ENERGY MINIMISATION TECHNIQUE IN WIRELESS SENSOR NETWORK USING COMPRESSIVE SENSING.
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ENERGY MINIMISATION TECHNIQUE IN WIRELESS SENSOR NETWORK USING COMPRESSIVE SENSING. THESIS PRELIMINARY REPORT Submitted by REJIRAM R Third Semester,M.Tech Applied Electronics and Instrumentation report1.pdf (Size: 223.35 KB / Downloads: 107) CHAPTER 1 INTRODUCTION Compressed sensing (CS) is a new method of signal and image measurement and reconstruction that takes advantage of the fact that many signals observed have sparse representation under some basis[1]. CS is an emerging model based framework for signal recovery at a rate significantly below the Nyquist sampling rate. The CS theory states that a signal having a sparse representation in some bases can be reconstructed from a small set of random project and implimentationions. Wireless sensor network (WSN) consists of a large number of spatially distributed signal processing devices (nodes), each with finite battery lifetime and thus limited computing and communication capabilities [3]. When properly programmed and networked, nodes in a WSN can cooperate to perform advanced signal processing tasks with unprecedented robustness and versatility, thus making WSN an attractive lowcost technology for a wide range of remote sensing and environmental monitoring applications. For applications involving distributed measurements of some physical phenomenon (e.g. temperature, vibration) that may be wirelessly transmitted, CS holds promising improvements to these limits. Compressive measurement of a sensor network is accomplished by prompting each sensor to communicate its value simultaneously to a central base station. Each sensor value is multiplied by a random number that changes each measurement and is known by the sensor and the base station [6]. CHAPTER 2 LITERATURE REVIEW 2.1 Wireless Sensor Networks(WSN) Sensor networks are the key to gathering the information needed by smart environments, whether in buildings, utilities, industrial, home, shipboard, transportation systems automation. Detecting the relevant quantities, monitoring and collecting the data, assessing and evaluating the information, formulating meaningful user displays, and performing decisionmaking and alarm functions [4] Wireless Sensor Networks have emerged as an important new area in wireless technology. Sensor network is subject to a unique set of resource constraints such as finite onboard battery power and limited network communication bandwidth. In a typical sensor network, each sensor node has a microprocessor and a small amount of memory for signal processing and task scheduling. Each node is also equipped with one or more sensing devices such as acoustic microphone arrays, video or still cameras, infrared (IR), magnetic sensors. Each sensor node communicates wirelessly with a few other local nodes within its radio communication range. [3,4,5] Wireless sensor networks (WSN) are gaining increasing research interest for their emerging potential in both consumer and national security applications. Sensor networks are envisioned to be used for surveillance, identification, and tracking of targets. They can also serve as the first line of detection for various types of biological hazards such as toxic gas attacks. In civilian applications, WSN can be used to monitor the environment and measure quantities such as temperature and pollution levels. In most application scenarios, WSN nodes are powered by small batteries, which are practically nonrechargable, either due to cost limitations or because they are deployed in hostile environments with high temperature, high pollution levels, or high nuclear radiation levels. These considerations motivate well energysaving and energyefficient WSN designs. The wireless sensor networks are expected to consist of thousands of inexpensive nodes, each having sensing capability with limited computational and communication power and which enable to deploy a largescale sensor network [3]. Recent advancement in wireless communications and electronics has enabled the development of lowcost, lowpower, multifunctional miniature devices for use in remote sensing applications. The combination of these factors has improved the viability of utilizing a sensor network consisting of a large number of intelligent sensors, enabling the collection, processing analysis and dissemination of valuable information gathered in a variety of environments. A sensor network is composed of a large number of sensor nodes which consist of sensing, data processing and communication capabilities. Sensor network protocols and algorithms must possess selforganizing capabilities. Another unique feature of sensor networks is the cooperative effort of sensor nodes. Sensor nodes are suitable with an onboard processor. Instead of sending the raw data to the nodes responsible for the fusion, they use their processing abilities to locally carry out simple computations and transmit only the required and partially processed data.