Ad Hoc Networks 11 (2013) 410–426

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Ad Hoc Networks

journal homepage: www.elsevier .com/locate /adhoc

A dual scheme for compression and restoration of sequentiallytransmitted images over Wireless Sensor Networks

G. Nikolakopoulos a,⇑, P. Stavrou b, D. Tsitsipis c, D. Kandris d, A. Tzes c, T. Theocharis b

a Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Swedenb Department of Informatics and Telecommunications, University of Athens, Greecec Department of Electrical and Computer Engineering, University of Patras, Greeced Department of Electronics, Technological Educational Institute of Athens, Greece

a r t i c l e i n f o

Article history:Received 3 December 2010Received in revised form 13 March 2012Accepted 6 July 2012Available online 2 August 2012

Keywords:Wireless Sensor NetworksQuad tree decompositionImage inpaintingMultimedia Wireless Sensor Networks

1570-8705/$ - see front matter � 2012 Elsevier B.Vhttp://dx.doi.org/10.1016/j.adhoc.2012.07.003

⇑ Corresponding author.E-mail address: george.nikolakopoulos@ltu.se (G

a b s t r a c t

A dual scheme for the compression and restoration of sequentially transmitted images overWireless Sensor Networks (WSNs) is presented. These networks are characterized by lowtransmission rates, extended packet losses and limited power resources, factors thatdegrade the overall system performance. Especially in the cases of transmitting multimediacontent, a considerable number of data packets are dropped, with a corresponding directeffect on the received image quality. For dealing with this loss of valuable information, var-ious retransmission schemes have been proposed that act as quality of service modules.However, this approach increases energy consumption and inserts considerable timedelays, due to the rise of the traffic load in the network. This article proposes a novel dualtransmission scheme, targeting mainly the area of Multimedia WSNs (WMSN), that aims todecrease the overall traffic load introduced by the retransmission schemes, by performingimage restoration for lost data packets, at the receiver side. The proposed novel dualscheme is based on: (a) the quad tree decomposition algorithm that is adopted for com-pressing the image data before transmission over the WSN, and (b) the fast image inpaint-ing algorithm for restoring the effect of the missing data packets. The overall proposedscheme has been applied in multiple experimental studies that prove its efficacy.

� 2012 Elsevier B.V. All rights reserved.

1. Introduction

Recent technological advances have enabled the inex-pensive development of sophisticated wireless sensornodes which have the ability to perform sensing, process-ing, communication and actuation tasks. A typical sensornode of this kind is a small sized device which consists ofone or more sensors, a data processor, a memory unit, apower supply, a transceiver and possibly an actuator (see[2,10,27]).

A sensor node, in accordance with the sensing units thatit incorporates, can measure electrical, mechanical, ther-

. All rights reserved.

. Nikolakopoulos).

mal, magnetic, biological, chemical or optical features ofthe physical phenomena taking place at its surroundings.Through the collaborative use of a large number of sensornodes, a WSN is able to perform concurrent data acquisi-tion of ambient conditions at various locations of interest.This is the main reason why WSNs, based on the sensingcapabilities of their nodes, are nowadays being utilized inan ever growing number of applications including surveil-lance and reconnaissance, environment and habitat moni-toring, fire detection, inventory control, biological andbiomedical applications, traffic control, agriculture, energymanagement, machine failure diagnosis, monitoring andhandling of emergency situations, as well as battlefieldmonitoring and control (see [4,14,29]).

In most cases, sensor nodes are deployed over wideareas and transmit data to a sink node, the base station.

G. Nikolakopoulos et al. / Ad Hoc Networks 11 (2013) 410–426 411

Usually in an arbitrary WSN deployment, the distance be-tween a source node and a destination node (the base sta-tion) may exceed the transmission range of thetransmitter. Therefore, relaying is required via intermedi-ary sensor nodes. That is why sensor nodes not only collect,process and transmit the sensed data, but are also respon-sible for forwarding received data from other nodes of thenetwork. Thus, the operation of a WSN is associated withthe transmission of large volumes of data.

However, most of the energy expenditure of a sensornode takes place during its wireless communication whileit is significantly lower during sensing and data processing[15]. The presence of heavy traffic load within a WSNcauses rapid depletion of a node’s energy reserves whichare, by principle of design, limited. Moreover, the presenceof increased traffic load raises the probability of conges-tion. High congestion causes the number of packet lossesto increase. In order to prevent data recipients from losingvaluable information due to failed transmissions, retrans-missions can be activated. Nevertheless, data retransmis-sions result in a further increase in traffic load and,consequently, energy consumption and congestion.

