提出了一个移动用户智能电网(Mobile User Smart Grid,MUSG)概念,给出了MUSG场景下的用电模式描述,并利用基于身份的密码技术给出了一种基于身份的MUSG认证方案。分析表明,利用该认证方案不仅可以有效解决智能电网中移动用户的认证问题...提出了一个移动用户智能电网(Mobile User Smart Grid,MUSG)概念,给出了MUSG场景下的用电模式描述,并利用基于身份的密码技术给出了一种基于身份的MUSG认证方案。分析表明,利用该认证方案不仅可以有效解决智能电网中移动用户的认证问题,满足移动用户即时用电和户外用电的需求,并且在保障系统安全性的同时有效降低了系统的计算和通信开销,减轻了系统的负载压力,提高了系统的使用效率。这为智能电网业务由固定用户向移动用户过渡奠定了基础。展开更多
随着群智感知的普及,以合理的成本招募最佳感知信息提供者的问题变得更加重要,但移动用户追求高回报的贪婪特性会使得招募成本偏高.为此,提出一种针对团体的群智感知招募的激励机制.首先,通过移动用户的属性和任务详细信息来迭代所有可...随着群智感知的普及,以合理的成本招募最佳感知信息提供者的问题变得更加重要,但移动用户追求高回报的贪婪特性会使得招募成本偏高.为此,提出一种针对团体的群智感知招募的激励机制.首先,通过移动用户的属性和任务详细信息来迭代所有可能团体;然后,评估生成的随机初始团体,删除其中违反任务约束的团体,并计算其余团体的信息质量QoI(the quality of information,QoI)比率,团体将经过轮盘赌程序从当前团体中选择候选人进行进化程序,选定的团体经过交叉,在团体之间随机交换成员;最后,进行突变,该过程随机替换团体的成员,从解决方案集中选择具有最佳QoI比率的团体,解决了移动用户对数据进行过高定价以提高利润的倾向.提出的激励机制包括选择和支付机制,避免了移动用户选择过程中的贪婪特性.通过与现有的团队招募框架方法的对比,以及实验数据集与原始模型进行的比较,表明了该激励机制的有效性.展开更多
There is a problem of unfairness in allocation of radio resources among heterogeneous mobile terminals in heterogeneous wireless networks. Low-capability mobile terminals (such as single-mode terminals) suffer high ca...There is a problem of unfairness in allocation of radio resources among heterogeneous mobile terminals in heterogeneous wireless networks. Low-capability mobile terminals (such as single-mode terminals) suffer high call blocking probability whereas high-capability mobile terminals (such as quad-mode terminals) experience very low call blocking probability, in the same heterogeneous wireless network. This paper proposes a Terminal-Modality-Based Joint Call Admission Control (TJCAC) algorithm to reduce this problem of unfairness. The proposed TJCAC algorithm makes call admission decisions based on mobile terminal modality (capability), network load, and radio access technology (RAT) terminal support index. The objectives of the proposed TJCAC algorithm are to reduce call blocking/dropping probability, and ensure fairness in allocation of radio resources among heterogeneous mobile terminals in heterogeneous networks. An analytical model is developed to evaluate the performance of the proposed TJCAC scheme in terms of call blocking/dropping probability in a heterogeneous wireless network. The performance of the proposed TJCAC algorithm is compared with that of other JCAC algorithms. Results show that the proposed algorithm reduces call blocking/dropping probability in the networks, and ensure fairness in allocation of radio resources among heterogeneous terminals.展开更多
The use of traditional positioning technologies, such as GPS and wireless local positioning, rely on un- derlying infrastructure. However, in a subway environment, such positioning systems are not available for the po...The use of traditional positioning technologies, such as GPS and wireless local positioning, rely on un- derlying infrastructure. However, in a subway environment, such positioning systems are not available for the position- ing tasks, such as the detection of the train arrivals for the passengers in the train. An alternative approach is to exploit the contextual information available in the mobile devices of subway riders to detect train arrivals. To this end, we pro- pose to exploit multiple contextual features extracted from the mobile devices of subway riders to precisely detecting train arrivals. Following this line, we first investigate poten- tial contextual features which may be effective to detect train arrivals according to the observations from 3D accelerome- ters and GSM radio. Furthermore, we propose to explore the maximum entropy （MaxEnt） model for training a train ar- rival detector by learning the correlation between contextual features and train arrivals. Finally, we perform extensive ex- periments on several real-world data sets collected from two major subway lines in the Beijing subway system. Experi- mental results validate both the effectiveness and efficiency of the proposed approach.展开更多
