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Investigating the Advantages and Limitations of Math Modeling for Wireless sensor Networks 5th

Investigating the Advantages and Limitations of Math Modelin...

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5th international conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Zaidi, Taskeen Ansari, Tabish Sindhuri, Bonda Prema Alam, Intekhab Karnataka Bangalore India Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

this paper investigates the blessings and obstacles of math modeling for wireless sensor Networks (WSNs). WSNs may be immediately evaluated on a performance basis or analyzed to address protection and reliability troubles. However, mathematical models ignore the sensors’ heterogeneity and the networks’ distributed nature. Similarly, the mission is to validate the results to ensure actual performance. Other WSNs that need attention and mathematical modeling include their management algorithms, optimizations, MAC protocols, power management, and *** comparison, sure advantages exist in math modeling, understanding complex problems, coming across new solutions and situations, and the capacity to expect effects. this paper aims to review the advantages and obstacles of math modeling for WSNs and concludes by providing hints on how WSNs can gain from mathematical modeling. Math modeling holds the splendid capacity for wireless sensor Networks (WSNs) because it comprehensively clarifies complex actual-global troubles. Math modeling offers an established framework to conceptualize, examine, and endorse answers for diverse WSN deployments. Its relative predictability and simulation accuracy can assist in perceiving hidden insights and generate reliable outcomes. However, it has some critical boundaries. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： modeling sensor simulation

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Improving Data Integrity for Gray Hole Attack Detection by using a Hash Signature Algorithm in WSN 5

Improving Data Integrity for Gray Hole Attack Detection by u...

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5th international conference on Smart systems and Inventive Technology, ICSSIT 2023

作者： Radhika, S. Srikanth, M.S. Anand, K. Saravanan, K. Southry, S. Sree Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Reva University School Computer Science and Engineering Karnataka Bangalore India Rajalakshmi Engineering College Department of Computer Science and Engineering Rajalakshmi NagarThandalam Chennai India R.M.D Engineering College Department of Information Technology Kavaraipettai Chennai India Sona College of Technology Department of Electronics and Communication Engineering Tamil Nadu Salem India

ISBN: (纸本)9781665474672

Nowadays, sensor nodes are utilized in widespread distribution. thus, security issues are a significant apprehension in the Wireless sensor Networks (WSN). the gray hole attack is a particularly damaging kind of assault that, in addition to bringing down WSNs, brings down whole networks and interferes with the flow of data. A hash signature algorithm (IGDH) is proposed to solve these problems to improve data integrity for Gray hole attack detection. During the process of route discovery in the network, this strategy makes use of an algorithm that generates a hash signature to detect the Gray hole attack. the recipient node gets the message from any sensor and verifies the sensor node hash signature. During data transmission, the recipient node checks the principal communication count (PCC) and size of the data packet;if the packet size is equal Accept the message;otherwise, the recipient node decides some packets are dropped and isolates that node is a grayhole attacker. the simulation results explain the proposed method has better packet delivery and minimizes the packet loss and miss detection ratio in the WSN. © 2023 ieee.

关键词： sensor nodes

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A distributed Adaptive Algorithm for Non-Smooth Spatial Filtering Problems 48

A Distributed Adaptive Algorithm for Non-Smooth Spatial Filt...

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48th ieee international conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Hovine, Charles Bertrand, Alexander Leuven Belgium Signal Processing and Data Analytics Stadius Center for Dynamical Systems Leuven Belgium Leuven Belgium

ISBN: (纸本)9781728163277

computing the optimal solution to a spatial filtering problems in a Wireless sensor Network can incur large bandwidth and computational requirements if an approach relying on data centralization is used. the so-called distributed adaptive signal fusion (DASF) algorithm solves this problem by having the nodes collaboratively solve low-dimensional versions of the original optimization problem, relying solely on the exchange of compressed views of the sensor data between the nodes. However, the DASF algorithm has only been shown to converge for filtering problems that can be expressed as smooth optimization problems. In this paper, we explore an extension of the DASF algorithm to a family of non-smooth spatial filtering problems, allowing the addition of non-smooth regularizers to the optimization problem, which could for example be used to perform node selection, and eliminate nodes not contributing to the filter objective, therefore further reducing communication costs. We provide a convergence proof of the non-smooth DASF algorithm and validate its convergence via simulations in both a static and adaptive setting. © 2023 ieee.

