Mobile clouds are the most common medium for aggregating,storing,and analyzing data from the medical Internet of Things(MIoT).It is employed to monitor a patient’s essential health signs for earlier disease diagnosis...Mobile clouds are the most common medium for aggregating,storing,and analyzing data from the medical Internet of Things(MIoT).It is employed to monitor a patient’s essential health signs for earlier disease diagnosis and prediction.Among the various disease,skin cancer was the wide variety of cancer,as well as enhances the endurance rate.In recent years,many skin cancer classification systems using machine and deep learning models have been developed for classifying skin tumors,including malignant melanoma(MM)and other skin cancers.However,accurate cancer detection was not performed with minimum time consumption.In order to address these existing problems,a novel Multidimensional Bregman Divergencive Feature Scaling Based Cophenetic Piecewise Regression Recurrent Deep Learning Classification(MBDFS-CPRRDLC)technique is introduced for detecting cancer at an earlier stage.The MBDFS-CPRRDLC performs skin cancer detection using different layers such as input,hidden,and output for feature selection and classification.The patient information is composed of IoT.The patient information was stored in mobile clouds server for performing predictive analytics.The collected data are sent to the recurrent deep learning classifier.In the first hidden layer,the feature selection process is carried out using the Multidimensional Bregman Divergencive Feature Scaling technique to find the significant features for disease identification resulting in decreases time consumption.Followed by,the disease classification is carried out in the second hidden layer using cophenetic correlative piecewise regression for analyzing the testing and training data.This process is repeatedly performed until the error gets minimized.In this way,disease classification is accurately performed with higher accuracy.Experimental evaluation is carried out for factors namely Accuracy,precision,recall,F-measure,as well as cancer detection time,by the amount of patient data.The observed result confirms that the proposed MBDFS-CPRRDLC technique increases accuracy as well as lesser cancer detection time compared to the conventional approaches.展开更多
Medical Internet of Things(MIoTs)is a collection of small and energyefficient wireless sensor devices that monitor the patient’s body.The healthcare networks transmit continuous data monitoring for the patients to su...Medical Internet of Things(MIoTs)is a collection of small and energyefficient wireless sensor devices that monitor the patient’s body.The healthcare networks transmit continuous data monitoring for the patients to survive them independently.There are many improvements in MIoTs,but still,there are critical issues that might affect the Quality of Service(QoS)of a network.Congestion handling is one of the critical factors that directly affect the QoS of the network.The congestion in MIoT can cause more energy consumption,delay,and important data loss.If a patient has an emergency,then the life-critical signals must transmit with minimum latency.During emergencies,the MIoTs have to monitor the patients continuously and transmit data(e.g.,ECG,BP,heart rate,etc.)with minimum delay.Therefore,there is an efficient technique required that can transmit emergency data of high-risk patients to the medical staff on time with maximum reliability.The main objective of this research is to monitor and transmit the patient’s real-time data efficiently and to prioritize the emergency data.In this paper,Emergency Prioritized and Congestion Handling Protocol for Medical IoTs(EPCP_MIoT)is proposed that efficiently monitors the patients and overcome the congestion by enabling different monitoring modes.Whereas the emergency data transmissions are prioritized and transmit at SIFS time.The proposed technique is implemented and compared with the previous technique,the comparison results show that the proposed technique outperforms the previous techniques in terms of network throughput,end to end delay,energy consumption,and packet loss ratio.展开更多
The sustainability of the Internet of Things(IoT)involves various issues,such as poor connectivity,scalability problems,interoperability issues,and energy inefficiency.Although the Sixth Generation of mobile networks(...The sustainability of the Internet of Things(IoT)involves various issues,such as poor connectivity,scalability problems,interoperability issues,and energy inefficiency.Although the Sixth Generation of mobile networks(6G)allows for Ultra-Reliable Low-Latency Communication(URLLC),enhanced Mobile Broadband(eMBB),and massive Machine-Type Communications(mMTC)services,it faces deployment challenges such as the short range of sub-THz and THz frequency bands,low capability to penetrate obstacles,and very high path loss.This paper presents a network architecture to enhance the connectivity of wireless IoT mesh networks that employ both 6G and Wi-Fi technologies.In this architecture,local communications are carried through the mesh network,which uses a virtual backbone to relay packets to local nodes,while remote communications are carried through the 6G network.The virtual backbone is created using a heuristic distributed ConnectedDominating Set(CDS)algorithm.In this algorithm,each node uses information collected from its one-and two-hop neighbors to determine its role and find the set of expansion nodes that are used to select the next CDS nodes.The proposed algorithm has O(n)message and O(K)time complexities,where n is the number of nodes in the network,and K is the depth of the cluster.The study proved that the approximation ratio of the algorithmhas an upper bound of 2.06748(3.4306MCDS+4.8185).Performance evaluations compared the size of the CDS against the theoretical limit and recent CDS clustering algorithms.Results indicate that the proposed algorithm has the smallest average slope for the size of the CDS as the number of nodes increases.展开更多
