2023 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET)最新文献

{"title":"DDoS Vulnerabilities Analysis in SDN Controllers: Understanding the Attacking Strategies","authors":"Mitali Sinha, P. Bera, M. Satpathy","doi":"10.1109/WiSPNET57748.2023.10134518","DOIUrl":"https://doi.org/10.1109/WiSPNET57748.2023.10134518","url":null,"abstract":"Modern technologies like 5G, IoT, and Cloud Computing are all adopting Software-Defined Networking (SDN) as a standard approach. The reason behind its widespread acceptance is its innovative design principle of decoupling the network's control logic from its data-forwarding hardware. SDN controller is the core of the network which manages the network traffic flows therefore the most important task in SDN is to provide security to the controller. Distributed Denial of Service attacks are types of attacks that slow down the performance of SDN where malicious users send a large volume of fake packets to the controller in an attempt to use up all of its resources. In this study, we analyze the vulnerability of DDoS attacks in different SDN controllers like POX, Ryu, Floodlight, and OpenDayLight through a comprehensive experimental study. From this study, it has been observed that said controllers are affected differently in terms of the CPU and memory utilization due to their routing policies. This study will help a network administrator to choose the right solution against DDoS attacks in SDN controllers.","PeriodicalId":150576,"journal":{"name":"2023 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET)","volume":"os-33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127775851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Supervised Machine Learning Model based Spectrum Sensing using NI USRP-2922 SDR","authors":"Rock Feller Singh Russells P, Merlin Gilbert Raj S","doi":"10.1109/WiSPNET57748.2023.10134276","DOIUrl":"https://doi.org/10.1109/WiSPNET57748.2023.10134276","url":null,"abstract":"Spectrum scarcity is a major problem in this millennial communication engineering. Machine learning based spectrum sensing approaches are getting more attention among research community. The spectrum sensing techniques detects the licensed and unlicensed bands and supports spectrum management. In this work, we have proposed the spectrum detection as two level classification problem and solved using a supervised machine learning model based support vector machine(SVM) algorithm. The data samples are collected in real campus environment ranging from line of sight to non-line of sight using the Labview enabled NI USRP-2922 software defined radio platform for 815 MHz. Correlation and moving average metrics are two features used for classifcation. The effectiveness of the classfication based on the feature vectors are observed through confusion matrix, prediction estimation and detection probability.","PeriodicalId":150576,"journal":{"name":"2023 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123196305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient FPGA Implementation of RSA Algorithm Using Vedic Multiplier","authors":"Jegadish Kumar K J, K. P., Gokhulesh V, S. K","doi":"10.1109/WiSPNET57748.2023.10134210","DOIUrl":"https://doi.org/10.1109/WiSPNET57748.2023.10134210","url":null,"abstract":"Technology cannot function effectively and securely without algorithms, which also enable integrity and encryption. To secure sensitive data, especially when it is delivered over an unsecured network like the Internet, we employ the RSA (Rivest-Shamir-Adleman) Algorithm, which forms the backbone of the cryptosystem that permits public key encryption. In a cryptosystem, multipliers are essential since they help to produce the desired results as efficiently as possible. The enormous number of adders and other digital circuits used in typical multipliers causes an increase in propagation delay, which eventually reduces the multiplier's efficiency. In contrast, the Vedic Multiplier can overcome this issue and operates at high efficiency. The objective is to develop an effective 8 X 8 Vedic multiplier and implement it in the Field Programmable Gate Array (FPGA) using the simulation tool Xilinx - ISE Design Suite 14.7. The effective performance metrics are compared with the pre-existing booth multiplier in terms of combinational path delay, number of slices, and number of Look Up Table (LUT)s. Further, the Modular exponentiation operation in RSA cryptosystem is replaced with the proposed Vedic multiplier and booth multiplier logics. The effectiveness of the RSA implementation with these operator logics is compared in terms of delay and area.","PeriodicalId":150576,"journal":{"name":"2023 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131004992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modified fractal-based Polarization-Reconfigurable Patch Antenna for 2.4 GHz ISM Band Applications","authors":"T. K. Das, Rupanita Das, Abinash Kumar, Ridhi Kumari, Priya Mandal, Rupesh Kumar Mukhi, Jagannath Dayal Pradhan","doi":"10.1109/WiSPNET57748.2023.10134042","DOIUrl":"https://doi.org/10.1109/WiSPNET57748.2023.10134042","url":null,"abstract":"A modified fractal-based planar structure with the ability to switch between circular polarization states is presented in this article. This design is made up of a basic radiating patch of circular-shape incorporated with repeated