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| 1 | Incentive-Driven Approach for Misbehavior Avoidance in Vehicular Networks显示文摘For efficient and robust information exchange in the vehicular adhoc network,a secure and trusted incentive reward is needed to avoid and reduce the intensity of misbehaving nodes and congestion especially in the case where the periodic beacons exploit the channel.In addition,we cannot be sure that all vehicular nodes eagerly share their communication assets to the system for message dissemination without any rewards.Unfortunately,there may be some misbehaving nodes and due to their selfish and greedy approach,these nodes may not help others on the network.To deal with this challenge,trust-based misbehavior avoidance schemes are generally reflected as the capable resolution.In this paper,we employed a fair incentive mechanism for cooperation aware vehicular communication systems.In order to deploy a comprehensive credit based rewarding scheme,the proposed rewardbased scheme fully depends on secure and reliable cryptographic procedures.In order to achieve the security goals,we used the cryptographic scheme to generate a certified public key for the authenticity of every message exchange over the network.We evaluated the friction of misbehaving vehicles and the effect of rewarding schemes in context with honest messages dissemination over the network. | Shahid Sultan Qaisar Javaid Eid Rehman Ahmad Aziz Alahmadi Nasim Ullah Wakeel Khan | 2022 | Computers, Materials & Continua2022，，3： | 0 |
| 2 | Speech Recognition-Based Automated Visual Acuity Testing with Adaptive Mel Filter Bank显示文摘One of the most commonly reported disabilities is vision loss,which can be diagnosed by an ophthalmologist in order to determine the visual system of a patient.This procedure,however,usually requires an appointment with an ophthalmologist,which is both time-consuming and expensive process.Other issues that can arise include a lack of appropriate equipment and trained practitioners,especially in rural areas.Centered on a cognitively motivated attribute extraction and speech recognition approach,this paper proposes a novel idea that immediately determines the eyesight deficiency.The proposed system uses an adaptive filter bank with weighted mel frequency cepstral coefficients for feature extraction.The adaptive filter bank implementation is inspired by the principle of spectrum sensing in cognitive radio that is aware of its environment and adapts to statistical variations in the input stimuli by learning from the environment.Comparative performance evaluation demonstrates the potential of our automated visual acuity test method to achieve comparable results to the clinical ground truth,established by the expert ophthalmologist’s tests.The overall accuracy achieved by the proposed model when compared with the expert ophthalmologist test is 91.875%.The proposed method potentially offers a second opinion to ophthalmologists,and serves as a cost-effective pre-screening test to predict eyesight loss at an early stage. | Shibli Nisar Muhammad Asghar Khan Fahad Algarni Abdul Wakeel M.Irfan Uddin Insaf Ullah | 2022 | Computers, Materials & Continua2022，，2： | 0 |
| 3 | Enhanced Fingerprinting Based Indoor Positioning Using Machine Learning显示文摘Due to the inability of the Global Positioning System(GPS)signals to penetrate through surfaces like roofs,walls,and other objects in indoor environments,numerous alternative methods for user positioning have been presented.Amongst those,the Wi-Fi fingerprinting method has gained considerable interest in Indoor Positioning Systems(IPS)as the need for lineof-sight measurements is minimal,and it achieves better efficiency in even complex indoor environments.Offline and online are the two phases of the fingerprinting method.Many researchers have highlighted the problems in the offline phase as it deals with huge datasets and validation of Fingerprints without pre-processing of data becomes a concern.Machine learning is used for the model training in the offline phase while the locations are estimated in the online phase.Many researchers have considered the concerns in the offline phase as it deals with huge datasets and validation of Fingerprints becomes an issue.Machine learning algorithms are a natural solution for winnowing through large datasets and determining the significant fragments of information for localization,creating precise models to predict an indoor location.Large training sets are a key for obtaining better results in machine learning problems.Therefore,an existing WLAN fingerprinting-based multistory building location database has been used with 21049 samples including 19938 training and 1111 testing samples.The proposed model consists of mean and median filtering as pre-processing techniques applied to the database for enhancing the accuracy by mitigating the impact of environmental dispersion and investigated machine learning algorithms(kNN,WkNN,FSkNN,and SVM)for estimating the location.The proposed SVM with median filtering algorithm gives a reduced mean positioning error of 0.7959 m and an improved efficiency of 92.84%as compared to all variants of the proposed method for 108703 m^(2) area. | Muhammad Waleed Pasha Mir Yasir Umair Alina Mirza Faizan Rao Abdul Wakeel Safia Akram Fazli Subhan Wazir Zada Khan | 2021 | Computers, Materials & Continua2021，，11： | 0 |
| 4 | Position Vectors Based Efcient Indoor Positioning System显示文摘With the advent and advancements in the wireless technologies,Wi-Fi ngerprinting-based Indoor Positioning System(IPS)has become one of the most promising solutions for localization in indoor environments.Unlike the outdoor environment,the lack of line-of-sight propagation in an indoor environment keeps the interest of the researchers to develop efcient and precise positioning systems that can later be incorporated in numerous applications involving Internet of Things(IoTs)and green computing.In this paper,we have proposed a technique that combines the capabilities of multiple algorithms to overcome the complexities experienced indoors.Initially,in the database development phase,Motley Kennan propagation model is used with Hough transformation to classify,detect,and assign different attenuation factors related to the types of walls.Furthermore,important parameters for system accuracy,such as,placement and geometry of Access Points(APs)in the coverage area are also considered.New algorithm for deployment of an additional AP to an already existing infrastructure is proposed by using Genetic Algorithm(GA)coupled with Enhanced Dilution of Precision(EDOP).Moreover,classication algorithm based on k-Nearest Neighbors(k-NN)is used to nd the position of a stationary or mobile user inside the given coverage area.For k-NN to provide low localization error and reduced space dimensionality,three APs are required to be selected optimally.In this paper,we have suggested an idea to select APs based on Position Vectors(PV)as an input to the localization algorithm.Deducing from our comprehensive investigations,it is revealed that the accuracy of indoor positioning system using the proposed technique unblemished the existing solutions with signicant improvements. | Ayesha Javed Mir Yasir Umair Alina Mirza Abdul Wakeel Fazli Subhan Wazir Zada Khan | 2021 | Computers, Materials & Continua2021，，5： | 0 |
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