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Image Acquisition and communication Using ZigBee Transceivers
Recently, there has been a acquiring demand to integrate multimedia content delivery over the Wireless Sensor Networks (WSNs). This feature could not only enhance several existing applications in the commercial, industrial, and medical domains, but could also results an array of new applications. Most wireless communication standards with high or moderate data throughputs do not focus primarily on energy efficiency. The IEEE 802.15.4 WPAN standard provides a widely accepted solution for low-cost and low-power wireless communication. In this work an attempt is made to capture an image and transmit over wireless sensor networks (WSN).
Image is captured from camera and transmitted over ZigBee Transceivers . The data rate required for an image is too high to transmit over ZigBee transceivers, since maximum data rate supported by ZigBee transceiver is 250Kbps.So the requirement for image compression is vital and JPEG Standard is used for compressing the captured image. Real time capturing of image and JPEG  compression is modeled in MATLAB for performance analysis. The compressed data transmitted in between peer to peer communication through ZigBee transceivers interfaces with systems through UART and controlled by MATLAB. Fast DCT implementation is also part of this work for reducing the power consumption.
Wireless Sensor Networks (WSN), IEEE 802.15.4, WPAN, ZigBee, JPEG and Matlab.
Recently most wireless communication standards focused on high speed and long range and have been applied successfully for cellular and local area data networks. The ZigBee Alliance (www.zigbee.org) is a consortium of over 100 companies that is developing a wireless network standard for commercial and residential control and automation applications. The Alliance has recently released its specifications for a low data rate wireless sensor network. The design goals for the network have been driven by the need for machine-to-machine communication of small simple control packet and sensor data and a desire to keep the cost of wireless transceivers to a minimum. Additionally, the network possesses self-organizing capability so that little or no network setup is required. Ideally, individual nodes should be battery powered with a long lifetime and should cost very little. The applications for such networks are numerous and include: Inventory management, product quality monitoring, factory process monitoring, disaster area monitoring, biometrics monitoring, and surveillance. ZigBee networks are similar to Ad-hoc networks in the sense that the networks borrows heavily on the self-organizing and routing technologies developed by the ad-hoc research community. However, a major design objective for ZigBee networks is reducing the cost of each node. For many of the above applications the desired cost for a wirelessly enable device is less than one dollar. While it is not a stated goal of the Alliance to support the transfer of images over the network, it is clearly a desirable capability especially for surveillance systems. Additionally, with the publication of the ZigBee standards it is expected that compliant transceivers will become readily available.
This paper examines the use of ZigBee transceivers for image transmission. Conceptual diagrammatic view of the proposed work depicted in Fig 1.The paper is organized as follows: Section II gives a brief overview of the key features of ZigBee . Section III discusses experimental setup of image acquisition and communication over ZigBee transceivers. Section IV discusses Fast DCT/IDCT implementation for reducing the power consumption of battery based systems . Section V discusses results of image transmission. This paper concludes with Fast DCT implementation and simulation results of the proposed work.
II Overview of ZigBee
ZigBee is described by referring to the OSI model for layered communication systems. The ZigBee Alliance specifies the bottom three layers (Physical, Data Link, and Network), as well an Application Programming Interface (API) that allows end developers the ability to design custom applications that use the services provided by the lower layers. Fig-2 shows the layered protocol architecture adopted by the alliance. It should be noted that the ZigBee Alliance chose to use an already existing data link and physical layers specification. These are the recently published IEEE 802.15.4 standards for low-rate personal area networks. We describe the key features of each layer in the following. Complete descriptions of the protocols used in ZigBee can be found in ,, .
A. Physical Layer Features
The IEEE 802.15.4 standard  defines three frequency bands of operation: 868MHz, 916MHz and the 2.4GHZ bands. We will focus on the 2.4GHz bands as these are the most commonly available products at the moment and in addition this band offers the highest achievable data rate of 250Kbps at the physical layer. The 2450 MHz PHY employs a 16- ary quasi-orthogonal modulation technique. During each data symbol period, four information bits are used to select one of 16 nearly orthogonal pseudo-random noise (PN) sequences to be transmitted. The PN sequences for successive data symbols are concatenated, and the aggregate chip sequence is modulated onto the carrier using offset -Quadrature phase-shift keying (O-QPSK). Essentially, this modulation format can be thought of as coded O-QPSK and is typically implemented with a table look-up for generating channel symbols which reduces transceiver cost.
B. Data Link Layer Features
The IEEE 802.15.4 is a light weight simple protocol that is based on CSMA (Channel Sense Multiple Access). Its responsibilities may also include transmitting beacon frames, synchronization and providing a reliable transmission mechanism. A key aspect of the data link layer is that individual packets are each acknowledged thus providing link level delivery guarantees. However, there are no quality of service guarantees or support for priority levels of network traffic. Essentially, ZigBee offers only best effort end-to-end delivery of individual packets.
C. Network Layer Features
The majority of the new technology development that has occurred within the ZigBee Alliance has been in the creation of the network layer. The responsibilities of the ZigBee network layer include mechanisms used to join and leave a network, and to route frames to their intended destinations. The routing of course may involve using multiple intermediate relay devices within the network.
