The cognitive radio concept has been a revolutionary development in wireless communications systems. Cognitive, software-defined radios are able to adjust link and network resources in order to optimize communications performance. However, high rate, robust communications is often just one of many possible network objectives. For example, in sensing applications, the goal is to maximize coverage, detect important events with high probability, and track objects of interest with high accuracy. These goals are often at odds with those for optimum communications; improved coverage requires more widely dispersed sensors, complicating network connectivity. High resolution sensing requires more bits of information, which in turn place a strain on network throughput. Power devoted to routing or packet forwarding reduces a sensors lifetime. Clearly, a different paradigm is needed when sensing performance is the critical factor, or perhaps most interestingly, when both communications and sensing performance must be considered in tandem. This research effort introduces Cognitive Sensing as a concept dual to that of cognitive communications, and investigates the competing objectives of sensing and communications networks. A cognitive sensor would adaptively adjust its operating parameters in response to the environment it finds itself in so as to optimize sensing performance, or perhaps a dual performance metric that includes both sensing and communications functions. Parameters relevant to sensing performance could include sensor position, speed and heading, antenna beampattern and polarization, transmit waveform type and bandwidth, imaging camera zoom, orientation, resolution, pointing angle, etc. The question of how to allocate such sensor resources is central to this effort.