[3] Since there are limited bandwidths in wireless sensor networks, it is important to reduce data bits communicated among sensor nodes to meet the application performance requirements. It also saves node energy since less bits are communicated between nodes.Energy limitation is one of the major differences between a WSN and other wireless networks such as wireless local area networks, where energy efficiency is of a lesser concern. Also, WSNs are often selfconfigured networks with little or no preestablished infrastructure as well as a topology that can change dynamically [3] A wireless sensor network (WSN) generally consists of a basestation (or "gateway") that can communicate with a number of wireless sensors via a radio link. Data is collected at the wireless sensor node, compressed, and transmitted to the gateway directly or, if required, uses other wireless sensor nodes to forward data to the gateway.[5] Two popular WSN characteristic are: Fusion center(FC) Adhoc WSNs 3 Fusion center(FC): In Fusion Center network, there is no inter sensor communication; communication is only between sensors and the FC (Fig 2.1). The FC collects locally processed data and produces a final estimate;[3]. In the figure s1,s2,s3 indicate the sensor nodes. Figure 2.1: A WSN topology with an FC Adhoc WSNs: In Ad hoc WSN,the network itself is responsible for processing the collected information, and to this end, sensors communicate with each other through the shared wireless medium (Fig 2.2). Figure 2.2: Ad hoc Wireless Sensor Network 4 2.2 Compressive Sensing Background 2.2.1 Consider Sparsity For large wireless sensor networks, the events are relatively sparse compared with the number of sources. Compressive sensing is an idea achieve much lower sampling rate for sparse signals .Consider a real valued, finite length, one dimensional, discrete time signal X, which view as an N 1 column vector in RN with elements x[n], n = 1, 2, . . . , N. Here it treats higherdimensional data by vectorizing it into a long one diamensional vector. Any signal in RN can be represented in terms of a basis of N 1 vectors f igN i=1 = 1. For simplicity assume that the basis is orthonormal. Forming the N N basis matrix := [ 1j 2j:::j n] by stacking the vectors f ig as columns, any signal X can be expressed as X = N i=1Si 1 or X = NNS (2.1) Where S is the N 1 column vector of weighting coefficients Si =< X; >= TX. Clearly, X and S are equivalent representations of the same signal,with X in the time domain and S in the domain. Focus on signals that have a sparse representation, where X is a linear combination of just K basis vectors, with K << N. That is, only K of the Si in (1) are nonzero and (NK) are zero. Sparsity is motivated by the fact that many natural and manmade signals are compressible in the sense that there exists a basis where the representation (1) has just a few large coefficients and many small coefficients. A typical compression algorithm would simply compute the nonzero coefficients of S and store their amplitudes and locations This method of sampling a signal and then compressing it suffers from a few inherent inefficiencies. First the entire N length signal must be measured which can be inefficient if N is very large. Second the encoder (compression algorithm) must compute all of the N transform coefficients even though many of them are small and can be discarded. Third the positions of each of the transform coefficients must be known 5 and will therefore require storage [1]. So the use of compressed sensing primarily as a tool to decrease the number of measurements required to accurately determine the sensor readings in a wireless sensor network. 2.2.2 Compressive Measurements Compressive sensing presents an alternative, a more general data acquisition approach that condenses the signal directly into a compressed representation without going through the intermediate stage of taking N samples. The remarkable characteristic of CS is that a K sparse signal can be encoded by multiplying it by a random matrix,MN,where M is much smaller than N but is larger than K i.e. K << M << N. The result of this encoding method is the compressive measurement vector, Y, which is defined by Y = MNXN1 (2.2) Substituting the representation of X from equation (2.1) in to equation (2.2) we get Y = MN NNS (2.3) Y = MNS (2.4) Where = is an M N matrix Figure 2.3: Compressive sensing measurement process 1a) with (random Gaussian) measurement matrix and transform matrix .The coefficient vector S is sparse with K=4. (b) Measurement process in terms of the matrix product = with the four columns corresponding to nonzero Si highlighted. The measurement vector Y is a linear combination of these four columns. 6 It is important to discuss how it is possible to reconstruct S from Y and to ensure that the probability of exact reconstruct can be made close to unity for this measurement scheme. This is a difficult problem because the locations of the K nonzero coefficients of X are unknown. The measurement vector Y is just a linear combination of the columns of which correspond to the nonzero coefficient in S. If the locations of the nonzero entries of S were known, finding a solution would simply be a matter of inverting the matrix corresponding to the ordered set of these entries. Here, reconstruction is possible so long as M K. A necessary and sufficient condition to show that the M Ksystem has a numerically stable inverse is that for any vector V sharing the same nonzero entries as S we have 1 