关键词： Adaptive algorithms

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A Layer-Based Sparsification Method For distributed DNN Training 24

A Layer-Based Sparsification Method For Distributed DNN Trai...

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24th ieee international conference on High Performance computing and Communications, 8th ieee international conference on Data Science and systems, 20th ieee international conference on Smart City and 8th ieee international conference on Dependability in sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Hu, Yanqing Ye, Qing Zhang, Zhongyu Lv, Jiancheng Sichuan University Chengdu China

ISBN: (纸本)9798350319934

With the increasing size of Deep Neural Networks (DNNs) and datasets, DNN training will consume a lot of time. Various distributed strategies have been utilized to speed up the training process. Nevertheless, the frequent communication between different computational nodes dramatically limits the scale of the cluster. Currently, many gradient compression methods (e.g., Sparsification, Quantization, Low-Rank) have been proposed to optimize the transmission process. Since each layer needs to be processed and compressed respectively before transmission, a rash of time-consuming tensor operations is demanded. To alleviate this issue, we propose a layer-based sparsification method, which stems from the observation that the learning efficiency and convergence speed of different layers of DNN is different during the training process. Instead of compressing all gradients in an iteration, some layers are preferentially selected using a well-designed sliding window, which largely avoids unnecessary tensor operations. the sliding window is adjusted according to the dynamic characteristics of the parameter during the DNN training. Extensive experiments on one Text task and two Image Classification tasks are conducted to validate the efficiency of the proposed method in the same environment configuration, where the gradient compression adopts the layer-based sparsification and the other three comparative state-of-the-art methods. the total training time, communication time, and model accuracy are collected and analyzed. Sufficient experimental results demonstrate that both training and communication time can be greatly reduced, which is especially up to 60% in some cases. Additionally, despite slightly decreased within 1% model quality, the results indicate that the improvement in throughput can be as high as 50%. © 2022 ieee.

关键词： Deep neural networks

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24th international conference on Parallel and distributed computing, Applications and Technologies, PDCAT 2023

24th International Conference on Parallel and Distributed Co...

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24th international conference on Parallel and distributed computing, Applications and Technologies, PDCAT 2023

ISBN: (纸本)9789819982103

the proceedings contain 28 papers. the special focus in this conference is on Parallel and distributed computing, Applications and Technologies. the topics include: Insider Trading Detection Algorithm in Industrial Chain Based on Logistics Time Interval Characteristics;link Attributes Based Multi-service Routing for Software-Defined Satellite Networks;A Fuzzy Logical RAT Selection Scheme in SDN-Enabled 5G HetNets;SSR-MGTI: Self-attention Sequential Recommendation Algorithm Based on Movie Genre Time Interval;fine Time Granularity Allocation Optimization of Multiple Networks Industrial Chains in Task Processing systems;Ε-Maximum Critic Deep Deterministic Policy Gradient for Multi-agent Reinforcement Learning;effective Density-Based Concept Drift Detection for Evolving Data Streams;an End-to-End Multiple Hyper-parameters Prediction Method for distributed Constraint Optimization Problem;Formalization and Verification of the Zab Protocol Using CSP;dynamic Priority Coflow Scheduling in Optical Circuit Switched Networks;Deep Reinforcement Learning Based Multi-WiFi Offloading of UAV Traffic;Triple-Path RNN Network: A Time-and-Frequency Joint Domain Speech Separation Model;design of Query Based Gallery Selector and Mask-Aware Loss for Person Search;a Privacy-Preserving Blockchain Scheme for the Reliable Exchange of IoT Data;R-RPT-A Reliable Routing Protocol for Industrial Wireless sensor Networks;action Segmentation Based on Encoder-Decoder and Global Timing Information;Security Challenges and Lightweight Cryptography in IoT: Comparative Study and Testing Method for PRESENT-32bit Cipher;the Prediction Model of Water Level in Front of the Check Gate of the LSTM Neural Network Based on AIW-CLPSO;Using MPIs Non-Blocking Allreduce for Health Checks in Dynamic Simulations;parallelizable Loop Detection using Pre-trained Transformer Models for Code Understanding;list-Based Workflow Scheduling Utilizing Deep Reinforcement Learning.

