Active Topology Inference using Network Coding

Our goal is to infer the topology of a network when (i) we can send probes between sources and receivers at the edge of the network and (ii) intermediate nodes can perform simple network coding operations, i.e., additions. Our key intuition is that network coding introduces topology-dependent correlation in the observations at the receivers, which can be exploited to infer the topology. For undirected tree topologies, we design hierarchical clustering algorithms, building on our prior work. For directed acyclic graphs (DAGs), first we decompose the topology into a number of two-source, two-receiver (2-by-2) subnetwork components and then we merge these components to reconstruct the topology. Our approach for DAGs builds on prior work on tomography, and improves upon it by employing network coding to accurately distinguish among all different 2-by-2 components. We evaluate our algorithms through simulation of a number of realistic topologies and compare them to active tomographic techniques without network coding. We also make connections between our approach and alternatives, including passive inference, traceroute, and packet marking

A reinforcement learning-based link quality estimation strategy for RPL and its impact on topology management

Over the last few years, standardisation efforts are consolidating the role of the Routing Protocol for Low-Power and Lossy Networks (RPL) as the standard routing protocol for IPv6-based Wireless Sensor Networks (WSNs). Although many core functionalities are well defined, others are left implementation dependent. Among them, the definition of an efficient link-quality estimation (LQE) strategy is of paramount importance, as it influences significantly both the quality of the selected network routes and nodesâ\u80\u99 energy consumption. In this paper, we present RL-Probe, a novel strategy for link quality monitoring in RPL, which accurately measures link quality with minimal overhead and energy waste. To achieve this goal, RL-Probe leverages both synchronous and asynchronous monitoring schemes to maintain up-to-date information on link quality and to promptly react to sudden topology changes, e.g. due to mobility. Our solution relies on a reinforcement learning model to drive the monitoring procedures in order to minimise the overhead caused by active probing operations. The performance of the proposed solution is assessed by means of simulations and real experiments. Results demonstrated that RL-Probe helps in effectively improving packet loss rates, allowing nodes to promptly react to link quality variations as well as to link failures due to node mobility

Using the Java Media Framework to build Adaptive Groupware Applications

Realtime audio and video conferencing has not yet been satisfactorily integrated into web-based groupware environments. Conferencing tools are at best only loosely linked to other parts of a shared working environment, and this is in part due to their implications for resource allocation and management. The Java Media Framework offers a promising means of redressing this situation. This paper describes an architecture for integrating the management of video and audio conferences into the resource allocation mechanism of an existing web-based groupware framework. The issue of adaptation is discussed and a means of initialising multimedia session parameters based on predicted QoS is described

Link Delay Inference in ANA Network

Estimating quality of service (QoS) parameters such as link delay distribution from the end-to-end delay of a multicast tree topology in network tomography cannot be achieved without multicast probing techniques or designing unicast probing packets that mimic the characteristics of multicast probing packets. Active probing is gradually giving way to passive measurement techniques. With the emergence of next generation networks such as Autonomic Network Architecture (ANA) network, which do not support active probing, a new way of thinking is required to provide network tomography support for such networks. This thesis is about investigating the possible solution to such problem in network tomography. Two approaches, queue model and adaptive learning model were implemented to minimized the uncertainty in the end-to-end delay measurements from passive data source so that we could obtain end-toend delay measurements that exhibit the characteristics of unicast or multicast probing packets. The result shows that the adaptive learning model performs better than the queue model. In spite of its good performance against the queue model, it fails to outperform the unicast model. Overall, the correlation between the adaptive learning model and multicast probing model is quite weak when the traffic intensity is low and strong when the traffic intensity is high. The adaptive model may be susceptible to low traffic. In general, this thesis is a paradigm shift from the investigation of ”deconvolution” algorithms that uncover link delay distributions to how to estimate link delay distributions without active probing.Master i nettverks- og systemadministrasjo

Secure Routing in Wireless Mesh Networks

Wireless mesh networks (WMNs) have emerged as a promising concept to meet the challenges in next-generation networks such as providing flexible, adaptive, and reconfigurable architecture while offering cost-effective solutions to the service providers. Unlike traditional Wi-Fi networks, with each access point (AP) connected to the wired network, in WMNs only a subset of the APs are required to be connected to the wired network. The APs that are connected to the wired network are called the Internet gateways (IGWs), while the APs that do not have wired connections are called the mesh routers (MRs). The MRs are connected to the IGWs using multi-hop communication. The IGWs provide access to conventional clients and interconnect ad hoc, sensor, cellular, and other networks to the Internet. However, most of the existing routing protocols for WMNs are extensions of protocols originally designed for mobile ad hoc networks (MANETs) and thus they perform sub-optimally. Moreover, most routing protocols for WMNs are designed without security issues in mind, where the nodes are all assumed to be honest. In practical deployment scenarios, this assumption does not hold. This chapter provides a comprehensive overview of security issues in WMNs and then particularly focuses on secure routing in these networks. First, it identifies security vulnerabilities in the medium access control (MAC) and the network layers. Various possibilities of compromising data confidentiality, data integrity, replay attacks and offline cryptanalysis are also discussed. Then various types of attacks in the MAC and the network layers are discussed. After enumerating the various types of attacks on the MAC and the network layer, the chapter briefly discusses on some of the preventive mechanisms for these attacks.Comment: 44 pages, 17 figures, 5 table