define the future use of AI and security standards necessary to exist in order to create health environment for the next generation telecommunication infrastructure. In the wireless world AI/Machine learning (ML) has great potential to shake the way we operate and to become foundation of the transformation that leads to the next industrial revolution. Network virtualization gives flexibility and freedom of the telco operators to choose the hardware and network topology they need for AI/ML platforms and big data sets. 5G and IoT create positive environment for AI and ML development and usage. As the network requirements are developed and the number of the users raises, gains are expected to grow with the number of variables and the interactions among them so it becomes impossible to relay on humans to control the network for increased number of variables and this is why AI with ML and automation become beneficial and necessity to run the future networks. AI generally is defined as capacity of mind or ability to acquire and apply knowledge and skills while ML is defined as learning that does not require explicit programming. Combined usage of AI and ML can optimize almost any component of the wireless network, this does not mean that it should be used everywhere mainly because at the end of the day the cost benefit analysis of its usage must be positive. Smart operation, management and infrastructure maintenance (SOMM) networks are defined as: Intelligent, data driven, integrated and agile. Today AI is introduced but in future it will represent the network engine. It is interesting to mention that network security must be upgraded because the network will provide services for massive number of IoT devices that will have variety of functions and requests. AI/ML can improve the security services and to be used in order to elevate them at advanced level. In this text we focus our attention at AI/ML and security scenarios defined for IoT in 5G environment.
6
Rathee, Geetanjali, Adel Khelifi, and Razi Iqbal. "Artificial Intelligence- (AI-) Enabled Internet of Things (IoT) for Secure Big Data Processing in Multihoming Networks." Wireless Communications and Mobile Computing 2021 (August 11, 2021): 1–9. http://dx.doi.org/10.1155/2021/5754322.
Full textAbstract:
The automated techniques enabled with Artificial Neural Networks (ANN), Internet of Things (IoT), and cloud-based services affect the real-time analysis and processing of information in a variety of applications. In addition, multihoming is a type of network that combines various types of networks into a single environment while managing a huge amount of data. Nowadays, the big data processing and monitoring in multihoming networks provide less attention while reducing the security risk and efficiency during processing or monitoring the information. The use of AI-based systems in multihoming big data with IoT- and AI-integrated systems may benefit in various aspects. Although multihoming security issues and their analysis have been well studied by various scientists and researchers; however, not much attention is paid towards big data security processing in multihoming especially using automated techniques and systems. The aim of this paper is to propose an IoT-based artificial network to process and compute big data processing by ensuring a secure communication multihoming network using the Bayesian Rule (BR) and Levenberg-Marquardt (LM) algorithms. Further, the efficiency and effect on multihoming information processing using an AI-assisted mechanism are experimented over various parameters such as classification accuracy, classification time, specificity, sensitivity, ROC, and F -measure.
7
Rojas, Elisa, David Carrascal, Diego Lopez-Pajares, et al. "A Survey on AI-Empowered Softwarized Industrial IoT Networks." Electronics 13, no. 10 (2024): 1979. http://dx.doi.org/10.3390/electronics13101979.
Full textAbstract:
The future generation of mobile networks envision Artificial Intelligence (AI) and the Internet of Things (IoT) as key enabling technologies that will foster the emergence of sophisticated use cases, with the industrial sector being one to benefit the most. This survey reviews related works in this field, with a particular focus on the specific role of network softwarization. Furthermore, the survey delves into their context and trends, categorizing works into several types and comparing them based on their contribution to the advancement of the state of the art. Since our analysis yields a lack of integrated practical implementations and a potential desynchronization with current standards, we finalize our study with a summary of challenges and future research ideas.
8
Rahman, Md Maruf, Mahrima Akter Mim, Debashon Chakraborty, Zihad Hasan Joy, and Nourin Nishat. "Anomaly-based Intrusion Detection System in Industrial IoT-Healthcare Environment Network." Journal of Engineering Research and Reports 26, no. 6 (2024): 113–23. http://dx.doi.org/10.9734/jerr/2024/v26i61166.
