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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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Integration of Fuzzy Model with the IOT Model to Achieve Better Health(Medical) Care Design System 4

Integration of Fuzzy Model with the IOT Model to Achieve Bet...

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4th ieee international conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Raman, Ramakrishnan Kumar, Vikram Pillai, Biju G. Verma, Apurv Rastogi, Shailesh Meenakshi, R. India Srv Media Pvt. Limited India Sri Balaji University Faculty of Management It & Business Analytics Pune India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India India Computer Science and Engineering Chennai Institute of Technology India

ISBN: (纸本)9798350360165

The Internet of Things is a pervasive network that utilizes sensor-equipped devices by the billions to observe the real-world environment. The Internet of Things technology presents considerable potential for the development of various industries, including medical treatment, where it can decrease the impact that the combination of old age and chronic ailments puts on health care systems. This paper tends to put forward a secure hierarchical routing approach, so-called Fuzzy Logic-based secure Hierarchical Routing using the Firefly Algorithm, to mitigate the issued challenges in IoT technologies for healthcare. A Fuzzy Trust Framework, an Inter-cluster Routing Framework, and a Firefly Algorithm-based Clustered Framework are the three main components of the proposed FSRF. The Fuzzy Security Framework identifies the trustworthiness of IoT devices in the network to resist routing attacks. The Firefly Algorithm-based Colony Framework selects IoT devices as cluster head nodes using the fitness function that measures the trust level, residual power hop count, connectivity radius, and centrality. FSRF goes further to apply an on-demand routing technique to ensure efficient data transmission. Simulation analysis compared with the current protocol was presented that showed that FSRF significantly improves the network lifetime, and energy efficiency, but security is the issue that needs further concern to improve the system. © 2024 ieee.

关键词： sensor nodes

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Data Mining Framework for Discovering and Clustering Phenotypes of Atypical Diabetic Lung 3

Data Mining Framework for Discovering and Clustering Phenoty...

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3rd international conference on Smart Technologies and systems for Next Generation computing, ICSTSN 2024

作者： Lekshmy, S. Sridhar, K.P. Roberts, Michaelraj Kingston Karpagam Academy of Higher Education Coimbatore India Karpagam Academy of Higher Education Department of Electronics and Communication Engineering Centre for Interdisciplinary Research Coimbatore India Sri Eshwar College of Engineering Centre for Advances in Signal Processing and Wireless Sensor Networks Department of ECE India

ISBN: (纸本)9798350391565

The global diabetes epidemic has exposed significant clinical variability in diabetic patients, including phenotypes that defy accepted characteristics. Atypical diabetic phenotypes in lungs are sifting through numerous data sets to establish connections and identify patterns to use data analysis to solve a certain issue. This article presents the Data Mining Framework based on the Atypical Diabetes Prediction Model (DMFADPM), built on the Data Mining Framework to identify and group the characteristics of A.D. in lungs. Data Mining Framework is used to solve the main problems of improving the prediction model's accuracy and enabling the model's adaptation to multiple datasets. The exclusion and inclusion processes are the two components of the preprocessing steps that form the foundation of the data mining technique. The experimental analysis of the proposed method has obtained an accuracy of 98.9%, prediction ratio of 0.22%, gain ratio of 95%, information gain of 92%. © 2024 ieee.

关键词： Atypical Diabetes Data Mining Diabetic Complications lungs Prediction Model

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Utilizing Consensus Mechanisms in Blockchain and IoT Edge Networks to Enhance Healthcare System

Utilizing Consensus Mechanisms in Blockchain and IoT Edge Ne...

