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Dynamic Key Management based ACO Routing for Wireless sensor Networks 6

Dynamic Key Management based ACO Routing for Wireless Sensor...

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6th international conference on computing Methodologies and Communication, ICCMC 2022

作者： Jemin, V.M. Kumar, A Senthil thirunavukkarasu, V Kumar, D Ravi Manikandan, R. RMK College of Engineering Technology Department of CSE Chennai India Kings Engineering College Department of ECE Government College of Engineering Department of ECE The Quaide Milleth College for Men Department of CS India

ISBN: (纸本)9781665410281

Ant Colony Optimization is applied to design a suitable and shortest route between the starting node point and the end node point in the Wireless sensor Network (WSN). In general ant colony algorithm plays a good role in path planning process that can also applied in improving the network security. therefore to protect the network from the malicious nodes an ACO based Dynamic Key Management (ACO-DKM) scheme is proposed. the routes are diagnosed through ACO method also the actual coverage distance and pheromone updating strategy is updated simultaneously that prevents the node from continuous monitoring. Simulation analysis gives the efficiency of the proposed scheme. © 2022 ieee.

关键词： Ant colony optimization

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A Survey on Byzantine Attack using Secure Cooperative Spectrum Sensing in Cognitive Radio sensor Network 6

A Survey on Byzantine Attack using Secure Cooperative Spectr...

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6th international conference on computing Methodologies and Communication, ICCMC 2022

作者： Patil, Siddarama R Rajashree, Rajashree Agarkhed, Jayashree P.D.A College of Engineering Department of Electronics and Communication Engineering Karnataka Kalaburagi585102 India P.D.A College of Engineering Department of Computer Science and Engineering Karnataka Kalaburagi585102 India

ISBN: (纸本)9781665410281

the strategy of permanently allocating a frequency band in a wireless communication network to one application has led to exceptionally low utilization of the vacant spectrum. By utilizing the unused licensed spectrum along with the unlicensed spectrum, Cognitive Radio sensor Network (CRSNs) ensures the efficiency of spectrum management. To utilize the spectrum dynamically it is important to safeguard the spectrum sensing. Cooperative Spectrum Sensing (CSS) is recommended for this task. CSS aims to provide reliable spectrum sensing. However, there are various vulnerabilities experienced in CSS which can influence the performance of the network. In this work, the focus is on the Byzantine attack in CSS and current security solutions available to avoid the Byzantines in CRSN. © 2022 ieee.

关键词： Cognitive radio

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Efficient. Scalable and Robust Data Shuffle Service for distributed MapReduce computing on Cloud 24

Efficient. Scalable and Robust Data Shuffle Service for Dist...

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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： Gu, Rong Huang, Xu Dai, Haipeng Geng, Xiaoyu Chen, Xiaofei Huang, Yihua Xiao, Fu Chen, Guihai Nanjing University State Key Laboratory for Novel Software Technology Nanjing China Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9798350319934

distributed Map Reduce computing frameworks, such as Hadoop, Spark, and Flink, are widely used in various domains which face big data challenges. Inside Map Reduce, Shuffle is a critical stage that bridges up the Map stage and Reduce stage through data partition and network transmission under the hood. With the rapidly growing scale of data and clusters, the all-to-all data transfer in Shuffle becomes inefficient and frequently fails, making Shuffle the bottleneck of many big data processing jobs. To improve the Shuffle efficiency, we propose a hybrid Shuffle model based on file pre-merging and partition reorganization mechanisms. To address the Shuf-fle stability issue, we design a novel data transmission model based on the replication mechanism. Finally, to support scalable Shuffle with high performance, we present a Shuffle parallelism degree tuning approach based on machine learning and searching techniques. We have implemented the proposed Shuffle models and strategies into the widely-used MapReduce-based big data platform Spark for evaluation. Experimental evaluation shows that, compared with the state-of-the-art systems, our proposed methods outperform them by 23% on average. As a practical application case, the proposed Shuffle service has been running in the real-world environment of ByteDance. Inc over one year with significant performance improvement. © 2022 ieee.

