International Journal of Embedded Systems (22 papers in press)

Regular Issues

• A survey on latency and power consumption estimation for embedded systems  
  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  
  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  
  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  
  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  
  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  
  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  
  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  
  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  
  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  
  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  
  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
   
  
• Mechanical design and key technology of automatic production line based on artificial intelligence  
  by Xiuhua Yu, Yuhao Shan 
  Abstract: In the field of mechanical design of motion production lines, artificial intelligence technology is mainly expressed in two ways: one is to use simulation methods to process data information, emphasise the practicality of information, and establish complementary mapping relationships; the other is to use traditional programming technology to visualise the internet. By comparing the production rhythm and production data of two traditional intelligent production lines of multiple companies, this article concludes that by improving the artificial intelligence of automated production line machinery, companies can increase output by an average of 49.6%. This article is based on the mechanical design and key technologies of automated production lines, aiming to improve the balance of complex automated production lines, which can fundamentally improve the productivity of enterprises in the field of dynamic production line mechanical design, and upgrade and modernise the entire production system in terms of conveyors, workshops and supply chains.
  Keywords: artificial intelligence; automated production; machine design; optimisation of production system; automatic production line; traditional manufacturing system.
  DOI: 10.1504/IJES.2023.10062820
   
  
• Computer intelligent device adjustment and fuzzy controller design for embedded ARM  
  by Hansong Ge, Ke Li 
  Abstract: There are more and more researches on fuzzy control. Fuzzy controllers in all walks of life have very successful application cases, but they can be affected by quantification factors in the development process, so most of the control rules obtained are based on personal experience and have great uncertainty. To solve these problems, in this paper, the intelligent device fuzzy controller was designed and studied with the help of advanced reduced instruction set computer (RISC). The optimal control rules were searched by advanced RISC machines (ARM). These rules were used to generate the corresponding fuzzy controller. The experimental results suggested that the fuzzy controller based on embedded ARM was more accurate for the regulation of computer intelligence devices than the controllers based on ant algorithm and genetic algorithm. The accuracy of the controller studied in this paper was above 94%, while the other two adjustments were below 91% and 92%, respectively. The performance of the controller studied in this paper is also better, which is conducive to improve the performance of computer intelligent equipment, improve the use value of equipment, better improve the accuracy of equipment adjustment, improve the processing speed of fuzzy controller for subset rules, and the running speed is faster.
  Keywords: fuzzy controller design; intelligent device adjustment; embedded ARM; ant algorithm; genetic algorithm.
  DOI: 10.1504/IJES.2023.10062880
   
  
• An improved 802.15.4 unslotted CSMA/CA algorithm for reducing collision probability and delay in wireless sensor networks  
  by Lei Niu, Xianchao Wang, Dongdong Liu, Bo Guo 
  Abstract: A smaller backoff exponent (BE) can lead to higher collision probability in high-density wireless sensor networks. Consequently, this gives rise to reduced throughput and increased delay. This article improves the unslotted CSMA/CA algorithm, including: firstly, by reducing the basic unit value of backoff period (BP) and increasing the contention window (CW), the collision probability can be significantly reduced when multiple nodes simultaneously backoff, and the delay can also be reduced; Secondly, in response to the lack of a dynamic mechanism to reduce BE in the original unslotted CSMA/CA algorithm, this article proposes an adaptive adjustment algorithm for BE. It can reasonably reduce BE to further reduce delay when network status allows. This article establishes two mathematical models for the improved algorithm for analysis. Compared with the original algorithm, the simulation results show that the collision probability and network delay are improved by at least 43.45% and 52.72%, respectively.
  Keywords: internet of things; wireless sensor networks; unslotted CSMA/CA; backoff exponent; BE; contention window; CW; network delay; collision probability.
  DOI: 10.1504/IJES.2024.10063102
   
  
• Design and application of digital network teaching resource system for network environment  
  by Guobin Jun 
  Abstract: As the information technique developing, resource construction has become an unavoidable practical problem in college education. The systematic integration of teaching resources has become an important breakthrough to solve this problem. Therefore, this study first extracts hidden structural features of digital network teaching resources through data pre-processing, and adds split and merge operations to K-means algorithm to extract main features. Then use LSTM to optimise CNN to form LSCN. Finally, LSCN is combined with the improved K-means algorithm and applied to the digital network teaching resource system. The results show that the objective function value of the final solution of the improved K-means algorithm is 115. The accuracy of LSCN model in online teaching resource database can reach 94.6% at most, and the running time is 38.6s. After combining the enhanced K-means with the LSCN model, the accuracy of the integration of online courses, digital materials and other resources in the college network education system is more than 93%. It shows that the teaching resources integration method proposed by the research has good effect and efficiency, and can provide a reference method for the further informatisation of the education system.
  Keywords: network environment; teaching resources; K-means; convolutional neural network; CNN; LSTM; data mining; K-means.
  DOI: 10.1504/IJES.2024.10063172
   
