Forthcoming and Online First Articles

International Journal of Embedded Systems

International Journal of Embedded Systems (IJES)

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International Journal of Embedded Systems (11 papers in press)

Regular Issues

  • A survey on latency and power consumption estimation for embedded systems   Order a copy of this article
    by Nejra Beganovic, Mattias O'Nils 
    Abstract: Performance evaluation of Internet of Things (IoT) platforms becomes inevitable as the number of IoT devices is constantly increasing. Discussing from the aspect of their interdependences, it is of utmost importance to provide an efficient framework for the analysis of causal relation between consumed power, processing latency, data size reduction, and algorithm computational complexity of embedded systems. As embedded devices, operating often on limited and unreliable energy sources such as batteries or other energy harvesters, are the devices with the highest need for optimal power use, the main focus of this contribution is to review energy consumption modelling approaches and their relation to a latency modelling framework. Such analysis is necessary to provide the basis for efficient system design from early design stage and to guarantee the fulfillment of all system requirements. Accordingly, the paper points out not only existing challenges but also the possibilities for improvements with respect to power/energy savings.
    Keywords: power estimation; energy consumption; internet of things; embedded systems;.

  • Construction of Simulink-CarSim joint simulation platform for distributed drive electric vehicles   Order a copy of this article
    by Hua Cui, Bin Guo 
    Abstract: With the rapid development of emerging power systems and electric vehicle technology, distributed drive electric vehicles based on multiple motors and linear control technology have become an important trend in the future development of the automotive industry. However, the long research and development time and high investment in traditional automotive simulation research greatly hinder the development of distributed electric vehicles. To better address these issues, this article establishes a joint simulation platform based on the discussion of CarSim vehicle model and Simulink motor model. The simulation platform can output the longitudinal and transverse vehicle speeds in real-time, simplifying the complex process of building a distributed drive electric vehicle model, and can conduct simulation experiments without establishing a driver model and simulation conditions, It has laid the foundation for studying the stability and active safety of electric vehicle handling and has become an important means of distributed electric vehicle research, playing an important role in promoting the development of distributed electric vehicles.
    Keywords: simulation platform construction; distributed drive electric vehicle; Simulink-CarSim co-simulation; double shift line condition; serpentine pile winding condition.
    DOI: 10.1504/IJES.2023.10060389
     
  • Field-embedded database query system based on natural language processing   Order a copy of this article
    by Fei Long 
    Abstract: This research seeks to develop a paradigm that will improve user-database interaction. To convert the user's queries into structured query language (SQL), natural language processing (NLP) is needed, and then the SQL can be processed quickly by the query system in the embedded database. The primary goal of NLP is to facilitate human-computer interaction with little reliance on programming knowledge. To access the data efficiently, field embedded database query system (FEDQS) uses NLP to take in 2880 structured queries about train prices and seat availability from the train reservation database and turn them into a SQL query. Therefore, field embedded database query system (FEDQS) is suggested in this research to help the users access the data efficiently. The simulation findings show that the proposed method achieves a translation accuracy of 92%, precision of 91%, RMSE of 7%, and MAE of 9%.
    Keywords: field-embedded database; query system; natural language processing; NLP; structured query language; SQL.
    DOI: 10.1504/IJES.2023.10060443
     
  • Application of machine learning algorithm in operator shop intelligent selection data Data   Order a copy of this article
    by Chao Liu 
    Abstract: In order to improve the accuracy of data analysis, this paper applies machine learning algorithms to the analysis of smart selection data in operator shops. This paper introduces common machine learning algorithms, analyses the data to be analysed for intelligent selection in operator shops, applies machine learning algorithms to intelligent selection data in operator shops, and finally analyses the effect analysis of the application of machine learning algorithms, finally concluding that the analysis of intelligent selection data in operator shops using machine learning algorithms can not only improve calculation speed and calculation accuracy, but also improve generalisation. It can also reduce the omission rate of data, in which the omission rate of smart selection data of shop 5 is reduced to 5.67%. Machine learning algorithms will need to be applied in many more ways in future life.
    Keywords: smart selection data; machine learning algorithms; operator stores; applied science.
    DOI: 10.1504/IJES.2023.10060689
     
