Energy conservation for image transmissionover wireless sensor networks

Mohsen Nasri, Abdelhamid Helali, Halim Sghaier & Hassen MaarefLaboratoire de Micro-Optoélectronique et Nanostructures(LMON)

Faculté des Sciences de Monastir.(nasrimohsen@yahoo.fr, abdelhamid.helali@isimm.rnu.tn, halim.sghaier@isimm.rnu.tn, hassen.maaref@fsm.rnu.tn )

Abstract—Images processing is one of the most importantapplications for wireless sensor networks. One of the majorchallenges in enabling images transmission in wireless sensornetwork will be the need to process and wirelessly transmit verylarge amounts of data. Therefore, optimized computation andenergy dissipation are critical requirements to maximize thelifetime of the sensor network. To address this problem, theenergy efficient image compression over a resource constrainedmulti-hop wireless network is considered. Adaptive WaveletImage Compression and Organization (AWICOA) is analgorithm that ensures efficient energy dissipation and lowcomputation overhead on the sensor nodes. The proposedalgorithm is investigated with respect to energy consumptionand image quality. Performance studies indicate that theproposed algorithm enabling significant reductions incomputation as well as communication energy needed.

Keywords- Wireless Sensor Network; Energy conservation;Adaptive Wavelet Image Compression andOptimization Algorithm (AWICOA).

I. INTRODUCTIONA wireless sensor network (WSN) is a set of very small,

inexpensive, resource constrained devices, called sensornodes, ranging from a few dozen to several thousandelements. In such network, each node is a veritable embeddedsystem with attached sensors that can process, exchangesensed-data, as well as, communicate wirelessly amongthemselves to perform various tasks. A typical sensor nodeconsists of a processing unit which is responsible forcontrolling all activities of node and executingcommunication protocols, a sensing module includes sensorsattaching to the node and the RF Radio module is responsiblefor wireless communication [1]. Replacing or renewingenergy resources after deployment becomes infeasible or toocostly in most cases, so applications must make judicious useof the finite energy resources. Sensor nodes avoid directcommunication with a distant destination due to the hightransmission power requirements for reliably sendingmessages across the deployment area which may cover alarge geographical area. Despite using multi-hopcommunication, the communication energy is the dominantpart in terms of energy dissipation. Therefore, approaches tooptimize data transmission are a critical issue to reduce thevolume of data to be transmitted over the wireless channel.Top of that, one of the major challenges in enablingmultimedia data transfer services will be the need to process

and wirelessly transmit very large volumes of data. This willimpose severe demands on the battery resources ofmultimedia-based applications as well as the bandwidth ofthe wireless network [2]. However, the most evident solutionis the data compression.

Since images processing will constitute a large part ofwireless application, we focus in this paper on developingenergy efficient and adaptive image compression andcommunication techniques. Based on wavelet imagecompression, we present an Adaptive Wavelet ImageCompression and Organization Algorithm (AWICOA),consisting of techniques to eliminate computation of certainhigh-pass coefficients of an image. The use of AWICOA cansignificantly reduce both (i) computation energy, byminimizing the computation needed to compress an image,and (ii) communication energy, consumed by the radiotransceiver, which is proportional to the number of bitstransmitted. The energy optimization is obtained withminimally perceptible loss in image quality. In this study, wewere interested by analyzing the impact of several parametersof AWICOA on computation and communication energy, andimage quality during wireless image communication. Basedon AWICOA and its parameters, we concentrate on theproblem of efficiently compressing and transmitting imagesin a resource-constrained multihop wireless network.

The rest of the paper is organized as follows: Section IIdescribes the system model. The wavelet transmissionscheme in WSN is proposed in section III. The sensor nodeorganization is described in section IV. Section V describesthe Adaptive Wavelet Image Compression and OrganizationAlgorithm (AWICOA). Experimental results and discussionsare shown in Section VI. Finally, section VII concludes thiswork.

