The effects of 5%Ni addition on the glass forming ability,thermal stability and crystallization behavior of Mg65Cu25Tb10 bulk metallic glass were investigated using X-ray diffractometry,differential scanning calorimet...The effects of 5%Ni addition on the glass forming ability,thermal stability and crystallization behavior of Mg65Cu25Tb10 bulk metallic glass were investigated using X-ray diffractometry,differential scanning calorimetry and transmission electron microscopy.The small amount of Ni addition reduces the glass forming ability and thermal stability due to a significant decrease in the crystallization activation energy.Analyses of crystallization kinetics give evidence to the existence of quenched-in nuclei in amorphous Mg65Cu20Ni5Tb10.Final crystallization products are basically same for Mg65Cu25Tb10 and Mg65Cu20Ni5Tb10.展开更多
Coronavirus is a potentially fatal disease that normally occurs in mammals and birds.Generally,in humans,the virus spreads through aerial droplets of any type of fluid secreted from the body of an infected person.Coro...Coronavirus is a potentially fatal disease that normally occurs in mammals and birds.Generally,in humans,the virus spreads through aerial droplets of any type of fluid secreted from the body of an infected person.Coronavirus is a family of viruses that is more lethal than other unpremeditated viruses.In December 2019,a new variant,i.e.,a novel coronavirus(COVID-19)developed in Wuhan province,China.Since January 23,2020,the number of infected individuals has increased rapidly,affecting the health and economies of many countries,including Pakistan.The objective of this research is to provide a system to classify and categorize the COVID-19 outbreak in Pakistan based on the data collected every day from different regions of Pakistan.This research also compares the performance of machine learning classifiers(i.e.,Decision Tree(DT),Naive Bayes(NB),Support Vector Machine,and Logistic Regression)on the COVID-19 dataset collected in Pakistan.According to the experimental results,DT and NB classifiers outperformed the other classifiers.In addition,the classified data is categorized by implementing a Bayesian Regularization Artificial Neural Network(BRANN)classifier.The results demonstrate that the BRANN classifier outperforms state-of-the-art classifiers.展开更多
In crowded cities,searching for the availability of parking lots is a herculean task as it results in the wastage of drivers’time,increases air pollution,and traffic congestion.Smart parking systems facilitate the dr...In crowded cities,searching for the availability of parking lots is a herculean task as it results in the wastage of drivers’time,increases air pollution,and traffic congestion.Smart parking systems facilitate the drivers to determine the information about the parking lot in real time and book them depending on the requirement.But the existing smart parking systems necessitate the drivers to reveal their sensitive information that includes their mobile number,personal identity,and desired destination.This disclosure of sensitive information makes the existing centralized smart parking systems more vulnerable to service providers’security breaches,single points of failure,and bottlenecks.In this paper,an Improved Asymmetric Consortium Blockchain and Homomorphically Computing Univariate Polynomial-based private information retrieval(IACB-HCUPPIR)scheme is proposed to ensure parking lots’availability with transparency security in a privacy-preserving smart parking system.In specific,an improved Asymmetric Consortium Blockchain is used for achieving secure transactions between different parties interacting in the smart parking environment.It further adopted the method of Homomorphically Computing Univariate Polynomial-based private information retrieval(HCUPPIR)scheme for preserving the location privacy of drivers.The results of IACB-HCUPPIR confirmed better results in terms of minimized computation and communication overload with throughput,latency,and response time with maximized drivers’privacy preservation.Moreover,the proposed fully homomorphic algorithm(FHE)was compared against partial-homomorphic encryption(PHE)and technique without encryption and found that the proposed model has quick communication in allocating the parking slots starting with 24.3 s,whereas PHE starts allocating from 24.7 s and the technique without encryption starts at 27.4 s.Thus,we ensure the proposed model performs well in allocating parking slots with less time and high security with privacy preservation.展开更多
In this paper, we survey a number of studies in the literature on improving lightweight systems in the Internet of Things (IoT). The paper illustrates recent development of Boolean cryptographic function Application a...In this paper, we survey a number of studies in the literature on improving lightweight systems in the Internet of Things (IoT). The paper illustrates recent development of Boolean cryptographic function Application and how it assists in using hardware such as the internet of things. For a long time there seems to be little progress in applying pure mathematics in providing security since the wide progress made by George Boole and Shannon. We discuss cryptanalysis of Boolean functions to avoid trapdoors and vulnerabilities in the development of block ciphers. It appears that there is significant progress. A comparative analysis of lightweight cryptographic schemes is reported in terms of execution time, code size and throughput. Depending on the schemes and the structure of the algorithms, these parameters change but remain within reasonable values making them suited for Internet of things applications. The driving force of lightweight cryptography (LWC) stems mainly from its direct applications in the real world since it provides solutions to actual problems faced by designers of IoT systems. Broadly speaking, lightweight cryptographic algorithms are designed to achieve two main goals. The first goal of a cryptographic algorithm is to withstand all known cryptanalytic attacks and thus to be secure in the black-box model. The second goal is to build the cryptographic primitive in such a way that its implementations satisfy a clearly specified set of constraints that depend on a case-by-case basis.展开更多
Due to mobile Internet technology's rapid popularization,the Industrial Internet of Things(IIoT)can be seen everywhere in our daily lives.While IIoT brings us much convenience,a series of security and scalability ...Due to mobile Internet technology's rapid popularization,the Industrial Internet of Things(IIoT)can be seen everywhere in our daily lives.While IIoT brings us much convenience,a series of security and scalability issues related to permission operations rise to the surface during device communications.Hence,at present,a reliable and dynamic access control management system for IIoT is in urgent need.Up till now,numerous access control architectures have been proposed for IIoT.However,owing to centralized models and heterogeneous devices,security and scalability requirements still cannot be met.In this paper,we offer a smart contract token-based solution for decentralized access control in IIoT systems.Specifically,there are three smart contracts in our system,including the Token Issue Contract(TIC),User Register Contract(URC),and Manage Contract(MC).These three contracts collaboratively supervise and manage various events in IIoT environments.We also utilize the lightweight and post-quantum encryption algorithm-Nth-degree Truncated Polynomial Ring Units(NTRU)to preserve user privacy during the registration process.Subsequently,to evaluate our proposed architecture's performance,we build a prototype platform that connects to the local blockchain.Finally,experiment results show that our scheme has achieved secure and dynamic access control for the IIoT system compared with related research.展开更多
