Many ontologies are provided to representing semantic sensors data.However,heterogeneity exists in different sensors which makes some service operators of Internet of Thing(IoT) difficult(such as such as semantic infe...Many ontologies are provided to representing semantic sensors data.However,heterogeneity exists in different sensors which makes some service operators of Internet of Thing(IoT) difficult(such as such as semantic inferring,non-linear inverted index establishing,service composing) .There is a great deal of research about sensor ontology alignment dealing with the heterogeneity between the different sensor ontologies,but fewer solutions focus on exploiting syntaxes in a sensor ontology and the pattern of accessing alignments.Our solution infers alignments by extending structural subsumption algorithms to analyze syntaxes in a sensor ontology,and then combines the alignments with the SKOS model to construct the integration sensor ontology,which can be accessed via the IoT.The experiments show that the integration senor ontology in the SKOS model can be utilized via the IoT service,and the accuracy of our prototype,in average,is higher than others over the four real ontologies.展开更多
This paper focuses on solving the delay constrained least cost routing problem, and propose a simple, distributed heuristic solution, called distributed recursive delay constrained least cost (DR DCLC) unicast routing...This paper focuses on solving the delay constrained least cost routing problem, and propose a simple, distributed heuristic solution, called distributed recursive delay constrained least cost (DR DCLC) unicast routing algorithm. DR DCLC only requires local information to find the near optimal solution. The correctness of DR DCLC is proued by showing that it is always capable of constructing a loop free delay constrained path within finite time, if such a path exists. Simulation is also used to compare DR DCLC to the optimal DCLC algorithm and other algorithms.展开更多
This paper addresses the transportation network design problem （NDP） wherein the dis- tance limit and en-route recharge of electric vehicles are taken into account. Specifically, in this work, the network design pro...This paper addresses the transportation network design problem （NDP） wherein the dis- tance limit and en-route recharge of electric vehicles are taken into account. Specifically, in this work, the network design problem aims to select the optimal planning policy from a set of infrastructure design scenarios considering both road expansions and charging station allocations under a specified construction budget. The user-equilibrium mixed-vehicular traffic assignment problem with en-route recharge （MVTAP-ER） is formulated into a novel convex optimization model and extended to a newly developed bi-level program of the aggregated NDP integrating recharge facility allocation （NDP-RFA）. In the algorithmic framework, a convex optimization technique and a tailored CA are adopted for, respectively, solving the subproblem MVTAP-ER and the primal problem NDP-RFA. Systematic ex- periments are conducted to test the efficacy of the proposed approaches. The results highlight the impacts of distance limits and budget levels on the project selection and evaluation, and the benefits of considering both road improvement policy and recharge service provision as compared to accounting for the latter only. The results also report that the two design objectives, to respectively minimize the total system travel time and vehicle miles travelled, are conflicting for certain scenarios.展开更多
基金Supported by National Natural Science Foundation of China(No.61601039)financially supported by the State Key Research Development Program of China(Grant No.2016YFC0801407)+3 种基金financially supported by the Natural Science Foundation of Beijing Information Science & Technology University(No.1625008)financially supported by the Opening Project of Beijing Key Laboratory of Internet Culture and Digital Dissemination Research(NO.ICDD201607)Open Foundation of State key Laboratory of Networking and Switching Technology(Beijing University of Posts and Telecommunications)(NO.SKLNST-2016-2-08)financially supported by the Importation and Development of High-Caliber Talents Project of Beijing Municipal Institutions(Grant No.CIT&TCD201504056)
文摘Many ontologies are provided to representing semantic sensors data.However,heterogeneity exists in different sensors which makes some service operators of Internet of Thing(IoT) difficult(such as such as semantic inferring,non-linear inverted index establishing,service composing) .There is a great deal of research about sensor ontology alignment dealing with the heterogeneity between the different sensor ontologies,but fewer solutions focus on exploiting syntaxes in a sensor ontology and the pattern of accessing alignments.Our solution infers alignments by extending structural subsumption algorithms to analyze syntaxes in a sensor ontology,and then combines the alignments with the SKOS model to construct the integration sensor ontology,which can be accessed via the IoT.The experiments show that the integration senor ontology in the SKOS model can be utilized via the IoT service,and the accuracy of our prototype,in average,is higher than others over the four real ontologies.
文摘This paper focuses on solving the delay constrained least cost routing problem, and propose a simple, distributed heuristic solution, called distributed recursive delay constrained least cost (DR DCLC) unicast routing algorithm. DR DCLC only requires local information to find the near optimal solution. The correctness of DR DCLC is proued by showing that it is always capable of constructing a loop free delay constrained path within finite time, if such a path exists. Simulation is also used to compare DR DCLC to the optimal DCLC algorithm and other algorithms.
基金supported by Research Centre for Integrated Transport Innovation,UNSW
文摘This paper addresses the transportation network design problem （NDP） wherein the dis- tance limit and en-route recharge of electric vehicles are taken into account. Specifically, in this work, the network design problem aims to select the optimal planning policy from a set of infrastructure design scenarios considering both road expansions and charging station allocations under a specified construction budget. The user-equilibrium mixed-vehicular traffic assignment problem with en-route recharge （MVTAP-ER） is formulated into a novel convex optimization model and extended to a newly developed bi-level program of the aggregated NDP integrating recharge facility allocation （NDP-RFA）. In the algorithmic framework, a convex optimization technique and a tailored CA are adopted for, respectively, solving the subproblem MVTAP-ER and the primal problem NDP-RFA. Systematic ex- periments are conducted to test the efficacy of the proposed approaches. The results highlight the impacts of distance limits and budget levels on the project selection and evaluation, and the benefits of considering both road improvement policy and recharge service provision as compared to accounting for the latter only. The results also report that the two design objectives, to respectively minimize the total system travel time and vehicle miles travelled, are conflicting for certain scenarios.
