Vehicular communications will increase road safety, traffic efficiency and driving comfort, by enabling vehicles to form Vehicular Ad-hoc Networks (VANETs) and to directly exchange information. Additionally, connecting the VANET to an IP based network infrastructure (e.g., the Internet) may enhance those applications, and creating the opportunity for others such as infotainment ones (e.g., games, web browsing, e-mail, etc.). One of the functionalities needed to bring IP to vehicular networks is the capability of vehicles to autoconfigure an IPv6 address. GeoSAC is a mechanism enabling IPv6 address autoconfiguration in vehicular networks based on geographic routing. In GeoSAC, as a result of the mobility of the vehicles, they cannot always use the same IP address. Each new address configuration introduces a delay during which communications are interrupted. We propose an improvement for GeoSAC, based on the caching of Router Advertisements, to avoid this disruption time. We also analytically model the probability of achieving seamless IP address reconfiguration as well as an expression for the average configuration time of nodes. The model is validated through extensive simulation. Results in different realistic scenarios show that the use of our proposed optimisation is valuable and would improve the performance in terms of configuration time and/or signaling overhead and the average configuration time expression would provide network administrators with a powerful tool that can be used during the network design.

Mohamed Hefeeda is an assistant professor in the School of Computing Science, Simon Fraser University, Canada, where he leads the Network Systems Lab. He holds a Ph.D. from Purdue University, USA, and M.Sc. and B.Sc. from Mansoura University, Egypt. His research interests include multimedia networking over wired and wireless networks, peer-to-peer systems, network security, and wireless sensor networks. 

The talk consists of two parts. The first part will present an overview on the current research activities of iNEXT (Centre for Innovation in IT applications and Services) at the University of Technology, Sydney. This includes a brief description of our Sensor Grid for Assistive Healthcare project.

Cognitive radio networks (CRNs) involve extensive exchange of control messages, which are used to coordinate critical network functions such as distributed spectrum sensing, medium access, and routing, to name a few. Typically, control messages are broadcasted on a pre-assigned common control channel (e.g., a separate frequency band, a given time slot, or a spreading sequence). Such a static channel allocation policy is contrary to the opportunistic access paradigm. In this work, we address the problem of dynamically assigning the control channel in CRNs according to spatiotemporally varying spectrum opportunities. We propose a cluster-based architecture that allocates different control channels to various clusters in the network. The clustering problem is formulated as a bipartite graph problem, for which we develop a class of algorithms that provide different tradeoffs between two conflicting factors: number of common channels in a cluster and the cluster size. Clusters are guaranteed to have a desirable number of common channels for control, which facilitates graceful channel migration when primary-radio activity is detected, without the need for frequent re-clustering. We use simulations to verify the agility of our algorithms in adapting to variations in spectrum availability.

Delay Tolerant Networks (DTN) are networks of self-organizing wireless nodes, where end-to-end connectivity is intermittent. In these networks, content or information between nodes is exchanged (opportunistically), whenever two nodes are within range ("in contact"). Forwarding decisions are generally probabilistic and based on locally collected knowledge about node behavior (e.g., past contacts between nodes) to predict future contact opportunities. The use of complex network analysis has been recently suggested to perform this prediction task and improve the performance of opportunistic (DTN) routing. Contacts seen in the past are aggregated to a "Social Graph", and a variety of metrics (e.g., entrality and similarity) or algorithms (e.g., community detection) can be used to assess the utility of a node to deliver a content or bring it closer to the destination.

The current Internet includes a large number of distributed services. In order to guarantee the QoS of the communication in these services, a client has to select a close-by server with enough available resources. In order to achieve this objective, in this Thesis, we propose a simple and practical solution for Dynamic and Location Aware Server Discovery based on a Distributed Hash Table (DHT). Specifically, we decide to use a Chord DHT system (although any other DHT scheme can be used). In more detail, the solution works as follows. The servers offering a given service form a Chord-like DHT. In addition, they register their location (topological and/or geographical) information in the DHT. Each client using the service is connected to at least one server from the DHT. Eventually, a given client realizes that it is connected to a server providing a bad QoS, then, it queries the DHT in order to find an appropriate server (i.e. a close-by server with enough available resources). We define 11 design criteria, and compare our solution to the State of the Art based on them. We show that our solution is the most complete one. Furthermore, we validate the performance of our solution in two different scenarios: NAT Traversal Server Discovery and Home Agent Discovery in Mobile IP scenarios. The former serves to validate our solution in a highly dynamic environment whereas the latter demonstrates the appropriateness of our solution in more classical environments where the servers are typically hosts.

The conference will be conducted in English

Wireless ad hoc networks have emerged to be used in scenarios where it is required or desired to have wireless communications among a variety of devices without relying on any infrastructure or central management. One of the fundamental operations in wireless ad hoc networks is broadcasting, where a wireless device (simply called a node) disseminates a message to all other nodes in the network. A major challenge of efficient broadcast algorithms is to reduce the number of transmissions required to disseminate a message. Unfortunately, minimizing the total number of required transmissions is an NP-hard problem even when the whole network topology is known by every node. Reducing the number of transmissions becomes more challenging in local broadcast algorithms, where each node makes decision (whether or not to transmit a received message) based on local neighborhood information. The common belief is that local broadcast algorithms are not able to guarantee both full delivery and a good bound on the number of transmissions.

In this talk I present a platform to extract models of security-relevant functionality from program binaries, enabling multiple security applications such as active botnet infiltration, finding deviations between implementations of the same functionality, vulnerability signature generation, and finding content-sniffing cross-site scripting (XSS) attacks. In this talk, I present two applications: active botnet infiltration and finding content-sniffing XSS attacks.