In this paper, we present a sparse expression model as well as a training method which can learn the sparse transformation basis from compressive sensing measurement results rather than original historical data.
compressive sensingindoor localizationreceived signal strengthsecure localizationwireless networksSecurity and accuracy are two issues in the localization of wireless sensor networks (WSNs) that are difficult to balance in hostile indoor environments. Massive numbers of malicious positioning requests may cause ...
Our paper aims to review most of the work that has been done so far in the above-mentioned area, so that the scientific community has a clear understanding of the immense potential CS holds for this field of study.Keywords: Compressed sensing,聽Wireless sensor networksAdditional informationAuthor...
Compressive sensing is a low-rate sampling approach for the signals that are known to be sparse. Suppose the signal x is an N-dimensional vector, which can be sampled using a sensing matrix Φ∈RM×N as (9.1)y=Φx. The constant M determines the sampling rate. Because M is smaller than...
Zhang J, Xiang Q, Yin Y, Chen C, Luo X (2017) Adaptive compressed sensing for wireless image sensor networks. Multimed Tools Appl 76(3):4227–4242 Article Google Scholar Zhang Q, Maldague X (2016) An adaptive fusion approach for infrared and visible images based on NSCT and compressed...
the observed signals of nearby sensors are known to be correlated, this paper firstly investigates the connection between network coding and compression concept of compressed sensing and then makes an in-depth combination between these two powerful concepts for error control in wireless sensor networks....
There will be Ng similar parallel processing required for estimating the massive MIMO channels with Ng sub-antenna groups; i.e., the same algorithm will be working simultaneously with user to estimate channels of Ng subgroups. Algorithm 1: Proposed SUCoSaMPrecovery algorithm. Input: Sensing matrix...
Compressive sensing is a revolutionary idea proposed recently to achieve much lower sampling rate for sparse signals. For large wireless sensor networks, the events are relatively sparse compared with the number of sources. Because of deployment cost, the number of sensors is limited, and due to ...
Energy and latency analysis for in-network computation with compressive sensing in wireless sensor networks INFOCOM, 2012 Proceedings IEEE (2012) Y. Zhang et al. A review of compressive sensing in information security field IEEE Access (2017) R. Gennaro et al. Non-interactive verifiable computing:...
Then the iteration was performed for all paths associated with far-field Lf. During iteration, the maximum correlation between the current residual R and the sensing matrix Af corresponding to the far-field was computed to obtain storage value t1 which guides the selection of the next column for...