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performance Assessment of Routing Protocols in an internet of Things by communicating in a Wireless Sensor networks 6

Performance Assessment of Routing Protocols in an Internet o...

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6th International conference on Intelligent Computing and control systems, ICICCS 2022

作者： Jaganathan, D. Archana, T. Saranya, S. Srinivasan, Sathiyandrakumar Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology India Saveetha Engineering College India Panimalar Institute of Technology India Kalasalingam Academy of Research and Education India

ISBN: (纸本)9781665410359

Protocols have an important role on the overall performance of Wireless Sensor networks, including throughput and power consumption, postponement and transmission rate. This research study has a portion of suggested computations that focus on network performance concerns in the proposed sensor network. The directing convention monitors the energy and draws out the steadiness time of organization as well as organization life time. As a result of this convention, sensor nodes will no longer establish pairs with one another and share information about what is being detected. Additionally, the Cluster Head selection technique has been improved and CHs are selected based on the remaining power of the sensor nodes. The reenactment has been done on MATLAB for assessing the exhibition of these conventions on various boundaries thinking about two situations. In all scenarios, the suggested convention outperforms alternative steering conventions, according to the findings of the simulations. © 2022 IEEE.

关键词： Sensor nodes

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10 Gigabit industrial thermal data acquisition and storage solution based on software-defined network 7

10 Gigabit industrial thermal data acquisition and storage s...

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7th IEEE International conference on Data Science in Cyberspace, DSC 2022

作者： Zhang, Ge Zhang, Zheyu Sun, Jun Wang, Zun Wang, Rui Wang, Shirui Xie, Chengyun China Industrial Control Systems Cyber Emergency Response Team Beijing China

ISBN: (数字)9781665474801

ISBN: (纸本)9781665474801

With the wide application of internet technology in the industrial control field, industrial control networks are getting larger and larger, and the industrial data generated by industrial control systems are increasing dramatically, and the performance requirements of the acquisition and storage systems are getting higher and higher. The collection and analysis of industrial equipment work logs and industrial timing data can realize comprehensive management and continuous monitoring of industrial control system work status, as well as intrusion detection and energy efficiency analysis in terms of traffic and data. In the face of increasingly large realtime industrial data, existing log collection systems and timing data gateways, such as packet loss and other phenomena [1], can not be more complete preservation of industrial control network thermal data. The emergence of software-defined networking provides a new solution to realize massive thermal data collection in industrial control networks. This paper proposes a 10-gigabit industrial thermal data acquisition and storage scheme based on software-defined networking, which uses software-defined networking technology to solve the problem of insufficient performance of existing gateways. © 2022 IEEE.

关键词： Digital storage

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Adaptive-Cost Shortest Path Based Heuristic for Space Division Multiplexing networks 5

Adaptive-Cost Shortest Path Based Heuristic for Space Divisi...

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5th IEEE International conference on Advanced systems and Emergent Technologies (IC_ASET)

作者： Mrad, Mehdi Suryahatmaja, Umar S. Elsayed, Abdelrahman Ossama Gharbi, Anis Esmail, Maged Abdullah King Saud Univ Coll Engn Dept Ind Engn Riyadh Saudi Arabia Prince Sultan Univ Coll Engn Communicat & Networks Engn Dept Riyadh Saudi Arabia

ISBN: (纸本)9781665427357

The total internet traffic is rapidly rising as a result of the growing popularity of various network services. One of the network types is mesh network where space division multiplexing (SDM) has become an interesting approach to deal with the upcoming capacity crisis issue. SDM's key concept is to take advantage of the spatial dimension to significantly increase transmission system power. In this paper, we present an adaptive cost shortest path based heuristic for flexgrid SDM optical networks. The preliminary experimental results show a promising performance in terms of minimizing the highest used number of slices over all the links/cores of the network.

关键词： heuristics internet traffic routing slice assignment SDM networks

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Using IoT Technology for the Skilled Crafts 24th

Using IoT Technology for the Skilled Crafts

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24th International conference on Enterprise Information systems (ICEIS)

作者： Pomp, Andre Burgdorf, Andreas Paulus, Alexander Meisen, Tobias Univ Wuppertal Chair Technol & Management Digital Transformat Wuppertal Germany

ISBN: (纸本)9783031393853;9783031393860

The internet of Things (IoT) is a rapidly growing technology that connects everyday objects to the internet, allowing them to send and receive data. This technology has the potential to revolutionize the way that craftsmen work, by providing them with access to real-time information and automation capabilities. For instance, devices that are equipped with sensors can transmit data about their usage and performance, providing craftsmen with valuable insights into their work. Moreover, IoT can also enable craftsmen to remotely monitor and control their tools and machines, increasing efficiency and productivity. In addition, IoT can help craftspeople to better manage their inventory and supply chain. By attaching sensors to raw materials and finished products, craftspeople can track the movement and status of their goods in real-time, improving the accuracy of their forecasts and reducing the risk of stock-outs. Thus, adopting the IoT technology has the potential to greatly enhance the capabilities of the skilled crafts, allowing them to work more efficiently and effectively, and to better serve the needs of their customers. The aim of the IoT4H project is to make IoT technology usable for craftsmen. For that, we develop a platform that helps craft businesses in exploring and implementing IoT solutions. The platform will help craftsmen to identify suitable IoT use cases and business models for their trades, provide an overview of required components, and allow them to set up use cases without technical expertise. It will also collect and manage data and allow craft businesses to share new use cases with other platform users. So far, the project has already identified many different IoT use cases for the skilled crafts sector, which are presented in this paper. In addition, initial platform architecture considerations have also been made based on these use cases and are presented in this paper. Overall, the results are intended to assist craft businesses from a wide ran