II. ESYSTEM MODELIn our study, we use the wavelet-based image

compression system that the image sample goes first througha wavelet transform step, which generates a set of frequencycoefficients. Then, the transformed coefficients are quantizedin order to reduce the volume of encoded data. The output ofthis step is a stream of integers; each integer corresponds toan index of a quantized binary number. Following thequantization step, an entropy encoding module provides asequence of binary symbols. Shorter binary symbols are usedto encode integers that generated with relatively highprobability. This helps to reduce the number of bits978-1-4577-2209-7/11/$26.00 ©2011 IEEE

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Distribution of high-pass coefficient values

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transmitted over wireless channel. The selection of acompression system for image transmission in wirelesssensor network depends not only on traditional criteria suchas the compression ratio and the image quality distortion, butalso on associated utilization energy and robustness to higherbit error rates.

As the wavelet transform is the dominant part in terms ofenergy dissipation of the wavelet-based image compressionprocess, it is obvious that optimizing algorithmic features ofthe wavelet transform step, would significantly improveperformance and energy requirements of the compressionsystem [3]. For this reason, we target the wavelet transformstep to minimize the energy consumption. The adoptedtransform is based on the CDF 9/7 discrete wavelettransforms implemented via the lifting scheme (LS 9/7). TheLS 9/7coefficients are widely used because they provide anear optimal performance in image compression applications[4]. For this study, The DWT is achieved by applying a low-pass filter LPF followed by a high-pass filter HPF on eachpixel, line by line and then column by column. At the end ofthis process, we get four sub-bands: low-low (LL1), low-high(LH1), high-low (HL1) and high-high (HH1). The low-passsub-band represents a down sampled of the original image.The high-pass sub-band represents residual information of theoriginal image, needed for the perfect reconstruction of theoriginal set from the low-resolution version. Specifically, theLL1 sub-band can be transformed again to form LL2, LH2,HL2, and HH2 sub-bands, producing a two-level wavelettransform. The information of LL2 is used for the third leveltransform. For each sample pixel, low-pass decompositionrequires 8 shifts (S) and 8 additions (A) instructions whereashigh-pass decomposition requires 2 shifts and 4 additions.The energy needed for low-pass/high-pass decompositionsmay be defined by the number of operations. This energycalled “computational load”. Therefore the low-passdecomposition requires 8S + 8A units of computational loadin a unit pixel and 2S + 4A units for the high-passes. At atransform level, each pixel is read and written twice. Weestimate that the “data-access load” is the number of read andwrite operations. Assuming that the input image size is ofM×N pixels and that the image is decomposed into presolution level, then the 2D-DWT is iteratively applied p-1levels. Since the image is divided into 4 sub-bands in eachtransform level, the total computational energy for thisprocess can be computed as a sum of the computational loadand data-access load as follows:

1

1)1(4

1)2.21210(),,(p

iimemmemDWT WRASMNpNME (1)

We first apply the decomposition in the horizontal direction.Since all even-positioned image pixels are decomposed intothe low-pass coefficients and odd-positioned image pixels aredecomposed into the high-pass coefficients, the totalcomputational energy involved in the horizontaldecomposition is:

)..2.2.12.10(.21),,( memmemH WRASMNpNME (2)

Where S , A , memR , and memW represent the energyconsumption for shift, add, read, and write of one-byteinstructions, respectively [5].

III. WAVELET IMAGE TRANSMISSION SCHEME IN WSNIn this section, we propose an image transmission scheme

driven by energy efficiency considerations in order to besuitable for wireless sensor networks. This scheme attemptsto minimize computation energy (by reducing the number ofarithmetic operations and memory accesses) andcommunication energy (by reducing the number oftransmitted bits) in order to prolong the network lifetime. Ittherefore uses wavelet coefficients (produced with DWT) tojudiciously eliminate a large number of samples fromconsideration in the image compression process. The idea ofthe proposed technique is based on the exploitation of high-pass coefficients. Fig. 1 illustrates the distribution of high-pass coefficients after applying a 1D wavelet transform to the256×256 Lena image. We notice that the high-passcoefficients are generally represented by small integer values.Indeed, the most of the high-pass coefficients are less than0.2. Consequently, the low-pass filtering can compact thesignificant coefficients in the Li sub-band. Based on thenumerical distribution, we can estimate the high-passcoefficients to be zeros and hence avoid computing themresulting in a minimum image quality loss.