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FNotify: A Low-Latency and Scalable Publish/Subscribe System using RDMA 24

FNotify: A Low-Latency and Scalable Publish/Subscribe System...

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作者： Xu, Yusen Shen, Sijie Han, Mingcong Chen, Rong Institute of Parallel and Distributed Systems Seiee Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798350319934

Publish/subscribe systems are widely used as messaging middleware in various scenarios, such as online business, financial trading, and social media. However, the network overhead intro-duced by the pub/sub system can severely impact application performance. Especially in the latency-sensitive applications of the data center, optimizing the latency of publishing or delivering messages of the pub/sub system is particularly important. RDMA network has gained popularity in relevant applications due to its low latency, high bandwidth and ability to bypass the CPU. Never-theless, it is still challenging to design and optimize the pub/sub system using RDMA. through an in-depth analysis of RDMA features, we design and implement FNotify, a high-performance RDMA-accelerated pub/sub system. FNotify optimizes latency and CPU load using in-memory message management and one-sided RDMA primitives for message collection. FNotify also proposes a topic-based partitioning scheme to achieve good scal-ability. We conducted a comprehensive evaluation and analysis on an 8-node RDMA cluster to verify the validity of our design by comparing the performance of FNotify with Apache Kafka (a state-of-the-art pub/sub system) in a typical pub/sub application scenario and various micro-benchmarks. Experimental results show that FNotify improves overall performance by 1.5× to 3.9× and reduces end-to-end latency by more than 20× compared to Apache Kafka. © 2022 ieee.

关键词： Middleware

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IoT-Sustainability Protection:Water Level MonitoringWith Micro Leak Detection and AutomaticWater Restoration 5

IoT-Sustainability Protection:Water Level MonitoringWith Mic...

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5th international conference on Smart sensors and Application, ICSSA 2024

作者： Zainuddin, Ahmad Anwar Bharudin, Mohamad Syafiq Asyraf Kamarudin, Saidatul Izyanie Hussin, Amir ‘Aatieff Amir Subramaniam, Krishnan Tamrin, Mohd. Izzuddin Mohd. Handayani, Dini Kamal, Nik Nor Muhammad Saifudin Nik Mohd Department of Computer Science Kulliyyah of ICT International Islamic University Malaysia Kuala Lumpur Malaysia Universiti Teknologi MARA Shah Alam Selangor Malaysia Department of Mechanical Engineering Mila University Negeri Sembilan Nilai Malaysia Department of Information Systems Kulliyyah of ICT International Islamic University Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9798350354294

Efficient water resource management stands as a linchpin for sustainable development initiatives worldwide. To this end, this work endeavours to pioneer a comprehensive water level monitoring system with a focus on leak detection and automatic water restoration. Tailored with specialized sensors with cutting-edge automation technology to monitor water levels against predefined thresholds, promptly triggering restoration processes when necessary and identifying leaks in real-time. Central idea of the design is the incorporation of a dedicated rain sensor module, bolstering the micro leak detection capabilities while an ultrasonic sensor ensures precise measurement of water levels in tanks. In the event of a detected leak, rain sensor promptly initiates a signal, process by the main board which also known as the "brain" of the system and triggers an automatic cessation of water input to prevent wastage. Moreover, the system ensures seamless data logging by uploading pertinent information to a dedicated IoT cloud database, facilitating comprehensive monitoring and analysis. this innovative solution underscores not only its viability but also its practical efficacy, showcasing its potential to optimize water utilization and conservation efforts. Specifically, it excels in detecting micro droplets indicative of leaks in the tank. this work aims to spearhead the development of an advanced water level monitoring system, leveraging state-of-the-art of Arduino hardware and a diverse array of sensors, including ultrasonic and rain sensors as well as presenting a holistic framework poised to revolutionize water management practices. © 2024 ieee.