Decision-making(DM)is a process in which several persons concur-rently engage,examine the problems,evaluate potential alternatives,and select an appropriate option to the problem.Technique for determining order prefer...Decision-making(DM)is a process in which several persons concur-rently engage,examine the problems,evaluate potential alternatives,and select an appropriate option to the problem.Technique for determining order preference by similarity to the ideal solution(TOPSIS)is an established DM process.The objective of this report happens to broaden the approach of TOPSIS to solve the DM issues designed with Hesitancy fuzzy data,in which evaluation evidence given by the experts on possible solutions is presents as Hesitancy fuzzy decision matrices,each of which is defined by Hesitancy fuzzy numbers.Findings:we represent analytical results,such as designing a satellite communication network and assessing reservoir operation methods,to demonstrate that our suggested thoughts may be used in DM.Aim:We studied a new testing method for the arti-ficial communication system to give proof of the future construction of satellite earth stations.We aim to identify the best one from the different testing places.We are alsofinding the best operation schemes in the reservoir.In this article,we present the concepts of Laplacian energy(LE)in Hesitancy fuzzy graphs(HFGs),the weight function of LE of HFGs,and the TOPSIS method technique is used to produce the hesitancy fuzzy weighted-average(HFWA).Also,consider practical examples to illustrate the applicability of thefinest design of satellite communication systems and also evaluation of reservoir schemes.展开更多
This research is focused on a highly effective and untapped feature called gammatone frequency cepstral coefficients(GFCC)for the detection of COVID-19 by using the nature-inspired meta-heuristic algorithm of deer hun...This research is focused on a highly effective and untapped feature called gammatone frequency cepstral coefficients(GFCC)for the detection of COVID-19 by using the nature-inspired meta-heuristic algorithm of deer hunting optimization and artificial neural network(DHO-ANN).The noisy crowdsourced cough datasets were collected from the public domain.This research work claimed that the GFCC yielded better results in terms of COVID-19 detection as compared to the widely used Mel-frequency cepstral coefficient in noisy crowdsourced speech corpora.The proposed algorithm's performance for detecting COVID-19 disease is rigorously validated using statistical measures,F1 score,confusion matrix,specificity,and sensitivity parameters.Besides,it is found that the proposed algorithm using GFCC performs well in terms of detecting the COVID-19 disease from the noisy crowdsourced cough dataset,COUGHVID.Moreover,the proposed algorithm and undertaken feature parameters have improved the detection of COVID-19 by 5%compared to the existing methods.展开更多
Wireless Body Area Network(WBAN)is a cutting-edge technology that is being used in healthcare applications to monitor critical events in the human body.WBAN is a collection of in-body and on-body sensors that monitor ...Wireless Body Area Network(WBAN)is a cutting-edge technology that is being used in healthcare applications to monitor critical events in the human body.WBAN is a collection of in-body and on-body sensors that monitor human physical parameters such as temperature,blood pressure,pulse rate,oxygen level,body motion,and so on.They sense the data and communicate it to the Body Area Network(BAN)Coordinator.The main challenge for the WBAN is energy consumption.These issues can be addressed by implementing an effective Medium Access Control(MAC)protocol that reduces energy consumption and increases network lifetime.The purpose of the study is to minimize the energy consumption and minimize the delay using IEEE 802.15.4 standard.In our proposed work,if any critical events have occurred the proposed work is to classify and prioritize the data.We gave priority to the highly critical data to get the Guarantee Tine Slots(GTS)in IEEE 802.15.4 standard superframe to achieve greater energy efficiency.The proposed MAC provides higher data rates for critical data based on the history and current condition and also provides the best reliable service to high critical data and critical data by predicting node similarity.As an outcome,we proposed a MAC protocol for Variable Data Rates(MVDR).When compared to existing MAC protocols,the MVDR performed very well with low energy intake,less interruption,and an enhanced packet-sharing ratio.展开更多
[Objective] The aim was to study the effects of heavy metal stress on the protein content of microorganisms.[Method] By using traditional microbiological culture method,four typical microorganisms(including Escherichi...[Objective] The aim was to study the effects of heavy metal stress on the protein content of microorganisms.[Method] By using traditional microbiological culture method,four typical microorganisms(including Escherichia coli,Bacillus subtilis,Saccharomyces cerevisiae Hansen and Streptomycetaceae) were cultured under the stress of heavy metal ions(like Hg2+,Cd2+,Cr6+ and Pb2+) with different concentrations,and the effects of heavy metal stress on the synthesis of protein in four typical microorganisms were discussed through measuring protein content.[Result] Heavy metals with low concentration were beneficial to the synthesis of protein in four typical microorganisms to a certain extent,but the synthesis of protein in four typical microorganisms was inhibited differently with the increase of heavy metal concentration.The tolerance of B.subtilis to four heavy metals was stronger compared with other three microorganisms,and the four heavy metals with concentration of 5-50 mg/L promoted the protein synthesis of B.subtilis.Cr6+ with low concentration promoted the protein synthesis of E.coli greatly;Pb2+ inhibited the protein synthesis of E.coli obviously,and promoted the protein synthesis of other three microorganisms under certain concentration;Cd2+ with low concentration was beneficial to the protein synthesis of four microorganisms.[Conclusion] The study could provide theoretical foundation for discussing the physiological response of microorganism to heavy metal stress.展开更多
