Pervasive Computing has become more personal with the widespread adoption of the Internet of Things(IoT)in our day-to-day lives.The emerging domain that encompasses devices,sensors,storage,and computing of personal us...Pervasive Computing has become more personal with the widespread adoption of the Internet of Things(IoT)in our day-to-day lives.The emerging domain that encompasses devices,sensors,storage,and computing of personal use and surroundings leads to Personal IoT(PIoT).PIoT offers users high levels of personalization,automation,and convenience.This proliferation of PIoT technology has extended into society,social engagement,and the interconnectivity of PIoT objects,resulting in the emergence of the Social Internet of Things(SIoT).The combination of PIoT and SIoT has spurred the need for autonomous learning,comprehension,and understanding of both the physical and social worlds.Current research on PIoT is dedicated to enabling seamless communication among devices,striking a balance between observation,sensing,and perceiving the extended physical and social environment,and facilitating information exchange.Furthermore,the virtualization of independent learning from the social environment has given rise to Artificial Social Intelligence(ASI)in PIoT systems.However,autonomous data communication between different nodes within a social setup presents various resource management challenges that require careful consideration.This paper provides a comprehensive review of the evolving domains of PIoT,SIoT,and ASI.Moreover,the paper offers insightful modeling and a case study exploring the role of PIoT in post-COVID scenarios.This study contributes to a deeper understanding of the intricacies of PIoT and its various dimensions,paving the way for further advancements in this transformative field.展开更多
Multi-mode power internet of things(PIoT)combines various communication media to provide spatio-temporal coverage for low-carbon operation in smart park.Edge-end collaboration is feasible to achieve the full utilizati...Multi-mode power internet of things(PIoT)combines various communication media to provide spatio-temporal coverage for low-carbon operation in smart park.Edge-end collaboration is feasible to achieve the full utilization of heterogeneous resources and anti-eavesdropping.However,edge-end collaboration-based multi-mode PIoT faces challenges of mutual contradiction in communication and security quality of service(QoS)guarantee,inadaptability of resource management,and multi-mode access conflict.We propose an Adaptive learning based delAysensitive and seCure Edge-End Collaboration algorithm(ACE_(2))to optimize multi-mode channel selection and split device power into artificial noise(AN)transmission and data transmission for secure data delivery.ACE_(2) can achieve multi-attribute QoS guarantee,adaptive resource management and security enhancement,and access conflict elimination with the combined power of deep actor-critic(DAC),“win or learn fast(WoLF)”mechanism,and edge-end collaboration.Simulations demonstrate its superior performance in queuing delay,energy consumption,secrecy capacity,and adaptability to differentiated low-carbon services.展开更多
Objective: To update the status of Gardnerella vaginalis(G. vaginalis) as a causative agent of bacterial vaginosis(BV) in Malaysia and to define its epidemiology, metronidazole resistance and virulence properties.Meth...Objective: To update the status of Gardnerella vaginalis(G. vaginalis) as a causative agent of bacterial vaginosis(BV) in Malaysia and to define its epidemiology, metronidazole resistance and virulence properties.Methods: It is a single-centre(Gynaecology clinic at the Hospital Kuala Lumpur,Malaysia) prospective study with laboratory-based microbiological follow up and analyses. Vaginal swabs collected from the patients suspected for BV were subjected to clinical BV diagnosis, isolation and identification of G. vaginalis, metronidazole susceptibility testing, vaginolysin and sialidase gene PCR, Piot's biotyping and amplified ribosomal DNA restriction analysis genotyping.Results: Among the 207 patients suspected for BV, G. vaginalis was isolated from 47 subjects. G. vaginalis coexisted with Trichomonas vaginalis and Candida albicans in 26 samples. Three G. vaginalis isolates were resistant to metronidazole. Biotyping revealed 1 and 7 as the common types. Amplified ribosomal DNA restriction analysis genotype II was found to be more common(n = 22; 46%) than I(n = 12; 25.53%) and III(n = 13;27.6%). All genotype I and III isolates carried the sialidase gene, while 91.6% and 84.6%contained the vaginolysin gene. Genotype I was significantly associated with postgynaecological surgical complications and abortions(P = 0.002).Conclusions: The existence of pathogenic G. vaginalis clones in Malaysia including drug resistant strains should not be taken lightly and needs to be monitored as these may bring more complications especially among women of child bearing age and pregnant women.展开更多
分布式能源、可调负荷及储能装置大规模接入配电网运行带动“源-网-荷-储”调控模式的转变,配电网与分布式资源之间频繁双向互动对通信网全面感知与广域传输能力提出更高要求。电力物联网与5G的融合通过云-边-端多层级资源的深度协同提...分布式能源、可调负荷及储能装置大规模接入配电网运行带动“源-网-荷-储”调控模式的转变,配电网与分布式资源之间频繁双向互动对通信网全面感知与广域传输能力提出更高要求。电力物联网与5G的融合通过云-边-端多层级资源的深度协同提供有效的解决方案。针对现有云-边-端协同技术在电力物联网与5G融合应用面临的与电力业务需求适配性不足、异构资源调度协同性差、数据隐私安全难以保障等挑战,文章提出电力物联网5G云-边-端多级协同框架,支撑分布式资源与配电网的协同互动;在此基础上,基于联邦深度Q学习,提出基于半分布式人工智能的云-边-端协同资源调度方法,在高可靠低时延约束下实现端侧任务卸载、功率控制与云侧/边侧计算资源分配的协同优化;最后,通过算例分析验证该技术在能耗、时延、吞吐量等方面的性能优势,同基于层次分析法和深度Q学习的边缘网络任务卸载算法(distribution offloading algorithm based on analytic hierarchy process and deep Q network,AHP-DQN)和能量感知边缘计算移动管理算法(energy-aware mobility management algorithm for mobile edge computing,EMM)相比,平均吞吐量分别提高15.29%和23.87%,总排队时延分别降低53.35%和62.20%,能够满足电力物联网业务差异化通信需求,支撑分布式资源接入配电网双向互动。展开更多
文摘Pervasive Computing has become more personal with the widespread adoption of the Internet of Things(IoT)in our day-to-day lives.The emerging domain that encompasses devices,sensors,storage,and computing of personal use and surroundings leads to Personal IoT(PIoT).PIoT offers users high levels of personalization,automation,and convenience.This proliferation of PIoT technology has extended into society,social engagement,and the interconnectivity of PIoT objects,resulting in the emergence of the Social Internet of Things(SIoT).The combination of PIoT and SIoT has spurred the need for autonomous learning,comprehension,and understanding of both the physical and social worlds.Current research on PIoT is dedicated to enabling seamless communication among devices,striking a balance between observation,sensing,and perceiving the extended physical and social environment,and facilitating information exchange.Furthermore,the virtualization of independent learning from the social environment has given rise to Artificial Social Intelligence(ASI)in PIoT systems.However,autonomous data communication between different nodes within a social setup presents various resource management challenges that require careful consideration.This paper provides a comprehensive review of the evolving domains of PIoT,SIoT,and ASI.Moreover,the paper offers insightful modeling and a case study exploring the role of PIoT in post-COVID scenarios.This study contributes to a deeper understanding of the intricacies of PIoT and its various dimensions,paving the way for further advancements in this transformative field.