Fig. 1. The distribution of high-pass coefficients after

wavelet transform through 1D-DWT

Based on the estimation technique presented, we havedeveloped our approach which consists of two techniquesattempting to conserve energy by avoiding the computationof high-pass coefficients: The first technique attempts toconserve energy by skipping the least significant sub-band.We call this technique “SHPS Skipped High-Pass Sub-bands”. After executing a 1-D sub-band decomposition, thelow-pass sub-band (L1) resulting is decomposed in thevertical direction, leading to LL1 and HL1 sub-bands. Thehigh-pass sub-band (H1) is skipped. Then, the firstdecomposition level results the LL1, HL1, and H1 sub-bands.After one transform level, the image is then processed by

applying the 2-D sub-band decomposition to the LLi sub-band while skipping the high-pass sub-band (Hi) in thevertical direction. In the second scheme, only the LLi and LHisub-bands are kept and the two other high-pass sub-bands(HLi, and HHi) are removed. We call this “EHPF:Elimination High-Pass Filtering”. This shame is achieved byapplying only low-pass decomposition filter (LPF) in thevertical direction in each compression level. We next

compare the two proposed data exchange schemes betweenthem with respect to image quality and energy consumption.

The proposed techniques are implemented by makingspecific modifications on the wavelet transform as shown inthe Fig. 2.

Fig. 2. Data flow of AWICOA

During the wavelet transform, each input image goesthrough the row and column transform decomposing. At theend of this process, we get four sub-bands (LL, LH, HL, andHH). However, to implement the SHPS technique, after therow transform, the high-pass coefficients are not processed.This avoids the generation of a diagonal sub-band (HH) inthe column transform step. To implement the EHPFtechnique, after the row transform step, the both sub-bands L1and H1 are processed through only the low-pass filter duringthe column transform steps. We can therefore remove allhigh-pass filtering during the column transform.

Instead of transmitting the data to the base station directly,the set of sensor nodes s1 send their processed data to the nextcluster head. The cluster head process data then forwardsthem to its cluster member nodes s2 for further dataprocessing. This process is repeated will getting the requestedcompression level. In the data exchange of distributed imagecompression we proposed two image compression exchangeschemes and compare between them with energyconsumption.

Applying the 2-D sub-band decomposition to the LLi sub-band while skipping the high-pass sub-band (Hi) in thevertical direction for SHPS technique and applying only thelow-pass filter during column transform for EHPF technique.This process can be repeated up to any level.

IV. SENSOR NODES ORGANIZATIONThe proposed sensor nodes organization is based on

distributing the task to several set of nodes along the pathfrom the source to the sink. In this study, multiple packets areforwarded through different paths and at certain intermediatecluster heads, they are combined, processed, and thenmultiple copies of the result are retransmitted [6]. To extendthe network lifetime it is important that multiple relayingnodes in each cluster should be chosen to provide specialredundancy and hence ensure continuous system working.For ease of illustration, we describe this process in moredetail using an example as shown in Fig. 3.

Fig.3. An example of sensor nodes organization. Twodecomposition levels are used.

IV.1. Image transport scheme based SHPS techniqueAfter the captured image data by camera node is SHPS

transformed, the source s queries its cluster head c1 for theroute to the destination. The cluster head c1 activates the s1relaying nodes which will take part in the distributed tasksthen informs source node. The source transmits the originalimage to set of nodes s1. Those nodes run the firstcompression level on their received data then send the results

to c2. After receiving the results, c2 chooses a part of theresults (corresponding to LL1 in Fig. 4 and distributes it to theset of nodes s2. The remaining part of the image (H1 and HL1)is coded and sent to the next cluster head c3. The set of nodessend their processed results (LL2, HL1, and H2) to c3 afterrunning 1D-SHPS transform algorithm on LL1 sub-band. Thecluster head c3 combines the sub-bands (LL2, HL2, H2, HL1,and H1) and forwards it to the sink.