Full textAbstract:
The Internet of Things (IoT) technology facilitates automation, monitoring, and control of tangible objects and surroundings by enabling connected devices to interact and exchange data over the Internet. Developments in edge computing, blockchain, and artificial intelligence (AI) are incorporated into IoT technologies for more reliable operations. Inadequate authorization, authentication, and encryption protocols could render IoT networks insecure and open the door to illegal access and data breaches which can have terrible consequences, most notably in the healthcare industry. In this regard, to identify malicious and incursion traffic, machine learning (ML) is crucial to Internet of Things (IoT) cybersecurity. The paper proposes a framework to detect intrusion or malicious traffic in IoT-enabled different medical equipment such as medical sensors, and controllers for real-time data collection, creating communication channels and data monitoring and analysis over locally available network nodes. IoT-Flock has been utilized for both normal and malicious traffic generation in a wide dataset found by the sensors connected to IoT integrated healthcare network. The feature selection-based proposed framework has been evaluated by three distinct machine learning classifiers, KNN, RF, and DT where corresponding accuracy, sensitivity, precision, and F1-score have been measured for performance analysis. With an accuracy of 99.74%, the KNN technique performed better than the other tactics used by RF and DT regarding intrusion detection in IoT networks. The suggested framework will be helpful in developing or analyzing security solutions in IoT-integrated network systems.
9
Battina Srinuvasu Kumar, Et al. "A Learning based Secure Routing Approach using Deep Reinforcement Learning in IoT Integrated Wireless Sensor Network." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 2707–14. http://dx.doi.org/10.17762/ijritcc.v11i9.9345.
Full textAbstract:
The usage of Wireless Sensor Network (WSN) is ubiquitous in nature. With the emergence of Internet of Things (IoT) technology and its unprecedented use cases, the role of sensor networks as part of IoT application became crucial. WSN became backbone of IoT to realize integration between physical and digital worlds and connectivity to Internet. However, IoT devices are resource constrained with limited computational capabilities. The entire network is distributed in nature and has increased complexity. Routing in such WSN integrated IoT network plays an important role in achieving meaningful communication among objects. In this context, it is indispensable to have more energy efficient routing method. Since the IoT integrated sensor network is highly complicated, it is very dynamic in nature. Thus routing decisions are also dynamic leading to much importance to routing in such use cases. With the emergence of Artificial Intelligence (AI), it became possible to solve complex real world problems through learning based approach which acquires desired intelligence prior to making decisions. In this paper we proposed a deep reinforcement learning based routing mechanism for energy efficient routing in WSN-IoT integrated application. We proposed novel algorithms for network setup, formation of clusters and routing. Our method adapts to network changes due to energy levels, mobility and makes learning based routing decisions. We enhanced the method further with security to ensure its Qualityof Service (QoS) in presence of attacks. Our simulation study using MATLAB has revealed that the proposed secure routing approach outperforms existing protocols.
10
Schiller, Eryk, Elfat Esati, and Burkhard Stiller. "IoT-Based Access Management Supported by AI and Blockchains." Electronics 11, no. 18 (2022): 2971. http://dx.doi.org/10.3390/electronics11182971.
Full textAbstract:
Internet-of-Things (IoT), Artificial Intelligence (AI), and Blockchains (BCs) are essential techniques that are heavily researched and investigated today. This work here specifies, implements, and evaluates an IoT architecture with integrated BC and AI functionality to manage access control based on facial detection and recognition by incorporating the most recent state-of-the-art techniques. The system developed uses IoT devices for video surveillance, AI for face recognition, and BCs for immutable permanent storage to provide excellent properties in terms of image quality, end-to-end delay, and energy efficiency.
Books on the topic "AI-Integrated IoT Networks"
1
Kasabov, Nikola. Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering. The MIT Press, 1996. http://dx.doi.org/10.7551/mitpress/3071.001.0001.
