The human motion data collected using wearables like smartwatches can be used for activity recognition and emergency event detection.This is especially applicable in the case of elderly or disabled people who live sel...The human motion data collected using wearables like smartwatches can be used for activity recognition and emergency event detection.This is especially applicable in the case of elderly or disabled people who live self-reliantly in their homes.These sensors produce a huge volume of physical activity data that necessitates real-time recognition,especially during emergencies.Falling is one of the most important problems confronted by older people and people with movement disabilities.Numerous previous techniques were introduced and a few used webcam to monitor the activity of elderly or disabled people.But,the costs incurred upon installation and operation are high,whereas the technology is relevant only for indoor environments.Currently,commercial wearables use a wireless emergency transmitter that produces a number of false alarms and restricts a user’s movements.Against this background,the current study develops an Improved WhaleOptimizationwithDeep Learning-Enabled Fall Detection for Disabled People(IWODL-FDDP)model.The presented IWODL-FDDP model aims to identify the fall events to assist disabled people.The presented IWODLFDDP model applies an image filtering approach to pre-process the image.Besides,the EfficientNet-B0 model is utilized to generate valuable feature vector sets.Next,the Bidirectional Long Short Term Memory(BiLSTM)model is used for the recognition and classification of fall events.Finally,the IWO method is leveraged to fine-tune the hyperparameters related to the BiLSTM method,which shows the novelty of the work.The experimental analysis outcomes established the superior performance of the proposed IWODL-FDDP method with a maximum accuracy of 97.02%.展开更多
Wireless Sensor Network(WSN)consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment.Designing the energy-efficient data collection methods in...Wireless Sensor Network(WSN)consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment.Designing the energy-efficient data collection methods in largescale wireless sensor networks is considered to be a difficult area in the research.Sensor node clustering is a popular approach for WSN.Moreover,the sensor nodes are grouped to form clusters in a cluster-based WSN environment.The battery performance of the sensor nodes is likewise constrained.As a result,the energy efficiency of WSNs is critical.In specific,the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station(BS).Therefore,energy efficiency and load balancing are very essential in WSN.In the proposed method,a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques(GW-IPSO-TS)was used.The selection of Cluster Heads(CHs)and routing path of every CH from the base station is enhanced by the proposed method.It provides the best routing path and increases the lifetime and energy efficiency of the network.End-to-end delay and packet loss rate have also been improved.The proposed GW-IPSO-TS method enhances the evaluation of alive nodes,dead nodes,network survival index,convergence rate,and standard deviation of sensor nodes.Compared to the existing algorithms,the proposed method outperforms better and improves the lifetime of the network.展开更多
Due to exponential increase in smart resource limited devices and high speed communication technologies,Internet of Things(IoT)have received significant attention in different application areas.However,IoT environment...Due to exponential increase in smart resource limited devices and high speed communication technologies,Internet of Things(IoT)have received significant attention in different application areas.However,IoT environment is highly susceptible to cyber-attacks because of memory,processing,and communication restrictions.Since traditional models are not adequate for accomplishing security in the IoT environment,the recent developments of deep learning(DL)models find beneficial.This study introduces novel hybrid metaheuristics feature selection with stacked deep learning enabled cyber-attack detection(HMFS-SDLCAD)model.The major intention of the HMFS-SDLCAD model is to recognize the occurrence of cyberattacks in the IoT environment.At the preliminary stage,data pre-processing is carried out to transform the input data into useful format.In addition,salp swarm optimization based on particle swarm optimization(SSOPSO)algorithm is used for feature selection process.Besides,stacked bidirectional gated recurrent unit(SBiGRU)model is utilized for the identification and classification of cyberattacks.Finally,whale optimization algorithm(WOA)is employed for optimal hyperparameter optimization process.The experimental analysis of the HMFS-SDLCAD model is validated using benchmark dataset and the results are assessed under several aspects.The simulation outcomes pointed out the improvements of the HMFS-SDLCAD model over recent approaches.展开更多
