Hash-MAC-DSDV: Mutual Authentication for Intelligent IoT-Based Cyber-Physical Systems

Adil, Muhammad; Jan, Mian Ahmad; Mastorakis, Spyridon; Song, Houbing; Jadoon, Muhammad Mohsin; Abbas, Safia; Farouk, Ahmed

doi:10.48550/arxiv.2105.07711

Cited by 5 publications

(1 citation statement)

References 14 publications

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“…Here, we have studied the related works. Adil et al (2021) proposed a lightweight Hash media access control destination sequence distance vector (Hash-MAC-DSDV) routing method to solve authentication issues in CPS technologies based on IoT networks. They developed a CPS of IoT devices from the local and public chains.…”

Section: Task Scheduling and Mapping Mechanismsmentioning

confidence: 99%

A new approach for task managing in the fog-based medical cyber-physical systems using a hybrid algorithm

Wang

J.H.

et al. 2021

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Purpose This paper aims to offer a hybrid genetic algorithm and the ant colony optimization (GA-ACO) algorithm for task mapping and resource management. The paper aims to reduce the makespan and total response time in fog computing- medical cyber-physical system (FC-MCPS). Design/methodology/approach Swift progress in today’s medical technologies has resulted in a new kind of health-care tool and therapy techniques like the MCPS. The MCPS is a smart and reliable mechanism of entrenched clinical equipment applied to check and manage the patients’ physiological condition. However, the extensive-delay connections among cloud data centers and medical devices are so problematic. FC has been introduced to handle these problems. It includes a group of near-user edge tools named fog points that are collaborating until executing the processing tasks, such as running applications, reducing the utilization of a momentous bulk of data and distributing the messages. Task mapping is a challenging problem for managing fog-based MCPS. As mapping is an non-deterministic pol ynomial-time-hard optimization issue, this paper has proposed a procedure depending on the hybrid GA-ACO to solve this problem in FC-MCPS. ACO and GA, that is applied in their standard formulation and combined as hybrid meta-heuristics to solve the problem. As such ACO-GA is a hybrid meta-heuristic using ACO as the main approach and GA as the local search. GA-ACO is a memetic algorithm using GA as the main approach and ACO as local search. Findings MATLAB is used to simulate the proposed method and compare it to the ACO and MACO algorithms. The experimental results have validated the improvement in makespan, which makes the method a suitable one for use in medical and real-time systems. Research limitations/implications The proposed method can achieve task mapping in FC-MCPS by attaining high efficiency, which is very significant in practice. Practical implications The proposed approach can achieve the goal of task scheduling in FC-MCPS by attaining the highest total computational efficiency, which is very significant in practice. Originality/value This research proposes a GA-ACO algorithm to solve the task mapping in FC-MCPS. It is the most significant originality of the paper.

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Section: Task Scheduling and Mapping Mechanismsmentioning

confidence: 99%

A new approach for task managing in the fog-based medical cyber-physical systems using a hybrid algorithm

Wang

J.H.

et al. 2021

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Natural Language Description Generation Method of Intelligent Image Internet of Things Based on Attention Mechanism

Ouyang

2022

Security and Communication Networks

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With the rapid development of Internet of Things technology, the image data on the Internet are growing at an amazing speed. How to describe the semantic content of massive image data is facing great challenges. Attentional mechanisms originate from the study of human vision. In cognitive science, due to bottlenecks in information processing, humans selectively attend to a portion of all information while ignoring the rest of the visible information. This study mainly discusses the natural language description generation method of Internet of Things intelligent image based on attention mechanism. In this study, a CMOS sensor based on Internet of Things technology is used for image data acquisition and display. FPGA samples cis16bit parallel port data, writes FIFO, stores image data, and then transmits it to host computer for display through network interface. In order to minimize the value of cross-entropy loss function, maximum-likelihood estimation is used to maximize the joint probability of word sequences in the language model when sentence descriptions are generated using the encoder-decoder framework. At each moment, in addition to image features, additional text features are input. Image feature vector and text feature vector are weighted and summed by attention mechanism at each time. In decoding, the attention mechanism gives each image region feature weight, and the long-term and short-term memory network decodes in turn, but the long-term and short-term memory network has limited decoding ability. We use bidirectional long-term and short-term memory network instead of long-term memory network, and dynamically focus on context information through forward LSTM and reverse LSTM. The specificity of the proposed network is 5% higher than that of the 3D convolution residual link network. The results show that the performance of image description model is improved by inputting image context and text context into long-term memory network decoder.

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Design and Implementation of Human Motion Monitoring System on Account of Intelligent Computing of Internet of Things

Liu¹,

Zhang²

2022

Journal of Electrical and Computer Engineering

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The era of big data network represented mainly by the Internet of Things is limited by various factors such as various environments, volumes, and calculations. This paper proposes and studies a human motion detection system based on the intelligent computing of the Internet of Things, which can effectively detect the daily motion of the human body. In the era of the Internet of Things, this paper designs and develops a human motion detection system based on the Internet of Things technology and intelligent computing and explores the law of normal human motion. This paper also analyzes the design ideas and technical advantages of the human motion detection system from many aspects. This research is a human motion detection system designed and developed under the comprehensive use of Internet of Things technology and intelligent computing technology, which can effectively detect the actual situation of the human body in the state of motion. Experimental results show that the overall accuracy of the system for monitoring and recognizing human walking is 96.91%, and the overall accuracy of monitoring and recognizing human jogging is 97.18%. It monitors and recognizes human jogging with an overall accuracy rate of 97.96%. It has great practical significance.

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Hash-MAC-DSDV: Mutual Authentication for Intelligent IoT-Based Cyber-Physical Systems

Cited by 5 publications

References 14 publications

A new approach for task managing in the fog-based medical cyber-physical systems using a hybrid algorithm

A new approach for task managing in the fog-based medical cyber-physical systems using a hybrid algorithm

Natural Language Description Generation Method of Intelligent Image Internet of Things Based on Attention Mechanism

Design and Implementation of Human Motion Monitoring System on Account of Intelligent Computing of Internet of Things

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