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Forecasting Naturally Occurring Forest Fires using AI and Machine Learning in Wireless sensor Networks 5

Forecasting Naturally Occurring Forest Fires using AI and Ma...

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5th international conference on Inventive Research in computing Applications, ICIRCA 2023

作者： Akila, T. Menaka, M. Vasanth, P. Victoria Jancee, B. Mahendra College of Engineering Department of Information Technology Tamil Nadu Salem India Kingston Engineering College Department of Information Technology Tamil Nadu Vellore India Sri Venkateswara College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India St.Joseph's College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350321425

Wildfires are recurrent and sometimes devastating in regions of subtropical deciduous forests. Protecting forest areas are essential to ensure the health and vitality of the ecosystem. the goal of this work is to detect forest fires based on Artificial Intelligence (AI) and Machine Learning (ML) algorithms. the information obtained from the images combined with the forest spatial data will be used to make the detection. this article introduces an AI and ML based algorithm to forecast the forest fire in the wireless sensor network. the Convolutional Neural Network (CNN) algorithm is a better image classification algorithm for detecting fire. the experimental results illustrate that the proposed approach enhances the accuracy of fire detection. Furthermore, the proposed system using the CNN algorithm is compared to several ML algorithms such as Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (KNN) algorithms. From the results, it is observed that the proposed algorithm minimizes the false positive and false negative ratio, and provides an accuracy of 97.35%. © 2023 ieee.

关键词： Fires

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distributed Trust Evaluation Mechanism of LEO Satellites for 6G Network 23

Distributed Trust Evaluation Mechanism of LEO Satellites for...

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23rd ieee international conference on High Performance computing and Communications, 7th ieee international conference on Data Science and systems, 19th ieee international conference on Smart City and 7th ieee international conference on Dependability in sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Li, Hui Shi, DongCong Zhou, Ruijin Liao, Dan Zhang, Ming Zhou, Yuliang School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Chengdu Research Institute University of Electronic Science and Technology of China Chengdu China School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781665494571

In 6G network, the LEO satellite communication is popular because of the lower development costs and lower transmission delay. However, due to the instability, openness and exposure of links between satellites, LEO satellite is vulnerable to internal attacks during data routing. Considering that trust management has a better performance in solving the internal attacks between satellite communications, this paper proposed a new distributed trust evaluation mechanism (DTEM) of LEO satellites based on D-S evidence theory. In the proposed trust evaluation mechanism, it evaluates the trust degree of satellite nodes by direct trust, indirect trust and aggregate trust. then it detects the malicious satellite nodes for 6G networks and takes corresponding isolation measures. the simulations verifies that the proposed trust evaluation mechanism has superior performance in packet delivery rate and packet loss rate for data routing. © 2021 ieee.

关键词： 6G mobile communication Satellites Smart cities Evidence theory Low earth orbit satellites Packet loss Routing

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distributed Consensus Problem in Federated Learning Paradigm 23

Distributed Consensus Problem in Federated Learning Paradigm

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作者： Yan, Xin Qin, Yiming Hu, Xiaodong School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China The University of Hong Kong The Faculty of Education Hong Kong Hong Kong

ISBN: (纸本)9781665494571

Federated learning (FL) framework facilitates more and more applications of deep learning algorithms on the existing network architectures, where the model parameters are aggregated in a centralized manner. However, some of FL participants are often inaccessible, such as in a power shortage or device dormant state. that will force us to explore the probability that the parameter aggregation is operated in an ad hoc manner, which is based on consensus computing. In this paper, we first propose a novel FL paradigm, which supports an ad hoc operation mode for FL participants. Second, a discrete-time dynamic equation and its control law are formulated to satisfy the demands from FL framework, with a quantized caching scheme designed to mask the uncertainties from asynchronous updates and measurement noises. then, the consensus conditions and the convergence of the consensus protocol are deduced analytically, and a strategy of quantized caching to optimize the convergence speed is provided. Last, the theoretical results are validated by numerical simulations. © 2021 ieee.

