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2nd International conference on Intelligent Data Communication Technologies and internet of Things, IDCIoT 2024

作者： Ramya, M. Balamurugan, R. Ganesh, J. St. Joseph's Institute of Technology Department of Department of Computer Science Chennai India

ISBN: (纸本)9798350327533

Despite their low resources and high node mobility, wireless mobile networks are essential to modern communication systems. Since these wireless networks don't have a physical backbone like traditional networks with a fixed base station do, it's critical to develop efficient routing strategies. The study focuses on investigating multipath routing, an idea that permits the creation of multiple paths between a source and destination node, to address these issues. The main objective of this research is to improve load balancing, quality of service, energy efficiency, and data transmission reliability through multipath routing to overcome the constraints of resource-constrained wireless networks. Nodes can easily join and leave wireless mobile networks as they move around due to their dynamic nature. Since wireless nodes have a limited transmission range, data exchange frequently necessitates multiple hops. The study essentially explores the dynamic and resource-constrained characteristics of wireless mobile networks, highlighting the importance of multipath routing to overcome obstacles and enhance different facets of network performance © 2024 IEEE.

关键词： Dynamic data exchange Energy efficiency Energy harvesting Load Balancing Multiple hope Power splitting Architecture Reliability Wireless mobile node

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performance Characteristics of Selected network Topology in a Software-Defined networking QoS Testing Framework 4

Performance Characteristics of Selected Network Topology in ...

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4th International conference on Frontiers of Computers and Communication Engineering, FCCE 2023

作者： Yoo, Hyeong Seon Yu, William Emmanuel S. Ateneo de Manila University Department of Information Systems and Computer Science Quezon City Philippines

ISBN: (纸本)9798350333169

Quality of Service in SDN/OpenFlow can be provided by making use of queues and their management techniques. This allows for better and more fine-grained control of network traffic. In this study, we test a Class-based Queuing (CBQ) algorithm for QoS provision that classifies network traffic according to the application (e.g., video traffic or HTTP traffic) over a selection of topology forms from the internet Topology Zoo (ITZ). To accomplish this, we utilize a Mininet-based QoS Testing framework that allows for testing of various QoS algorithms. To select the networks to be tested, we use K-means clustering over the ITZ dataset according to network size for random sampling within each cluster. We show that under CBQ, HTTP stress test performance was better in clusters with smaller network size, while VLC video performance remained similar throughout clusters. KPI analysis also shows that clients closer to servers exhibited better performance than those farther away, while VLC video performance remained consistent throughout the network. The results show that the framework is able to satisfy QoS requirements while showing performance differences across various simulated network topologies. © 2023 IEEE.

关键词： network topology

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Wireless Sensor networks Anomaly Detection Using Machine Learning: A Survey

Wireless Sensor Networks Anomaly Detection Using Machine Lea...

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Intelligent systems conference (IntelliSys)

作者： Haque, Ahshanul Chowdhury, Naseef-Ur-Rahman Soliman, Hamdy Hossen, Mohammad Sahinur Fatima, Tanjim Ahmed, Imtiaz New Mexico Inst Min & Technol 801 Leroy PL Socorro NM 87801 USA

ISBN: (纸本)9783031477140;9783031477157

Wireless Sensor networks (WSNs) have become increasingly valuable in various civil/military applications like industrial process control, civil engineering applications such as buildings' structural strength monitoring, environmental monitoring, border intrusion, IoT (internet of Things), and healthcare. However, the sensed data generated by WSNs is often noisy and unreliable, making it a challenge to detect and diagnose anomalies. Machine learning (ML) techniques have been widely used to address this problem by detecting and identifying unusual patterns in the sensed data. This survey paper provides an overview of the state-of-the-art applications of ML techniques for data anomaly detection in WSN domains. We first introduce the characteristics of WSNs and the challenges of anomaly detection in WSNs. Then, we review various ML techniques such as supervised, unsupervised, and semi-supervised learning that have been applied to WSN data anomaly detection. We also compare different ML-based approaches and their performance evaluation metrics. Finally, we discuss open research challenges and future directions for applying ML techniques in WSNs sensed data anomaly detection.

