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Duarte, Dr Sofia, and Jiwon Park. "SECURING LARGE-SCALE IOT NETWORKS: A FEDERATED TRANSFER LEARNING APPROACH FOR REAL-TIME INTRUSION DETECTION." International Journal of Modern Computer Science and IT Innovations 2, no. 6 (2025): 1–7. https://doi.org/10.55640/ijmcsit-v02i06-01.
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The pervasive deployment of Internet of Things (IoT) devices has ushered in an era of unprecedented connectivity and data generation. However, this expansive network also presents a vast attack surface, making robust intrusion detection critical. Traditional centralized Intrusion Detection Systems (IDS) face significant challenges in large-scale IoT environments, including privacy concerns, communication overhead, and the sheer volume and heterogeneity of data. This article proposes an enhanced real-time intrusion detection framework that leverages the synergistic capabilities of Federated Learning (FL) and Transfer Learning (TL). The framework allows IoT devices to collaboratively train a global intrusion detection model without sharing raw data, thereby preserving privacy, while utilizing pre-trained knowledge to enhance detection capabilities and adapt to evolving threats. We discuss the architectural components, data handling strategies, and the integration of FL and TL, highlighting how this approach can significantly improve detection accuracy, reduce latency, and maintain data privacy in dynamic and resource-constrained large-scale IoT networks.
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Tiruvayipati, Sujanavan, and Ramadevi Yellasiri. "Leveraging Nano Block-Lattice for Cost-Effective Large-Scale Wi-Fi-Based IoT Network Expansion." International Journal of Interactive Mobile Technologies (iJIM) 19, no. 08 (2025): 224–38. https://doi.org/10.3991/ijim.v19i08.53319.
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The rapid expansion of Internet of Things (IoT) networks necessitates cost-effective and scalable solutions for secure data transmission and storage. This study explores the integration of Nano (XNO) cryptocurrency with Wi-Fi repeater networks to address the challenges of IoT scalability and data security. By leveraging XNO’s block-lattice structure, this study demonstrates how the cryptocurrency’s minimal transaction fees, high transaction throughput, and low latency make it an optimal solution for real-time IoT data handling. In combination with Wi-Fi repeater networks, which extend coverage and reduce signal loss, the proposed system enables secure, decentralized storage of IoT data. Performance metrics, including latency, transaction throughput, and cost, show that the XNO-integrated IoT network outperforms traditional blockchain technologies such as Ethereum and IOTA, offering a promising solution for large-scale IoT deployments.
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Shao, Yulin, Soung Chang Liew, He Chen, and Yuyang Du. "Flow Sampling: Network Monitoring in Large-Scale Software-Defined IoT Networks." IEEE Transactions on Communications 69, no. 9 (2021): 6120–33. http://dx.doi.org/10.1109/tcomm.2021.3093320.
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Moe, Z. Win, Meyer Florian, Liu Zhenyu, Dai Wenhan, Bartoletti Stefania, and Conti Andrea. "Efficient Multisensor Localization for the Internet of Things: Exploring a New Class of Scalable Localization Algorithms." IEEE Signal Processing Magazine 35, no. 5 (2018): 153–67. https://doi.org/10.1109/MSP.2018.2845907.
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In the era of the Internet of Things (IoT), efficient localization is essential for emerging mass-market services and applications. IoT devices are heterogeneous in signaling, sensing, and mobility, and their resources for computation and communication are typically limited. Therefore, to enable location awareness in large-scale IoT networks, there is a need for efficient, scalable, and distributed multisensor fusion algorithms. This article presents a framework for designing network localization and navigation (NLN) for the IoT. Multisensor localization and operation algorithms developed within NLN can exploit spatiotemporal cooperation, are suitable for arbitrary, large network sizes, and only rely on an information exchange among neighboring devices. The advantages of NLN are evaluated in a large-scale IoT network with 500 agents. In particular, because of multisensor fusion and cooperation, the presented network localization and operation algorithms can provide attractive localization performance and reduce communication overhead and energy consumption.
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Salva-Garcia, Pablo, Jose M. Alcaraz-Calero, Qi Wang, Jorge Bernal Bernabe, and Antonio Skarmeta. "5G NB-IoT: Efficient Network Traffic Filtering for Multitenant IoT Cellular Networks." Security and Communication Networks 2018 (December 10, 2018): 1–21. http://dx.doi.org/10.1155/2018/9291506.
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Internet of Things (IoT) is a key business driver for the upcoming fifth-generation (5G) mobile networks, which in turn will enable numerous innovative IoT applications such as smart city, mobile health, and other massive IoT use cases being defined in 5G standards. To truly unlock the hidden value of such mission-critical IoT applications in a large scale in the 5G era, advanced self-protection capabilities are entailed in 5G-based Narrowband IoT (NB-IoT) networks to efficiently fight off cyber-attacks such as widespread Distributed Denial of Service (DDoS) attacks. However, insufficient research has been conducted in this crucial area, in particular, few if any solutions are capable of dealing with the multiple encapsulated 5G traffic for IoT security management. This paper proposes and prototypes a new security framework to achieve the highly desirable self-organizing networking capabilities to secure virtualized, multitenant 5G-based IoT traffic through an autonomic control loop featured with efficient 5G-aware traffic filtering. Empirical results have validated the design and implementation and demonstrated the efficiency of the proposed system, which is capable of processing thousands of 5G-aware traffic filtering rules and thus enables timely protection against large-scale attacks.
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Wu, Xing, Jing Duan, Mingyu Zhong, Peng Li, and Jianjia Wang. "VNF Chain Placement for Large Scale IoT of Intelligent Transportation." Sensors 20, no. 14 (2020): 3819. http://dx.doi.org/10.3390/s20143819.
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With the advent of the Internet of things (IoT), intelligent transportation has evolved over time to improve traffic safety and efficiency as well as to reduce congestion and environmental pollution. However, there are some challenging issues to be addressed so that it can be implemented to its full potential. The major challenge in intelligent transportation is that vehicles and pedestrians, as the main types of edge nodes in IoT infrastructure, are on the constant move. Hence, the topology of the large scale network is changing rapidly over time and the service chain may need reestablishment frequently. Existing Virtual Network Function (VNF) chain placement methods are mostly good at static network topology and any evolvement of the network requires global computation, which leads to the inefficiency in computing and the waste of resources. Mapping the network topology to a graph, we propose a novel VNF placement method called BVCP (Border VNF Chain Placement) to address this problem by elaborately dividing the graph into multiple subgraphs and fully exploiting border hypervisors. Experimental results show that BVCP outperforms the state-of-the-art method in VNF chain placement, which is highly efficient in large scale IoT of intelligent transportation.
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Shreyas, J., N. V. Priya, P. K. Udayprasad, N. N. Srinidhi, Chouhan Dharmendra, and Kumar S. M. Dilip. "Opportunistic Routing for Large Scale IoT Network to Reduce Transmission Overhead." Journal of Advancement in Parallel Computing 5, no. 1 (2022): 1–8. https://doi.org/10.5281/zenodo.6379125.
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<em>Increase in popularity of sensor electronics have gained much attention for wireless sensor technologies and demands many IoT (Internet of things) applications for real time and industrial applications. In IoT the proliferation of devices which are able to directly connected to internet and can be monitored. Sensed data from the device has to be forwarded to base station or end user (EU) which is achieved by efficient routing protocols to improve data transmission for large scale IoT. During routing process redundant data may be forwarded by nodes causing more overhead which may lead to congestion. There exist many challenges like low power links, multiple disjoint path, and energy while designing efficient communication protocol. In this paper we propose an enhanced opportunistic routing (e-OR) protocol for self-disciplined and self-healing large scale IoT devices. Enhanced opportunistic routing protocol uses best fit traversing algorithm to find optimal and reliable routes. The e- OR estimates link quality of nodes to avoid frequent disconnections. During route discovery process e-OR adapts greedy behaviour for finding optimal and shortest routes. Further we integrate congestion avoidance using clear channel assignment (CCA) for better channel availability to avoid packet loss and achieve QoS.</em>
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Pan, Pengyu, Xiaobo Ma, Yingjie Fu, and Feitong Chen. "Automating Group Management of Large-Scale IoT Botnets for Antitracking." Security and Communication Networks 2022 (April 14, 2022): 1–10. http://dx.doi.org/10.1155/2022/4196945.