Nowadays numerous novel applications have beendeveloped for use in Multimedia Wireless Sensor Networks(WMSNs) [23]. These networks are actually WSNs equippedwith sensor nodes capable of sensing, processing and trans-mitting multimedia data. In image based applications, thevolumes of data that have to be transmitted are even greaterthan in most other applications of WSNs [19]. Therefore, insuch applications the need for data traffic constriction be-comes even more evident. In order to compensate for errorsand packet losses during data transmission in WMSNs, tech-niques that improve the perceptual quality of the transmit-ted multimedia content are employed in the Transport andApplication Layers of the network [3]. Such erroneousbehavior is mainly attributed to multi-path fading andinterference. Popular compensative techniques include For-ward Error Correction (FEC) and Automatic Repeat Request(ARQ), which both induce limitations to the transmissionand energy consumption efficiency of a WMSN. ARQ bur-dens the allocated bandwidth by requesting retransmissionof lost data packets while the decoding complexity of FEC re-quires additional computational resources of a node in aWMSN, as it has to be built-into the transport layer. In addi-tion, multimedia content requires extended functionality ofthe application layer of the WMSN due to the nature of thetransmitted data, especially concerning video streaming.Additional functionality includes application-specificsource coding techniques and collaboration of networknodes for in-network multimedia processing.

Until now, WMSNs have been a focal point of researchefforts but there is still a lack of real-life applications whileapplications that have been experimentally verified stillmostly utilize low resolution optical sensors (30 � 30 pix-els). As stated in [1], experiments with wireless multime-dia sensor networks are inherently complex, typicallytime-consuming to set-up and execute, and hard to repeatby other researchers. Thus until now, simulations havebeen the main methodology of researchers, while nowa-days the research community is becoming aware that in

most cases the simulation results are often questionableand of limited credibility. One of the major novelties of thisarticle is to bridge the gap between simulated and experi-mental test cases and to advance theoretical design andanalysis of WMSNs with sound experimental validations.More specifically, this article proposes a novel scheme fortransmitting multimedia content over a WSN by attempt-ing to render invasive techniques to the transport andapplication layer obsolete. The proposed approach isexperimentally applied to image transmission and is char-acterized by its simplicity, as it is network-independentand does not rely on node functionality enhancement.The proposed scheme comprises of two fundamental tasks,the first being the coding of the images to be transmittedusing the QTD algorithm. QTD compresses the volume ofimage data by clustering the image in sets of variable sizeand of similar type of color information, which in turnleads to a significant reduction of the volume of transmit-ted data. The second task is the application of a fastinpainting algorithm over the received images in order torestore, based on existing surrounding information, thedamaged or missing image portions. The method aims tooptimally compensate for the loss of information, causedby failed data transmissions, by circumvention of retrans-missions, while retaining basic node functionality. Thetasks described are applied by the transmitter and receiverbase stations, which in principle are equipped with ex-tended computational resources with respect to nodes ofa WSN.

Although both QTD and inpainting algorithms are gen-erally utilized for the coding and restoration of images,they have not been so far jointly applied and experimen-tally verified in image based applications over WMSNs.Thus, the novelty of the proposed scheme lies in the com-bined application of lightweight compression techniquesand fast inpainting methods to the sequential image trans-mission over WSNs, achieving the reception of good qual-ity images at considerably reduced communication cost,as the preliminary results in this field have been indicatedin [22]. Moreover this article will provide an extensive setof results that concern: (a) time cost vs. transmitted imageanalysis and number of hops, (b) energy consumptionmeasurements with respect to image analysis and numberof hops, and (c) experimental comparison of QTD versusother popular image compression techniques such as JPEG,Progressive and Error resilient techniques for WMSNs, (d)qualitative analysis and comparison of the lossy receivedimages and the reconstructed ones, and (e) discussion onthe trade offs for the proposed scheme. Finally, it shouldbe noted that to the best knowledge of the authors, thisis the first extensive experimental application of QTD andan inpainting algorithm for image restoration in the fieldof WMSNs.