关键词： Restoration

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Integrated Detection sensor of Metal and Magnet in Wireless Power Transfer System 5

Integrated Detection Sensor of Metal and Magnet in Wireless ...

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5th international conference on Electrical Engineering and Control Technologies, CEECT 2023

作者： Zhang, Jiabao Yu, Chunlai Liu, Yancheng Wang, Zhikai Zhu, Junkang Ding, Haolun Dalian Maritime University Maritime Engineering College Dalian China Hefei University of Technology Hefei China

ISBN: (纸本)9798350342253

this paper addresses the issue of reduced transmission efficiency in wireless charging systems due to foreign metal object and misalignment of coils. It proposes an integrated method for the detection of metals and magnets, which can also enhance the system's transmission efficiency. First, the theoretical analysis and finite element analysis verify that when a metal and a magnet approach the parallel resonant circuit, the equivalent resistance and inductance of the circuit change, leading to different variations in the quality factor. By detecting the changes in the resonant voltage of the parallel resonant circuit, a distinctive and integrated detection circuit can be designed. Experimental verification shows that the detection device can simultaneously detect magnets and metals, and the output state changes by different output ports. the design of the detection hardware reduces system losses, enhances the cost-effectiveness, and improves the system safety. © 2023 ieee.

关键词： Cost effectiveness

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Economic dispatch of multi-agent network systems based on distributed finite-step consensus algorithm 5

Economic dispatch of multi-agent network systems based on di...

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5th ieee international Electrical and Energy conference, CIEEC 2022

作者： Wu, Xiaopeng He, Ping Dong, Jie Zhengzhou University of Light Industry School of Electrical and Information Engineering China

ISBN: (数字)9781665411042

ISBN: (纸本)9781665411042

In this paper, the economic dispatch problem of multi-agent network systems is investigated based on distributed finite-step consensus iteration algorithm. In the proposed consensus strategy, the finite iteration steps can be decided based on the numbers of distinct nonzero eigenvalues of constructed Laplacian matrix, and different network topologies own the same results once given the same initial values. Besides, the power constraints of each generator node are considered to ensure the demand-supply balance of the whole multi-agents network systems. Finally, several case studies with different multi-agents network topologies are provided to verify the validity and correctness of the proposed iteration algorithm. © 2022 ieee.

关键词： Network topology

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distributed Reinforcement Learning for Real-Time Batteries Control Using Lagrangian Decomposition 5

Distributed Reinforcement Learning for Real-Time Batteries C...

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5th international conference on Smart Energy systems and Technologies (SEST)

作者： Stai, Eleni Stanojev, Ognjen di Prata, Riccardo de Nardis Hug, Gabriela Swiss Fed Inst Technol EEH Power Syst Lab Phys Str 3 CH-8092 Zurich Switzerland

ISBN: (数字)9781665405577

ISBN: (纸本)9781665405577

this paper presents an efficient distributed realtime control scheme for a set of batteries in power grids to follow a day-ahead dispatch plan. the problem of controlling the batteries is cast as a multistep optimization problem aiming to minimize the error in dispatch following while satisfying lookahead constraints of the individual batteries. To achieve a computationally efficient solution, Lagrangian decomposition is used to distribute the task into subproblems pertaining to each battery. Finally, each subproblem is reformulated as a Markov Decision Process and solved with the fitted Q-iteration. Communication among subproblems is achieved via the common Lagrange multiplier value. Simulation results demonstrate that this approach has a similar cost performance to the state-of-the-art model predictive control solution, while achieving better performance in terms of computation time.

关键词： active distribution grids battery energy storage real-time control distributed reinforcement learning

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