The smart city comprises various interlinked elements which communicate data and offers urban life to citizen.Unmanned Aerial Vehicles(UAV)or drones were commonly employed in different application areas like agricultu...The smart city comprises various interlinked elements which communicate data and offers urban life to citizen.Unmanned Aerial Vehicles(UAV)or drones were commonly employed in different application areas like agriculture,logistics,and surveillance.For improving the drone flying safety and quality of services,a significant solution is for designing the Internet of Drones(IoD)where the drones are utilized to gather data and people communicate to the drones of a specific flying region using the mobile devices is for constructing the Internet-of-Drones,where the drones were utilized for collecting the data,and communicate with others.In addition,the SIRSS-CIoD technique derives a tuna swarm algorithm-based clustering(TSA-C)technique to choose cluster heads(CHs)and organize clusters in IoV networks.Besides,the SIRSS-CIoD technique involves the design of a biogeography-based optimization(BBO)technique to an optimum route selection(RS)process.The design of clustering and routing techniques for IoD networks in smart cities shows the novelty of the study.A wide range of experimental analyses is carried out and the comparative study highlighted the improved performance of the SIRSS-CIoD technique over the other approaches.展开更多
The fifth generation(5G)system is the forthcoming generation of the mobile communication system.It has numerous additional features and offers an extensively high data rate,more capacity,and low latency.However,these ...The fifth generation(5G)system is the forthcoming generation of the mobile communication system.It has numerous additional features and offers an extensively high data rate,more capacity,and low latency.However,these features and applications have many problems and issues in terms of security,which has become a great challenge in the telecommunication industry.This paper aimed to propose a solution to preserve the user identity privacy in the 5G system that can identify permanent identity by using Variable Mobile Subscriber Identity,which randomly changes and does not use the permanent identity between the user equipment and home network.Through this mechanism,the user identity privacy would be secured and hidden.Moreover,it improves the synchronization between mobile users and home networks.Additionally,its compliance with the Authentication and Key Agreement(AKA)structure was adopted in the previous generations.It can be deployed efficiently in the preceding generations because the current architecture imposes minimal modifications on the network parties without changes in the authentication vector’s message size.Moreover,the addition of any hardware to the AKA carries minor adjustments on the network parties.In this paper,the ProVerif is used to verify the proposed scheme.展开更多
Sentiment analysis or Opinion Mining (OM) has gained significant interest among research communities and entrepreneurs in the recentyears. Likewise, Machine Learning (ML) approaches is one of the interestingresearch d...Sentiment analysis or Opinion Mining (OM) has gained significant interest among research communities and entrepreneurs in the recentyears. Likewise, Machine Learning (ML) approaches is one of the interestingresearch domains that are highly helpful and are increasingly applied in severalbusiness domains. In this background, the current research paper focuses onthe design of automated opinion mining model using Deer Hunting Optimization Algorithm (DHOA) with Fuzzy Neural Network (FNN) abbreviatedas DHOA-FNN model. The proposed DHOA-FNN technique involves fourdifferent stages namely, preprocessing, feature extraction, classification, andparameter tuning. In addition to the above, the proposed DHOA-FNN modelhas two stages of feature extraction namely, Glove and N-gram approach.Moreover, FNN model is utilized as a classification model whereas GTOA isused for the optimization of parameters. The novelty of current work is thatthe GTOA is designed to tune the parameters of FNN model. An extensiverange of simulations was carried out on the benchmark dataset and the resultswere examined under diverse measures. The experimental results highlightedthe promising performance of DHOA-FNN model over recent state-of-the-arttechniques with a maximum accuracy of 0.9928.展开更多
In recent times,Internet of Things(IoT)and Cloud Computing(CC)paradigms are commonly employed in different healthcare applications.IoT gadgets generate huge volumes of patient data in healthcare domain,which can be ex...In recent times,Internet of Things(IoT)and Cloud Computing(CC)paradigms are commonly employed in different healthcare applications.IoT gadgets generate huge volumes of patient data in healthcare domain,which can be examined on cloud over the available storage and computation resources in mobile gadgets.Chronic Kidney Disease(CKD)is one of the deadliest diseases that has high mortality rate across the globe.The current research work presents a novel IoT and cloud-based CKD diagnosis model called Flower Pollination Algorithm(FPA)-based Deep Neural Network(DNN)model abbreviated as FPA-DNN.The steps involved in the presented FPA-DNN model are data collection,preprocessing,Feature Selection(FS),and classification.Primarily,the IoT gadgets are utilized in the collection of a patient’s health information.The proposed FPA-DNN model deploys Oppositional Crow Search(OCS)algorithm for FS,which selects the optimal subset of features from the preprocessed data.The application of FPA helps in tuning the DNN parameters for better classification performance.The simulation analysis of the proposed FPA-DNN model was performed against the benchmark CKD dataset.The results were examined under different aspects.The simulation outcomes established the superior performance of FPA-DNN technique by achieving the highest sensitivity of 98.80%,specificity of 98.66%,accuracy of 98.75%,F-score of 99%,and kappa of 97.33%.展开更多