关键词： internet of things Semantic systems Engineering Usability Skilled crafts

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Joint Cooperation Clustering and Downlink Power control for Cell-Free Massive MIMO with Deep Reinforcement Learning 23

Joint Cooperation Clustering and Downlink Power Control for ...

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23rd IEEE International conference on Communication Technology, ICCT 2023

作者： Du, Mingjun Sun, Xinghua Zhang, Yue Wang, Junyuan Liu, Pei School of Electronics and Communication Engineering Sun Yat-sen University Shenzhen518107 China Shantou University Department of Electronic Information Engineering Shantou515063 China College of Electronic and Information Engineering Tongji University Shanghai201804 China School of Information Engineering Wuhan University of Technology Wuhan430070 China Nanjing University of Posts and Telecommunications Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing210003 China

ISBN: (纸本)9798350325959

Recently, numerous clustering and power control approaches have been developed to optimize network performance for cell-free massive multiple-input multiple-output (MIMO) systems. Notably, deep reinforcement learning (DRL) has emerged as a promising solution for network optimization. This paper investigates the joint cooperation clustering and downlink power control problem in cell-free massive MIMO, utilizing the deep deterministic policy gradient (DDPG) algorithm to maximize the long-term global average proportional fairness (PF). Our results demonstrate that the proposed DDPG method effectively outperforms the baselines in both clustering and power control. Furthermore, the proposed scheme exhibits robustness as it maintains superior performance even as the number of clusters varies. © 2023 IEEE.

关键词： cell-free massive MIMO clustering Deep reinforcement learning power control

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Learning Neural Contracting Dynamics: Extended Linearization and Global Guarantees 38

Learning Neural Contracting Dynamics: Extended Linearization...

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38th conference on Neural Information Processing systems, NeurIPS 2024

作者： Jaffe, Sean Davydov, Alexander Lapsekili, Deniz Singh, Ambuj K. Bullo, Francesco Center for Control Dynamical Systems and Computation University of California Santa Barbara United States Department of Computer Science University of California Santa Barbara United States

Global stability and robustness guarantees in learned dynamical systems are essential to ensure well-behavedness of the systems in the face of uncertainty. We present Extended Linearized Contracting Dynamics (ELCD), the first neural network-based dynamical system with global contractivity guarantees in arbitrary metrics. The key feature of ELCD is a parametrization of the extended linearization of the nonlinear vector field. In its most basic form, ELCD is guaranteed to be (i) globally exponentially stable, (ii) equilibrium contracting, and (iii) globally contracting with respect to some metric. To allow for contraction with respect to more general metrics in the data space, we train diffeomorphisms between the data space and a latent space and enforce contractivity in the latent space, which ensures global contractivity in the data space. We demonstrate the performance of ELCD on the high dimensional LASA, multi-link pendulum, and Rosenbrock datasets. © 2024 Neural information processing systems foundation. All rights reserved.

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Application of Deep Reinforcement Learning in Optimization of Traffic Signal control 23

Application of Deep Reinforcement Learning in Optimization o...

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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： Huang, Jian Wang, Wei Wang, Lirong Chen, Hui Deng, Qingshan Fan, Heying Yu, Yaohua School of Software and Internet of Things Engineering Jiangxi University of Finance and Economics Nanchang China School of Data Science Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9781665494571

Traffic signal control is the most basic and pivotal task in road traffic problems. The optimization of traffic signal control is for improving vehicle passing efficiency of each intersection in order to improve the traffic capacity of the whole road net. Currently, traditional traffic control methods are not able to satisfy the needs of continuously expanding traffic conditions. Thus, with ideal self-adapting and exploring abilities, deep reinforcement learning methods are widely applied in model traffic signal control. However, due to the problems of overestimation and instability during the learning process of Deep Q-learning (DQN), we came up with a model based on Double-Dueling-Deep Q network (3DQN). This model combines dueling network, double learning method and target network by natural DQN to relieve overestimation and improve data using efficiency. According to the experimental results, compared with traditional traffic control and DQN, the model of 3DQN has better outcomes in traffic signal control. © 2021 IEEE.

关键词： Learning systems Q-learning Smart cities Law enforcement Roads Traffic control Data models

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Research on Real-time Monitoring System of Multi-scenario Health Data Based on internet of Things 3

Research on Real-time Monitoring System of Multi-scenario He...