The wireless sensor network (WSN) has been widely used in various fields,but it still remains in the preliminary discovery and research phase with a lack of various related mature technologies.Traditional encryption s...The wireless sensor network (WSN) has been widely used in various fields,but it still remains in the preliminary discovery and research phase with a lack of various related mature technologies.Traditional encryption schemes are not suitable for wireless sensor networks due to intrinsic features of the nodes such as low energy,limited computation capability,and lack of storage resources.In this paper,we present a novel block encryption scheme based on the integer discretization of a chaotic map,the Feistel network structure,and an S-box.The novel scheme is fast,secure,has low resource consumption and is suitable for wireless sensor network node encryption schemes.The experimental tests are carried out with detailed analysis,showing that the novel block algorithm has a large key space,very good diffusion and disruptive performances,a strict avalanche effect,excellent statistical balance,and fast encryption speed.These features enable the encryption scheme to pass the SP800-22 test.Meanwhile,the analysis and the testing of speed,time,and storage space on the simulator platform show that this new encryption scheme is well able to hide data information in wireless sensor networks.展开更多
We report on the generation of a high energy and long pulse for pumping optical parametric chirped-pulse amplification(OPCPA) by a high-birefringence photonic crystal fibre(HB-PCF) and a laser-diode-pumped regenerativ...We report on the generation of a high energy and long pulse for pumping optical parametric chirped-pulse amplification(OPCPA) by a high-birefringence photonic crystal fibre(HB-PCF) and a laser-diode-pumped regenerative chirped pulse amplifier.Using the femtosecond pump pulse centred at 815 nm,a 1064 nm soliton pulse is produced in the HB-PCF.After injecting it into an Nd:YAG regenerative amplifier with the glass etalons,a narrow-band amplified pulse with an energy of ～4 mJ and a duration of 235 ps is achieved at a repetition rate of 10 Hz,which is suitable for being used as a pump source in the 800 nm OPCPA system.展开更多
The cumulative sum (CUSUM) algorithm is proposed to detect the selfish behavior of a node in a wireless ad hoc network. By tracing the statistics characteristic of the backoff time between successful transmissions, a ...The cumulative sum (CUSUM) algorithm is proposed to detect the selfish behavior of a node in a wireless ad hoc network. By tracing the statistics characteristic of the backoff time between successful transmissions, a wireless node can distinguish if there is a selfish behavior in the wireless network. The detection efficiency is validated using a Qualnet simulator. An IEEE 802.11 wireless ad hoc network with 20 senders and 20 receivers spreading out randomly in a given area is evaluated. The well-behaved senders use minimum contention window size of 32 and maximum contention window size of 1 024, and the selfish nodes are assumed not to use the binary exponential strategy for which the contention window sizes are both fixed as 16. The transmission radius of all nodes is 250 m. Two scenarios are investigated: a single-hop network with nodes spreading out in 100 m×100 m, and all the nodes are in the range of each other; and a multi-hop network with nodes spreading out in 1 000 m×1 000 m . The node can monitor the backoff time from all the other nodes and run the detection algorithms over those samples. It is noted that the threshold can significantly affect the detection time and the detection accuracy. For a given threshold of 0.3 s, the false alarm rates and the missed alarm rates are less than 5%. The detection delay is less than 1.0 s. The simulation results show that the algorithm has short detection time and high detection accuracy.展开更多
The internet of things(IoT)has a wide variety of applications,which in turn raisesmany challenging issues.IoT technology enables devices to closely monitor their environment,providing context-aware intelligence based ...The internet of things(IoT)has a wide variety of applications,which in turn raisesmany challenging issues.IoT technology enables devices to closely monitor their environment,providing context-aware intelligence based on the real-time data collected by their sensor nodes.The IoT not only controls these devices but also monitors their user’s behaviour.One of the major issues related to IoT is the need for an energy-efficient communication protocol which uses the heterogeneity and diversity of the objects connected through the internet.Minimizing energy consumption is a requirement for energyconstrained nodes and outsourced nodes.The IoT nodes deployed in different geographical regions typically have different energy levels.This paper focuses on creating an energy-efficient protocol for IoTwhich can deal with the clustering of nodes and the cluster head selection process.An energy thresholdmodel is developed to select the appropriate cluster heads and also to ensure uniform distribution of energy to those heads andmember nodes.The proposed model envisages an IoT network with three different types of nodes,described here as advanced,intermediate and normal nodes.Normal nodes are first-level nodes,which have the lowest energy use;intermediate nodes are second-level nodes,which have a medium energy requirement;and the advanced class are thirdlevel nodes with the highest energy use.The simulation results demonstrate that the proposed algorithm outperforms other existing algorithms.In tests,it shows a 26%improvement in network lifetime compared with existing algorithms.展开更多
The pandemic situation in 2020 brought about a‘digitized new normal’and created various issues within the current education systems.One of the issues is the monitoring of students during online examination situation...The pandemic situation in 2020 brought about a‘digitized new normal’and created various issues within the current education systems.One of the issues is the monitoring of students during online examination situations.A system to determine the student’s eye gazes during an examination can help to eradicate malpractices.In this work,we track the users’eye gazes by incorporating twelve facial landmarks around both eyes in conjunction with computer vision and the HAAR classifier.We aim to implement eye gaze detection by considering facial landmarks with two different Convolutional Neural Network(CNN)models,namely the AlexNet model and the VGG16 model.The proposed system outperforms the traditional eye gaze detection system which only uses computer vision and the HAAR classifier in several evaluation metric scores.The proposed system is accurate without the need for complex hardware.Therefore,it can be implemented in educational institutes for the fair conduct of examinations,as well as in other instances where eye gaze detection is required.展开更多
The prodigious advancements in contemporary technologies have also brought in the situation of unprecedented cyber-attacks.Further,the pin-based security system is an inadequate mechanism for handling such a scenario....The prodigious advancements in contemporary technologies have also brought in the situation of unprecedented cyber-attacks.Further,the pin-based security system is an inadequate mechanism for handling such a scenario.The reason is that hackers use multiple strategies for evading security systems and thereby gaining access to private data.This research proposes to deploy diverse approaches for authenticating and securing a connection amongst two devices/gadgets via sound,thereby disregarding the pins’manual verification.Further,the results demonstrate that the proposed approaches outperform conventional pin-based authentication or QR authentication approaches.Firstly,a random signal is encrypted,and then it is transformed into a wave file,after which it gets transmitted in a short burst via the device’s speakers.Subsequently,the other device/gadget captures these audio bursts through its microphone and decrypts the audio signal for getting the essential data for pairing.Besides,this model requires two devices/gadgets with speakers and a microphone,and no extra hardware such as a camera,for reading the QR code is required.The first module is tested with realtime data and generates high scores for the widely accepted accuracy metrics,including precision,Recall,F1 score,entropy,and mutual information(MI).Additionally,this work also proposes a module helps in a secured transmission of sensitive data by encrypting it over images and other files.This steganographic module includes two-stage encryption with two different encryption algorithms to transmit data by embedding inside a file.Several encryption algorithms and their combinations are taken for this system to compare the resultant file size.Both these systems engender high accuracies and provide secure connectivity,leading to a sustainable communication ecosystem.展开更多
