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intelligent Data Communication Technologies and Internet of Things (IDCIoT), International conference on

作者： P. Vishnu Kumar Adnan Ali Akula Zaheer Sha Siddem Rajesh Department of Internet of Things G. Pullaiah College of Engineering and Technology Kurnool Andhra Pradesh India

This paper introduces an intelligent vehicle system implemented through Internet of Things (IoT) technology, employing various sensors for comprehensive vehicle monitoring. The ESP32 microcontroller processes sensor data and manages the system's actuators. The system focuses on enhancing vehicle safety by detecting drunk driving, accidents, and driver fatigue. Recent techniques, including machine learning algorithms for improved detection accuracy and edge computing for real-time processing, are explored. Challenges faced by such systems, such as sensor calibration, data accuracy, and the integration of complex algorithms, are acknowledged. Despite these challenges, real-world evaluations confirm the system's effectiveness in identifying instances of drunk driving, accidents, and driver fatigue. The system consistently delivers real-time vehicle tracking information and antitheft alerts. The proposed intelligent vehicle system aims to address these challenges and significantly improve vehicle safety and convenience. It is designed to be innovative, cost-effective, and user-friendly, making it a valuable asset for any driver.

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Diffusion Bayesian Subband Adaptive Filters for Distributed Estimation Over sensor networks

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IEEE TRANSACTIONS ON COMMUNICATIONS 2021年第10期69卷 6909-6925页

作者： Huang, Fuyi Zhang, Jiashu Zhang, Sheng Chen, Hongyang Poor, H. Vincent Guangxi Univ Sch Comp Elect & Informat Nanning 530004 Peoples R China Guangxi Univ Guangxi Key Lab Multimedia Commun & Network Techn Nanning 530004 Peoples R China Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu 611756 Peoples R China Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 611756 Peoples R China Res Ctr Intelligent Network Zhejiang Lab Hangzhou 311121 Peoples R China Princeton Univ Dept Elect & Comp Engn Princeton NJ 08544 USA

sensor networks are an indispensable part of the Internet of Things (IoT), where sensors perform data acquisition and information processing tasks to obtain the parameters of interest so that IoT-based monitoring, diagnosis and other systems respond quickly to the changing conditions, instantaneous faults, etc. Distributed estimation algorithms are usually employed to estimate the parameters of interest in these IoT-based applications. However, when sensor networks have highly correlated input signals and nonstationary behavior in which the parameters of interest are time-varying, conventional distributed estimation algorithms suffer from severely degraded learning performance due to the large eigenvalue spread in the covariance matrix of the input signals and the random perturbation of the parameters of interest. To address these problems, this paper proposes two diffusion Bayesian subband adaptive filter (DBSAF) algorithms from a Bayesian learning perspective. As the highly-correlated input signal is whitened in a multiband structure and an estimate of the uncertainty in the parameters of interest is obtained by performing Bayesian inference, the proposed DBSAF algorithms are able to achieve better learning performance in comparison with the competing diffusion algorithms. The transient and steady-state mean square error performance of the proposed DBSAF algorithms are analyzed, and are verified by numerical simulations. A lower bound on the time-varying step-size is derived to maintain the optimal steady-state performance in nonstationary scenarios. A new method for the estimation of the noise variance is also proposed. Numerical simulations demonstrate the excellent learning performance of the proposed algorithms in comparison with benchmark algorithms.

关键词： Estimation Bayes methods Steady-state Signal processing algorithms Inference algorithms Internet of Things Covariance matrices Adaptive signal processing distributed estimation algorithm IoT multiband structure sensor networks

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Smart Automated Zebra Crossing System Using PIR sensors, IoT Integration, and IP Camera-Based Headcount for Enhanced Road Safety

Smart Automated Zebra Crossing System Using PIR Sensors, IoT...

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International conference on Computer and Communications Technologies (ICCCT)

作者： M.kumar M.E G. Adiline Macriga Department of Information Technology Computer Science and Engineering (with specialization in networks) Sri Sairam Engineering College Department of Information Technology Sri Sairam Engineering College

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

The Smart Automated Zebra Crossing System operationalizes Passive Infrared (PIR) sensors and Internet of Things (IoT) technology with IP camera-based headcount capabilities to foster pedestrian safety. A data-driven control system protected pedestrian safety by integrating adaptive management structures and intelligent traffic methods. The system actively switches on its crossing systems only after PIR sensors detect approaching pedestrians. IP cameras enabled by advanced computer vision algorithms provide real-time pedestrian counting and density measurement services. networks based on IoT technology carry system data to produce shifting crosswalk time lengths through dynamic adjustments based on variations in pedestrian numbers. Smart LED warning indicators inform drivers about traffic while adaptive timing systems shorten the time pedestrians and drivers need to wait. Real-time decision capabilities and reduced web dependence emerge from the system's edge computing architecture which ensures operational reliability. Through solar power technology all system components underwent upgrades to become durable in varying weather conditions and maintain sustainable operation in any environment. The gathered system data helps analytical experts predict dangerous traffic areas and peak times which supports better precautionary traffic management and future infrastructure development strategies. This modern framework takes pedestrian safety to new heights while efficiently utilizing roads through combination of real-time detection systems and dynamic adaptiveness with data analytical features. Researchers studied architectural elements of the system together with implementation methods and performance results in order to show how this technology will transform smart transportation infrastructure in the future.

