An Overview of Moving Object Trajectory Compression Algorithms <s> Others. <s> Highly efficient compression provides a promising approach to address the transmission and computation challenges imposed by moving object tracking applications on resource constrained Wireless Sensor Networks (WSNs). In this paper, we propose and design a Compressive Sensing (CS) based trajectory approximation algorithm, Adaptive Algorithm for Compressive Approximation of Trajectory (AACAT), which performs trajectory compression, so as to maximize the information about the trajectory subject to limited bandwidth. Our extensive evaluation using "real" trajectories of three different object groups (animals, pedestrians and vehicles) shows that CS-based trajectory compression reduces up to 30% transmission overheads, for given information loss bounds, compared to the state-of-the-art trajectory compression algorithms. We implement AACAT on the resource-impoverished sensor nodes, which shows that AACAT achieves high compression performance with very limited resource (computation power and energy) overheads. <s> BIB001 </s> An Overview of Moving Object Trajectory Compression Algorithms <s> Others. <s> 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. <s> BIB002
Considering that if the movement pattern and internal features are neglected, applications, such as trajectory clustering, outlier detection, and activity discovery may be not so accurate as we expected. Therefore, we expect that a new algorithm called structure features based trajectory compression which compresses trajectories based on movement pattern and structure features in trajectories, such as moving direction of objects, internal fluctuation in trajectories, and trajectory velocity or acceleration, will attract more attention of researchers, for instance, a polynomial-time algorithm for optimal direction-preserving simplification proposed by Long et al., which supports border application range than position-preserving simplification  . At present, most of the portable equipment used for data collection is inexpensive, power saving, and of lower computational capability, while the data processing procedure is often performed in supercomputers which have a higher computational capability. In order to effectively reduce the transport cost, we expect that compressive sensing based trajectory compression, which reduces the data scale in the process of acquiring data by combining compressive sensing with trajectory features, will attract more attention of researchers, for instance, Rana et al. present an adaptive algorithm for compressive approximation of trajectory in 2011, which performs trajectory compression, so as to maximize the information about the trajectory subject to limited bandwidth BIB001 . Four years later, another compression method called adaptive trajectory (lossy) compression algorithm based on compressive sensing has been proposed by Rana et al., which has two innovative elements BIB002 . First, they propose a method to compute a deterministic projection matrix from a learnt dictionary. Second, they propose a method for the mobile nodes to adaptively predict the number of projections needed based on the speed of the mobile nodes.