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With the popularity of Internet of Things (IoT) devices, IoT botnets like Mirai have been infecting as many devices as possible such as IP cameras and home routers. Because of the sheer volume and continual operation of many vulnerabilities (many users do not pay much attention to IoT update alerts and leave the configurations by default) of IoT devices, the population of an IoT botnet becomes increasingly tremendous. The growing population, though making a botnet powerful, results in an increased risk of exposure. Specifically, once a bot is captured, the command and control (C&C) channel may be cracked and then tracked, potentially rendering more bots being discovered. To solve this problem, this paper proposes an automated approach to group management of large-scale IoT bots. The basic idea of the proposed approach is to establish a reliable and unsuspicious social network-based C&C channel capable of automatically grouping bots, wherein a group of bots have a unique ID that is against cross-group tracking. The Diffie–Hellman key exchange method is leveraged for efficiently generating the unique group ID, thereby scaling up automatic bot grouping. We refer to the botnet proposed in this paper as a multichannel automatic grouping botnet (MCG botnet) and conduct verification experiments using social networks and more than 2,000 docker nodes. The experimental results show that the MCG botnet has the ability of automatic grouping and antitracking.
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Yin, Lihua, Weizhe Chen, Xi Luo, and Hongyu Yang. "Efficient Large-Scale IoT Botnet Detection through GraphSAINT-Based Subgraph Sampling and Graph Isomorphism Network." Mathematics 12, no. 9 (2024): 1315. http://dx.doi.org/10.3390/math12091315.
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In recent years, with the rapid development of the Internet of Things, large-scale botnet attacks have occurred frequently and have become an important challenge to network security. As artificial intelligence technology continues to evolve, intelligent detection solutions for botnets are constantly emerging. Although graph neural networks are widely used for botnet detection, directly handling large-scale botnet data becomes inefficient and challenging as the number of infected hosts increases and the network scale expands. Especially in the process of node level learning and inference, a large number of nodes and edges need to be processed, leading to a significant increase in computational complexity and posing new challenges to network security. This paper presents a novel approach that can accurately identify diverse intricate botnet architectures in extensive IoT networks based on the aforementioned circumstance. By utilizing GraphSAINT to process large-scale IoT botnet graph data, efficient and unbiased subgraph sampling has been achieved. In addition, a solution with enhanced information representation capability has been developed based on the Graph Isomorphism Network (GIN) for botnet detection. Compared with the five currently popular graph neural network (GNN) models, our approach has been tested on C2, P2P, and Chord datasets, and higher accuracy has been achieved.
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Qian, Kexiang, Hongyu Yang, Ruyu Li, Weizhe Chen, Xi Luo, and Lihua Yin. "Distributed Detection of Large-Scale Internet of Things Botnets Based on Graph Partitioning." Applied Sciences 14, no. 4 (2024): 1615. http://dx.doi.org/10.3390/app14041615.
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With the rapid growth of IoT devices, the threat of botnets is becoming increasingly worrying. There are more and more intelligent detection solutions for botnets that have been proposed with the development of artificial intelligence. However, due to the current lack of computing power in IoT devices, these intelligent methods often cannot be well-applied to IoT devices. Based on the above situation, this paper proposes a distributed botnet detection method based on graph partitioning, efficiently detecting botnets using graph convolutional networks. In order to alleviate the wide range of IoT environments and the limited computing power of IoT devices, the algorithm named METIS is used to divide the network traffic structure graph into small graphs. To ensure robust information flow between nodes while preventing gradient explosion, diagonal enhancement is applied to refine the embedding representations at each layer, facilitating accurate botnet attack detection. Through comparative analysis with GATv2, GraphSAGE, and GCN across the C2, P2P, and Chord datasets, our method demonstrates superior performance in both accuracy and F1 score metrics. Moreover, an exploration into the effects of varying cluster numbers and depths revealed that six cluster levels yielded optimal results on the C2 dataset. This research significantly contributes to mitigating the IoT botnet threat, offering a scalable and effective solution for diverse IoT ecosystems.
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Y. Khan, Jamil, Dong Chen, and Oliver Hulin. "Enabling Technologies for Effective Deployment of Internet of Things (IoT) Systems." Journal of Telecommunications and the Digital Economy 2, no. 4 (2020): 21. http://dx.doi.org/10.18080/jtde.v2n4.276.
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The demand for IoT (Internet of Things) systems that encompass cloud computing, the multitude of low power sensing and data collection electronic devices and distributed communications architecture is increasing at an exponential pace. With increasing interests from different industrial, business and social groups, in the near future it will be necessary to support massive deployment of diverse IoT systems in different geographical areas. Large scale deployment of IoT systems will introduce challenging problems for the communication designers, as the networking is one of the key enabling technologies for the IoT systems. Major challenges include cost effective network architecture, support of large area of coverage and diverse QoS (Quality of Service) requirements, reliability, spectrum requirements, energy requirements, and many other related issues. The paper initially reviews different classes of IoT applications and their communication requirements. Following the review, different communications and networking technologies that can potentially support large scale deployment of IoT systems for different industrial, business and social applications are discussed. The paper then concentrates on wireless networking technologies for IoT systems with specific focus on deployment issues. The deployment discussion concentrates on different IoT systems QoS and networking requirements, cost, coverage area and energy supply requirements. We introduce a sustainable low cost heterogeneous network design using short range radio standards such as IEEE 802.15.4/Zigbee, IEEE 802.11/WLAN that can be used to develop a wide area networks to support large number of IoT devices for various applications. Finally the paper makes some general recommendations towards sustainable network design techniques for future IoT systems that can reduce the OPEX and CAPEX requirements.
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Yang, Xing, Zhen Qian, Xuhong Zhang, et al. "Cascading-Failures Effect on Heterogeneous Internet of Things Systems under Targeted Selective Attack." Security and Communication Networks 2022 (March 21, 2022): 1–13. http://dx.doi.org/10.1155/2022/6848156.
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With the rapid development of the Internet of Things (IoT), the physical system and the network space are further deeply integrated, forming a larger-scale IoT heterogeneous fusion system. The attack mode considered in the security mechanism research of traditional large-scale complex systems is relatively simple; only simple attack types such as random attacks on physical systems or network systems are considered. In addition, existing attack modalities such as selectivity, locality, and distribution cannot fully consider the characteristics of security threats in the IoT system. In this paper, for large-scale heterogeneous IoT system scenarios, attackers can attack network systems or physical systems through cyberspace. We conduct situational awareness analysis on important traffic nodes or backbone nodes and study the cascading failures of two interdependent heterogeneous space systems. In view of the existence of such targeted attack threats in large-scale IoT heterogeneous systems, we focus on security assessment and risk prediction issues. First, this paper analyzes and models different IoT heterogeneous systems. Then using the penetration theory, we analyze the cascading failure process step by step and obtain the critical threshold for system collapse failure. Finally, we further verify the correctness of the theoretical values through simulation to effectively analyze and illustrate the reliability of the parameters affecting the system risk. The experimental results show that the large-scale IoT heterogeneous system presents a first-order discontinuous transition value near the critical threshold and the power-law index of the SF network has little effect on the system security.