关键词： Data transfer

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Design of a 45 nm Complementary Metal Oxide Semiconductor Low Noise Amplifier for a 30 GHz Millimeter-Wave Wireless Transceiver in Radar sensor Applications 6

Design of a 45 nm Complementary Metal Oxide Semiconductor Lo...

引用

6th international conference on Artificial Intelligence, Big Data, computing and Data Communication systems, icABCD 2023

作者： Chakoma, Shingirirai M. Ogudo, Kingsley A. University of Johannesburg Faculty of Engineering and The built Environment Department of Electrical & Electronics Engineering Johannesburg South Africa

ISBN: (纸本)9798350314809

the millimeter-wave (mmWave) frequency band is rapidly becoming utilized in wireless technologies due to its large bandwidth and high data throughput. Wireless technology is increasingly becoming the backbone of the Internet of things (IoT). this has resulted in increased applications of the radio frequency (RF) spectrum and congestion of the microwave band. this can be solved by utilizing more bandwidth at higher frequency bands. One notable application of IoT pertains to radar sensing, which has experienced increased popularity across various domains such as autonomous vehicles, gesture recognition, drones, and health monitoring. Radar sensors have been employed in these applications to perform tasks including proximity sensing, direction detection, speed measurement, target localization, and capturing physiological indicators such as heartbeat and breathing. Several factors have an impact on the performance of radar sensors, encompassing the maximum range for target detection, measurement precision, capability to differentiate between multiple targets, and ability to operate effectively in environments with high levels of noise. this paper presents the design of a 45 nm complementary metal-oxide-semiconductor (CMOS) low noise amplifier (LNA) for a mmWave Ka-band wireless transceiver for radar sensors. the LNA was designed to operate at 0.6V and 700 μA for low power consumption. the LNA consists of an inductive degenerated common source (CS) and a common gate (CG) diode-connected load. the LNA achieves a power gain of 31.19 dB and a noise figure (NF) of 0.133 dB at 30 GHz consuming 0.42 mW of power. © 2023 ieee.

关键词： Millimeter waves

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Introducing Natural Language-based Instruction Protocol for Intelligent Machine Collaborations 19

Introducing Natural Language-based Instruction Protocol for ...

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19th ieee international Wireless Communications and Mobile computing (ieee IWCMC)

作者： Gong, Haobing Gao, Hui Feng, Yuting Yuan, Caixia Minist Educ Sch Informat & Commun Engn Key Lab Trustworthy Distributed Comp & Serv Beijing Peoples R China Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing 100876 Peoples R China

ISBN: (纸本)9798350333398

the rapid development of various autonomous unmanned systems has increased the endogenous intelligence of machines, which empowers functional collaborations among intelligent machines. However, traditional protocols for intelligent machine collaboration have limitations regarding functionality, efficiency, and scalability. Moreover, existing research on intelligent machine collaboration is not yet approaching a unified paradigm that facilitates interactions among machines and between machines and humans. therefore, aiming to enable a more efficient functional collaboration among intelligent machines, we propose a natural language-based instruction (NLI) protocol, which enjoys the advantages of autonomy, robustness and efficiency. In particular, we specify the NLI protocol architecture by introducing a corpus of NLIs, an NLI generation module, and an NLI parsing module, wherein the corpus contains control instructions, intents and slots, the generation module is used to generate NLIs, and the parsing module is used to parse the intents and slots in the instruction. Furthermore, we case-study the proposed NLI protocol in an electromagnetic interference avoidance scenario based on semi-physical simulation with software-defined radio. Simulation results show that the NLI protocol is more robust and efficient than traditional control protocol in severe wireless channels, which validates the feasibility and effectiveness of implementing intelligent machine collaboration based on NLI.

关键词： Natural language-based instructions intelligent machine collaboration network control electromagnetic interference avoidance software-defined radio

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6th international conference on Soft computing and Signal Processing, ICSCSP 2023

6th International Conference on Soft Computing and Signal Pr...