关键词： Uncertainty Smart cities Delay effects Packet loss Collaborative work Particle measurements Numerical simulation

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IoT-based Autonomous Robot for Smart Farming 5

IoT-based Autonomous Robot for Smart Farming

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5th ieee international conference on Emerging Trends in Electrical, Electronic and Communications Engineering, ELECOM 2024

作者： Akib, A.S.M. Ahsanul Sarkar Rishad, N.M.Raziul Hassan Sourov, Salahin Mozumder, Md Shahin Alam Rahman, Md. Shahriyar Sifa, Fatema Jahan Alom, Md Sharif Ahasan, Md Abdullah Al Robo Tech Valley Dept. of Robotics Dhaka Bangladesh American International University Bangladesh Dept. of CSE Dhaka Bangladesh Washington University of Science and Technology Dept. of IT United States Ahsanullah University of Science and Technology Dept. of EEE Dhaka Bangladesh Independent University Bangladesh Dept. of Pharmacy Dhaka Bangladesh Z. H. Sikder University of Science and Technology Dept. of CSE Bangladesh University of Regina Dept. of CS Regina Canada

ISBN: (纸本)9798331532475

the integration of the Internet of things (IoT) and artificial intelligence (AI) has revolutionized agriculture and smart farming. these advanced technologies have not only improved cognitive processes but also enabled autonomous systems to navigate and communicate intelligently within agricultural environments. the adoption of smart sensor systems, in conjunction with IoT, has modernized traditional farming practices, particularly through AI-driven crop management solutions. this paper explores how a robotic vehicle (robocar), equipped with sensors to detect nitrogen (N), phosphorus (P), potassium (K), soil moisture, and humidity, collects data and transmits it to a cloud server for analysis. One mobility feature of the robot is its ability to navigate fields autonomously using GPS-guided paths. Starting from a designated home location, the robot follows a pre-set route to the field, where its robotic arm automatically collects soil samples for testing. By utilizing sensors that measure water content, soil moisture, and NPK levels, the system assesses soil conditions and sends the data to the IoT server. Based on this information, AI provides recommendations for the most suitable crops for the area. Furthermore, the robot monitors soil properties and alerts the operator to significant changes in the environment or infrastructure. After completing its tasks, the robot uses its navigation capabilities to autonomously return to its home position by activating the 'return home' function. © 2024 ieee.

关键词： Smart agriculture

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Soft Normal and Shear Force sensor for Underwater Robotic Applications 5

Soft Normal and Shear Force Sensor for Underwater Robotic Ap...

引用

5th ieee international conference on Flexible and Printable sensors and systems, FLEPS 2023

作者： Kamp, David Subad, Rafsan Al Shafatul Islam Park, Kihan University of Massachusetts Dartmouth Department of Mechanical Engineering DartmouthMA02747 United States

ISBN: (纸本)9781665457330

Tactile perception is a fundamental requirement to perceive and control the manipulation of an object via physical interaction. However, contemporary sensing technologies have some shortcomings in terms of directional force recognition such as inflexibility, intricate design, expensive fabrication methodology, and most importantly incompatibility with underwater applications. In this work, we report a facile and cost-effective methodology to build a soft and multi-directional force sensor especially focusing on its applicability in the underwater environment. the sensor consists of a silicone cover, a 3D printed pyramidal shape, piezo-resistive sensor nodes, and a silicone base. Whenever there is any force applied normal to the sensor, the pyramid shape would distribute the force equally to all the nodes and provide similar outputs from them whereas any other directional force would result in putting differential forces on the nodes and generate distinctive output patterns. the developed sensor was characterized experimentally and tested successfully for underwater demonstrations. © 2023 ieee.

关键词： Silicones

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AI and IoT based Intrusion Detection System for Cybersecurity 5

AI and IoT based Intrusion Detection System for Cybersecurit...

