Forthcoming and Online First Articles

International Journal of Autonomous and Adaptive Communications Systems

International Journal of Autonomous and Adaptive Communications Systems (IJAACS)

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International Journal of Autonomous and Adaptive Communications Systems (37 papers in press)

Regular Issues

  • Layered Routing Algorithm For Wireless Sensor Networks Based On Energy Balance   Order a copy of this article
    by Danxia Luo, Chang’an Ren 
    Abstract: Aiming at the shortcomings of traditional LEACH Routing Protocol in wireless sensor network data transmission applications, such as high total energy consumption, low residual energy and short network life, a hierarchical routing algorithm based on energy balance is proposed. This algorithm is based on the energy consumption model of wireless sensor network communication, and adopts the non-uniform clustering algorithm to introduce the threshold. According to the relationship between the distance between cluster head node and sink node and the threshold, the implementation method of network communication is selected. In addition, a simple correlation multi-path route is designed to realize the multi hop communication between clusters. By considering the communication cost and the residual energy value of nodes, the hierarchical route with balanced energy consumption is realized. Experimental results show that the algorithm has obvious advantages in balancing network energy consumption and prolonging network lifetime.
    Keywords: Library and Information Management; Coding Information; Automatic Extraction.
    DOI: 10.1504/IJAACS.2023.10033563
  • A Hole Repair Algorithm For Wireless Sensor Networks Based On Virtual Attractive Force Constraint   Order a copy of this article
    by Ting Hu 
    Abstract: There are some problems in the traditional algorithm, such as long running time and poor coverage effect. In this paper, a new algorithm based on virtual attractive force constraint is proposed. Based on the virtual attractive force model of intensity-based virtual force algorithm with boundary forces (IVFA-B), aiming at the particularity of ideal distance between heterogeneous network nodes, this paper analyzes the relationship between the perception radius of two heterogeneous nodes and the optimal distance between nodes when realizing the maximum coverage in grid. By combining the best distance and the best distance threshold of virtual force algorithm, the adaptability of heterogeneous network is provided. At the same time, the node moving probability is introduced into the node’s moving distance formula to repair the hole in wireless sensor network node. The simulation results show that the proposed algorithm can achieve better coverage effect and reduce the running time effectively, which proves that the proposed algorithm has better application performance.
    Keywords: Virtual attractive force constraint; Wireless sensor; Hole repair of network node.
    DOI: 10.1504/IJAACS.2023.10034566
  • Vulnerability Detection Of The Authentication Protocol In The Iot Based On Improved Wavelet Packet   Order a copy of this article
    by Shihong Chen 
    Abstract: In order to overcome the problems of long detection time and large detection error in traditional vulnerability detection methods for the authentication protocol in the IOT, this paper proposes a new method based on improved wavelet packet for vulnerability detection of the authentication protocol in the IOT. This method uses the improved wavelet packet to preprocess the data packet and form a small amount of original data. Combined with the method of protocol state diagram, it improves the coverage of traversal path and the effectiveness of trial cases. At the same time, it uses the method of sending TCP detection packets to detect whether there is vulnerability in the IOT authentication protocol. The experimental results show that the proposed method can effectively reduce the detection time and improve the detection accuracy, with the highest detection accuracy of 98.2%.
    Keywords: Improved wavelet packet; IOT; Authentication protocol; Vulnerability detection; Traversal path.
    DOI: 10.1504/IJAACS.2023.10034567
  • Optimum Design of Distance Education Assistant System based on Wireless Network   Order a copy of this article
    by Zixiang Yan 
    Abstract: Due to the constraints of various environments, the existing distance education assistant system can not meet the needs of the present stage. Aiming at the above problems, a new distance education assistant system based on wireless network is designed. Firstly, the function of the hardware part of the distance education assistant system is designed, the functions of several subsystems are introduced, and the business process of the hardware part of the system is analyzed. Combining the video and audio signal coding technology in the software design, the characteristics of the editing code are analyzed, and the software part of the system is optimized by using MMX technology. The simulation results show that the proposed system effectively reduces the response time of the system, improves the stability of the system, lays a solid foundation for the stable operation of the system, and realizes the optimization of the distance education assistant system.
    Keywords: Wireless network; Distance education; Assistant system; Optimization.
