近几年,面向跨社交平台识别分布在不同社交网络上的同一用户依然是一个未解决的难题。该研究可以解决商业应用、资源整合、好友推荐等方面的相关问题。现有的算法如通过文本挖掘、单纯的用户属性无法取得良好的效果。提出CLA(Combined L...近几年,面向跨社交平台识别分布在不同社交网络上的同一用户依然是一个未解决的难题。该研究可以解决商业应用、资源整合、好友推荐等方面的相关问题。现有的算法如通过文本挖掘、单纯的用户属性无法取得良好的效果。提出CLA(Combined Link and Attribute)算法实现用户身份匹配。通过好友亲密度获得候选用户,结合基于网络结构的链接信息和用户属性信息进行用户匹配度计算。其中,链接信息相似度利用朋友匹配度计算得到。将该算法应用于多种社交网络,实验结果表明,该算法效果优越于传统的算法效果。展开更多
As much as accurate or precise position estimation is always desirable, coarse accuracy due to sensor node localization is often sufficient. For such level of accuracy, Range-free localization techniques are being exp...As much as accurate or precise position estimation is always desirable, coarse accuracy due to sensor node localization is often sufficient. For such level of accuracy, Range-free localization techniques are being explored as low cost alternatives to range based localization techniques. To manage cost, few location aware nodes, called anchors are deployed in the wireless sensor environment. It is from these anchors that all other free nodes are expected to estimate their own positions. This paper therefore, takes a look at some of the foremost Range-free localization algorithms, detailing their limitations, with a view to proposing a modified form of Centroid Localization Algorithm called Reach Centroid Localization Algorithm. The algorithm employs a form of anchor nodes position validation mechanism by looking at the consistency in the quality of Received Signal Strength. Each anchor within the vicinity of a free node seeks to validate the actual position or proximity of other anchors within its vicinity using received signal strength. This process mitigates multipath effects of radio waves, particularly in an enclosed environment, and consequently limits localization estimation errors and uncertainties. Centroid Localization Algorithm is then used to estimate the location of a node using the anchors selected through the validation mechanism. Our approach to localization becomes more significant, particularly in indoor environments, where radio signal signatures are inconsistent or outrightly unreliable. Simulated results show a significant improvement in localization accuracy when compared with the original Centroid Localization Algorithm, Approximate Point in Triangulation and DV-Hop.展开更多
Cognitive radio(CR) is a promising solution to improve the spectrum utilization.The cognitive radio networks includes the primary user(PU) system with authorized spectrum and the secondly user(SU) system without autho...Cognitive radio(CR) is a promising solution to improve the spectrum utilization.The cognitive radio networks includes the primary user(PU) system with authorized spectrum and the secondly user(SU) system without authorized spectrum. When the SUs want to use the spectrum, they have to find the idle channels that are not occupied by the PUs. So the QoS of the SUs will be affected not only by the characteristic of their own business, but also by the behavior of the PUs.Currently, in order to ensure the quality of the SU services, the M-LDWF algorithm is widely used in scheduling. However, the M-LWDF algorithm didn't fully consider the difference among the SUs. For those SUs who are in the process of communication but have to change channel due to the return of the PU, they should have higher scheduling priority. In this paper, we put forward an improved algorithm based on M-LWDF. In order to guarantee the QoS of the SUs those were in the processing of communication, we gave the higher scheduling priority. Simulation results show that the improved algorithm can effectively decrease the dropping rate and improve the QoS of the SUs and the performance of the whole system.展开更多
文摘近几年,面向跨社交平台识别分布在不同社交网络上的同一用户依然是一个未解决的难题。该研究可以解决商业应用、资源整合、好友推荐等方面的相关问题。现有的算法如通过文本挖掘、单纯的用户属性无法取得良好的效果。提出CLA(Combined Link and Attribute)算法实现用户身份匹配。通过好友亲密度获得候选用户,结合基于网络结构的链接信息和用户属性信息进行用户匹配度计算。其中,链接信息相似度利用朋友匹配度计算得到。将该算法应用于多种社交网络,实验结果表明,该算法效果优越于传统的算法效果。
文摘As much as accurate or precise position estimation is always desirable, coarse accuracy due to sensor node localization is often sufficient. For such level of accuracy, Range-free localization techniques are being explored as low cost alternatives to range based localization techniques. To manage cost, few location aware nodes, called anchors are deployed in the wireless sensor environment. It is from these anchors that all other free nodes are expected to estimate their own positions. This paper therefore, takes a look at some of the foremost Range-free localization algorithms, detailing their limitations, with a view to proposing a modified form of Centroid Localization Algorithm called Reach Centroid Localization Algorithm. The algorithm employs a form of anchor nodes position validation mechanism by looking at the consistency in the quality of Received Signal Strength. Each anchor within the vicinity of a free node seeks to validate the actual position or proximity of other anchors within its vicinity using received signal strength. This process mitigates multipath effects of radio waves, particularly in an enclosed environment, and consequently limits localization estimation errors and uncertainties. Centroid Localization Algorithm is then used to estimate the location of a node using the anchors selected through the validation mechanism. Our approach to localization becomes more significant, particularly in indoor environments, where radio signal signatures are inconsistent or outrightly unreliable. Simulated results show a significant improvement in localization accuracy when compared with the original Centroid Localization Algorithm, Approximate Point in Triangulation and DV-Hop.
基金supported by Beijing Key Laboratory of Work Safety Intelligent Monitoring (Beijing University of Posts and Telecommunications)
文摘Cognitive radio(CR) is a promising solution to improve the spectrum utilization.The cognitive radio networks includes the primary user(PU) system with authorized spectrum and the secondly user(SU) system without authorized spectrum. When the SUs want to use the spectrum, they have to find the idle channels that are not occupied by the PUs. So the QoS of the SUs will be affected not only by the characteristic of their own business, but also by the behavior of the PUs.Currently, in order to ensure the quality of the SU services, the M-LDWF algorithm is widely used in scheduling. However, the M-LWDF algorithm didn't fully consider the difference among the SUs. For those SUs who are in the process of communication but have to change channel due to the return of the PU, they should have higher scheduling priority. In this paper, we put forward an improved algorithm based on M-LWDF. In order to guarantee the QoS of the SUs those were in the processing of communication, we gave the higher scheduling priority. Simulation results show that the improved algorithm can effectively decrease the dropping rate and improve the QoS of the SUs and the performance of the whole system.