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Lachowski, Rosana, Marcelo Pellenz, Edgard Jamhour, et al. "ICENET: An Information Centric Protocol for Big Data Wireless Sensor Networks." Sensors 19, no. 4 (2019): 930. http://dx.doi.org/10.3390/s19040930.
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Wireless Sensors Networks (WSNs) are an essential element of the Internet of Things (IoT), and are the main producers of big data. Collecting a huge amount of data produced by a resource-constrained network is a very difficult task, presenting several challenges. Big data gathering involves not only periodic data sensing, but also the forwarding of queries and commands to the network. Conventional network protocols present unfeasible strategies for large-scale networks and may not be directly applicable to IoT environments. Information-Centric Networking is a revolutionary paradigm that can overcome such big data gathering challenges. In this work, we propose a soft-state information-centric protocol, ICENET (Information Centric protocol for sEnsor NETworks), for big data gathering in large-scale WSNs. ICENET can efficiently propagate user queries in a wireless network by using a soft-state recovery mechanism for lossy links. The scalability of our solution is evaluated in different network scenarios. Results show that the proposed protocol presents approximately 84% less overhead and a higher data delivery rate than the CoAP (Constrained Application Protocol), which is a popular protocol for IoT environments.
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Afifie, Nur Azzurin, Adam Wong Yoon Khang, Abd Shukur Bin Ja'afar, et al. "Evaluation Method of Mesh Protocol over ESP32 and ESP8266." Baghdad Science Journal 18, no. 4(Suppl.) (2021): 1397. http://dx.doi.org/10.21123/bsj.2021.18.4(suppl.).1397.
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Internet of Things (IoT) is one of the newest matters in both industry and academia of the communication engineering world. On the other hand, wireless mesh networks, a network topology that has been debate for decades that haven’t been put into use in great scale, can make a transformation when it arises to the network in the IoT world nowadays. A Mesh IoT network is a local network architecture in which linked devices cooperate and route data using a specified protocol. Typically, IoT devices exchange sensor data by connecting to an IoT gateway. However, there are certain limitations if it involves to large number of sensors and the data that should be received is difficult to analyze. The aim of the work here is to implement a self-configuring mesh network in IoT sensor devices for better independent data collection quality. The research conducted in this paper is to build a mesh network using NodeMCU ESP 8266 and NodeMCU ESP 32 with two types of sensor, DHT 11 and DHT 22. Hence, the work here has evaluated on the delay performance metric in Line-of-Sight (LoS) and Non-Line-of-Sight (nLos) situation based on different network connectivity. The results give shorter delay time in LoS condition for all connected nodes as well as when any node fail to function in the mesh network compared to nLoS condition. The paper demonstrates that the IoT sensor devices composing the mesh network is a must to leverage the link communication performance for data collection in order to be used in IoT-based application such as fertigation system. It will certainly make a difference in the industry once being deployed on large scale in the IoT world and make the IoT more accessible to a wider audience.
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Pan, Xiangnan, Shingo Yamaguchi, Taku Kageyama, and Mohd Hafizuddin Bin Kamilin. "Machine-Learning-Based White-Hat Worm Launcher in Botnet Defense System." International Journal of Software Science and Computational Intelligence 14, no. 1 (2022): 1–14. http://dx.doi.org/10.4018/ijssci.291713.
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This article proposes a white-hat worm launcher based on machine learning (ML) adaptable to large-scale IoT network for Botnet Defense System (BDS). BDS is a cyber-security system that uses white-hat worms to exterminate malicious botnets. White-hat worms defend an IoT system against malicious bots, the BDS decides the number of white-hat worms, but there is no discussion on the white-hat worms' deployment in IoT network. Therefore, the authors propose a machine-learning-based launcher to launch the white-hat worms effectively along with a divide and conquer algorithm to deploy the launcher to large-scale IoT networks. Then the authors modeled BDS and the launcher with agent-oriented Petri net and confirmed the effect through the simulation of the PN2 model. The result showed that the proposed launcher can reduce the number of infected devices by about 30-40%.
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Pham, Van-Nam, Md Delowar Hossain, Ga-Won Lee, and Eui-Nam Huh. "Efficient Data Delivery Scheme for Large-Scale Microservices in Distributed Cloud Environment." Applied Sciences 13, no. 2 (2023): 886. http://dx.doi.org/10.3390/app13020886.
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The edge computing paradigm has emerged as a new scope within the domain of the Internet of Things (IoT) by bringing cloud services to the network edge in order to construct distributed architectures. To efficiently deploy latency-sensitive and bandwidth-hungry IoT application services, edge computing paradigms make use of devices on the network periphery that are distributed and resource-constrained. On the other hand, microservice architectures are becoming increasingly popular for developing IoT applications owing to their maintainability and scalability advantages. Providing an efficient communication medium for large-scale microservice-based IoT applications constructed from small and independent services to cooperate to deliver value-added services remains a challenge. This paper introduces an event-driven communication medium that takes advantage of Edge–Cloud publish/subscribe brokers for microservice-based IoT applications at scale. Using the interaction model, the involved microservices can collaborate and exchange data through triggered events flexibly and efficiently without changing their underlying business logic. In the proposed model, edge brokers are grouped according to their similarities in event channels and the proximity of their geolocations, reducing the data delivery latency. Moreover, in the proposed system a technique is designed to construct a broker-based utility matrix with constraints in order to strike a balance between delay, relay traffic, and scalability while arranging brokers into proper clusters for efficient data delivery. Rigorous simulation results prove that the proposed publish/subscribe model can provide an efficient interaction medium for microservice-based IoT applications to collaborate and exchange data with low latency, modest relay traffic, and high scalability at scale.
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Li, He, Kaoru Ota, and Mianxiong Dong. "LS-SDV: Virtual Network Management in Large-Scale Software-Defined IoT." IEEE Journal on Selected Areas in Communications 37, no. 8 (2019): 1783–93. http://dx.doi.org/10.1109/jsac.2019.2927099.
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Khan, Shaikhul Arefin, Md Mokarram Hossain Chowdhury, and Uthso Nandy. "LTE/LTE-A Based Advanced Wireless Networks." Journal of Engineering Research and Reports 25, no. 10 (2023): 195–99. http://dx.doi.org/10.9734/jerr/2023/v25i101012.
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LTE/LTE-A is a wireless broadband standard that provides quick and reliable connectivity for the Internet of Things (IoT) and other connected devices. It is the de facto standard for 4G wireless, offering high-speed communication, extensive network coverage, and low latency. LTE/LTE-A networks are crucial for real-time IoT applications, offering high-speed data transfer, extensive coverage, low latency, and scalability. However, challenges in evaluating LTE/LTE-A-based wireless networks for IoT include compatibility, cost, power consumption, network coverage, and security. Large-scale deployments are hindered by cost, power usage, real-time data transfer, security vulnerabilities, and interoperability. Network performance may be impacted by high data traffic levels, expensive setup and maintenance, and interference from other wireless devices. LTE/LTE-A networks are complex and require sophisticated network technologies and protocols, making them difficult for some IoT stakeholders to build and administer.
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Mshvidobadze, Tinatin. "Heterogeneous IoT platform in Network Architecture." International Journal of Computers 16 (May 16, 2022): 60–65. http://dx.doi.org/10.46300/9108.2022.16.12.
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Heterogeneous Internet of Things (HetIoT) is an emerging research field that has strong potential to transform both our understanding of fundamental computer science principles and our future living. This paper proposes a four-layer HetIoT architecture consisting of sensing, networking, cloud computing, and applications. This paper also suggests several potential solutions to address the challenges facing future HetIoT, including data integration and processing in large-scale HetIoT
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Qasim, Nameer Hashim. "5G Network Segmantation for IoT Applications: Perspective on Scalability and Flexibility Growth by 2025 Times." Radioelectronics. Nanosystems. Information Technologies. 17, no. 2 (2025): 229–48. https://doi.org/10.17725/j.rensit.2025.17.229.