IV.2. Image transport scheme based EHPF techniqueThe proposed implementation of the image compression

using EHPF technique is almost similar to the SHPSimplementation. After receiving a query from a source nodes, the cluster head c1 selects a set of nodes s1 in the clusterwhich will take part in the distributed EHPF transform theninforms s. The source transmits the original image to set ofrelaying nodes s1. Those nodes run 1D-EHPF decompositionon their received packets and send them back to the nextcluster head c2. In this decomposition, both sub-bands (L1 andH1) resulting from the horizontal direction are only fed intothe low-pass filter (LPF) and not the high-pass filter (HPF) inthe column transform step, leading to LL1 and LH1 sub-bands. After receiving the packets, c2 selects a part of LL1sub-band and forwards it to the set of nodes s2. Theremaining part of the image (LH1) is coded and sent directlyto the next cluster head c3. The relaying nodes s2 run 1D-EHPF decomposition on LL1 sub-band and send the results tothe next cluster head c3. The cluster head c3 combines thesub-bands (LL2, LH2, and LH1) and forwards it to the sink.Depending on the image quality specified (which isapplication-dependent), this procedure may repeat severaltimes until the requested QoS is satisfied.

IV.3. Energy efficiency of the proposed techniques

To estimate the energy efficiency of the proposedtechniques, we measure the computational and data accessloads using the same method outlined in section III. Weassume that the elimination techniques are applied to the firstE transform levels out of the p-1 total transform levels. Thisis because the advantage of the two image transmissionschemes is more significant at lower transform levels.

In the SHPS technique, the computation load during therow transform is the same as with the LS 9/7 transform.However, during the column transform of the two sub-bandsresulting from the previous row transform, the high-passedpixels are not processed. However, during the columntransform the high-pass sub-band (HH1) and (LH1) are notcomputed, resulting in less computed operation and hencesaving computational load. The computation load saving is

given by: )1012(41 SAMN operation units of

computational load (25 % compared to the LS 9/7 transform).Therefore, the total computational load when using SHPStechnique is represented as:

Computational load:

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Because the low-pass sub-band L1 and the high-pass sub-band H1 resulting from the row transform is still required tocompute the HL sub-band during the column transform.However, we can save on a half of “write and read”operations (25% savings) during the column transformcorresponding to )(

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load. Therefore, the SHPS technique reduces some data-access loads during the transform steps by skipping two outof every four sub-bands. The total data-access load is givenby:

Data-access load:

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The SHPS technique also results in significantcommunication energy savings. For each transform level thatthe SHPS technique is applied, 25 % of the image data isremoved during the column transforms leading to lessinformation to be transmitted over the wireless channel.

In the same way, the EHPF technique reduces somecomputation loads during the transform steps. In the EHPFtechnique (Fig. 2), only the LL and LH sub-bands aregenerated and all other high pass sub-bands HL and HH areremoved. Thus, only low-pass filter is used in columntransform. This leads to a savings of

)24(21 SAMN operation units of computational load

(over 13, 7% compared to the LS 9/7 transform). Therefore,the total computational load when using EHPF is representedas:

Computational load:

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As the high-pass sub-band resulting from the rowtransform is still required to compute the HL sub-band duringthe column transform, we cannot save on “read” accessesusing the EHPF technique. Similarly, since only (L, H, LL,and HL) are written to memory and accessed through the nexttransform steps, write operations are saved by 25 %. The totaldata-access load is given by:

Data-access load:

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The EHPF technique can result in significant savings incommunication energy since two out of four sub-bands areremoved from the compressed results. The extent of savingsin computation and communication energy using thesetechniques will be demonstrated in the next section.

According to the theoretical part, we notice that theEHPF technique reduces some computation loads during the

transform steps by eliminating the high-pass sub-bands (HH,and HL), while the SHPS technique targets more significantcomputation energy savings.

