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Natural Disaster Sustainability Forest Fire Detection System Based on Computer Vision Using Smart IoT sensor networks and Deep Learning Techniques

Natural Disaster Sustainability Forest Fire Detection System...

引用

Advances in Computation, Communication and information Technology (ICAICCIT), International conference on

作者： T. LeoNila G. A Senthil S Geerthik K Saravanan G Arun Kumar M Kamatchi Saranya Department of Information Technology Agni College of Technology Chennai India

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

Forest Fire pose significant threats to ecosystems, human life, and property. Early detection and rapid response are crucial for effective firefighting and mitigation efforts. In this paper, we propose a novel forest fire detection system leveraging smart sensor networks and deep learning techniques. Our system employs computer vision algorithms to analyze real-time video feeds from strategically placed cameras in forested areas. Deep learning models, particularly convolutional neural networks (CNNs) and Artificial Neural networks (ANNs) are utilized to detect and classify fire-related patterns in the video streams. The proposed system aims to enhance the speed and high accuracy of forest fire detection, thereby enabling prompt intervention and minimizing the devastating impacts of such natural disasters. The accuracy and efficiency results produced ANN is 89% and CNN is 95%. This technology is used to minimize forest fire losses.

关键词： Deep learning Computer vision Accuracy Prevention and mitigation Disasters Forestry Streaming media Convolutional neural networks Sustainable development intelligent sensors

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Integration of Artificial Intelligence and BIM in intelligent Decision Support System for Water Conservancy Projects

Integration of Artificial Intelligence and BIM in Intelligen...

引用

Electronics, Energy Systems and Power Engineering (EESPE), IEEE International conference on

作者： Zekun Chang Shandong Vocational College of Industry Zibo China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

A kind of intelligent DSS based on AI and Architecture information Modeling (BIM) is put forward. The system is composed of several modules, including data collection, processing, intelligence decision-making, feedback and optimizing. The data acquisition and processing module collects environmental data, equipment status and water flow information of water conservancy projects in real time through a variety of sensors and remote sensing technologies; the intelligent decision module uses deep learning algorithms, especially GCN is to model and predict the collected data, so as to make effective risk assessment and scheduling decisions; the feedback and optimization module continuously optimizes the decision process according to real-time feedback information, and improves decision accuracy and response speed. Through a series of experimental simulations, this paper verifies the effectiveness of the system. The GCN-based model improves the forecasting precision by 15% compared with the conventional ANN, and the calculation efficiency is improved 20%. Furthermore, it is proved by experiments that the system has remarkable superiority in dealing with complicated data in real time, data fusion and decision-making.

关键词： Decision support systems Accuracy Graph convolutional networks Building information management Water conservation Predictive models Prediction algorithms Real-time systems Artificial intelligence Water resources

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Fusion BASED AGV Robot Navigation Solution Comparative Analysis and Vrep Simulation 8

Fusion BASED AGV Robot Navigation Solution Comparative Analy...

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8th International Iranian conference on Signal processing and intelligent Systems, ICSPIS 2022

作者： Moshayedi, Ata Jahangir Zanjani, S. Mohammadali Xu, Dangling Chen, Xi Wang, Guijiang Yang, Shuxin Jiangxi University of Science and Technology School of Information Engineering Jiangxi Ganzhou China Islamic Azad University Smart Microgrid Research Center Najafabad Branch Najafabad Iran Islamic Azad University Department of Electrical Engineering Najafabad Branch Najafabad Iran

ISBN: (纸本)9781665476232

Today, Automated Guided Vehicle (AGV) robots are integral to many factories. One of the basic problems of these robots is accurate navigation, which, in addition to creating security in performing tasks, it helps the robot to manage the battery power and energy and move on a predetermined path. Over the various method, the visions are based on good performance, recently been widely used. In this research, a simple but effective fusion method as the combination of vision(camera) and infrared (IR) sensors with the minimum number of sensors is proposed and implemented. The proposed method has been simulated and evaluated using the Vrep simulator with real dimensions of our previous design AGV system named Hongma and the Python API. In the simulation, the proposed method was carried out. In the experiment, five paths named Circle, Elliptical, Spiral,8 shapes, and Special path, different paths with different complexity were tested, and the experiment aimed to find the maximum speed at which the proposed algorithm and the vision sensor (camera sensors) can track the path with a 100% success rate. The results obtained in the experiment show the fusion method's effectiveness over the five mentioned scenarios in tracking the planned path compared to the routine vision method. © 2022 IEEE.

关键词： Computer software

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SmartPark: AI and IoT-Enabled Automated Parking System for Urban Efficiency

SmartPark: AI and IoT-Enabled Automated Parking System for U...

