International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395 -0056
Volume: 04 Issue: 03 | Mar -2017
p-ISSN: 2395-0072
www.irjet.net
Compressive Data Gathering using NACS in Wireless Sensor Network Sabari Girison P.S.1, Vinoth S.2, Ranjeeth Kumar S.O.3, Veeralakshmi P.4 1,2,3Student,
Department of IT, Prince Shri Venkateshwara Padmavathy Engineering College, Chennai, Tamilnadu, India 4Associate Professor, Department of IT, Prince Shri Venkateshwara Padmavathy Engineering College, Chennai, Tamilnadu, India
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Abstract - Wireless sensor networks are useful in such area
compress has to be sparse in some basis to be able to recover the original signal from the shorter compressed version.
where the human being is unable to go and monitor. Compressive sensing (CS) has been used widely for the gathering of data in wireless sensor networks for the purpose of the communication overhead recent years. Structured Random Matrix (SRM) offers a practical method to sampling. It has high sparsity, low complexity, fast computation properties and has good sensing performance comparable to that of completely random sensing matrices. In this paper, Neighbor-Aided Compressive Sensing(NACS) scheme is proposed for efficient gathering of data without any data loss in spatial and temporal correlated WSNs. During every sensing period, the sensor node sends the raw readings within the sensing period to a uniquely selected shortest neighbor. Simulation results demonstrate that compared with the conventional KCS (Kronecker Compressive Sensing) models and SRM, the proposed NACS model can achieve efficient data gathering and recovery performance with much fewer transmissions.
Fig -1: Wireless sensor network Structured Random Matrix [3] (SRM) is a sensing matrix that offers a practical method of sampling. It has high sparsity, low complexity, fast computation properties and has sensing performance comparable to that of completely random sensing matrices.
Key Words: Compressive sensing, WSNs, Data gathering, Structured Random Matrix, Kronecker Compressive Sensing, Neighbor-Aided Compressive Sensing.
Kronecker compressive sensing [4] (KCS) is recently introduced compressive sensing method to exploit general correlation patterns by combining the possibly distinct sparsifying bases from each signal dimension into a single basis matrix. In terms of improving compression performance and decreasing sensor energy expenditure with signals featuring typical WSN data characteristics, KCS has been empirically shown to outperform.
I. INTRODUCTION Wireless Sensor Networks [1] (WSNs) is a collection of sensors that are spatially connected together with the network to monitor the environmental and physical condition such as pressure, sound, temperature, humidity etc. and transfer the data to the server location. Fig.1 shows the connection of sensor nodes with gateway node. Wireless sensor network is used in some of the applications like precision agriculture, medicine and health care, machine surveillance and preventive measures and so on.
Neighbor-Aided Compressive Sensing [5] (NACS) scheme is a proposed system for data gathering through wireless sensor network for transfer of data to sink node with efficient performance.
Compressed Sensing or Compressive Sensing [2] is about acquiring and recovering a sparse signal in the most efficient way possible (subsampling) with the help of an incoherent projecting basis. Unlike conventional sampling methods, Compressive Sensing provides a new framework for acquiring sparse signals in a mutiplexed manner. Compressive Sensing (CS) provides a new approach to simultaneous sensing and compression that promises a potentially large reduction in sampling costs and the required number of measurements to recover the original signal. The main requirement for CS is that the signal to
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Notations: We use boldface letters to denote vectors (lowercase) and matrices (capital), and calligraphy letters to denote sets. An entry of matrix A at its i-th row and j-th column is denoted as aij . The matrix A of size N × N is denoted as AN and when A is the i-th matrix of a matrices set, it is denoted as A(i) . (·)T denotes the matrix transpose, ⊗ denotes the Kronecker product, vec (A) stacks the columns of A into a column vector, and ℜs,t (A) reshapes matrix A of size s × t to a matrix of size p × q (st = pq).
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