Applied linguistics is one of the fields in the linguistics domain and deals with the practical applications of the language studies such as speech processing,language teaching,translation and speech therapy.The ever-...Applied linguistics is one of the fields in the linguistics domain and deals with the practical applications of the language studies such as speech processing,language teaching,translation and speech therapy.The ever-growing Online Social Networks(OSNs)experience a vital issue to confront,i.e.,hate speech.Amongst the OSN-oriented security problems,the usage of offensive language is the most important threat that is prevalently found across the Internet.Based on the group targeted,the offensive language varies in terms of adult content,hate speech,racism,cyberbullying,abuse,trolling and profanity.Amongst these,hate speech is the most intimidating form of using offensive language in which the targeted groups or individuals are intimidated with the intent of creating harm,social chaos or violence.Machine Learning(ML)techniques have recently been applied to recognize hate speech-related content.The current research article introduces a Grasshopper Optimization with an Attentive Recurrent Network for Offensive Speech Detection(GOARN-OSD)model for social media.The GOARNOSD technique integrates the concepts of DL and metaheuristic algorithms for detecting hate speech.In the presented GOARN-OSD technique,the primary stage involves the data pre-processing and word embedding processes.Then,this study utilizes the Attentive Recurrent Network(ARN)model for hate speech recognition and classification.At last,the Grasshopper Optimization Algorithm(GOA)is exploited as a hyperparameter optimizer to boost the performance of the hate speech recognition process.To depict the promising performance of the proposed GOARN-OSD method,a widespread experimental analysis was conducted.The comparison study outcomes demonstrate the superior performance of the proposed GOARN-OSD model over other state-of-the-art approaches.展开更多
The Internet of Things(IoT)offers a new era of connectivity,which goes beyond laptops and smart connected devices for connected vehicles,smart homes,smart cities,and connected healthcare.The massive quantity of data g...The Internet of Things(IoT)offers a new era of connectivity,which goes beyond laptops and smart connected devices for connected vehicles,smart homes,smart cities,and connected healthcare.The massive quantity of data gathered from numerous IoT devices poses security and privacy concerns for users.With the increasing use of multimedia in communications,the content security of remote-sensing images attracted much attention in academia and industry.Image encryption is important for securing remote sensing images in the IoT environment.Recently,researchers have introduced plenty of algorithms for encrypting images.This study introduces an Improved Sine Cosine Algorithm with Chaotic Encryption based Remote Sensing Image Encryption(ISCACE-RSI)technique in IoT Environment.The proposed model follows a three-stage process,namely pre-processing,encryption,and optimal key generation.The remote sensing images were preprocessed at the initial stage to enhance the image quality.Next,the ISCACERSI technique exploits the double-layer remote sensing image encryption(DLRSIE)algorithm for encrypting the images.The DLRSIE methodology incorporates the design of Chaotic Maps and deoxyribonucleic acid(DNA)Strand Displacement(DNASD)approach.The chaotic map is employed for generating pseudorandom sequences and implementing routine scrambling and diffusion processes on the plaintext images.Then,the study presents three DNASD-related encryption rules based on the variety of DNASD,and those rules are applied for encrypting the images at the DNA sequence level.For an optimal key generation of the DLRSIE technique,the ISCA is applied with an objective function of the maximization of peak signal to noise ratio(PSNR).To examine the performance of the ISCACE-RSI model,a detailed set of simulations were conducted.The comparative study reported the better performance of the ISCACE-RSI model over other existing approaches.展开更多