基金supported by the Science and Technology Project of State Grid Corporation of China under Grant Number 52094021N010 (5400202199534A-0-5-ZN)
文摘Multi-mode power internet of things(PIoT)combines various communication media to provide spatio-temporal coverage for low-carbon operation in smart park.Edge-end collaboration is feasible to achieve the full utilization of heterogeneous resources and anti-eavesdropping.However,edge-end collaboration-based multi-mode PIoT faces challenges of mutual contradiction in communication and security quality of service(QoS)guarantee,inadaptability of resource management,and multi-mode access conflict.We propose an Adaptive learning based delAysensitive and seCure Edge-End Collaboration algorithm(ACE_(2))to optimize multi-mode channel selection and split device power into artificial noise(AN)transmission and data transmission for secure data delivery.ACE_(2) can achieve multi-attribute QoS guarantee,adaptive resource management and security enhancement,and access conflict elimination with the combined power of deep actor-critic(DAC),“win or learn fast(WoLF)”mechanism,and edge-end collaboration.Simulations demonstrate its superior performance in queuing delay,energy consumption,secrecy capacity,and adaptability to differentiated low-carbon services.
基金supported by Universiti Putra Malaysia through Research University Grant Scheme(RUGS 04-02-12-1756RU)
文摘Objective: To update the status of Gardnerella vaginalis(G. vaginalis) as a causative agent of bacterial vaginosis(BV) in Malaysia and to define its epidemiology, metronidazole resistance and virulence properties.Methods: It is a single-centre(Gynaecology clinic at the Hospital Kuala Lumpur,Malaysia) prospective study with laboratory-based microbiological follow up and analyses. Vaginal swabs collected from the patients suspected for BV were subjected to clinical BV diagnosis, isolation and identification of G. vaginalis, metronidazole susceptibility testing, vaginolysin and sialidase gene PCR, Piot's biotyping and amplified ribosomal DNA restriction analysis genotyping.Results: Among the 207 patients suspected for BV, G. vaginalis was isolated from 47 subjects. G. vaginalis coexisted with Trichomonas vaginalis and Candida albicans in 26 samples. Three G. vaginalis isolates were resistant to metronidazole. Biotyping revealed 1 and 7 as the common types. Amplified ribosomal DNA restriction analysis genotype II was found to be more common(n = 22; 46%) than I(n = 12; 25.53%) and III(n = 13;27.6%). All genotype I and III isolates carried the sialidase gene, while 91.6% and 84.6%contained the vaginolysin gene. Genotype I was significantly associated with postgynaecological surgical complications and abortions(P = 0.002).Conclusions: The existence of pathogenic G. vaginalis clones in Malaysia including drug resistant strains should not be taken lightly and needs to be monitored as these may bring more complications especially among women of child bearing age and pregnant women.
文摘分布式能源、可调负荷及储能装置大规模接入配电网运行带动“源-网-荷-储”调控模式的转变,配电网与分布式资源之间频繁双向互动对通信网全面感知与广域传输能力提出更高要求。电力物联网与5G的融合通过云-边-端多层级资源的深度协同提供有效的解决方案。针对现有云-边-端协同技术在电力物联网与5G融合应用面临的与电力业务需求适配性不足、异构资源调度协同性差、数据隐私安全难以保障等挑战,文章提出电力物联网5G云-边-端多级协同框架,支撑分布式资源与配电网的协同互动;在此基础上,基于联邦深度Q学习,提出基于半分布式人工智能的云-边-端协同资源调度方法,在高可靠低时延约束下实现端侧任务卸载、功率控制与云侧/边侧计算资源分配的协同优化;最后,通过算例分析验证该技术在能耗、时延、吞吐量等方面的性能优势,同基于层次分析法和深度Q学习的边缘网络任务卸载算法(distribution offloading algorithm based on analytic hierarchy process and deep Q network,AHP-DQN)和能量感知边缘计算移动管理算法(energy-aware mobility management algorithm for mobile edge computing,EMM)相比,平均吞吐量分别提高15.29%和23.87%,总排队时延分别降低53.35%和62.20%,能够满足电力物联网业务差异化通信需求,支撑分布式资源接入配电网双向互动。
