Wireless and Mobile Computing
1. Using (mobile) Agents in Mobile Computing and Sensor Networks
Recently, a new approach for learning features of a domain of interest has been proposed. The main characteristic of the novel approach is the fact that global information about the domain is obtained by combining in some clever way local information gathered by independent agents. In fact, for a large number of practically-relevant domains the following paradigm can be used: (1) a large number of agents, each with a specific mandate is being sent into the domain, (2) each agent learns a given characteristic or feature of the domain, (3) a subset of the agents is recovered and debriefed. Somewhat surprisingly, for many domains it is possibly to recover a strict subset of the agents and still obtain “full” knowledge about the domain. It would be very useful to implement strategies for 1-3 above for a number of particular domains arising in various practical applications. Of a particular interest is the area of mobile computing and wireless networks.
2. Applying Geometric Graphs in Wireless Networks (in collaboration with Dr. Weisheng Si, University of Western Sydney)
With the availability of GPS services, the position information of nodes has become available in many types of wireless networks such as sensor networks, mesh networks, and vehicular ad hoc networks. With the knowledge of node positions, the network topologies of these networks can be modelled by geometric graphs. This project investigates how to utilize the theories of geometric graphs to address the following issues in wireless networks: topology control, connectivity analysis, routing algorithm design, and fault tolerance. Many results have been achieved in this area, but some of the fundamental research problems remain unsolved and novel research problems keep arising due to the new applications of these networks. So this project will explore many challenging and interesting problems.
3. Self-Organising Protocols for Wireless Sensor Networks
In a wireless sensor network, energy conservation is the primary design goal. Research shows that in a low-energy radio network, the energy consumed by receiving and listening (or attempting to receive) messages is of the same order of magnitude as transmitting them. The most efficient way to save energy is keeping sensor nodes turned off as long as possible. These sleeping-or-awaking nodes need the capabilities of self-organisation and re-organisation to adapt to dynamic environment and network settings. This work address issues of energy efficient self-organisation in sensor networks. The work also deals with situations in which the network needs to efficiently adapt in catastrophe scenarios by maintaining reasonable energy levels that keep the network active for the longest period of time.
4. Federating Autonomous Sensor Networks
An important component of our research is motivated by the need to use the inherent capacity of sensor networks for data collection, surveillance and target tracking as a key ingredient for establishing ubiquitous monitoring and control capabilities in support of civilian and defence applications. Indeed, a single sensor network cannot satisfy the broad spectrum of application requirements, especially when these requirements change drastically along the dimensions of time, space, and context. On the other hand, deploying numerous sensor networks in an area of interest may be infeasible. The goal is to develop a new sensor network system that will act as a distributed service provider. To build such a distributed system, we are looking at innovative sensor network system architectures that will facilitate rapid self-organization and dynamic reconfiguration of component sensor networks in support of adaptive service deployment, composition, and federation to cover the dynamic needs of numerous applications.