With a population of 440 million,Arabic language users form the rapidly growing language group on the web in terms of the number of Internet users.11 million monthly Twitter users were active and posted nearly 27.4 mi...With a population of 440 million,Arabic language users form the rapidly growing language group on the web in terms of the number of Internet users.11 million monthly Twitter users were active and posted nearly 27.4 million tweets every day.In order to develop a classification system for the Arabic lan-guage there comes a need of understanding the syntactic framework of the words thereby manipulating and representing the words for making their classification effective.In this view,this article introduces a Dolphin Swarm Optimization with Convolutional Deep Belief Network for Short Text Classification(DSOCDBN-STC)model on Arabic Corpus.The presented DSOCDBN-STC model majorly aims to classify Arabic short text in social media.The presented DSOCDBN-STC model encompasses preprocessing and word2vec word embedding at the preliminary stage.Besides,the DSOCDBN-STC model involves CDBN based classification model for Arabic short text.At last,the DSO technique can be exploited for optimal modification of the hyperparameters related to the CDBN method.To establish the enhanced performance of the DSOCDBN-STC model,a wide range of simulations have been performed.The simulation results con-firmed the supremacy of the DSOCDBN-STC model over existing models with improved accuracy of 99.26%.展开更多
ive Arabic Text Summarization using Hyperparameter Tuned Denoising Deep Neural Network(AATS-HTDDNN)technique.The presented AATS-HTDDNN technique aims to generate summaries of Arabic text.In the presented AATS-HTDDNN t...ive Arabic Text Summarization using Hyperparameter Tuned Denoising Deep Neural Network(AATS-HTDDNN)technique.The presented AATS-HTDDNN technique aims to generate summaries of Arabic text.In the presented AATS-HTDDNN technique,the DDNN model is utilized to generate the summary.This study exploits the Chameleon Swarm Optimization(CSO)algorithm to fine-tune the hyperparameters relevant to the DDNN model since it considerably affects the summarization efficiency.This phase shows the novelty of the current study.To validate the enhanced summarization performance of the proposed AATS-HTDDNN model,a comprehensive experimental analysis was conducted.The comparison study outcomes confirmed the better performance of the AATS-HTDDNN model over other approaches.展开更多
Wireless Sensor Networks(WSN)interlink numerous Sensor Nodes(SN)to support Internet of Things(loT)services.But the data gathered from SNs can be divulged,tempered,and forged.Conventional WSN data processes manage the ...Wireless Sensor Networks(WSN)interlink numerous Sensor Nodes(SN)to support Internet of Things(loT)services.But the data gathered from SNs can be divulged,tempered,and forged.Conventional WSN data processes manage the data in a centralized format at terminal gadgets.These devices are prone to attacks and the security of systems can get compromised.Blockchain is a distributed and decentralized technique that has the ability to handle security issues in WSN.The security issues include transactions that may be copied and spread across numerous nodes in a peer-peer network system.This breaches the mutual trust and allows data immutability which in turn permits the network to go on.At some instances,few nodes die or get compromised due to heavy power utilization.The current article develops an Energy Aware Chaotic Pigeon Inspired Optimization based Clustering scheme for Blockchain assisted WSN technique abbreviated as EACPIO-CB technique.The primary objective of the proposed EACPIO-CB model is to proficiently group the sensor nodes into clusters and exploit Blockchain(BC)for inter-cluster communication in the network.To select ClusterHeads(CHs)and organize the clusters,the presented EACPIO-CB model designs a fitness function that involves distinct input parameters.Further,BC technology enables the communication between one CH and the other and with the Base Station(BS)in the network.The authors conducted comprehensive set of simulations and the outcomes were investigated under different aspects.The simulation results confirmed the better performance of EACPIO-CB method over recent methodologies.展开更多
