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Design and Development of Hybrid Hierarchical Routing Protocol for Wireless sensor Networks 5

Design and Development of Hybrid Hierarchical Routing Protoc...

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5th international conference on Circuits, Control, Communication and computing, I4C 2024

作者： Raghunandan, G.H. Patil, Tarun Sri Srujan Hari, T. Bms Institute of Technology & Management Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798331528539

Primarily employed to achieve this objective, wireless sensor networks (WSNs) are designed to track environmental or physical conditions. they obtain data from their environment efficiently and deliver it all at once to a specific location. the main issues are network longevity, usage of energy, and coordinated transmission, even though their efficacy is well established. this study proposes a unique approach to improve network endurance and address these issues. the technique seeks to improve WSN performance as a whole. In particular, a hybrid hierarchical routing system is presented to address problems with the current protocols, such as excessive energy usage in addition to high latency. For boosting the longevity of the WSN, the primary goals are to lower intra-cluster transmission and limit contact with base station. To lower quantity of energy spent while distributing to distant base stations (BS), the suggested approach encompasses gateway nodes, hybrid time synchronization, locality-sensitive hashing (LSH) based data aggregation technique, and cluster choice focused on centrality. To demonstrate effectiveness of the presented technique, a comparison with a traditional method is carried out, assessing several performance indicators. In regards to gauges of performance including latency, throughput, total dead nodes, network energy consumption, and data packets transmitted, hybrid approach outperforms conventional approaches. © 2024 ieee.

关键词： Base stations

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Generating a distributed network attack dataset based on weather station IoT Cyber Physical systems 7

Generating a distributed network attack dataset based on wea...

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7th international conference on Artificial Intelligence, Big Data, computing and Data Communication systems (ICABCD)

作者： Johnson, David Roy, Kaushik North Carolina Agr & Tech State Univ Dept Comp Sci Greensboro NC 27411 USA

ISBN: (纸本)9798350387919;9798350387902

there has been a large increase in Internet of things (IoT) devices used in many parts of life. these devices, also called edge devices, have been known to be vulnerable to attackers and to be used as a medium for network attacks. A simple loT device uses one or more sensors to read data from the real world and performs a simple action based on the sensor readings, such as a motion sensor in a home turning on the lights when motion is detected. Building a dataset to model real-world network traffic, along with pseudo-realistic uses of network attacks is essential for creating an intrusion detection system (IDS). In this paper, we develop an loT network anomaly dataset using synthetic and real-world network attacks. Virtual machines with Ubuntu 22.04 were used to contain the network data in a sandboxed environment. For our edge devices, we use Le Potato by Libre Computers as an alternative to Raspberry Pi 3, which is a popular single board computer used for home automation and hobbyist projects where the Raspberry Pi acts as the computer for the smart device. Each device has Waveshare Weather sensor attached to it, which reads climate data such as temperature, humidity, and barometric pressure. the edge devices are located within the Cyber Defense and AI lab at North Carolina Agriculture and Technology State University. Real network attack tools were used to generate traffic to label the current data as attack data, along with the attack type. In the absence of the attack tools in use, the network traffic is labeled normal, or benign. the dataset is comprised of homogeneous data using environmental sensors that measure local climate data. In this study, we use XGBoost, LSTM+CNN, and Multilayer Perceptron along with various resampling methods to evaluate our synthetic dataset. In our results, we find that XGBoost produces the best performance at classifying attacks when using RandomUndersampler.

关键词： anomaly detection deep neural network network attack IoT Internet of things distributed learning federated learning

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Designing distributed Deep Learning Architectures for 5G Network Slicing 15

Designing Distributed Deep Learning Architectures for 5G Net...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Kumari, Sweta Shree, Ritu Kumar, R. thanga Department of Law ARKA JAIN University Jharkhand Jamshedpur India Department of Computer Science & Application Vivekananda Global University Jaipur India Karnataka Bangalore India

ISBN: (纸本)9798350370249

the intention present day this paper is to advise a dispensed deep ultra-modern (DL) architecture for 5G network slicing. the proposed allotted DL structure utilizes distributed nodes to learn about the network slices. the dispensed nodes and the community slices are interconnected via an orchestrator, for this reason, supplying a comprehensive view present day the environment. Furthermore, the distributed DL structure leverages switch mastering to enhance its overall performance in dispensed situations. In addition, it is also an aspect computing node to research visitors' styles present day the 5G slices. Eventually, the proposed allotted DL architecture has been aligned with the 5G architecture, and the disbursed nodes are related through a 5G cellular community. the experimental consequences demonstrate that the proposed allotted DL architecture provides performance for network cutting while lowering the strength consumption and improving community security. ©2024 ieee.

关键词： 5G mobile communication systems

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Congestion Based Adaptive Association Mechanism for ieee 802.11ah Based Large IoT Network 5th

Congestion Based Adaptive Association Mechanism for IEEE 80...

