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检索条件"任意字段=IEEE International Conference on Distributed Computing in Sensor Systems"

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Backup Placement in WSNs in the Network Management distributed Setting 41

Backup Placement in WSNs in the Network Management Distribut...

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ieee 41st international conference on distributed computing systems (ICDCS)

作者： Oren, Gal Barenboim, Leonid Technion Israel Inst Technol Dept Comp Sci Haifa Israel Nucl Res Ctr Negev Sci Comp Ctr Beer Sheva Israel Open Univ Israel Dept Math & Comp Sci Raanana Israel

ISBN: (纸本)9781665449328

sensor nodes are inherently a cheap piece of hardware - due to the common need to use many of them over a large area - and usually contain a small amount of RAM and flash memory, which are insufficient in case of high degree of data sampling. An overloaded sensor can harm the data integrity, or even completely reject incoming messages. The problem gets even worse when data should be received from many nodes, as missing data becomes a more common phenomenon as deployed WSNs grow in scale. As a solution, we consider the Backup Placement problem in networks in the network management distributed setting: Given a network graph G = (V, E), the goal of each vertex v is an element of V is selecting a neighbor, such that the maximum number of vertices in V that select the same vertex is minimized. In cases of an overflow, our distributed Adaptive Clustering algorithm (D-ACR) reconfigures the network, by adaptively and hierarchically re-clustering parts of it, based on the rate of incoming data packages in order to minimize the energy-consumption, and prevent premature death of nodes. In the network management setting of a grid, we proved that a backup placement of load at most O(1) can be maintained within O(log(2) n) update time, and an O(n log(2) n) energy consumption using the constructed ACR tree.

关键词： Backup Placement WSNs Network Management distributed Setting

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Energy-Efficient computing Offloading Policy for MEC-assisted Power sensor Network: A D3QN Approach

Energy-Efficient Computing Offloading Policy for MEC-assiste...

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2023 international conference on Cyber-Physical Social Intelligence, ICCSI 2023

作者： Guan, Ti Yao, Yushun Yuan, Chao Liu, Fuhao Liu, Yirong Gu, Rentao State Grid Shandong Electric Power Company Jinan China Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing China State Grid Laiwu Electric Power Supply Company Jinan China State Grid Binzhou Electric Power Supply Company Binzhou China

ISBN: (纸本)9798350312492

With the deep and close integration of massive renewable and controllable terminals into the next generation smart power grid, the amount of data and various applications with different quality requirements will increase greatly, which will be tough challenges for the power sensor network to provide real-time data collecting and processing, global monitoring, and quick response, etc. Meanwhile, due to the limitation in energy supplement and computing capacity, it is a second issue for the sensor network how to work in an energy-efficient way. In this paper, our work sets out to study a reinforcement learning (RL) based computing offloading policy in the mobile edge computing (MEC) assisted power sensor network. By jointly optimizing task scheduling and resource allocation, we propose a new scheme to optimize data latency and energy efficiency. The optimization problem is modeled as a Markov decision process (MDP), and Dueling Double Deep Q Network (D3QN) algorithm is employed to solve this MDP problem. Simulation results demonstrate the effectiveness of the proposed algorithm and its outperformance in reduction of data latency and energy consumption as well as convergence and stability, compared with other benchmarks. © 2023 ieee.

关键词： Markov processes

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mpiQulacs: A Scalable distributed Quantum Computer Simulator for ARM-based Clusters 4

mpiQulacs: A Scalable Distributed Quantum Computer Simulator...