Wireless Sensor Network(WSN)is a vital element in Internet of Things(IoT)as the former enables the collection of huge quantities of data in energy-constrained environment.WSN offers independent access to the target re...Wireless Sensor Network(WSN)is a vital element in Internet of Things(IoT)as the former enables the collection of huge quantities of data in energy-constrained environment.WSN offers independent access to the target region and performs data collection in an effective manner.But energy constraints remain a challenging issue in WSN since it operates on in-built battery.The studies conducted earlier recommended that the energy spent on communication processmust be considerably reduced to improve the efficiency of WSN.Cluster organization and optimal selection of the routes are considered as NP hard optimization problems which can be resolved with the help of metaheuristic algorithms.Clustering and routing are considered as effective approaches in enhancing the energy effectiveness and lifespan of WSN.In this background,the current study develops an Improved Duck and Traveller Optimization(IDTO)-enabled cluster-based Multi-Hop Routing(IDTOMHR)technique for WSN.Primarily,IDTO algorithm is exploited for the selection of Cluster Head(CH)and construction of clusters.Besides,Artificial Gorilla Troops Optimization(ATGO)technique is also used to derive an optimal set of routes to the destination.Both clustering and routing approaches derive a fitness function with the inclusion of multiple input parameters.The proposed IDTOMHR model was experimentally validated for its performance under different aspects.The extensive experimental results confirmed the better performance of IDTOMHR model over other recent approaches.展开更多
Nowadays,vehicular ad hoc networks(VANET)turn out to be a core portion of intelligent transportation systems(ITSs),that mainly focus on achieving continual Internet connectivity amongst vehicles on the road.The VANET ...Nowadays,vehicular ad hoc networks(VANET)turn out to be a core portion of intelligent transportation systems(ITSs),that mainly focus on achieving continual Internet connectivity amongst vehicles on the road.The VANET was utilized to enhance driving safety and build an ITS in modern cities.Driving safety is a main portion of VANET,the privacy and security of these messages should be protected.In this aspect,this article presents a blockchain with sunflower optimization enabled route planning scheme(BCSFO-RPS)for secure VANET.The presented BCSFO-RPSmodel focuses on the identification of routes in such a way that vehicular communication is secure.In addition,the BCSFO-RPS model employs SFO algorithm with a fitness function for effectual identification of routes.Besides,the proposed BCSFO-RPS model derives an intrusion detection system(IDS)encompassing two processes namely feature selection and classification.To detect intrusions,correlation based feature selection(CFS)and kernel extreme machine learning(KELM)classifier is applied.The performance of the BCSFO-RPS model is tested using a series of experiments and the results reported the enhancements of the BCSFO-RPS model over other approaches with maximum accuracy of 98.70%.展开更多
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups Project under grant number(158/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R77)+1 种基金Princess Nourah bint Abdulrahman University,Riyadh,Saudi ArabiaThe authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4310373DSR52).
文摘The human motion data collected using wearables like smartwatches can be used for activity recognition and emergency event detection.This is especially applicable in the case of elderly or disabled people who live self-reliantly in their homes.These sensors produce a huge volume of physical activity data that necessitates real-time recognition,especially during emergencies.Falling is one of the most important problems confronted by older people and people with movement disabilities.Numerous previous techniques were introduced and a few used webcam to monitor the activity of elderly or disabled people.But,the costs incurred upon installation and operation are high,whereas the technology is relevant only for indoor environments.Currently,commercial wearables use a wireless emergency transmitter that produces a number of false alarms and restricts a user’s movements.Against this background,the current study develops an Improved WhaleOptimizationwithDeep Learning-Enabled Fall Detection for Disabled People(IWODL-FDDP)model.The presented IWODL-FDDP model aims to identify the fall events to assist disabled people.The presented IWODLFDDP model applies an image filtering approach to pre-process the image.Besides,the EfficientNet-B0 model is utilized to generate valuable feature vector sets.Next,the Bidirectional Long Short Term Memory(BiLSTM)model is used for the recognition and classification of fall events.Finally,the IWO method is leveraged to fine-tune the hyperparameters related to the BiLSTM method,which shows the novelty of the work.The experimental analysis outcomes established the superior performance of the proposed IWODL-FDDP method with a maximum accuracy of 97.02%.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Larg Groups project Under Grant Number(71/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R238)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:22UQU4340237DSR20.
