Quickest detection is an important technique to detect the change of probability distribution in a random process being monitored. It is widely used in problems like financial decision making, environmental monitoring and industrial quality control. With the rapid development of networking techniques, there exist pressing demands to carry out quickest detection based on observations from many nodes and make decision at more than one node. Motivated by this demand, this research studies collaborative quickest detection in ad hoc networks, in which nodes exchange observation statistics and make local decisions about distribution change. In contrast to existing theory of decentralized quickest detection, the collaborative quickest detection does not need a data processing center, thus avoiding the round-trip time overhead and possible data congestion. Moreover, collaboration can enhance the agility and robustness of the detection of change. The research involves aspects of statistical signal processing (e.g. detection rule), information theory (e.g. source coding) and networking (e.g. scheduling or broadcast). An important application of collaborative quickest detection is spectrum sensing in cognitive radio systems. In such a system, secondary nodes need to monitor the activity of primary users and should quit the frequency band once primary users emerge. It is essentially a problem of quickest detection since the secondary nodes need to detect the change as quickly as possible. Therefore, this research also involves the design of collaborative quickest detection in cognitive radio networks. Inter-disciplinary essence of the research also lends itself to cross-disciplinary education. A one-semester graduate level course will be devised, which introduces quickest detection, cooperative communication and cognitive radio. This project also expects to attract traditionally underrepresented groups.
