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1st international conference on Advanced Network Technologies and Intelligent computing (ANTIC)

作者： Pavan, Badarla Sri Harigovindan, V. P. Natl Inst Technol Puducherry Dept ECE Karaikal 609609 India

ISBN: (纸本)9783030960407;9783030960391

The ieee 802.11 WLAN standards are very popular since three decades due to its flexibility and operation in unlicensed frequency bands. It provides physical and MAC layer specifications. We present an analytical model with a channel access scheme to improve the aggregate utility of WLAN using non-orthogonal multiple access (NOMA). The NOMA is used at physical layer and in MAC layer distributed coordination function (DCF) is considered. With the inclusion of NOMA in WLAN, multiple transmissions are possible in a resource block. Moreover, it mitigates the contention process to enhance the performance of WLAN. With the analytical model, we show the performance of NOMA based WLAN improved compared to conventional WLAN. The analytical results are validated with simulations.

关键词： Internet of things ieee 802.11 WLAN NOMA

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Fault Identification Using Fuzzy In Renewable Energy Interfaced ieee 13 Bus System 1

Fault Identification Using Fuzzy In Renewable Energy Interfa...

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1st international conference on Intelligent Controller and computing for Smart Power, ICICCSP 2022

作者： Ekka, Aradhana Yadav, Anamika National Institute of Technology Raipur Department of Electrical Engineering Raipur India

ISBN: (数字)9781665472586

ISBN: (纸本)9781665472586

Nowadays, distributed generation (DG) based on renewable energy resources has accelerated due to policymakers' encouragement, worldwide worries about CO2 emissions, energy scarcity, and the desire for sustainable energy generation. This paper discusses the use of fuzzy logic to recognize and identify power transmission faults. Current waveforms are affected by transients when a defect occurs in the organization, and their pattern varies depending on what type of failure. The MATLAB-generated three-phase alternating current, zero-sequence, and positive-sequence recent data are directly processed for classification. The results show that the suggested technique is capable of effectively identifying and classifying the type of failure in an ieee 13 bus system with a solar panel system as a source. The efficacy of the proposed controller for detecting faults in the line has been determined by the analysis. © 2022 ieee.

关键词： Fuzzy logic

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Clinical application of remote fetal heart rate monitoring system for AI early warning1 25

Clinical application of remote fetal heart rate monitoring s...

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25th ieee international conferences on High Performance computing and Communications, 9th international conference on Data Science and systems, 21st ieee international conference on Smart City and 9th ieee international conference on Dependability in sensor, Cloud and Big Data systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Zhuang, Chunyu Chen, Xiaojing Lin, Chunlan Han, Yan Zhang, Qingmei Du, Qiongliang Duan, Yucong Chen, Jiacheng Haikou Maternal and Child Health Care Hospital Nursing Department Hainan Haikou570203 China Haikou Maternal and Child Health Care Hospital Neonatology Hainan Haikou570203 China Haikou Maternal and Child Health Care Hospital Pediatric Intensive Care Unit Hainan Haikou570203 China School of Computer Science and Technology Hainan University Hainan Haikou570203 China Hainan General Hospital Department of Hepatobiliary and Pancreatic Surgery Hainan Haikou570311 China

ISBN: (纸本)9798350330014

To compare the safety, effectiveness and effect of the AI early warning system and the traditional fetal monitoring system. Methods: Establish and deploy AI remote fetal monitoring system;From January 1, 2022 to December 31, 2022, 440 pregnant women in the third trimester of pregnancy admitted to the Maternal and Child Health Hospital of Haikou City and meeting the admission criteria were selected and randomly assigned to the artificial intelligence early warning remote fetal visit group 220 cases and the traditional group 220 cases. In the AI group, remote fetal monitoring system was used for examination and treatment, while in the traditional group, pregnant women went to the hospital for examination and delivery according to the usual standard procedures. The effectiveness and safety of the AI-early warning impromptu fetal heart test system were observed, and the rates of perinatal adverse outcomes (low birth weight, perinatal death, premature delivery, neonatal asphyxia, cord blood oxygen saturation and fetal distress) of pregnant women in the two groups were compared. Results: In the test of artificial intelligence early warning fetal monitoring system, the AI group detected 332 normal fetal cardiogram cases of pregnant women, 206 suspected pathological changes in 18 cases;In the traditional group, 421 cases of normal fetal cardiogram were detected, 117 cases of suspected pathological changes were detected in 16 cases. The sensitivity and specificity of normal pregnant women (true positive was normal group, false negative was suspicious + lesion) were 81.55% and 96.98% respectively. The sensitivity and specificity of pregnant women with abnormal fetal monitoring map were 100% and 82%. The low birth weight rate was 6/220 in the AI group and 13/220 in the control group, with a total value of 2.695 (p value 0. 101). Perinatal death was 0 in both groups. The rate of preterm infants in AI group was 7/220, compared with 15/220 in control group, and peason2 was 3.062,