According to various worldwide statistics,most car accidents occur solely due to human error.The person driving a car needs to be alert,especially when travelling through high traffic volumes that permit high-speed tr...According to various worldwide statistics,most car accidents occur solely due to human error.The person driving a car needs to be alert,especially when travelling through high traffic volumes that permit high-speed transit since a slight distraction can cause a fatal accident.Even though semiautomated checks,such as speed detecting cameras and speed barriers,are deployed,controlling human errors is an arduous task.The key causes of driver’s distraction include drunken driving,conversing with co-passengers,fatigue,and operating gadgets while driving.If these distractions are accurately predicted,the drivers can be alerted through an alarm system.Further,this research develops a deep convolutional neural network(deep CNN)models for predicting the reason behind the driver’s distraction.The deep CNN models are trained using numerous images of distracted drivers.The performance of deep CNN models,namely the VGG16,ResNet,and Xception network,is assessed based on the evaluation metrics,such as the precision score,the recall/sensitivity score,the F1 score,and the specificity score.The ResNet model outperformed all other models as the best detection model for predicting and accurately determining the drivers’activities.展开更多
Urbanization affects the quality of the air,which has drastically degraded in the past decades.Air quality level is determined by measures of several air pollutant concentrations.To create awareness among people,an au...Urbanization affects the quality of the air,which has drastically degraded in the past decades.Air quality level is determined by measures of several air pollutant concentrations.To create awareness among people,an automation system that forecasts the quality is needed.The COVID-19 pandemic and the restrictions it has imposed on anthropogenic activities have resulted in a drop in air pollution in various cities in India.The overall air quality index(AQI)at any particular time is given as the maximum band for any pollutant.PM2.5 is a fine particulate matter of a size less than 2.5 micrometers,the inhalation of which causes adverse effects in people suffering from acute respiratory syndrome and other cardiovascular diseases.PM2.5 is a crucial factor in deciding the overall AQI.The proposed forecasting model is designed to predict the annual PM2.5 and AQI.The forecasting models are designed using Seasonal Autoregressive Integrated Moving Average and Facebook’s Prophet Library through optimal hyperparameters for better prediction.An AQI category classification model is also presented using classical machine learning techniques.The experimental results confirm the substantial improvement in air quality and greater reduction in PM2.5 due to the lockdown imposed during the COVID-19 crisis.展开更多
Data offloading at the network with less time and reduced energy con-sumption are highly important for every technology.Smart applications process the data very quickly with less power consumption.As technology grows t...Data offloading at the network with less time and reduced energy con-sumption are highly important for every technology.Smart applications process the data very quickly with less power consumption.As technology grows towards 5G communication architecture,identifying a solution for QoS in 5G through energy-efficient computing is important.In this proposed model,we perform data offloading at 5G using the fuzzification concept.Mobile IoT devices create tasks in the network and are offloaded in the cloud or mobile edge nodes based on energy consumption.Two base stations,small(SB)and macro(MB)stations,are initialized and thefirst tasks randomly computed.Then,the tasks are pro-cessed using a fuzzification algorithm to select SB or MB in the central server.The optimization is performed using a grasshopper algorithm for improving the QoS of the 5G network.The result is compared with existing algorithms and indi-cates that the proposed system improves the performance of the system with a cost of 44.64 J for computing 250 benchmark tasks.展开更多
Retinopathy is a human eye disease that causes changes in retinal blood vessels that leads to bleed,leak fluid and vision impairment.Symptoms of retinopathy are blurred vision,changes in color perception,red spots,and...Retinopathy is a human eye disease that causes changes in retinal blood vessels that leads to bleed,leak fluid and vision impairment.Symptoms of retinopathy are blurred vision,changes in color perception,red spots,and eye pain and it cannot be detected with a naked eye.In this paper,a new methodology based on Convolutional Neural Networks(CNN)is developed and proposed to intelligent retinopathy prediction and give a decision about the presence of retinopathy with automatic diabetic retinopathy screening with accurate diagnoses.The CNN model is trained by different images of eyes that have retinopathy and those which do not have retinopathy.The fully connected layers perform the classification process of the images from the dataset with the pooling layers minimize the coherence among the adjacent layers.The feature loss factor increases the label value to identify the patterns with the kernel-based matching.The performance of the proposed model is compared with the related methods of DREAM,KNN,GD-CNN and SVM.Experimental results show that the proposed CNN performs better.展开更多
基金Projects(50601011,50432020) supported by the National Natural Science Foundation of ChinaProject(A2720060295) supported by the Basic Research Project of National Defense of China+1 种基金Project(BK2006533) supported by the Natural Science Foundation of Jiangsu Province, ChinaProject supported by the New Century Excellent Talents in University Program
文摘The effects of 5%Ni addition on the glass forming ability,thermal stability and crystallization behavior of Mg65Cu25Tb10 bulk metallic glass were investigated using X-ray diffractometry,differential scanning calorimetry and transmission electron microscopy.The small amount of Ni addition reduces the glass forming ability and thermal stability due to a significant decrease in the crystallization activation energy.Analyses of crystallization kinetics give evidence to the existence of quenched-in nuclei in amorphous Mg65Cu20Ni5Tb10.Final crystallization products are basically same for Mg65Cu25Tb10 and Mg65Cu20Ni5Tb10.