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As the applications of Internet of Things (IoT) are in exponential rise, there has been increasing emphasis on large, elastic and adaptable networking. It is difficult to fulfill the various demands of IoT devices at the same time due to the limitations of traditional networks regarding latency, bandwidth, and energy consumption. Network slicing in the 5G context provides an opportunity to launch separate virtual isolated networks for different purposes. This research aims at evaluating the applicability of 5G network slicing for handling scalability and flexibility issues in IoT applications by 2025 with special attention to network performance. To investigate the 5G network slicing performance of the IoT use cases, we deployed a simulation approach considering smart city, industrial automation, and healthcare application. Specific performance parameters, which include latency, bandwidth consumption and scale were recorded against traditional networks implementations. Compared to the standard networks, the technical advantages of 5G network slicing were seen in a 40% higher latency and a 30% better Bandwidth on Demand. New approaches to scalability enhanced by 50% facilitating incorporation of devices totaling to 10,000 dev/km2 including low power IoT devices energy requirement into account. Scalability and flexibility, network slicing in 5G network, IoT applications are expected to have growth of around 2025 times more and customized most suited mobile networks will be made to improve overall IoT performance.
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Schizas, Nikolaos, Aristeidis Karras, Christos Karras, and Spyros Sioutas. "TinyML for Ultra-Low Power AI and Large Scale IoT Deployments: A Systematic Review." Future Internet 14, no. 12 (2022): 363. http://dx.doi.org/10.3390/fi14120363.
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The rapid emergence of low-power embedded devices and modern machine learning (ML) algorithms has created a new Internet of Things (IoT) era where lightweight ML frameworks such as TinyML have created new opportunities for ML algorithms running within edge devices. In particular, the TinyML framework in such devices aims to deliver reduced latency, efficient bandwidth consumption, improved data security, increased privacy, lower costs and overall network cost reduction in cloud environments. Its ability to enable IoT devices to work effectively without constant connectivity to cloud services, while nevertheless providing accurate ML services, offers a viable alternative for IoT applications seeking cost-effective solutions. TinyML intends to deliver on-premises analytics that bring significant value to IoT services, particularly in environments with limited connection. This review article defines TinyML, presents an overview of its benefits and uses and provides background information based on up-to-date literature. Then, we demonstrate the TensorFlow Lite framework which supports TinyML along with analytical steps for an ML model creation. In addition, we explore the integration of TinyML with network technologies such as 5G and LPWAN. Ultimately, we anticipate that this analysis will serve as an informational pillar for the IoT/Cloud research community and pave the way for future studies.
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Ullah, Imtiaz, Ayaz Ullah, and Mazhar Sajjad. "Towards a Hybrid Deep Learning Model for Anomalous Activities Detection in Internet of Things Networks." IoT 2, no. 3 (2021): 428–48. http://dx.doi.org/10.3390/iot2030022.
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The tremendous number of Internet of Things (IoT) applications, with their ubiquity, has provided us with unprecedented productivity and simplified our daily life. At the same time, the insecurity of these technologies ensures that our daily lives are surrounded by vulnerable computers, allowing for the launch of multiple attacks via large-scale botnets through the IoT. These attacks have been successful in achieving their heinous objectives. A strong identification strategy is essential to keep devices secured. This paper proposes and implements a model for anomaly-based intrusion detection in IoT networks that uses a convolutional neural network (CNN) and gated recurrent unit (GRU) to detect and classify binary and multiclass IoT network data. The proposed model is validated using the BoT-IoT, IoT Network Intrusion, MQTT-IoT-IDS2020, and IoT-23 intrusion detection datasets. Our proposed binary and multiclass classification model achieved an exceptionally high level of accuracy, precision, recall, and F1 score.
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Jia, Guanwei, Zhaoyu Shen, Hongye Sun, Jingbo Xin, and Dongyu Wang. "RWA-BFT: Reputation-Weighted Asynchronous BFT for Large-Scale IoT." Sensors 25, no. 2 (2025): 413. https://doi.org/10.3390/s25020413.
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This paper introduces RWA-BFT, a reputation-weighted asynchronous Byzantine Fault Tolerance (BFT) consensus algorithm designed to address the scalability and performance challenges of blockchain systems in large-scale IoT scenarios. Traditional centralized IoT architectures often face issues such as single points of failure and insufficient reliability, while blockchain, with its decentralized and tamper-resistant properties, offers a promising solution. However, existing blockchain consensus mechanisms struggle to meet the high throughput, low latency, and scalability demands of IoT applications. To address these limitations, RWA-BFT adopts a two-layer blockchain architecture; the first layer leverages reputation-based filtering to reduce computational complexity by excluding low-reputation nodes, while the second layer employs an asynchronous consensus mechanism to ensure efficient and secure communication among high-reputation nodes, even under network delays. This dual-layer design significantly improves performance, achieving higher throughput, lower latency, and enhanced scalability, while maintaining strong fault tolerance even in the presence of a substantial proportion of malicious nodes. Experimental results demonstrate that RWA-BFT outperforms HB-BFT and PBFT algorithms, making it a scalable and secure blockchain solution for decentralized IoT applications.
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Neto, Euclides Carlos Pinto, Sajjad Dadkhah, Raphael Ferreira, Alireza Zohourian, Rongxing Lu, and Ali A. Ghorbani. "CICIoT2023: A Real-Time Dataset and Benchmark for Large-Scale Attacks in IoT Environment." Sensors 23, no. 13 (2023): 5941. http://dx.doi.org/10.3390/s23135941.
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Nowadays, the Internet of Things (IoT) concept plays a pivotal role in society and brings new capabilities to different industries. The number of IoT solutions in areas such as transportation and healthcare is increasing and new services are under development. In the last decade, society has experienced a drastic increase in IoT connections. In fact, IoT connections will increase in the next few years across different areas. Conversely, several challenges still need to be faced to enable efficient and secure operations (e.g., interoperability, security, and standards). Furthermore, although efforts have been made to produce datasets composed of attacks against IoT devices, several possible attacks are not considered. Most existing efforts do not consider an extensive network topology with real IoT devices. The main goal of this research is to propose a novel and extensive IoT attack dataset to foster the development of security analytics applications in real IoT operations. To accomplish this, 33 attacks are executed in an IoT topology composed of 105 devices. These attacks are classified into seven categories, namely DDoS, DoS, Recon, Web-based, brute force, spoofing, and Mirai. Finally, all attacks are executed by malicious IoT devices targeting other IoT devices. The dataset is available on the CIC Dataset website.
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Ntayagabiri, Jean Pierre, Youssef Bentaleb, Jeremie Ndikumagenge, and Hind El Makhtoum. "OMIC: A Bagging-Based Ensemble Learning Framework for Large-Scale IoT Intrusion Detection." Journal of Future Artificial Intelligence and Technologies 1, no. 4 (2025): 401–16. https://doi.org/10.62411/faith.3048-3719-63.
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The research focuses on developing an Optimized Multiclass Intrusion Classifier (OMIC), an advanced framework for large-scale network intrusion detection in IoT environments. Traditional intrusion detection systems face significant challenges with increasing network complexity, attack sophistication, and the exponential growth of IoT devices, particularly in handling class imbalance, computational efficiency, and real-time processing of massive data volumes. OMIC introduces a novel ensemble approach combining LightGBM and XGBoost classifiers with a memory-optimized processing pipeline to address these limitations. The framework implements sophisticated data handling techniques, including dynamic chunk-based processing, adaptive sampling methods, and cost-sensitive learning to manage class imbalance. Experimental evaluation using the comprehensive CICIoT2023 dataset, comprising over 1 million records and 33 distinct attack types, demonstrates OMIC's exceptional performance with an overall accuracy of 99.26%. The framework achieves perfect precision, recall, and F1-scores for most DDoS and DoS attack categories, significantly outperforming traditional machine learning and deep learning approaches. While excelling in most attack categories, OMIC shows limitations in detecting certain web-based attacks and reconnaissance activities, suggesting areas for future enhancement. The framework's superior performance in handling large-scale data while maintaining high detection accuracy positions it as a significant advancement in IoT network security, offering practical solutions for real-world deployments.