关键词： Obstetrics

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The smart secured harvesting technique for embedded wireless sensor networking using network deep learning model 1

The smart secured harvesting technique for embedded wireless...

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1st ieee international Interdisciplinary Humanitarian conference for Sustainability, IIHC 2022

作者： Sharma, Neeraj Singh, Kushdeep Vivekananda Global University Department of Electrical Engineering Jaipur India Chandigarh Engineering College Department of Mechanical Engineering Jhanjeri India

ISBN: (纸本)9781665456876

The well-established term wireless sensor network (WSN) refers to a new class of wireless systems, which are networks of distributed, self-organizing and fault-tolerant miniature electronic devices. Autonomic sources of nutrients. Intelligent nodes of such a network are capable of sending messages along the chain, providing a significant system coverage area with low transmitter power, and as a result, the high energy efficiency of the system becomes a potential component. To ensure high reliability and security of information transmitted in WSN, it is necessary to develop native radio protocols that are resistant to changes in the characteristics of the communication channel, radio congestion, interception and data imitation. In this paper the secured harvesting model was proposed for embedded wireless sensor networking. In this case, it is better to use spread spectrum technologies DSSS (direct number sequence) and FHSS (frequency hopping) methods. Regarding the means of accessing the data transmission medium, mutually exclusive requirements appear for the high energy efficiency of the system and the minimum time delay for data propagation in the WSN. Using CSMA/CA (Carrier Sense Multiple Media Access with Collision Avoidance) as the underlying mechanism has its drawback - network devices must be constantly listening over the air, which leads to increased power consumption. © 2022 ieee.

关键词： Harvesting

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SOA Based Improved Convolutional Neural Network for Detection of DDoS Attack in Banking Dataset 1

SOA Based Improved Convolutional Neural Network for Detectio...

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1st ieee international conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Kumar, M. Giridhar Kolla, Nagaraju Kotagi, Veeranna Mathapati, Mahantesh G. Pullaiah College of Engineering and Technology Kurnool India School of Management Studies Jawaharlal Nehru Technological University Anantapuram India Nitte Meenakshi Institute of Technology Dept. of Ise Bengaluru India Amruta Institute of Engineering and Management Sciences Dept. of Cse Bengaluru India

ISBN: (纸本)9798350316469

Due to the anticipated rise of the IoT, in which sensors and actuators interact autonomously to provide various forms of smart services, there has been a surge in the demand for embedded devices. The expansion of Internet of Things (IoT) applications now relies heavily on fog computing. However, a DDoS assault on a mining pool in 2017 revealed serious vulnerabilities in the blockchain-enabled IoT. The banking sector is especially vulnerable because of the high pecuniary charge of the data it possesses. As banks increase their digital imprints, so does the attack surface for cybercriminals. This paper's goal is to identify Banking Dataset-based DDOS attacks against banking institutions. In order to upsurge the reliability of the categorization results, this study pre-processes the data. Then, the hybrid model known as an Improved Convolutional Neural Network is fed the extracted features as an input. Using a randomised parametric rectified linear unit (RPReLU) function, this network is constructed. In contrast, the low learning efficiency that results from the standard dropout procedure's zero-setting operation of weights is due to its random nature and its inability to differentiate among features with varying weights. In order to boost the manual orientation and the learning efficiency of the dropout process, this study presents a new dropout approach based on a support vector machine (SVM) and calls it RSCNN. Using the Sandpiper Optimisation algorithm (SOA), the suggested representation's weights are chosen optimally. The experimental consequences demonstrate that the suggested model has a high degree of accuracy (94.66%), precision (95.5%), sensitivity (0.95), and F-score (0.95). The RSCNN-SOA has been found to be more stable than standard methods. © 2023 ieee.

