You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: An Overview of Moving Object Trajectory Compression Algorithms 
Chapter Title:  Compressive Sensing. 
1. Abstract of Cited Paper (BIB001):  Some mobile sensor network applications require the sensor nodes to transfer their trajectories to a data sink. This paper proposes an adaptive trajectory (lossy) compression algorithm based on compressive sensing. The algorithm has two innovative elements. First, we propose a method to compute a deterministic projection matrix from a learnt dictionary. Second, we propose a method for the mobile nodes to adaptively predict the number of projections needed based on the speed of the mobile nodes. Extensive evaluation of the proposed algorithm using 6 datasets shows that our proposed algorithm can achieve sub-metre accuracy. In addition, our method of computing projection matrices outperforms two existing methods. Finally, comparison of our algorithm against a state-of-the-art trajectory compression algorithm show that our algorithm can reduce the error by 10-60 cm for the same compression ratio.

Literature Review:
Compressive sensing (CS) is an efficient signal processing technique to acquire and reconstruct a signal by finding solutions to underdetermined linear systems. It is also known as compressed sensing, compressive sampling, or sparse sampling. CS is with the principle that, through optimization, the sparsity of a signal can be exploited to recovery from far fewer samples than required by the Shannon-Nyquist sampling theorem. There are two conditions under which recovery is possible. The first one is sparsity which requires the signal to be sparse in some domain. The second one is incoherence which is applied through the isometric property which is sufficient for sparse signals BIB001 . In this section, we will discuss CS given in literature BIB002 briefly. Given a vector ∈ R , the representation ∈ R can be computed on a basis ∈ R × by solving the linear equation = which is said to be compressible if has a large number of elements with small magnitude. If there is a basis on which a given vector has a compressible representation, then is also compressible. Compressive sensing considers the problem of recovering an unknown compressible vector from its projections. Let Φ be an × projection matrix with < . Consider the equation where ∈ R is a noise vector whose norm is bounded by . Compressive sensing aims to reconstruct from and Φ given the knowledge that is compressible on the basis . Compressive sensing shows that under certain conditions it is possible to recover by solving the following ℓ 1 optimization problem: Given̂, can be estimated from̂=̂. In the context of trajectory compression, is the trajectory measured by a Mobile Sensor Networks node. The dimension of is large. The MSN node computes = Φ and transmits to the server. The server can compute an estimated trajectorŷby using , Φ, and to solve the aforementioned ℓ 1 optimization problem as shown in  . Note that the compression is lossy with 1 − / representing both space savings and reduction in wireless transmission requirement.