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2024 international conference on Emerging Innovations and Advanced computing, INNOCOMP 2024

作者： Singh, Puneeta Sagar, Shrddha Galgotias University Greater Noida India

ISBN: (纸本)9798350376470

The mathematical processes of sharing for sensors and block nodes are as follows: prime number p and base g, and i. The sensor chooses Private Key A and provides Node with Public Key A. A = ga mod p is the formula used. Node chooses the private key b and provides the sensor with the public key b. The sensor calculates the shared key. Calculate shared key with Ksensor = Ba mod p v- Nodes. The transaction's signature is produced by HMA C when Kbc = Ksensor is used. By identifying outliers, HMAC and after the validation of signatures. The BIoT design was theoretically demonstrated to be one of the greatest options in the most recent trend, but actual testing is necessary for the research to be successful. The tests below were conducted using the DEVS toolbox by RED network to examine the relationship between sensors and actuators. Three characteristics were tested: latency, network utilization, and energy *** has advanced the digitalization of several industries, including healthcare, manufacturing, education, shipping, and retail systems. High data consumption occurs connection between the IoT application network and its network of sensors. As a result, issues with cloud architecture, privacy, security, excessive latency, and low data integrity arise from the operation of simulated IoT devices. While the BC-IoT paradigm includes many elements that foster trust, confidentiality, and data protection, a standalone IoT system still has several disadvantages. The most portable blockchain consensus technique was created for Internet of Things devices. The Rivest-Shamir-Adleman (RSA) algorithm is used to negotiate keys with blockchain nodes and sensors. These nodes collaborate to create Optimal Asymmetric Encryption Padding (OAEP) for sensor-aware transactions. Not just facilitating key sharing but also enabling. © 2024 ieee.

关键词： sensor nodes

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4th international conference on Dynamic Data Driven Applications systems, DDDAS 2022

4th International Conference on Dynamic Data Driven Applicat...

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4th international conference on Dynamic Data Driven Applications systems, DDDAS 2022

ISBN: (纸本)9783031526695

The proceedings contain 42 papers. The special focus in this conference is on Dynamic Data Driven Applications systems. The topics include: Physics-Aware Machine Learning for Dynamic, Data-Driven Radar Target Recognition;DDDAS for Optimized Design and Management of 5G and Beyond 5G (6G) Networks;DDDAS-Based Learning for Edge computing at 5G and Beyond 5G;monitoring and Secure Communications for Small Modular Reactors;Data Augmentation of High-Rate Dynamic Testing via a Physics-Informed GAN Approach;unsupervised Wave Physics-Informed Representation Learning for Guided Wavefield Reconstruction;passive Radio Frequency-Based 3D Indoor Positioning System via Ensemble Learning;deep Learning Approach for Data and computing Efficient Situational Assessment and Awareness in Human Assistance and Disaster Response and Battlefield Damage Assessment Applications;SpecAL: Towards Active Learning for Semantic Segmentation of Hyperspectral Imagery;generalized Multifidelity Active Learning for Gaussian-process-based Reliability Analysis;Multimodal IR and RF Based sensor System for Real-Time Human Target Detection, Identification, and Geolocation;learning Interacting Dynamic systems with Neural Ordinary Differential Equations;Relational Active Feature Elicitation for DDDAS;explainable Human-in-the-Loop Dynamic Data-Driven Digital Twins;transmission Censoring and Information Fusion for Communication-Efficient distributed Nonlinear Filtering;distributed Estimation of the Pelagic Scattering Layer Using a Buoyancy Controlled Robotic System;towards a Data-Driven Bilinear Koopman Operator for Controlled Nonlinear systems and Sensitivity Analysis;tracking Dynamic Gaussian Density with a Theoretically Optimal Sliding Window Approach;dynamic Data-Driven Digital Twins for Blockchain systems;Adversarial Forecasting Through Adversarial Risk Analysis Within a DDDAS Framework;essential Properties of a Multimodal Hypersonic Object Detection and Tracking System;power Grid Resilience: Data Gaps for Da

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Crop Prediction using NPK sensor: Novel Approach Using Machine Learning Algorithms

Crop Prediction using NPK Sensor: Novel Approach Using Machi...