III Experimental setup of Image Acquisition and communication using Zigbee Transceivers:
Experimental setup of this work depicted in Fig-1. Camera is connected to PC-1 and image capturing and image analysis modeled in Matlab. JPEG standard is implemented to compress the image. Compressed data is transmitted through ZigBee Transceivers. XBee   model ZigBee transceivers are used. The compressed data transmitted in between peer to peer communication through ZigBee transceivers interfaces with systems through UART and controlled by MATLAB. the computer (PC-1) having JPEG Encoder analysis and the other computer (PC-2) having JPEG Decoder analysis for reconstruction of the image and the image viewed in Matlab.
IV Fast DCT and inverse fast DCT Implementation
The discrete cosine transform (DCT) is a frequency transform used in still and moving video compression. This section addresses fast implementation of DCT based on algorithm-architecture transformations and the decimation in frequency approach.
The DCT of data sequence x(n),n=0,1,....N-1,by X(k),k=0,1,...N-1. The DCT and inverse DCT (IDCT) algorithms are described by the following equations:
Dct is an orthogonal transform, i.e the transformation matrix for IDCT is a scaled version of the transpose of that for the DCT and vice versa. Therefore the DCT architecture can be obtained by “transposing” the IDCT i.e., reversing the direction of the arrows in the flow graph of IDCT, and the IDCT can be obtained by transposing the DCT.
It is easy to verify that a direct implementation of DCT or IDCT requires N(N-1) multiplication operations i.e. which is very hardware expensive. Therefore an algorithm that can minimize the number of calculations required is a problem of particular interest for DCT. This algorithm reduces the number of multiplications to about.
The Fast DCT and Fast IDCT  architectures are depicted in Fig-5 and Fig-6.Cosine values of Fast DCT/IDCT are calculated by the following equation
Scaling Factor ½ is used at the end of each and every element.
Fast DCT Architecture:
a) Two Dimensional FastDCT Implementation:
Consider a two dimensional array (Matrix) and apply the 8-point DCT for every individual row. Output is a two dimensional array. Apply the 8-point DCT to every individual column of resultant Matrix. Resultant matrix is two dimensional DCT.
* Take a two dimensional array (Matrix).
* Read each row and apply 8-point Fast DCT. Result should be saved to new matrix
* Now apply the 8-point Fast DCT each column. And resultant is a Matrix. (Note: to apply for a column, column should be transposed, at the re-transpose the result)
* Now resultant is a two dimensional Fast DCT matrix. This can be used in JPEG standard algorithm.
Pseudo code in Matlab:
b=x(1:8,1:8); % read a 8x8 matrix
c=b(i,:); %read each row
d=dct(c); %function calling for each row
e(i,:)=d; %storing each row into new matrix
g=e(:,i); %read each column
h=g‘; %Transpose the column to apply dct
l is a two dimensional array.
Two dimensional FastIDCT is also done by using above procedure.
V Experimental Results
The compilation time for the compressed image is 20% faster than the original image and we can save the power around 5 mW and also increase the battery life for 1-2 more years. By using the Fast DCT based JPEG standard we can reduce 50% time taken for execution which yields power consumption around 20% of general DCT based JPEG standard. Table 1 depicts the scenario of various images sizes with different transmissions.
Table 1: Practical considerations
Simulation results of this work are depicted in Fig 7(a), 7(b) &7(c). Fig-7(a) is an original image which image we are intending to transmit over ZigBee Transciver.Fig-7(b) is an Fast DCT implemented result. Encoded data is transmitted using ZigBee Transceivers. The decompressed image is depicted in Fig-7(c)
The PSNR compute the peak signal-to-noise ratio, in decibels, between two images. This ratio is often used as a quality measurement between the original and a compressed image. The higher the PSNR, the better is the quality of the compressed or reconstructed image.
The Mean Square Error (MSE) and the Peak Signal to Noise Ratio (PSNR) are the two error metrics used to compare image compression quality. The MSE represents the cumulative squared error between the compressed and the original image, whereas PSNR represents a measure of the peak error. To compute the PSNR, the block first calculates the mean-squared error using the following equation
In the previous equation, M and N are the number of rows and columns in the input images. PSNR is computed using the following equation
In the previous equation, R is the maximum fluctuation in the input image data type. If it has an 8-bit unsigned integer data type, R is 255, etc.
Wireless communication of JPEG real time images are tested over ZigBee Transceivers. JPEG standard provides better compression algorithm though for wireless sensor networks it is not sufficient. SPIHT algorithm is provides better compression than JPEG. Fast DCT multiplications can also reduce using optimum algorithms like Loffler DCT. Future work of this paper is extended towards these algorithms.
 Shanin Farahani, Zigbee wireless networks and Transceivers, Newens Publications.
 Rafael C Gonzalez, Richard E.Woods and Steven L.Eddins, “Digital Image Processing”
 IEEE Standard for Part 15.4: Wireless Medium Access Control (MAC) and Physical Layer (PHY) specifications for Low Rate Wireless Personal Area Networks (LR-WPANs), 2003.
 ZigBee Alliance Document 02130: Network Layer Specification, July 2004.
 ZigBee Alliance Document 03525: ZigBee Application Framework, March 2004
 Keshab K Parhi,VLSI Digital Signal Processing systems, Design and Implementations
Mr R Prakash has received B.E degree in Electronics and communication university from and received M.E degree from .He is Asst Professor (Senior) in Vellore Institute of Technology, India. His current research interests are speech processing and image processing for wireless networks and Embedded systems.
Vijay Davuluri received his M.Sc Degree in Electronics from Andhra University, India, in 2006.Now he is pursuing M.Tech communication Engineering at VIT University, India. His current research areas include wireless sensor networks and Embedded systems.