This paper introduces two schemes to put the decoding of the convolutional network code (CNC) into practice, which are named the Intermittent Packet Transmission Scheme (IPTS) and the Redundancy Packet Transmission Scheme (RPTS). According to the decoding formula of the sink nodes, we can see that, at the time k+δ in order to decode the source packet generated at time k, the sink node should know all the source packets generated before k-1. This is impractical. The two schemes we devised make it unnecessary. A construction algorithm is also given about the RPTS networks. For the two schemes, we analyze the strengths and weaknesses and point out their implemented condition.

This letter investigates the physical layer security for a buffer-aided underlay cooperative cognitive radio network in the presence of an eavesdropper, wherein, the relay is equipped with a buffer so that it can store packets received from the secondary source. To improve the secure performance of cognitive radio networks, we propose a novel cognitive secure link selection scheme which incorporates the instantaneous strength of the wireless links as well as the status of relay's buffer, the proposed scheme adapts the link selection decision on the strongest available link by dynamically switching between relay reception and transmission. Closed-form expressions of secrecy outage probability (SOP) for cognitive radio network is obtained based on the Markov chain. Numerical results demonstrate that the proposed scheme can significantly enhance the secure performance compared to the conventional relay selection scheme.

Routing algorithms with low overhead, stable link and independence of the total number of nodes in the network are essential for the design and operation of the large-scale wireless mobile ad hoc networks (MANET). In this paper, we develop and analyze the Cluster Based Location-Aided Routing Protocol for MANET (C-LAR), a scalable and effective routing algorithm for MANET. C-LAR runs on top of an adaptive cluster cover of the MANET, which can be created and maintained using, for instance, the weight-based distributed algorithm. This algorithm takes into consideration the node degree, mobility, relative distance, battery power and link stability of mobile nodes. The hierarchical structure stabilizes the end-to-end communication paths and improves the networks' scalability such that the routing overhead does not become tremendous in large scale MANET. The clusterheads form a connected virtual backbone in the network, determine the network's topology and stability, and provide an efficient approach to minimizing the flooding traffic during route discovery and speeding up this process as well. Furthermore, it is fascinating and important to investigate how to control the total number of nodes participating in a routing establishment process so as to improve the network layer performance of MANET. C-LAR is to use geographical location information provided by Global Position System to assist routing. The location information of destination node is used to predict a smaller rectangle, isosceles triangle, or circle request zone, which is selected according to the relative location of the source and the destination, that covers the estimated region in which the destination may be located. Thus, instead of searching the route in the entire network blindly, C-LAR confines the route searching space into a much smaller estimated range. Simulation results have shown that C-LAR outperforms other protocols significantly in route set up time, routing overhead, mean delay and packet collision, and simultaneously maintains low average end-to-end delay, high success delivery ratio, low control overhead, as well as low route discovery frequency.

This work presents a hybrid formulation of the stochastic reduced order model (SROM) algorithm, which makes use of Gauss quadrature, a key ingredient of the stochastic collocation method, to avoid the cumbersome optimization process required by SROM for optimal extraction of the sample set. With respect to classic SROM algorithms, the proposed formulation allows a significant reduction in computation time and burden as well as a remarkable improvement in the accuracy and convergence rate in the estimation of statistical moments. The method is here applied to a specific case study, that is the prediction of crosstalk in a two-conductor wiring structure with electrical and geometrical parameters not perfectly known. Both univariate and multivariate analyses are carried out, with the final objective being to compare the performance of the two SROM formulations with respected to Monte Carlo simulations.