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5th international conference on Inventive Research in computing Applications, ICIRCA 2023

作者： Konda, Rajasekhar Ayyannan, Manimuthu Chandramouli, Deepak Mohanakumar Software Engineering Manager & Ieee Senior Member Bay area San FranciscoCA United States AI/ML and Data Expert | Ieee Member Sr. Software Engineering Manager Bay Area San FranciscoCA United States Data Platform and Distributed Systems Sr. Software Engineer | Ml Platform Bay Area San JoseCA United States

ISBN: (纸本)9798350321425

A network's traffic can be monitored by an Intrusion Detection System (IDS), which then sends out warnings if any malicious behavior is detected. It's a program that checks if a website link leads to malicious content or if there was a policy violation on the network. As a case study for IDS, identifying phishing websites has been performed. One of the most effective tools for hackers to carry out a variety of security attacks is the widespread distribution of fake website Uniform Resource Locators (URLs) via messages, email, etc., with the goal of fooling unaware consumers into providing over sensitive information or worse. Traditional methods for detecting phishing websites, such as employing blacklists, are used to make predictions about phishing URLs. Traditional solutions based on blacklists are not effective at predicting many phishing websites, because so many new ones are created and released on the Internet every day. three distinct Artificial Intelligence (AI) models-Random Forest (RF), Naive Bayes (NB), and XGBoost-are used to accurately detect dangerous URLs. Both the authentic and the fraudulent URLs were obtained from various sources. the AI model is trained with the retrieved relevant features. Following this, performance indicators are used to assess the quality of the AI models. According to the outcome of the testing data, the XGBoost model achieved a maximum accuracy of 96.98%, which is higher than the accuracy of the remaining models tested. then the XGBoost model has been finalized and deployed as a website, anyone can immediately recognize the phishing URL. © 2023 ieee.

关键词： Websites

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Survivable Virtual sensor Networks Embedding Strategy for Link Failures 21

Survivable Virtual Sensor Networks Embedding Strategy for Li...

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21st ieee international conference on Ubiquitous computing and Communications / 5th ieee international conference on Data Science and Computational Intelligence / 21st ieee international conference on Computer and Information Technology

作者： Zhou, Guangxu Guo, Yue Hou, Junfeng Zhang, Hongjun Zhang, Genqin Chai, Yajie Henan Prov Tobacco Co Xuchang Branch Xuchang 461000 Henan Peoples R China

ISBN: (纸本)9781665477260

the introduction of network virtualization into wireless sensor networks can effectively improve resource utilization. However, in a virtualized environment, the failure of the physical layer will affect the availability of virtual sensor networks, resulting in network service interruption. Aiming at the virtual sensor network mapping problem under the condition of link failure in the physical network, a virtual sensor network survivability embedding model is constructed, and a differentiated service survivability virtual network embedding algorithm is proposed. For high-priority virtual sensor network requests, a protection mechanism is adopted to construct a protection path for the virtual sensor network during virtual network mapping;for low-priority virtual sensor network requests, a fault virtual line migration strategy based on link availability is proposed. Migration strategy to maximize the integrity of the virtual sensor network and the continuity of network services. Simulation results show the superiority of the proposed algorithm in the fault recovery rate and resource utilization of the virtual sensor network.

关键词： wireless sensor network network virtualization survivable embedding link failure

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distributed sensor-Assisted DNN-Based Massive-MIMO Channel Reconstruction 5

Distributed Sensor-Assisted DNN-Based Massive-MIMO Channel R...

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5th international Science and Technology conference Modern Network Technologies, MoNeTec 2024

作者： Artemasov, Dmitry Sherstobitov, Alexander Lyashev, Vladimir Skolkovo Institute of Science and Technology Center for Next Generation Wireless and IoT Moscow Russia Moscow Institute of Physics and Technology The Department of Multimedia Technology and Telecommunication Moscow Russia