    DOI: 10.1504/IJAACS.2023.10034574
  • Collaborative Variational Factorization Machine For Recommender System   Order a copy of this article
    by Jiwei Qin, HongLin Dai 
    Abstract: At present, the recommendation systems are confronting the huge challenge of data sparsity and high complexity of algorithm. Like the traditional collaborative filtering recommendation methods, they are difficult to adapt to the data sparse environment, resulting in low prediction accuracy. To address the aforementioned issues, this paper presents a novel Factorization Machine based on Collaborative filtering framework called Collaborative variational Factorization Machine (CVFM) that considers the user-user relations with the interaction data for Recommender systems. First, the user-item explicit ratings are used to build the user-user relationship by the similarity calculation. Next, we develop a variational Factorization Machine with a linear process to exploit the inherent relationship of latent variables from interaction information. The experimental results on three different datasets show that the presented CVFM is superior to other popular methods in prediction accuracy, at the same time, maintain the stability of our algorithm with dealing with sparse data.
    Keywords: Service recommendation; factorization machine; collaborative filtering; Service calculation.
    DOI: 10.1504/IJAACS.2023.10034575
  • Integrated Radar Radio: Enabling technology for Smart Vehicle of Smart Cities
    by MITHUN CHAKRABORTY, Debdatta Kandar, Bansibadan Maji 
    Abstract: The growing technological development in the field of information and communication technology has evolved the futuristic concept of smart cities, wherein the objects, embedded with high speed processors and memory, would be intelligent in the sense that they are capable to communicate among each other and can take decision. The smart cities will ensure increased road safety, traffic mobility, sustain environment and economic development. To ensure these features smart vehicle becomes an integral component of the smart cities. The smart vehicles should be equipped with simultaneous intelligent sensing and communication technologies at the back end to enable for increased road safety, traffic mobility etc. This requires the joint operation of radar and communication without interference. The aim of the paper is to develop an integrated radar radio platform without interference between the radar and the radio, facilitating smart vehicles. The concept substantiated here for integrated radar radio
    Keywords: OFDM; radar radio; UWB; IV; IVC; V 2 V; V 2 I; FMCW; ICI; mmW.

  • Network Dynamic Routing And Spectrum Allocation Algorithm Based On Blockchain Technology   Order a copy of this article
    by Jue Ma  
    Abstract: To overcome the problems of low resource utilization rate and high bandwidth blocking rate of traditional network dynamic routing and spectrum allocation, a network dynamic routing and spectrum allocation algorithm based on blockchain technology is proposed. In this algorithm, a hybrid integer linear model of network dynamic routing and spectrum allocation is constructed to minimize spectrum consumption and frequency. Based on the extended static heuristic algorithm of blockchain, the link with the largest load is selected to optimize the spectrum allocation, and the linear model and extended static heuristic algorithm are combined to update the frequency gap state of the link where the path is located, so as to achieve the purpose of dynamic routing and spectrum allocation of the network. The experimental results show that the spectrum utilization rate is as high as 99.66%, and the bandwidth blocking rate is as low as 0.
    Keywords: Blockchain technology; Network dynamic routing; Spectrum allocation; Bandwidth blocking.
    DOI: 10.1504/IJAACS.2023.10038427
  • Security Key Distribution Method of Wireless Sensor Network Based on DV-Hop Algorithm   Order a copy of this article
    by Fei Gao 
    Abstract: In order to overcome the problems of low security connectivity and poor distribution accuracy of traditional key distribution methods for network security, this paper proposes a security key distribution method for wireless sensor networks based on DV-Hop algorithm. In this method, the improved DV-Hop algorithm is used to locate the network security key distribution points, and the distributable points are separated according to the location results. According to the separation results, the key management tree is introduced to manage the distributable points in a centralized way, and the key management tree is used to complete the authentication, key distribution and update of wireless sensor network equipment. The experimental results show that the energy consumption of key establishment and update is low, and the minimum energy consumption of key update is only 25 ? J, which has strong anti-attack performance and high overall security.
    Keywords: DV-Hop; Wireless sensor network; Key management tree; Key distribution.
    DOI: 10.1504/IJAACS.2023.10039241
  • Detection Of Malicious Rank Attack Nodes In Communication Network Based On Windowed Frequency Shift Algorithm   Order a copy of this article
    by Hao Yang, Yibo Xia, Wen Cai, Xin Xie 
    Abstract: In order to overcome the problem of low detection efficiency and accuracy in the existing detection methods of malicious nodes in communication networks, a detection method of malicious Rank attack nodes in communication networks based on windowed frequency shift algorithm is proposed. The original signal samples are collected, and the windowed signal spectrum is obtained by windowed truncation and DTFT processing. The frequency shift of signal is calculate, the direction of frequency shift is judged, the amplitude and frequency parameters of sampling signal are calculated, and the abnormal detection of communication network signal is realized according to the calculation results of parameters. The experimental results show that compared with the traditional methods, the proposed method has higher detection efficiency and accuracy, the highest detection rate can reach more than 98%, which can effectively protect the security of the communication network.
    Keywords: Windowed frequency shift; Communication network; Malicious Rank attack; Node detection.
