Decentralized algorithms enable the calculation of compressive measurements at each sensor in the network, thus being useful for applications where monitoring agents traverse the network during operation.

**Randomized gossiping**

In randomized gossiping [4], each sensor communicates

The method can also be applied when each sensor observes a compressible signal. In this case, each sensor computes multiple random projections of the data and transmits them using randomized gossiping to the rest of the network.
A potential drawback of this technique is the amount of storage required per sensor, as it could be considerable for large networks .
In this case, each sensor can store the data from only a subset of the sensors, where each group of sensors of a certain size will be known to contain CS measurements for all the data in the network. To maintain a constant error as the network size grows, the number of transmissions becomes

**Distributed sparse random projections**

A second method modifies the randomized gossiping approach by limiting the number of communications each node must perform, in order to reduce overall power consumption [5]. Each data node takes