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CleanComm: A Hybrid ACO-WOA-MOA Model for Sustainable Communication

CleanComm: A Hybrid ACO-WOA-MOA Model for Sustainable Commun...

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international conference on ICT in Business Industry & Government (ICTBIG)

作者： Vaishali Aggarwal Kashvi Goel Ishika Gupta Prerna Ajmani School of Information Technology Vivekananda Institute of Professional Studies-Technical Campus Delhi India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Wireless sensor networks (WSNs) and their applications are on an increasing trend owing to the users’ demands with interest in environmental activities, intelligent cities, and medical assistance. However, because of the inefficiency and limitations of wireless sensor networks, it is still very difficult to optimize the network parameters and performance (energy consumption, area coverage and reliable information transfer). In this paper optimization algorithms including Ant Colony Optimization (ACO), Moth Flame Optimization (MFO) and Whale Optimization Algorithm (WOA) to enhance the effectiveness of wireless sensor systems are presented. In this approach, ACO captures and explores functional properties, MFO coordinates the optimization and exploration processes, and WOA concentrates on global exploration. Wide-ranging simulations demonstrate that the recommended approach is more effective than the existing approaches in enhancing energy efficiency, network lifetime and effectiveness of packet delivery. the findings in this paper indicate that the proposed hybrid algorithms support the various requirements of the WSN, thus promoting development of networks that are secure and dependable.

关键词： Wireless sensor networks Energy consumption Smart cities Green communications Power control Interference Routing Whale optimization algorithms Reliability Optimization

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Multi-Agent Path Finding using Dynamic distributed Deep Learning Model 4

Multi-Agent Path Finding using Dynamic Distributed Deep Lear...

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4th international conference on Communication, Information and computing Technology, ICCICT 2021

作者： Sinkar, Manas Izhan, Mohammad Nimkar, Sai Kurhade, Swapnali Mumbai India Mumbai India

ISBN: (纸本)9781665404303

Multi-Agent Pathfinding (MAPF) is the task of finding paths for more than one agent in a common environment such that each agent reaches its goal and does not collide. Multi-Agent Path Finding is a problem that is related to various applications ranging from robot movements, game simulations, self-driving vehicles, etc. Most of the existing solutions to these problems are providing paths to each agent which they shall follow. these paths are optimal but are calculated once for a certain fixed and known scenario. there might be collisions and time-based overlapping paths if another agent or an obstacle is dynamically added to the grid. thus, there is a need to have a system that deals with such situations while providing the most possible optimal path. Hence, we propose a solution to this problem by using a Deep Learning Model trained on a dataset made using an A* pathfinding algorithm that can take decisions at run time for each agent while making sure it does not collide with any obstacle or any other agent and handles all sort of dynamic variations in the environment at any point of time while not relying on precomputation. © 2021 ieee.

关键词： Multi agent systems

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Prototyping of Low-Cost Configurable Sparse Neural Processing Unit with Buffer and Mixed-Precision Reshapeable MAC Array

Prototyping of Low-Cost Configurable Sparse Neural Processin...

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international conference on Parallel and distributed systems (ICPADS)

作者： Binyi Wu Wolfgang Furtner Bernd Waschneck Christian Mayr Faculty of Electrical and Computer Engineering Technische Universit¨at Dresden Dresden Germany Division of Power and Sensor Systems Infineon Technologies Munich Germany Centre for Tactile Internet with Human-in-the-loop (CeTI) Technische Universität Dresden Dresden Germany

More recently, it has become possible to run deep learning algorithms on edge devices such as microcontrollers due to continuous improvements in neural network optimization algorithms such as quantization and neural architecture search. Nonetheless, most of the embedded hardware available today still falls short of the requirements of running deep neural networks. As a result, specialized processors have emerged to improve the inference efficiency of deep learning algorithms. However, most are not for edge applications that require efficient and low-cost hardware. therefore, we design and prototype a low-cost configurable sparse Neural Processing Unit (NPU). the NPU has a built-in buffer and a reshapable mixed-precision multiply-accumulator (MAC) array. the computing and memory resources of the NPU are parameterized, and different NPUs can be derived. Besides, users can also conFigure the NPU at runtime to fully utilize the resources. In our experiments, the 200MHz NPU with only 32 MACs is more than 32 times faster than the 400MHzSTM32H7 when inferring MobileNet-Vl. Besides, the yielded NPUs can achieve roofline or even beyond roofline performance. the buffer and reshapeable MAC array push the NPU’s attainable performance to the roofline, while the feature of supporting sparsity allows the NPU to obtain performance beyond the roofline.