基金The authors are grateful to the Raytheon Chair for Systems Engineering for funding.
文摘Coronavirus is a potentially fatal disease that normally occurs in mammals and birds.Generally,in humans,the virus spreads through aerial droplets of any type of fluid secreted from the body of an infected person.Coronavirus is a family of viruses that is more lethal than other unpremeditated viruses.In December 2019,a new variant,i.e.,a novel coronavirus(COVID-19)developed in Wuhan province,China.Since January 23,2020,the number of infected individuals has increased rapidly,affecting the health and economies of many countries,including Pakistan.The objective of this research is to provide a system to classify and categorize the COVID-19 outbreak in Pakistan based on the data collected every day from different regions of Pakistan.This research also compares the performance of machine learning classifiers(i.e.,Decision Tree(DT),Naive Bayes(NB),Support Vector Machine,and Logistic Regression)on the COVID-19 dataset collected in Pakistan.According to the experimental results,DT and NB classifiers outperformed the other classifiers.In addition,the classified data is categorized by implementing a Bayesian Regularization Artificial Neural Network(BRANN)classifier.The results demonstrate that the BRANN classifier outperforms state-of-the-art classifiers.
基金The research was funded by the School of Information Technology and Engineering,Vellore Institute of Technology,Vellore 632014,Tamil Nadu,India.
文摘In crowded cities,searching for the availability of parking lots is a herculean task as it results in the wastage of drivers’time,increases air pollution,and traffic congestion.Smart parking systems facilitate the drivers to determine the information about the parking lot in real time and book them depending on the requirement.But the existing smart parking systems necessitate the drivers to reveal their sensitive information that includes their mobile number,personal identity,and desired destination.This disclosure of sensitive information makes the existing centralized smart parking systems more vulnerable to service providers’security breaches,single points of failure,and bottlenecks.In this paper,an Improved Asymmetric Consortium Blockchain and Homomorphically Computing Univariate Polynomial-based private information retrieval(IACB-HCUPPIR)scheme is proposed to ensure parking lots’availability with transparency security in a privacy-preserving smart parking system.In specific,an improved Asymmetric Consortium Blockchain is used for achieving secure transactions between different parties interacting in the smart parking environment.It further adopted the method of Homomorphically Computing Univariate Polynomial-based private information retrieval(HCUPPIR)scheme for preserving the location privacy of drivers.The results of IACB-HCUPPIR confirmed better results in terms of minimized computation and communication overload with throughput,latency,and response time with maximized drivers’privacy preservation.Moreover,the proposed fully homomorphic algorithm(FHE)was compared against partial-homomorphic encryption(PHE)and technique without encryption and found that the proposed model has quick communication in allocating the parking slots starting with 24.3 s,whereas PHE starts allocating from 24.7 s and the technique without encryption starts at 27.4 s.Thus,we ensure the proposed model performs well in allocating parking slots with less time and high security with privacy preservation.
文摘In this paper, we survey a number of studies in the literature on improving lightweight systems in the Internet of Things (IoT). The paper illustrates recent development of Boolean cryptographic function Application and how it assists in using hardware such as the internet of things. For a long time there seems to be little progress in applying pure mathematics in providing security since the wide progress made by George Boole and Shannon. We discuss cryptanalysis of Boolean functions to avoid trapdoors and vulnerabilities in the development of block ciphers. It appears that there is significant progress. A comparative analysis of lightweight cryptographic schemes is reported in terms of execution time, code size and throughput. Depending on the schemes and the structure of the algorithms, these parameters change but remain within reasonable values making them suited for Internet of things applications. The driving force of lightweight cryptography (LWC) stems mainly from its direct applications in the real world since it provides solutions to actual problems faced by designers of IoT systems. Broadly speaking, lightweight cryptographic algorithms are designed to achieve two main goals. The first goal of a cryptographic algorithm is to withstand all known cryptanalytic attacks and thus to be secure in the black-box model. The second goal is to build the cryptographic primitive in such a way that its implementations satisfy a clearly specified set of constraints that depend on a case-by-case basis.
