Survey on power analysis attacks and its impact on intelligent sensor networks

Shanmugham, Shanthi Rekha; Paramasivam, Saravanan

doi:10.1049/iet-wss.2018.5157

Cited by 16 publications

(10 citation statements)

References 106 publications

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“…e Internet of ings connects all items to the Internet through information sensing methods such as radio frequency identification, infrared induction, optical induction, and barcode scanning, so as to achieve information exchange and communication and achieve intelligent identification, positioning, tracking, monitoring, and management [17,18]. Figure 1 shows a diagram of the IoT system architecture.…”

Section: Internet Of Ings Intelligent Control Systemmentioning

confidence: 99%

Medical Equipment Comprehensive Management System Based on Cloud Computing and Internet of Things

Shang

Zhao

et al. 2021

Journal of Healthcare Engineering

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The continuous progress in modern medicine is not only the level of medical technology, but also various high-tech medical auxiliary equipment. With the rapid development of hospital information construction, medical equipment plays a very important role in the diagnosis, treatment, and prognosis observation of the disease. However, the continuous growth of the types and quantity of medical equipment has caused considerable difficulties in the management of hospital equipment. In order to improve the efficiency of medical equipment management in hospital, based on cloud computing and the Internet of Things, this paper develops a comprehensive management system of medical equipment and uses the improved particle swarm optimization algorithm and chicken swarm algorithm to help the system reasonably achieve dynamic task scheduling. The purpose of this paper is to develop a comprehensive intelligent management system to master the procurement, maintenance, and use of all medical equipment in the hospital, so as to maximize the scientific management of medical equipment in the hospital. Scientific Management. It is very necessary to develop a preventive maintenance plan for medical equipment. From the experimental data, it can be seen that when the system simultaneously accesses 100 simulated users online, the corresponding time for submitting the equipment maintenance application form is 1228 ms, and the accuracy rate is 99.8%. When there are 1000 simulated online users, the corresponding time for submitting the equipment maintenance application form is 5123 ms, and the correct rate is 99.4%. On the whole, the medical equipment management information system has excellent performance in stress testing. It not only predicts the initial performance requirements, but also provides a large amount of data support for equipment management and maintenance.

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Section: Internet Of Ings Intelligent Control Systemmentioning

confidence: 99%

Medical Equipment Comprehensive Management System Based on Cloud Computing and Internet of Things

Shang

Zhao

et al. 2021

Journal of Healthcare Engineering

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“…Edge computing refers to an open platform that integrates network, computing, storage and application core capabilities on the side close to the source of objects or data, and provides services nearby. Its applications are launched on the edge side, resulting in faster network service response and meeting the basic needs of the industry in real-time processing, intelligent application, security and privacy protection [6]. Edge computing is between physical entities and industrial connections, or at the top of physical entities, while cloud computing can still access the historical data of edge computing.…”

Section: Edge Calculationmentioning

confidence: 99%

Research on Intelligent Power Automation Technology Based on Edge Computing of Power Internet of Things

Su¹,

Lai²,

Zhang³

et al. 2020

E3S Web Conf.

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With the rapid development of modern economy and the rapid promotion of science and technology, electricity is the realistic need for further transformation of various industries in contemporary society. Because the traditional power management mode is relatively backward and the automation level of different industries is uneven, these will limit the development of intelligent power automation. Therefore, based on the edge computing of power Internet of Things, this paper studies intelligent power automation technology, which is of great value for realizing power automation and improving the management level of power equipment. In this paper, aiming at the research of intelligent power automation technology, based on the edge computing of power Internet of Things, the Internet of Things system based on RFID technology is studied. This system constructs the basic architecture of Internet of Things for smart power applications. The architecture is composed of sensing layer, network layer and application layer, which is used to realize information collection, identification and transmission, and its application in the production management of power Internet of Things. This paper analyzes the functional requirements, factors to be considered, main features and main functions of power equipment management system, gives the design ideas of power equipment management system, and designs an intelligent power equipment system based on edge computing of power Internet of Things. The research results show that RFID technology based on edge computing of power Internet of Things is feasible to realize intelligent power automation, can solve some problems in current power technology, and is of great significance to realize intelligent power automation.

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“…Initially, a few pre-selected trials are replicated precisely like in normal flow. Once a certain test encompassing the worst-case possibilities has been accurately analyzed, it is feasible to have other trials that create power problems [7]. Also, for such designs, the remedial repetition doesn't always work for others.…”

Section: Introduction To Power Estimationmentioning

confidence: 99%

Power Prediction of VLSI Circuits Using Machine Learning

Poovannan¹,

Karthik²

2023

Computers, Materials &Amp; Continua

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The difference between circuit design stage and time requirements has broadened with the increasing complexity of the circuit. A big database is needed to undertake important analytical work like statistical method, heat research, and IR-drop research that results in extended running times. This unit focuses on the assessment of test strength. Because of the enormous number of successful designs for current models and the unnecessary time required for every test, maximum energy ratings with all tests cannot be achieved. Nevertheless, test safety is important for producing trustworthy findings to avoid loss of output and harm to the chip. Generally, effective power assessment is only possible in a limited sample of pre-selected experiments. Thus, a key objective is to find the experiments that might give the worst situations again for testing power. It offers a machine-based circuit power estimation (ML-CPE) system for the selection of exams. Two distinct techniques of predicting are utilized. Firstly, to find testings with power dissipation, it forecasts the behavior of testing. Secondly, the change movement and energy data are linked to the semiconductor design, identifying small problem areas. Several types of algorithms are utilized. In particular, the methods compared. The findings show great accuracy and efficiency in forecasting. That enables such methods suitable for selecting the worst scenario.

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Survey on power analysis attacks and its impact on intelligent sensor networks

Cited by 16 publications

References 106 publications

Medical Equipment Comprehensive Management System Based on Cloud Computing and Internet of Things

Medical Equipment Comprehensive Management System Based on Cloud Computing and Internet of Things

Research on Intelligent Power Automation Technology Based on Edge Computing of Power Internet of Things

Power Prediction of VLSI Circuits Using Machine Learning

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