The rest of this article is organized as it follows. In Sec-tion 2 the QTD method is described while in Section 3, thestate of the art in inpainting algorithms is presented. Thearchitecture of the proposed experimental system andmultiple experimental results, that prove the efficacy ofthe proposed scheme, are presented in Section 4. Finally,in Section 5 the conclusions are drawn.

Fig. 1. QTD example in the form of a multilayer set of blocks and acorresponding tree structure.

Fig. 2. Example of image formation through QTD based on the homoge-neity criterion.

412 G. Nikolakopoulos et al. / Ad Hoc Networks 11 (2013) 410–426

2. Image compression via quad tree decomposition

Most image coding methods are based on image decom-position. Typically, a natural image consists of several re-gions that possess local similarity and others that haveextensively varying content. Therefore, it is wise whencoding such an image to allocate less data for homoge-neous neighborhood decomposition and more data forareas containing edges and texture. In the proposedscheme, QTD is utilized. QTD is an image segmentationmethod generally used for hierarchical decomposition.The key behind hierarchical decomposition is to divide animage into sufficiently homogeneous areas, the levels ofwhich can be compactly encoded. In the correspondingbibliography, there are several image compression algo-rithms, with the most popular being the Discrete CosineTransform, Fractal compression and the Discrete WaveletTransform. The aforementioned techniques tend to bemathematically complex, except for the QTD algorithm.QTD has been widely utilized not only for its low-complex-ity but also due to its powerful compression potential[24,28].

These attributes constitute QTD an ideal candidate forapplication in image based compression applications overWMSNs. In these applications most images are stored inraster format. Hence, any access to a raster image issequential, starting from the top left-most pixel and endingat the bottom right-most pixel. The QTD can be applied intwo alternative approaches [28]. The first is Bottom–Updecomposition, where each image is initially segmentedinto blocks of the minimum possible size. In sequel, everyfour adjacent blocks of equal size are joined together if thenew joint block is homogeneous. The overall procedure isrepeated until no more blocks can be merged. The secondimplementation approach is Top–Down decomposition,where each image is initially divided into four blocks ofequal size. Next, each of the newly generated blocks recur-sively splits into four new blocks if it is inhomogeneousand its size is greater than the minimum allowable blocksize. In general, in terms of processing speed, the Top–Down QTD is considered to outperform Bottom–Up QTDfor images which are either big or smooth while the latterperforms better for images which have either small size orextensive textural features.

The image compression performed in this work, isbased on the Top–Down QTD method. Thus, each imagecan be divided in half along both axes, all the way downto pixel level. This recursive subdividing of blocks allowsfor the image data to be organized in groups, in accordancewith the neighboring blocks. Specifically, every subdivisionexists as one of four neighboring blocks, which is actuallycomparable to having a tree-like structure, where the rootof the tree is the entire image recursively splitting into fourbranches, until its leaves are pixels. Thus the nodes of thequad tree are either leaves (pixels) or have four children.As a result, each block is either a single color block or con-sists of four smaller sub-blocks. An example of the QTDprocess is presented in Fig. 1.

The tree like evaluation of an image enables the re-moval of the unnecessary leaves and branches of the tree,

which result in the reduction of the QTD representationsize. This is achieved by checking every individual blockagainst a criterion of homogeneity. If this criterion is satis-fied, the corresponding block is not divided any further.However, if the homogeneity criterion is not satisfied, theblock is subdivided into four blocks. The process is exe-cuted iteratively until each block satisfies the homogeneitycriterion [25,20], which is expressed as:

maxðMX4 � AVG4;AVG4 �MN4Þ 6RS

AVGð1�c

2Þ4

q

!ð1Þ

In this formula, MX4 represents the maximum value of thefour leaves of a branch, while MN4 expresses the minimumvalue found on this branch, and AVG4 symbolizes the linearaverage of the values found on this specific branch. At theright part of Eq. (1), which represents the threshold for

G. Nikolakopoulos et al. / Ad Hoc Networks 11 (2013) 410–426 413

removing the branches, R is the decomposition factor,expressing the degree of compression. Parameter S refersto a scaling factor which corresponds to the size of the im-age region. For instance, S is equal to 1 for simple pixels, 2for regions of size 2 � 2 and so on. Moreover, c representsgamma correction, and q denotes the ratio of the region toimage size. For instance, when c gets the commonly usedvalue 2 and q is equal to 128, the quantity inside the paren-thesis simplifies to 1/128. Thus, for a pixel array derivedfrom an image of size 256 � 256, it represents 1/128 ofthe image size. This means that there are 128 pixel arraysin a 256 � 256 sized image. If a leaf is removed, a quadrantwill be represented by the average of the pixels it con-tained prior to pruning. The adoption of the homogeneitycriterion expressed by Eq. 1, leads to the formation ofimages of reduced size created through QTD, similar tothe QTD partitioned image illustrated in Fig. 2.

