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| 1 | A Bio-Inspired Routing Optimization in UAV-enabled Internet of Everything显示文摘Internet of Everything(IoE)indicates a fantastic vision of the future,where everything is connected to the internet,providing intelligent services and facilitating decision making.IoE is the collection of static and moving objects able to coordinate and communicate with each other.The moving objects may consist of ground segments and ying segments.The speed of ying segment e.g.,Unmanned Ariel Vehicles(UAVs)may high as compared to ground segment objects.The topology changes occur very frequently due to high speed nature of objects in UAV-enabled IoE(Ue-IoE).The routing maintenance overhead may increase when scaling the Ue-IoE(number of objects increases).A single change in topology can force all the objects of the Ue-IoE to update their routing tables.Similarly,the frequent updating in routing table entries will result more energy dissipation and the lifetime of the Ue-IoE may decrease.The objects consume more energy on routing computations.To prevent the frequent updation of routing tables associated with each object,the computation of routes from source to destination may be limited to optimum number of objects in the Ue-IoE.In this article,we propose a routing scheme in which the responsibility of route computation(from neighbor objects to destination)is assigned to some IoE-objects in the Ue-IoE.The route computation objects(RCO)are selected on the basis of certain parameters like remaining energy and mobility.The RCO send the routing information of destination objects to their neighbors once they want to communicate with other objects.The proposed protocol is simulated and the results show that it outperform state-of-the-art protocols in terms of average energy consumption,messages overhead,throughput,delay etc. | Masood Ahmad Fasee Ullah Ishtiaq Wahid Atif Khan M.Irfan Uddin Abdullah Alharbi Wael Alosaimi | 2021 | Computers, Materials & Continua2021，，4： | 0 |
| 2 | Application of the Deep Convolutional Neural Network for the Classification of Auto Immune Diseases显示文摘IIF(Indirect Immune Florescence)has gained much attention recently due to its importance in medical sciences.The primary purpose of this work is to highlight a step-by-step methodology for detecting autoimmune diseases.The use of IIF for detecting autoimmune diseases is widespread in different medical areas.Nearly 80 different types of autoimmune diseases have existed in various body parts.The IIF has been used for image classification in both ways,manually and by using the Computer-Aided Detection(CAD)system.The data scientists conducted various research works using an automatic CAD system with low accuracy.The diseases in the human body can be detected with the help of Transfer Learning(TL),an advanced Convolutional Neural Network(CNN)approach.The baseline paper applied the manual classification to the MIVIA dataset of Human Epithelial cells(HEP)type II cells and the Sub Class Discriminant(SDA)analysis technique used to detect autoimmune diseases.The technique yielded an accuracy of up to 90.03%,which was not reliable for detecting autoimmune disease in the mitotic cells of the body.In the current research,the work has been performed on the MIVIA data set of HEP type II cells by using four well-known models of TL.Data augmentation and normalization have been applied to the dataset to overcome the problem of overfitting and are also used to improve the performance of TL models.These models are named Inception V3,Dens Net 121,VGG-16,and Mobile Net,and their performance can be calculated through parameters of the confusion matrix(accuracy,precision,recall,and F1 measures).The results show that the accuracy value of VGG-16 is 78.00%,Inception V3 is 92.00%,Dense Net 121 is 95.00%,and Mobile Net shows 88.00%accuracy,respectively.Therefore,DenseNet-121 shows the highest performance with suitable analysis of autoimmune diseases.The overall performance highlighted that TL is a suitable and enhanced technique compared to its counterparts.Also,the proposed technique is used to detect autoimmune diseases with a minimal margin of errors and flaws. | Fayaz Muhammad Jahangir Khan Asad Ullah Fasee Ullah Razaullah Khan Inayat Khan Mohammed ElAffendi Gauhar Ali | 2023 | Computers, Materials & Continua2023，77，10： | 0 |
| 3 | Quality of Service Aware Cluster Routing in Vehicular Ad Hoc Networks显示文摘In vehicular ad hoc networks(VANETs),the topology information(TI)is updated frequently due to vehicle mobility.These frequent changes in topology increase the topology maintenance overhead.To reduce the control message overhead,cluster-based routing schemes are proposed.In clusterbased routing schemes,the nodes are divided into different virtual groups,and each group(logical node)is considered a cluster.The topology changes are accommodated within each cluster,and broadcasting TI to the whole VANET is not required.The cluster head(CH)is responsible for managing the communication of a node with other nodes outside the cluster.However,transmitting real-time data via a CH may cause delays in VANETs.Such real-time data require quick service and should be routed through the shortest path when the quality of service(QoS)is required.This paper proposes a hybrid scheme which transmits time-critical data through the QoS shortest path and normal data through CHs.In this way,the real-time data are delivered efciently to the destination on time.Similarly,the routine data are transmitted through CHs to reduce the topology maintenance overhead.The work is validated through a series of simulations,and results show that the proposed scheme outperforms existing algorithms in terms of topology maintenance overhead,QoS and real-time and routine packet transmission. | Ishtiaq Wahid Fasee Ullah Masood Ahmad Atif Khan M.Irfan Uddin Abdullah Alharbi Wael Alosaimi | 2021 | Computers, Materials & Continua2021，，6： | 0 |
| 4 | Adaptation of Vehicular Ad hoc Network Clustering Protocol for Smart Transportation显示文摘Clustering algorithms optimization can minimize topology maintenance overhead in large scale vehicular Ad hoc networks(VANETs)for smart transportation that results from dynamic topology,limited resources and noncentralized architecture.The performance of a clustering algorithm varies with the underlying mobility model to address the topology maintenance overhead issue in VANETs for smart transportation.To design a robust clustering algorithm,careful attention must be paid to components like mobility models and performance objectives.A clustering algorithm may not perform well with every mobility pattern.Therefore,we propose a supervisory protocol(SP)that observes the mobility pattern of vehicles and identies the realistic Mobility model through microscopic features.An analytical model can be used to determine an efcient clustering algorithm for a specic mobility model(MM).SP selects the best clustering scheme according to the mobility model and guarantees a consistent performance throughout VANET operations.The simulation has performed in three parts that is the central part simulation for setting up the clustering environment,In the second part the clustering algorithms are tested for efciency in a constrained atmosphere for some time and the third part represents the proposed scheme.The simulation results show that the proposed scheme outperforms clustering algorithms such as honey bee algorithm-based clustering and memetic clustering in terms of cluster count,re-afliation rate,control overhead and cluster lifetime. | Masood Ahmad Abdul Hameed Fasee Ullah Ishtiaq Wahid Atif Khan M.Irfan Uddin Shaq Ahmad Ahmed M.El-Sherbeeny | 2021 | Computers, Materials & Continua2021，，5： | 0 |
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