引用

Sustainable Communication networks and Application (ICSCNA), International conference on

作者： Balaram Reddy Jebarson Raj Vijay Vasanth Aroulanandam Department of Networking and Communications Faculty of Engineering and Technology School of Computing SRM IST Kattankulathur Tamil Nadu India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

There have been a lot of traffic issues in cities due to parking anomalies and unavailability of parking. That is why SmartPark was created. It is a smart parking system that uses computer vision and sensors to improve parking efficiency and make it easier for drivers. This parking system uses computer vision to see if parking spaces are available and shares that information through a mobile app in real-time. Furthermore, sensors are used to help drivers park properly in the specified parking slot stopping them from blocking pathways or interfering with other vehicles nearby. With the fusion of AI and IoT, this parking system aims to reduce traffic congestion and minimize parking delays.

关键词： Computer vision Accuracy Urban areas Automated parking sensor systems Real-time systems Mobile applications Artificial intelligence intelligent sensors Traffic congestion

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A Comparative Study of Deep Learning Robustness for sensor-based Human Activity Recognition

A Comparative Study of Deep Learning Robustness for Sensor-b...

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International conference on Telecommunications and Signal processing (TSP)

作者： Sakorn Mekruksavanich Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Department of Mathematics Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut’s University of Technology North Bangkok Bangkok Thailand

Recognition of human activities has become a highly significant area of research in the current era of the Internet of Things and A.I. technologies. Human activity recognition (HAR) plays a crucial role in monitoring and analyzing human behaviors using various types of sensors. Such sensor-based HAR systems are being used in various industry domains, including healthcare, professional sports, and worker assessment in intelligent factories. The integration of micro electronics mechanical systems sensor technology in smart wearable devices has significantly improved the capabilities of HAR using machine learning and deep learning techniques. However, processing longtime dependent sequence samples with noisy data is still a significant challenge, affecting the classification time and accuracy. This study aimed to analyze noise-tolerant deep learning models for recognizing physical activities from noisy smartphone data. The study analyzed the recognition performances of the five most popular deep learning networks for HAR under different levels of Gaussian noise. The results indicated that the BiGRU network demonstrated outstanding robustness to Gaussian noise of different levels.

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Demo Abstract: Bio-inspired Tactile Sensing for MAV Landing with Extreme Low-cost sensors

Demo Abstract: Bio-inspired Tactile Sensing for MAV Landing ...

引用

International Symposium on information processing in sensor networks (IPSN)

作者： Chenyu Zhao Ciyu Ruan Shengbo Wang Jirong Zha Haoyang Wang Jiaqi Li Yuxuan Liu Xuzhe Wang Xinlei Chen Shenzhen International Graduate School Tsinghua University Shenzhen China Pengcheng Laboratory Shenzhen China RISC-V International Open Source Laboratory Shenzhen China

ISBN: (数字)9798350362015

ISBN: (纸本)9798350362022

MAV (Micro Aerial Vehicle) requires landing on a docking platform for recharging during or after missions due to their limited energy capacity. Inspired by biological tactile sensing, we propose a proprioceptive sensing system that allows MAV to "touch", recognize, and locate the landing platform even when visual or other positioning systems are not functioning properly. We leverage a physical phenomenon: as the MAV approaches a beneath obstacle, it experiences attitude disturbances caused by the airflow generated by the rotor’s reflections from the ground. By employing traditional signal processing and learning-based techniques to analyze signals from the IMU (Inertial Measurement Unit) and motors, the MAV can sense the edges of the platform and further calculate the precise landing coordinates. With a power consumption of less than 40 mW, our system achieves an edge detection error of less than 2 cm and a landing success rate exceeding 90%.CCS CONCEPTS• Applied computing → Aerospace; • Computing methodologies → Machine learning approaches; • Computer systems organization → sensors and actuators.

关键词： Visualization Power demand Image edge detection Propioception Organizations Signal processing sensor systems

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Fair Distributed Cooperative Bandit Learning on networks for intelligent Internet of Things Systems

Fair Distributed Cooperative Bandit Learning on Networks for...

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IEEE International conference on Communications Workshops, ICC

作者： Ziqun Chen Kechao Cai Jinbei Zhang Y. Zhigang School of Electronics and Communication Engineering Sun Yat-sen University Shenzhen China China Academy of Electronics and Information Technology Beijing China

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

In intelligent Internet of Things (IoT) systems, edge servers within a network exchange information with their neighbors and collect data from sensors to complete delivered tasks. In this paper, we propose a multiplayer multi-armed bandit model for intelligent IoT systems to facilitate data collection and incorporate fairness considerations. In our model, we establish an effective communication protocol that helps servers cooperate with their neighbors. Then we design a distributed cooperative bandit algorithm, DC-ULCB, enabling servers to collaboratively select sensors to maximize data rates while maintaining fairness in their choices. We conduct an analysis of the reward regret and fairness regret of DC-ULCB, and prove that both regrets have logarithmic instance-dependent upper bounds. Additionally, through extensive simulations, we validate that DC-ULCB outperforms existing algorithms in maximizing reward and ensuring fairness.