文摘Due to mobile Internet technology's rapid popularization,the Industrial Internet of Things(IIoT)can be seen everywhere in our daily lives.While IIoT brings us much convenience,a series of security and scalability issues related to permission operations rise to the surface during device communications.Hence,at present,a reliable and dynamic access control management system for IIoT is in urgent need.Up till now,numerous access control architectures have been proposed for IIoT.However,owing to centralized models and heterogeneous devices,security and scalability requirements still cannot be met.In this paper,we offer a smart contract token-based solution for decentralized access control in IIoT systems.Specifically,there are three smart contracts in our system,including the Token Issue Contract(TIC),User Register Contract(URC),and Manage Contract(MC).These three contracts collaboratively supervise and manage various events in IIoT environments.We also utilize the lightweight and post-quantum encryption algorithm-Nth-degree Truncated Polynomial Ring Units(NTRU)to preserve user privacy during the registration process.Subsequently,to evaluate our proposed architecture's performance,we build a prototype platform that connects to the local blockchain.Finally,experiment results show that our scheme has achieved secure and dynamic access control for the IIoT system compared with related research.
文摘Decision-making(DM)is a process in which several persons concur-rently engage,examine the problems,evaluate potential alternatives,and select an appropriate option to the problem.Technique for determining order preference by similarity to the ideal solution(TOPSIS)is an established DM process.The objective of this report happens to broaden the approach of TOPSIS to solve the DM issues designed with Hesitancy fuzzy data,in which evaluation evidence given by the experts on possible solutions is presents as Hesitancy fuzzy decision matrices,each of which is defined by Hesitancy fuzzy numbers.Findings:we represent analytical results,such as designing a satellite communication network and assessing reservoir operation methods,to demonstrate that our suggested thoughts may be used in DM.Aim:We studied a new testing method for the arti-ficial communication system to give proof of the future construction of satellite earth stations.We aim to identify the best one from the different testing places.We are alsofinding the best operation schemes in the reservoir.In this article,we present the concepts of Laplacian energy(LE)in Hesitancy fuzzy graphs(HFGs),the weight function of LE of HFGs,and the TOPSIS method technique is used to produce the hesitancy fuzzy weighted-average(HFWA).Also,consider practical examples to illustrate the applicability of thefinest design of satellite communication systems and also evaluation of reservoir schemes.
文摘This research is focused on a highly effective and untapped feature called gammatone frequency cepstral coefficients(GFCC)for the detection of COVID-19 by using the nature-inspired meta-heuristic algorithm of deer hunting optimization and artificial neural network(DHO-ANN).The noisy crowdsourced cough datasets were collected from the public domain.This research work claimed that the GFCC yielded better results in terms of COVID-19 detection as compared to the widely used Mel-frequency cepstral coefficient in noisy crowdsourced speech corpora.The proposed algorithm's performance for detecting COVID-19 disease is rigorously validated using statistical measures,F1 score,confusion matrix,specificity,and sensitivity parameters.Besides,it is found that the proposed algorithm using GFCC performs well in terms of detecting the COVID-19 disease from the noisy crowdsourced cough dataset,COUGHVID.Moreover,the proposed algorithm and undertaken feature parameters have improved the detection of COVID-19 by 5%compared to the existing methods.
文摘Wireless Body Area Network(WBAN)is a cutting-edge technology that is being used in healthcare applications to monitor critical events in the human body.WBAN is a collection of in-body and on-body sensors that monitor human physical parameters such as temperature,blood pressure,pulse rate,oxygen level,body motion,and so on.They sense the data and communicate it to the Body Area Network(BAN)Coordinator.The main challenge for the WBAN is energy consumption.These issues can be addressed by implementing an effective Medium Access Control(MAC)protocol that reduces energy consumption and increases network lifetime.The purpose of the study is to minimize the energy consumption and minimize the delay using IEEE 802.15.4 standard.In our proposed work,if any critical events have occurred the proposed work is to classify and prioritize the data.We gave priority to the highly critical data to get the Guarantee Tine Slots(GTS)in IEEE 802.15.4 standard superframe to achieve greater energy efficiency.The proposed MAC provides higher data rates for critical data based on the history and current condition and also provides the best reliable service to high critical data and critical data by predicting node similarity.As an outcome,we proposed a MAC protocol for Variable Data Rates(MVDR).When compared to existing MAC protocols,the MVDR performed very well with low energy intake,less interruption,and an enhanced packet-sharing ratio.
基金Supported by Scientific Research Foundation of Educational Commission of Yunnan Province (09Y0382)Natural Science Foundation of Yunnan Province (2008ZC161M)
文摘[Objective] The aim was to study the effects of heavy metal stress on the protein content of microorganisms.[Method] By using traditional microbiological culture method,four typical microorganisms(including Escherichia coli,Bacillus subtilis,Saccharomyces cerevisiae Hansen and Streptomycetaceae) were cultured under the stress of heavy metal ions(like Hg2+,Cd2+,Cr6+ and Pb2+) with different concentrations,and the effects of heavy metal stress on the synthesis of protein in four typical microorganisms were discussed through measuring protein content.[Result] Heavy metals with low concentration were beneficial to the synthesis of protein in four typical microorganisms to a certain extent,but the synthesis of protein in four typical microorganisms was inhibited differently with the increase of heavy metal concentration.The tolerance of B.subtilis to four heavy metals was stronger compared with other three microorganisms,and the four heavy metals with concentration of 5-50 mg/L promoted the protein synthesis of B.subtilis.Cr6+ with low concentration promoted the protein synthesis of E.coli greatly;Pb2+ inhibited the protein synthesis of E.coli obviously,and promoted the protein synthesis of other three microorganisms under certain concentration;Cd2+ with low concentration was beneficial to the protein synthesis of four microorganisms.[Conclusion] The study could provide theoretical foundation for discussing the physiological response of microorganism to heavy metal stress.
