In randomized gossiping [4], each sensor communicates MM random projection of its data sample to a random set of nodes, in each stage aggregating and forwarding the observations received to a new set of random nodes. In essence, a spatial dot product is being performed as each node collects and aggregates information, compiling a sum of the weighted samples to obtain MM CS measurements which becomes more accurate as more rounds of random gossiping occur. To recover the data, a basis that provides data sparsity (or at least compressibility) is required, as well as the random projections used. However, this information does not need to be known while the data is being passed.

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 Θ(kMn2)Θ(kMn2), where kk represents the number of groups in which the data is partitioned, MM is the number of values desired from each sensor and n are the number of nodes in the network. The results can be improved by using geographic gossiping algorithms [2].

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 MM projections of its data, passing along information to a small set of LL neighbors, and summing the observations; the resulting CS measurements are sparse, since N-LN-L of each row's entries will be zero. Nonetheless, these projections can still be used as CS measurements with quality similar to that of full random projections. Since the CS measurement matrix formed by the data nodes is sparse, a relatively small amount of communication is performed by each encoding node and the overall power required for transmission is reduced.

Compressive wireless sensing (CWS) emphasizes the use of synchronous communication to reduce the transmission power of each sensor [1]. In CWS, each sensor calculates a noisy projection of their data sample. Each sensor then transmits the calculated value by analog modulation and transmission of a communication waveform. The projections are aggregated at the central location by the receiving antenna, with further noise being added. In this way, the fusion center receives the CS measurements, from which it can perform reconstruction using knowledge of the random projections.

A drawback of this method is the required accurate synchronization. Although CWS is constraining the power of each node, it is also relying on constructive interference to increase the power received by the data center. The nodes themselves must be accurately synchronized to know when to transmit their data. In addition, CWS assumes that the nodes are all at approximately equal distances from the fusion center, an assumption that is acceptable only when the receiver is far away from the sensor network. Mobile nodes could also increase the complexity of the transmission protocols. Interference or path issues also would have a large effect on CWS, limiting its applicability.

If these limitations are addressed for a suitable application, CWS does offer great power benefits when very little is known about the data beyond sparsity in a fixed basis. Distortion will be proportional to M-2α/(2α+1)M-2α/(2α+1), where αα is some positive constant based on the network structure. With much more a priori information about the sensed data, other methods will achieve distortions proportional to M-2αM-2α.

Distributed Compressive Sensing (DCS) provides several models for combining neighboring sparse signals, relying on the fact that such sparse signals may be similar to each other, a concept that is termed joint sparsity [3]. In an example model, each signal has a common component and a local innovation, with the commonality only needing to be encoded once while each innovation can be encoded at a lower measurement rate. Three different joint sparsity models (JSMs) have been developed:

Although JSM 1 would seem preferable due to the relatively limited amount of data, only JSM 2 is computationally feasible for large sensor networks; it has been used in many applications [3]. JSMs 1 and 3 can be solved using a linear program, which has cubic complexity on the number of sensors in the network.

DCS, however, does not address the communication or networking necessary to transmit the measurements to a central location; it relies on standard communication and networking techniques for measurement transmission, which can be tailored to the specific network topology.