Decision Making in Internet of Vehicles Using Pervasive Trusted Computing Scheme

Pervasive schemes are the significant techniques that allow intelligent communication among the devices without any human intervention.Recently Internet of Vehicles(IoVs)has been introduced as one of the applications of pervasive computing that addresses the road safety challenges.Vehicles participating within the IoV are embedded with a wide range of sensors which operate in a real time environment to improve the road safety issues.Various mechanisms have been proposed which allow automatic actions based on uncertainty of sensory and managed data.Due to the lack of existing transportation integration schemes,IoV has not been completely explored by business organizations.In order to tackle this problem,we have proposed a novel trusted mechanism in IoV during communication,sensing,and record storing.Our proposed method uses trust based analysis and subjective logic functions with the aim of creating a trust environment for vehicles to communicate.In addition,the subjective logic function is integrated with multi-attribute SAW scheme to improve the decision metrics of authenticating nodes.The trust analysis depends on a variety of metrics to ensure an accurate identification of legitimate vehicles embedded with IoT devices ecosystem.The proposed scheme is determined and verified rigorously through various IoT devices and decision making metrics against a baseline solution.The simulation results show that the proposed scheme leads to 88%improvement in terms of better identification of legitimate nodes,road accidents and message alteration records during data transmission among vehicles as compared to the baseline approach.

Disease Diagnosis System Using IoT Empowered with Fuzzy Inference System

Disease diagnosis is a challenging task due to a large number of associated factors.Uncertainty in the diagnosis process arises frominaccuracy in patient attributes,missing data,and limitation in the medical expert’s ability to define cause and effect relationships when there are multiple interrelated variables.This paper aims to demonstrate an integrated view of deploying smart disease diagnosis using the Internet of Things(IoT)empowered by the fuzzy inference system(FIS)to diagnose various diseases.The Fuzzy Systemis one of the best systems to diagnose medical conditions because every disease diagnosis involves many uncertainties,and fuzzy logic is the best way to handle uncertainties.Our proposed system differentiates new cases provided symptoms of the disease.Generally,it becomes a time-sensitive task to discriminate symptomatic diseases.The proposed system can track symptoms firmly to diagnose diseases through IoT and FIS smartly and efficiently.Different coefficients have been employed to predict and compute the identified disease’s severity for each sign of disease.This study aims to differentiate and diagnose COVID-19,Typhoid,Malaria,and Pneumonia.This study used the FIS method to figure out the disease over the use of given data related to correlating with input symptoms.MATLAB tool is utilised for the implementation of FIS.Fuzzy procedure on the aforementioned given data presents that affectionate disease can derive from the symptoms.The results of our proposed method proved that FIS could be utilised for the diagnosis of other diseases.This study may assist doctors,patients,medical practitioners,and other healthcare professionals in early diagnosis and better treat diseases.

Type II Fuzzy Logic Based Cluster Head Selection for Wireless Sensor Network

Wireless Sensor Network(WSN)forms an essential part of IoT.It is embedded in the target environment to observe the physical parameters based on the type of application.Sensor nodes inWSN are constrained by different features such as memory,bandwidth,energy,and its processing capabilities.In WSN,data transmission process consumes the maximum amount of energy than sensing and processing of the sensors.So,diverse clustering and data aggregation techniques are designed to achieve excellent energy efficiency in WSN.In this view,the current research article presents a novel Type II Fuzzy Logic-based Cluster Head selection with Low Complexity Data Aggregation(T2FLCH-LCDA)technique for WSN.The presented model involves a two-stage process such as clustering and data aggregation.Initially,three input parameters such as residual energy,distance to Base Station(BS),and node centrality are used in T2FLCH technique for CH selection and cluster construction.Besides,the LCDA technique which follows Dictionary Based Encoding(DBE)process is used to perform the data aggregation at CHs.Finally,the aggregated data is transmitted to the BS where it achieves energy efficiency.The experimental validation of the T2FLCH-LCDAtechnique was executed under three different scenarios based on the position of BS.The experimental results revealed that the T2FLCH-LCDA technique achieved maximum energy efficiency,lifetime,Compression Ratio(CR),and power saving than the compared methods.