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7th International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Bin Karim, Md. Asif Rahman, Md. Mukidur Shahriar, Saquib Islam, Md. Manirul Department of Mechatronics Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350341485

This research study explores the wireless sensor networking, where sensor data is transmitted to a local database via a nearby server. The primary focus of this research study is real-time environment monitoring systems and wireless control and actuation systems. This research study incorporates the DHT 11 (Temperature and Humidity) and MQ-6 (LPG Gas) sensors. Selecting an appropriate controller is crucial to attaining desired system attributes like high-speed precision and meeting requirements for precise motion control at high speeds. That is why the ESP-8266-based NodeMCU is adopted for this ***, both feedback and feedforward closed-loop control mechanisms are implemented. As a result, the system can either control the actuator itself or by user demand. Leveraging sensor-derived data, the controller can automatically trigger the actuation system precisely within estimated time. Further, this study also depicts a clear differentiation between conventional methods and the proposed novel approach. Moreover, an IP (internet Protocol) based monitoring system allows to monitor the sensor-derived data instantly, even without internet connectivity. Notably, the entire system operates on solar power, ensuring uninterrupted functionality even during power outages. © 2023 IEEE.

关键词： internet protocols

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A Secure Dynamic Edge Resource Federation Architecture for Cross-Domain IoT systems 31

A Secure Dynamic Edge Resource Federation Architecture for C...

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31st International conference on Computer Communications and networks (ICCCN)

作者： Xu, Ronghua Chen, Yu Li, Xiaohua Blasch, Erik Binghamton Univ Dept Elect & Comp Engn Binghamton NY 13902 USA US Air Force Res Lab Rome NY Italy

ISBN: (数字)9781665497268

ISBN: (纸本)9781665497268

The fast integration of 5G communication, Artificial Intelligence (AI), and internet-of-Things (IoT) technologies is envisioned to enable Next Generation networks (NGNs) for diverse smart services and user-defined applications for Smart Cities. However, it is still challenging to build a scalable and efficient infrastructure that satisfies the various performance, security, and management demands by heterogeneous IoT applications across multiple administrative domains. This paper presents a dynamic edge resource federation architecture, which integrates the concept of network slicing (NS) and blockchain to improve scalability, dynamicity, and security for multi-domain IoT applications. A NS-enabled dynamic edge resource federation framework adopts intelligent mechanisms to support efficient multi-domain service coordination that satisfies diverse quality of Service (QoS) and security requirements. We propose a Hierarchical Integrated Federated Ledger (HIFL), which aims to guarantee decentralized security and privacy-preserving properties in multi-domain resource orchestration and service re-adjustment. As a secure-by-design solution, HIFL is promising to support efficient, trust and secured end-to-end IoT services. A preliminary proof-of-concept prototype has been implemented for comparing intra- and inter-domain performance expectations.

关键词： Next-Generation networks (NGNs) Smart Cities internet of Things (IoT) Edge network network Slicing Blockchain

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Reinforcement Learning-Based Secure Video Transmission For IOV systems

Reinforcement Learning-Based Secure Video Transmission For I...

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IEEE International conference on Image Processing

作者： Lixin Liu Zhibo Liu Xiaozhen Lu Yanling Bu Bin Han Liang Xiao College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China School of Communications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China Department of Informatics and Communication Engineering Xiamen University Xiamen China

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

The rapid growth in the number of vehicles and types of services such as video transmission improves the quality-of-service (QoS) requirements and increases the difficulty in resisting eavesdropping attacks on the internet of Vehicles (IoV). Existing video transmission schemes that either ignore the impact of eavesdropping attacks or have the full knowledge of the attack model have performance degradation in highly dynamic IoV systems. In this paper, we propose a reinforcement learning-based secure video transmission scheme for IoV systems, which jointly optimizes the access control policy for each vehicle (i.e., the selection of access nodes such as the base stations or unmanned aerial vehicles) and the corresponding transmit power level against active eavesdropping. This scheme uses the QoS and eavesdropping rate as the criteria to evaluate the long-term risk of each state-action pair, which is estimated by a designed deep Q-network to avoid the risky access control policies that cause severe data leakage or video transmission failure. Simulation results show that our scheme reduces the energy consumption, transmission latency, and eavesdropping rate compared with the benchmark.