3. Image restoration through inpainting techniques

Various methods have been proposed for restoring andreconstructing images. The two most popular categories ofthese methods are namely texture synthesis and imageinpainting. Image inpainting is the process of modifyingan image by reconstructing its damaged or missing por-tions. Such techniques have been applied manually forcenturies while popular texture synthesis techniques aimto reproduce large areas of missing pixels from images byextracting statistical features from textures. In this way,texture synthesis algorithms are able to repair textured re-gions of an image with high quality. However, this is notthe case when dealing with regions containing structuralinformation.

The approach proposed in this article focuses on pre-serving structural image information, since this kind ofinformation is of greater importance in WSN applications,where recovering packet losses resulting from image trans-mission, requires a more subtle and natural approach.Thus, the image restoration process is based on a variationof the original inpainting algorithm.

Nowadays, there is a wide range of algorithms devel-oped for digital image inpainting, which to some extentreplicate the basic techniques used by restorers. The maindifferences concern their mathematical foundation, qualityof results, topology dependence, speed of execution andtype of application. Until now, the most popular inpaintingalgorithms were based on: (a) isophotes for joining pointsof equal light [18,17], (b) Euler–Lagrange equation alongwith anisotropic diffusion (Total Variational) [9], (c) Curva-ture-Driven Diffusion [8] (d) partial differential equations(PDEs) [16,7], (e) edge identification [21], and (f) global ap-proach deriving from the global heat principle and its laws[5].

Recently, the results from [16], have been extended in[6], by deriving a third order PDE that performs inpainting.The inpainting problem in this work is perceived as a caseof image interpolation and level lines are propagated byexpressing them in terms of local neighborhoods. Usingthe Taylor expansion, a third order PDE is derived, whichoptimally ensures the continuation of level lines. The

method outperforms previous work in terms of both accu-racy and contrast invariance. However, the execution ofthe algorithm is quite time consuming.

An alternative and faster approach has been presentedin [26], which proposes the incorporation of the inpaintingapproach to complete missing regions in video sequencesof complex dynamic scenes. Completion is achieved byan optimization of a global visual coherence function.The method fills in missing regions of the video sequenceby using similar space-time patches extrapolated fromthe video sequence. In order to measure similarity amongspace-time patches, the Sum of Squared Differences mea-sure is adopted and applied to space-time points that arecharacterized by the RGB color coefficients and verticalas well as horizontal motion coefficients, thus comprisinga 5� D representation for each point. To further optimizethe algorithm, a confidence measure has been incorporatedand the iterative process is able to perform in multiplescales using spatio-temporal pyramids. The proposedmethod performs impressively for video-completion andcan also be adapted to image completion by eliminatingthe temporal extent. The algorithm has been incorporatedin our work as it fits the need of a fast restoration scheme,for sequential images transmitted over a WSN.

The procedure adopted is based on a fusion of the afore-mentioned methods proposed by Bertalmio [16] and Wex-ler [26]. It aims to simultaneously achieve high quality ofrestoration and high speed of execution. More specifically,the utilization of the PDEs of Chan [8] aids in preservingthe image’s structural information while the global visualcoherence optimization defined in [26] prevents solutionswith local inconsistency to be selected as viablecandidates.

Let I0ði; jÞ be our discrete gray level image and X the re-gion to be inpainted, where ði; jÞ are the pixel coordinates.As the algorithm execution progresses, a sequence ofimages Iði; j;nÞ, where Iði; j;0Þ ¼ I0ði; jÞ and limn!1ði; j;nÞ ¼IRði; jÞ, are produced at each iteration n. This procedureultimately results at the final inpainted image IRði; jÞ. Atany step of its execution the algorithm can be generallydescribed by:

Inþ1ði; jÞ ¼ Inði; jÞ þ DtInt ði; jÞ;8ði; jÞ 2 X ð2Þ

where n corresponds to the current iteration, Dt denotesthe rate of improvement and In

t ði; jÞ represents the updateat each step.