ISBN: (纸本)9798350369007

Modern massive multiple-input multiple-output (MIMO) systems allow for a great increase in cell throughput by means of spatial multiplexing. However, urban environmental conditions, such as low signal-to-noise ratio (SNR) and non-line-of-sight (NLoS) cell areas, can limit their full potential. To overcome these issues, techniques such as beam sweeping and beam tracking may be used to establish the correct beamforming for signal reception. Nevertheless, beam sweeping can induce overhead in terms of the time required to scan the site area, and beam tracking can only be performed after the initial connection, what may restrict the practical application of these methods. In this paper, we propose to utilize the set of passive sensors equipped with small antenna arrays to receive the uplink pilot signals and apply the state-of-the-art deep neural network transformer architecture for the base station (BS) channel state information (CSI) reconstruction task. We show that it is possible to recover the BS high-dimensional (128 ports) CSI in the negative SNR region with the help of passive synchronized sensors equipped with 4 cross-polarized antenna arrays. the developed method allows for the reconstruction of the 1 transmission layer precoding matrix with an accuracy of approximately 84% for more than 70% of UEs deployed in the NLoS area, while being trained on only 10% of samples. © 2024 ieee.

关键词： Deep neural networks

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Introducing Natural Language-based Instruction Protocol for Intelligent Machine Collaborations 19

Introducing Natural Language-based Instruction Protocol for ...

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19th ieee international Wireless Communications and Mobile computing (ieee IWCMC)

作者： Gong, Haobing Gao, Hui Feng, Yuting Yuan, Caixia Minist Educ Sch Informat & Commun Engn Key Lab Trustworthy Distributed Comp & Serv Beijing Peoples R China Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing 100876 Peoples R China

ISBN: (纸本)9798350333398

the rapid development of various autonomous unmanned systems has increased the endogenous intelligence of machines, which empowers functional collaborations among intelligent machines. However, traditional protocols for intelligent machine collaboration have limitations regarding functionality, efficiency, and scalability. Moreover, existing research on intelligent machine collaboration is not yet approaching a unified paradigm that facilitates interactions among machines and between machines and humans. therefore, aiming to enable a more efficient functional collaboration among intelligent machines, we propose a natural language-based instruction (NLI) protocol, which enjoys the advantages of autonomy, robustness and efficiency. In particular, we specify the NLI protocol architecture by introducing a corpus of NLIs, an NLI generation module, and an NLI parsing module, wherein the corpus contains control instructions, intents and slots, the generation module is used to generate NLIs, and the parsing module is used to parse the intents and slots in the instruction. Furthermore, we case-study the proposed NLI protocol in an electromagnetic interference avoidance scenario based on semi-physical simulation with software-defined radio. Simulation results show that the NLI protocol is more robust and efficient than traditional control protocol in severe wireless channels, which validates the feasibility and effectiveness of implementing intelligent machine collaboration based on NLI.

关键词： Natural language-based instructions intelligent machine collaboration network control electromagnetic interference avoidance software-defined radio

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Higher Reconstruction Performance with Dual Deep Neural Networks for Wireless sensor Networks Compressed Temporal Signals 12

Higher Reconstruction Performance with Dual Deep Neural Netw...

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12th ieee international conference on Intelligent Data Acquisition and Advanced computing systems: Technology and Applications, IDAACS 2023

作者： Rousseau, Sebastien Negrier, Romain Courreges, Fabien Engoti, Frank Itoua Quere, Raymond University of Limoges Xlim Institute Brive-la-Gaillarde19100 France NAIoBEE Company Brive-la-Gaillarde19100 France

ISBN: (纸本)9798350358056

In many applications using wireless sensor networks, the reliability of monitored data is crucial to analyze situations and take decisions. Compressed sensing methods are effective to ensure durability of a wireless sensor network installation and to overcome communication failures by drastically lowering the amount of data to transmit. the method proposed in this paper aims to perform an abstraction of the signals sparsity model required by compressed sensing in order to use a deep learning-based approach. this method is designed to be effective for all kinds of transient or temporally dynamic signals sufficiently regular to be predictable, that is to say signals that can be implicitly modelled by a sparse representation. through the collaboration of two recurrent neural networks and signal processing algorithms, our temporal method outperforms, in terms of accuracy, state-of-art methods, including distributed compressed sensing, time series imputation and forecasting deep learning methods. thus, high compression rates up to 95% can be reached while leading to an error of less than 0.5°C in reconstructing temperature signals. © 2023 ieee.

关键词： compressed sensing deep learning recurrent neural networks signal processing algorithms wireless sensor networks

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