关键词： Upper bound Protocols conferences Simulation Distributed databases sensor systems Servers

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Data Aided Sensing with Variational Autoencoder

Data Aided Sensing with Variational Autoencoder

引用

International conference on Artificial Intelligence in information and Communication (ICAIIC)

作者： Muhammad Awais Jinho Choi Jihong Park Yun Hee Kim Department of Electronics and Information Convergence Engineering Kyung Hee University Yongin Korea School of Information Technology Deakin University Melbourne Australia

In this paper, we propose an intelligent data-aided sensing method exploiting variational auto-encoder (VAE) for wireless sensor networks. A set of measurements of all sensors is analyzed to identify the underlying cause. For this purpose, we employ $\beta$ - VAE to find the cause with a partial observation of the measurement vector and utilize the inferred cause for communication efficient data collection in uploading the data. The resulting approach can be used when collecting all measurements would be expensive, or excessively time consuming.

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Integrated RNNs for Rainfall Sensing with Wireless Communication networks

Integrated RNNs for Rainfall Sensing with Wireless Communica...

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Acoustics, Speech, and Signal processing Workshops (ICASSPW), IEEE International conference on

作者： Dror Jacoby Jonatan Ostrometzky Hagit Messer School of Electrical Engineering Tel Aviv University Israel

ISBN: (数字)9798350374513

ISBN: (纸本)9798350374520

Incorporating physical characteristics effectively into dynamic time series data is vital for improving machine learning models. Our research focuses on utilizing Wireless Communication Links (CMLs) as sensors for rainfall prediction. We explore the advantages of incorporating the physical attributes of CMLs into the learning mechanism, aiming to enhance the adaptability and applicability of the sensing capabilities of communication networks. We investigate these benefits through two distinct approaches for embedding sensor features in Integrated Recurrent Neural networks (I-RNNs). Aligned with the opportunistic use of communication networks within the Integrated Sensing, Communication, and Computation (ISCC) framework, our research aims to optimize the integration of sensor capabilities. We conduct a comprehensive analysis with real-world measurements from operational CMLs, including unique data from smart cities, aiming to enhance sensing strategies in both existing and emerging landscape of Beyond 5G/6G systems in the ISCC. Our results demonstrate the value of incorporating static information into RNNs for sensor differentiation, thereby enhancing the accuracy, generalization, and robustness of weather sensing with communication networks.

关键词： Wireless communication Adaptation models Rain Smart cities Estimation sensor phenomena and characterization Data models

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Wireless sensor Network Localization with Molecular Force

Wireless Sensor Network Localization with Molecular Force

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Software System and information processing (ICSSIP), IEEE International conference on

作者： Jingyao Ouyang Jiang Feng Yang School of Electrical and Information Engineering Yunnan Minzu University Yunnan Key Laboratory of Unmanned Autonomous System Kunming China

ISBN: (数字)9798331506131

ISBN: (纸本)9798331506148

Wireless sensor networks (WSNs) consist of spatially distributed autonomous sensors that monitor physical or environmental conditions, such as temperature, sound, and pressure. Locating in WSNs is crucial for various applications, including environmental monitoring, military surveillance, and healthcare. Accurate positioning enables the network to provide location-based services, improve routing efficiency, and support the detection of events and objects. His molecular force-based localization algorithm represents an advancement in WSN positioning technology. Its iterative approach, driven by a well-defined update mechanism, ensures precise and efficient localization, making it an invaluable tool for enhancing the functionality and performance of wireless sensor networks. In this study, a novel iterative algorithm WLMF is proposed to solve the localization problem of WSN. This algorithm is a wireless positioning algorithm based on RSSI technology, and its core idea is to use the current distance and target distance to determine whether the attraction component or the repulsion component is generated, and finally sum the multiple force components to determine the iterative update direction. To speed up algorithm convergence, the concept of momentum was introduced into the proposed algorithm. In order to estimate the convergence and localization performance of the proposed algorithm, we set up several different test environments, including different node numbers and model parameters. Experimental results show that the proposed algorithm has good positioning accuracy and fast convergence speed. This makes it a versatile and reliable choice for real-world applications, where precise localization is vital for tasks such as environmental monitoring, asset tracking, and security surveillance. Experimental results demonstrate that the proposed localization algorithm achieves good accuracy.

关键词： Location awareness Temperature sensors Wireless communication Wireless sensor networks Accuracy Surveillance Force Software algorithms Iterative algorithms Convergence

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