文摘Wireless Sensor Network(WSN)consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment.Designing the energy-efficient data collection methods in largescale wireless sensor networks is considered to be a difficult area in the research.Sensor node clustering is a popular approach for WSN.Moreover,the sensor nodes are grouped to form clusters in a cluster-based WSN environment.The battery performance of the sensor nodes is likewise constrained.As a result,the energy efficiency of WSNs is critical.In specific,the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station(BS).Therefore,energy efficiency and load balancing are very essential in WSN.In the proposed method,a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques(GW-IPSO-TS)was used.The selection of Cluster Heads(CHs)and routing path of every CH from the base station is enhanced by the proposed method.It provides the best routing path and increases the lifetime and energy efficiency of the network.End-to-end delay and packet loss rate have also been improved.The proposed GW-IPSO-TS method enhances the evaluation of alive nodes,dead nodes,network survival index,convergence rate,and standard deviation of sensor nodes.Compared to the existing algorithms,the proposed method outperforms better and improves the lifetime of the network.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups Project under grant number(45/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R140)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4310373DSR16).
文摘Due to exponential increase in smart resource limited devices and high speed communication technologies,Internet of Things(IoT)have received significant attention in different application areas.However,IoT environment is highly susceptible to cyber-attacks because of memory,processing,and communication restrictions.Since traditional models are not adequate for accomplishing security in the IoT environment,the recent developments of deep learning(DL)models find beneficial.This study introduces novel hybrid metaheuristics feature selection with stacked deep learning enabled cyber-attack detection(HMFS-SDLCAD)model.The major intention of the HMFS-SDLCAD model is to recognize the occurrence of cyberattacks in the IoT environment.At the preliminary stage,data pre-processing is carried out to transform the input data into useful format.In addition,salp swarm optimization based on particle swarm optimization(SSOPSO)algorithm is used for feature selection process.Besides,stacked bidirectional gated recurrent unit(SBiGRU)model is utilized for the identification and classification of cyberattacks.Finally,whale optimization algorithm(WOA)is employed for optimal hyperparameter optimization process.The experimental analysis of the HMFS-SDLCAD model is validated using benchmark dataset and the results are assessed under several aspects.The simulation outcomes pointed out the improvements of the HMFS-SDLCAD model over recent approaches.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R281)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia+1 种基金Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code: (22UQU4331004DSR031)supported via funding from Prince Sattam bin Abdulaziz University project number (PSAU/2023/R/1444).
文摘Applied linguistics is one of the fields in the linguistics domain and deals with the practical applications of the language studies such as speech processing,language teaching,translation and speech therapy.The ever-growing Online Social Networks(OSNs)experience a vital issue to confront,i.e.,hate speech.Amongst the OSN-oriented security problems,the usage of offensive language is the most important threat that is prevalently found across the Internet.Based on the group targeted,the offensive language varies in terms of adult content,hate speech,racism,cyberbullying,abuse,trolling and profanity.Amongst these,hate speech is the most intimidating form of using offensive language in which the targeted groups or individuals are intimidated with the intent of creating harm,social chaos or violence.Machine Learning(ML)techniques have recently been applied to recognize hate speech-related content.The current research article introduces a Grasshopper Optimization with an Attentive Recurrent Network for Offensive Speech Detection(GOARN-OSD)model for social media.The GOARNOSD technique integrates the concepts of DL and metaheuristic algorithms for detecting hate speech.In the presented GOARN-OSD technique,the primary stage involves the data pre-processing and word embedding processes.Then,this study utilizes the Attentive Recurrent Network(ARN)model for hate speech recognition and classification.At last,the Grasshopper Optimization Algorithm(GOA)is exploited as a hyperparameter optimizer to boost the performance of the hate speech recognition process.To depict the promising performance of the proposed GOARN-OSD method,a widespread experimental analysis was conducted.The comparison study outcomes demonstrate the superior performance of the proposed GOARN-OSD model over other state-of-the-art approaches.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R319)PrincessNourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4210118DSR48).
