Spectrum sensing in cognitive radio sensor networks: towards ultra low overhead, distributed channel findings

Cognitive radios can utilise the idle spectrum holes that are not occupied by the Primary Users (PUs) (i.e. licensed users) for temporary wireless communication tasks. A challenging issue is to efficiently reach global/regional views among the Secondary Users (SUs) on channel availability. The SUs need to exchange control messages on their spectrum sensing results. A traditional way is to send out 'raw' channel information from each individual node. It consumes much wireless communication bandwidth. This research proposes a new cognitive radio spectrum sensing scheme based on our proposed Spatially-Decaying, Time-Incremental (SDTI) updating algorithm. Our SDTI algorithm is based on the extension of Gossiping Updates for Efficient Spectrum Sensing (GUESS) scheme that adopts Flajolet-Martin (FM) aggregation to reduce data amount. Our SDTI automatically assigns weights to channel information based on the distance between a source node and an observing node. Further, distance nodes have less important information to the observing node.