  
• Psychophysiological state recognition of middle school students based on vibraimage technology and k-means cluster analysis algorithm  
  by Rui Huang, Xiaoquan Liu, Yunzhen Xue, Zhu Zhang 
  Abstract: Adolescence is a special period for middle school students to have rebellious psychology. How to effectively evaluate the mental health of middle school students and help middle school students successfully pass adolescence has always been the focus and difficulty of psychologists’ research. The typical emotion recognition of middle school students in adolescence is the basis for completing this work. In order to identify the psychological and physiological state of middle school students in adolescence, this paper proposes a method of adolescent psychological and physiological state recognition based on vibration imaging technology-K-means clustering analysis algorithm. In order to verify the feasibility of this method, 74,011 middle school students from 59 schools in Taiyuan City were selected as experimental subjects, and the experimental data were obtained by face-to-face interviews and capturing the facial expression video stream of the interviewees. The research results show that the vibration imaging technology-K-means clustering combination model is feasible for the identification of the psychological and physiological state of middle school students in adolescence, and has certain reference significance for the research work in this field.
  Keywords: K-means clustering; vibration imaging technology; descriptive statistical analysis; adolescence.
  DOI: 10.1504/IJES.2024.10063193
   
  
• Design and optimisation strategy of linear traffic spatial dynamic vision guidance system based on multi-source data  
  by Liang Xu, Shiyong Hu 
  Abstract: With the rapid advancement of urbanisation and the increasingly prominent problem of traffic congestion, the traditional navigation system mainly relies on GPS and map data, often can not provide real-time road information and personalised navigation suggestions. Therefore, it has become an important task to study and design a dynamic visual guidance system for linear traffic space based on multi-source data. The purpose of this study is to design and optimise a linear traffic space dynamic visual navigation system based on multi-source data, so as to provide more accurate, real-time and personalised traffic navigation services. The research shows that dynamic vision has better guiding effect, improves the score of spatial visual art, and effectively conveys the local traditional culture. Multi-source data fusion technology can improve user satisfaction, and the user satisfaction increased by 7.01%.
  Keywords: linear traffic space; dynamic visual guidance; multi-source data; international sustainable transport; optimise the design.
  DOI: 10.1504/IJES.2024.10063278
   
  
• Construction and application of online learning mental state diagnosis model based on student learning behaviour data  
  by Xiaohui Ma, Zhongwang Li 
  Abstract: This study addresses the issue of burnout psychology in online learning, which has become prevalent due to educational reforms and the push for educational informatisation, leading to a disinterest in learning among students. It defines the concept and dimensions of online learning burnout psychology using student data, and develops an early warning model using the XGBoost algorithm to predict student burnout effectively. Results indicate the XGBoost algorithm outperforms three other classification algorithms in iteration quality, with minimal difference between actual and training loss, and demonstrates an average absolute error between 1.5 and 2.0, and a mean square error around 1.0. In tests, the model’s accuracy, recall rate, and F1 score were 93.1%, 93.5%, and 0.93, respectively, surpassing comparative models. Thus, this early warning model is highly effective for diagnosing online learning burnout, offering significant improvements over existing methods.
  Keywords: learning data; online diagnosis; educational psychology; promotion of information technology; reform in education.
  DOI: 10.1504/IJES.2024.10063285
   