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6th international conference on Soft computing and Signal Processing, ICSCSP 2023

ISBN: (纸本)9789819984503

the proceedings contain 54 papers. the special focus in this conference is on Soft computing and Signal Processing. the topics include: Stock Price Prediction Using LSTM, CNN and ANN;IoT-Based Smart Wearable Devices Using Very Large Scale Integration (VLSI) Technology;plant Disease Detection and Classification Using Artificial Intelligence Approach;camera and Voice Control-Based Human–Computer Interaction Using Machine Learning;optimal Crop Recommendation by Soil Extraction and Classification Techniques Using Machine Learning;ioT-Based Smart Irrigation System in Aquaponics Using Ensemble Machine Learning;Performance Evaluation of MFSK Techniques Under Various Fading Environments in Wireless Communication;prediction of Breast Cancer Using Feature Extraction-Based Methods;review and Design of Integrated Dashboard Model for Performance Measurements;anomaly Detection in Classroom Using Convolutional Neural Networks;Efficient VLSI Architectures of Multimode 2D FIR Filter Bank using distributed Arithmetic Methodology;implementation of an Efficient Image Inpainting Algorithm using Optimization Techniques;A Systematic Study and Detailed Performance Assessment of SDN Controllers Across a Wide Range of Network Architectures;Classification and Localization of Objects Using Faster RCNN;an Image Processing Approach for Weed Detection Using Deep Convolutional Neural Network;detection of Leaf Black Sigatoka Disease in Enset Using Convolutional Neural Network;detecting Communities Using Network Embedding and Graph Clustering Approach;Deep Learning Approaches-Based Brain Tumor Detection Using MRI Images—A Comprehensive Review;Predicting Crop Yield with AI—A Comparative Study of DL and ML Approaches;An Empirical Analysis of Lung Cancer Detection and Classification Using CT Images;trust and Secured Routing in Mobile Ad Hoc Network Using Block Chain.

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Optimal Trajectory Planning and Task Assignment for UAV-assisted Fog computing 24

Optimal Trajectory Planning and Task Assignment for UAV-assi...

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作者： Liu, Shuaijun Yin, Jiaying Zeng, Zishu Wu, Jingjin BNU-HKBU United International College Department of Statistics and Data Science Zhuhai519087 China The University of Hong Kong Li Ka Shing Faculty of Medicine Pokfulam Hong Kong School of Engineering The Hong Kong University of Science and Technology Kowloon Hong Kong Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science China

ISBN: (纸本)9798350319934

Fog computing is an emerging distributed computing model for the realization of the Internet of things (IoT). It extends computing and caching functions to the edge of wireless networks. Unmanned Aerial Vehicles (UAVs) provide effective support for fog computing, as UAVs can not only act as a relay between mobile users and physically remote edge devices to avoid costly long-range wireless communications, but also are equipped with computing facilities that can take over certain tasks. In this paper, we aim to optimize the energy efficiency of a fog computing system assisted by a single UAV, by planning the trajectories of the UAV and assigning computing tasks to different devices including the UAV itself. We propose two algorithms based on the classical Ant Colony and Particle Swarm Optimization techniques, and solve the problem by continuous convex approximation. Different from most existing studies where the trajectories are assumed to be straight lines, we account for the effect of obstacles, such as buildings, and deliberately avoid them during the trajectory planning phase. We demonstrate, through extensive simulation experiments, that our proposed approach can achieve significantly better energy efficiency compared to existing benchmark algorithms. © 2022 ieee.

关键词： Fog computing

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Higher Reconstruction Performance with Dual Deep Neural Networks for Wireless sensor Networks Compressed Temporal Signals 12

Higher Reconstruction Performance with Dual Deep Neural Netw...