  • Mobile sensors-based detection of road conditions and quality   Order a copy of this article
    by Prabhat Singh, Abhay Bansal, Ahmed E. Kamal, Sunil Kumar 
    Abstract: As road infrastructure is a lifeline of transportation in modern society. Due to the frequent use of roads, maintenance, and monitoring at regular intervals become important. Indian roads have many anomalies factors such as poor construction quality, heavy traffic, poor drainage, weak sub grade, and large variations in temperature that can contribute to the creation of potholes, cracks, etc. Hence, authors are focusing on developing the most efficient and accessible application for road quality detection, that can focus on more problematic areas. In the first part the work is done on the collection of data sets with the help of Android in-built mobile sensors. The second part employs the machine learning algorithm on the dataset to depict the quality of the road. The third part focuses on the deployment of the machine learning model on the server-side and reverting the results to the application. The algorithm is based on machine learning algorithms and comparing the accuracies based on accelerometer data. Best accuracy was received by gradient boosting classifier technique. The accuracy obtained was 94.07% with 88% precisions core for detection of road quality so that accident can be reduced.
    Keywords: real-time road monitoring; smart phone; sensor; Android; machine learning; flutter.
    DOI: 10.1504/IJES.2023.10061009
     
  • A malicious traffic detection method based on Bayesian meta-learning for few samples   Order a copy of this article
    by Zhibin Liu, Zhanpeng Lv, Lixin Zhao, Min Li, Xin Liu 
    Abstract: Realistic network environments have difficulties collecting malicious traffic data, and training network models with virtually generated traffic data are inevitably disconnected from the real network situation. To address few sample problem, we propose a Bayesian meta-learning-based technique to detect encrypted malicious traffic. The internal loop of this meta-learning method is replaced by an analytical marginal likelihood calculation that can be directly implemented as a single optimiser. Experiments show that when the sample size of malicious traffic is reduced to 100, our model still detects up to 96.35%.
    Keywords: meta-learning; few samples; cross-domain detection; encrypted traffic.
    DOI: 10.1504/IJES.2023.10061091
     
  • Evaluation of CNN-based computer vision recommended treatments for recognised guava disease   Order a copy of this article
    by Vishal Kanaujia, Satya Prakash Yadav, Awadhesh Kumar, Victor Hugo C. De Albuquerque, Caio Dos Santos Nascimento 
    Abstract: Climate change poses a particular threat to the agricultural crop production sector. The entire food industry is affected by this issue, not just the farming sector. The diagnosis of plant diseases could be improved by using deep learning strategies, according to several studies. These samples are rarely analysed for their ability to predict quality. Extreme caution is required to organise agricultural output surgically. Detecting high incidence rates in commercial production is difficult because of the unfair model’s unpredictability, resulting in more difficulty in diagnosing reflex plant diseases. The proposed model is designed to identify the guava disease using convolutional neural networks (CNNs) and machine learning for classification. In which autoencoder is used to divide the neural network design in the encoder and decoder. The linear support vector machine is used as a classification to analyse the outcomes of our experiments. Preliminary results from the suggested model indicate a remarkable degree of accuracy (97.5%).
    Keywords: CNN feature extraction; guava disease; auto encoder preprocessing; data augmentation; plant disease detection.
    DOI: 10.1504/IJES.2023.10061388
     