关键词： Pedestrians Adaptive systems Traffic control sensor systems Road safety Safety IP networks Vehicle dynamics intelligent sensors Vehicles

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Real Time Kernel Learning for sensor networks using Principles of Federated Learning

Real Time Kernel Learning for Sensor Networks using Principl...

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Asia-Pacific-Signal-and-information-processing-Association Annual Summit and conference (APSIPA ASC)

作者： Kuh, Anthony Univ Hawaii Honolulu HI 96822 USA

ISBN: (纸本)9789881476890

This paper considers online kernel learning at the edge. Increasingly, we are seeing more networks consisting of edge processors and a central processor or coordinator. The edge processor could be a sensor, mobile phone, cognitive assistant, and/or IoT devices. These processors likely have limited power and communication capabilities, but have enough processing ability to make intelligent decisions and learn. With the advent of federated learning, these edge processors do not pass data to the central processor, but assist in learning by adjusting parameters of the learning model based on data the edge processor receives before passing information about the updated model to the central processor. Here we consider where the edge processors use kernel methods to perform the learning. The learning algorithms are online with edge processors adjusting the model parameters using stochastic gradient algorithms. We establish a framework for online federated kernel learning.

关键词： federated learning online kernel methods

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An Overview of Multi-sensor information Fusion 6

An Overview of Multi-sensor Information Fusion

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6th International conference on intelligent Informatics and Biomedical Sciences, ICIIBMS 2021

作者： Lu, Peng Dai, Fengzhi Tianjin University of Science and Technology College of Electronic Information and Automation Tianjin China

ISBN: (纸本)9781728167145

Multi-sensor information fusion technology has been widely used in target recognition, home appliances, robotics, health care, image processing, pattern recognition and other fields. With the development of multi-sensor information fusion technology, the application field of sensors is expanding rapidly. This paper introduces the development history, concept and fusion model of multi-sensor information fusion technology. An overview of multi-sensor information fusion algorithms and their features in recent years is introduced. In this paper, the development trend of multi-sensor information fusion technology and fusion algorithm is also predicted. © 2021 IEEE.

关键词： Domestic appliances

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Submodular Optimization via Reinforcement Learning for Active Control of sensor networks

Submodular Optimization via Reinforcement Learning for Activ...

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2022 IEEE Aerospace conference, AERO 2022

作者： Garagic, Denis Ravier, Robert Peskoe, Jacob Galoppo, Travis Zulch, Peter Sarcos Group Lc 360 Wakara Way Salt Lake CityUT84108 United States AFRL/RI 26 Electronics Parkway RomeNY13441 United States Bae Systems Fast LabsTM 600 District Avenue BurlingtonMA01803 United States

ISBN: (数字)9781665437608

ISBN: (纸本)9781665437608

Autonomous heterogeneous sensor networks (HSNs) consist of platforms equipped with various sensors. Platforms and sensors have limited range, so they must make decisions on where and when to point these sensors, what information to share among the cooperating platforms, and how to exploit multi-modal dependencies for accurate detection, tracking, and classification (DTC) of time-critical objects. We posit that cooperating platforms within HSN s that share and fuse multimodal data among themselves (using constrained inter-platform communication) will be more effective in accomplishing their required tasks and have more accurate target detection and recognition. We study a scenario where a group of cooperating platforms, each equipped with multi-modal sensors are collecting measurements of an object and the measurements are transmitted over a bandwidth-limited communication channel to a centralized node for processing. The communication channel presents an information transfer bottleneck as the sensors collect measurements at a much higher rate than what is feasible to transmit over the communication channel. In order to minimize the estimation error at the centralized node, only a carefully selected subset of measurements should be transmitted. We propose a unique synthesis of novel submodular optimization based reinforcement learning (RL) for active sensor control/management. Our approach solves the measurement selection problem by employing a model-free RL with sub modular objective functions, where instead of maximizing the cumulative reward, the goal is to maximize the objective value induced by a sub modular function. We make evident the effectiveness of this approach with simulated and real data (the latter including ESCAPE Government-furnished information which we have been extensively using on our previous efforts). © 2022 IEEE.

关键词： Reinforcement learning

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Achievable rate-exponent region for parallel distributed detection via binary symmetric channels 6

Achievable rate-exponent region for parallel distributed det...