关键词： Deep learning Runtime Quantization (signal) Program processors Neural networks Random access memory Prototypes

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Enhanced Cyberattack Detection in Wireless sensor Networks Using Gradient Boosting Decision Tree

Enhanced Cyberattack Detection in Wireless Sensor Networks U...

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Mobile Networks and Wireless Communications (ICMNWC), ieee international conference on

作者： R. Mohandas Pavithra A E.S. Vinothkumar Amarnath Prabhakaran A Sumit Kumar Gupta Neeru Malik Department of Electronics & Communication Engineering Chennai Institute of Technology Chennai India Department of Information Technology K. Ramakrishnan College of Engineering (Autonomous) Trichy India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Thiruvallur India Department of Biomedical Engineering Nandha Engineering College Erode India Department of Physics St. Wilfred's P.G College Jaipur India Department of Engineering and Technology Pimpri Chinchwad University Pune India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

Wireless sensor Networks (WSNs) are essential to the Internet of things despite their power constraints. As sensor deployment has increased, so have security risks, requiring effective attack detection and prevention solutions. Machine Learning (ML) might build strong systems that detect cyber threats in real time. Data preparation, feature extraction, and model training are used to improve WSN security in this research. Preprocessing included data purification, conversion, standardisation, and splitting. Principal Component Analysis (PCA) reduced dimensionality, preserved variance, and improved computing speed. Model training uses Gradient Boosting Decision Trees (GBDT), notable for its classification accuracy in complex datasets. Averaging 93.11 % accuracy, the GBDT model outperformed the BiLSTM and DCNN models. this high accuracy shows GBDT's capacity to identify and mitigate WSN-based IoT security threats. the findings show that machine learning, particularly GBDT, can efficiently identify WSN intrusions to improve IoT network security and resilience. this study establishes complete cybersecurity safeguards for the IoT ecosystem, enabling more secure and robust applications.

关键词： Training Wireless sensor networks Accuracy Computational modeling Bidirectional long short term memory Boosting Feature extraction Decision trees Cyberattack Principal component analysis

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A Low-Complexity Design for Complex Multiplication Using Radix-4 Booth Encoding

A Low-Complexity Design for Complex Multiplication Using Rad...

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international conference on VLSI systems, Architecture, Technology and Applications (VLSI-SATA)

作者： Anil Kali B. S. Khuntia S. L. Sabat P. K. Meher CASEST University of Hyderabad Hyderabad India Department of CSE C. V. Raman Global University Bhubaneswar India

ISBN: (数字)9798350362268

ISBN: (纸本)9798350362275

In this paper, we present a novel design strategy for complex-number multiplication where the real part, as well as the imaginary part, are considered as a 2-point inner-product, and those inner-products are computed by radix-4 modified Booth-encoded distributed arithmetic approach where the partial inner products are reduced by the carry-save reduction process. Reduction of area complexity is achieved by sharing of Booth encoders by the pair of inner-product units. Time complexity is reduced by avoiding the vector-merging adder, which conventionally follows the carry-save reduction of partial products. the post-layout synthesis results using Cadence Innovus with a GPDK 90nm technology library and FPGA synthesis show that the proposed complex multiplier offers significant advantages in area-delay product and energy consumption over the real-number multiplier-based reference architecture.

关键词： Power demand Neural networks systems architecture Very large scale integration Encoding Libraries Hardware

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Temperature sensor Integration into the Node-RED Platform for Transformer Monitoring 4

Temperature Sensor Integration into the Node-RED Platform fo...

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4th Engineering Physics international conference 2023, EPIC 2023

作者： Nugraha, H. Hermawan, A.D. Mulya, M.A.J. Firmansyah, I. Suryadi Research Center for Energy Conversion and Conservation BRIN KST BJ Habibie Serpong Banten Tangerang Selatan Indonesia Research Center for Electronics BRIN KST BJ Habibie Serpong Banten Tangerang Selatan Indonesia Research Center for Telecommunication BRIN KST Samaun Samadikun Jawa Barat Bandung Indonesia Research Center for Photonics BRIN KST BJ Habibie Serpong Banten Tangerang Selatan Indonesia

