An abstract to calculate big O factors of time and space complexity of machine code

Devi, Swagata; Selvam, K.; Rajagopalan, Sridhar

doi:10.1049/cp.2011.0483

Cited by 30 publications

(12 citation statements)

References 1 publication

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“…The measure of an algorithm's complexity is popularly using the big O notation, which essentially is a mathematical notation that describes the limiting behavior of a function when the argument tends to a value or infinity [36]. According to [37], this is frequently used in the analysis of algorithms to describe an algorithm's usage of computational resources: the worst case or average case running time or memory usage of an algorithm is often expressed as a function of the length of its input. The input sized of the proposed algorithm for constrained IoT devices is a fixed 16bytes block size of input, and thus of O(1) computational complexity in terms of the big O notation with respect to the input size and O(m), with a growing message size m, as there would be m blocks to encrypt.…”

Section: B Results and Analysis Of Computation Complexity Of The Effmentioning

confidence: 99%

Efficient Security Algorithm for Power-Constrained IoT Devices

Mamvong

Goteng

Zhou

et al. 2021

IEEE Internet Things J.

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Internet of Things (IoT) devices characterized by low power and low processing capabilities do not exactly fit into the provision of existing security techniques, due to their constrained nature. Classical security algorithms which are built on complex cryptographic functions often require a level of processing that low power IoT devices are incapable to effectively achieve due to limited power and processing resources. Consequently, the option for constrained IoT devices lies in either developing new security schemes or modifying existing ones to be more suitable for constrained IoT devices. In this work, an Efficient security Algorithm for Constrained IoT devices; based on the Advanced Encryption Standard is proposed. We present a cryptanalytic overview of the consequence of complexity reduction together with a supporting mathematical justification, and provisioned a secure element (ATECC608A) as a trade-off. The ATECC608A doubles for authentication and guarding against implementation attacks on the associated IoT device (ARM Cortex M4 microprocessor) in line with our analysis. The software implementation of the efficient algorithm for constrained IoT devices shows up to 35% reduction in the time it takes to complete the encryption of a single block (16bytes) of plain text, in comparison to the currently used standard AES-128 algorithm, and in comparison to current results in literature at 26.6%

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Section: B Results and Analysis Of Computation Complexity Of The Effmentioning

confidence: 99%

Efficient Security Algorithm for Power-Constrained IoT Devices

Mamvong

Goteng

Zhou

et al. 2021

IEEE Internet Things J.

View full text Add to dashboard Cite

show abstract

“…Generally, the O() notation [51] is used in computer science to analyze the performance or measure algorithm complexity. To illustrate this, if the algorithm has an N iteration loop for calculation, the O() is approximately O(N ) for this algorithm.…”

Section: ) Complexitymentioning

confidence: 99%

Empirical Analysis of Feature Reduction in Deep Learning and Conventional Methods for Foot Image Classification

Jaruenpunyasak

Duangsoithong

2021

IEEE Access

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Deep learning algorithms are employed in many applications, especially in medical fields such as gait analysis and human pose detection for rehabilitation. However, creating the desired model with deep learning algorithms requires high memory and computing costs, which is problematic because deep learning technologies must be run on low-power devices such as edge computing equipment. To deal with these problems, feature reduction methods reduce the memory and energy costs. This paper presents an empirical analysis of deep learning with feature reduction. The method classifies foot images for knee rehabilitation using convolutional and dense autoencoders. The obtained results are compared with those of conventional methods (histograms of oriented gradients and local binary pattern algorithms). The features were classified and compared using support vector machine, k-nearest neighbor, and multilayer perceptron methods. The experimental results demonstrate that the conventional method uses fewer features than the deep learning method with higher accuracy because its algorithm projects pixels onto the histogram. In addition, using fewer features in deep learning layers maintains high accuracy, which is beneficial for edge computing implementations.

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“…AHP is used to obtain weights that should be used for datasets, and since it is only dependent on the number of features used, it may follow that regardless of the size of the dataset the process always takes a constant time. Therefore, in computing time complexity the constant time (C) is added to the dataset size (n) [31]. The classification setting is also a common method hence its time complexity is considered.…”

Section: Time and Space Complexitymentioning

confidence: 99%

A multi-criteria assessment of decision support systems in educational environments

Alowaigl

Al-Shqeerat

Hadwan

2021

IJEECS

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<span>Decision support systems (DSS) are useful business intelligence (BI) tools as they help managers in large organizations make the best out of many decisions. Decisions are based on various types of raw data, models, documents, knowledge, and past experiences. This paper examines numerous criteria of decision support systems in the educational environment. Two effective methods were discovered and applied in this research, the analytic hierarchy process (AHP) and simple multi-attribute rating technique (SMART). These methods were selected due to their abilities to deal with complex decisional environments in general and widely used in practice for the educational environment in specific. The performance of methods is compared using two datasets called xApi-Education and IPEDS datasets. The obtained results based on the measurement of space complexity showed the level of convergence and similarity between these two methods. However, the experiments show that the Simple Multi-Attribute Rating Technique outperformed the analytic hierarchy process in terms of accuracy, deviation, and time complexity measurement.</span>

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An abstract to calculate big O factors of time and space complexity of machine code

Cited by 30 publications

References 1 publication

Efficient Security Algorithm for Power-Constrained IoT Devices

Efficient Security Algorithm for Power-Constrained IoT Devices

Empirical Analysis of Feature Reduction in Deep Learning and Conventional Methods for Foot Image Classification

A multi-criteria assessment of decision support systems in educational environments

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