In cognitive radio networks(CoR),the performance of cooperative spectrum sensing is improved by reducing the overall error rate or maximizing the detection probability.Several optimization methods are usually used to ...In cognitive radio networks(CoR),the performance of cooperative spectrum sensing is improved by reducing the overall error rate or maximizing the detection probability.Several optimization methods are usually used to optimize the number of user-chosen for cooperation and the threshold selection.However,these methods do not take into account the effect of sample size and its effect on improving CoR performance.In general,a large sample size results in more reliable detection,but takes longer sensing time and increases complexity.Thus,the locally sensed sample size is an optimization problem.Therefore,optimizing the local sample size for each cognitive user helps to improve CoR performance.In this study,two new methods are proposed to find the optimum sample size to achieve objective-based improved(single/double)threshold energy detection,these methods are the optimum sample size N^(*)and neural networks(NN)optimization.Through the evaluation,it was found that the proposed methods outperform the traditional sample size selection in terms of the total error rate,detection probability,and throughput.展开更多
Privacy,identity preserving and integrity have become key problems for telecommunication standards.Significant privacy threats are expected in 5G networks considering the large number of devices that will be deployed....Privacy,identity preserving and integrity have become key problems for telecommunication standards.Significant privacy threats are expected in 5G networks considering the large number of devices that will be deployed.As Internet of Things(IoT)and long-term evolution for machine type(LTE-m)are growing very fast with massive data traffic the risk of privacy attacks will be greatly increase.For all the above issues standards’bodies should ensure users’identity and privacy in order to gain the trust of service providers and industries.Against such threats,5G specifications require a rigid and robust privacy procedure.Many research studies have addressed user privacy in 5G networks.This paper proposes a method to enhance user identity privacy in 5G systems through a scheme to protect the international mobile subscriber identity(IMSI)using a mutable mobile subscriber identity(MMSI)that changes randomly and avoids the exchange of IMSIs.It maintains authentication and key agreement(AKA)structure compatibility with previous mobile generations and improves user equipment(UE)synchronization with home networks.The proposed algorithm adds no computation overhead to UE or the network except a small amount in the home subscriber server(HSS).The proposed pseudonym mutable uses the XOR function to send the MMSI from the HSS to the UE which is reducing the encryption overhead significantly.The proposed solution was verified by ProVerif.展开更多
基金This project was supported financially by Institution Fund projects under Grant No.(IFPIP-1266-611-1442).
文摘The smart city comprises various interlinked elements which communicate data and offers urban life to citizen.Unmanned Aerial Vehicles(UAV)or drones were commonly employed in different application areas like agriculture,logistics,and surveillance.For improving the drone flying safety and quality of services,a significant solution is for designing the Internet of Drones(IoD)where the drones are utilized to gather data and people communicate to the drones of a specific flying region using the mobile devices is for constructing the Internet-of-Drones,where the drones were utilized for collecting the data,and communicate with others.In addition,the SIRSS-CIoD technique derives a tuna swarm algorithm-based clustering(TSA-C)technique to choose cluster heads(CHs)and organize clusters in IoV networks.Besides,the SIRSS-CIoD technique involves the design of a biogeography-based optimization(BBO)technique to an optimum route selection(RS)process.The design of clustering and routing techniques for IoD networks in smart cities shows the novelty of the study.A wide range of experimental analyses is carried out and the comparative study highlighted the improved performance of the SIRSS-CIoD technique over the other approaches.
基金The Universiti Kebangsaan Malaysia(UKM)Research Grant Scheme GGPM-2020-028 funded this research.
文摘The fifth generation(5G)system is the forthcoming generation of the mobile communication system.It has numerous additional features and offers an extensively high data rate,more capacity,and low latency.However,these features and applications have many problems and issues in terms of security,which has become a great challenge in the telecommunication industry.This paper aimed to propose a solution to preserve the user identity privacy in the 5G system that can identify permanent identity by using Variable Mobile Subscriber Identity,which randomly changes and does not use the permanent identity between the user equipment and home network.Through this mechanism,the user identity privacy would be secured and hidden.Moreover,it improves the synchronization between mobile users and home networks.Additionally,its compliance with the Authentication and Key Agreement(AKA)structure was adopted in the previous generations.It can be deployed efficiently in the preceding generations because the current architecture imposes minimal modifications on the network parties without changes in the authentication vector’s message size.Moreover,the addition of any hardware to the AKA carries minor adjustments on the network parties.In this paper,the ProVerif is used to verify the proposed scheme.