Transformers must be continuously monitored to guarantee dependable and effective performance. the combination of Internet of things (IoT) technology and sophisticated data processing systems opens up new possibilities for improved transformer monitoring in this context. To enable real-time monitoring of transformer conditions, this study focuses on the integration of IoT temperature sensors into the Node-RED platform. the suggested method gathers temperature information from several locations inside the scale laboratory transformer using IoT temperature sensors. the Node-RED platform receives the collected data and processes, analyzes, and visualizes it in real time. Comprehensive monitoring is made possible by the platform, allowing operators to quickly identify any unusual temperature swings or potential problems inside the transformer. For older transformers built with normal Kraft paper, the rated hot spot temperature is either 90°C according to ieee or 97°C according to IEC. the Node-RED platform's incorporation of IoT devices not only provides real-time monitoring capabilities but also offers the flexibility to implement predictive maintenance strategies. By harnessing the power of data analytics and visualization, operators can make informed decisions to optimize transformer performance, extend its lifespan, and minimize downtime. the results of this study demonstrate the effectiveness of the IoT temperature sensor integration into the Node-RED platform for laboratory transformer monitoring. Based on the measurement winding temperature at the transformer has reached 90 °C at full load, and then the system starts to activate the blower to cool the transformer. the approach contributes to the advancement of transformer management practices, ensuring the reliability and efficiency of critical energy infrastructure. © 2023 Institute of Physics Publishing. All rights reserved.

关键词： Internet of things

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10th international conference on New Trends in Model and Data Engineering, MEDI 2021 held with 4th international Workshop on Modeling, Verification and Testing of Dependable Critical systems, DETECT 2021, Symposium on Intelligent and Autonomous systems, SIAS 2021, 1st Workshop on Control Software: Methods, Models, and Languages, CSMML 2021, Workshop on Blockchain for Inter-Organizational Collaboration, BIOC 2021 and 1st international Health Data Workshop, HEDA 2021

10th International Conference on New Trends in Model and Dat...

引用

10th international conference on New Trends in Model and Data Engineering, MEDI 2021 held with 4th international Workshop on Modeling, Verification and Testing of Dependable Critical systems, DETECT 2021, Symposium on Intelligent and Autonomous systems, SIAS 2021, 1st Workshop on Control Software: Methods, Models, and Languages, CSMML 2021, Workshop on Blockchain for Inter-Organizational Collaboration, BIOC 2021 and 1st international Health Data Workshop, HEDA 2021

ISBN: (纸本)9783030876562

the proceedings contain 24 papers. the special focus in this conference is on New Trends in Model and Data Engineering. the topics include: Record Linkage for Auto-tuning of High Performance computing systems;protecting Sensitive Data in Web of Data;COVID-DETECT: A Deep Learning Based Approach to Accelerate COVID-19 Detection;time Insertion Functions;static Checking Consistency of Temporal Requirements for Control Software;visual Language for Device Management in Telecommunication Product Line;Using Process-Oriented Structured Text for IEC 61499 Function Block Specification;the DibiChain Protocol: Privacy-Preserving Discovery and Exchange of Supply Chain Information;towards a Resource-Aware Formal Modelling Language for Workflow Planning;Development of Critical systems with UML/OCL and FoCaLiZe;systematic Literature Review of Methods for Maintaining Data Integrity;medical Data Engineering – theory and Practice;querying Medical Imaging Datasets Using Spatial Logics (Position Paper);evaluation of Anonymization Tools for Health Data;usages of the ContSys Standard: A Position Paper;systematic Assessment of Formal Methods Based Models Quality Criteria;deriving Interaction Scenarios for Timed distributed systems by Symbolic Execution;energy Efficient Real-Time Calibration of Wireless sensor Networks for Smart Buildings;edge-to-Fog Collaborative computing in a Swarm of Drones;Coverage Maximization in WSN Deployment Using Particle Swarm Optimization with Voronoi Diagram;EPSAAV: An Extensible Platform for Safety Analysis of Autonomous Vehicles;bridging Trust in Runtime Open Evaluation Scenarios.

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Reactive Power Optimization in Distribution Network with Renewable distributed Power Sources

Reactive Power Optimization in Distribution Network with Ren...