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Amalarethinam, Dr D. I. George, and Ms P. Mercy. "Enhanced Quality of Service Strategy for Improving Network Coverage in IOT Applications." Journal of University of Shanghai for Science and Technology 23, no. 05 (2021): 694–707. http://dx.doi.org/10.51201/jusst/21/05201.
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The Internet of Things (IoT) is a network that includes physical things capable of aggregating and communicating electronic information. With the advancement in wireless sensor networks, IoT provides highly efficient communication for various real-time applications. IoT networks are large-scale networks where routing can be improved by focusing on the Quality of Service (QoS) Parameter. Network coverage can be enhanced by hierarchical clustering of the nodes which increases the network lifetime. The proposed algorithm Enhanced Fuzzy Based Clustering and Routing Algorithm (EFCRA) performs distance and energy-based cluster head selection to find a new path from source to destination. The algorithm uses Fuzzy c-means clustering to provide optimization in forming cluster centers. The cluster head (CH) is identified based on the minimum distance and maximum energy of the sensor node. The cluster head is updated when its energy is lesser than the threshold value. The distance between sensor nodes and its CH node and then to the destination is computed using Dijkstra’s algorithm. The proposed routing strategy provides improved network coverage and throughput which extends the lifetime of the IoT network.
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Khandale, Shreyas Pradeepkumar. "IOT based Network Attached Storage." International Journal for Research in Applied Science and Engineering Technology 12, no. 11 (2024): 2247–55. https://doi.org/10.22214/ijraset.2024.65616.
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Internet of Things (IoT) devices grow in many areas, the need for quality data storage and retrieval solutions becomes more important. This research paper presents a comprehensive study on the implementation and evaluation of Internet of Things based on Network Attached Storage systems. The de- sign concept leverages the capabilities of IoT devices to provide large scale, secure and reliable solutions for the growth of data generated by IoT ap- plications. NAS systems are designed to integrate with the IoT ecosystem by allowing the collection, processing and storage of data. In addition, the NAS system adopts security measures that include access, management control and authentication procedures to protect data sensitive documents from unauthorized access or interception. This article also discusses the impact of these security features on compliance with industry specific policies and privacy standards.
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Aouthu, Srilakshmi, Veeramreddy Jyothsna, Kuraparthi Swaraja, and Ravilla Dilli. "GAUSS-NEWTON MULTILATERATION LOCALIZATION ALGORITHM IN LARGE-SCALE WIRELESS SENSOR NETWORKS FOR IoT APPLICATIONS." Telecommunications and Radio Engineering 82, no. 11 (2023): 13–29. http://dx.doi.org/10.1615/telecomradeng.v82.i11.20.
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The location information of sensor nodes plays an important role in critical applications like health monitoring, fire detection, and intruder detection. Installing global positioning system (GPS) modules with the sensor node hardware is not a cost-effective solution for knowing the location coordinates. This has lead to rigorous research in defining nascent localization techniques for wireless sensor networks. But, the existing localization techniques use more number of anchor nodes to compute the location coordinates of sensor nodes, and the network deployment becomes costly. This article presents a low complex, range-based localization algorithm called gauss-newton multilateration that uses received signal strength indicator (RSSI) values of the anchor nodes' signals received at the target nodes. The proposed algorithm uses only four static anchor nodes, which are deployed at the corners of the network terrain to locate the sensor nodes with localization accuracy of 90.21&#37; and increased up to 98.59&#37;. Based on the results obtained, the proposed algorithm provides higher localization accuracy, and it is well suited for locating sensor nodes with high accuracy in large scale wireless sensor networks.
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Cuomo, Francesca, Domenico Garlisi, Alessio Martino, and Antonio Martino. "Predicting LoRaWAN Behavior: How Machine Learning Can Help." Computers 9, no. 3 (2020): 60. http://dx.doi.org/10.3390/computers9030060.
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Large scale deployments of Internet of Things (IoT) networks are becoming reality. From a technology perspective, a lot of information related to device parameters, channel states, network and application data are stored in databases and can be used for an extensive analysis to improve the functionality of IoT systems in terms of network performance and user services. LoRaWAN (Long Range Wide Area Network) is one of the emerging IoT technologies, with a simple protocol based on LoRa modulation. In this work, we discuss how machine learning approaches can be used to improve network performance (and if and how they can help). To this aim, we describe a methodology to process LoRaWAN packets and apply a machine learning pipeline to: (i) perform device profiling, and (ii) predict the inter-arrival of IoT packets. This latter analysis is very related to the channel and network usage and can be leveraged in the future for system performance enhancements. Our analysis mainly focuses on the use of k-means, Long Short-Term Memory Neural Networks and Decision Trees. We test these approaches on a real large-scale LoRaWAN network where the overall captured traffic is stored in a proprietary database. Our study shows how profiling techniques enable a machine learning prediction algorithm even when training is not possible because of high error rates perceived by some devices. In this challenging case, the prediction of the inter-arrival time of packets has an error of about 3.5% for 77% of real sequence cases.
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Amin, Farhan, Rashid Abbasi, Abdul Mateen, Muhammad Ali Abid, and Salabat Khan. "A Step toward Next-Generation Advancements in the Internet of Things Technologies." Sensors 22, no. 20 (2022): 8072. http://dx.doi.org/10.3390/s22208072.
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The Internet of Things (IoT) devices generate a large amount of data over networks; therefore, the efficiency, complexity, interfaces, dynamics, robustness, and interaction need to be re-examined on a large scale. This phenomenon will lead to seamless network connectivity and the capability to provide support for the IoT. The traditional IoT is not enough to provide support. Therefore, we designed this study to provide a systematic analysis of next-generation advancements in the IoT. We propose a systematic catalog that covers the most recent advances in the traditional IoT. An overview of the IoT from the perspectives of big data, data science, and network science disciplines and also connecting technologies is given. We highlight the conceptual view of the IoT, key concepts, growth, and most recent trends. We discuss and highlight the importance and the integration of big data, data science, and network science along with key applications such as artificial intelligence, machine learning, blockchain, federated learning, etc. Finally, we discuss various challenges and issues of IoT such as architecture, integration, data provenance, and important applications such as cloud and edge computing, etc. This article will provide aid to the readers and other researchers in an understanding of the IoT’s next-generation developments and tell how they apply to the real world.
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Kumar, Sharan, Venkata Ramana Kaneti, and Vandana Sharma. "Ensembled combination of Q-Learning and Deep Extreme learning machine to achieve the high performance and less latency to handle the large IoT and Fog Nodes." Journal of Smart Internet of Things 2024, no. 2 (2024): 106–19. https://doi.org/10.2478/jsiot-2024-0015.
