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Simulating Distributed Wireless Sensor networks for Edge-AI 5

Simulating Distributed Wireless Sensor Networks for Edge-AI

引用

5th conference on Cloud and internet of Things (CIoT)

作者： Prajapati, Ambar Banerjee, Bonny Proc Control Automat & Integrat Int Paper Memphis TN 38113 USA Univ Memphis Inst Intelligent Syst Memphis TN USA Univ Memphis Dept Elect & Comp Engn Memphis TN USA

ISBN: (纸本)9781665479646

This paper presents the simulation of distributed wireless sensor networks (WSNs) consisting of autonomous mobile nodes that communicate, with or without a central/root node, as desired for edge artificial intelligence (edge-AI). We harness the high-resolution and multidimensional sensing characteristics of IEEE 802.15.4 standard and Routing Protocol for Low-Power and Lossy networks (RPL) to implement dynamic, asynchronous, event-driven, targeted communication in distributed WSNs. We choose Contiki-NG/Cooja to simulate two WSNs, one with and the other without a root node. The simulations are assessed on the network Quality of Service (QoS) parameters such as throughput, network lifetime, power consumption, and packet delivery ratio. The simulation outputs show that the sensor nodes at the edge communicate successfully with the specific targets responding to particular events in an autonomous and asynchronous manner. The performance is slightly degraded when using the RPL WSN with a root node. This work shows how to simulate and evaluate distributed WSNs using the Cooja simulator which would be useful for designing such networks for edge-AI applications, such as visual surveillance, monitoring in assisted living facilities, intelligent transportation with connected vehicles, automated factory floors, immersive social media experience, and so on.

关键词： Edge-AI Contiki OS Cooja Wireless sensor networks simulation Routing Protocol for Low-Power and Lossy networks IEEE 802.15.4 event-driven and targeted communication

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Achieving Seamless IoT Interoperability Through Data Plane Programmability

Achieving Seamless IoT Interoperability Through Data Plane P...

引用

Consumer Communications and networking conference, CCNC IEEE

作者： Alexandre Heideker Dener Silva Carlos Kamienski Angelo Trotta Department of Computer Science and Engineering University of Bologna Italy Federal University of the ABC CMCC Santo André Brazil

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

The rapid proliferation and vast diversity of internet of Things (IoT) devices present significant challenges to achieving seamless interoperability within modern networks. Many legacy IoT systems still rely on outdated protocols, rendering them incompatible with newer devices and creating communication bottlenecks. Additionally, the need for real-time traffic adaptation and modification further complicates the integration of these heterogeneous systems. This paper explores the potential of data plane programmability as a solution to address these challenges, enabling dynamic control over network behavior and improving compatibility between diverse IoT devices. By allowing real-time adjustments to traffic flows and adapting protocols on the fly, programmable data planes offer a flexible and scalable approach to ensuring interoperability across both modern and legacy IoT systems. Experimental results demonstrate the feasibility and effectiveness of this approach, highlighting its potential to enhance IoT network performance and longevity.

关键词： performance evaluation Protocols Scalability Switches Rendering (computer graphics) Real-time systems Hardware internet of Things Interoperability Payloads

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Accident Detection and Reporting System for Powered Two-Wheelers Using internet of Things and Edge AI augmented with MCUSUM features

Accident Detection and Reporting System for Powered Two-Whee...

引用

2025 International conference on Emerging Technologies for Intelligent systems, ETIS 2025

作者： Viswanath, Vijay Unnikrishnan, S. Saeed, S. Dhanya, V.S. Deepa, P.P. Selvan, Kalai Emerging Technologies Group Centre for Development of Advanced Computing Thiruvananthapuram India

ISBN: (纸本)9798331507541

According to data provided by the Ministry of Road Transport and Highways (MORTH), Government of India, about 1.68 lakh people have lost their lives in accidents and nearly 4.4 lakh people have suffered injuries [1]. The number of accidents has increased by approximately 12% year on year. The data also show that people affected by accidents are likely to be in the age group of 15 to 49 years. This age group is virtually the pillar that supports the nation's development. Therefore, road fatalities can indirectly influence the nations development. In this work, we have implemented a system aimed towards powered two-wheelers (PTWs) that detects accidents and sends an SOS signal to a preconfigured contact. The system, upon detecting an accident/critical event, can attempt to convey the SOS signal in multiple modalities for redundancy. The SOS signal includes the last known GNSS coordinates of the vehicle to be able to pinpoint the location of the incident. The system also has a manual SOS trigger option that can be used as a panic button. Detection is performed by a Convolutional Neural network (CNN) based classifier running on the system using Edge AI capability that uses sensory output from an inertial measurement unit (IMU) and its derivative data for input features. In this work, we have experimented with the possibility of improving the classification performance of the classifier by augmenting the input feature set with Multivariate Cumulative Summation (MCUSUM) [2] control chart statistic of the IMU data. The results show that this technique can give marked improvements in the overall accuracy of the classification system. © 2025 IEEE.