    DOI: 10.1504/IJAACS.2023.10039900
  • Performance of RPL under various mobility models in IoT   Order a copy of this article
    by Spoorthi Shetty 
    Abstract: The Internet of Things is a system used primarily for communication where various devices are connected for the collection, analysis and execution of the task required The main challenge in IoT device is, they are resource-constrained Hence, usage of an effective data transmission routing protocol plays an vital role in IoT It is identified from the research that, IPv6 Routing Protocol for Low Power and Lossy Networks (RPL)is an effective routing protocol for static IoT network Along with static network, it is necessary to evaluate the effectiveness of the RPL for different mobility models The energy consumption of the Reference Point Mobility Model (RPGM) is compared in this document with the Column Mobility Model (CMM) for RPL at distinct concentrations of salability using Cooja simulator with Contiki operating system By the extensive experimental analysis, it is identified that the CMM is more energy efficient than the model of RPGM model.
    Keywords: Reference Point Group Mobility Model; Column Mobility model; Internet of Things; Routing Protocol for Low power and Lossy networks.
    DOI: 10.1504/IJAACS.2023.10040423
    by VIMAL SHANMUGANATHAN, Kaliappan M, Vijayalakshmi K, Muthulakshmi S, Selva Ishwarya 
    Abstract: In our routine life, we tend to stumble upon several birds. Bird-watching may be an in-style hobby that offers relaxation in way of life. Infinite individuals look at the class of various bird species while visiting bird sanctuaries., to make the bird watchers easy tool for developed where we can assist birders to acknowledge 60 bird species however we tend to can not ready to acknowledge the kind of that bird species. To beat this downside we tend to stumble upon an answer of building a package as a project. From DCNN formula may be foreseen at 88. We can notice additional correct and stable prediction of the image exploitation random formula in Jupyter notebook.
    Keywords: image recognition; random forest algorithm; internet of birds; deep learning; DCNN.
    DOI: 10.1504/IJAACS.2023.10042235
  • Research On Parallel Association Rules Mining Of Big Data Based On Improved K-Means Clustering Algorithm   Order a copy of this article
    by Li Hao, Tuanbu Wang, Chaoping Guo 
    Abstract: In order to overcome the problems of time-consuming, low precision and redundant rules in association rules mining of big data, a parallel association rule mining method based on improved K-means clustering algorithm is proposed. This paper introduces the matter-element theory of extension, combines matter-element theory and database, and constructs the matter-element relation database model of extension, to realize the mining of parallel association rules of big data on the basis of extension. Redundant algorithm and equivalent transformation are used to eliminate redundant association rules. The experimental results show that the proposed method has high mining efficiency, high mining accuracy and high rule association, which proves that the proposed method has better application performance.
    Keywords: K-means clustering algorithm; Association rules; Data mining; Redundancy algorithm; Equivalence transformation.
    DOI: 10.1504/IJAACS.2023.10042660
  • Dynamic Key Distribution Method For Wireless Sensor Networks Based On Exponential Algorithm   Order a copy of this article
    by Yun ZHAO, Yong XIAO, Weibin LIN, Chao CUI, Di XU 
    Abstract: In order to overcome the problem of low robustness of key distribution of wireless sensor networks, a dynamic key distribution method for wireless sensor networks based on exponential algorithm is proposed in this paper. In this method, the collusion characteristics of newly added and cancelled nodes in wireless sensor networks are used to establish the wireless sensor’s security model. Based on the wireless sensor’s security model, the exponential algorithm is used to achieve dynamic key distribution through the five indicators of initialization, session key self-repair, session key mutual repair, joining node and withdrawing node. The experimental results show that when the number of dynamic key nodes is 600, the probability of communication failure is 47%; when the number of hops is 10, the energy cost is only 1.64mJ, and the network robustness is high.
    Keywords: Exponential algorithm; Wireless sensor; Network; Dynamic key; Distribution; Method.
    DOI: 10.1504/IJAACS.2023.10043736
  • Heuristic Positioning Method Of Intrusion Nodes In Sensor Networks Based On Quantum Annealing Algorithm
    by Yun ZHAO, Ziwen CAI, Tao HUANG, Bin QIAN, Mi ZHOU 
    Abstract: In order to overcome the problems of low positioning accuracy and long time-consuming in traditional heuristic positioning methods, a new heuristic positioning method of intrusion nodes in sensor network based on quantum annealing algorithm is proposed. This method analyzes the result graph of sensor network and node system, selects the multi-communication radius method to communicate and broadcast among each sensor node, at the same time, refines the number of hops of nodes, and selects the weighting factor to calculate the average hopping moment of unknown nodes. On the basis of the above, through quantum tunneling effect, combined with quantum annealing algorithm, the heuristic positioning of intrusion nodes in sensor network is completed. The simulation results show that the proposed method can effectively improve the positioning accuracy and reduce the running time. The maximum positioning time is only 0.2min.