基金Taif University Researchers Supporting Project Number(TURSP-2020/216),Taif University,Taif,Saudi Arabia.
文摘Sentiment analysis or Opinion Mining (OM) has gained significant interest among research communities and entrepreneurs in the recentyears. Likewise, Machine Learning (ML) approaches is one of the interestingresearch domains that are highly helpful and are increasingly applied in severalbusiness domains. In this background, the current research paper focuses onthe design of automated opinion mining model using Deer Hunting Optimization Algorithm (DHOA) with Fuzzy Neural Network (FNN) abbreviatedas DHOA-FNN model. The proposed DHOA-FNN technique involves fourdifferent stages namely, preprocessing, feature extraction, classification, andparameter tuning. In addition to the above, the proposed DHOA-FNN modelhas two stages of feature extraction namely, Glove and N-gram approach.Moreover, FNN model is utilized as a classification model whereas GTOA isused for the optimization of parameters. The novelty of current work is thatthe GTOA is designed to tune the parameters of FNN model. An extensiverange of simulations was carried out on the benchmark dataset and the resultswere examined under diverse measures. The experimental results highlightedthe promising performance of DHOA-FNN model over recent state-of-the-arttechniques with a maximum accuracy of 0.9928.
基金This research was supported by Taif University Researchers Supporting Project Number(TURSP-2020/216),Taif University,Taif,Saudi Arabia.
文摘In recent times,Internet of Things(IoT)and Cloud Computing(CC)paradigms are commonly employed in different healthcare applications.IoT gadgets generate huge volumes of patient data in healthcare domain,which can be examined on cloud over the available storage and computation resources in mobile gadgets.Chronic Kidney Disease(CKD)is one of the deadliest diseases that has high mortality rate across the globe.The current research work presents a novel IoT and cloud-based CKD diagnosis model called Flower Pollination Algorithm(FPA)-based Deep Neural Network(DNN)model abbreviated as FPA-DNN.The steps involved in the presented FPA-DNN model are data collection,preprocessing,Feature Selection(FS),and classification.Primarily,the IoT gadgets are utilized in the collection of a patient’s health information.The proposed FPA-DNN model deploys Oppositional Crow Search(OCS)algorithm for FS,which selects the optimal subset of features from the preprocessed data.The application of FPA helps in tuning the DNN parameters for better classification performance.The simulation analysis of the proposed FPA-DNN model was performed against the benchmark CKD dataset.The results were examined under different aspects.The simulation outcomes established the superior performance of FPA-DNN technique by achieving the highest sensitivity of 98.80%,specificity of 98.66%,accuracy of 98.75%,F-score of 99%,and kappa of 97.33%.
基金This research was funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R97),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘In cognitive radio networks(CoR),the performance of cooperative spectrum sensing is improved by reducing the overall error rate or maximizing the detection probability.Several optimization methods are usually used to optimize the number of user-chosen for cooperation and the threshold selection.However,these methods do not take into account the effect of sample size and its effect on improving CoR performance.In general,a large sample size results in more reliable detection,but takes longer sensing time and increases complexity.Thus,the locally sensed sample size is an optimization problem.Therefore,optimizing the local sample size for each cognitive user helps to improve CoR performance.In this study,two new methods are proposed to find the optimum sample size to achieve objective-based improved(single/double)threshold energy detection,these methods are the optimum sample size N^(*)and neural networks(NN)optimization.Through the evaluation,it was found that the proposed methods outperform the traditional sample size selection in terms of the total error rate,detection probability,and throughput.
基金This Research was supported by Taif University Researchers Supporting Project Number(TURSP-2020/216),Taif University,Taif,Saudi Arabia。
文摘Privacy,identity preserving and integrity have become key problems for telecommunication standards.Significant privacy threats are expected in 5G networks considering the large number of devices that will be deployed.As Internet of Things(IoT)and long-term evolution for machine type(LTE-m)are growing very fast with massive data traffic the risk of privacy attacks will be greatly increase.For all the above issues standards’bodies should ensure users’identity and privacy in order to gain the trust of service providers and industries.Against such threats,5G specifications require a rigid and robust privacy procedure.Many research studies have addressed user privacy in 5G networks.This paper proposes a method to enhance user identity privacy in 5G systems through a scheme to protect the international mobile subscriber identity(IMSI)using a mutable mobile subscriber identity(MMSI)that changes randomly and avoids the exchange of IMSIs.It maintains authentication and key agreement(AKA)structure compatibility with previous mobile generations and improves user equipment(UE)synchronization with home networks.The proposed algorithm adds no computation overhead to UE or the network except a small amount in the home subscriber server(HSS).The proposed pseudonym mutable uses the XOR function to send the MMSI from the HSS to the UE which is reducing the encryption overhead significantly.The proposed solution was verified by ProVerif.
