Why should I consider a future in research? What sort of opportunities does a PhD open up for me? What makes research in the science of networks such an exciting career choice? How can we ensure that the Internet can grow and adapt to ever-changing needs over the coming decades? What lies beyond the Internet? What inspires network research?

A sensor is a device capable of monitoring physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants. Sensor networks composed of hundreds, or sometime of thousands of nodes, are able to gather large quantities of information enabling dynamic large scale surveillance applications to be deployed. Most of surveillance applications have a high level of criticality and can not be deployed with the current state of technology. Besides military applications that possess an obvious criticality level and have a very specific usage, surveillance applications oriented toward Critical Infrastructures and disaster relief are also very important applications that many countries have identified as critical in the near future. In this presentation, we will present the challenges in deploying mission-critical surveillance applications and will present our latest works on criticaity modeling and management.

espite the broadly title, the seminar will be focused in the notion of max-min fairness (MMF) and its application to routing optimization in communication networks: 1. Basic notions 2. Convex MMF problems 3. Non-convex MMF problems 4. Examples of MMF routing optimization problems

The optimal configuration of the contention parameters of a WLAN depends on the network conditions in terms of number of stations and the traffic they generate. Following this observation, a considerable effort in the literature has been devoted to the design of distributed algorithms that optimally configure the WLAN parameters based on current conditions. In this work we propose a novel algorithm that, in contrast to previous proposals which are mostly based on heuristics, is sustained by mathematical foundations from multivariable control theory. A key advantage of the algorithm over existing approaches is that it is compliant with the 802.11 standard and can be implemented with current wireless cards without introducing any changes into the hardware or firmware. We study the performance of our proposal by means of theoretical analysis, simulations and a real implementation. Results show that the algorithm substantially outperforms previous approaches in terms of throughput and delay.

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.