关键词： Banking data distributed denial-of-service Improved Convolutional Neural Network Randomized parametric rectified linear units Sandpiper Optimization Support vector machine

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Intelligent Hybrid Low Energy Adaptive Cluster Head Selection Algorithm for Effective Data Communication in Underwater sensor Network 1

Intelligent Hybrid Low Energy Adaptive Cluster Head Selectio...

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1st international conference on Applied Artificial Intelligence and computing, ICAAIC 2022

作者： Majitha, A. Christina, Chitra Evangelin Gifta, Irine Sophiya. T. B. E. Archana, M. Tirunelveli India

ISBN: (纸本)9781665497107

There are different issues in planning underwater wireless sensor networks (WSNs and UWSNs). These challenges are basically because of the remarkable attributes of networks like, Limited battery and confined stockpiling. Clustering is one of the methods utilized in WSNs and UWSNs to diminish energy utilization. It involves portioning networks into clusters, with each group comprising of a cluster head and sensor nodes. Clustering is a method for eliminating repetitive information from a few sensor nodes simultaneously or from similar sensor nodes at various time steps. To safeguard the organization's coherence and give appropriate interconnection between sensor nodes conveyed in the antagonistic climate, compelling energy utilization is fundamental. In this substance, boosting the Lifetime of the WSN submerged Distribution framework space is basic objective for guaranteeing its persistent activity and empowering an expected answer for water driven harm discovery in light of an assortment of execution measures. To build the UWSNs Lifetime, Intelligent Hybrid Low Energy Adaptive Clustering Head and relay node selection algorithm is proposed. In this paper a new equation for deciding the threshold value for head selection to utilizing node's normal energy to get most noteworthy life expectancy. © 2022 ieee.

关键词： sensor nodes

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A distributed Agent-Oriented Framework for Blockchain-Enabled Supply Chain Management 1

A Distributed Agent-Oriented Framework for Blockchain-Enable...

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1st ieee international conference on Blockchain and distributed systems Security, ICBDS 2022

作者： Swain, Satyananda Patra, Manas Ranjan Berhampur University Department of Computer Science Odisha Berhampur India

ISBN: (数字)9781665428323

ISBN: (纸本)9781665428323

Blockchain has emerged as a leading technological innovation because of its indisputable safety and services in a distributed setup. Applications of blockchain are rising covering varied fields such as financial transactions, supply chains, maintenance of land records, etc. Supply chain management is a potential area that can immensely benefit from blockchain technology (BCT) along with smart contracts, making supply chain operations more reliable, safer, and trustworthy for all its stakeholders. However, there are numerous challenges such as scalability, coordination, and safety-related issues which are yet to be resolved. Multi-agent systems (MAS) offer a completely new dimension for scalability, cooperation, and coordination in distributed culture. MAS consists of a collection of automated agents who can perform a specific task intelligently in a distributed environment. In this work, an attempt has been made to develop a framework for implementing a multi-agent system for a large-scale product manufacturing supply chain with blockchain technology wherein the agents communicate with each other to monitor and organize supply chain operations. This framework eliminates many of the weaknesses of supply chain management systems. The overall goal is to enhance the performance of SCM in terms of transparency, traceability, trustworthiness, and resilience by using MAS and BCT. © 2022 ieee.

