Compressive sensing optimization over ZigBee networks

Efficient data aggregation and compression in sensor networks is becoming fundamental with the increase of the number of nodes in the network. Although several data aggregation and compression techniques have been proposed in the literature only few of them can perform in-network compression and can extend lifetime without prior knowledge of the sensed data or without a central coordination. In this paper we consider a scenario where a wireless sensor network (WSN) exploits ZigBee protocols for smart building application. We study a classical gathering scheme and a distributed compressive sampling approach. We discuss limitations and we propose a new distributed mixed algorithm for in-network compression. With this algorithm each node takes a decision about which scheme to adopt aiming at the reducing the number of packets to transmit. We are interested in scalability of this new method and lifetime of the system with respect to the increase of network dimension. Simulations are performed using real data sets and results show that the use of this algorithm permits to obtain longer network lifetime with small computational complexity. The performances of the algorithm are also investigated when some sensor parameters are modified and sporadic readings rise in the network.