    Keywords: Quantum annealing algorithm; Sensor network; Intrusion node; Heuristic positioning.

  • Research on Abnormal Data Recognition Method of Optical Network Based on WIFI Triangular Location
    by Bingchen Lin 
    Abstract: In order to overcome the problems of low recognition accuracy and poor reliability of traditional optical network abnormal data identification methods, a new optical network abnormal data recognition method based on WiFi triangulation positioning is proposed. Time series analysis method is used to analyze the channel model of optical network to obtain the temporal characteristics of abnormal data in optical network. Hyperbolic frequency modulation decomposition method is used to detect the time domain characteristics of abnormal data, and the total energy of abnormal data in time and frequency domain is obtained. The abnormal data signal model is established by the energy density characteristics of abnormal data, and the specific position of abnormal data in the abnormal data signal model after filtering is identified by using WiFi triangle positioning algorithm. The experimental results show that the accuracy of the method is higher than 95%, and the recognition performance is good.
    Keywords: WiFi; triangulation; channel model; total time-frequency energy; energy density characteristics.

  • Response Efficiency Optimization of Data Cube Online Analysis for Network user's behavior   Order a copy of this article
    by Hui Zhang, Su Zhang, Xiaoling Jiang 
    Abstract: Data cube plays an important role in online analysis and processing of multi-dimensional data warehouse. Aiming at the problem of long response time and poor compression performance of data query in current methods, the optimization performance of the method is reduced, and a response efficiency optimization method for online analysis data cube based on formal concept lattice is proposed. Firstly, the data of network user's behavior is analyzed and combined with the access frequency of network user. Secondly, the time-varying and stability of data warehouse are analyzed in detail. Finally, slicing and dicing operations in online analysis are analyzed. The experimental results show that the proposed method has a shorter query response time and can quickly retrieve data encoding with better compression performance when the number of fact tables and dimension tables is increasing.
    Keywords: Network user's behavior; Data cube; Online analysis; Response efficiency optimization.
    DOI: 10.1504/IJAACS.2023.10044612
  • IoT Based Vehicular Accident Detection Using Deep Learning Model   Order a copy of this article
    by Ishu Rani, Bhushan Thakre, K. Jairam Naik 
    Abstract: With increase of population and running valuable time, the demand for cars has skyrocketed creating an unprecedented condition in spite of traffic risks and road collisions. The crashes are growing at an unprecedented pace hence it causes death. Now, when Machine Learning has taken over, the previously complex problems have become feasible, and the real-life applications of these artificial ML models have been very promising. In this article, a learning model that learns over an image dataset, thereby classifying never before seen images and data has been proposed. It aims at classifying the real-time accidents based on the level of damage. For that an ANN is utilized to train the model and to learn the similarities among images and accident data. The proposed solution is efficient as it was tried to improve the efficiency of existing model using certain literature mentioned, augmenting different extractions and leaning techniques.
    Keywords: Vehicles; Accident detection; Classification; Accuracy; Deep Learning; IoT; Training model; Image polarity.
    DOI: 10.1504/IJAACS.2025.10046127
  • Twitter Sentiment Analysis using Ensemble Classifiers on Tamil and Malayalam Languages   Order a copy of this article
    by Gokula Krishnan V, Deepa J, Pinagadi Venkateswara Rao, Divya V 
    Abstract: The proliferation of social network is generating a huge amount of texts and drawing attentions Sentiment Analysis (SA) extracts useful information from such data Maximum researches on SA have been done on the English language, but others main languages such as Tamil and Malaya requests obligation too It is pivotal to work on Tamil and Malaya social posts because it is the most spoken language by native speakers and heavily used in social media Although such a crowd, modest work has been done on different languages SA This paper proposes to automatically classify the overall polarity of sentiments expressed in Tamil and Malaya tweets posts by Twitter users in three classes: Positive, Negative and Neutral, and determine a fruitful approach to solve this problem Two samples of Tamil and Malaya languages are collected and later divided into two different types of corpuses Each sample in both corpuses is annotated
    Keywords: English Language; Malaya; Polarity; Tamil; Twitter; Sentiment Analysis; Long-Short Term Memory; Sentiment Analysis; Ensemble Classifiers.
