David Munoz

Network Coding for Detection and Defense of Sink Holes in Wireless Reconfigurable Networks

Wireless reconfigurable networks are prone to several security attacks such as selective forwarding, black holes, and wormholes. In this paper, we introduce a network coding method that detects security attacks related to routing. The proposed method works on a distributed fashion performing random network coding over nodes composing a given route, not only to distribute content, but also to provide data confidentiality by cooperation as a mechanism of detection. The method presents a robust, accurate and fast response under security attacks for varying network conditions, such as density and interference, and it also increases successfully received packets without a significant sacrifice of the bandwidth usage.

Analysis of IP network path capacity estimation using a variable packet size method

Capacity estimation effective in an IP network path is a key enabling technology for proactive and reactive resource management as well as intelligent planning in next generation IP networks. We analyze capacity measurements at different Internet destinations. Particularly, we use the variable packet size (VPS) method to measure the per-hop capacity in some network paths. This method is based on round trip time (RTT) measurements as well as on the Internet control message protocol, ICMP. The RTT delay is approximated by a gamma distribution. We observe how the measurement accuracy of this active probing is affected by factors such as layer 2 device latencies, limited clock resolution at the probing host, or queuing delay. These factors cause non linearity on the RTT curves obtained and, consequently, inaccuracies on the estimation of path capacities. The use of robust regression to improve linear approximation in the VPS method is proposed. As a result, we obtain better qualitative and quantitative results.

A Discrete-Time Approach for Heavy-Tailed Modeling

It is important in teletraffic modeling to provide mathematical models to study and evaluate performance. We introduce a traffic source based on Polyas urn model and study this in a semi-Markov process and relate the heavy-tailed characteristics and long-range dependence to the model parameters. The first and second moments are obtained from excess measures. Tail decay and long-range dependence are shown to be adequately engineered by selection of the parameters. Q–Q plots show agreement of the proposed model to Paretian-type distributions. The model could be used in applications such as queueing theory, networking, etc.

Epidemic routing in vehicular delay-tolerant networks: The use of heterogeneous conditions to increase packet delivery ratio

Vehicular networks can facilitate a set of new different services such as automated payments, security alerts, commerce, infotainment, etc., so they are relevant in the context of Intelligent Transportation Systems as well as in the context of Smart Cities. However, due to the harsh conditions in which they are developed, for example nodes moving at very high speeds, end-to-end communication is not always possible, so communication under traditional network schemes is not feasible. Vehicular Delay-Tolerant Networks are an extension of Delay-Tolerant Networks in which vehicles are the main agents in the communication process, and they make possible communication even when no end-to-end links are available in the network. In such kind of networks, alternative paths and packet distribution schemes are necessary. Hence, the analysis of dissemination algorithms is important as it provides insight of alternatives to ensure message delivery. In this paper we show how in heterogeneous environments the use of different conditions can lead to an increase in packet delivery ratio under simple epidemic spreading, one of the most used dissemination schemes.

Corrections to: A Discrete-Time Approach for Heavy-Tailed Modeling

Corrections to some equations in Ref.[ 1 ], are introduced that describe the heavy-tailed behavior in the standard form and help to improve comprehension of the model.