Piggybacking Codes for Network Coding: The High/Low SNR Regime

We propose a piggybacking scheme for network coding where strong source inputs piggyback the weaker ones, a scheme necessary and sufficient to achieve the cut-set upper bound at high/low-snr regime, a new asymptotically optimal operational regime for the multihop Amplify and Forward (AF) networks

Accountability and Moral Competence Promote Ethical Leadership

Accountability and moral competence are two factors that may have a positive effect on ethical leadership in organizations. This study utilized a survey methodology to investigate the relationship among accountability, moral competence and ethical leadership in a sample of 103 leaders from a variety of industries and different countries. Accountability was found to be a significant positive predictor of ethical leadership. Moral competence was also found to moderate this relationship such that increases in moral competence enhanced the positive effects of accountability on ethical leadership. The results of the study suggest that organizations can increase ethical leadership throughout the company via accountability (especially self-accountability) and moral competence by training their leaders to use self-monitoring behaviors and increasing moral education

Network Coding: Connections Between Information Theory And Estimation Theory

In this paper, we prove the existence of fundamental relations between information theory and estimation theory for network-coded flows. When the network is represented by a directed graph G=(V, E) and under the assumption of uncorrelated noise over information flows between the directed links connecting transmitters, switches (relays), and receivers. We unveil that there yet exist closed-form relations for the gradient of the mutual information with respect to different components of the system matrix M. On the one hand, this result opens a new class of problems casting further insights into effects of the network topology, topological changes when nodes are mobile, and the impact of errors and delays in certain links into the network capacity which can be further studied in scenarios where one source multi-sinks multicasts and multi-source multicast where the invertibility and the rank of matrix M plays a significant role in the decoding process and therefore, on the network capacity. On the other hand, it opens further research questions of finding precoding solutions adapted to the network level.Comment: IEEE Wireless Communications and Networking Conference (WCNC), April, 201

Energy Efficient Adaptive Network Coding Schemes for Satellite Communications

In this paper, we propose novel energy efficient adaptive network coding and modulation schemes for time variant channels. We evaluate such schemes under a realistic channel model for open area environments and Geostationary Earth Orbit (GEO) satellites. Compared to non-adaptive network coding and adaptive rate efficient network-coded schemes for time variant channels, we show that our proposed schemes, through physical layer awareness can be designed to transmit only if a target quality of service (QoS) is achieved. As a result, such schemes can provide remarkable energy savings.Comment: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 24 March 201

Network Coding Channel Virtualization Schemes for Satellite Multicast Communications

In this paper, we propose two novel schemes to solve the problem of finding a quasi-optimal number of coded packets to multicast to a set of independent wireless receivers suffering different channel conditions. In particular, we propose two network channel virtualization schemes that allow for representing the set of intended receivers in a multicast group to be virtualized as one receiver. Such approach allows for a transmission scheme not only adapted to per-receiver channel variation over time, but to the network-virtualized channel representing all receivers in the multicast group. The first scheme capitalizes on a maximum erasure criterion introduced via the creation of a virtual worst per receiver per slot reference channel of the network. The second scheme capitalizes on a maximum completion time criterion by the use of the worst performing receiver channel as a virtual reference to the network. We apply such schemes to a GEO satellite scenario. We demonstrate the benefits of the proposed schemes comparing them to a per-receiver point-to-point adaptive strategy

Support Vector Machine for Network Intrusion and Cyber-Attack Detection

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Cyber-security threats are a growing concern in networked environments. The development of Intrusion Detection Systems (IDSs) is fundamental in order to provide extra level of security. We have developed an unsupervised anomaly-based IDS that uses statistical techniques to conduct the detection process. Despite providing many advantages, anomaly-based IDSs tend to generate a high number of false alarms. Machine Learning (ML) techniques have gained wide interest in tasks of intrusion detection. In this work, Support Vector Machine (SVM) is deemed as an ML technique that could complement the performance of our IDS, providing a second line of detection to reduce the number of false alarms, or as an alternative detection technique. We assess the performance of our IDS against one-class and two-class SVMs, using linear and non-linear forms. The results that we present show that linear two-class SVM generates highly accurate results, and the accuracy of the linear one-class SVM is very comparable, and it does not need training datasets associated with malicious data. Similarly, the results evidence that our IDS could benefit from the use of ML techniques to increase its accuracy when analysing datasets comprising of non-homogeneous features