关键词： Emergency services

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UAV Enabled Sustainable IoT network with OTPDRL 26

UAV Enabled Sustainable IoT Network with OTPDRL

引用

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

作者： Wang, Tiantian Liu, Lei Sun, Hongbo Hu, Jia Shandong University School of Software Jinan China YanTai University School of Computer and Control Engineering YanTai China Exeter University Computer Science United Kingdom

ISBN: (纸本)9798350331684

Integrating large-scale sensors into the network has become a research hotspot for its promising flexibility in monitoring vitally critical wild areas. However, the existing internet of Things (IoT) systems are limited due to the lack of a stable power supply, which seriously affects the system's sustainability. The combination of sensors equipped with cordless power batteries and long-distance power transmission has ushered in a new era. Using the unmanned aerial vehicles (UAVs) to charge the battery ensures the flexibility and sustainability of the sensor in environmental detection. In this work, we aim to provide a solution for maintaining the sustainability of the sensors while optimizing UAV trajectory to minimize the overall energy consumption of UAV. Since deep reinforcement learning successfully solves the NP-hard combinatorial optimization problem, deep reinforcement learning is introduced in this work to obtain a feasible solution. We formulate the trajectory planning of UAV as a Markov decision problem and employ a deep reinforcement learning (DRL) model based on an attention mechanism to find the optimal policy efficiently, named the optimal trajectory planning algorithm based on DRL (OTPDRL). The experimental results suggest the OTPDRL obtains a good trade-off between performance gain and computational time. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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A Lightweight Blockchain Scheme for Secure Data Communication in internet of Things-enabled Wireless Sensor network

A Lightweight Blockchain Scheme for Secure Data Communicatio...

引用

Smart systems for applications in Electrical Sciences (ICSSES), International conference on

作者： Lavina Balraj A. Prasanth K.K. Devi Sowndarya T. Kuntavai Department of Electronics and Communication Engineering Sri Venkateswara College of Engineering Sriperumbudur Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu India Department of Electronics and Communication Engineering DMI College of Engineering Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Adhipara Sakthi Engineering College Chengalpattu Tamil Nadu India

ISBN: (数字)9798350364040

ISBN: (纸本)9798350364057

In today’s world, there will probably be an enormous rise in the volume of data sensed by sensors on the internet of Things-enabled Wireless Sensor network (IWSN). The sensed data from sensor nodes is gathered and transmitted to the sink. These sensor nodes suffer from several consequences including security, transparency, trust, and immutability. Furthermore, other technical impairments like computation, storage of sensed data, privacy, and energy conservation enhance the issues of the IWSN. To overcome this, a novel Blockchain Interplanetary File Access control System (BIFACS) has been proposed to manage intelligent autonomous access control for IWSN. Efficient data security and data transmission can be achieved in the proposed scheme by using the BIFACS algorithms. Further, the proposed scenario is implemented in the Solidity programming language and deployed on the local Ethereum. The efficacy of the proposed system has been analyzed based on computing, storage, and network performance.

关键词： Access control performance evaluation Wireless sensor networks Memory Sensor systems and applications internet Blockchains

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On the use of conditional TimeGAN to enhance the robustness of a reinforcement learning agent in the building domain 22

On the use of conditional TimeGAN to enhance the robustness ...

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9th ACM International conference on systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys)

作者： Fochesato, Marta Khayatian, Fazel Lima, Doris Fonseca Nagy, Zoltan Swiss Fed Inst Technol Automat Control Lab Zurich Switzerland EMPA Urban Energy Syst Lab Dubendorf Switzerland Univ Texas Austin Intelligent Environm Lab Austin TX USA

ISBN: (纸本)9781450398909

This paper develops an end-to-end data-driven pipeline to improve the out-of-sample performance of a Reinforcement Learning (RL) agent operating in the domain of building energy management. The approach can benefit researchers and practitioners that are confronted with the challenge of training robust control architectures when only few historical data are available to them. Under these circumstances, in fact, the RL agent is generally unable to respond robustly to unseen (possible, rare) events. To tackle this issue, we propose a data-driven procedure composed of two steps: (i) we develop a novel Generative Adversarial network (GAN) architecture to create synthetic time series profiles of building performance;(ii) we infuse these artificial profiles into the original training dataset. The procedure is found to increase the robustness of the RL agent to rare events, without compromising the performance during "standard" operations. Extended simulations conducted on the CityLearn OpenAI Gym environement show that the GAN-enhanced RL agent's response displays better performance metrics with respect to a rule-based controller, with results generally improving with the data-enhancement process.

关键词： Generative Adversarial network Time-series forecasting Reinforcement Learning Building energy management.

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A Multi-Attention Mechanisms Based Transfer Learning Framework for State of Health Estimation of Lithium-ion Battery 4

A Multi-Attention Mechanisms Based Transfer Learning Framewo...