关键词： Wireless sensor network Anomaly detection Machine learning Survey Energy efficiency Hybrid networks Techniques Algorithms Data performance metrics

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EEOMA: End-to-end oriented management architecture for 6G-enabled drone communications

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PEER-TO-PEER networkING AND APPLICATIONS 2022年第2期15卷 1232-1254页

作者： Ali, Zainab H. Ali, Hesham A. Kafrelsheikh Univ Fac Artificial Intelligence Dept Embedded Network Syst & Technol El Geish St Kafrelsheikh 33516 Egypt Fac Engn Dept Comp Engn & Control Syst El Gomhoria St Talkha 33511 Eldakahlia Egypt Mansoura Univ Delta Univ Sci & Technol Fac Artificial Intelligence El Gomhoria St Talkha 33511 Eldakahlia Egypt

We are witnessing a new era in autonomous systems with unprecedented user experiences, excessively recovered road safety and air quality, a wide range of transportation conditions and utilization cases, as well as a plethora of advanced time-sensitive applications. To realise this ambitious vision, Vehicular Ad-hoc networks (VANET) and drones must have significantly improved communication. They must be remarkably intelligent and capable of simultaneously supporting hyper-fast, ultra-reliable, uninterrupted service, as well as low-latency massive information exchange. Although the Sixth Generation (6G) is successful in providing a seamless integration of heterogeneous elements as well as offering high coverage area with maximizing resource utilization;however, the accomplishment of faster computation, reducing bandwidth usage, low power consumption, and high throughput is remaining critical more than other purposes. This study introduces an End-To-End Oriented Management Architecture (EEOMA) based on fog computing and software-defined network (SDN) technologies for boosting the performance in hybrid networks of Vehicular Ad hoc network (VANET) and Drone. Two techniques are proposed through EEOMA to manage the data transmission on both the SDN and the drones and meet network constraints: (i) a transport service aggregation approach for simplifying the amount of traffic data traveling across the network bandwidth and (ii) a normalized throughput of a channel with adjusting the interference ratio that indicates medium access control (MAC) layer overhead. The experimental results show that there is 1.2% to 6.1% enhancement of packet delivery and packet loss ratios. More trustworthiness in performance measurement is reached by the proposed EEOMA in terms of total throughput, response time, and power consumption.

关键词： Drone networks Data transmission Cellular network Fog computing internet of things network throughput Vehicular ad hoc network Software-defined network

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Modeling network Traffic and Exploring Distribution Fitting: A Case Study on Spotify 8

Modeling Network Traffic and Exploring Distribution Fitting:...

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8th South-East Europe Design Automation, Computer Engineering, Computer networks and Social Media conference, SEEDA-CECNSM 2023

作者： Karadimas, Odysseas Florou, Aikaterini Margariti, Spiridoula V. Stergiou, Eleftherios Stylios, Chrysostomos D. University of Ioannina Dept. of Informatics and Telecommunications Arta Greece Industrial Systems Institute Athena Research Center Patra Greece

ISBN: (纸本)9798350383676

As internet traffic grows exponentially due to user demands and economic competition, the characterization of network workload and its resource utilization becomes more complex but also more important. This study aims to gain insights into the network's dynamics and explore various aspects of its behavior and performance. It presents an empirical analysis of network traffic based on a 24-hour dataset obtained at a university campus from internet users on the 'Spotify' platform. The analysis is built on data features, and it reveals several traffic models. More specifically, using the Cullen and Frey graphs, it investigates which are the optimal distributions fitting to data traffic. It focuses on patterns observed in the number of connections per minute and brings out an initial burst of activity followed by a stable level of connectivity. The study also examines the duration of TCP (Transmission control Protocol) connections and the size of transfer data, indicating that most connections are short-lived and data transfers predominantly in-volve smaller sizes. These findings contribute to a comprehensive understanding of the network's behavior and can shape network management strategies for optimizing performance and resource allocation. © 2023 IEEE.