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2024 international conference on Smart systems for Applications in Electrical Sciences, ICSSES 2024

作者： Siddique, Abbubukar Harikishan, K.N. Kaif, Mohammed Gowda, Nachiketha N. Aparanji, V.M. Karthik, S. Siddaganga Institute of Technology Department of ECE Tumakuru India Cyphertek solutions Bangalore India

ISBN: (纸本)9798350364040

Agriculture, a vital element in nourishing the world's growing population, faces significant challenges such as climate change, soil degradation, and water scarcity. Accurate crop prediction is essential for maximizing yield and efficient farming practices. However, traditional manual observation methods have limitations in accuracy, scalability, and timeliness. To address these issues, an automated solution integrating NPK sensors emerges as a viable option. These sensors measure key nutrients like Nitrogen, Phosphorus, and Potassium in the soil, providing real-time data on crop nutrition. By feeding NPK sensor data to machine learning models, accurate predictions can be generated. Implementation involves integrating NPK sensors with Raspberry Pi via RS485 to TTL converter and utilizing algorithms like Random Forest and Naïve Bayes for insights on crop suitability. Leveraging edge computing, it provides on-field accuracy, eliminating the need to transport soil samples to testing centers. This integration has the potential to transform modern agriculture, increasing yields while minimizing waste. © 2024 ieee.

关键词： Crops

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Advancements in Human Activity Recognition: A comprehensive Review of Smartphone and Wearable sensor Integration 7

Advancements in Human Activity Recognition: A comprehensive ...

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7th international conference on Inventive Computation Technologies, ICICT 2024

作者： Dhohada, Cheruku Sumalatha, Konatham Vellore Institute of Technology Computer Science And Engineering Vellore India Vellore Institute of Technology Department of Database Systems Vellore India

ISBN: (纸本)9798350359299

In recent years, deep learning and machine learning methods have been extensively employed in almost every field due to their capability of data processing and analysis. These are the subdomains of Artificial intelligence that gather information in different formats and from different sources. Human Activity Recognition using smartphones and wearable sensors has evolved into a dynamic field with numerous applications and challenges. Ongoing research efforts continue to refine methodologies, address challenges, and expand the scope of Human activity recognition, making it an exciting area with significant potential. Nowadays everyone is using smartphones, so it is easy to find their applications in health monitoring, fitness tracking, context-aware computing, and security. The ability to automatically detect activities such as walking, running, and sleeping opens avenues for personalized services and interventions that help in people's daily lives. While traditional machine learning and deep learning models offer valuable insights individually, ensemble learning presents an opportunity to further enhance accuracy and robustness. This research work aims to give a concise overview of the various deep learning and machine learning approaches, their convergence, and an analysis of the applications of these algorithms in the field of smartphone sensor-based human activity recognition. © 2024 ieee.

关键词： Learning systems

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Probabilistic Information Matrix Fusion in a Multi-Object Environment

Probabilistic Information Matrix Fusion in a Multi-Object En...

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ieee international conference on Multisensor Fusion and Integration for Intelligent systems (MFI)

作者： Yang, Kaipei Bar-Shalom, Yaakov Univ Connecticut Dept ECE Storrs CT 06269 USA

ISBN: (数字)9781665460262

ISBN: (纸本)9781665460262

In distributed sensor fusion systems, each of the local sensors has its own tracker processing local measurements for measurement-to-track association and state estimation. Only the processed data, local tracks (LT) comprising state vector estimates and their covariance matrices are transmitted to the fusion center (FC). In this work, a multi-object hybrid probabilistic information matrix fusion (MO-HPIMF) is derived taking into account all association hypotheses. In MO-HPIMF, the association carried out is between the FC track states (prediction) and the LT state estimates from local sensors. When having a large number of objects and sensors in fusion, only the m-best FC-track-to-LT association hypotheses should be incorporated in MO-HPIMF to reduce the computational complexity. A Sequential m-best 2-D method is used for solving the multidimensional assignment problem in this work. It is shown in the simulations that MO-HPIMF can successfully track all targets of interest and is superior to track-to-track fusion (T2TF, a commonly used approach in distributed sensor fusion system) which relies on hard association decisions.

关键词： Target tracking Uncertainty Computational modeling sensor fusion Probabilistic logic Covariance matrices Computational complexity

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Optimizing Cloud Energy Consumption Using Static Task Scheduling Algorithms: A Comparative Study 14

Optimizing Cloud Energy Consumption Using Static Task Schedu...