Full textAbstract:
In a clear and accessible style, Kasabov describes rule-based and connectionist techniques and then their combinations, with fuzzy logic included, showing the application of the different techniques to a set of simple prototype problems, which makes comparisons possible. A particularly strong feature of the text is that it is filled with applications in engineering, business, and finance. AI problems that cover most of the application-oriented research in the field (pattern recognition, speech and image processing, classification, planning, optimization, prediction, control, decision making, and game simulations) are discussed and illustrated with concrete examples. Intended both as a text for advanced undergraduate and postgraduate students as well as a reference for researchers in the field of knowledge engineering, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering has chapters structured for various levels of teaching and includes original work by the author along with the classic material. Data sets for the examples in the book as well as an integrated software environment that can be used to solve the problems and do the exercises at the end of each chapter are available free through anonymous ftp. Bradford Books imprint
Book chapters on the topic "AI-Integrated IoT Networks"
1
Anh, P. T. N., Vladimir Hahanov, Triwiyanto, et al. "Artificial Intelligence (AI) Models for Disease Diagnosis and Prediction of Heart Disease with Artificial Neural Networks (ANN)." In Computer Vision and AI-Integrated IoT Technologies in the Medical Ecosystem. CRC Press, 2024. http://dx.doi.org/10.1201/9781003429609-9.
Full text
2
Raymahapatra, Prithwish, Avijit Kumar Chaudhuri, Sulekha Das, and Alex Khang. "Lung Cancer Prediction Using Convolutional Neural Network (CNN) with VGG16 Model." In Computer Vision and AI-Integrated IoT Technologies in the Medical Ecosystem. CRC Press, 2024. http://dx.doi.org/10.1201/9781003429609-15.
Full text
3
Krishna Pasupuleti, Murali. "Next-Gen Connectivity: AI and IoT for Space-Terrestrial Integrated Networks." In Future Networks: AI, IoT, and Sustainable Communications from Earth to Orbit. National Education Services, 2024. http://dx.doi.org/10.62311/nesx/7202.
Full textAbstract:
Abstract: This chapter explores the transformative potential of artificial intelligence (AI) and the Internet of Things (IoT) in developing space-terrestrial integrated networks. It discusses the core technologies and architectural frameworks driving next-gen connectivity, including AI-driven network optimization, IoT integration, and advanced satellite communication protocols. The chapter highlights practical applications, such as expanding global internet access, enhancing disaster response, and enabling smart city infrastructure. It also addresses the challenges of latency, security, and regulatory barriers while providing a forward-looking view of future innovations, such as AI-enhanced network intelligence and emerging satellite technologies. The chapter concludes with a call for collaboration and strategic investment to create a more connected and resilient world. Keywords: AI, IoT, space-terrestrial networks, next-gen connectivity, network optimization, satellite technology, global internet access, disaster response, smart cities, latency, security, data privacy, regulatory challenges, AI-driven automation, future innovations, digital divide, global communication.
4
Ahmed, Ameer Asra, P. Vinish, and Harold Andrew Patrick. "Technology-Integrated Smart Living and Era of Smart Homes." In Advances in Logistics, Operations, and Management Science. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-2346-5.ch006.
Full textAbstract:
Businesses and users communicate via networks. Businesses use Skype, RSS, blogs, etc. Communication networks are becoming more complicated and customised as technology develops. Smart networks help carriers launch and manage services. The Bell Communications Research phone network allows device service feature addition or removal. Smart networks may add modules to provide services without replacing equipment. Integrating services with the intelligent network may need new skills. Service addition to an intelligent network may be similar. Improved network management enables smart homes. IoT, AI, and IoE smart houses enable tech-savvy living. IoE and IoT may assist many people, particularly those who spend a lot of time outside while their kids and elderly parents stay home. Home automation may improve life. But every gain has drawbacks. Integrated smart technology may cause complications. Thus, this chapter examines smart living in smart homes, its concepts, intelligent network technologies, administration, benefits, and drawbacks.