关键词： Data transfer

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Traffic control, Congestion Management and Smart Parking through VANET, ML, and IoT: A Review 10

Traffic Control, Congestion Management and Smart Parking thr...

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10th International conference on Wireless networks and Mobile Communications, WINCOM 2023

作者： Ouhmidou, Hajar Nabou, Abdellah Ikidid, Abdelouafi Bouassaba, Wafa Ouzzif, Mohammed El Kiram, Moulay Ahmed Cadi Ayyad University Computing Systems Enginnering Laboratory Computer Sciences Dept Marrakesh Morocco Est Ensem Hassan Ii University of Casablanca Computer Science and Smart Systems Laboratory Casablanca Morocco

ISBN: (纸本)9798350329674

As the quantity of cars increases on the road, so does the focus on improving road safety and in-vehicle entertainment. In response to this need, the creation of new in-car applications and services is on the rise. Common applications include collision avoidance, safety and traffic monitoring, multimedia streaming, and smart city data collection using wireless sensor networks. The internet of Things (IOT) is a recent innovation that is accelerating the universe towards automated procedures and intelligent transportation management solutions. This is an important contribution to automation and intelligent civilization. Another way to improve the transport system is through the machine learning (ML). an Existing traffic management solution face a number of problems, resulting in traffic congestion, delays and high fatality rates. In addition, vehicular ad hoc network (VANET) has emerged as a new technology capable of supporting such emerging vehicular applications. This paper presents a comprehensive review of the latest state of the traffic control, traffic management and parking and smart city strategy of the vehicle by using VANET, ML and internet of Things technology. © 2023 IEEE.

关键词： internet of things

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Utilizing a CNN for Automatic Detection of Military Reconnaissance and Surveillance Objects in Aerial Images: Concept and Challenges 10th

Utilizing a CNN for Automatic Detection of Military Reconnai...

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10th International conference on Modelling and Simulation for Autonomous systems

作者： Ligocki, Adam Gabrlik, Petr Zalud, Ludek Michenka, Karel Brno Univ Technol Fac Elect Engn & Commun Dept Control & Instrumentat Brno 61600 Czech Republic Univ Def Fac Mil Leadership Dept Intelligence Support Brno 66210 Czech Republic

ISBN: (纸本)9783031713965;9783031713972

The utilization of compact unmanned aircraft systems (UAS) for military reconnaissance and surveillance is experiencing growth in the intelligence branch. Obtaining a large amounts of data by these means leads to the need for their quick and efficient processing for further use within the commander's decision-making process (battle management). This paper focuses on the automatic detection of military reconnaissance and surveillance objects, such as vehicles or soldiers, in aerial images by employing the YOLOv8 object detector, a convolutional neural network (CNN) model. To achieve a high detection success rate across diverse military equipment, weather conditions, and geographic locations, a comprehensive dataset comprising thousands of images is essential for training the neural network. However, publicly available datasets of this nature are scarce, presenting a significant challenge. This study utilizes a custom image set with more than ten thousand annotated objects, whereas the data were collected from internet databases, social networks, and military training the authors participated in. The data enabled us to examine distinct preprocessing approaches and model training setups to deliver beneficial findings for future research directions. The model performance results indicate a promising detection success rate according to standard evaluation metrics;however, to ensure the algorithm's robustness for practical applications, a considerably larger amount of data will be required.

关键词： Object detection CNN YOLOv8 Military objects UAS Aerial images

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Sensing the Future: Challenges and Trends in IoT Sensor Technology 4

Sensing the Future: Challenges and Trends in IoT Sensor Tech...

