A good archetype for a sensor network is that of a technology that consists of large set of independent devices that, through communications, can reach consensus on the fly on events that occur in an area they monitor, a region, which is large, compared to their size and communication range. This project has three main research thrusts that will contribute to build such infrastructure: 1) the scheduling of the sensor transmissions; 2) the synchronization 3) the physical layer transmission architecture. The ideas we propose are distributed, cooperative and data driven and challenge some of the conventional wisdom used in building communication networks today which neglect fundamental aspects of the problem, such as: 1) the sensors are not end users; 2) the sensors can cooperate rather than contend for the medium to deliver critical information; 3) the aggregate entropy/complexity of the data in space and time is low. In fact, sensor networks are deployed to record unexpected dispersed events, sudden but localized changes, inconsistencies or discontinuities that occur rarely. Current network technology applied to this problem fails in one fundamental aspect: the scalability. The interactions among nodes are heavily structured in a number of layered functionalities.
Scalability will result from un-complicating the interactions between the large population of devices and constraining their operations and message exchange to be through a broadcast, noisy, limited range interface. The most original contribution of this research is in finding a class of scheduling strategies that operate within these restrictions and is driven by the sensors' data, achieving efficient transmission from the distributed sources without requiring any prior exchange of data among them.