IV.4. Impact of the proposed techniques on the image quality

The image quality is measured by using the peak signal tonoise ratio (PSNR) metric, which is defined (in decibels) by:

(7)

Where, q is the number of bits per pixel (bpp) of theoriginal image, and MSE is the mean-square-error which isdefined by:

(8)Where, ),( nmi is the pixel values of the original image,

),( nmî is the pixel values of the reconstructed image.Although, metric PSNR does not always accurate imageperception, we confirm it because it is a commonly used inthe literature.

Using the two proposed techniques, the image qualitydegradation depends on the importance of the eliminated HL1and LH1 sub-bans. Fig.4 illustrates the difference of the LH1and HL1sub-bands in terms of perception after applying thefirst compression level to the 256*256 Photograph image.

(a) LH1 sub-band (b) HL1 sub-band

Fig.4. Difference of LH1, and HL1 sub-band respectivelyin terms of perception after the first compression level

It is observed that the LH1 sub-bands coefficients are moreimportant than the HL1 sub-bands coefficients. Therefore, theEHPF is performed with loss in image quality compared tothe SHPS technique, while the SHPS technique is moreperformed in term of computation energy. To analyze thetrade-off between computation energy and image qualitywhen processing and transmitting an image, we propose anAdaptive Wavelet Image Compression and OrganizationAlgorithm in WSNs (AWICO) consisting of the tow proposedtechniques.

V. ADAPTIVE WAVELET IMAGE COMPRESSION ANDORGANISATION ALGORITHM (AWICAO)

Our proposed AWICOA is based on wavelet imagetransform and distributed image compression to achieveenergy conservation. This algorithm is consisting of both

SHPS and EHPF techniques to eliminate computation ofcertain high-pass coefficients of an image. The choicebetween these approaches is determined by the wirelessapplications. Some wireless applications prefer savingconsumption energy over image quality for remote wirelesssensors network application, while other applications requireno perceivable image quality without taking into accountsaving consumption energy. Top of that, depending on theimage quality desired by a wireless service, and the state ofthe battery of the wireless appliances, when applying theSHPS and EHPF techniques, different trade-offs can beobtained between the image quality and the savingconsumption energy in compressing the image andtransmitting the compressed image. Based on AWICOA, wehave developed an adaptive image codec, which canminimize energy consumption and service cost needed for animage-based wireless service.

In order to make the image transmission scheme possiblevia energy preservation and allocated memory basedheuristic, we use an image request based scenario. Therequest is initiated by the sink and then conveyed through theintermediate nodes using a multi-hop communication. Eachsensor node involved in the transmission request processsaves in its memory all the requested parameters. In thisscenario a request specifying the necessary constraints ofQoS is required to initiate the AWICOA parameters. The mostimportant parameters are PSNR and compression level.These parameters will be checked during the compressionprocess to make the decision to choice the optimal technique(SHPS or EHPF).

During the image transmission schemes, each sensor nodeinvolved in the transmission request process saves in itsmemory all the requested parameters. Theses parameters willbe checked during the compression process to determine thenumber of hops needed from source to sink. This techniquemakes the number of hops depending on the routingalgorithm and the distance from source to sink as well.

VI. RESULTS AND DISCUSSIONSIn this section, we evaluate the energy savings made

possible by using the proposed Adaptive Wavelet ImageCompression and Organization Algorithm (AWICOA) inWSN with respect to two performance metrics: Total energyconsumption and image quality.

VI.1. Effects of compression levels on computation energyand image quality

In this paragraph, we analyze the effect of thecompression level on the computation energy and the imagequality generated by each of the two proposed techniques asdescribed in the previous section, and compares the resultswith the LS 9/7 algorithm. In this study, we used Photographimage of size 256×256 pixels with 8 bits per pixel, andmeasured the computation energy and the PSNR of thecompressed image.

