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A Compressive Sensing Method 时间:2017-05-22 13:27   来源:未知   作者:admin   点击: 次 Abstract-Compressive Sensing (CS) is a stable and robust technique that allows for the sub-sampling of data at a given data rate: ‘compressive sampling’ or ‘compressive sensing’ at rates smaller than the Nyquist sampling rate. It makes it possible to create standalone and net-centric applications with fewer resources required in Internet of Things (IoT). In this paper, we investigate how CS can provide new insights into coexisting heterogeneous IoT environments. First, we briefly introduce the CS theory with respect to the sampling through providing a compressive sampling process with low computation costs. Then, a CS-based framework is proposed for IoT, in which the hub nodes measure, transmit, and store the sampled data into the fusion center. Then, an efficient cluster-sparse reconstruction algorithm is proposed for innetwork compression aiming at more accurate data reconstruction and lower energy efficiency. Therefore, compression should be erformed locally at each hub node and reconstruction is executed jointly to consider dependencies in the acquired data by the final fusion center.
Keywords: Internet of Things (IoT), Compressive Sensing(CS), Wireless Sensor Networks (WSN)
1. Introduction
  Internet of Things (IoT) environment is growing tremendously in a few decades, thereby posing new challenges for both existing Information Communication Technology (ICT), designed with human communication in mind. Based on a growth speed for IoT, there is not much time left to become an era of Massive IoT which says an environment for pen device per one square meters. With several standards specifically targeting the connectivity requirements of Massive IoT applications, for example, cellular networks can deliver reliable, secure and diverse IoT services using existing network infrastructure in nowadays. There will be a wide range of IoT use cases in the future, and the market is now expanding toward both Massive IoT deployment as well as more advanced solutions as like smart building, smart agriculture, logistics, tracking and fleet management, and etc. Researchers found that, in information systems, Wireless Sensor Networks (WSNs) and IoT, many types of information have a property called sparseness in the transformation process which allows a certain number of samples enabling capturing all required information without loss of information [1]. IoT is expected to be a worldwide network of interconnected objects, and its development depends on a number of new technologies, such as WSNs, cloud computing, and information sensing [2]. In IoT-based information systems, a low-cost data acquisition system is necessary to effectively collect and process the data and information at IoT end nodes.
  In IoT, a desirable data compression ratio is very important, which cannot be obtained by current methods without introducing unacceptable distortions. Furthermore, for most data compression solutions in IoT, three main problems must be solved: resolution, sensitivity, and reliability. This reality has driven much of the recent research on compressive data acquisition, in which data is acquired directly in a compressed format [3]. Recovery of the data typically requires finding a solution to an undetermined linear system, which becomes feasible when the underlying data possesses special structure. Compressive Sensing (CS) is a stable and robust technique that allows for the subsampling of data at a given data rate: `compressive sampling'or ‘compressive sensing’ at rates smaller than the Nyquist sampling rate [4][5]. The theory of CS states that if a signal
is sparse in a transform domain, then it can be reconstructed exactly from a small set of linear measurements using tractable optimization algorithms. The CS changes the rules of data acquisition in information systems by exploiting a priori data sparsity information. The applications of CS for data acquisition in WSNs have been studied recently [6]. Authors in [6] investigated CS for networked data in IoT through considering the distributed data sources and their sampling, transmission, and storage.
However, for the first time, our work studies information acquisition in IoT with CS from the perspective of datacompressed sampling, robust transmission, and accurate reconstruction to reduce the energy consumption, computation costs, and data redundancy and increase the network capacity. A common task of an IoT end node is to transmit the sensed data to a specific node or fusion center(FC); however, how to efficiently acquire, store, and transmit among a large number of source nodes remains a challenge.It is obvious that various IoT environments or platformsshould co-exist with other IoT ones in the real life, because tremendous IoT devices and platforms come out nowadays. In this paper, assume that multiple IoT platforms are utilized simultaneously, we describe that how can huge amounts of information from massive IoT devices be handled with at the same time.