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Abstract The proliferation of IoT devices and the adoption of Fog computing architectures have transformed data processing and real-time decision-making across various domains. These advancements enable seamless connectivity and distributed computational power, fostering the development of more intelligent systems. However, managing large-scale IoT and Fog networks presents critical challenges, including high latency, inefficient resource utilization, and scalability limitations, which can undermine system performance. To address these challenges, this research proposes an innovative framework combining Q-Learning and Deep Extreme Learning Machine (DELM). Q-Learning optimizes resource allocation by intelligently learning and adapting to dynamic network conditions, ensuring efficient utilization of resources. It enhances decision-making processes by identifying optimal strategies to manage complex IoT and Fog environments. Meanwhile, DELM provides high-speed and accurate data processing capabilities, enabling it to handle the intensive computational demands of large-scale networks. By leveraging the complementary strengths of these methods, the framework aims to enhance latency, resource utilization, and scalability in large-scale environments. Extensive experimental evaluations validate the framework’s effectiveness, demonstrating significant reductions in latency, improved computational efficiency, and enhanced throughput. Furthermore, the framework efficiently handles complex data processing tasks with minimal overhead, making it suitable for diverse real-time applications across IoT and Fog systems. This study highlights the transformative potential of the proposed approach, offering high performance and real-time efficiency for complex, large-scale IoT and Fog computing environments.
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Li, Heng, Yonghe Liu, Zheng Qin, Huigui Rong, and Qin Liu. "A Large-Scale Urban Vehicular Network Framework for IoT in Smart Cities." IEEE Access 7 (2019): 74437–49. http://dx.doi.org/10.1109/access.2019.2919544.
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Asad Ullah, Muhammad, Junnaid Iqbal, Arliones Hoeller, Richard Souza, and Hirley Alves. "K-Means Spreading Factor Allocation for Large-Scale LoRa Networks." Sensors 19, no. 21 (2019): 4723. http://dx.doi.org/10.3390/s19214723.
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Low-power wide-area networks (LPWANs) are emerging rapidly as a fundamental Internet of Things (IoT) technology because of their low-power consumption, long-range connectivity, and ability to support massive numbers of users. With its high growth rate, Long-Range (LoRa) is becoming the most adopted LPWAN technology. This research work contributes to the problem of LoRa spreading factor (SF) allocation by proposing an algorithm on the basis of K-means clustering. We assess the network performance considering the outage probabilities of a large-scale unconfirmed-mode class-A LoRa Wide Area Network (LoRaWAN) model, without retransmissions. The proposed algorithm allows for different user distribution over SFs, thus rendering SF allocation flexible. Such distribution translates into network parameters that are application dependent. Simulation results consider different network scenarios and realistic parameters to illustrate how the distance from the gateway and the number of nodes in each SF affects transmission reliability. Theoretical and simulation results show that our SF allocation approach improves the network’s average coverage probability up to 5 percentage points when compared to the baseline model. Moreover, our results show a fairer network operation where the performance difference between the best- and worst-case nodes is significantly reduced. This happens because our method seeks to equalize the usage of each SF. We show that the worst-case performance in one deployment scenario can be enhanced by 1 . 53 times.
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Nuriev, Marat, Timur Aygumov, Rimma Zaripova, Svetlana Nikolaeva, and Guzel Gumerova. "Evolving network systems through blockchain innovation for smart agriculture IoT networks." BIO Web of Conferences 138 (2024): 02002. http://dx.doi.org/10.1051/bioconf/202413802002.
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This article provides an in-depth analysis of how blockchain technology is driving innovation in network systems for smart agriculture IoT networks. It begins by exploring the foundational principles of blockchain, focusing on its decentralized nature, cryptographic security, and consensus mechanisms, which ensure data integrity and transparency in agricultural IoT ecosystems. The article then delves into the specific applications of blockchain within smart agriculture, such as enhancing supply chain transparency, improving data exchange between IoT devices, and optimizing resource distribution on farms. Challenges to blockchain adoption in this sector are also addressed, including scalability, performance issues, security concerns, and regulatory challenges unique to agriculture. To overcome these hurdles, the article discusses cutting-edge solutions like Layer 2 scaling, advanced cryptographic methods, and sustainable consensus models that can support large-scale IoT networks. Ultimately, the article envisions blockchain as a key enabler for evolving smart agriculture networks, driving increased efficiency, reliability, and security in IoTenabled farming operations, and paving the way for a more connected and sustainable agricultural future.
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Khatib, Amine, Mohamed Hamlich, and Denis Hamad. "Machine Learning based Intrusion Detection for Cyber-Security in IoT Networks." E3S Web of Conferences 297 (2021): 01057. http://dx.doi.org/10.1051/e3sconf/202129701057.
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IoT network is a promising technology, IoT implementation is growing rapidly but cybersecurity is still a loophole, detection of attacks in IOT infrastructures is a growing concern in the field of IoT. With the increased use of Internet of Things in different areas, cyber-attacks are also increasing proportionately and can cause failures in the system. IDS becomes the leading security solution. Anomaly based network intrusion detection (IDS) detection plays a major role in protecting networks against various malicious activities. Improving the security of loT networks has become one of the most critical issues. This is due to the large-scale development and deployment of loT devices and the insufficiency of Intrusion Detection Systems (IDS) to be deployed for the use of special purpose networks. In this article, the performance of several machine learning models has been compared to accurately predict attacks on IoT systems, the case of imbalanced classes was subsequently treated using the SMOTE technique. The Nystrom based kernel SVM is the first time used to detect attacks in the IoT network and the results are promising. The evaluation metrics used in the performance comparison are accuracy, precision, recall, f1 score, and auc-roc curve.
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Roberto, Rome, and Benua Arto. "Implementation of Support Vector Machine Architecture for Anomaly Detection in IoT Networks." Instal : Jurnal Komputer 17, no. 03 (2025): 120–26. https://doi.org/10.54209/jurnalinstall.v17i03.365.
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Internet of Things (IoT) is a technology that allows various physical devices to connect to each other and exchange data via the internet network. The application of IoT is increasingly widespread in various sectors such as smart homes, manufacturing industries, smart agriculture, and healthcare. However, along with the increasing number of devices and the volume of data traffic sent, the potential risk of cybersecurity threats is also increasing. The large number of IoT devices that have limited computing capabilities makes the system more vulnerable to various attacks, including intrusion, exploitation of system weaknesses, and Distributed Denial of Service (DDoS) attacks. Therefore, early detection of anomalies in network traffic is a crucial aspect to maintain the security and stability of IoT systems. This study aims to develop and implement a Support Vector Machine (SVM)-based architecture as a classification method in an anomaly detection system on an IoT network. SVM was chosen because of its ability to handle high-dimensional data and non-linear classification effectively. The methodology used includes the process of extracting features from IoT network traffic datasets, data normalization, model training using the SVM algorithm, and evaluating model performance in distinguishing between normal and anomalous traffic. Thus, the implementation of SVM architecture can be an effective and efficient solution in intrusion detection systems for IoT networks. This research also opens up opportunities for the development of more adaptive security systems by integrating machine learning-based detection models into large-scale IoT infrastructures.
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Padmavathi, V., R. Saminathan, and S. Selvamuthukumaran. "An adaptive QoS supportive approach for user based services using Krill Herd Approach over Internet of Things (KHAI)." International Journal of Knowledge-based and Intelligent Engineering Systems 25, no. 1 (2021): 109–17. http://dx.doi.org/10.3233/kes-210056.
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The demand for a providing QoS adaptive routing over IoT networks is always a challenge among current research community. This research work KHAI proposes a framework for QoS-adaptive routing approach, which incorporates Krill Herd optimization model over IoT network. Variable QoS user preference and handling differential service types over a scalable IoT network shows that challenge for designing an adaptive QoS is a must. Research survey suggest that major works have been carried out on bandwidth appreciable services and route management approaches. Hence QoS adaptive user defined services, which adapt to variable service priority levels based on user demand and network resource utilization is proposed in this research work. The performance analysis of proposed approach shows an improved throughput of 97.51 Mbps and minimal packet loss of 37.29% over a session in comparison to traditional computational approaches. Considering large scale of interconnected IoT devices, proposed approach delivers near optimal solution of throughput and adaptive utilization of network resources.
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Jin, Jianian, Suchuan Xing, Enkai Ji, and Wenhe Liu. "XGate: Explainable Reinforcement Learning for Transparent and Trustworthy API Traffic Management in IoT Sensor Networks." Sensors 25, no. 7 (2025): 2183. https://doi.org/10.3390/s25072183.