文摘The Internet of Things(IoT)offers a new era of connectivity,which goes beyond laptops and smart connected devices for connected vehicles,smart homes,smart cities,and connected healthcare.The massive quantity of data gathered from numerous IoT devices poses security and privacy concerns for users.With the increasing use of multimedia in communications,the content security of remote-sensing images attracted much attention in academia and industry.Image encryption is important for securing remote sensing images in the IoT environment.Recently,researchers have introduced plenty of algorithms for encrypting images.This study introduces an Improved Sine Cosine Algorithm with Chaotic Encryption based Remote Sensing Image Encryption(ISCACE-RSI)technique in IoT Environment.The proposed model follows a three-stage process,namely pre-processing,encryption,and optimal key generation.The remote sensing images were preprocessed at the initial stage to enhance the image quality.Next,the ISCACERSI technique exploits the double-layer remote sensing image encryption(DLRSIE)algorithm for encrypting the images.The DLRSIE methodology incorporates the design of Chaotic Maps and deoxyribonucleic acid(DNA)Strand Displacement(DNASD)approach.The chaotic map is employed for generating pseudorandom sequences and implementing routine scrambling and diffusion processes on the plaintext images.Then,the study presents three DNASD-related encryption rules based on the variety of DNASD,and those rules are applied for encrypting the images at the DNA sequence level.For an optimal key generation of the DLRSIE technique,the ISCA is applied with an objective function of the maximization of peak signal to noise ratio(PSNR).To examine the performance of the ISCACE-RSI model,a detailed set of simulations were conducted.The comparative study reported the better performance of the ISCACE-RSI model over other existing approaches.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R263)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:22UQU4340237DSR40.
文摘With a population of 440 million,Arabic language users form the rapidly growing language group on the web in terms of the number of Internet users.11 million monthly Twitter users were active and posted nearly 27.4 million tweets every day.In order to develop a classification system for the Arabic lan-guage there comes a need of understanding the syntactic framework of the words thereby manipulating and representing the words for making their classification effective.In this view,this article introduces a Dolphin Swarm Optimization with Convolutional Deep Belief Network for Short Text Classification(DSOCDBN-STC)model on Arabic Corpus.The presented DSOCDBN-STC model majorly aims to classify Arabic short text in social media.The presented DSOCDBN-STC model encompasses preprocessing and word2vec word embedding at the preliminary stage.Besides,the DSOCDBN-STC model involves CDBN based classification model for Arabic short text.At last,the DSO technique can be exploited for optimal modification of the hyperparameters related to the CDBN method.To establish the enhanced performance of the DSOCDBN-STC model,a wide range of simulations have been performed.The simulation results con-firmed the supremacy of the DSOCDBN-STC model over existing models with improved accuracy of 99.26%.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R281)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia+1 种基金The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:22UQU4210118DSR33The authors are thankful to the Deanship of ScientificResearch atNajranUniversity for funding thiswork under theResearch Groups Funding Program Grant Code(NU/RG/SERC/11/7).
文摘ive Arabic Text Summarization using Hyperparameter Tuned Denoising Deep Neural Network(AATS-HTDDNN)technique.The presented AATS-HTDDNN technique aims to generate summaries of Arabic text.In the presented AATS-HTDDNN technique,the DDNN model is utilized to generate the summary.This study exploits the Chameleon Swarm Optimization(CSO)algorithm to fine-tune the hyperparameters relevant to the DDNN model since it considerably affects the summarization efficiency.This phase shows the novelty of the current study.To validate the enhanced summarization performance of the proposed AATS-HTDDNN model,a comprehensive experimental analysis was conducted.The comparison study outcomes confirmed the better performance of the AATS-HTDDNN model over other approaches.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups Project under grant number(142/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R238)+1 种基金Princess Nourah bint Abdulrahman University,Riyadh,Saudi ArabiaThe authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4340237DSR24).