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4th ieee international conference on Quantum computing and Engineering, QCE 2023

作者： Tabuchi, Akihiro Imamura, Satoshi Yamazaki, Masafumi Honda, Takumi Kasagi, Akihiko Nakao, Hiroshi Fukumoto, Naoto Nakashima, Kohta Computing Laboratory Fujitsu Limited Kawasaki Japan

ISBN: (纸本)9798350343236

Quantum computer simulators running on classical computers are essential for developing real quantum computers and emerging quantum applications. In particular, state vector simulators, which store a full state vector in memory and update it in every quantum operation, can be used to simulate an arbitrary form of quantum circuits, debug quantum applications, and validate future quantum computers. Since the time and space complexity grows exponentially with the number of qubits and easily exceeds the capability of a single machine, various distributed state vector simulators are developed. However, these are tuned for common CPUs and GPUs, not for ARM CPUs. Therefore, we develop a distributed state vector simulator, mpiQulacs, that is optimized for large-scale simulation on A64FX-based cluster systems. A64FX is a 48-core ARM-based CPU having high-bandwidth memory (HBM2) and also used in one of the world's top supercomputers, Fugaku. As computational optimization, we vectorize qubit operations with Scalable Vector Extension (SVE) to fully utilize its HBM2. To reduce the amount of inter-node communication for scalable simulation, mpiQulacs provides a fused-swap gate that collectively performs multiple swap operations as one operation and a quantum circuit optimizer that automatically adds fused-swap gates to a circuit. We evaluate weak and strong scaling of mpiQulacs with up to 39 qubits on our 512-node A64FX-based cluster system. The result shows that mpiQulacs achieves high-scalability on hundreds of nodes in benchmark and practical quantum circuits, and its performance is comparable with the existing well-known distributed state vector simulators. © 2023 ieee.

关键词： Benchmarking

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Development of Verification Model for Intelligence of Multiagent systems 12

Development of Verification Model for Intelligence of Multia...

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12th ieee international conference on Intelligent Data Acquisition and Advanced computing systems: Technology and Applications, IDAACS 2023

作者： Martynyuk, Oleksandr Drozd, Oleksandr Sachenko, Anatoliy Martynyuk, Dmitry Mileiko, Igor Kopytchuk, Igor Hladiy, Grygoriy National University 'Odesskaya Polytechnica' Odessa650044 Ukraine West Ukrainian National University Ternopil46009 Ukraine Kazimierz Pulaski University of Technology and Humanities Radom26-600 Poland Join Venture 'Nippon Auto' Odessa65000 Ukraine

ISBN: (纸本)9798350358056

The development of distributed information systems essentially relies on the complex application of Web services technologies, cloud, 'green', GRID, multi-agent and many others. In many of these technologies, intellectualization is increasingly manifested, which is clearly inherent, for example, in semantic and intelligent Web services, multi-agent systems. Intelligence, often defined as a set of properties of knowledge and mechanisms for their formation, requires its own ways of formalization and implementation, which is also reflected in the problems of analysis and synthesis of intelligent systems, including the problems of their identification and verification. This paper consider the fuzzy and component development of a model for verifying the intelligence of multi-agent systems based on the previously proposed model for verifying the intelligence of multi-agent systems, which, in turn, is based on behavioral experiments for extended Petri nets. The features of the proposed model for verifying intelligence are the presentation of fuzzy production models of all components of intelligence for parallel, associative-evolutionary search for known identifying primitives and recognition of fragments of system behavior based on them, the construction of new identifying primitives as part of a three-level model of new knowledge, as well as a fuzzy inference mechanism, as the basis for obtaining new knowledge, and for identifying and verifying the behavioral properties of the system. The intelligence verification model makes it possible to reduce the computational complexity of the behavioral verification of multi-agent systems and increase its flexibility. © 2023 ieee.