引用

4th International conference on Smart Power and internet Energy systems (SPIES)

作者： Lu, Dong Cui, Naxin Li, Changlong Shandong Univ Sch Control Sci & Engn Jinan Peoples R China

ISBN: (纸本)9781665489577

Accurate state of health (SOH) estimation is essential for ensuring the stable and safe operation of lithium-ion batteries. However, the adaptability of the estimation method for batteries with different formulations remains challenging. In this paper, partial charging segments are collected and processed. A pre-training convolutional neural network (CNN), which combines attention mechanisms for heightening the estimation performance, is proposed for integrating the inputs and extracting the hidden features automatically. Experiments are performed to show that the proposed method could reduce the estimation error by 80.9%, 41.3% and 25.6% for LCO, LFP and NCA respectively. Moreover, to reduce the computation burden between different kinds of batteries, a transfer learning (TL) strategy is utilized by fine-tuning the dense layers. The transfer learning results show that the estimation root mean square error (RMSE) of LFP and NCA are only 1.3% and 2.6%, respectively.

关键词： Lithium-ion battery state of health (SOH) partial charging segments convolutional neural network transfer learning

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Enhancing Node Selection in Blockchain-Enabled Edge internet of Things (IoT): A Fuzzy Logic Approach for Improved performance 7

Enhancing Node Selection in Blockchain-Enabled Edge Internet...

引用

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

作者： Imamguluyev, Rahib Hasanov, Ashraf Mikayilova, Rena Odlar Yurdu University Department of It and Engineering Baku Azerbaijan Western Caspian University Philosophy in Economics Baku Azerbaijan Azerbaijan State University of Economics Department of Digital Technologies and Applied Informatics Baku Azerbaijan

ISBN: (纸本)9798350341485

The rise of connected and smart devices has heightened the demand for robust internet of Things (IoT) applications, necessitating enhanced data storage and processing. Traditional cloud-based IoT systems, often distant from users, grapple with issues like server load, slow responses, and limited mobility. Edge computing counters some of these problems by bringing processing closer to users. Selecting appropriate nodes also addresses common IoT challenges such as network lifetime, resource allocation, and data reliability, ensuring optimal intervals for node selection. Integrating edge and blockchain tech in IoT introduces new perspectives on access control design. This study introduces an innovative approach using fuzzy logic for node selection in blockchain-powered edge IoT. Fuzzy logic processes numeric and linguistic data concurrently, alongside the Technique of Preference Ranking by Similarity of Ideal Solution (TOPSIS) for Multi-Criteria Decision Making (MCDM). This fuzzy-based method enhances IoT node selection efficiency, with three entry criteria, significantly ameliorating considered parameters, as demonstrated by experimental results. © 2023 IEEE.

关键词： Fuzzy logic

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Data-Driven Physics Based Ultra-short-term Wind Power Prediction Model: A VMD-LSTM and WRF Based Approach 4

Data-Driven Physics Based Ultra-short-term Wind Power Predic...

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4th International conference on Smart Power and internet Energy systems (SPIES)

作者： Cui, Xiyou Li, Hongwei Pan, Zhiyuan Zhang, Zhengmao State Grid China Technol Coll Jinan Peoples R China

ISBN: (纸本)9781665489577

In this paper, an ultra-short-term wind power prediction model is proposed. First, based on the Weather Research and Forecast (WRF) model, the predicted wind speed of the wind power plant is obtained. Second, extract log history data from wind farm data acquisition and supervisory control system (SCADA). After, a variational mode decomposition (VMD) is applied to process historical wind speed of the wind farm to create 5 training data sets. Finally, Using long short-term memory (LSTM) network as a predictive model. The historical wind speed, historical wind power and WRF predicted wind speed are used as inputs to output the predicted power for the next 4 hours. For evaluating the prediction accuracy of this model, considering root mean square error (RMSE) and mean absolute error (MAE) as error evaluation metrics, the proposed model is compared with 3 other algorithms. The results show that LSTM has the best prediction performance.

关键词： wind power prediction ultra-short-term long and short-term memory network WRF model

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Microservices Integrated performance and Reliability Testing 3

Microservices Integrated Performance and Reliability Testing

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3rd ACM/IEEE International conference on Automation of Software Test (AST)

作者： Camilli, Matteo Guerriero, Antonio Janes, Andrea Russo, Barbara Russo, Stefano Free Univ Bozen Bolzano Bolzano Italy Univ Napoli Federico II Naples Italy

ISBN: (纸本)9781450392860

Continuous quality assurance for extra-functional properties of modern software systems is today a big challenge as their complexity is constantly increasing to satisfy market demands. This is the case of microservice systems. They provide high control on the scale of operation by means of fine-grained service decomposition, but this demands careful consideration of the relations between performance of individual microservices and service failures. In this work, we propose MIPaRT, a novel methodology, and platform to automatically test microservice operations for performance and reliability in combination. The proposed platform can be integrated into a DevOps cycle to support continuous testing and monitoring by the automatic (1) generation and execution of performance-reliability ex-vivo testing sessions, (2) collection of monitoring data, (3) computation of performance and reliability metrics, and (4) integrated visualization of the results. We apply our approach by operating the platform on an open source benchmark. Results show that our integrated approach can provide additional insights into the performance and reliability behaviour of microservices as well as their mutual relationships.

关键词： Microservices systems reliability testing performance testing

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