文摘Wireless Sensor Networks(WSN)interlink numerous Sensor Nodes(SN)to support Internet of Things(loT)services.But the data gathered from SNs can be divulged,tempered,and forged.Conventional WSN data processes manage the data in a centralized format at terminal gadgets.These devices are prone to attacks and the security of systems can get compromised.Blockchain is a distributed and decentralized technique that has the ability to handle security issues in WSN.The security issues include transactions that may be copied and spread across numerous nodes in a peer-peer network system.This breaches the mutual trust and allows data immutability which in turn permits the network to go on.At some instances,few nodes die or get compromised due to heavy power utilization.The current article develops an Energy Aware Chaotic Pigeon Inspired Optimization based Clustering scheme for Blockchain assisted WSN technique abbreviated as EACPIO-CB technique.The primary objective of the proposed EACPIO-CB model is to proficiently group the sensor nodes into clusters and exploit Blockchain(BC)for inter-cluster communication in the network.To select ClusterHeads(CHs)and organize the clusters,the presented EACPIO-CB model designs a fitness function that involves distinct input parameters.Further,BC technology enables the communication between one CH and the other and with the Base Station(BS)in the network.The authors conducted comprehensive set of simulations and the outcomes were investigated under different aspects.The simulation results confirmed the better performance of EACPIO-CB method over recent methodologies.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups Project under grant number(45/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R238)+1 种基金Princess Nourah bint Abdulrahman University,Riyadh,Saudi ArabiaThe authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:22UQU4210118DSR14。
文摘Wireless Sensor Network(WSN)is a vital element in Internet of Things(IoT)as the former enables the collection of huge quantities of data in energy-constrained environment.WSN offers independent access to the target region and performs data collection in an effective manner.But energy constraints remain a challenging issue in WSN since it operates on in-built battery.The studies conducted earlier recommended that the energy spent on communication processmust be considerably reduced to improve the efficiency of WSN.Cluster organization and optimal selection of the routes are considered as NP hard optimization problems which can be resolved with the help of metaheuristic algorithms.Clustering and routing are considered as effective approaches in enhancing the energy effectiveness and lifespan of WSN.In this background,the current study develops an Improved Duck and Traveller Optimization(IDTO)-enabled cluster-based Multi-Hop Routing(IDTOMHR)technique for WSN.Primarily,IDTO algorithm is exploited for the selection of Cluster Head(CH)and construction of clusters.Besides,Artificial Gorilla Troops Optimization(ATGO)technique is also used to derive an optimal set of routes to the destination.Both clustering and routing approaches derive a fitness function with the inclusion of multiple input parameters.The proposed IDTOMHR model was experimentally validated for its performance under different aspects.The extensive experimental results confirmed the better performance of IDTOMHR model over other recent approaches.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups Project under Grant Number(25/43)Taif University Researchers Supporting Project Number(TURSP-2020/346)+1 种基金Taif University,Taif,Saudi Arabia.Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R303)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Ara-bia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4210118DSR17).
文摘Nowadays,vehicular ad hoc networks(VANET)turn out to be a core portion of intelligent transportation systems(ITSs),that mainly focus on achieving continual Internet connectivity amongst vehicles on the road.The VANET was utilized to enhance driving safety and build an ITS in modern cities.Driving safety is a main portion of VANET,the privacy and security of these messages should be protected.In this aspect,this article presents a blockchain with sunflower optimization enabled route planning scheme(BCSFO-RPS)for secure VANET.The presented BCSFO-RPSmodel focuses on the identification of routes in such a way that vehicular communication is secure.In addition,the BCSFO-RPS model employs SFO algorithm with a fitness function for effectual identification of routes.Besides,the proposed BCSFO-RPS model derives an intrusion detection system(IDS)encompassing two processes namely feature selection and classification.To detect intrusions,correlation based feature selection(CFS)and kernel extreme machine learning(KELM)classifier is applied.The performance of the BCSFO-RPS model is tested using a series of experiments and the results reported the enhancements of the BCSFO-RPS model over other approaches with maximum accuracy of 98.70%.