5
Koshariya, Ashok Kumar, D. Kalaiyarasi, A. Arokiaraj Jovith, T. Sivakami, Dler Salih Hasan, and Sampath Boopathi. "AI-Enabled IoT and WSN-Integrated Smart Agriculture System." In Artificial Intelligence Tools and Technologies for Smart Farming and Agriculture Practices. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-8516-3.ch011.
Full textAbstract:
Agriculture and farming have gotten smarter as a result of the use of current technology such as Wireless Sensor Networks (WSN) and the Internet of Things (IoT). Smart farming is an enhanced agriculture system that offers data such as temperature, soil moisture, and so on, to assist in the growth of plants and cattle. It integrates wireless sensors and the internet to collect and communicate information with farmers. The priority event-based energy efficient algorithm developed in this study is utilized for accurate and efficient information transmission regarding power consumption and node priority. The major goal of the IoT-sensor network in this chapter is to increase farm productivity and extend its lifespan by applying intelligent algorithms such as Artificial Neural Network (ANN) to recognize environmental conditions and improve total production. Priority event-based energy efficient method reduces energy usage and increases the lifetime using Dijkstra's algorithm.
6
Pise, Ganesh Shivaji, and Sachin D. Babar. "AI for IoT Application." In Handbook of Research on AI and Knowledge Engineering for Real-Time Business Intelligence. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-6519-6.ch008.
Full textAbstract:
The popularity of the Internet of Things (IoT) has increased due to the development of faster internet networks and more advanced digital devices (Smarter Hardware), such as sensors integrated into a microcontroller. Currently, sensors and other digital devices used in diverse geographical sectors, such as agriculture, hospitals, smart homes, and smart cities, generate and share vast quantities of data. These IoT data must be collected and mined using AI for knowledge management (AI). Collaboration between AI and IoT results in intelligence system automation. This continuum will influence all emerging industries, including healthcare, transportation, manufacturing, and retail. IoT devices generate vast quantities of data, and AI will aid in more intelligent data planning for various IoT applications. In the software engineering and technical research processes, AI techniques are utilized for automatic problem solving and problem identification.
7
Fadi, Oumaima, Sara Lahlou, Adil Bahaj, Karim Zkik, Abdelatif El Ghazi, and Mohammed Boulmalf. "An integrated framework for securing IoT networks with blockchain and AI." In Empowering IoT with Big Data Analytics. Elsevier, 2025. https://doi.org/10.1016/b978-0-443-21640-4.00012-0.
Full text
8
Yamini, G. "Exploring Internet of Things and Artificial Intelligence for Smart Healthcare Solutions." In Deep Neural Networks for Multimodal Imaging and Biomedical Applications. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-3591-2.ch013.
Full textAbstract:
Artificial intelligence integrated with the internet of things network could be used in the healthcare sector to improve patient care. The data obtained from the patient with the help of certain medical healthcare devices that include fitness trackers, mobile healthcare applications, and several wireless sensor networks integrated into the body of the patients promoted digital data that could be stored in the form of digital records. AI integrated with IoT could be able to predict diseases, monitor heartbeat rate, recommend preventive maintenance, measure temperature and body mass, and promote drug administration by having a review with the patient's medical history and detecting health defects. This chapter explores IoT and artificial intelligence for smart healthcare solutions.
9
Komala, C. R., Mehfooza Munavar Basha, S. Farook, R. Niranchana, M. Rajendiran, and B. Subhi. "Smart Energy Systems-Integrated Machine Learning, IoT, and AI Tools." In Advances in Environmental Engineering and Green Technologies. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-2351-9.ch011.
Full textAbstract:
The integration of renewable energy sources and AI-driven solutions is revolutionizing the energy landscape, offering efficiency, sustainability, and innovation. This chapter explores the interplay between renewable energy sources and AI-driven solutions, highlighting its role in forecasting, real-time monitoring, predictive maintenance, and demand response. Comparative studies between traditional and novel energy technologies highlight the potential for cleaner, more efficient alternatives. Challenges of integration complexity, data privacy, and scalability are discussed, while a vision of future directions includes holistic energy systems, autonomous energy networks, and collaborative innovation. By harnessing AI's capabilities and embracing innovative power generation methods, the energy sector can create a resilient, sustainable, and intelligent energy future.