关键词： associative-evolutionary search behavior identifying behavior verification distributed information system intelligence verification model multiagent system

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A Review on SPR Based PCF Refractometric sensor

A Review on SPR Based PCF Refractometric Sensor

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2024 international conference on Integrated Circuits, Communication, and computing systems, ICIC3S 2024

作者： Tyagi, Ayush Mishra, Anurag Tiwari, Avisha Chaudhary, Anubhav Singh, Shivam Bhatia, Kamal ABES Engineering college Electronics and communication Engineering Ghaziabad India ABES Engineering College Electronics and communication Engineering Noida Ghaziabad India

ISBN: (纸本)9798350364088

Many research institutions expressed interest in photonic crystal fibers (PCFs) due to their potential features. Samples of liquids or gases can be placed into PCF's air holes. This makes it possible for constrained light and sensing samples to interact in a well-controlled manner, opening up new possibilities for sensing applications. Depending on the parameter being monitored, physical and biological detectors are the two categories into which PCF applications in sensing domains fall. Physical sensors measure factors such as refractive index (RI), displacement, pressure, temperature, and electric field. In contrast, chemical and biological substances can be detected using biochemical sensors. An essential part of biological sensors is the evaluation of the chemical RI. Chemical sensors are sometimes called biochemical sensors because of their near resemblance to biosensors. Overall, this article delves into the capabilities of surface plasmon resonance (SPR) based PCF sensors to identify biological and chemical components. It also explores numerous strategies aimed at augmenting their sensing efficacy. © 2024 ieee.

关键词： Surface plasmon resonance

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A Robust Framework for Fixing The Vulnerability of Compressed distributed Learning 26

A Robust Framework for Fixing The Vulnerability of Compresse...

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26th international conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Chen, Yueyao Wang, Beilun Zhang, Yan Kuang, Jingyu Southeast University School of Computer Science and Engineering Nanjing211102 China

ISBN: (纸本)9798350331684

Nowadays, as a prevailing paradigm for large-scale machine learning, distributed learning has been faced with two challenges, communication bottleneck and limited robustness. For the communication challenge, compression is widely-used as a solution. For the robustness challenge, some robust defence methods have been proposed. However, previous works that simultaneously consider these two challenges are limited. Through an experiment on the w8a dataset, we found that compressed distributed learning with rand-K is vulnerable to poisoning attacks. Therefore, in this paper, we propose a robust compressed distributed framework for distributed learning settings. Experimental evaluations on a9a and w8a datasets have shown the effectiveness of our proposed framework, which markedly decreases the average optimality gap from 1.47E -2 and 2.15E -2 to 3.98E -4 and 4.33E -4 respectively. © 2023 ieee.

关键词： Machine learning

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Analysis of Military Security Surveillance Applications Using Wireless sensor Networks 1

Analysis of Military Security Surveillance Applications Usin...

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2023 1st DMIHER international conference on Artificial Intelligence in Education and Industry 4.0, IDICAIEI 2023

作者： Singla, Chaitanya Gill, Rupali Srivastava, Durgesh Hooda, Susheela Singh, Janpreet Institute of Engineering and Technology Chitkara University Punjab India School of Computer Science and Engineering Lovely Professional University Punjab India

ISBN: (纸本)9798350338423

Wireless sensor Networks are a type of device that converts any of the physical quantity to an understandable, and observable quantity. sensors are lightweight and distributed in large numbers to monitor the type of system and environment around them. Wireless sensor Networks are deployed in many areas like agriculture, military security surveillance, animal husbandry and tracking, smart surroundings and buildings, health sciences of patient monitoring and care, air pressure temperature around us, noise levels, and many others. This paper tells various executions and uses of WSNs with military surveillance and security systems. © 2023 ieee.

关键词： Wireless sensor networks

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Ingenious Waste Management: Implementing Machine Learning in Waste Segregation 6

Ingenious Waste Management: Implementing Machine Learning in...