  
• Evaluation of hidden danger types of optical channel performance degradation based on machine learning cascading technology  
  by Qing Wang, Qiong Cheng, Yuzhi Jing, Shuxin Nie, Jun Yang 
  Abstract: In order to improve the accuracy of performance degradation hazard type assessment and improve the transmission effect of optical channel signal, this paper uses machine learning cascade technology to conduct in-depth research on optical channel performance degradation hazard type assessment. This paper firstly analyses the causes and characteristics of potential degradation hazards of optical channel (OC) performance, and then classifies OC performance degradation hazards by using machine learning algorithm. Meanwhile, in order to verify the effectiveness of the machine learning cascade technology, this paper takes the real OC performance degradation data of an optical communication enterprise as a sample set to conduct precision experiment analysis. The results show that the ML algorithm can effectively and accurately classify the potential degradation hazards of OC performance. By concatenating decision tree, support vector machine and neural network, the accuracy of identifying potential degradation hazards of OC performance can reach 92.37%.
  Keywords: optical channel; machine learning; types of potential performance degradation hazards; cascade technology; support vector machine; SVM.
  DOI: 10.1504/IJES.2024.10063384
   
  
• Empowering intrusion detection in 5G embedded and cyber-physical networks  
  by Nitesh Singh Bhati, Manju Khari 
  Abstract: As intrusion detection systems (IDS) continue to evolve in response to emerging threats to edge devices and embedded devices, various approaches, such as anomaly-based and fuzzy logic-based techniques, have been employed to construct effective IDSs. More recently, with the introduction of 5G to the public usage, the data is dynamic and heterogeneous in nature due to which the integration of machine learning methodologies has gained prominence in IDS development. This research paper introduces a novel ensemble-based approach for enhancing intrusion detection within the context of modern 5G embedded and cyber-physical network security. The proposed technique leverages an optimised CatBoost classifier to fortify the defences of contemporary networks against potential breaches. To evaluate the efficacy of the proposed approach, experimentation was conducted using the KDDCup99 dataset. The results yielded by the proposed technique exhibit a remarkable 99.96% accuracy in detecting intrusions. This research contributes valuable insights to the realm of 5G embedded and cyber-physical by leveraging an ensemble-based approach with a focus on CatBoost optimisation, this study advances the field’s understanding of bolstering intrusion detection capabilities within the evolving landscape of modern distributed networks.
  Keywords: intrusion detection technique; 5G embedded; cyber-physical network; machine learning; CatBoost.
  DOI: 10.1504/IJES.2024.10063474
   
  
• Secured IoT node design through protected ITULES Family Group-II (ULWC) implementation against physical attacks for ubiquitous computing  
  by Swapnil Sutar, Priyanka Mekala, U. Surya Kameswari 
  Abstract: The next-generation communication system will heavily rely on tiny connected devices to sense, transmit and receive data worldwide using IoT infrastructure. Such an IoT ecosystem empowers automation in smart homes, smart cities, and smart grids to increase human comfort. The IoT environment consists of low-resourced tiny nodes connected over the internet through several gateways. These nodes are vulnerable to security attacks due to accessibility via the internet, which highlights the importance of security in the IoT ecosystem. Several encryption standards for the resource-constrained environment were proposed and implemented after mathematical cryptanalysis. However, evaluating the proposed encryption standards against powerful side-channel attacks is essential to validate the IoT node security concerning the open deployment scenarios and physical tampering. This paper investigated the recently proposed novel ultra-lightweight block ciphers for simulated power attacks using differential power cryptanalysis. Also, we provided the mitigation and performance evaluation of ULWC on tiny devices.
  Keywords: ultra-lightweight cipher; internet of things; side channel attack; differential power attack; node security; physical tampering; resource-constrained; cryptanalysis.
  DOI: 10.1504/IJES.2024.10063686
   
  
• Evaluation and design of changes in optical cable and fibre optic online monitoring system based on digital communications technology  
  by Qiong Cheng, Huaijun Li, Yurong Zhen, Qing Wang, Zhiyi Jia 
  Abstract: In order to solve the problem of low signal transmission efficiency and susceptibility to noise interference in online monitoring systems in fibre optic communication construction, this paper conducts effective research on the analysis and design of changes in fibre optic cable online monitoring systems using digital communications technology (CT). This paper conducted tests from three aspects: real-time analysis of changes, accuracy, and signal transmission efficiency. The test results show that at the accuracy level of change analysis, the average relative error level of digital CT used for fibre optic cable change analysis is about 6.381%, while the average relative error result of fibre optic change analysis in traditional online monitoring systems is about 7.595%. From the comparison of accuracy results, digital CT can enhance the stability and reliability of online monitoring systems, improve the accuracy of fibre optic change analysis, and promote the healthy development of fibre optic communication.
  Keywords: optical fibre; digital communications technology; online monitoring system; change analysis and design.
  DOI: 10.1504/IJES.2023.10063701