  • Energy-efficient hybrid node localisation underwater wireless sensor network scheme   Order a copy of this article
    by Parul Gupta, Wajahat Gh. Mohd, Nitin Goyal, Sachin Kumar Gupta, Ashutosh Mishra 
    Abstract: The underwater network consists of a huge number of sensor nodes deployed sparsely and interconnected with each node to gather information about the ocean. The method by which the location of deployed sensor node is determined is called node localisation. But it is difficult to achieve the exact location coordinates of underwater sensor nodes. Since there are several localisation algorithms for terrestrial networks but those are not feasible for underwater wireless sensor networks (UWSN) because of the harsh environment of the ocean. In this paper, various UWSN localisation schemes are classified on the basis of range. Also, a hybrid model of node localisation is also suggested for better output and real-time detection of node position. Here, various underwater localisation schemes are reviewed and compared to the existing schemes. This comparison is based on NS2 simulator parameters to showcase better performance out of existing UWSN localisation techniques. Further, this examined similar survey papers to identify subtopics that have not been reviewed till then. On behalf of reported research gaps from the literature study, an improved node localisation scheme for mobile UWSN to explore the ocean is proposed that will perform better in terms of delay, error, cost, and energy consumption for localisation.
    Keywords: angel of arrival; AOA; challenges; classification; node localisation; received signal strength; RSS; underwater wireless sensor networks; UWSN.
    DOI: 10.1504/IJES.2023.10061597
     
  • Simulation and application of computer network security monitoring based on multi-difference embedded model   Order a copy of this article
    by Yuping Li, Ke Li 
    Abstract: In order to strengthen the maintenance of computer network security, this article uses the multi-differential embedding model to monitor, simulate and apply research on computer network security. This article analyses the accuracy, stability and time period of network security through application experiments on two computers of different brands (Dell Precision 3551 and HP ZBook Fury 17 G7). The results showed that the neural network algorithm model had the highest average accuracy, with Dell Precision 3551 at 93.3% and HP ZBook Fury 17 G7 at 95.6%. The Math OS model had the highest average stability, with the Dell Precision 3551 at 77.5% and the HP ZBook Fury 17 G7 at 77.7%. The mathematical operating system model on the Dell Precision 3551 had the shortest average time period at 32.8 seconds, and the UML model on the HP ZBook Fury 17 G7 had the shortest time period at 30.6 seconds.
    Keywords: computer network security; neural network algorithm; embedded model; unified modelling language; UML; network security monitoring.
    DOI: 10.1504/IJES.2023.10061925
     
  • Evaluation on application of intelligent traffic image recognition system in vehicle detection and tracking   Order a copy of this article
    by Cheng Liu 
    Abstract: This paper studied from three aspects: the structure of vehicle detection system and the use of intelligent traffic image recognition system video information collection and analysis, the use of intelligent traffic image recognition system to design vehicle detection algorithms, and the use of intelligent traffic image recognition system to track the application of moving vehicles. Through experiments and research, this paper built a new vehicle detection and tracking system, and the satisfaction rate was 19% higher than that of the traditional vehicle detection and tracking system. Compared with the traditional vehicle detection and tracking system, the accuracy of the new vehicle detection and tracking system was increased by 0.28, and the definition was increased by 0.4. This can be in order to better serve people and solve traffic problems such as urban congestion. Therefore, the construction of intelligent transportation system is very important.
    Keywords: intelligent traffic imagery; image recognition system; vehicle detection and tracking; video image processing; intelligent transportation system; ITS.
    DOI: 10.1504/IJES.2023.10062167
     
  • Application of cloud and fog networks and QoS routing optimisation strategies for low delay   Order a copy of this article
    by Fei Zhou, Huaibao Ding, Xiaomei Ding 
    Abstract: This article explores optimisation techniques for cloud networks and low latency QoS routing to improve the efficiency of QoS routing. Research has found that compared with cloud computing networks, the low latency model reduces processing time by 11.95 seconds when processing 1000 MB of data, achieving results in just 18.47 seconds. Using low latency can improve network throughput, increase operation speed, reduce packet loss, and ensure scalable router system performance.
    Keywords: quality of service; QoS; cloud network; low delay; single fog node; cloud computing network.
    DOI: 10.1504/IJES.2023.10062242