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5th EAI international conference on Cognitive computing and Cyber Physical systems, IC4S 2024

作者： R, Nandhini R, Radha School of Computer Science and Engineering Vellore Institute of Technology–Chennai Chennai India

ISBN: (纸本)9783031770807

the ieee 802.11 ah standard is the prominent protocol for medium range communication in the domain of Internet of things (IoT), facilitating interaction between wireless sensor nodes and access points. Supporting up to approximately 8192 nodes connected to a single access point through single hop strategies, ieee 802.11 ah employs CSMA/CA for the authentication and association process of sensor nodes with access point. However, the standard encounters challenges such as significant collisions and prolonged network setup times when attempting to associate an excessive number of wireless sensor nodes with a single access point, primarily due to authentication thresholds set at intervals. In this research, we propose a dynamic adjustment of the authentication threshold based on network congestion status. Learning parameters including the number of requests served successfully by the access point in the past, present and a congestion threshold are leveraged to adapt to current network congestion levels. through evaluation using the LILD method in the ns-3 simulator, our approach demonstrates considerable enhancements in terms of collision reduction and network setup efficiency. this research endeavours to promote responsible network management practices for improving the performance and reliability of ieee 802.11 ah based IoT deployments. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Internet of things

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Robust sensor Networks: Applying AI for Fault Identification in Challenging Operational Contexts 5

Robust Sensor Networks: Applying AI for Fault Identification...

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5th international conference on IoT Based Control Networks and Intelligent systems, ICICNIS 2024

作者： Manjunatha, A.S. Venkatramana Bhat, P. Ramakrishna, M. Prabhu, Matti Nidhi Dept. of Computer and Communication Engineering NMAM Institute of Technology Nitte Karnataka Mangaluru India Dept. of Computer Science and Engineering NMAM Institute of Technology Nitte Karnataka Mangaluru India Manipal Academy of Higher Education Dept. of Data Science and Computer Applications Manipal Institute of Technology Manipal576104 India

ISBN: (纸本)9798331518097

Wireless sensor Networks (WSNs) are highly implemented in harsh and dynamic environments. In such configurations, faults and anomalies easily occur, causing unwanted disruptions in data accuracy and network performance. In the context of reliability and stability in WSN operations, effective fault detection techniques are vital. this research work discusses how several machine learning models may be applied, including Logistic Regression, Decision Tree, Random Forest, and K-Nearest Neighbors in order to detect fault in WSNs. the performance evaluation was performed based on major criteria, which are accuracy, precision, recall, and Fl score. this, therefore, makes these approaches to machine learning effective in identifying faults efficiently, hence offering a well-balanced solution that is scalable and robust for integrity and operational effectiveness of WSN s. © 2024 ieee.

关键词： Random forests

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Efficient Localness Transformer for Smart sensor-Based Energy Disaggregation 18

Efficient Localness Transformer for Smart Sensor-Based Energ...

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18th Annual international conference on distributed computing in sensor systems (DCOSS)

作者： Yue, Zhenrui Zeng, Huimin Kou, Ziyi Shang, Lanyu Wang, Dong Univ Illinois Sch Informat Sci Champaign IL 61820 USA

ISBN: (纸本)9781665495127

Modern smart sensor-based energy management systems leverage non-intrusive load monitoring (NILM) to predict and optimize appliance load distribution in real-time. NILM, or energy disaggregation, refers to the decomposition of electricity usage conditioned on the aggregated power signals (i.e., smart sensor on the main channel). Based on real-time appliance power prediction using sensory technology, energy disaggregation has great potential to increase electricity efficiency and reduce energy expenditure. With the introduction of transformer models, NILM has achieved significant improvements in predicting device power readings. Nevertheless, transformers are less efficient due to O(l2) complexity w.r.t. sequence length l. Moreover, transformers can fail to capture local signal patterns in sequenceto-point settings due to the lack of inductive bias in local context. In this work, we propose an efficient localness transformer for non-intrusive load monitoring (ELTransformer). Specifically, we leverage normalization functions and switch the order of matrix multiplication to approximate self-attention and reduce computational complexity. Additionally, we introduce localness modeling with sparse local attention heads and relative position encodings to enhance the model capacity in extracting short-term local patterns. To the best of our knowledge, ELTransformer is the first NILM model that addresses computational complexity and localness modeling in NILM. With extensive experiments and quantitative analyses, we demonstrate the efficiency and effectiveness of the the proposed ELTransformer with considerable improvements compared to state-of-the-art baselines.