    DOI: 10.1504/IJAACS.2023.10046016
  • A multi-level autopoietic system to develop an artificial embryogenesis process   Order a copy of this article
    by Rima HIOUANI, Nour Eddine DJEDI, Sylvain Cussat-blanc, Yves Duthen 
    Abstract: This paper presents a new model for the self-creation of an artificial multicellular organism from one cell, which is inspired by “The Autopoietic System Theory” at different levels. This theory has been proposed to define the universal self-organization and the self-creation of living systems, the use of this concept allows the development of the artificial organism as a closed organization, and it has been widely used to understand the living systems and their capabilities such as self-organization, self-creation, autonomy, evolution, reproduction... We proposed MLAS “Multi-level Autopoietic System" beside the self-organization to embody this autopoietic system. However, in contrast to the proposed system by Varela, we set it up according to various levels (organs autopoietic machine, tissues autopoietic machine, and cells autopoietic machine). Inside the level of cell autopoietic machine, we proposed the second contribution in this paper, which is a Boolean Artificial GRN with an epigenetic part; lead the cells to create its history during evolution.
    Keywords: Autopoietic System; self-organization; self-adaptation; Artificial Gene regulatory network; evolution; diversity.
    DOI: 10.1504/IJAACS.2024.10047330
  • Unsupervised learning of local features for person re-identi?cation with loss funciton   Order a copy of this article
    by Lunzheng Tan, GuoLuan Chen, Rui Ding, Xia Limin 
    Abstract: Many methods for person re-identi?cation focus on making full use of local features, which typically requires either a comprehensive manual labeling or complex pretreatment. This paper proposes a novel loss function, termed feature channels dropout and de-similarity loss, which drives the autonomous learning of discriminative local features in Convolutional Neural Networks. The proposed loss function consists of two components. The first is a feature channels dropout component designed to compel each feature channel to be discriminative. A novel channel-dropout function and a cross-channel-element-max function are applied in this component. The second component is a de-similarity component that uses Pearson correlation coe?cient to constrain feature channels and ensure they differ from each other. This component is conducive to diverse local features mining. Extensive experiments on three large-scale re-identification datasets demonstrate that the feature channels dropout and de-similarity loss achieves superior performance compared with state-of-the-art methods.
    Keywords: Person re-identi?cation; Local feature; Unsupervised learning; Loss function.
    DOI: 10.1504/IJAACS.2024.10046623
  • Fingerprint Liveness Detection Approaches: A SURVEY   Order a copy of this article
    by Mingyu Chen, Chengsheng Yuan, Ying Lv 
    Abstract: In contemporary society, with the popularity of smart wearable devices, people are more inclined to use convenient and efficient identity verification based on biometrics. Human fingerprints are one of the most commonly used biometric factors, which have the advantages of uniqueness, convenience and security. Compared with traditional password authentication, fingerprint authentication system has extremely high security, but it is still vulnerable to fingerprint spoofing attacks. Counterfeiters can imitate user fingerprints by using various human body simulation materials, thereby realizing illegal authentication and infringing user rights and interests, so liveness detection is quite necessary. According to the fingerprint image and biometric information obtained by the sensor, the fingerprint liveness detection (FLD) can distinguish whether the fingerprint is from a real person. This paper reviews the development history and the latest progress in the field of FLD. Both hardware and software based state-of-the-art methods are thoroughly presented to help researchers to carry out further research.
    Keywords: Fingerprint Liveness Detection; Biometrics; Understand; Software; Hardware.
    DOI: 10.1504/IJAACS.2024.10046755
  • Face Forgery Detection with Cross-Level Attention
    by Yaju Liu, Jianwei Fei, Peipeng Yu, Chengsheng Yuan, Haopeng Liang 
    Abstract: Currently, face videos manipulated using deep learning models are widely spread on social media, which violates personal privacy and may disturb social security. In this study, we start by discovering the essential differences between real and fake faces. To extract Multi-scale artifacts and increase the perceptual field of the downsampling layer, we introduce atrous spatial pyramid pooling (ASPP). Considering the drawback that ASPP does not use all pixels for computation and may lose information, we design a Cross-Level Attention(CLA) module to interact with the output of the ASPP block with the backbone. Our proposed attention mechanism allows the network to focus on locally manipulated areas without destroying other features of the model. Experimental results on the large publicly available facial manipulation database Faceforensics++ show that our method effectively improves detection accuracy and generalization, and confirms that great detection performance is achieved even for compressed images.
    Keywords: Face forgery detection ASPP Attention mechanism.

  • Analysis and optimization of RON loss via compound variable selection and BP neural network
    by Yunshu Dai, Jianwei Fei, Fei Gu, Chengsheng Yuan 
    Abstract: The loss of octane in gasoline refining process can cause huge economic losses. Reducing the loss of octane has high practical significance. However, octane loss involves many operations in gasoline refining process, which are coupled with each other and have a highly nonlinear relationship with octane loss. Therefore, the analyze and optimization of octane loss is a high-dimensional nonlinear programming problem. Therefore, this paper proposes a compound variable selection scheme. Based on the selection of independent variables by outlier filtering and high correlation filtering, the representative operations are selected by random forest and grey correlation analysis, and the octane loss is predicted by BP neural network and XGBoost algorithm. To optimize the octane loss, an operation optimization scheme based on fast gradient modification is proposed. Based on the octane loss prediction network, the main operations are gradually fine-tuned to reduce the octane loss.
