A Compressive Sensing Based Clustering Scheme for UAS-aided Networks

The development of the Unmanned Aircraft Systems (UASs) makes it possible to collect data from ground sensor nodes by UASs. Special cluster heads (CHs) and the mobility pattern of the UAS make the UAS-aided network different from normal sensor networks. There are works which have addressed the energy efficiency problem of the CHs, but no works have done with the energy efficiency of the normal sensor nodes within the UAS-aided networks. Both the Compressive Sensing (CS) and clustering techniques can be applied for saving energy of the network, however, existing works barely integrate them. Although few works integrate CS with clustering, they ignored the sparsity difference in temporal and spacial domain, moreover, they assume nodes can transmit data to the CH by one hop, which is usually unreal for UAS-aided networks. In this paper, a CS based clustering scheme for UAS-aided networks is proposed. By jointly consider the effect of the compressive ratio variation of a cluster and the distances variation between cluster members and the CH in the clustering process, the energy consumption of sensor nodes is minimized. We evaluate our proposed scheme based on the real data traces. The simulation results show our proposed scheme can significantly reduce the power consumption of the sensor nodes within UAS-aided networks.