International Journal on Future Revolution in Computer Science & Communication Engineering
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5G Technology based Edge Computing in UAV Networks for Resource Allocation with Routing using Federated Learning Access Network and Trajectory Routing Protocol
Get PDFUAVs (Unmanned aerial vehicles) are being utilised more frequently in wireless communication networks of the Beyond Fifth Generation (B5G) that are equipped with a high-computation paradigm and intelligent applications. Due to the growing number of IoT (Internet of Things) devices in smart environments, these networks have the potential to produce a sizeable volume of heterogeneous data.This research propose novel technique in UAV based edge computing resource allocation and routing by machine learning technique. here the UAV-enabled MEC method regarding emerging IoT applications as well as role of machine learning (ML) has been analysed. In this research the UAV assisted edge computing resource allocation has been carried out using Monte Carlo federated learning based access network. Then the routing through UAV network has been carried out using trajectory based deterministic reinforcement collaborative routing protocol.We specifically conduct an experimental investigation of the tradeoff between the communication cost and the computation of the two possible methodologies.The key findings show that, despite the longer connection latency, the computation offloading strategy enables us to give a significantly greater throughput than the edge computing approach
Analysis of Machine Learning Models for Heart Disease Prediction using Different Algorithms: A Review
Get PDFNow a days the heart diseases are growing very rapidly making it an important and apprehensive task of prediction of these kinds of diseases in advance. The diagnosis is also a tough chore because it has to be performed in a precise and efficient manner. The emerging technology in modern life style integrated with internet of thing which having sensors and huge amount of data is sent to various clouds for further investigation using different algorithms to fetch out precise information for various domains. Across the world approximately 3 quintillion bytes/day information generated and this data stored for further examination. As data is in huge quantity therefore, appropriate methods applied to examine the perfect analysis so that prediction can be carried out optimally. Clinical decision making is dominant to all patient care happenings which includes choosing a deed, between replacements. These days emerging field like Machine Learning play prime role in healthcare to analyze and predict the diseases. After investigating numerous research article on Machine Learning, it was found that for same data set accuracy was different for various algorithms. In our research work different machine learning techniques will be implemented and will be tested for various parameters like accuracy, precision, recall on validated dataset. ML and Neural Networks are more capable in supporting deciding and predicting from the enormous data formed by health care systems
A Fussy Based Neural Genetic Algorithm for Securing Data in Cyber Security
Get PDFBusinesses are using cyber security technologies more and more to upgrade their operations. These businesses are prone to hazards and cyber security breaches because to the very specialized characteristics of such settings, including their sensitive exchange of cyber security data and the weak design of connected devices. Our main goal is to develop a cyber security system that can take into account all potential forms of assaults while staying within the allocated budget. To achieve this, a financial strategy based on portfolio management is utilized by enabling the selection of a portfolio of security controls that maximizes security level control while minimizing direct expenses. To solve this problem we proposed Fussy Based Neural Genetic Algorithm for authenticity, reliability and confidentiality of cyber security data and it decreases the danger of cyber security data integrity. Using a complex key, the plaintext is first transformed into a complex cipher text. The key is created using logical operators and is randomly chosen from the cyber security data. By applying principles of proposed algorithm, the cipher text acquired in the first step is rendered even more unreadable in the second phase. Feature Extraction of cyber security data is done by Principle Component Analysis (PCA).The data is encrypted by using Data Encryption Standard (DES). The data is decrypted using the proposed Fussy Based Neural Genetic Algorithm with Particle Swarm Optimization (FNGA-PSO).The suggested model's metrics are examined and compared to various traditional algorithms. This model solves the lack of difference in the authenticity of cyber security information, as well as it will give real and effective information to the organizational companies
Research on Smart Environment Monitoring Systems based on Secure Internet of Things (IoT)