rectangular stubs at an angle of 10° on the outer perimeter excited diagonally by co-axial feeding. On the radiating patch, slots in the shape of a U, are inserted to place PIN diodes (silicon) to achieve polarization reconfigurability. To validate the performance, the design is simulated on an FR4 substrate. The simulated results of the design show an -10 dB bandwidth in the frequency range of 2.36-2.48 GHz. It also indicates the 3 dB axial ratio bandwidth of 2.38-2.41 GHz with a 2.4 GHz resonating frequency. The peak realized gain of the modified fractal structure is found to be 3.26 dBic with stable radiation performance at the ISM (Industrial, Scientific, and Medical) band.","PeriodicalId":150576,"journal":{"name":"2023 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET)","volume":"196 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116359311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RF Energy Harvesting from Handheld Mobile Band signals","authors":"K. J. Kumar, Hariharan A, R. L. Yedidiah","doi":"10.1109/WiSPNET57748.2023.10134078","DOIUrl":"https://doi.org/10.1109/WiSPNET57748.2023.10134078","url":null,"abstract":"This paper presents the design and test a RF energy harvester that can harvest / scavenge ambient RF signals from the GSM Band and Wi-Fi band. The main application of this proposal is to power low energy devices at inaccessible locations and require frequent battery changes using the available wireless power sources. These devices will contain a suitable antenna to catch these RF signals from mentioned bands and convert the RF waves to DC voltages. The RF input signal first passes through a matching circuit followed by a rectifier with optional electrostatic storage devices like Capacitors followed by the load. The RF energy harvesting device is first designed and simulated using the ADS software. The simulated device is then fabricated, and its efficiency is analyzed and compared to the simulated values. An overall average power efficiency of 53.76% was observed in a laboratory setup. The device is then tested in a real environment, and the performance was analyzed for various network loads and varying distance from the RF Source which was a Smart Phone in this case.","PeriodicalId":150576,"journal":{"name":"2023 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116743906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of Millimeter Wave Path Loss Prediction using Machine Learning Techniques","authors":"Vinu Abinayaa. R, V. G, Shwathi Ramanathan, M. K","doi":"10.1109/WiSPNET57748.2023.10134020","DOIUrl":"https://doi.org/10.1109/WiSPNET57748.2023.10134020","url":null,"abstract":"In this paper, different machine learning algorithms like ridge regression, linear regression, Random forest regression and K-Nearest Neighbors Algorithm (KNN) were used to predict the path loss of millimeter waves (mmWave)under different scenarios like Uma (Urban Macro) and Umi (Urban Micro) thereby comparing the accuracy of each algorithm. mmWaves are prone to attenuation due to different environmental factors like foliage, size and rate of raindrops, etc. as the size of these objects are comparable to the wavelength of the mmWaves. Since mmWave is the basis for 5G communication it is imperative to analyze and predict the path loss exponent under different scenarios. From the analysis performed it is seen that linear regression provides better accuracy compared to the other models.","PeriodicalId":150576,"journal":{"name":"2023 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125072511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Texture Image Classification with Dilated Convolution Layers","authors":"S. G, P. N","doi":"10.1109/WiSPNET57748.2023.10133964","DOIUrl":"https://doi.org/10.1109/WiSPNET57748.2023.10133964","url":null,"abstract":"This work develops a compact deep-learning architecture to learn and recognize texture features. The suggested approach primarily concentrates on the feature-extracting layers of the neural network. The proposed Texture-Dilated Convolutional Neural Network (T-DCNN) is supported by blocks with dilated convolution layers. These blocks assist the model in retrieving the underlying texture attributes required for categorizing images. The built network was trained and evaluated on the kylberg texture database v.1.0. The model produced a result with 98.88% accuracy rate. The investigation shows that under same environment, the proposed model outperforms the conventional CNN model by lowering the required training time and parameters to categorize textures.","PeriodicalId":150576,"journal":{"name":"2023 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127366770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design of Noise Transfer Function for Delta Sigma Modulator based on Modified Jacobi Polynomial Approximations","authors":"J. Twinkle, P. Chandramani, R. Srinivasan","doi":"10.1109/WiSPNET57748.2023.10134293","DOIUrl":"https://doi.org/10.1109/WiSPNET57748.2023.10134293","url":null,"abstract":"This paper presents the design of Noise Transfer Function (NTF) for Delta Sigma Modulator (DSM) based on continuous time high pass filter approximated by modified Jacobi polynomial. $boldsymbol{upalpha}, boldsymbol{upbeta}$ are the orders of the Jacobian polynomial. Different combination of $boldsymbol{upalpha}, boldsymbol{upbeta}$ results