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Energy Efficient Ambience Awake Routing with OpenFlow Approach

Computers, Materials & Continua 2021年第5期67卷 2049-2059页

作者： Hima Bindu Valiveti SNV Ganesh budati anil kumar Dileep kumar Yadav Department of ECE GRIETHyderabad500090India Department of EEE Avanthi Institute of Engineering and TechnologyVisakhapatnam535006India Department of CSE Galgotia’s UniversityNoida201310India

A major problem in networking has always been energy *** life is one parameter which could help improve Energy *** research on wireless networking stresses on reducing signaling messages or time required for data transfer for addressing energy consumption *** or Forwarding packets in a network between the network elements like routers,switches,wireless access points,etc.,is complex in conventional *** the advent of Software Defined Networking(SDN)for 5G network architectures,the distributed networking has embarked onto centralized networking,wherein the SDN Controller is responsible for decision *** controller pushes its decision onto the network elements with the help of a control plane protocol termed *** networks have been largely in use because of their ease in physical and logically setting the administrative *** centralized controller deals with the policy funding and the protocols used for routing procedures are designated by the decentralized *** Awake is a location centered routing protocol deployed in the 5G network architecture with OpenFlow *** Ambience Awake mechanism relies on the power consumption of the network elements during the packet transmission for unicast and multicast *** signalling load and the routing overhead witnessed an improvement of 30%during the routing *** proposed routing mechanism run on the top of the decentralized SDN controller proves to be 19.59%more efficient than the existing routing solutions.

关键词： Energy efficiency routing smart networks 5G software defined networking openflow

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Guest editorial: Machine Learning in Wireless Networks

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CAAI Transactions on Intelligence Technology 2021年第2期6卷 133-134页

作者： anil kumar budati Steve S.H.Ling Department of Electronics and Communication Engineering Gokaraju Rangaraju Institute of Engineering and TechnologyHyderabadIndia School of Biomedical Engineering University of Technology SydneySydneyNew South WalesAustralia

This dedicated Special Section on Machine Learning in Wireless Networks aims to provide improved knowledge for state-of-the-art worldwide R&D communities in wireless networks and machine learning(ML)*** aims to do so by calling for novel formulations,innovative techniques,and optimised solutions to highlight the key issues related to these problems in a forum for shared research and ideas.

关键词： Wireless wireless formulation

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Content-based image retrieval using Gaussian-Hermite moments and firefly and grey wolf optimization

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CAAI Transactions on Intelligence Technology 2021年第2期6卷 135-146页

作者： Yasasvy Tadepalli Meenakshi Kollati Swaraja Kuraparthi Padmavathi Kora anil kumar budati Lakshmi Kala Pampana Department of ECE GRIETHyderabadIndia Department of ECE VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadIndia

Rapid growth in the transfer of multimedia information over the Internet requires algorithms to retrieve a queried image from large image database *** proposed content-based image retrieval(CBIR)uses Gaussian-Hermite moments as the low-level *** these features are compressed with principal component *** compressed feature set is multiplied with the weight matrix array,which has the same size as the feature *** firefly and grey wolf optimization(FAGWO)is used to prevent the premature convergence of optimization in the firefly *** retrieval of images in CBIR is carried out in an OpenCV python environment with K-nearest neighbours and random forest algorithm *** fitness function for FAGWO is the accuracyof the *** FAGWO algorithm derives the optimum weights from a randomlygenerated initial *** these optimized weights are applied,the proposed algorithm shows better precision/recall and efficiency than other techniques such as exact legendre moments,Region-based image retrieval,K-means clustering and Color descriptor wavelet-based texture descriptor retrieval *** terms of optimization,hybrid FAGWO outperformed various optimization techniques(when used alone)like Particle Swarm Optmization,Genetic Algorithm,Grey-Wolf Optimization and FireFly algorithm.

关键词： python optimization image

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Performance Analysis of Three Spectrum Sensing Detection Techniques with Ambient Backscatter Communication in Cognitive Radio Networks

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Computer Modeling in Engineering & Sciences 2023年第10期137卷 813-825页

作者： Shayla Islam anil kumar budati Mohammad Kamrul Hasan Saoucene Mahfoudh Syed Bilal Hussian Shah Institute of Computer Science&Digital Innovation ICSDIUCSI UniversityKuala Lumpur56000Malaysia Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia(UKM)Selangor43600Malaysia School of Engineering Computing and InformaticsDar Al-Hekma UniversityJeddah22246Saudi Arabia

In wireless communications, the Ambient Backscatter Communication (AmBC) technique is a promisingapproach, detecting user presence accurately at low power levels. At low power or a low Signal-to-Noise Ratio(SNR), there is no dedicated power for the users. Instead, they can transmit information by reflecting the ambientRadio Frequency (RF) signals in the spectrum. Therefore, it is essential to detect user presence in the spectrum forthe transmission of data without loss or without collision at a specific time. In this paper, the authors proposed anovel Spectrum Sensing (SS) detection technique in the Cognitive Radio (CR) spectrum, by developing the *** Matched Filter Detection with Inverse covariance (MFDI), Cyclostationary Feature Detection with Inversecovariance (CFDI) and Hybrid Filter Detection with Inverse covariance (HFDI) approaches are used with AmBCto detect the presence of users at low power levels. The performance of the three detection techniques is measuredusing the parameters of Probability of Detection (PD), Probability of False Alarms (Pfa), Probability of MissedDetection (Pmd), sensing time and throughput at low power or low SNR. The results show that there is a significantimprovement via the HFDI technique for all the parameters.

关键词： Ambient backscatter communication cognitive radio MFDI CFDI HFDI

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