Frequent joining and withdrawal of resources and services in a grid make dynamic discovery of grid resource and service quite difficult. In this paper, a two-dimensional model of service discovery （TDMSD） is present...Frequent joining and withdrawal of resources and services in a grid make dynamic discovery of grid resource and service quite difficult. In this paper, a two-dimensional model of service discovery （TDMSD） is presented for use of dynamic service discovery. Description and proof of the model and the route algorithm of service discovery are proposed. The complexity analysis and simulation results show that the TDMSD model works well.展开更多
In recent years,with the rapid development of Internet of things(IoT)technology,radio frequency identification(RFID)technology as the core of IoT technology has been paid more and more attention,and RFID network plann...In recent years,with the rapid development of Internet of things(IoT)technology,radio frequency identification(RFID)technology as the core of IoT technology has been paid more and more attention,and RFID network planning(RNP)has become the primary concern.Compared with the traditional methods,meta-heuristic method is widely used in RNP.Aiming at the target requirements of RFID,such as fewer readers,covering more tags,reducing the interference between readers and saving costs,this paper proposes a hybrid gray wolf optimization-cuckoo search(GWO-CS)algorithm.This method uses the input representation based on random gray wolf search and evaluates the tag density and location to determine the combination performance of the reader's propagation area.Compared with particle swarm optimization(PSO)algorithm,cuckoo search(CS)algorithm and gray wolf optimization(GWO)algorithm under the same experimental conditions,the coverage of GWO-CS is 9.306%higher than that of PSO algorithm,6.963%higher than that of CS algorithm,and 3.488%higher than that of GWO algorithm.The results show that the GWO-CS algorithm cannot only improve the global search range,but also improve the local search depth.展开更多
基金Project supported by the National Natural Science Foundation of China （Grant No.60273075）, and the Postgraduate Research Foundation of Huazhong University of Science and Technology （Grant No.x0219）
文摘Frequent joining and withdrawal of resources and services in a grid make dynamic discovery of grid resource and service quite difficult. In this paper, a two-dimensional model of service discovery （TDMSD） is presented for use of dynamic service discovery. Description and proof of the model and the route algorithm of service discovery are proposed. The complexity analysis and simulation results show that the TDMSD model works well.
基金supported by the National Natural Science Foundation of China (61761004)the Natural Science Foundation of Guangxi Province,China (2019GXNSFAA245045)。
文摘In recent years,with the rapid development of Internet of things(IoT)technology,radio frequency identification(RFID)technology as the core of IoT technology has been paid more and more attention,and RFID network planning(RNP)has become the primary concern.Compared with the traditional methods,meta-heuristic method is widely used in RNP.Aiming at the target requirements of RFID,such as fewer readers,covering more tags,reducing the interference between readers and saving costs,this paper proposes a hybrid gray wolf optimization-cuckoo search(GWO-CS)algorithm.This method uses the input representation based on random gray wolf search and evaluates the tag density and location to determine the combination performance of the reader's propagation area.Compared with particle swarm optimization(PSO)algorithm,cuckoo search(CS)algorithm and gray wolf optimization(GWO)algorithm under the same experimental conditions,the coverage of GWO-CS is 9.306%higher than that of PSO algorithm,6.963%higher than that of CS algorithm,and 3.488%higher than that of GWO algorithm.The results show that the GWO-CS algorithm cannot only improve the global search range,but also improve the local search depth.