    Keywords: RON loss optimization; variable selection; XGBoost; BP neural network.

  • A Survey on Neural Network-based Image Data Hiding for Secure Communication   Order a copy of this article
    by Yue Wu, Peipeng Yu, Chengsheng Yuan 
    Abstract: Data hiding has always been a hot research topic in the field of information security, and has attracted more and more attention from the academic community. At the same time, the rise of deep learning has also injected new development directions into the field. According to the characteristics of data hiding for images, many scholars have made corresponding improvements to the neural network and achieved many creative results. This review summarizes the main methods and representative research results of data hiding for images based on neural network. The principles and methods of neural network-based steganography and watermarking are introduced in detail, Finally we discuss problems of existing research and point out the direction for further research.
    Keywords: Data hiding; Steganography; Image watermarking; deep neural network.
    DOI: 10.1504/IJAACS.2024.10048413
  • Research and design of in-loop virtual simulation system of tread winding control software based on MCD
    by Mingxia Chen, Jijing He, Haitao Zheng, Hanyu Shi 
    Abstract: To improve the efficiency of industrial equipment design and debugging, virtual debugging technology is used to save the cost of industrial equipment debugging and reduce the risk of physical debugging. In this paper, an MCD-based tread winding virtual simulation system is presented, and the Software-In-Loop Virtual Debug of this system is used to study the application effect of fuzzy PID control algorithm in winding control devices. The simulation results show that compared with the traditional PID control and open-loop control, under the closed-loop control formed by the PID control algorithm with fuzzy control, the speed of the roller head is closer to the expected speed, the operation is more stable, the operation trajectory is more smooth, and a good control effect is achieved. The feasibility and effectiveness of the MCD-based tread winding virtual simulation debugging scheme are verified, and an idea is provided for the design of industrial equipment.
    Keywords: in-loop virtual simulation system; MCD; tread winding control software.

  • Augmented Data Control in Cipher Security using Functional Procedures
    by Raghvan M, Krishnmoorthy Prabu 
    Abstract: Data security, integrity and confidentiality are the main challenges of today’s digital world Even the highly secured data can be easily broken down by a simple hacking algorithm Though, data protection raises in terms of exponential growth, on the other side there is also a tremendous growth to break down the protection The scheme for data protection attracts more researchers and still the research is going on In the first part of this paper, we present a brief study of various techniques which supports data security, and the applications corresponding to the methods of cryptography.
    Keywords: Cryptographic techniques; Genetic Algorithm; Data security; Cloud database.

  • The Security Storage Method Of Dynamic Data In Internet Of Things Based On Blockchain   Order a copy of this article
    by Li Sun  
    Abstract: This paper proposes a method based on dynamic storage chain to overcome the problem of large amount of data in the Internet of things. In this method, ECC and D-H are used as encryption tools of the whole architecture to realize encrypted communication between lot devices. Combined with the blockchain technology, the IOT node access and the IOT dynamic data are stored to promote the dynamic data between IOT nodes to be stored in the offline storage structure, so as to achieve the purpose of secure storage of IOT dynamic data. The experimental results show that the average data storage time is 0.40s, the maximum root mean square error is 0.06, and the cost is controlled within 34500 yuan, which can effectively realize the security of IOT nodes.
    Keywords: Blockchain; Internet of things; Dynamic data; Security storage.
    DOI: 10.1504/IJAACS.2023.10049022
  • Unsupervised Clustering Algorithm For Database Based On Density Peak Optimization   Order a copy of this article
    by Xiaochuan Pu, Wonchul Seo, Ning QI 
    Abstract: In order to improve the clustering effect of traditional unsupervised clustering algorithm for database, an unsupervised clustering algorithm based on density peak optimization is designed and proposed. K-nearest neighbor is used to set a new method to measure the sample density and sample distance, and a decision diagram of sample distance relative to sample density is drawn. The selected sample is the initial cluster center, and the number of clusters is automatically determined. In order to further improve the clustering results, the improved K-means algorithm and particle swarm optimization algorithm are introduced to optimize the convergence process of the algorithm. In order to verify the effectiveness of the proposed algorithm, a simulation experiment is designed. Experimental results show that the proposed algorithm is effective and feasible.
    Keywords: Density peak optimization; Database; Unsupervised clustering; Initial cluster center; K-means algorithm; Particle swarm optimization algorithm.