Let #X denote the boundary of the region to be inpaint-ed. The goal of the proposed approach is to smoothly prop-agate the missing information into X. In order to achievethis, the propagation direction!nði; jÞ and the informationto be propagated Lnði; jÞ must be computed. These twocoefficients define In

t ði; jÞ as:

Int ði; jÞ ¼ dLnði; jÞ � Nnði; jÞ ð3Þ

where dLnði; jÞ denotes a measure of change in the informa-tion Lnði; jÞ.

To achieve smoothness in the resulting image, whichstatistically balances the pixel intensity over a given regionproviding a more visually coherent result, a simple imple-mentation of the discrete Laplacian is used for Lnði; jÞ:

414 G. Nikolakopoulos et al. / Ad Hoc Networks 11 (2013) 410–426

Lnði; jÞ ¼ Inxxði; jÞ þ In

yyði; jÞ ð4Þ

If the discrete Laplacian in (4) was not incorporated, theresulting image would present high sharpness, especiallyaround the area of the boundary pixels of the missing re-gion. Smoothness statistically balances the pixel intensityover a given region and provides results which are not per-ceptually visible.

For the direction coefficient Nn!ði; jÞ, the direction of the

smallest spatial change r?Inði; jÞ has been utilized as pro-posed in [16]. In addition to the procedure describedabove, an anisotropic diffusion technique is iteratively ap-plied every few steps of the execution of the inpaintingalgorithm to ensure the correct evolution of the directioncoefficient. Anisotropic diffusion reduces noise. As the in-painting algorithm progresses, driving towards the innerarea of a missing region, the noise introduced increasesas less original information is available for the specificarea, resulting in deviation of the direction coefficient.Such information is propagated from the reconstructedpixels inwards and requires noise reduction in order to re-tain the algorithm efficiency.

The discrete 2D anisotropic diffusion utilized in the pro-posed algorithm incorporates a 3 � 3 pixel neighborhoodto contribute information and is described by:

Iði; j; tþDtÞ

¼ Iði; j; tÞþDt

P1k¼�1;k–0Iðxþk;y; tÞþ Iðx;yþk; tÞ

þffiffiffi2p

=2ðIðx�1;y�1; tÞþ Iðx�1;yþ1; tÞþIðxþ1;y�1; tÞþ Iðx�1;yþ1; tÞÞ

264

375ð5Þ

In order to increase the execution speed of the digitalinpainting algorithm a multi-scale resolution approach isadopted. An iterative process over multiple scales is imple-mented. Each level contains 1/4th of the pixels of the high-er scale image, which is also the case for the missing regionX.

The iterative process begins at the lower scale applyingthe inpainting algorithm. As the execution progresses, thedirection of N

!nði; jÞ is propagated to the next level alongwith an estimation of Lnði; jÞ which acts as an initializationfor the missing pixels of the image, resulting in a fasterconvergence of the algorithm. The main algorithm param-eters are the depth N of the multi-scale resolution and themaximum M of iterations of the inpainting algorithm.Moreover, the global visual coherence optimizationproposed by Wexler [26] is incorporated to determinealgorithm convergence and the SSD of the color (or gray-level) information is adopted to measure the change ofinformation in the image from step In to Inþ1. If the resultis below a predefined threshold, the inpainting algorithmcompletes its execution providing output to the higherexecution level until the initial resolution is reached. Inthe proposed scheme: (a) the advantages of the originalinpainting algorithm with respect to information propaga-tion are maintained, (b) a global metric is adopted for qual-ity measurement, and (c) convergence is achieved rapidlyby applying a multi-scale resolution scheme, which makesthe method ideal for near real-time performance, a factor

that is of paramount importance when dealing with im-age-based applications over WSNs.

