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.
Some other adaptive compression algorithms, although not requiring enormous inter-node communication, however require a large number of on-node processing. For example, @cite_27 proposed an adaptive wavelet compression algorithm for WSNs. In the proposed method each receiving sensor computes the space savings, and calculates the total energy dissipation to make a decision about whether to adjust the wavelet transform level. Clearly, this method will involve enormous computation given that for each trajectory segment it has to iterate multiple times to determine the optimal transform level for the best compression and energy trade-off.
@cite_27
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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. In a wireless multimedia sensor network (WMSN), the minimization of network energy consumption is a crucial task not just for scalar data but also for multimedia. In this network, a camera node (CN) captures images and transmits them to a base station (BS). Several sensor nodes (SNs) are also placed throughout the network to facilitate the proper functioning of the network. Transmitting an image requires a large amount of energy due to the image size and distance; however, SNs are resource constrained. Image compression is used to scale down image size; however, it is accompanied by a computational complexity trade-off. Moreover, direct image transmission to a BS requires more energy. Thus, in this paper, we present a distributed image compression architecture over WMSN for prolonging the overall network lifetime (at high throughput). Our scheme consists of three subtasks: determining the optimal camera radius for prolonging the CN lifetime, distributing image compression tasks among the potential SNs to balance the energy, and, finally, adopting a multihop hierarchical routing scheme to reduce the long-distance transmission energy. Simulation results show that our scheme can prolong the overall network lifetime and achieve high throughput, in comparison with a traditional routing scheme and its state-of-the-art variants. We design lifting-based wavelet transforms for any arbitrary communication graph in a wireless sensor network (WSN). Since transmitting raw data bits along the routing trees in WSN usually requires more bits than transmitting encoded data, we seek to minimize raw data transmissions in the network. We especially focus on unidirectional transforms which are computed as data is forwarded towards the sink on a routing tree. We formalize the problem of minimizing the number of raw data transmitting nodes as a weighted set cover problem and provide greedy approximations. We compare our method with existing distributed wavelet transforms on communication graphs. The results validate that our proposed transforms reduce the total energy consumption in the network with respect to existing designs.