LP (Logic Programming) has been successfully applied to knowledge discovery in many fields. The execution of the LP is based on the evaluation of the first order predicate. Usually the information involved in the pred...LP (Logic Programming) has been successfully applied to knowledge discovery in many fields. The execution of the LP is based on the evaluation of the first order predicate. Usually the information involved in the predicates are local and homogenous, thus the evaluation process is relatively simple. However, the evaluation process become much more complicated when applied to KDD on the Internet where the information involved in the predicates maybe heterogeneous and distributed over many different sits. Therefor, we try to attack the problem in a multi agent system's framework so that the logic program can be written in a site independent style and deal easily with heterogeneous represented information.展开更多
In order to solve the high latency of traditional cloud computing and the processing capacity limitation of Internet of Things(IoT)users,Multi-access Edge Computing(MEC)migrates computing and storage capabilities from...In order to solve the high latency of traditional cloud computing and the processing capacity limitation of Internet of Things(IoT)users,Multi-access Edge Computing(MEC)migrates computing and storage capabilities from the remote data center to the edge of network,providing users with computation services quickly and directly.In this paper,we investigate the impact of the randomness caused by the movement of the IoT user on decision-making for offloading,where the connection between the IoT user and the MEC servers is uncertain.This uncertainty would be the main obstacle to assign the task accurately.Consequently,if the assigned task cannot match well with the real connection time,a migration(connection time is not enough to process)would be caused.In order to address the impact of this uncertainty,we formulate the offloading decision as an optimization problem considering the transmission,computation and migration.With the help of Stochastic Programming(SP),we use the posteriori recourse to compensate for inaccurate predictions.Meanwhile,in heterogeneous networks,considering multiple candidate MEC servers could be selected simultaneously due to overlapping,we also introduce the Multi-Arm Bandit(MAB)theory for MEC selection.The extensive simulations validate the improvement and effectiveness of the proposed SP-based Multi-arm bandit Method(SMM)for offloading in terms of reward,cost,energy consumption and delay.The results showthat SMMcan achieve about 20%improvement compared with the traditional offloading method that does not consider the randomness,and it also outperforms the existing SP/MAB based method for offloading.展开更多
Due to the overwhelming characteristics of the Internet of Things(IoT)and its adoption in approximately every aspect of our lives,the concept of individual devices’privacy has gained prominent attention from both cus...Due to the overwhelming characteristics of the Internet of Things(IoT)and its adoption in approximately every aspect of our lives,the concept of individual devices’privacy has gained prominent attention from both customers,i.e.,people,and industries as wearable devices collect sensitive information about patients(both admitted and outdoor)in smart healthcare infrastructures.In addition to privacy,outliers or noise are among the crucial issues,which are directly correlated with IoT infrastructures,as most member devices are resource-limited and could generate or transmit false data that is required to be refined before processing,i.e.,transmitting.Therefore,the development of privacy-preserving information fusion techniques is highly encouraged,especially those designed for smart IoT-enabled domains.In this paper,we are going to present an effective hybrid approach that can refine raw data values captured by the respectivemember device before transmission while preserving its privacy through the utilization of the differential privacy technique in IoT infrastructures.Sliding window,i.e.,δi based dynamic programming methodology,is implemented at the device level to ensure precise and accurate detection of outliers or noisy data,and refine it prior to activation of the respective transmission activity.Additionally,an appropriate privacy budget has been selected,which is enough to ensure the privacy of every individualmodule,i.e.,a wearable device such as a smartwatch attached to the patient’s body.In contrast,the end module,i.e.,the server in this case,can extract important information with approximately the maximum level of accuracy.Moreover,refined data has been processed by adding an appropriate nose through the Laplace mechanism to make it useless or meaningless for the adversary modules in the IoT.The proposed hybrid approach is trusted from both the device’s privacy and the integrity of the transmitted information perspectives.Simulation and analytical results have proved that the proposed privacy-preserving information fusion technique for wearable devices is an ideal solution for resource-constrained infrastructures such as IoT and the Internet ofMedical Things,where both device privacy and information integrity are important.Finally,the proposed hybrid approach is proven against well-known intruder attacks,especially those related to the privacy of the respective device in IoT infrastructures.展开更多
基于Internet的呼叫中心使呼叫中心不再局限于传统的PSTN,而是扩展到Internet的各种媒体.提出一个客户/服务器网络结构的Internet呼叫中心的模型,使得用户可以通过电话网或者Internet访问本呼叫中心.讨论了如何利用Microsoft Windows 20...基于Internet的呼叫中心使呼叫中心不再局限于传统的PSTN,而是扩展到Internet的各种媒体.提出一个客户/服务器网络结构的Internet呼叫中心的模型,使得用户可以通过电话网或者Internet访问本呼叫中心.讨论了如何利用Microsoft Windows 2000 Sever平台内置的可编程接口TAPI 3.0具体实现Internet呼叫中心的各种功能.展开更多
文摘LP (Logic Programming) has been successfully applied to knowledge discovery in many fields. The execution of the LP is based on the evaluation of the first order predicate. Usually the information involved in the predicates are local and homogenous, thus the evaluation process is relatively simple. However, the evaluation process become much more complicated when applied to KDD on the Internet where the information involved in the predicates maybe heterogeneous and distributed over many different sits. Therefor, we try to attack the problem in a multi agent system's framework so that the logic program can be written in a site independent style and deal easily with heterogeneous represented information.