基金This research is funded by Princess Nourah Bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R194)Princess Nourah Bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Mobile clouds are the most common medium for aggregating,storing,and analyzing data from the medical Internet of Things(MIoT).It is employed to monitor a patient’s essential health signs for earlier disease diagnosis and prediction.Among the various disease,skin cancer was the wide variety of cancer,as well as enhances the endurance rate.In recent years,many skin cancer classification systems using machine and deep learning models have been developed for classifying skin tumors,including malignant melanoma(MM)and other skin cancers.However,accurate cancer detection was not performed with minimum time consumption.In order to address these existing problems,a novel Multidimensional Bregman Divergencive Feature Scaling Based Cophenetic Piecewise Regression Recurrent Deep Learning Classification(MBDFS-CPRRDLC)technique is introduced for detecting cancer at an earlier stage.The MBDFS-CPRRDLC performs skin cancer detection using different layers such as input,hidden,and output for feature selection and classification.The patient information is composed of IoT.The patient information was stored in mobile clouds server for performing predictive analytics.The collected data are sent to the recurrent deep learning classifier.In the first hidden layer,the feature selection process is carried out using the Multidimensional Bregman Divergencive Feature Scaling technique to find the significant features for disease identification resulting in decreases time consumption.Followed by,the disease classification is carried out in the second hidden layer using cophenetic correlative piecewise regression for analyzing the testing and training data.This process is repeatedly performed until the error gets minimized.In this way,disease classification is accurately performed with higher accuracy.Experimental evaluation is carried out for factors namely Accuracy,precision,recall,F-measure,as well as cancer detection time,by the amount of patient data.The observed result confirms that the proposed MBDFS-CPRRDLC technique increases accuracy as well as lesser cancer detection time compared to the conventional approaches.
基金the Deanship of Scientific Research(DSR),at KingAbdulaziz University,Jeddah,under grant no.G:292-612-1440.
文摘Medical Internet of Things(MIoTs)is a collection of small and energyefficient wireless sensor devices that monitor the patient’s body.The healthcare networks transmit continuous data monitoring for the patients to survive them independently.There are many improvements in MIoTs,but still,there are critical issues that might affect the Quality of Service(QoS)of a network.Congestion handling is one of the critical factors that directly affect the QoS of the network.The congestion in MIoT can cause more energy consumption,delay,and important data loss.If a patient has an emergency,then the life-critical signals must transmit with minimum latency.During emergencies,the MIoTs have to monitor the patients continuously and transmit data(e.g.,ECG,BP,heart rate,etc.)with minimum delay.Therefore,there is an efficient technique required that can transmit emergency data of high-risk patients to the medical staff on time with maximum reliability.The main objective of this research is to monitor and transmit the patient’s real-time data efficiently and to prioritize the emergency data.In this paper,Emergency Prioritized and Congestion Handling Protocol for Medical IoTs(EPCP_MIoT)is proposed that efficiently monitors the patients and overcome the congestion by enabling different monitoring modes.Whereas the emergency data transmissions are prioritized and transmit at SIFS time.The proposed technique is implemented and compared with the previous technique,the comparison results show that the proposed technique outperforms the previous techniques in terms of network throughput,end to end delay,energy consumption,and packet loss ratio.
基金Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through the project number RI-44-0028.
文摘The sustainability of the Internet of Things(IoT)involves various issues,such as poor connectivity,scalability problems,interoperability issues,and energy inefficiency.Although the Sixth Generation of mobile networks(6G)allows for Ultra-Reliable Low-Latency Communication(URLLC),enhanced Mobile Broadband(eMBB),and massive Machine-Type Communications(mMTC)services,it faces deployment challenges such as the short range of sub-THz and THz frequency bands,low capability to penetrate obstacles,and very high path loss.This paper presents a network architecture to enhance the connectivity of wireless IoT mesh networks that employ both 6G and Wi-Fi technologies.In this architecture,local communications are carried through the mesh network,which uses a virtual backbone to relay packets to local nodes,while remote communications are carried through the 6G network.The virtual backbone is created using a heuristic distributed ConnectedDominating Set(CDS)algorithm.In this algorithm,each node uses information collected from its one-and two-hop neighbors to determine its role and find the set of expansion nodes that are used to select the next CDS nodes.The proposed algorithm has O(n)message and O(K)time complexities,where n is the number of nodes in the network,and K is the depth of the cluster.The study proved that the approximation ratio of the algorithmhas an upper bound of 2.06748(3.4306MCDS+4.8185).Performance evaluations compared the size of the CDS against the theoretical limit and recent CDS clustering algorithms.Results indicate that the proposed algorithm has the smallest average slope for the size of the CDS as the number of nodes increases.