    DOI: 10.1504/IJAACS.2023.10049212
  • Research On e-Business Requirement Information Resource Extraction Method In Network Big Data   Order a copy of this article
    by Yawen Li 
    Abstract: For the challenge of the data sparsity of user-behavior in the current e-business personalized recommendation system, an information resource extraction method for e-business requirements based on similar case analysis is proposed. A recommendation model for e-commerce users’ requirements information resources is built, including static information, browsing behavior information, selection information of network user, and user interest. According to the built user requirement information resource recommendation model, the method based on similar case analysis is introduced into the personalized recommendation of e-business under the background of the personalized recommendation of e-business considering the potential requirement. The feature attribute similarity and comprehensive similarity of customer registration information are calculated. Experimental results show that the proposed method has good effect on product coverage, product exposure rate, and feedback rate. It can overcome the behavior sparsity of user-product, and extract the dark information in e-business requirement information resources, and overcome the long tail recommendation.
    Keywords: network big data; e-business; requirement information resources; extraction method.
    DOI: 10.1504/IJAACS.2023.10049564
  • Exposing deepfakes in online communication:detection based on ensemble strategy   Order a copy of this article
    by Jie Xu, Guoqiang Wang, Tianxiong Zhou 
    Abstract: In recent years, deepfake techniques appeared in people's lives. As a product of deep learning, it can generate realistic face-swapping videos. Due to high fidelity, deepfake is often used to produce porn videos and guide public opinion, so as to pose a great threat to social stability. Previous studies have been able to get better detection accuracy. This paper aims to improve the detection ability of existing schemes by using the ensemble learning scheme from the perspective of model learning. Specifically, our scheme includes feature extraction, feature selection, feature classification and combination strategy. The experimental results on several datasets demonstrate that our scheme can effectively improve the detection ability of the model.
    Keywords: deepfake detection; ensemble strategy; online communication; video forensics; deep learning.
    DOI: 10.1504/IJAACS.2022.10049685
  • Nonintrusive Power Load Feature Recognition Based On Internet Of Things Technology   Order a copy of this article
    by Jing Liu, Di Zhao 
    Abstract: In order to overcome the problems of low recognition efficiency, low information credibility and low recognition accuracy existing in the existing nonintrusive power load feature recognition methods, a nonintrusive power load feature recognition method based on the Internet of things technology is proposed. With the support of Internet of things technology, the feature parameters of power consumption information are obtained by Fourier transform method, and the feature parameters are fused according to the correct time sequence to realize the recognition of power consumption equipment. Based on the detection results, a nonintrusive power load feature recognition model is constructed by C4.5 decision tree algorithm, and the nonintrusive power load feature recognition model is realized by using the nonintrusive power load feature recognition model. The experimental results show that the proposed method has high recognition efficiency, high information reliability and high recognition accuracy.
    Keywords: Internet of things technology; Fourier transform; Parameter feature; C4.5 decision tree algorithm; Load feature recognition.
    DOI: 10.1504/IJAACS.2023.10051271
  • Optimal Load Balancing Strategy-based Centralized Sensor for WSN-based Cloud-IoT Framework using Hybrid Meta-heuristic Strategy   Order a copy of this article
    by Yogaraja G. S. R, Thippeswamy M. N, Venkatesh K 
    Abstract: This paper is to implement a load balancing centralized server to control the Wireless Sensor Networks (WSN) connected with IoT and cloud. The WSN gathers data pertaining to diverse applications and transfer it to centralized server in the cloud through IoT channel. Sever controls the routing of each node in the WSN through optimal load balancing strategy. A hybrid meta-heuristic algorithm with Forest-Cat Optimization Algorithm (F-COA) is introduced for accomplishing centralized load balanced strategy in communication system. The fundamental constraints used in the proposed models are clustering parameters like distance between nodes, energy, and delay, load balancing parameters like response time, turnaround time, server load, and QoS parameters like resource utilization, execution time, and throughput. The experimental results present the superior performance through multi objectives optimization when compared to the other approaches in terms of different constraints.
    Keywords: Internet of Things; Cloud Computing; Wireless Sensor Networks; Optimal Routing; Forest-Cat Optimization Algorithm; Load Balancing; Multi-Objective Function.
    DOI: 10.1504/IJAACS.2024.10051757
  • Automated Anomaly Detection and Multi-label Anomaly Classification in Crowd Scenes based on Optimal Thresholding and Deep Learning Strategy   Order a copy of this article
    by Harshadkumar S. Modi, Dhaval A. Parikh 
    Abstract: This paper plan to develop the anomaly detection and multi-label anomaly classification in crowd scenes using the enhanced deep learning strategy. The two main phases of the proposed model are the anomaly detection and the multi-label anomaly classification. In the first phase of anomaly detection, pre-processing of frames is done by the Histogram Equalization, and patches are extracted from the video frames. The extracted patches are further subjected to the Convolutional Neural Network for obtaining the movement score and appearance score of the frame. The extraction of movement score and appearance score helps to know the deep insight of the object behavior in the video, which thus helps to detect whether the objects are anomaly or not. For detecting that, a threshold is fixed for the movement score and appearance score.