基金This work was supported in part by the Zhejiang Lab under Grant 20210AB02in part by the Sichuan International Science and Technology Innovation Cooperation/Hong Kong,Macao and Taiwan Science and Technology Innovation Cooperation Project under Grant 2019YFH0163in part by the Key Research and Development Project of Sichuan Provincial Department of Science and Technology under Grant 2018JZ0071.
文摘In order to solve the high latency of traditional cloud computing and the processing capacity limitation of Internet of Things(IoT)users,Multi-access Edge Computing(MEC)migrates computing and storage capabilities from the remote data center to the edge of network,providing users with computation services quickly and directly.In this paper,we investigate the impact of the randomness caused by the movement of the IoT user on decision-making for offloading,where the connection between the IoT user and the MEC servers is uncertain.This uncertainty would be the main obstacle to assign the task accurately.Consequently,if the assigned task cannot match well with the real connection time,a migration(connection time is not enough to process)would be caused.In order to address the impact of this uncertainty,we formulate the offloading decision as an optimization problem considering the transmission,computation and migration.With the help of Stochastic Programming(SP),we use the posteriori recourse to compensate for inaccurate predictions.Meanwhile,in heterogeneous networks,considering multiple candidate MEC servers could be selected simultaneously due to overlapping,we also introduce the Multi-Arm Bandit(MAB)theory for MEC selection.The extensive simulations validate the improvement and effectiveness of the proposed SP-based Multi-arm bandit Method(SMM)for offloading in terms of reward,cost,energy consumption and delay.The results showthat SMMcan achieve about 20%improvement compared with the traditional offloading method that does not consider the randomness,and it also outperforms the existing SP/MAB based method for offloading.
基金Ministry of Higher Education of Malaysia under theResearch GrantLRGS/1/2019/UKM-UKM/5/2 and Princess Nourah bint Abdulrahman University for financing this researcher through Supporting Project Number(PNURSP2024R235),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Due to the overwhelming characteristics of the Internet of Things(IoT)and its adoption in approximately every aspect of our lives,the concept of individual devices’privacy has gained prominent attention from both customers,i.e.,people,and industries as wearable devices collect sensitive information about patients(both admitted and outdoor)in smart healthcare infrastructures.In addition to privacy,outliers or noise are among the crucial issues,which are directly correlated with IoT infrastructures,as most member devices are resource-limited and could generate or transmit false data that is required to be refined before processing,i.e.,transmitting.Therefore,the development of privacy-preserving information fusion techniques is highly encouraged,especially those designed for smart IoT-enabled domains.In this paper,we are going to present an effective hybrid approach that can refine raw data values captured by the respectivemember device before transmission while preserving its privacy through the utilization of the differential privacy technique in IoT infrastructures.Sliding window,i.e.,δi based dynamic programming methodology,is implemented at the device level to ensure precise and accurate detection of outliers or noisy data,and refine it prior to activation of the respective transmission activity.Additionally,an appropriate privacy budget has been selected,which is enough to ensure the privacy of every individualmodule,i.e.,a wearable device such as a smartwatch attached to the patient’s body.In contrast,the end module,i.e.,the server in this case,can extract important information with approximately the maximum level of accuracy.Moreover,refined data has been processed by adding an appropriate nose through the Laplace mechanism to make it useless or meaningless for the adversary modules in the IoT.The proposed hybrid approach is trusted from both the device’s privacy and the integrity of the transmitted information perspectives.Simulation and analytical results have proved that the proposed privacy-preserving information fusion technique for wearable devices is an ideal solution for resource-constrained infrastructures such as IoT and the Internet ofMedical Things,where both device privacy and information integrity are important.Finally,the proposed hybrid approach is proven against well-known intruder attacks,especially those related to the privacy of the respective device in IoT infrastructures.
文摘基于Internet的呼叫中心使呼叫中心不再局限于传统的PSTN,而是扩展到Internet的各种媒体.提出一个客户/服务器网络结构的Internet呼叫中心的模型,使得用户可以通过电话网或者Internet访问本呼叫中心.讨论了如何利用Microsoft Windows 2000 Sever平台内置的可编程接口TAPI 3.0具体实现Internet呼叫中心的各种功能.