基金supported by the National Natural Science Foundation of China (Grant No.60973162)the Natural Science Foundation of Shandong Province,China (Grant No.ZR2009GM037)+4 种基金the Key Natural Science Foundation of Shandong Province,China (Grant No.Z2006G01)the Science and Technology Fund of Shandong Province,China (Grant No.2010GGX10132)the Scientific Research Foundation of Harbin Institute of Technology at Weihai,China (Grant No.HIT(WH)ZB200909)the Technology Research and Development Program of Weihai High-Technology Development Zone in Shandong Province,China (Grant No.201025)the Technology Research and Development Program of Weihai,China (Grant No.2008011)
文摘The wireless sensor network (WSN) has been widely used in various fields,but it still remains in the preliminary discovery and research phase with a lack of various related mature technologies.Traditional encryption schemes are not suitable for wireless sensor networks due to intrinsic features of the nodes such as low energy,limited computation capability,and lack of storage resources.In this paper,we present a novel block encryption scheme based on the integer discretization of a chaotic map,the Feistel network structure,and an S-box.The novel scheme is fast,secure,has low resource consumption and is suitable for wireless sensor network node encryption schemes.The experimental tests are carried out with detailed analysis,showing that the novel block algorithm has a large key space,very good diffusion and disruptive performances,a strict avalanche effect,excellent statistical balance,and fast encryption speed.These features enable the encryption scheme to pass the SP800-22 test.Meanwhile,the analysis and the testing of speed,time,and storage space on the simulator platform show that this new encryption scheme is well able to hide data information in wireless sensor networks.
基金Project supported by the National Basic Research Program of China (Grant No. 2006CB806001)the Knowledge Innovation Program of the Chinese Academy of Sciences (Grant No. KGCX-YW-417-2)the Fund of the State Key Laboratory of High Field Laser Physics and Shanghai Commission of Science and Technology,China (Grant No. 07JC14055)
文摘We report on the generation of a high energy and long pulse for pumping optical parametric chirped-pulse amplification(OPCPA) by a high-birefringence photonic crystal fibre(HB-PCF) and a laser-diode-pumped regenerative chirped pulse amplifier.Using the femtosecond pump pulse centred at 815 nm,a 1064 nm soliton pulse is produced in the HB-PCF.After injecting it into an Nd:YAG regenerative amplifier with the glass etalons,a narrow-band amplified pulse with an energy of ～4 mJ and a duration of 235 ps is achieved at a repetition rate of 10 Hz,which is suitable for being used as a pump source in the 800 nm OPCPA system.
基金Supported by National Natural Science Foundation of China (No. 60702038)National High Technology Research and Development Program of China ("863"Program, No. 2007AA01Z220)Cultivation Fund of Innovation Project,Ministry of Education of China (No. 708024)
文摘The cumulative sum (CUSUM) algorithm is proposed to detect the selfish behavior of a node in a wireless ad hoc network. By tracing the statistics characteristic of the backoff time between successful transmissions, a wireless node can distinguish if there is a selfish behavior in the wireless network. The detection efficiency is validated using a Qualnet simulator. An IEEE 802.11 wireless ad hoc network with 20 senders and 20 receivers spreading out randomly in a given area is evaluated. The well-behaved senders use minimum contention window size of 32 and maximum contention window size of 1 024, and the selfish nodes are assumed not to use the binary exponential strategy for which the contention window sizes are both fixed as 16. The transmission radius of all nodes is 250 m. Two scenarios are investigated: a single-hop network with nodes spreading out in 100 m×100 m, and all the nodes are in the range of each other; and a multi-hop network with nodes spreading out in 1 000 m×1 000 m . The node can monitor the backoff time from all the other nodes and run the detection algorithms over those samples. It is noted that the threshold can significantly affect the detection time and the detection accuracy. For a given threshold of 0.3 s, the false alarm rates and the missed alarm rates are less than 5%. The detection delay is less than 1.0 s. The simulation results show that the algorithm has short detection time and high detection accuracy.
文摘The internet of things(IoT)has a wide variety of applications,which in turn raisesmany challenging issues.IoT technology enables devices to closely monitor their environment,providing context-aware intelligence based on the real-time data collected by their sensor nodes.The IoT not only controls these devices but also monitors their user’s behaviour.One of the major issues related to IoT is the need for an energy-efficient communication protocol which uses the heterogeneity and diversity of the objects connected through the internet.Minimizing energy consumption is a requirement for energyconstrained nodes and outsourced nodes.The IoT nodes deployed in different geographical regions typically have different energy levels.This paper focuses on creating an energy-efficient protocol for IoTwhich can deal with the clustering of nodes and the cluster head selection process.An energy thresholdmodel is developed to select the appropriate cluster heads and also to ensure uniform distribution of energy to those heads andmember nodes.The proposed model envisages an IoT network with three different types of nodes,described here as advanced,intermediate and normal nodes.Normal nodes are first-level nodes,which have the lowest energy use;intermediate nodes are second-level nodes,which have a medium energy requirement;and the advanced class are thirdlevel nodes with the highest energy use.The simulation results demonstrate that the proposed algorithm outperforms other existing algorithms.In tests,it shows a 26%improvement in network lifetime compared with existing algorithms.
基金funded by the“Intelligent Recognition Industry Service Research Center”from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education(MOE)in Taiwan.Grant Number:N/A and the APC was funded by the aforementioned Project.
文摘The pandemic situation in 2020 brought about a‘digitized new normal’and created various issues within the current education systems.One of the issues is the monitoring of students during online examination situations.A system to determine the student’s eye gazes during an examination can help to eradicate malpractices.In this work,we track the users’eye gazes by incorporating twelve facial landmarks around both eyes in conjunction with computer vision and the HAAR classifier.We aim to implement eye gaze detection by considering facial landmarks with two different Convolutional Neural Network(CNN)models,namely the AlexNet model and the VGG16 model.The proposed system outperforms the traditional eye gaze detection system which only uses computer vision and the HAAR classifier in several evaluation metric scores.The proposed system is accurate without the need for complex hardware.Therefore,it can be implemented in educational institutes for the fair conduct of examinations,as well as in other instances where eye gaze detection is required.
