Performance analysis of DSDV and OLSR wireless sensor network routing protocols using FPGA hardware and machine learning

Gupta, Namit; Jain, Arpit; Vaisla, Kunwar Singh; Kumar, Adesh; Kumar, Rajeev

doi:10.1007/s11042-021-10820-4

Cited by 39 publications

(13 citation statements)

References 30 publications

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“…The Xilinx device summary for ANN-8, ANN-16, ANN-24, ANN-32, ANN-40 ANN-48, ANN-66, and ANN-64 is given in Table 5 . The target device is Virtex-5 FPGA with device xc5vlx20t-2-ff323 used for simulation and synthesis [ 24 ]. Figure 10 presents the hardware utilization curve for ANN-8 to ANN-64 hardware chips.…”

Section: Resultsmentioning

confidence: 99%

Performance analysis of multiple input single layer neural network hardware chip

Goel

Kumar

2023

Multimed Tools Appl

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Section: Resultsmentioning

confidence: 99%

Performance analysis of multiple input single layer neural network hardware chip

Goel

Kumar

2023

Multimed Tools Appl

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“…The significance of the MATLAB simulation time is that the particular algorithm can predict the estimated delay when the algorithm will be realized in the MATLAB-HDL simulator or Simulink for tumor detection system implementation. The MATLAB simulation predicts the time complexity for the algorithm and estimates the designer to choose the machine learning model [26,40]. The performance of the algorithms is evaluated based on the performance measures such as precision, recall, F-measure, and accuracy [43].…”

Section: Resultsmentioning

confidence: 99%

Study and analysis of different segmentation methods for brain tumor MRI application

Kumar

2022

Multimed Tools Appl

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Medical Resonance Imaging (MRI) is one of the preferred imaging methods for brain tumor diagnosis and getting detailed information on tumor type, location, size, identification, and detection. Segmentation divides an image into multiple segments and describes the separation of the suspicious region from pre-processed MRI images to make the simpler image that is more meaningful and easier to examine. There are many segmentation methods, embedded with detection devices, and the response of each method is different. The study article focuses on comparing the performance of several image segmentation algorithms for brain tumor diagnosis, such as Otsu’s, watershed, level set, K-means, HAAR Discrete Wavelet Transform (DWT), and Convolutional Neural Network (CNN). All of the techniques are simulated in MATLAB using online images from the Brain Tumor Image Segmentation Benchmark (BRATS) dataset-2018. The performance of these methods is analyzed based on response time and measures such as recall, precision, F-measures, and accuracy. The measured accuracy of Otsu’s, watershed, level set, K-means, DWT, and CNN methods is 71.42%, 78.26%, 80.45%, 84.34%, 86.95%, and 91.39 respectively. The response time of CNN is 2.519 s in the MATLAB simulation environment for the designed algorithm. The novelty of the work is that CNN has been proven the best algorithm in comparison to all other methods for brain tumor image segmentation. The simulated and estimated parameters provide the direction to researchers to choose the specific algorithm for embedded hardware solutions and develop the optimal machine-learning models, as the industries are looking for the optimal solutions of CNN and deep learning-based hardware models for the brain tumor.

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“…The chip design of the AODV routing algorithm is carried out in Xilinx ISE 14.7. The steps are: design constraints, algorithm selection, modeling approach, register transfer level (RTL) analysis, function simulation and data communication with test benches FPGA synthesis, hardware parameters report analysis, and perform comparative analysis [22] based on performance indices. The waveform is functionally checked on the Xilinx integrated synthesis environment simulator (ISIM).…”

Section: Methodsmentioning

confidence: 99%

Performance Analysis of AODV Routing for Wireless Sensor Network in FPGA Hardware

Gupta¹,

Vaisla²,

Jain³

et al. 2022

Computer Systems Science and Engineering

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Wireless sensor network (WSN) is a group of interconnected sensor nodes that work wirelessly to capture the information of surroundings. The routing of the network is a challenging task. The routing of WSN is classified as proactive, reactive, and hybrid. Adhoc on-demand distance vector (AODV) routing is an example of reactive routing based on the demand route formations among different nodes in the network. The research article emphasizes the design and simulation of the AODV routing hardware chip using very-high-speed integrated circuit hardware description language (VHDL) programming in Xilinx integrated synthesis environment (ISE) 14.7 software. The performance of the chip is studied based on the field-programmable gate array (FPGA) hardware parameters such as slices, lookup table (LUTs), input/output block (IOB), flipflops, and memory for the different configurations of the network (N = 10, 20 ….100). The delay and frequency are also estimated on the Virtex-5 FPGA. The performance of the WSN with AODV routing is also analyzed based on the packet delivery ratio, throughput, delay, and control overhead. The simulation test cases verified the 8-bit, 64-bit, and 128-bit data communication within the nodes.

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Performance analysis of DSDV and OLSR wireless sensor network routing protocols using FPGA hardware and machine learning

Cited by 39 publications

References 30 publications

Performance analysis of multiple input single layer neural network hardware chip

Performance analysis of multiple input single layer neural network hardware chip

Study and analysis of different segmentation methods for brain tumor MRI application

Performance Analysis of AODV Routing for Wireless Sensor Network in FPGA Hardware

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