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Radio resource management in energy harvesting cooperative cognitive UAV assisted IoT networks:A multi-objective approach

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Digital Communications and Networks 2024年第4期10卷 1088-1102页

作者： muhammad rashid ramzan muhammad Naeem Omer Chughtai Waleed Ejaz Mohammad Altaf Department of Electrical and Computer Engineering COMSATS University Islamabad(CUI)Wah CampusPakistan Department of Electrical Engineering Lakehead UniversityBarrieONL4M 3X9Canada

Cooperative communication through energy harvested relays in Cognitive Internet of Things(CIoT)has been envisioned as a promising solution to support massive connectivity of Cognitive Radio(CR)based IoT devices and to achieve maximal energy and spectral efficiency in upcoming wireless *** this work,a cooperative CIoT system is contemplated,in which a source acts as a satellite,communicating with multiple CIoT devices over numerous *** Aerial Vehicles(UAVs)are used as relays,which are equipped with onboard Energy Harvesting(EH)*** adopted a Power Splitting(PS)method for EH at relays,which are harvested from the Radio frequency(RF)*** conjunction with this,the Decode and Forward(DF)relaying strategy is used at UAV relays to transmit the messages from the satellite source to the CIoT *** developed a Multi-Objective Optimization(MOO)framework for joint optimization of source power allocation,CIoT device selection,UAV relay assignment,and PS ratio *** formulated three objectives:maximizing the sum rate and the number of admitted CIoT in the network and minimizing the carbon dioxide *** MOO formulation is a Mixed-Integer Non-Linear Programming(MINLP)problem,which is challenging to *** address the joint optimization problem for an epsilon optimal solution,an Outer Approximation Algorithm(OAA)is proposed with reduced *** simulation results show that the proposed OAA is superior in terms of CIoT device selection and network utility maximization when compared to those obtained using the Nonlinear Optimization with Mesh Adaptive Direct-search(NOMAD)algorithm.

关键词： Cooperative communication Energy harvesting Power splitting Unmanned aerial vehicles Cognitive radio Internet of things Multi-objective optimization Relay assignment Power allocation

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Seed germination and seedling emergence of four tree species of southern China in response to acid rain

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Journal of Forestry Research 2021年第2期32卷 471-481页

作者： Matoor Mohsin Gilani Mulualem Tigabu Bo Liu Taimoor Hassan Farooq muhammad Haroon Urashid muhammad ramzan Xiangqing Ma Forestry College Fujian Agriculture and Forestry UniversityFuzhou 350002People’s Republic of China Fujian Provincial Colleges and University Engineering Research Center of Plantation Sustainable Management Fuzhou 350002People’s Republic of China Faculty of Forest Science Southern Swedish Forest Research CenterSwedish University of Agricultural SciencesP.O.Box 4923053 AlnarpSweden National Engineering Laboratory for Applied Technology of Forestry and Ecology in South China Central South University of Forestry and TechnologyChangsha 410000People’s Republic of China School of Soil and Water Conservation and Desertification Combating Beijing Forestry UniversityBeijing 100083People’s Republic of China

Acid rain has become a major concern due to increasing atmospheric pollution,particularly in *** investigated whether acid rain inhibits the germination of seeds and subsequent emergence of seedling of four tree species from southern China:Cunninghamia lanceolata,Fokienia hodginisi,Pinus massoniana and Phoebe zhennan by simulating acid rain with pH of 2.5,3.5,4.5 and *** hypothesized that the inhibitory effect of acid rain on germination of seeds and emergence of seedling varies between species and the degree of acidity.A solution of 1 N H2SO4 and 1 N HNO 3 in the ratio of 10–1 was prepared and diluted to four pH levels,and seeds were supplied with solutions of these pH values and distilled water as control and tested for germination in a controlled growth *** results revealed that simulated acid rain of pH 2.5 adversely affected the germination capacity of *** and ***;while all acid solutions significantly increased germination of ***;but had no effect on germination of *** *** acid solution(pH of 2.5)adversely affected elongation of radicle and hypocotyl as well as fresh and dry weights of radicle and hypocotyl of tender *** result demonstrated that seedling emergence is more sensitive than seed germination to simulated acid rain,and germination of conifer species are less sensitive than broad leaved species to simulated acid *** a whole,acid rain of pH of 3.5 is the threshold level and acid rain below this value will have a detrimental effect on seed germination and seedling emergence.

关键词： Cunninghamia lanceolata Fokienia hodginisi Pinus massoniana Phoebe zhennan Seed viability Simulated acid rain

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Fluid Mechanics and Heat-Transfer Operations Combination Involved in Urea Unit of Fertilizer Complex

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Non-Metallic Material Science 2019年第1期1卷 5-10页

作者： muhammad Imran rashid Naveed ramzan Department of Chemical Polymer and Composite Material EngineeringUniversity of Engineering and Technology (UET)-KSK CampusLahorePakistan Department of Chemical Engineering University of Engineering and TechnologyLahorePakistan

Fertilizer plants are most complex plants in the world (rashid et al., 2013, Process Safety Progress) and its good opportunity to learn science from operations involved in these plants. Fluid mechanics and heat transf... 详细信息

关键词： Fertilizer plants Fertilizer complex Fluid mechanics Heat-transfer Urea

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Detection Collision Flows in SDN Based 5G Using Machine Learning Algorithms

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Computers, Materials & Continua 2023年第1期74卷 1413-1435页

作者： Aqsa Aqdus rashid Amin Sadia ramzan Sultan S.Alshamrani Abdullah Alshehri El-Sayed M.El-kenawy Department of Computer Science University of Engineering and Technology Taxila47050Pakistan Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Information Technology Al Baha UniversityAl BahaSaudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt

The rapid advancement of wireless communication is forming a hyper-connected 5G network in which billions of linked devices generate massive amounts of *** traffic control and data forwarding functions are decoupled in software-defined networking(SDN)and allow the network to be *** switch in SDN keeps track of forwarding information in a flow *** SDN switches must search the flow table for the flow rules that match the packets to handle the incoming *** to the obvious vast quantity of data in data centres,the capacity of the flow table restricts the data plane’s forwarding ***,the SDN must handle traffic from across the whole *** flow table depends on Ternary Content Addressable Memorable Memory(TCAM)for storing and a quick search of regulations;it is restricted in capacity owing to its elevated cost and energy *** the flow table is abused and overflowing,the usual regulations cannot be executed *** this case,we consider lowrate flow table overflowing that causes collision flow rules to be installed and consumes excessive existing flow table capacity by delivering packets that don’t fit the flow table at a low *** study introduces machine learning techniques for detecting and categorizing low-rate collision flows table in SDN,using Feed ForwardNeuralNetwork(FFNN),K-Means,and Decision Tree(DT).We generate two network topologies,Fat Tree and Simple Tree Topologies,with the Mininet simulator and coupled to the OpenDayLight(ODL)*** efficiency and efficacy of the suggested algorithms are assessed using several assessment indicators such as success rate query,propagation delay,overall dropped packets,energy consumption,bandwidth usage,latency rate,and *** findings showed that the suggested technique to tackle the flow table congestion problem minimizes the number of flows while retaining the statistical consistency of the 5G *** putting the proposed flow method

关键词： 5G networks software-defined networking(SDN) OpenFlow load balancing machine learning(ML) feed forward neural network(FFNN) k-means and decision tree(DT)

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