10
Sarkar, Swagata, Ciro Rodriguez, Nithi Ravya S., and Jeena A. Thankachan. "IoT and AI Integration in Wool Classification." In Leveraging AI-Powered Marketing in the Experience-Driven Economy. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-9561-5.ch010.
Full textAbstract:
This chapter introduces an integrated wool classification system that leverages the Internet of Things (IoT) and Artificial Intelligence (AI) technologies to enhance the customer experience in textile marketing. The primary objectives are to: develop an automated wool classification system using IoT sensors to measure critical wool characteristics; implement advanced AI algorithms, specifically Convolutional Neural Networks (CNN) and VGG16, to enable precise and efficient wool quality classification; and address the limitations of traditional manual classification methods to ensure superior product consistency, ultimately enhancing customer satisfaction. Using IoT-enabled sensors, the system gathers key physical characteristics of wool. These data points are then processed by powerful AI algorithms, enabling accurate and rapid wool quality classification. For brands in the textile industry, this innovative system offers a marketing advantage through quality assurance, transparency in production, and the ability to deliver superior customer experiences.
Conference papers on the topic "AI-Integrated IoT Networks"
1
N, Nishmitha, Bharat R. Mhalsekar, Disha, Navami, and Pavanlaxmi. "AI Integrated Support System for Detecting Autism Spectrum Disorder in Children." In 2024 International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS). IEEE, 2024. https://doi.org/10.1109/icicnis64247.2024.10823202.
Full text
2
William, P., Shailaja Salagrama, Meenakshi Maindola, Anurag Shrivastava, Muntather Almusawi, and Kanchan Yadav. "Reconfigurable Intelligent Surfaces to Enable Energy-Efficient IoT Networks." In 2024 1st International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI). IEEE, 2024. https://doi.org/10.1109/icscai61790.2024.10866148.
Full text
3
Rahmati, Afrooz, Afra Mashhadi, and Geethapriya Thamilarasu. "Building a Robust Federated Learning based Intrusion Detection System in Internet of Things." In 4th International Conference on AI, Machine Learning and Applications. Academy & Industry Research Collaboration Center, 2024. http://dx.doi.org/10.5121/csit.2024.140201.
Full textAbstract:
The Internet of Things (IoT) has emerged as the next big technological revolution in recent years with the potential to transform every sphere of human life. As devices, applications, and communication networks become increasingly connected and integrated, security and privacy concerns in IoT are growing at an alarming rate as well. While existing research has largely focused on centralized systems to detect security attacks, these systems do not scale well with the rapid growth of IoT devices and pose a single-point of failure risk. Furthermore, since data is extensively dispersed across huge networks of connected devices, decentralized computing is critical. Federated learning (FL) systems in the recent times has gained popularity as the distributed machine learning model that enables IoT edge devices to collaboratively train models in a decentralized manner while ensuring that data on a user’s device stays private without the contents or details of that data ever leaving that device. In this paper, we propose a federated learning based intrusion detection system using LSTM Autoencoder. The proposed technique allows IoT devices to train a global model without revealing their private data, enabling the training model to grow in size while protecting each participants local data. We conduct extensive experiments using the BoT-IoT data set and demonstrate that our solution can not only effectively improve IoT security against unknown attacks but also ensure users data privacy.
4
Abili, N., and S. Hemeda. "Insight-Driven Digital Engineering – A Key Enabler Driving Operational Intelligence in the Energy Industry." In SPE Annual Technical Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/214900-ms.