文摘The prodigious advancements in contemporary technologies have also brought in the situation of unprecedented cyber-attacks.Further,the pin-based security system is an inadequate mechanism for handling such a scenario.The reason is that hackers use multiple strategies for evading security systems and thereby gaining access to private data.This research proposes to deploy diverse approaches for authenticating and securing a connection amongst two devices/gadgets via sound,thereby disregarding the pins’manual verification.Further,the results demonstrate that the proposed approaches outperform conventional pin-based authentication or QR authentication approaches.Firstly,a random signal is encrypted,and then it is transformed into a wave file,after which it gets transmitted in a short burst via the device’s speakers.Subsequently,the other device/gadget captures these audio bursts through its microphone and decrypts the audio signal for getting the essential data for pairing.Besides,this model requires two devices/gadgets with speakers and a microphone,and no extra hardware such as a camera,for reading the QR code is required.The first module is tested with realtime data and generates high scores for the widely accepted accuracy metrics,including precision,Recall,F1 score,entropy,and mutual information(MI).Additionally,this work also proposes a module helps in a secured transmission of sensitive data by encrypting it over images and other files.This steganographic module includes two-stage encryption with two different encryption algorithms to transmit data by embedding inside a file.Several encryption algorithms and their combinations are taken for this system to compare the resultant file size.Both these systems engender high accuracies and provide secure connectivity,leading to a sustainable communication ecosystem.
文摘According to various worldwide statistics,most car accidents occur solely due to human error.The person driving a car needs to be alert,especially when travelling through high traffic volumes that permit high-speed transit since a slight distraction can cause a fatal accident.Even though semiautomated checks,such as speed detecting cameras and speed barriers,are deployed,controlling human errors is an arduous task.The key causes of driver’s distraction include drunken driving,conversing with co-passengers,fatigue,and operating gadgets while driving.If these distractions are accurately predicted,the drivers can be alerted through an alarm system.Further,this research develops a deep convolutional neural network(deep CNN)models for predicting the reason behind the driver’s distraction.The deep CNN models are trained using numerous images of distracted drivers.The performance of deep CNN models,namely the VGG16,ResNet,and Xception network,is assessed based on the evaluation metrics,such as the precision score,the recall/sensitivity score,the F1 score,and the specificity score.The ResNet model outperformed all other models as the best detection model for predicting and accurately determining the drivers’activities.
基金funded by grant number 14-INF1015-10 from the National ScienceTechnology,and Innovation Plan(MAARIFAH)+1 种基金the King Abdul-Aziz City for Science and Technology(KACST)Kingdom of Saudi Arabia.We thank the Science and Technology Unit at Umm Al-Qura University for their continued logistics support.
文摘Urbanization affects the quality of the air,which has drastically degraded in the past decades.Air quality level is determined by measures of several air pollutant concentrations.To create awareness among people,an automation system that forecasts the quality is needed.The COVID-19 pandemic and the restrictions it has imposed on anthropogenic activities have resulted in a drop in air pollution in various cities in India.The overall air quality index(AQI)at any particular time is given as the maximum band for any pollutant.PM2.5 is a fine particulate matter of a size less than 2.5 micrometers,the inhalation of which causes adverse effects in people suffering from acute respiratory syndrome and other cardiovascular diseases.PM2.5 is a crucial factor in deciding the overall AQI.The proposed forecasting model is designed to predict the annual PM2.5 and AQI.The forecasting models are designed using Seasonal Autoregressive Integrated Moving Average and Facebook’s Prophet Library through optimal hyperparameters for better prediction.An AQI category classification model is also presented using classical machine learning techniques.The experimental results confirm the substantial improvement in air quality and greater reduction in PM2.5 due to the lockdown imposed during the COVID-19 crisis.
文摘Data offloading at the network with less time and reduced energy con-sumption are highly important for every technology.Smart applications process the data very quickly with less power consumption.As technology grows towards 5G communication architecture,identifying a solution for QoS in 5G through energy-efficient computing is important.In this proposed model,we perform data offloading at 5G using the fuzzification concept.Mobile IoT devices create tasks in the network and are offloaded in the cloud or mobile edge nodes based on energy consumption.Two base stations,small(SB)and macro(MB)stations,are initialized and thefirst tasks randomly computed.Then,the tasks are pro-cessed using a fuzzification algorithm to select SB or MB in the central server.The optimization is performed using a grasshopper algorithm for improving the QoS of the 5G network.The result is compared with existing algorithms and indi-cates that the proposed system improves the performance of the system with a cost of 44.64 J for computing 250 benchmark tasks.
基金the MSIT(Ministry of Science and ICT),Korea,under the ICAN(ICT Challenge and Advanced Network of HRD)program(IITP-2020-0-01832)supervised by the IITP(Institute of Information&Communications Technology Planning&Evaluation)and the Soonchunhyang University Research Fund.
文摘Retinopathy is a human eye disease that causes changes in retinal blood vessels that leads to bleed,leak fluid and vision impairment.Symptoms of retinopathy are blurred vision,changes in color perception,red spots,and eye pain and it cannot be detected with a naked eye.In this paper,a new methodology based on Convolutional Neural Networks(CNN)is developed and proposed to intelligent retinopathy prediction and give a decision about the presence of retinopathy with automatic diabetic retinopathy screening with accurate diagnoses.The CNN model is trained by different images of eyes that have retinopathy and those which do not have retinopathy.The fully connected layers perform the classification process of the images from the dataset with the pooling layers minimize the coherence among the adjacent layers.The feature loss factor increases the label value to identify the patterns with the kernel-based matching.The performance of the proposed model is compared with the related methods of DREAM,KNN,GD-CNN and SVM.Experimental results show that the proposed CNN performs better.
