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.
Finally, in @cite_1 authors present an adaptive lossless data compression (ALDC) algorithm for wireless sensor networks. The data sequence to be compressed is partitioned into blocks, and the optimal compression scheme is applied for each block. However, the proposed algorithm is lossless, therefore, it is not robust to data loss of the wireless sensor network platform.
@cite_1
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Energy is an important consideration in the design and deployment of wireless sensor networks (WSNs) since sensor nodes are typically powered by batteries with limited capacity. Since the communication unit on a wireless sensor node is the major power consumer, data compression is one of possible techniques that can help reduce the amount of data exchanged between wireless sensor nodes resulting in power saving. However, wireless sensor networks possess significant limitations in communication, processing, storage, bandwidth, and power. Thus, any data compression scheme proposed for WSNs must be lightweight. In this paper, we present an adaptive lossless data compression (ALDC) algorithm for wireless sensor networks. Our proposed ALDC scheme performs compression losslessly using multiple code options. Adaptive compression schemes allow compression to dynamically adjust to a changing source. The data sequence to be compressed is partitioned into blocks, and the optimal compression scheme is applied for each block. Using various real-world sensor datasets we demonstrate the merits of our proposed compression algorithm in comparison with other recently proposed lossless compression algorithms for WSNs. In this paper we proposed a novel Adaptive Distributed Wavelet Compression (ADWC) algorithm for reducing energy consumption in a wireless sensor network, where each of the sensors has limited power. This algorithm is characterized by a distributed lifting factorization, which matching well with the transmission strategy employed in wireless sensor networks., it also present an adaptive algorithm to selects the optimal wavelet compression parameters to minimize total energy dissipation. The simulation results showed that these approaches can achieve significant energy savings without sacrificing the quality of the data reconstruction. Compressed sensing (CS) based image compression can achieve a very low sampling rate, which is ideal for wireless sensor networks with respect to their energy consumption and data transmission. In this paper, an adaptive compressed sensing rate assignment algorithm that is based on the standard deviations of image blocks is proposed. Specifically, each image block is first assigned a fixed sampling rate. In addition to the fixed sampling rate, an adaptive sampling rate is then given to each block based on the standard deviation of the block. With this adaptive sampling strategy, higher sampling rates are assigned to blocks that are less compressible (e.g., blocks with complex textures are less compressible than blocks with a smooth background). The sensing matrix is constructed based on the assigned sampling rate. The fixed measurements and the adaptive measurements are concatenated to form the final measurements. Finally, the measurements are used to reconstruct the image on the decoding side. The experimental results demonstrate that the proposed algorithm can achieve image progressive transmission and improve the reconstruction quality of the images. In the past few years, lossy compression has been widely applied in the field of wireless sensor networks (WSN), where energy efficiency is a crucial concern due to the constrained nature of the transmission devices. Often, the common thinking among researchers and implementers is that compression is always a good choice, because the major source of energy consumption in a sensor node comes from the transmission of the data. Lossy compression is deemed a viable solution as the imperfect reconstruction of the signal is often acceptable in WSN, subject to some application dependent maximum error tolerance. Nevertheless, this is seldom supported by quantitative evidence. In this paper, we thoroughly review a number of lossy compression methods from the literature, and analyze their performance in terms of compression efficiency, computational complexity and energy consumption. We consider two different scenarios, namely, wireless and underwater communications, and show that signal compression may or may not help in the reduction of the overall energy consumption, depending on factors such as the compression algorithm, the signal statistics and the hardware characteristics, i.e., micro-controller and transmission technology. The lesson that we have learned, is that signal compression may in fact provide some energy savings. However, its usage should be carefully evaluated, as in quite a few cases processing and transmission costs are of the same order of magnitude, whereas, in some other cases, the former may even dominate the latter. In this paper, we show quantitative comparisons to assess these tradeoffs in the above mentioned scenarios (i.e., wireless versus underwater). In addition, we consider recently proposed and lightweight algorithms such as Lightweight Temporal Compression (LTC) as well as more sophisticated FFT- or DCT-based schemes and show that the former are the best option in wireless settings, whereas the latter solutions are preferable for underwater networks. Finally, we provide formulas, obtained through numerical fittings, to gauge the computational complexity, the overall energy consumption and the signal representation accuracy of the best performing algorithms as a function of the most relevant system parameters.