Full textAbstract:
Abstract The energy industry is undergoing fundamental change with industry 4.0 enabling intelligent operations, production optimization, prescriptive maintenance and efficiency boosting, embedding insights, hyper-automation and enhance agility across production assets with the objectives of increasing productivity, delivering cost savings, and improving superior customer experience. Leveraging digital solution accelerators, APIs and network gateways connectivity for event data analytics, these digital native products can expedite development on energy assets with infrastructure as code - the single source of truth. Adopting insight-driven digital engineering is crucial for the energy industry to remain resilient, profitable, and sustainable in meeting net-zero carbon emission in the energy transition. There is urgent need for the energy industry to partner with the digital native companies and integrators to co-create solutions in driving digital enablement across oil and gas, offshore developments, LNGs, renewables, hydrogen, and utilities. The energy industry does not need to reinvent the will, as the technology exist. What is needed is collaborations of ideas and application of disruptive use cases across the digital and energy industry to pivot sustainable novel solutions for the ecosystem. Deployments of autonomous systems with AI for remote operations optimization, IoT connectivity for pervasive platform across assets, digital twin for visibility of entire assets, edge computing for resilience and low cost of operations, data analytic platform for prescriptive maintenance, and the protection of critical infrastructural assets in an ecosystem of vulnerabilities, are key processes for net-zero carbon emission and sustainability in the energy industry. The energy and digital tech leaders must collaborate to revolutionize the energy industry with insight-driven digital engineering in this age of digital disruptions. Designing for speed in an EPC project without compromising quality relies on digital information from concept and FEED, to inform a digital engineering solution platform, which have resulted in enabling Energy Operators get to a final investment decision up to 45% faster. A typical use case is where an EPC company begun the process of reducing their engineering software and technology portfolio from over 175 distinct products down to less than 32. The benefits include expedited evaluation of concepts through process simulation software, automated plant layout and piping designs through solution accelerators enablement, and coordinated estimates tied to the engineering information. With over 200 digital accelerator models deployed in the first year of an energy operating asset, the solution correctly identified 49 major early warnings, a value of $16.8 million saving in ROI was achieved. Thus, with support for the entire concept to FEED process now enabled with integrated digital design, layout and estimating software, EPCs can explore and optimize options for both offshore greenfield and brownfield projects quickly and efficiently with solution accelerators, providing Energy Operators the highest value from their investments.
Reports on the topic "AI-Integrated IoT Networks"
1
Pasupuleti, Murali Krishna. Smart Nanomaterials and AI-Integrated Grids for Sustainable Renewable Energy. National Education Services, 2025. https://doi.org/10.62311/nesx/rr1025.
Full textAbstract:
Abstract: The transition to sustainable and intelligent renewable energy systems is being driven by advancements in smart nanomaterials and AI-integrated smart grids. Nanotechnology has enabled the development of high-performance energy materials, such as graphene, perovskites, quantum dots, and MXenes, which enhance the efficiency, durability, and scalability of renewable energy solutions. Simultaneously, AI-driven smart grids leverage machine learning, deep learning, and digital twins to optimize energy distribution, predictive maintenance, and real-time load balancing in renewable energy networks. This research explores the synergistic integration of AI and nanomaterials to develop self-regulating, adaptive, and fault-tolerant energy infrastructures. The study examines AI-powered energy storage, decentralized smart microgrids, quantum AI for grid cybersecurity, and blockchain-integrated energy trading. Furthermore, the report assesses global industry adoption, policy frameworks, and economic growth trends, providing a strategic roadmap for the large-scale implementation of AI-enhanced nanomaterial-based energy systems. Through case studies and real-world applications, this research highlights how AI and nanotechnology will drive the next-generation sustainable energy revolution. Keywords Smart nanomaterials, AI-integrated grids, sustainable renewable energy, graphene-based solar cells, perovskite photovoltaics, quantum dots in energy, MXenes for energy storage, AI-driven energy optimization, machine learning for smart grids, deep learning energy forecasting, predictive maintenance in energy grids, digital twins for grid management, AI-powered decentralized microgrids, blockchain energy trading, hydrogen storage nanomaterials, AI-enhanced lithium-ion batteries, reinforcement learning in energy distribution, AI for demand-side energy management, quantum AI for grid cybersecurity, scalable nanomaterial-based energy solutions, AI-driven self-healing energy materials.