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The rapid proliferation of Internet of Things (IoT) devices and their associated application programming interfaces (APIs) has significantly increased the complexity of sensor network traffic management, necessitating more sophisticated and transparent control mechanisms. In this paper, we introduce XGate, a novel explainable reinforcement learning framework designed specifically for API traffic management in sensor networks. XGate addresses the critical challenge of balancing optimal routing decisions with the interpretability demands of network administrators operating large-scale IoT deployments. Our approach integrates transformer-based attention mechanisms with counterfactual reasoning to provide human-comprehensible explanations for each traffic management decision across distributed sensor data streams. Through extensive experimentation on three large-scale sensor API traffic datasets, we demonstrate that XGate achieves 23.7% lower latency and 18.5% higher throughput compared to state-of-the-art black-box reinforcement learning approaches. More importantly, our user studies with sensor network administrators (n=42) reveal that XGate’s explanation capabilities improve operator trust by 67% and reduce intervention time by 41% during anomalous sensor traffic events. The theoretical analysis further establishes probabilistic guarantees on explanation fidelity while maintaining computational efficiency suitable for real-time sensor data management. XGate represents a significant advancement toward trustworthy AI systems for critical IoT infrastructure, providing transparent decision making without sacrificing performance in dynamic sensor network environments.
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Yin, Ting, Decai Zou, Rui Zhang, Dongliang Jing, Yongzhao Li, and Xiaochun Lu. "Evolutionary Overlapping Coalitional Game-Based Link Selection for Distributed Cooperative Localization in Mobile Networks." Wireless Communications and Mobile Computing 2022 (February 16, 2022): 1–8. http://dx.doi.org/10.1155/2022/6588197.
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With the increasing application of Internet of Things (IoT), the localization of IoT devices has been widely used. The distributed cooperative localization is expected to be applied in a large-scale dynamic network, such as IoT. It is located through the exchange of information among multiple nodes. For a large amount of battery-based users, the high-computational complexity and heavy communication overhead will lead to huge energy consumption. In this paper, we propose a link selection algorithm based on the evolutionary overlapping coalitional (EOC) game to mitigate the energy consumption for distributed cooperative localization in the dynamic network. The equivalent Fisher information matrix (EFIM) and the Cramér–Rao lower bound (CRLB) are employed to keep location accuracy. Numerical results verify that the distributed cooperative localization based on the EOC game achieves lower energy consumption while keeping localization accuracy in the dynamic networks.
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Tilwari, Valmik, Kaharudin Dimyati, MHD Hindia, Tengku Mohmed Noor Izam, and Iraj Amiri. "EMBLR: A High-Performance Optimal Routing Approach for D2D Communications in Large-scale IoT 5G Network." Symmetry 12, no. 3 (2020): 438. http://dx.doi.org/10.3390/sym12030438.
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Coping with the skyrocketing needs for massive amounts of data for the future Fifth Generation (5G) network, Device-to-Device (D2D) communications technology will provide seamless connectivity, high data rates, extended network coverage, and spectral efficiency. The D2D communications are a prevalent emerging technology to achieve the vision of symmetry in the Internet of Things (IoT) services. However, energy resource constraints, network stability, traffic congestion, and link failure of the devices are the crucial impediments to establish an optimal route in the D2D communications based IoT 5G network. These obstacles induced packet drop, rapid energy depletion, higher end-to-end delay, and unfairness across the network, leading to significant route and network performance degradation. Therefore, in this paper, an energy, mobility, queue length, and link quality-aware routing (EMBLR) approach is proposed to overcome the challenges and boost network performance. Moreover, a multicriteria decision making (MCDM) technique is utilized for the selection of the intermediate device in an optimal route. Extensive simulation has been conducted and proven that the proposed routing approach significantly enhances network performance. Overall, results have been carried out in Quality of Service (QoS) performance metrics and compared with other well-known routing approaches.
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Chen, Dong, Guiran Chang, Dawei Sun, Jiajia Li, Jie Jia, and Xingwei Wang. "TRM-IoT: A trust management model based on fuzzy reputation for internet of things." Computer Science and Information Systems 8, no. 4 (2011): 1207–28. http://dx.doi.org/10.2298/csis110303056c.
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Since a large scale Wireless Sensor Network (WSN) is to be completely integrated into Internet as a core part of Internet of Things (IoT) or Cyber Physical System (CPS), it is necessary to consider various security challenges that come with IoT/CPS, such as the detection of malicious attacks. Sensors or sensor embedded things may establish direct communication between each other using 6LoWPAN protocol. A trust and reputation model is recognized as an important approach to defend a large distributed sensor networks in IoT/CPS against malicious node attacks, since trust establishment mechanisms can stimulate collaboration among distributed computing and communication entities, facilitate the detection of untrustworthy entities, and assist decision-making process of various protocols. In this paper, based on in-depth understanding of trust establishment process and quantitative comparison among trust establishment methods, we present a trust and reputation model TRM-IoT to enforce the cooperation between things in a network of IoT/CPS based on their behaviors. The accuracy, robustness and lightness of the proposed model is validated through a wide set of simulations.
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Liu, Shuai, Xubo Tang, and Chunhua Zang. "Design of Large Monitoring System Based on Heterogeneous Network." Journal of Physics: Conference Series 2218, no. 1 (2022): 012025. http://dx.doi.org/10.1088/1742-6596/2218/1/012025.
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Abstract In order to ensure the quality of tobacco products, it is necessary to strictly control the environment of tobacco during storage. In order to save manpower and material resources and realize accurate monitoring of the storage environment, a large-scale tobacco storage monitoring system based on heterogeneous network was designed. Combining the cross-regional characteristics of NB-IoT and low-frequency characteristics of LoRa, NB-IoT was used as a wide area network and LoRa as a subordinate local area network to form a two-layer network structure. Its structure includes sensor node and gateway node, and the gateway node is composed of LoRa gateway and NB-IoT module. This paper introduces the composition and working mode of each part in detail.
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Zong, Liang, Yong Bai, Chenglin Zhao, Gaofeng Luo, Zeyu Zhang, and Huawei Ma. "On Enhancing TCP to Deal with High Latency and Transmission Errors in Geostationary Satellite Network for 5G-IoT." Security and Communication Networks 2020 (December 8, 2020): 1–7. http://dx.doi.org/10.1155/2020/6693094.
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The geostationary (GEO) satellite networks have two important influencing factors: high latency and transmission errors. Similarly, they will happen in the large-scale multihop network of the Internet of things (IoT), which will affect the application of 5G- (5th-generation mobile networks-) IoT. In this paper, we propose an enhanced TCP mechanism that increases the amount of data transferred in the slow start phase of TCP Hybla to mitigate the effect of long RTT and incorporates a refined mechanism of TCP Veno, which can distinguish packet loss between random and congestion. This scheme is evaluated and compared with NewReno, Hybla, and Veno by simulation, and the performance improvement of the proposed TCP scheme for GEO satellite network in the presence of random packet losses is demonstrated. At the same time, the enhanced TCP scheme can improve the transmission performance in the future 5G-IoT heterogeneous network with high delay and transmission .
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Zhao, Hong-Yan, Jia-Chen Wang, Xin Guan, Zhi-Hong Wang, Yong-Hui He, and Hong-Lin Xie. "Ant Colony System for Energy Consumption Optimization in Mobile IoT Networks." Journal of Circuits, Systems and Computers 29, no. 09 (2019): 2050150. http://dx.doi.org/10.1142/s0218126620501509.