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6th ieee international conference on Emerging Smart computing and Informatics, ESCI 2024

作者： Asha, V. Nithya, B. Govindaraj, M. Brunda, T.M. Hiremath, Ashwini Jayaprakash, B. New Horizon College of Engineering Bengaluru India

ISBN: (纸本)9798350306613

Waste management is a pressing global issue, and the need for efficient waste separation processes is becoming increasingly important. Incorporating Machine Learning techniques with waste separation has yielded promising results and revolutionized the waste separation process. However, this research paper highlights the gaps that exist in the understanding and efficacy of machine learning for waste separation. This study provides an overview of state-of-the-art techniques for waste separation using machine learning, including the use of computer vision, sensor technology, and neural networks. This paper portrays case studies with empirical data from various waste management establishments to demonstrate the potential advantages of machine learning for recycling and waste classifying. Although progress has been made, this article highlights some significant gaps in our knowledge. These gaps include the need for standardized datasets, optimization of algorithms for real-world scenarios, the impact of technology on the environment, and the socio-economic aspects of integrating automation into waste management. The essential and most important tasks of intelligent mechanisms in the digital age is waste separation. Segregation of waste is necessary to apply proper disposal and waste management strategies. The existing systems use drones to identify waste by using deep learning, image processing, GPS, and GSM approaches, locating and notifying the authorities about them. The improvement made is in the analysis and utilize multi-object detection and picture classification to accomplish trash segregation. Waste management may be effectively accomplished with higher accuracy through Machine Learning practices which therefore considerably lowers labor expenses. © 2024 ieee.

关键词： Image Detection Machine Learning Prediction sensor Waste Segregation

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TADA: A Toolkit for Approximate distributed Agreement 23rd

TADA: A Toolkit for Approximate Distributed Agreement

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23rd IFIP WG 6.1 international conference on distributed Applications and Interoperable systems (DAIS) Held as Part of the 18th international Federated conference on distributed computing Techniques (DisCoTec)

作者： da Conceicao, Eduardo Lourenco Alonso, Ana Nunes Oliveira, Rui Carlos Pereira, Jose Orlando Univ Minho Braga Portugal INESC TEC Braga Portugal

ISBN: (纸本)9783031352591;9783031352607

Approximate agreement has long been relegated to the sidelines compared to exact consensus, with its most notable application being clock synchronisation. Other proposed applications stemming from control theory target multi-agent consensus, namely for sensor stabilisation, coordination in robotics, and trust estimation. Several proposals for approximate agreement follow the Mean Subsequence Reduce approach, simply applying different functions at each phase. However, taking clock synchronisation as an example, applications do not fit neatly into the MSR model: Instead they require adapting the algorithms' internals. Our contribution is two-fold. First, we identify additional configuration points, establishing a more general template of MSR approximate agreement algorithms. We then show how this allows us to implement not only generic algorithms but also those tailored for specific purposes (clock synchronisation). Second, we propose a toolkit for making approximate agreement practical, providing classical implementations as well as allow these to be configured for specific purposes. We validate the implementation with classical algorithms and clock synchronisation.

关键词： approximate agreement distributed systems primitives practical toolkit clock synchronisation

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Implementation of Yolo-Based Object Detection and sensor Integration Model for Visually Challenged Person

Implementation of Yolo-Based Object Detection and Sensor Int...

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2024 international conference on Innovative computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Kamarunisha, M. Jayasri, K. Sharin, S. Priyadharshini, M. Anusuya, T. Malathi, R. Dhanalakshmi Srinivasan College of Arts and Science for Women Autonomous Department of Computer Applications Perambalur India

ISBN: (纸本)9798331543617

It is suggested that visually impaired individuals use an assistive device to receive automatic direction and navigation. The gadget may provide obstacle-free navigation and use real-time image processing to detect impediments. The goal of the strategy is to maximise information flow by completing all activities in real-time and gathering all the information regarding path impediments. The suggested navigation stick is an inexpensive item, obstacle, and obstacle identifier. For safety and navigation, the suggested assistance system for the blind makes use of cutting-edge technology such as tilt and ultrasonic sensors, as well as YOLO(YouOnly Look Once) object recognition. It updates its location via GPS, haptic feedback, and real-time object detection. The user interface is independent and accessible through non-visual means. The suggested work focuses on a multi-label method for detecting items in indoor and outdoor settings utilising vision technology. It improves accuracy and cuts down on recognition time by using classification and clustering techniques. © 2024 ieee.

关键词： Ultrasonic sensors

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