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Intelligent Power and systems (ICIPS), international conference on

作者： Yufan Zhang Aiqiang Pan Xuejun Xiong Yajun Zhang Hanxiao Liu Liwei Li State Grid Shanghai Municipal Electric Power Company Shanghai China College of Computer Science and Technology Shandong University Qingdao China

ISBN: (数字)9798331532765

ISBN: (纸本)9798331532772

In order to solve the problems of reactive power optimization in distribution networks which includes the high proportion of distributed new energy power generation, this paper proposes a multi time scale reactive power optimization method. Firstly, on the day-ahead scale, the slow motion control variables were optimized with the objective function of minimizing the expected network loss, and a master-slave classification period decoupling strategy was proposed to transform the dynamic optimization of discrete variables into Static optimization to simplify the optimization process; Secondly, on the 15 minute scale, rolling optimization of reactive power output of new energy is adopted, with the goal of minimizing the system voltage deviations; Finally, in real time, reactive power output of new energy is optimized at any time with the goal of maintaining a constant voltage at the grid connection point within each time window to suppress voltage stability issues caused by various random fluctuations. Finally, the effectiveness and feasibility of the proposed method were verified using the modified ieee33 node as an example.

关键词： Reactive power Renewable energy sources Fluctuations Voltage fluctuations Distribution networks Transforms Real-time systems Voltage control Optimization Power generation

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ChronoPilot - Modulating Time Perception 4

ChronoPilot - Modulating Time Perception

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4th ieee international conference on Artificial Intelligence and Virtual Reality (AIVR)

作者： Botev, Jean Drewing, Knut Hamann, Heiko Khaluf, Yara Simoens, Pieter Vatakis, Argiro Univ Luxembourg VR AR Lab Esch Sur Alzette Luxembourg Justus Liebig Univ Giessen Expt Psychol HapLab Giessen Germany Univ Lubeck Inst Comp Engn Lubeck Germany Wageningen Univ & Res Informat Technol Grp Wageningen Netherlands Univ Ghent IMEC Dept Informat Technol IDLab Ghent Belgium Panteion Univ MultiTimeLab Athens Greece

ISBN: (纸本)9781665432252

Although time can be measured objectively, human time perception is remarkably subjective and influenced by cognitive states, individual motivations, and social factors. this malleability of perceived time can be evidenced, for instance, in stressful situations where one might experience a lack of time, while one might lose track of time in more relaxing circumstances. Based on fundamental knowledge from psychology and cognitive science, the ChronoPilot project aims at developing a prototype technology driven by artificial intelligence to extend or compress human subjective time adaptively and whenever required. Mediated-reality approaches, such as virtual and augmented reality, have enormous potential for presenting the users with visual, auditory, and haptic stimulation patterns that directly or indirectly influence their subjective time and which are difficult to reproduce in the real world. Going beyond individual settings, ChronoPilot will also investigate how to coordinate time plasticity in collaborative environments where one group member's actions may affect other members' perception. Different scenarios, where humans alone or humans and robots have to collaborate in realistic and virtual environments, will validate the planned research. In this paper, we present the fundamental concepts of our project ChronoPilot, which is a work in progress.

关键词： user-centered design artificial intelligence distributed system sensor system intelligent actuator virtual reality augmented reality collaboration cognitive science

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A Proactive Resilience Enhancement Strategy to Electric Distribution systems during Hurricanes 4

A Proactive Resilience Enhancement Strategy to Electric Dist...

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4th international conference on Smart Energy systems and Technologies (SEST)

作者： Abdelmalak, Michael Benidris, Mohammed Univ Nevada Dept Elect & Biomed Engn Reno NV 89557 USA

ISBN: (纸本)9781728176604

this paper proposes a probabilistic proactive strategy to enhance operational resilience of distribution power grids in preparation to and during hurricanes. Many of current resilience enhancement strategies focus on determining optimal locations and sizes of distributed Energy Resources (DERs) in electric distribution systems to reduce load curtailments during extreme events. Other approaches enhance distribution system resilience through utilizing the concept of microgrid formulation to split the grid into smaller and stable standalone grids. However, the rapid changing conditions and constraints of the power system during extreme events exhibit further computational burden and complexities to provide a fast acting decision process. In this work, a multi-objective mixed integer linear programming optimization problem is formulated to minimize the amount of load curtailments and operational costs. A Markov decision process is developed to model transitions between system states to determine the optimal generation level of each DER considering component failure probabilities, hurricane spatiotemporal properties, and load variations. To maintain reliable and stable operation of electric distribution systems integrated with DERs, system operational and technical constraints should be fulfilled. the CPLEX solver is integrated with MATLAB to formulate and solve the optimization problem. the proposed strategy is demonstrated using a modified ieee 33-feeder distribution system under various system characteristics. Results show the role of proactive generation redispatch to improve resilience levels and reduce the total amount of load curtailments by 30%-45%.

关键词： distributed energy resources extreme weather event hurricanes Markov decision process resilience

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