关键词： Access control Simulation Image processing quality of service performance gain Autonomous aerial vehicles internet

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Implementation and Evaluation of networked Model Predictive control System on Universal Robot

Implementation and Evaluation of Networked Model Predictive ...

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International conference on Robotics and Automation Engineering (ICRAE)

作者： Mahsa Noroozi Kai Wang Institute of Communications Technology Leibniz Universität Hannover Hanover Germany

networked control systems are closed-loop feedback control systems containing system components that may be distributed geographically in different locations and interconnected via a communication network such as the internet. The quality of network communication is a crucial factor that significantly affects the performance of remote control. This is due to the fact that network uncertainties can occur in the transmission of packets in the forward and backward channels of the system. The two most significant among these uncertainties are network time delay and packet loss. To overcome these challenges, the networked predictive control system has been proposed to provide improved performance and robustness using predictive controllers and compensation strategies. In particular, the model predictive control method is well-suited as an advanced approach compared to conventional methods. In this paper, a networked model predictive control system consisting of a model predictive control method and compensation strategies is implemented to control and stabilize a robot arm as a physical system. In particular, this work aims to analyze the performance of the system under the influence of network time delay and packet loss. Using appropriate performance and robustness metrics, an in-depth investigation of the impacts of these network uncertainties is performed. Furthermore, the forward and backward channels of the network are examined in detail in this study.

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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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Emotion-oriented recommender system for personalized control of indoor environmental quality

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BUILDING AND ENVIRONMENT 2024年 254卷

作者： Kim, Hakpyeong Hong, Taehoon Yonsei Univ Dept Architecture & Architectural Engn Seoul South Korea Yonsei Univ 50 Yonsei Ro Seoul 03722 South Korea

Indoor Environmental quality (IEQ) significantly influences health, cognitive abilities, and even moods. With the rise of smart homes and the internet of Things (IoT), the need for personalized IEQ control has become paramount. Traditional systems, primarily focusing on explicit feedback (i.e., preference), often neglect the role of emotions in shaping this feedback. Therefore, this study presents the Emotion-oriented Recommender System for personalized control of IEQ (ERS-IEQ), based on the R -E -C -S ontology, focusing on (i) Recognizing user's continuous emotional states, (ii) Estimating emotional similarity between users, (iii) Collecting user's feedback on IEQ conditions, and (iv) Systemizing an emotion-oriented recommender system using a graph neural network. In order to confirm the predictive performance of the ERS-IEQ and verify its higher level of excellence compared to traditional recommender systems, a private dataset composed of IEQ conditions, users' explicit feedback, and users' emotional states was built via a human participant experiment using a climate chamber. As a result, the ERS-IEQ significantly enhances the recommender system's predictive performance, particularly in thermal preferences. The number of linguistic terms of emotional similarity has a profound effect on the system's predictions, with four terms proving most effective. In the near future, ERS-IEQ will play a role as a personal assistant in smart home automation, offering emotion-based recommendations. It addresses key challenges in traditional recommender systems, such as the cold start problem and rating sparsity, and ensures personalized adjustments to IEQ conditions in both private and public spaces.

关键词： Indoor environmental quality Affective computing Fuzzy logic Recommender system Graph attention network

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Sensor-Based Cattle Behavior Classification Using Deep Learning Approaches

Sensor-Based Cattle Behavior Classification Using Deep Learn...