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4th International conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Rajora, Ricky Rajora, Aarju Sharma, Bhanu Aggarwal, Priyanshi Thapliyal, Siddhant Chitkara University Institute of Engineering and Technology Chitkara University Punjab 140401 India Chandigarh Engineering College Department of Computer Science & Engineering Mohali India Computer Science & Engineering Graphic Era Hill University Uttarakhand Dehradun248002 India Computer Science & Engineering Graphic Era Deemed to Be University Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350307757

In the ever-evolving world of the internet of Things (IoT), sensors play a crucial role as essential parts, linking and merging different elements to create a smart worldwide network. This study examines the crucial function of sensors in the internet of Things (IoT) ecosystem, where they serve as sensory organs that translate the intricacies of the physical world into a uniform language for networked systems. The main emphasis is on their ability to gather and evaluate factual data from the surroundings, which impacts the performance and effectiveness of IoT applications in many industries. This research explores the uses and relevance of many sensor types, including temperature, humidity, proximity, motion, gas, biometric, vibration, image, infrared (IR), occupancy, water quality, pressure, light, and sound sensors. Although sensors contribute to the improvement of automation, control systems, and predictive analysis, they also give rise to issues in terms of security, interoperability, and power efficiency. The convergence of edge computing, artificial intelligence (AI), quantum sensors, 5G connection, and improved sensor integration has the potential to bring about significant changes in the internet of Things (IoT) environment. The study provides a thorough survey of sensor applications in several domain including healthcare, agriculture, smart cities, and automotive sectors. Lastly, it examines the difficulties and potential opportunities, highlighting the vital significance of sensors in influencing the future of interconnection and data-driven decision-making. © 2024 IEEE.

关键词： internet of things

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Multi-node Trusted Remote Attestation and Recovery Scheme for Power Industrial control Terminal 3

Multi-node Trusted Remote Attestation and Recovery Scheme fo...

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3rd International conference on Intelligent Power and systems, ICIPS 2023

作者： Liu, Wei Yu, Jianye Qi, Longyun Yang, Kangle Li, Ke Xu, Zhichao Shang, Ketong Wu, Qiuxin Nanjing NARI Information and Communication Technology Company Limited Jiangsu Province Nanjing210000 China Trusted Computing and Information Assurance Laboratory Institute of Software Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9798350328691

With the development of energy internet, the scale of power network is expanding day by day. Therefore, a large number of electric devices are exposed to the network, and its security has become the focus of attention. In addition, electric devices frequently enter and exit the network and update software configuration items. Based on the above problems, this paper proposes a multi-node trusted remote attestation and repair scheme for power industrial control terminal. Firstly, based on the non-interactive attestation protocol, it can not only prevent DDoS attacks, but also be suitable for highly dynamic swarm networks. Secondly, the hash algorithm based on the complete binary tree can not only accurately recovery the compromised device, but also reduce the burden of hash calculation for new software configuration items of the device. Finally, the security analysis and performance evaluation show that this scheme is a secure and efficient swarm attestation and recovery scheme. ©2023 IEEE.

关键词： internet of Energy

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Attak Detection in RSU-OBU Communication Using Deep Neural network 23

Attak Detection in RSU-OBU Communication Using Deep Neural N...

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23rd International conference on control, Automation and systems, ICCAS 2023

作者： An, Youngwoo Eun, Yongsoon Dgist Department of Electrical Engineering & Computer Science Daegu42988 Korea Republic of

ISBN: (纸本)9788993215274

This paper proposes three types of neural network-based attack detectors for internet of Vehicles (IoV) networks. The proposed attack detectors consist of Long Short-Term Memory (LSTM) layers. The fault detector is implemented in the Road Side Unit (RSU) that communicates with On Board Unit (OBU) installed in the autonomous vehicles. We consider the multiplicative attacks in the RSU-OBU communication messages. The training and validation data sets are generated by using an automated driving toolbox in MATLAB. Attack detector performance is evaluated using the validation data set that is not included in the training data sets. performance comparison of the three detectors are given. © 2023 ICROS.

关键词： Autonomous vehicles

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