文摘提出了一个移动用户智能电网(Mobile User Smart Grid,MUSG)概念,给出了MUSG场景下的用电模式描述,并利用基于身份的密码技术给出了一种基于身份的MUSG认证方案。分析表明,利用该认证方案不仅可以有效解决智能电网中移动用户的认证问题,满足移动用户即时用电和户外用电的需求,并且在保障系统安全性的同时有效降低了系统的计算和通信开销,减轻了系统的负载压力,提高了系统的使用效率。这为智能电网业务由固定用户向移动用户过渡奠定了基础。
文摘随着群智感知的普及,以合理的成本招募最佳感知信息提供者的问题变得更加重要,但移动用户追求高回报的贪婪特性会使得招募成本偏高.为此,提出一种针对团体的群智感知招募的激励机制.首先,通过移动用户的属性和任务详细信息来迭代所有可能团体;然后,评估生成的随机初始团体,删除其中违反任务约束的团体,并计算其余团体的信息质量QoI(the quality of information,QoI)比率,团体将经过轮盘赌程序从当前团体中选择候选人进行进化程序,选定的团体经过交叉,在团体之间随机交换成员;最后,进行突变,该过程随机替换团体的成员,从解决方案集中选择具有最佳QoI比率的团体,解决了移动用户对数据进行过高定价以提高利润的倾向.提出的激励机制包括选择和支付机制,避免了移动用户选择过程中的贪婪特性.通过与现有的团队招募框架方法的对比,以及实验数据集与原始模型进行的比较,表明了该激励机制的有效性.
文摘There is a problem of unfairness in allocation of radio resources among heterogeneous mobile terminals in heterogeneous wireless networks. Low-capability mobile terminals (such as single-mode terminals) suffer high call blocking probability whereas high-capability mobile terminals (such as quad-mode terminals) experience very low call blocking probability, in the same heterogeneous wireless network. This paper proposes a Terminal-Modality-Based Joint Call Admission Control (TJCAC) algorithm to reduce this problem of unfairness. The proposed TJCAC algorithm makes call admission decisions based on mobile terminal modality (capability), network load, and radio access technology (RAT) terminal support index. The objectives of the proposed TJCAC algorithm are to reduce call blocking/dropping probability, and ensure fairness in allocation of radio resources among heterogeneous mobile terminals in heterogeneous networks. An analytical model is developed to evaluate the performance of the proposed TJCAC scheme in terms of call blocking/dropping probability in a heterogeneous wireless network. The performance of the proposed TJCAC algorithm is compared with that of other JCAC algorithms. Results show that the proposed algorithm reduces call blocking/dropping probability in the networks, and ensure fairness in allocation of radio resources among heterogeneous terminals.
文摘The use of traditional positioning technologies, such as GPS and wireless local positioning, rely on un- derlying infrastructure. However, in a subway environment, such positioning systems are not available for the position- ing tasks, such as the detection of the train arrivals for the passengers in the train. An alternative approach is to exploit the contextual information available in the mobile devices of subway riders to detect train arrivals. To this end, we pro- pose to exploit multiple contextual features extracted from the mobile devices of subway riders to precisely detecting train arrivals. Following this line, we first investigate poten- tial contextual features which may be effective to detect train arrivals according to the observations from 3D accelerome- ters and GSM radio. Furthermore, we propose to explore the maximum entropy （MaxEnt） model for training a train ar- rival detector by learning the correlation between contextual features and train arrivals. Finally, we perform extensive ex- periments on several real-world data sets collected from two major subway lines in the Beijing subway system. Experi- mental results validate both the effectiveness and efficiency of the proposed approach.