关键词： Smart sensor systems non-intrusive load monitoring energy disaggregation transformer

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Multi-sensor fusion visual SLAM for uncertain observations 5

Multi-sensor fusion visual SLAM for uncertain observations

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5th international conference on Computer Vision, Image and Deep Learning, CVIDL 2024

作者： Feng, Mingchi Yi, Xuan Wang, Kun Cheng, Zhengyu Chongqing University of Posts and Telecommunications School of Advanced Manufacturing Engineering Chongqing China

ISBN: (纸本)9798350373820

With the continued progress of autonomous driving technology, the use of multi-sensor fusion assisted visual simultaneous localization and mapping (VSLAM) has emerged as a new research focus. However, when sensors sensitive to environmental factors output invalid signals, the positioning accuracy of multi-sensor fusion SLAM systems can sharply decline, even leading to system crashes. To address this issue, we propose a multi-sensor fusion VSLAM scheme that can handle uncertain observations. this scheme maintains the stable operation of the SLAM system even when GNSS and image information are lost. By constructing a GNSS simulation neural network based on IMU, we can output position, velocity, and attitude data similar to those measured by dual-antenna GNSS. Subsequently, an error state Kalman filter is used to fuse the network output, GNSS measurement position information, and IMU data. the fusion results assist in the stable operation of the visual SLAM system. through comparisons with VINS-Fusion on public datasets, experimental results show that our proposed method reduces the average root mean square error by 11.6%. Additionally, simulation experiments under extreme conditions verify the effectiveness of our approach. © 2024 ieee.

关键词： Antennas

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Visualization Analysis of Artificial Intelligence Applications in Rural Areas Based on Citespace 5

Visualization Analysis of Artificial Intelligence Applicatio...

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5th international conference on Information Science, Parallel and distributed systems, ISPDS 2024

作者： Zhang, Qian Zhao, Jing Xia, Liping Shaanxi Normal University International Business School Xi'an China Changji University School of Economics and Management Changji China Changji University School of Information Engineering Changji China

ISBN: (纸本)9798350352719

With the rapid development of artificial intelligence, research on its application in rural development is increasing. this paper focuses on the literature on artificial intelligence empowering rural and agricultural development from the Web of Science core from 2014 to 2023. Using CiteSpace software, it conducts a visualization analysis of the literature over the past decade, examining the current status, hotpots, and trends of research from six aspects: publication quantity, disciplinary distribution, institutions, countries and regions distribution, research hotpots, and trends. Conclusion: Regarding the current status, there is rich international research on the application of artificial intelligence in rural areas, with an overall increasing trend in research quantity since 2018. Collaboration networks among institutions have begun to form, with China, the United States, and India conducting more research with higher impact. Regarding research hotpots, machine learning and deep learning are popular application technologies, with digital agriculture and precision agriculture being the main research objects over the past decade. Regarding research trends, integrating artificial intelligence technology with smart rural development and smart agriculture will further deepen. © 2024 ieee.

关键词： artificial intelligence CiteSpace rural areas Visualization Analysis

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Design of Induction Equipment Complex using the theory of distributed Parameter systems 5

Design of Induction Equipment Complex using the Theory of Di...

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5th international conference on Control in Technical systems, CTS 2023

作者： Ilyushina, Alena N. Pershin, Ivan M. Trushnikov, Vyacheslav E. Novozhilov, Igor M. Pervukhin, Dmitrii A. Tukeyev, Dmitrii L. St. Petersburg Technical College of Management and Commerce St. Petersburg Russia North-Caucasus Federal University Pyatigorsk Russia Saint Petersburg Mining University St. Petersburg Russia Saint Petersburg Electrotechnical University 'LETI' St. Petersburg Russia

this study sets the task of designing a set of induction equipment and automated means of work infeed system to inductors using the theory of distributed parameter systems. the results of this research are obtained in... 详细信息

ISBN: (纸本)9798350304664

关键词： analysis measurement sensoru system identification

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Drone-based Optimal and Heuristic Orienteering Algorithms Towards Bug Detection in Orchards 18

Drone-based Optimal and Heuristic Orienteering Algorithms To...

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18th Annual international conference on distributed computing in sensor systems (DCOSS)

作者： Sorbelli, Francesco Betti Coro, Federico Das, Sajal K. Palazzetti, Lorenzo Pinotti, Cristina M. Univ Perugia Dept Comp Sci & Math Perugia Italy Missouri Univ Sci & Technol Dept Comp Sci Rolla MO 65409 USA Univ Florence Dept Comp Sci & Math Florence Italy

ISBN: (纸本)9781665495127

In this paper, we consider the problem of using a drone to collect information within orchards in order to detect bugs. An orchard can be modeled as an aisle-graph, which is a regular data structure formed by consecutive aisles where trees are arranged in a straight line. For monitoring the presence of bugs, a drone flies close to the trees and takes videos and/or pictures that will be analyzed offline. As the drone's energy is limited, only a subset of locations in the orchard can be visited with a fully charged battery. those places that are most likely to be infested should be selected to promptly detect the parasite. We study the budgeted constrained position selection problem in the orchard from an algorithmic point of view. We present the Single-drone Orienteering Aisle-graph Problem (SOAP), a variant of the well-known orienteering problem where the finite resource is the drone's battery. We first show that SOAP can be optimally solved for aisle-graphs in polynomial time. However, the optimal solution is not efficient for large orchards. then, we propose two efficient heuristics that work even for large (orchard) instances. After a thorough analysis of the proposed solutions, we evaluate their performance by simulation experiments on both synthetic and real data sets.

关键词： Heuristic algorithms Computer bugs Data models sensor systems Batteries Simple object access protocol distributed computing

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