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14th international conference on Information and Communication systems, ICICS 2023

作者： Khiat, Abdelhamid Research Center on Scientific and Technical Information Networks and Distributed Systems Division Algiers Algeria

ISBN: (纸本)9798350307870

Cloud data centers, comprising a diverse set of heterogeneous resources working collaboratively to achieve high-performance computing, face the challenge of resource dynamism, where performance fluctuates over time. This dynamism poses complexities in task scheduling, warranting further research on the resilience of existing static task scheduling algorithms when deployed in dynamic cloud environments. This study adapts three well-known task scheduling algorithms to the cloud computing context and conducts a comprehensive comparison to assess their resilience to dynamic conditions. The evaluation, employing simulation techniques, analyzes total energy consumption and total response time as key metrics. The results offer detailed insights into the effectiveness of the adapted algorithms, providing valuable guidance for optimizing task scheduling in dynamic cloud data centers. © 2023 ieee.

关键词： Energy utilization

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Minimum Utilization of Energy Resource via MAC Routing Protocol for Mobile sensor Network 4

Minimum Utilization of Energy Resource via MAC Routing Proto...

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4th ieee Annual Flagship India Council international Subsections conference, INDISCON 2023

作者： Singh, Alka Desai, Meena College of Education Iimt University Uttar Pradesh Meerut India Isme - School of Management & Entrepreneurship Atlas Skill Tech University Maharashtra Mumbai India

ISBN: (纸本)9798350333558

The MAC routing protocol for mobile sensor networks is a method of communication that enables the data transfer between sensors over an established network. This protocol is designed to provide reliable, fast and efficient data delivery between nodes within a mobile sensor network. It differs from the traditional routing protocol in that it takes into consideration the mobility of the nodes and changes the routes dynamically to meet the dynamic requirements of the network. The main features of MAC for mobile sensor networks include route discovery and maintenance, dynamic path selection and dynamic quality of service. It also ensures reliability by providing multiple paths for data transmission and path diversity for redundant data transfer. Additionally, it provides fault tolerance and security via neighbor discovery and authentication. The protocol is also suited to heterogeneous networks due to the support for different types of communication technologies such as 802.11, ZigBee, etc. Furthermore, the MAC routing protocol provides multiple solutions such as deferred ordering, probabilistic forwarding and distributed routing. The MAC routing protocol, therefore, provides an effective way to connect sensors in mobile sensor networks. © 2023 ieee.

关键词： Routing protocols

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Edge Intelligence-Based E-Health Wireless sensor Network systems 23

Edge Intelligence-Based E-Health Wireless Sensor Network Sys...

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23rd ieee/ACIS international conference on Computer and Information Science, ICIS 2023

作者： Hu, Bintao Isaac, Matilda Abdul Majeed, Anwar P.P. Liu, Hengyan School of Internet of Things Xi'an Jiaotong-Liverpool University Suzhou China School of Robotics Xi'an Jiaotong-Liverpool University Suzhou China School of Ai and Advanced Computing Xi'an Jiaotong-Liverpool University Suzhou China

ISBN: (纸本)9798350345841

In order to support delay-sensitive applications of healthcare internet of things (HIoT) network systems, it is necessary to allow e-health wearable devices to process their computational applications at an artificial intelligence (AI) based mobile edge computing (MEC) server. Existing works mainly focused on optimising the offloading decisions of all users while minimising their corresponding energy consumption and/or transmission/processing delays. However, the huge data volumes offloading and/or privacy-preserving data processing have been largely ignored. In this paper, we consider an edge intelligence-based e-health wireless sensor network system, where the queues at the MEC server follow M/M/1 queueing model. To minimise energy consumption among all e-health wearable devices, we propose to jointly optimise the FL model task offloading decisions of all e-health wearable devices while guaranteeing delay constraints (which include the transmission delay, queueing delay and processing delay) and global/local FL training accuracy. This is achieved by devising an FL-based edge intelligence queueing offloading decision optimisation algorithm. Simulation results demonstrate that our proposed algorithm achieves a much lower maximum service delay than the benchmarks. © 2023 ieee.

关键词： Energy utilization

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