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3rd International conference on Digital Society and Intelligent systems, DSInS 2023

作者： Wang, Hongyang Huo, Jiuyuan Hu, Jun Wang, Tingjuan School of Electronic and Information Engineering Lanzhou Jiaotong University Lanzhou730070 China Gansu YiWangKeJi Network Technology Co. LTD Lanzhou730070 China

ISBN: (纸本)9798350331387

para>With the continuous development of society and people's growing concern for health, health monitoring technology has become an important part of our lives. For this reason, this study designs and implements a multi-scene real-time health data monitoring system based on the internet of Things. The system adopts innovative technologies such as smart bracelet, Bluetooth gateway and message queue telemetry transfer (MQTT) to collect and transmit health data in real time. In addition, users can adjust the parameters of the data collection terminal through the remote control function. Through the web interface, users are able to conveniently view their health data in real time. The experimental results show that the data collected by this monitoring system has high accuracy, multi-scene adaptability and real-time performance. Meanwhile, the system is easy to operate and understand, providing users with a convenient experience. This will bring important technological innovation and practical application value to the field of health monitoring, providing people with more convenient and efficient health monitoring services. Future research directions can include optimising system performance, expanding application scenarios, etc., which will help promote the further development of health monitoring technology and protect people's health. © 2023 IEEE.

关键词： internet of things

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Investigation of Sensor Probe performance on Soil Moisture Conditioning Monitoring System 7th

Investigation of Sensor Probe Performance on Soil Moisture C...

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7th International conference on Electrical, control, and Computer Engineering, InECCE 2023

作者： Faris, Nik Adham Osman, Muhammad Arif Sulaiman, Aisyah Illani Ramli, Roshahliza M. Mokhtar, Nadzirah Mohd Faculty of Electrical and Electronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah Pahang Pekan 26600 Malaysia Faculty of Civil Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah Pahang Gambang 26300 Malaysia

ISBN: (纸本)9789819738502

The internet of Things (IoT) is continuously advancing with improved internet technology, providing new opportunities for innovation such as in agriculture that can benefit from IoT devices. This paper focuses on investigating the performance of a sensor probe in an environmental conditioning monitoring system. The project aims to develop an IoT-based moisture sensor capable of collecting and storing data on the cloud using a cloud server. This study explores optimal conditions for accurate data collection and analyzes the sensor’s performance in different soil conditions, including dry, waterlogged, and normal. A wireless sensor network integrated with the moisture sensor is designed and implemented for real-time data collection through the IoT infrastructure. Then, the acquired information is stored on a cloud platform. With varying soil conditions, the sensor’s performance is thoroughly examined, providing insights into its accuracy and reliability. This knowledge is valuable for effective strategies in agriculture and water resource management, improving decision-making and resource allocation. The research contributes to the advancement of environmental conditioning monitoring systems, informing the development of enhanced sensor technologies, and promoting more efficient and sustainable agricultural practices, with the potential to enhance the growth of crops. By providing real-time and precise data on environmental conditions, the improved monitoring systems and sensor technologies enable farmers to optimize their crop management strategies, ensuring that crops receive the ideal conditions for growth. This heightened level of environmental control and management can lead to increased crop yields, improved crop quality, and overall enhanced agricultural productivity. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： internet of Things Sensor performance Soil moisture sensor

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Distributionally Robust Statistical Verification with Imprecise Neural networks 28

Distributionally Robust Statistical Verification with Imprec...

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28th International conference on Hybrid systems: Computation and control, HSCC 2025, held as part of the 18th Cyber-Physical systems and internet-of-Things Week, CPS-IoT Week 2025

作者： Dutta, Souradeep Caprio, Michele Lin, Vivian Cleaveland, Matthew Jang, Kuk Jin Ruchkin, Ivan Sokolsky, Oleg Lee, Insup University of British Columbia Canada University of Manchester United Kingdom University of Pennsylvania United States University of Florida United States

ISBN: (纸本)9798400715044

A particularly challenging problem in AI safety is providing guarantees on the behavior of high-dimensional autonomous systems. Verification approaches centered around reachability analysis fail to scale, and purely statistical approaches are constrained by the distributional assumptions about the sampling process. Instead, we pose a distributionally robust version of the statistical verification problem for black-box systems, where our performance guarantees hold over a large family of distributions. This paper proposes a novel approach based on uncertainty quantification using concepts from imprecise probabilities. A central piece of our approach is an ensemble technique called Imprecise Neural networks, which provides the uncertainty quantification. Additionally, we solve the allied problem of exploring the input set using active learning. The active learning uses an exhaustive neural-network verification tool Sherlock to collect samples. An evaluation on multiple physical simulators in the openAI gym Mujoco environments with reinforcement-learned controllers demonstrates that our approach can provide useful and scalable guarantees for high-dimensional systems. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： imprecise probabilities neural network verification reinforcement learning Statistical verification

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