Get PDFSignificant environmental threats include poor air quality, water contamination, and radiation pollution. A healthy society must be maintained for the planet to experience sustained growth. Environmental monitoring has transformed into smart environment monitoring (SEM) systems in recent years due to the growth of an internet of things (IoT). The Internet of Things (IoT) concept has developed into technology for creating smart environments and also has its disadvantage. To collect, evaluate, and recommend specific actions in smart environments for various purposes, a secure IoT-based platform is proposed. The proposed method follows the flow outlined here: data collection, normalization technique is used for data preprocessing, Linear Discriminant Analysis (LDA) is used for feature extraction, then data stored in IoT, Advanced Twofish encryption algorithm is proposed for securing the data, then user decryption, and finally performance is analyzed for smart environment monitoring using secure IoT. The proposed work aims to complete a critical evaluation of significant contributions to SEM that focus on the monitoring of water quality, air quality, radiation contamination, and agricultural systems. Secure IoT is based on the optimal integration and use of data gathered from several sources. This algorithm provides smart environment monitoring and also exhibits optimal integration
Multipath Routing in Cloud Computing using Fuzzy based Multi-Objective Optimization System in Autonomous Networks
Get PDFIntelligent houses and buildings, autonomous automobiles, drones, robots, and other items that are successfully incorporated into daily life are examples of autonomous systems and the Internet of Things (IoT) that have advanced as research areas. Secured data transfer in untrusted cloud applications has been one of the most significant requirements in the cloud in recent times. In order to safeguard user data from unauthorised users, encrypted data is stored on cloud servers. Existing techniques offer either security or efficiency for data transformation. They fail to retain complete security while undergoing significant changes. This research proposes novel technique in multipath routing based energy optimization of autonomous networks. The main goal of this research is to enhance the secure data transmission in cloud computing with network energy optimization. The secure data transmission is carried out using multi-authentication attribute based encryption with multipath routing protocol. Then the network energy has been optimized using multi-objective fuzzy based reinforcement learning. The experimental analysis has been carried out based on secure data transmission and energy optimization of the network. The parameters analysed in terms of scalability of 79%, QoS of 75%, encryption time of 42%, latency of 96%, energy efficiency of 98%, end-end delay of 45%
Smart Grid based Wireless Communication in 5G Network for Monitoring and Control Systems in Renewable Energy Management
Get PDFWireless networks are becoming ubiquitous and as the cost of equipment decreases and performance increases, it becomes both economically and technologically feasible to deploy wireless networks in power systems and industrial environments for a wide range of applications. They have advantage of providing diverse controlling features through a unified communication platform. Application of such networks in the smart grid/industrial environments is under active research and expected to become an integral part of the power system. This research propose novel technique smart grid communication in wireless 5G networks for monitoring and controlling management. Here the smart grid designing has been done based on wireless communication networks. The smart grid network for renewable energy has been controlled using Stackelberg equilibrium based SCADA (supervisory control and data acquisition) method. The control method based collected data has been monitored for detection of malicious activities in the network using supervised radial basis fuzzy systems. The experimental analysis has been carried out based on control system and network malicious activities. Here the control system based parameters analysed are Scalability of 65%, QoS of 71%, Power consumption of 41%, Network Efficiency of 92%. Then machine learning based malicious activities detection in terms of accuarcy of 96%, network security of 88%, throughput of 94%, Network delay of 41%. Proposed method supports interoperability of multiple types of inverters, is scalable and flexible, and transmits data over a secure communication channel
Numerical Simulation and Assessment of Meta Heuristic Optimization Based Multi Objective Dynamic Job Shop Scheduling System
Get PDFIn today's world of manufacturing, cost reduction becomes one of the most important issues. A successful business needs to reduce its cost to be competitive. The programming of the machine is playing an important role in production planning and control as a tool to help manufacturers reduce their costs maximizing the use of their resources. The programming problem is not only limited to the programming of the machine, but also covers many other areas such such as computer and information technology and communication. From the definition, programming is an art that involves allocating, planning the allocation and utilization of resources to achieve a goal. The aim of the program is complete tasks in a reasonable amount of time. This reasonableness is a performance measure of how well the resources are allocated to tasks. Time or time-dependent functions are always it used as performance measures. The objectives of this research are to develop Intelligent Search Heuristic algorithms (ISHA) for equal and variable size sub lot for m machine flow shop problems, to Implement Particle Swarm Optimization algorithm (PSO) in matlab, to develop PSO based Optimization program for efficient job shop scheduling problem. The work also address solution to observe and verify results of PSO based Job Shop Scheduling with help of graft chart