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2023 International Technical conference on Circuits/systems, Computers, and Communications, ITC-CSCC 2023

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Tancharoen, Datchakorn Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Panyapiwat Institute of Management Faculty of Engineering and Technology Nonthaburi Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350326413

The usage of precision livestock has grown due to the need for higher efficiency and productivity in response to the high demand for food. To ensure sustainable development and quality control of the inputs required by the industry, it is essential to monitor and classify the behavior of cattle. Sensor-based monitoring systems provide accurate information by capturing raw data and identifying behavior through machine learning and deep learning algorithms. This approach has allowed farmers to better understand the individual needs of their animals. This study presents a deep residual neural network for the classification of cattle behavior. The performance of the ResNeXt model was evaluated using a public real-world dataset collected from sensors attached to the neck of six different Japanese black beef cows. The experimental results showed that the presented ResNeXt model achieved the highest average accuracy of 94.96% and the highest average F1-score of 93.66%. Compared to other baseline deep learning models and the current state-of-the-art model for cattle behavior classification, the presented model outperformed them and achieved better performance. © 2023 IEEE.

关键词： quality control

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A large-scale holistic measurement of crowdsourced edge cloud platform

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WORLD WIDE WEB-internet AND WEB INFORMATION systems 2023年第5期26卷 3561-3584页

作者： Feng, Yicheng Shen, Shihao Xu, Mengwei Zhang, Cheng Wang, Xin Wang, Xiaofei Wang, Wenyu Leung, Victor C. M. Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China Tianjin Univ Finance & Econ Inst Technol Tianjin Peoples R China Paiou Cloud Comp Shanghai Co Ltd Shanghai Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Univ British Columbia Dept Elect & Comp Engn Vancouver BC Canada

Edge clouds have become a de-facto paradigm to deliver low and stable networks to delay-critical applications such as Web services and AR/VR. A unique form of edge clouds is those crowdsourced from third parties, e.g., idle PCs or workstations. Such crowdsourced edge platforms can better sink computations closer to users, reduce the purchase cost, and eliminates the carbon generated during manufacturing. Yet, they also face the challenge of out-of-control hardware, e.g., a server dropping in/out anytime. In this paper, we perform the first-of-its-kind measurement of quality of Service (QoS) for a large-scale crowdsourced edge platform, which covers over 10,000 edge servers, 100,000 users and 10,000,000 user requests. The measurement takes a holistic QoS view: First, we look at how much hardware resources are provided by edge servers, how much time they are available for service deployment, how geographic distance affects network performance, and what are the major abnormal behaviors. Second, we analyze the factors affecting service stability and quantify the resource utilization pattern of containerized services hosted on those edge servers. Third, we investigate the spatial and temporal features of user requests handled by the platform. Many useful and somehow surprising findings are obtained through the above measurements. We also derive insightful implications that could help edge platforms and edge applications to better deliver their services to users.

关键词： Edge computing performance analysis network measurement

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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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RL-based Transmission Power control Algorithm for Interference Minimization in UAV Swarms 3

RL-based Transmission Power Control Algorithm for Interferen...

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3rd International conference on Computer Science, Electronic Information Engineering and Intelligent control Technology, CEI 2023

作者： Li, Jun School of Computer and Information Anhui Polytechnic University Wuhu China

ISBN: (纸本)9798350372052

Advancements in embedded system technology and wireless communication have facilitated the extensive use of micro unmanned aerial vehicles (UAVs) in civil and public service applications. Given their high survivability, broad coverage, efficient task execution, and significant autonomy, micro UAVs are assembled into a networked aerial robot system, termed as a UAV swarm, to undertake information gathering and control tasks. The pronounced mobility of these systems frequently leads to variations in the distance between the UAV node and its adjacent nodes, engendering a highly dynamic topology, altering link bandwidth, and interference. These phenomena can potentially degrade the performance and reliability of communication within the UAV swarm. Acknowledging that dependable communication is pivotal for UAV swarm control and coordination, this paper introduces an RL-based power control algorithm to mitigate interference among UAV nodes in a UAV swarm. Our approach dynamically modulates the transmission power level to maintain a high-quality wireless link within the UAV swarm. Simulation outcomes demonstrate that our method can enhance the network performance of the UAV swarm under highly dynamic topologies. © 2023 IEEE.

关键词： Reinforcement learning

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