Currently cellular networks do not have sufficient capacity to accommodate the exponential growth of mobile data requirements.Data can be delivered between mobile terminals through peer-to-peer WiFi communications(e.g...Currently cellular networks do not have sufficient capacity to accommodate the exponential growth of mobile data requirements.Data can be delivered between mobile terminals through peer-to-peer WiFi communications(e.g.WiFi direct),but contacts between mobile terminals are frequently disrupted because of the user mobility.In this paper,we propose a Subscribe-and-Send architecture and an opportunistic forwarding protocol for it called HPRO.Under Subscribe-and-Send,a user subscribes contents on the Content Service Provider(CSP) but does not download the subscribed contents.Some users who have these contents deliver them to the subscribers through WiFi opportunistic peer-to-peer communications.Numerical simulations provide a robust evaluation of the forwarding performance and the traffic offloading performance of Subscribe-and-Send and HPRO.展开更多
In Delay Tolerant Networks (DTNs), establishing routing path from a source node to a destination node may not be possible, so the opportunistic routings are widely used. The energy and buffer constraints are general i...In Delay Tolerant Networks (DTNs), establishing routing path from a source node to a destination node may not be possible, so the opportunistic routings are widely used. The energy and buffer constraints are general in DTNs composed of the mobile phones or Pads. This paper proposes a novel opportunistic routing protocol, denoted by Large Opporturioty (LAOP), for the energy and buffer constrained DTNs. The objective of LAOP is to reach many receivers of a message with a small number of transmissions. By LAOP, the sender floods a message when the number of its neighbors is not less than a threshold. We compare the delivery performance of LAOP with other four widely used Delay or Disruption Tolerant Network (DTN) routing protocols, Direct Delivery, Epidemic routing, SprayAndWait and PRoPHET and demonstrate that LAOP can improve the delivery performance and decrease the delivery latency simultaneously.展开更多
Feature extraction of signals plays an important role in classification problems because of data dimension reduction property and potential improvement of a classification accuracy rate. Principal component analysis (...Feature extraction of signals plays an important role in classification problems because of data dimension reduction property and potential improvement of a classification accuracy rate. Principal component analysis (PCA), wavelets transform or Fourier transform methods are often used for feature extraction. In this paper, we propose a multi-scale PCA, which combines discrete wavelet transform, and PCA for feature extraction of signals in both the spatial and temporal domains. Our study shows that the multi-scale PCA combined with the proposed new classification methods leads to high classification accuracy for the considered signals.展开更多
基金supported by the National Natural Science Foundation of China under Grants No. 61100208,No. 61100205the Natural Science Foundation of Jiangsu Province under Grant No. BK2011169+1 种基金the Foundation of Beijing University of Posts and Telecommunications under Grant No. 2013RC0309supported by the EU FP7 Project REC-OGNITION:Relevance and Cognition for SelfAwareness in a Content-Centric Internet
文摘Currently cellular networks do not have sufficient capacity to accommodate the exponential growth of mobile data requirements.Data can be delivered between mobile terminals through peer-to-peer WiFi communications(e.g.WiFi direct),but contacts between mobile terminals are frequently disrupted because of the user mobility.In this paper,we propose a Subscribe-and-Send architecture and an opportunistic forwarding protocol for it called HPRO.Under Subscribe-and-Send,a user subscribes contents on the Content Service Provider(CSP) but does not download the subscribed contents.Some users who have these contents deliver them to the subscribers through WiFi opportunistic peer-to-peer communications.Numerical simulations provide a robust evaluation of the forwarding performance and the traffic offloading performance of Subscribe-and-Send and HPRO.
基金This work was supported by the National Natural Science Foundation of China under Grants No. 61100208, No. 61100205 the Natural Science Foundation of Jiangsu under Grant No. BK2011169. Pietro lio was supported by the EU FP7 project RECOGNITION： Relevance and Cognition for Self-Awareness in a Content-Centric Intemet.
文摘In Delay Tolerant Networks (DTNs), establishing routing path from a source node to a destination node may not be possible, so the opportunistic routings are widely used. The energy and buffer constraints are general in DTNs composed of the mobile phones or Pads. This paper proposes a novel opportunistic routing protocol, denoted by Large Opporturioty (LAOP), for the energy and buffer constrained DTNs. The objective of LAOP is to reach many receivers of a message with a small number of transmissions. By LAOP, the sender floods a message when the number of its neighbors is not less than a threshold. We compare the delivery performance of LAOP with other four widely used Delay or Disruption Tolerant Network (DTN) routing protocols, Direct Delivery, Epidemic routing, SprayAndWait and PRoPHET and demonstrate that LAOP can improve the delivery performance and decrease the delivery latency simultaneously.
文摘Feature extraction of signals plays an important role in classification problems because of data dimension reduction property and potential improvement of a classification accuracy rate. Principal component analysis (PCA), wavelets transform or Fourier transform methods are often used for feature extraction. In this paper, we propose a multi-scale PCA, which combines discrete wavelet transform, and PCA for feature extraction of signals in both the spatial and temporal domains. Our study shows that the multi-scale PCA combined with the proposed new classification methods leads to high classification accuracy for the considered signals.