关键词： Smart contract

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A Knowledge Graph-Based Deep Learning Framework for Efficient Content Similarity Search of Sustainable Development Goals Data

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Data Intelligence 2023年第3期5卷 663-684页

作者： Irene Kilanioti George A.Papadopoulos School of Electrical and Computer Engineering National Technical University of Athens9 Heroon Polytechneiou St.Zografou Campus Athens15780Greece Department of Computer Science University of Cyprus1 University AvenueAglantziaCY-2109NicosiaCyprus

Sustainable development denotes the enhancement ofliving standards in the present without compromising future generations'*** Development Goals(SDGs)quantify the accomplishment of sustainable development and pave the way for a world worth living in for future *** can contribute to the achievement of the SDGs by guiding the actions of practitioners based on the analysis of SDG data,as intended by this *** propose a framework of algorithms based on dimensionality reduction methods with the use of Hilbert Space Filling Curves(HSFCs)in order to semantically cluster new uncategorised SDG data and novel indicators,and efficiently place them in the environment of a distributed knowledge graph ***,a framework of algorithms for insertion of new indicators and projection on the HSFC curve based on their transformer-based similarity assessment,for retrieval of indicators and loadbalancing along with an approach for data classification of entrant-indicators is ***,a thorough case study in a distributed knowledge graph environment experimentally evaluates our *** results are presented and discussed in light of theory along with the actual impact that can have for practitioners analysing SDG data,including intergovernmental organizations,government agencies and social welfare *** approach empowers SDG knowledge graphs for causal analysis,inference,and manifold interpretations of the societal implications of SDG-related actions,as data are accessed in reduced retrieval *** facilitates quicker measurement of influence of users and communities on specific goals and serves for faster distributed knowledge matching,as semantic cohesion of data is preserved.

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SmartON: Just-in-Time Active Event Detection on Energy Harvesting systems 17

SmartON: Just-in-Time Active Event Detection on Energy Harve...

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17th Annual ieee international conference on distributed computing in sensor systems (ieee DCOSS)

作者： Luo, Yubo Nirjon, Shahriar Univ N Carolina Dept Comp Sci Chapel Hill NC 27515 USA

ISBN: (纸本)9781665439299

We propose SmartON, a batteryless system that learns to wake up proactively at the right moment in order to detect events of interest. It does so by adapting the duty cycle to match the distribution of event arrival times under the constraints of harvested energy. While existing energy harvesting systems either wake up periodically at a fixed rate to sense and process the data, or wake up only in accordance with the availability of the energy source, SmartON employs a three-phase learning framework to learn the energy harvesting pattern as well as the pattern of events at run-time, and uses that knowledge to wake itself up when events are most likely to occur. The three-phase learning framework enables rapid adaptation to environmental changes in both short and long terms. Being able to remain asleep more often than a CTID (charging-then-immediate-discharging) wake-up system and adapt to the event pattern, SmartON is able to reduce energy waste, increase energy efficiency, and capture more events. To realize SmartON we have developed a dedicated hardware platform whose power management module activates capacitors on-the-fly to dynamically increase its storage capacitance. We conduct both simulation-driven and real-system experiments to demonstrate that SmartON captures 1X-7X more events and is 8X-17X more energy-efficient than a CTID system. All source code and hardware design files are open-sourced(1).

关键词： Event detection Power system management Capacitance Energy efficiency Hardware Software sensor systems

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The Parallelized Cuckoo Filter for Cold Data Representation 23

The Parallelized Cuckoo Filter for Cold Data Representation

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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： Sun, Bowen Luo, Lailong Li, Shangsen Chen, Yingwen Guo, Deke College of Computer National University of Defense Technology China National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory China College of Computer National University of Defense Technology National Lab for Parallel and Distributed Processing China

ISBN: (纸本)9781665494571

Cold data contributes a large portion of the big data today and is usually stored in secondary storage. Various sketch data structures are implemented to represent the stored elements and provide constant-time membership queries. Considering the large volume and infrequent access of cold data, we envision three rationales of cold data representation: 1) parallelized insertion, 2) accurate representation, and 3) constant-time query/deletion. The existing sketches, however, fail to achieve these rationales simultaneously. To this end, we propose a novel sketch named Parallelized cuckoo Filter (PCF) in this paper. PCF divides the standard cuckoo filter into multiple homogenous segments. An additional hash function is introduced to assign an element to a segment. The element will be inserted into the selected segment. With such a design, element insertions in PCF can be naturally parallelized. Besides, as a variant of the cuckoo filter, PCF naturally realizes the accurate representation and constant-time query/deletion requirements. Comprehensive experiments further indicate that PCF achieves the three rationales simultaneously. © 2021 ieee.

关键词： Hash functions Smart cities Big Data Data structures standards

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