## 2 Background
### Other Existing Methods
#### 2.1.1 Data Compression  
Data compression often studied in the field of machine learning and artificial intelligence has been widely used to tackle various application-specific problems. This method involves encoding information by using fewer bits than the original encoding. Compression is useful because it reduces resource usage. When applied to WSNs, it helps to reduce storage space and the burden on transmission capacity, which is vital for the network lifetime. However, because compressed data must be decompressed in order to use it, this extra processing imposes a computational cost, which is not easily offset. In WSNs it even becomes difficult to practically implement a data compression scheme due to the limited memory space and the battery capacity of the nodes. Thus requiring a space-time complexity tradeoffs to accomplish this in real-time. In recent literature, a new alternative to the traditional data compression known as the Compressed Sensing (CS) has been proposed [18, 19]. This new method offers a promising solution for data compression. Our focus in this article is to show the necessary tradeoffs that can be accomplished when this new method is used for intelligent data acquisition in wireless sensor networks. For data gathering application, CS is usually adopted to increase the overall networking efficiency [20, 21]. In this case, CS aggregates the data during the collection in a multi-hop network, thus avoiding a progressive increase of the payload that is being transmitted from the peripheral nodes to the sink [22]. The work in [23] introduces an approach to minimize the network energy consumption through joint routing and compressed aggregation. Differently from our approach, there is no adaptive mechanism to activate the CS. The method provides an optimal solution which is Nondeterministic-Polynomial (NP)-complete, hence the authors present a greedy heuristic that delivers near-optimal solutions for large-scale WSNs. Similarly, the approach presented in [24] uses the results from studies in vehicle routing. The protocol is applied to data collection in WSN and integrated with CS. Unfortunately, even this approach is NP-hard, and the authors define both a centralized and a distributed heuristic to reduce the computational effort. These works analyze the routing and aggregation problem in WSNs, while in our work, we focus on the adaptive strategies when the networks is required to operate at different rates; since it allows the sensor nodes to have duty-cycle activity and hence reducing the overall payload transmitted in the network. In recent literature [25] proposed the use of compressed sensing with a scheduling approach. They used compressed sensing with measurement scheduling and estimation on in-situ soil moisture sensing. In addition, [26] proposed a Non-uniform Compressed Sensing (NCS) method that exploits compressibility and heterogeneity for an improved performance. Their approach is compared with the traditional CS using a rain-forest application that monitors wind speed and direction. Although, these works addressed the problem of how to apply CS to soil moisture processes and NCS to rain forest restoration processes in a non-real-time fashion, they both differ from our work. These works focusedon the accuracy of the sensing phenomenon, while tuning the selection of the measurement matrix and representation basis to conform to the application domain. Instead, our work tackles the joint problem of energy consumption and accuracy by using a combination of CS and data prediction. In another related work [27], the authors propose an adaptive data gathering scheme based on CS to address the problem of reconstruction quality degradation due to sensed data variation. They incorporate the Auto-regressive (AR) model to exploit the local spatial correlation between sensed data of neighboring sensor nodes, the reconstruction is thus adaptive to the variation of sensed data by adjusting the AR parameters.