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In this paper, a new algorithm is proposed for computing the node-disjoint optimal transmission energy consumption route for coded cooperative mobile IoT networks. Inspired by the potential benefits user cooperation can provide, we incorporate user cooperation to the mobile ad-hoc multi-hop IoT networks. Our results include a novel ant colony system-based node-disjoint energy-efficient routing algorithm. Ant colony system can approximate the optimal route by local information and is thus very suitable for mobile IoT network environment. In particular, ant algorithm makes history-sensitive choice and thus can significantly outperform the greedy algorithm. In addition, it can efficiently handle the case of multiple sources and multiple destinations. For a large IoT network, we investigate the multi-scale ant colony system and when compared to Dijkstra algorithm, such an algorithm usually shortens the runtime by a factor of several hundred.
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Chetan Kumar, Gopal Ram,. "Hybrid Clustering and Routing Algorithm for Node Performance Improvement in Wireless Sensor Networks." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 6 (2021): 1892–99. http://dx.doi.org/10.17762/turcomat.v12i6.4364.
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These days Wireless Sensor Networks (WSN) have been used in various Internet of Things (IoT) applications viz., healthcare monitoring, disaster management, smart buildings, smart farming etc. it is one of the substitutes for solving distinct problems of IoT in various areas. This paper proposed a hybrid clustering and routing algorithm. We develop a simulation area for node movement and communication. A hybrid clustering and routing (HCR) protocol proposed for reliable and efficient data collection in large-scale wireless sensor network. Theoretical analysis and simulation results will prove the connectivity and efficiency of the network topology generated by HCR.
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Pascu, Liviu, Attila Simo, and Alin Mihai Vernica. "Integrating Microsoft IoT, machine learning in a large scale power metre reading." Global Journal of Computer Sciences: Theory and Research 8, no. 3 (2018): 136–42. http://dx.doi.org/10.18844/gjcs.v8i3.4025.
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Due to fast technological progress in the power engineering field, the need for new information/communication technologies is more and more underlined. e-Learning has become a viable alternative to traditional teaching/learning techniques, adopted especially because of the advantages offered by the possibility of continuous training. This paper presents a Microsoft Internet of Things Platform for a very large scale smart power metre reading, used for training operative staff of the Distribution Network Operator, but also to help end-users to control their electrical energy consumption. The strength of this platform for the Distribution Network Operator is that the read data can be used for energy forecast, which is very useful for the future energy consumption optimisation. The platform can be reached via the Internet using a user name and password. A comparison between the results provided by classical teaching/learning methods and the ones achieved using this platform is presented. Keywords: Machine learning, internet of things (IoT), training.
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Pascu, Liviu, Attila Simo, and Alin Mihai Vernica. "Integrating Microsoft IoT, machine learning in a large-scale power meter reading." International Journal of New Trends in Social Sciences 3, no. 1 (2019): 10–16. http://dx.doi.org/10.18844/ijntss.v3i1.3815.
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Due to fast technological progress in the power engineering field, the need of new information/communication technologies is more and more underlined. e-Learning has become a viable alternative to traditional teaching/learning techniques, adopted especially due to the advantages offered by the possibility of continuous training. This paper presents a Microsoft internet of things platform for a very large-scale smart power meter reading, used not only for training operative staff of the distribution network operator but also to help end users to control electrical energy that they consume. The strength of this platform for the distribution network operator is that the read data can be used for energy forecast, which is very useful for the future energy consumption optimisation. The platform can be reached through the Internet using a user name and password. A comparison between the results provided by classical teaching/learning methods and the ones achieved using this platform is presented.
 Keywords: Machine learning, internet of things (IoT), training.
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Michael Oladipo Akinsanya, Cynthia Chizoba Ekechi, and Chukwuekem David Okeke. "SECURITY PARADIGMS FOR IOT IN TELECOM NETWORKS: CONCEPTUAL CHALLENGES AND SOLUTION PATHWAYS." Engineering Science & Technology Journal 5, no. 4 (2024): 1431–51. http://dx.doi.org/10.51594/estj.v5i4.1075.
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The concept paper explores the critical security challenges posed by the Internet of Things (IoT) in telecom networks and proposes solution pathways to address them. As IoT devices proliferate in telecom networks, they introduce new vulnerabilities and security threats that must be mitigated to ensure the integrity, confidentiality, and availability of network resources and data. The paper begins by highlighting the unique security challenges posed by IoT devices, including their large attack surface, resource constraints, and diverse communication protocols. These challenges are further exacerbated in telecom networks due to the scale and complexity of the infrastructure. To address these challenges, the paper proposes a multi-faceted approach that combines technical, organizational, and regulatory measures. Technical solutions include the use of secure communication protocols, device authentication mechanisms, and encryption techniques to protect data in transit and at rest. Organizational measures focus on improving security awareness and training among network operators and ensuring the secure development and deployment of IoT devices. Regulatory measures advocate for the implementation of standards and regulations that promote security and privacy in IoT deployments. The paper also discusses the importance of collaboration among stakeholders, including network operators, device manufacturers, and regulatory bodies, to address security challenges effectively. By working together, stakeholders can develop and implement best practices that enhance the security of IoT devices and telecom networks. In conclusion, the concept paper highlights the urgent need for robust security paradigms in IoT deployments within telecom networks. By implementing the proposed solution pathways and fostering collaboration among stakeholders, telecom networks can mitigate security risks and ensure the safe and secure deployment of IoT devices. Keywords: Security, IoT, Telecom, Solutions, Network.
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Sun, Bin, Renkang Geng, Yuan Xu, and Tao Shen. "Prediction of Emergency Mobility Under Diverse IoT Availability." EAI Endorsed Transactions on Pervasive Health and Technology 8, no. 4 (2022): e2. http://dx.doi.org/10.4108/eetpht.v8i4.274.
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INTRODUCTION: Prediction of emergency mobility needs to consider more scenarios as Internet of Things (IoT) develops at a high speed, which influences the quality and quantity of data, manageable resources and algorithms. OBJECTIVES: This work investigates differences in dynamic emergency mobility prediction when facing dynamic temporal IoT data with different quality and quantity considering diverse computing resources and algorithm availability. METHODS: A node construction scheme under a small range of traffic networks is adopted in this work, which can effectively convert the road to graph network structure data which has been proved to be feasible and used for the small-scale traffic network data here. Besides, two different datasets are formed using public large scale traffic network data. Representative widely used and proven algorithms from typical types of methods are selected respectively with different datasets to conduct experiments. RESULTS: The experimental results show that the graphed data and neural network algorithm can deal with the dynamic time series data with complex nodes and edges in a better way, while the non-neural network algorithm can predict the with a simple graph network structure. CONCLUSION: Our proposed graph construction with graph neural network improves dynamic emergency mobility prediction. The prediction should consider the scenarios of availability of computing resources, quantity and quality of data among other IoT features to improve the results. Later, automation and data enrichment should be improved.
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Amadou, Mbagnick Gning, Gueswende Kabore Donatien, Cissé Cheikh, and SARR Cheikh. "IoT based system for humidity monitoring applied to smart agriculture." Journal of Scientific and Engineering Research 9, no. 1 (2022): 41–46. https://doi.org/10.5281/zenodo.10497938.
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<strong>Abstract</strong> With the growth of the Internet of Things (IoT), LPWAN technology becomes more and more popular. LPWAN networks are well designed for large-scale IoT deployment due to low power consumption and long-range wireless communications especially in urban and metropolitan areas. A large number of devices typically communicate directly to a gateway node which forwards packets to a remote server for specific treatments. Internet of things (IoT) is now widely used in smart agriculture scheme. Sensors gather information about environment such as humidity, temperature for example and send it to a collection point through a LoRa network. Humidity is one of the most commonly measured physical quantities and is of great importance in smart agriculture. Thus, in this paper, we present a new connected system for monitoring humidity of the ground. LoRa is used as LPWAN technology to create a local network and send information through servers.