Compressive Sensing, which offers exact reconstruction of sparse signal from a small number of measurements, has tremendous potential for trajectory compression. In order to optimize the compression, trajectory compression algorithms need to adapt compression ratio subject to the compressibility of the trajectory. Intuitively, the trajectory of an object moving in starlight road is more compressible compared to the trajectory of a object moving in winding roads, therefore, higher compression is achievable in the former case compared to the later. We propose an in-situ compression technique underpinning the support vector regression theory, which accurately predicts the compressibility of a trajectory given the mean speed of the object and then apply compressive sensing to adapt the compression to the compressibility of the trajectory. The conventional encoding and decoding process of compressive sensing uses predefined dictionary and measurement (or projection) matrix pairs. However, the selection of an optimal pair is nontrivial and exhaustive, and random selection of a pair does not guarantee the best compression performance. In this paper, we propose a deterministic and data driven construction for the projection matrix which is obtained by applying singular value decomposition to a sparsifying dictionary learned from the dataset. We analyze case studies of pedestrian and animal trajectory datasets including GPS trajectory data from 127 subjects. The experimental results suggest that the proposed adaptive compression algorithm, incorporating the deterministic construction of projection matrix, offers significantly better compression performance compared to the state-of-the-art alternatives.
A slightly different adaptive compression principle is applied in the algorithm proposed in @cite_18 . Authors design an on-line adaptive algorithm that dynamically makes compression decisions to accommodate the changing state of WSNs. In the algorithm, a queueing model is adopted to estimate the queueing behavior of sensors with the assistance of only local information of each sensor node. By using the queueing model, the algorithm predicts the compression effect on the average packet delay and performs compression only when it can reduce the packet delay. This algorithm is quite elegant since it does not require lots of on node processing and intra-node communications, however, this algorithm may not be suitable for any trajectory in general. Instead, this algorithm will be suitable for those trajectories, where objects keep stationary for substantial amount of time, therefore, compression will be applied only when they are moving. Note that our proposed method is more general. The compression ratio is adapted to the speed of the object, therefore, when the object is not moving maximum compression will be achieved, and as the object starts moving instead of maintaining a common compression ratio, we adapt compression ration to the speed.
@cite_18
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In this paper, architectures for two-dimensional and three-dimensional underwater sensor networks are discussed. A detailed overview on the current solutions for medium access control, network, and transport layer protocols are given and open research issues are discussed.