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6th IEEE International conference on intelligent Computing and Signal processing, ICSP 2021

作者： Liu, Xiangyang Wang, Gang Wang, Qiang Li, Xian Ma, Simin National University of Defense Technology College of Information and Communication Xi'an Shaanxi Province China

ISBN: (纸本)9780738143705

Distributed detection with multiple sensors and a fusion center over binary symmetric channels has been investigated. The fusion center receives noisy observations of the local binary decisions. A fundamental tradeoff between achievable information rate and the error exponent of the missing probability exists. The tradeoff is well characterized by the rate-exponent region. The paper first derived a closed-form analytic expression for the error exponent and information rate by means of the theory of relative entropy typical set. Second, the properties of the optimal error exponent and information rate are discussed in detail. It was demonstrated that they are monotonically decreasing in the bit error probability of the binary symmetric channel. Simulation results validated the conclusions. © 2021 IEEE.

关键词： Wireless sensor networks

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Image processing Based on Inter-frame Color Feature Matching for Visible Light Positioning

Image Processing Based on Inter-frame Color Feature Matching...

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Signal processing and intelligent Computing (SPIC), International conference on

作者： Baozhu Yu Xiangyu Liu Yaoheng Chen Lei Guo Jianbo Bao Jiandong Sun School of Information Science and Engineering Shenyang Ligong University Shenyang China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China State Grid Electric Power Research Institute Nari Group Corporation Nanjing China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

The indoor imaging visible light positioning (VLP) technology based on the image sensor (IS) utilizes existing indoor lighting infrastructure to provide high-precision indoor positioning services. However, due to the high complexity of traditional image processing algorithms, existing VLP systems are unable to provide real-time positioning services. In response to the above issue, we propose an image processing mechanism based on the inter-frame color feature matching method, which includes the equidistant sampling processing, low complexity decoding algorithm, and color feature matching-based LED region of interest (ROI) determination method. The results show that the system average positioning delay is 80.2ms, indicating better system real-time performance.

关键词： Image sensors Image color analysis Education Signal processing algorithms Lighting Signal processing Light emitting diodes Real-time systems Decoding Delays

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An Anti-electromagnetic Interference Parking Detection Method Based on Wireless Sensing Technology

An Anti-electromagnetic Interference Parking Detection Metho...

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information Communication Technologies conference (ICTC)

作者： Jian Wang Xiyuan Wang Ruyin Cai Liangliang Lou Boyu Xu Gaojie Cheng Taizhou Environmental Science Design and Research Institute Taizhou China Taizhou Fuxing Office Equipment Co Taizhou China Institute of Intelligent Information Processing Taizhou University Taizhou China

ISBN: (数字)9798350373523

ISBN: (纸本)9798350373530

The management level of Smart Parking System (SPS) can be guaranteed by the accurate parking information, so that the low cost and high precision magnetometer-based wireless parking sensors (WPS) powered by batteries have been widely used in outdoor parking lots. However, the magnetic field around existing magnetometer-based WPS is easily interfered from the electromagnetic emission generated by underground high-voltage cables and subways in city. To address the above problems, an Anti-electromagnetic Interference Parking Detection (AeIPD) method is proposed in the paper, the band-pass filter of wireless transceiver is fully exploited. In AeIPD, the channel attenuation of wireless signals filtered by the bandpass filter assembled in the wireless transceiver is used to achieve parking detection. Namely, a channel attenuation measurement mechanism based on wireless transceivers is proposed in the paper, and the Receiving Signal Strength (RSS) unit assembled in wireless transceiver is used to collect the data required by AeIPD. Finally, the AeIPD performance is evaluated in a real testbed. The experimental results show that AeIPD achieves 99.17% accuracy in detecting 599 vehicles interfered by electromagnetic emission, which can provide a valuable reference for the development of wireless parking detector industry.

关键词： Wireless communication Band-pass filters Wireless sensor networks Energy consumption Accuracy Magnetometers Magnetic sensors

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Underground pipeline corridor trespassing monitoring system based on fiber power over fiber with signal

Underground pipeline corridor trespassing monitoring system ...

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OptoElectronics and Communications conference, OECC

作者： Shaolong Chang Zhiguo Zhang Yuemei Li Xueliang Gu Guangxin Li Tong Zhai State Key Lab of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350308396

ISBN: (纸本)9798350308402

We propose an novel underground pipeline corridor trespassing monitoring system based on power over fiber with signal. This system consists of a monitoring network containing up to 64 low-power processing unit mini-nodes, each of which transmits power and signals through a single optical fiber, the completion of a time of all the nodes of the data monitoring and pooling of processing time of two seconds.

关键词： Optical fibers Optical fiber sensors Surveillance Pipelines Optical fiber networks sensor systems sensors Security Reliability Monitoring

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