    Keywords: Automated Anomaly Detection; Multi-label Anomaly Classification; Optimal Thresholding; Convolutional Neural Network; Enhanced Recurrent Neural Network; Elephant Herding-Grey Wolf Optimization.
    DOI: 10.1504/IJAACS.2024.10051758
  • Research On A New Multipath Transmission Optimization Algorithm For Multichannel Wireless Sensor Based On Optimized Clustering And Multi-Hop   Order a copy of this article
    by Ting Hu, Shaohui Zhong 
    Abstract: In view of the poor data transmission accuracy in traditional wireless sensor transmission methods, a multipath transmission optimization algorithm for multichannel wireless sensor based on optimized clustering and multi-hop is proposed. The internal structure and node structure of multichannel wireless sensor network are analyzed firstly, a multichannel routing algorithm for wireless sensor according to the internal structure characteristics of multichannel wireless sensor is built, and the cluster head and number of multichannel wireless sensors are determined and optimized with optimized cluster and multi-hop; then, ant colony algorithm is introduced into multipath search of multichannel wireless sensor; finally, by using path coding and decoding, the corresponding fitness function is constructed to optimize the multipath transmission of multichannel wireless sensor. The simulation results show that the proposed method can reduce the data transmission delay, the accuracy of multipath transmission is up to 98 %, and the network energy consumption is low.
    Keywords: Optimized clustering and multi-hop; Multichannel wireless sensor; Multipath transmission; Path coding.
    DOI: 10.1504/IJAACS.2023.10051765
  • Deepfake detection and localization based on illumination inconsistency   Order a copy of this article
    by Fei Gu, Yunshu Dai, Jianwei Fei, Xianyi Chen 
    Abstract: The rapid development of image synthesis technology has encouraged the spread of some fake news, making people gradually lose trust in digital media. The compression in the process of image propagation brings a major challenge to the existing face forgery detection method. In this paper, we propose a multi-task Deepfake detection method according to the motivation of illumination inconsistency between tampered and non-tampered areas. Specifically, we trained a Siamese network as a feature extractor to estimate the illumination, then distinguish the face image and predict the forged region through a U-shaped network. Our method has achieved great accuracy in classification tasks and can still maintain a good performance in compressing data. In addition, we can also show the intensity of tampering while locating the forged area.
    Keywords: Deepfakes; illumination estimation; Siamese network; UNet.
    DOI: 10.1504/IJAACS.2024.10052496
  • ASER analysis of DF relay assisted communication systems with diversity receiver at destination subject to Nakagami-m fading channels   Order a copy of this article
    by RAJKISHUR MUDOI, Darilangi S. Lyngdoh 
    Abstract: In recent years, relay assisted communications have been extensively used for low power and long-distance transmission of information. The average symbol error rate (ASER) performance of a decode and forward (DF) relay assisted communication method is analysed using maximal ratio combining (MRC) as well as selection combining (SC) receiver at the destination node. All links of the wireless system are influenced by Nakagami-m fading distribution. The closed-form representation of ASER is derived using the MGF based approach for coherent as well as noncoherent modulation techniques. The results show an improvement in the ASER performance with the MRC receiver compared to the SC receiver at the destination node. The ASER performance improves with an enhancement of the fading parameter. The mathematical expressions are supported using computer simulations that give the correctness of the results.
    Keywords: ASER; Decode-and-Forward; Maximal Ratio Combining; MGF; Nakagami-m fading; and Selection Combining.
    DOI: 10.1504/IJAACS.2024.10052604
  • The Cleaning Method Of Duplicate Big Data Based On Association Rule Mining Algorithm   Order a copy of this article
    by Ming Wu  
    Abstract: In order to overcome the problems of low cleaning efficiency and serious memory occupation in traditional cleaning methods for duplicate big data, a new cleaning method based on association rule mining algorithm is proposed. In this method, association rules mining algorithm is used to get frequent itemsets of duplicate big data. At the same time, the output mode of the algorithm is optimized in parallel, the Hadoop interface is modified, and the read-in mode of MapReduce is changed. The first frequent item set is used to clean the duplicate big data. Experimental results show that the proposed method can effectively reduce the execution time and memory consumption, and the shortest cleaning time is only 1.28 minutes, which is feasible.
    Keywords: Association rule mining algorithm; Duplicate big data; Cleaning; Frequent items.
    DOI: 10.1504/IJAACS.2023.10052715