Numerical Simulation and Design of Low PAPR FBMC Communication System for 5G Applications
Get PDFUnlike SC-FDMA (Single-Carrier Frequency Division Multiple Access), merging only DFT (Discrete Fourier Transform) addition with FBMC-OQAM (filter group multi-carrier with offset quadrature amplitude modulation) only cuts the marginal PAPR. (Peak-to-average power ratio). To take advantage of the single carrier effect of DFT extension, special conditions for the coefficients of the IQ (in-phase and quadrature phase) channels of every single subcarrier ought to be met. As a beginning point, we first originate this form, which we call the ITSM (Identical Time-Shifted Multi-Carrier) condition. Then, depending on this condition, we put forward a new FBMC for low PAPR. The foremost features of the offered way out are summarized as: First, to additionally raise the PAPR reduction, we created four candidate versions of the FBMC waveform for DFT spreading out and ITSM conditions and carefully chosen one with the least peak power. Even with various candidate generations, unlike the traditional SI (Side information) based PAPR reduction scheme, the focal computational fragments (such as DFT and IDFT) are shared and need only be executed one time. Therefore, matched to the prior DFT-expanded FBMC, the overhead in complexity is small, and the recommended pattern can realize a PAPR reduction comparable to SC-FDMA. Second, in the projected pattern each one pass on only two bits of SI from a block of FBMC-OQAM symbols. And so, the SI overhead is meaningfully lesser than a conventional SI-based scheme such as SLM (Selective Mapping) or PTS (Partial Transmission Sequence).The whole work is executed using MATLAB software. The PAPR of FBMC system has been significantly reduced after the application of proposed algorithm. PAPR was reduced by 25 % after the use of DFT spreading and ITSM conditioning
Booth Multiplier Based on Low Power High Speed Full Adder With Fin_FET Technology
Get PDFThis paper proposes a novel Fin FET-based HSFA for the multiplier in order to overcome the issues of low speed operation. It is advantageous to use Fin FETs to construct the arithmetic circuit while assessing the available works. The carry propagation and slow operation of the old technique are disadvantages. The CMOS-based compressor circuit, on the other hand, suffers from leakage current, which reduces its driving capabilities. High current DSP applications are well matched to the design's specifications. Even with a supply voltage of 1 volt, the proposed device has a decent driving capability. As a result, the circuit runs more quickly and has less latency. A transmission gate is used in the design of the suggested adder structure to selectively block or transfer data from the input to output. Half adder and adder are shown in the following illustrations. The smaller the transistor count, the less power it uses. The suggested Fin FET design for the smaller transistors has a superior driving capability than the CMOS equivalent. Additionally, when cascading, the Fin FET based adder may contribute to superior switch performance, such as when using ripple carry adder. There is also the possibility of a low operation, which may operate at Low wattage Electronic designs for high-performance and small devices have become increasingly dependent on the use of VLSI circuits. The power of a processor is determined in large part by the multiplier used in its design. Multiplier factor booth coding is being used to reorder the input bits in order to reduce facility use. The booth decoder works by rearranging the specified booth equivalent. The Booth decoder has the ability to expand the range of zeros. As a result, the power consumption of the design will be decreased even more. As soon as the input bit constant drops below zero, related rows or columns of an adder must be disabled, if possible
Design Simulation and Analysis of Deep Convolutional Neural Network Based Complex Image Classification System
No full textThere are 350 families and over 250,000 known varieties of flowering plants. Furthermore, effective flower classification, including content-based image recovery, is essential for the order, plant inspections of buildings, the gardening sector, live plantations, and scientific flower classification guidelines. The representation of flowers has a broad variety of uses. However, manual categorization is time-consuming and exhausting, particularly when the image basis is confusing, has a large number of images, and is perhaps erroneous for several flower groupings. Therefore, effective flower division, discovery, and categorization processes are of great significance. To ensure robust, trustworthy, and ongoing characterization during the preparation stage, new approaches are proposed in this work. On three datasets of flowers that are undeniably known, our technique is tested. Results that are better than the best in this aim for all data sets with accuracy over 98 percent. The categorization of flowers from a wide variety of animal groups is attempted in this research using a unique two-way deep learning method. In order for the foundation box to be placed around the floral area, it was first separated into sections. In a system that uses just convolutional networks, the suggested method for floral distribution is shown to be a parallel classifier. Make a powerful classification using convolutional neural networks in order to recognize the various flower types