in different NTFs. The objective of this work is to determine the $upalpha, upbeta$ values for which the optimum attenuation characteristic is achieved in the stopband of the NTF while satisfying the realizability condition of the DSM. For the $boldsymbol{5}^{mathbf{th}}$ order DSM with an Oversampling Ratio (OSR) of 32 and $gamma$ of 1.5 an SQNR of 69.9 dB is achieved which is an improvement of 5.15 dB compared to the Delta Sigma Toolbox (DST) method.","PeriodicalId":150576,"journal":{"name":"2023 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129432075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ML-Aided Dynamic Clustering and Classification of UEs as VBs in D2D Communication Networks","authors":"I. Ioannou, P. Nagaradjane, A. Khalifeh, C. Christophorou, V. Vassiliou, G. Sashank, Charu Jain, A. Pitsillides","doi":"10.1109/WiSPNET57748.2023.10134336","DOIUrl":"https://doi.org/10.1109/WiSPNET57748.2023.10134336","url":null,"abstract":"With the next generation of mobile devices and streaming services such as Virtual Reality, Augmented Reality, and Meta being available worldwide, the network's data rate, latency, and connectivity must be improved. Even though 5G provides the user with the required Quality of Service (QoS) in terms of data rate and reduced delays by transmitting signals in the higher frequencies (called New Radio (NR)), it faces a lot of attenuation, leading to a short communication range. However, to meet goals set by utilising 6G requirements, many technological advancements and components must be incorporated into the network. The dense disposition of small cells will help reduce the network traffic in hotspot areas and increase the coverage and spectral efficiency. Nonetheless, the current deployment of 5G Base Stations (BSs) and small cells is static and cannot move around even though they are deployed in hot spot areas, leading to high operational costs. Furthermore, more than these static deployments of base stations can be required in an unpredictable scenario of extreme crowd movement. To overcome these issues, Device to Device (D2D) Communication with the dynamic deployment of Virtual Base stations (VBSs) can be called upon, which can be achieved by using User Equipment (UE) such as phones or laptops to mimic the functions of a Base Station (BS). Therefore, in this paper, a User Equipment based Virtual Base Station (UE-VBS) is studied, which will act as a secondary base station and, in turn, help alleviate the traffic load in the network. Specifically, as one UE cannot relieve the entire network traffic load, the network area is split into different clusters by using an unsupervised Machine Learning (ML) clustering technique(i.e., K-Means with Mean Shift Clustering), and a single UE is selected to act as a VBS for that cluster with the utilisation of supervised ML classification techniques (i.e., Decision Trees, Logistic Regression, Linear Discriminant Analysis And Quadratic Discriminant Analysis, Linear Support Vector). In our work, we utilise the K-means along with mean shift clustering techniques to cluster simulated network areas accurately. Also, we use and compare different classification machine learning techniques to predict/classify whether user equipment can be employed as a VBS and become UE-VBs. Our simulation study reveals that the Decision Tree algorithm achieves the highest accuracy in categorising the eligible UEs as UE-VBs.","PeriodicalId":150576,"journal":{"name":"2023 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129047642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-Sensor Fusion Based Off-Road Drivable Region Detection and Its ROS Implementation","authors":"Palmani Duraisamy, S. Natarajan","doi":"10.1109/WiSPNET57748.2023.10134440","DOIUrl":"https://doi.org/10.1109/WiSPNET57748.2023.10134440","url":null,"abstract":"There is a growing demand for multi-sensor fusion based off-road drivable region detection in the field of autonomous vehicles and robotics. This technology allows for improved navigation and localization in off-road environments, such as rough terrain, by combining data from multiple sensors. This can lead to more accurate and reliable detection of drivable regions, which is crucial for the safe operation of autonomous vehicles in off-road environments. In this work, a deep learning architecture is employed to identify drivable and obstacle regions on images. It learns to classify and cluster the regions simultaneously using semantic segmentation. Further, a LiDAR-based ground segmentation method is introduced to classify drivable regions more effectively. The ground segmentation method splits the regions into small bins and applies the ground fitting technique with adaptive likelihood estimation. Finally, a late fusion method is proposed to fuse both results better to classify the drivable region. The entire fusion architecture was implemented on ROS. On the RELLIS3D dataset, the semantic segmentation achieves a mean accuracy of 84.3%. Furthermore, it is observed that certain regions misclassified by the semantic segmentation are corrected by LiDAR-based ground segmentation and the fusion provides a better representation of the drivable region.","PeriodicalId":150576,"journal":{"name":"2023 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131751450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}