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

作者： Rousseau, Sebastien Negrier, Romain Courreges, Fabien Engoti, Frank Itoua Quere, Raymond University of Limoges Xlim Institute Brive-la-Gaillarde19100 France NAIoBEE Company Brive-la-Gaillarde19100 France

ISBN: (纸本)9798350358056

In many applications using wireless sensor networks, the reliability of monitored data is crucial to analyze situations and take decisions. Compressed sensing methods are effective to ensure durability of a wireless sensor network installation and to overcome communication failures by drastically lowering the amount of data to transmit. the method proposed in this paper aims to perform an abstraction of the signals sparsity model required by compressed sensing in order to use a deep learning-based approach. this method is designed to be effective for all kinds of transient or temporally dynamic signals sufficiently regular to be predictable, that is to say signals that can be implicitly modelled by a sparse representation. through the collaboration of two recurrent neural networks and signal processing algorithms, our temporal method outperforms, in terms of accuracy, state-of-art methods, including distributed compressed sensing, time series imputation and forecasting deep learning methods. thus, high compression rates up to 95% can be reached while leading to an error of less than 0.5°C in reconstructing temperature signals. © 2023 ieee.

关键词： compressed sensing deep learning recurrent neural networks signal processing algorithms wireless sensor networks

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t-path Based Reliable and Real-time Routing in Battery-Free Wireless sensor Network 23

t-path Based Reliable and Real-time Routing in Battery-Free ...

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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： Chen, Kunyi Gao, Hong Cheng, Siyao Chen, Quan Li, Jianzhong Harbin Institute of Technology China Guangdong University of Technology China

ISBN: (纸本)9781665494571

Wireless sensor Networks are deployed to sense data from the physical world and transmit these data to the server(s). To reduce the maintenance cost of replacing batteries, Energy-Harvesting nodes are developed which can get recharged from Solar Power, Radio Frequency, and other environmental energy resources. We term the network consist of energy-harvesting nodes as Battery-Free WSNs (BF-WSNs). Environmental energy is inherently time-varying and exhibits a high degree of heterogeneity. these features bring challenges to routing design. In this paper, we focus on the reliability and real-time issues and propose an opportunistic strategy as time synchronization is hard and expensive to reach in BF-WSN. We first model BF-WSNs as a directed weighted multigraph and propose a novel definition called t-path to describe the time-varying availability feature of BF-nodes. Further, we show that the problem is NP-Hard. We present a centralized approximate algorithm and prove that the ratio bound is 2 under two conditions. this paper also presents a distributed algorithm. Comprehensive simulations have been performed to evaluate the performance of the proposed algorithms and the results show that the efficiency of our algorithms. © 2021 ieee.

关键词： Wireless sensor networks Simulation Reliability theory Approximation algorithms Routing Reliability engineering Real-time systems

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Optimizing Techniques for Energy Efficiency in Wireless sensor Network 6

Optimizing Techniques for Energy Efficiency in Wireless Sens...

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6th international conference on computing Methodologies and Communication, ICCMC 2022

作者： Narmadha, C. Hamsadhwani, V. Poonguzhali, R. Kavitha, T. Periyar Maniammai Institute of Science and Technology Department of Electronics and Communication Engineering Thanjavur India Periyar Maniammai Institute of Science and Technology Department of Electrical and Electronics Engineering Thanjavur India Periyar Maniammai Institute of Science and Technology Department of Computer Science and Engineering Thanjavur India

ISBN: (纸本)9781665410281

the establishment of a Wireless sensor Networks (WSN) faces the main challenge of managing the available energy efficiently. With Particle Swarm Optimization (PSO) and Harmony Memory Search (HMS) algorithms optimising routing and cluster formation in the sensor Network, energy efficiency can be assured and the life span of WSN can be extended. this paper implements a new HMS-PSO protocol to ensure the most efficient cluster head selection process and also find an optimised routing path that confirms WSNs to be highly energy efficient. this substantiates the configuration of a hierarchical un-equal clustering architecture for selecting CHs and relay nodes as well as optimises the routing path between CHs to BS to accomplish efficient use of the constrained energy resources. © 2022 ieee.

关键词： Particle swarm optimization (PSO)

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