Fig.5 represent the computation energy savings obtainedusing the SHPS and EHPF techniques, as normalized to theLS 9/7 transform. We notice that when the EHPF techniqueis applied through compression level 1, the energy savings is13.7% less compared to the LS 9/7 transform with negligibleloss in image quality. At compression level 2, the EHPF

)()12(log.102

10 dBMSE

PSNRq

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m

N

nnmînmi

MNMSE

technique yields 17% energy savings at nominal loss inimage quality. While the EHPF technique reduces somecomputation energy saving, the SHPS technique targets moresignificant computation energy savings. The SHPS techniqueis more performed in term of computation energy. Thistechnique can save up to 25% for the compression level 1 and31% for the compression level 2 with a loss in the imagequality.

Fig.5. Effects of compression level on computationalenergy saving

VI.2. The trade-off analyzes between energy consumption andimage quality

The computation energy of the SHPS technique is associatedwith a loss in the image quality while the EHPF techniqueprovides a less saving computation energy with a guaranteefor the image quality as shown in fig 6.

Fig.6. Effects of compression level on image quality

It is observed that in SHPS technique if the number ofcompression levels increase, the savings in communicationenergy increases. However, in doing so, the quality of imagealso degrades. While the EHPF technique achieves somecomputation energy saving with less degradation for theimage quality. This is achieved again when a monochrome

Lena image of 256×256 pixels is applied. When DWT isapplied once, we obtained a PSNR=23.56 dB andPSNR=24.24 dB for the SHPS and EHPF techniquesrespectively. Thus, when applying the SHPS and EHPFtechniques at different compression levels, different trade-offs can be obtained between the image quality and theenergy expended. The trade-off between computation energyand image quality when processing and transmitting an imageis determined by the wireless application and the battery'sstate-of-charge.

VII. CONCLUSIONSWhen using wireless sensor networks for real-time image

transmission, data services will require very large amounts ofdata to be transmitted which require high energy that cannotbe fulfilled by limited growth in battery technologies. Thispaper presents a potential solution to the emerging problemsrelated to image transport in wireless application, bydeveloping an adaptive wavelet image compression andorganization in WSN (AWICOA) that can significantlyminimize the energy required for wireless imagecommunication while meeting bandwidth constraints ofwireless network and image quality. This algorithm haspresented two image transmission schemes driven by energyefficiency considerations. Both of them are based on waveletimage transform and distributed image compression. Thechoice between the two techniques depends on the wirelessapplication and the battery's state-of-charge. Performanceevaluation shows that this adopted scheme can savesignificantly the computational energy by reducing thenumber of arithmetic operations. In addition, the proposedapproach should minimize communication energy which isproportional to the number of transmitted bits and therefore,extends the overall network lifetime.

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[2] W. Zhang, Z. Deng, G. Wang, L. Wittenburg, Z. Xing.Distributed problem solving in sensor networks. In:Proceedings of the First International Joint Conference onAutonomous Agents and Multiagent Systems. ACMPress; 2002. p. 352-357.

[3] D.G. Lee, S. Dey. Adaptive and energy efficient waveletimage compression for mobile multimedia data services.In: International Conference on Communications(ICC’02); 2002, p. 2484 – 2490

[4] N. Kanvel, E.Ch. Monie. Adaptive lifting based imagecompression scheme for narrow band transmissionsystem. In International Journal of Physical Sciences, vol.4, no. 4; 2009. p. 19–164.

[5] V. Lecuire, C.D. Faundez, N. Krommenacker. Energy-Efficient Transmission of Wavelet-Based Images inWireless Sensor Networks. In: Eurasip journal on Imageand Video Processing, vol. 28, no. 1; 2007.

[6] H. Wu, A.A. Abouzeid. Error resilient image transport inwireless sensor networks. In: Elsevier ComputerNetworks Journal, vol. 58, no. 15; 2006. p. 2873–2887.

1 2 3 40

5

10

15

20

25

30

Compression level

PS

NR

(dB

)

SHPS techniqueEHPF technique

2.38 dB

2.9 dB

0.98 dB

2.06 dB

1 2 3 40

5

10

15

20

25

30

35

Compression level

Com

puta

tona

l ene

rgy

savi

ng (%

)

SHPS techniqueEHPF technique

11.3%

14.13%14.81% 15%