Dynamic Selection of Symmetric Key Cryptographic Algorithms for Securing Data Based on Various Parameters

Masram, Ranjeet; Shahare, Vivek; Abraham, Jibi; Moona, Rajni; Sinha, Pradeep K.; Sunder, Gaur; Bendale, Prashant; Pophalkar, Sayali

doi:10.5121/csit.2014.4519

Cited by 4 publications

(1 citation statement)

References 2 publications

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“…Compression Ratio is a ratio between the size of the compressed file and the size of the source file. The compression factor is the inverse of the compression ratio [3][24] [25][26] [27].…”

Section: Implementation and Indicatormentioning

confidence: 99%

Comparison of Lossless Compression Schemes for WAV Audio Data 16-Bit Between Huffman and Coding Arithmetic

Hidayat

Zakaria

Pee

2019

International Journal of Simulation: Systems, Science &Amp; Technology

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In this paper we give an overview and compare Huffman and Arithmetic algorithm using PCM audio data 16 bits stereo. The motivation for the study is to increase the transmission speed in the information exchange which is greatly dependent upon the size of the information. Compression has become one of the solutions to the above problem. The quest for efficient audio data compression rate is still ongoing and good quality compression particularly for music files are greatly sought after by the industry as well as users. Currently music files are stored in various formats and the paper will focus on a lossless technique. The technique is applied to raw WAV files with a comparison of two algorithms, namely Huffman and Arithmetic, and to compare the parameters of file size, ratio, speed, the factor of compression and waveform. The results are then compared to previous existing research and then present the difference of the test result on other data such as text and images.

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“…Compression Ratio is a ratio between the size of the compressed file and the size of the source file. The compression factor is the inverse of the compression ratio [3][24] [25][26] [27].…”

Section: Implementation and Indicatormentioning

confidence: 99%

Comparison of Lossless Compression Schemes for WAV Audio Data 16-Bit Between Huffman and Coding Arithmetic

Hidayat

Zakaria

Pee

2019

International Journal of Simulation: Systems, Science &Amp; Technology

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Advanced lightweight encryption algorithms for IoT devices: survey, challenges and solutions

Singh

Sharma

Moon

et al. 2017

J Ambient Intell Human Comput

258

111

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Design and Simulation of an Edge Compute Architecture for IoT-Based Clinical Decision Support System

Kumar,

Rajaram

2024

IEEE Access

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Clinical Decision Support Systems (CDSS) have revolutionized healthcare by leveraging modern technologies such as internet of things (IoT), artificial intelligence (AI), predictive analysis, nanomedicine, and virtual & augmented reality. IoT-based CDSS is of interest in particular. In a hospital scenario, a patient's vital signs like heart rate, blood pressure, respiration rate, ECG, EEG etc. are monitored with the use of embedded sensor devices, also called smart medical devices. These devices collect real-time data which is relayed to a compute device where several algorithms are employed to perform computations on said data to arrive at a prognosis e.g. real-time onset of hypotension can be detected by running predictive algorithms on real-time blood pressure data. The computation in IoT-based CDSS is done predominantly on the cloud, wherein the real-time data collected is relayed to a centralized cloud server. However, latency is a major drawback in a cloud-based monitoring system. Increased latency is of greater concern in healthcare applications as the decision-making process is time-sensitive. Edge computing can potentially overcome this drawback, wherein computation is done on edge-network devices rather than the cloud. While edge computing for IoT-based CDSS has been explored in literature, there are gaps in their implementations. A majority of literature dealing with edge computing for IoT-based healthcare only demonstrates a single application and does not address the varying data acquisition rates for different vital signs. Each prognosis or diagnosis requires different subsets of vital signs, and the underlying algorithm uses different sizes of data e.g. detecting arrhythmia requires processing of ECG data which is a time series data, and detecting cardiovascular disease requires blood pressure, cholesterol and certain habits of the patient which are mostly single points of data. This paper explores the use of edge computing in CDSS, quantifies its performance with respect to number of devices, sense time interval or intertransmission rate, and the size of data, and proposes a unified IoT edge gateway architecture to combine multiple patterns of data and computation algorithms to achieve reduced latency and network utilization. Simulation results show that edge computing reduces the latency of decision by approximately 87 times, and the network utilization by 1.5 times. The results show the efficacy of edge computing for implementing IoT-based CDSS and also demonstrate scalability with regard to the number of devices and the size and intertransmission rate of data. INDEX TERMSEdge computing , smart healthcare, vital sign monitoring, Internet of things, Clinical Decision support system. I. INTRODUCTIONC LINICAL Decision Support Systems (CDSS) are used in healthcare to provide stakeholders of the healthcare system including doctors, nursing staff, and patients with information necessary to make efficient decisions [1]. A traditional CDSS is a software system which aids in clinicaldecision-making wherei...

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Dynamic Selection of Symmetric Key Cryptographic Algorithms for Securing Data Based on Various Parameters

Abstract: Most of the information is in the form of electronic data. A lot of electronic data exchanged

Cited by 4 publications

References 2 publications

Comparison of Lossless Compression Schemes for WAV Audio Data 16-Bit Between Huffman and Coding Arithmetic

Comparison of Lossless Compression Schemes for WAV Audio Data 16-Bit Between Huffman and Coding Arithmetic

Advanced lightweight encryption algorithms for IoT devices: survey, challenges and solutions

Design and Simulation of an Edge Compute Architecture for IoT-Based Clinical Decision Support System

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