4. Experimental results

4.1. Experimental setup

The proposed overall dual transmission scheme is illus-trated in Fig. 3. This figure depicts the general setup whereimages captured from a camera are coded according toQTD prior to being transmitted over the WSN. The selec-tion of the acquiring source (digital camera or digital videoequipment) and the image acquisition settings are of para-mount importance for the overall application. Images ofhigher analysis will require greater transmission times,while images of low analysis, will speed up the transmis-sion scheme but will reduce significantly the quality ofreconstruction. Thus for every application, a compromiseshould be made between transmission time and recon-structed image quality and the Peak Signal to Noise Ration(PSNR) quality measurement should be adopted as a gen-eral criterion. At the receiver side, the streamed data pack-ets received are quad tree decomposed. The QTD algorithmfor the decomposition and the reconstruction of the trans-mitted images, can be implemented at the node level byattaching the WSN-node to a microcontroller or to anembedded PC. Due to probable losses of data packets,which contain information for specific areas of the imagestransmitted, the received images may contain black-col-ored areas. These areas are identified by a masking opera-tion on the received image and the inpainting algorithm forimage restoration is applied in order to restore the missingareas. For the experimental verification of the proposedscheme, a network of TelosB motes has been utilized. Theunderlying motes have been laid out in variable hop countlinear formations, in which sequential image transmissionshave been performed vs. different numbers of data hops. Inthis implementation the software for the image transmis-sion has been coded as a module on top of the ContikiOperation System [12], utilizing its Rime communicationStack [11], and more specifically the unicast module, whilethe MAC protocol adopted was CSMA based, with no radioduty cycling. The transmission speed was controlled byutilizing variable timers, which specifically controlled theinter-packet time interval.

The data payload size of the packets utilized in thisimplementation was 100 bytes, which is close enough tothe maximum packet size supported by the TelosB motes,but also leaves reasonable space even for complex headers.The utilized headers included a two byte final destinationRime address and a single byte ‘‘packet type’’ parameterused to distinguish image packets from other transmis-sions, as well as the source and next hop Rime addressesutilized by the unicast module. For non-QTD images, 4 by-tes of the 100 byte payload were used for offset transmis-sion, leaving 96 bytes for the actual data. The onlystructure that needs to be maintained in the QTD imagesare the quad byte blocks, a restriction which holds if filechunks are being sent with a 4-byte packet size. Therefore,no offset needs to be added to the packets, and the full

Fig. 4. Utilized transmission packet formats.

Table 1Configuration parameters for the utilized experimental WSN .

Network characteristics Values

Number of nodes (N) 6MAC sub-layer protocol IEEE 802.15.4Routing protocol StaticData size per packet (Bytes) 100Data size per packet (plus overhead) (Bytes) 107Distance between nodes (m) 5

Quad-TreeDecomposition

TRANSMITTER

Wireless Sensor Network

SequentialImage Frames

ImageDecompression

ImageInpainting

RestoredImages

Fig. 3. Overall system architecture for sequential image transmission over WSN.

G. Nikolakopoulos et al. / Ad Hoc Networks 11 (2013) 410–426 415

100 bytes can be utilized for data transmission. The uti-lized data packet structure is displayed in Fig. 4.

An overview of the parameters that have been utilizedin the WMSN for the presented test case is given in Table 1.

4.2. Image transmission measurements

For the presented experimental results, the benchmark8-bit gray scale images of Lenna and cameraman at variouspixel resolutions: (a) 256 � 256, (b) 128 � 128, (d) 64 � 64and (e) 32 � 32 have been utilized. These images,

416 G. Nikolakopoulos et al. / Ad Hoc Networks 11 (2013) 410–426

displayed in Fig. 5 have been also utilized to producesequential transmitted images, in order to simplify thereader’s comprehension of the variations in the quality ofthe received image, based on different QTDs, without a lossof the generality of the presented approach.

The validation scenarios included the application of dif-ferent decomposition factors to the same image and thesequential examination of: (a) the effect of packet losseson the same image, (b) the capabilities of the imageinpainting algorithm, and (c) the power consumption ofthe proposed scheme, and the advantages are being com-pared with classic image compression schemes.

The experiments have been performed in the followingsequence: First, the aforementioned images of Lenna atvarious resolutions with an 8-bit color depth have beendecomposed to their Quad-Tree equivalent with variousdecomposition factors, varying from 0.1 to 0.8 with a 0.1discrete step and subsequently the images have beentransmitted over the network. The initial aim has beenthe measurement of the effect of QTD decomposition on

Fig. 5. Utilized gray scale images

Fig. 6. Decomposed image of Len

the size of the transmitted compressed images and the cor-responding transmission time needed, with respect to im-age size.

As an example, the effect of the decomposition factor onthe quality of the original 256 � 256 gray scale image ofLenna is depicted in Fig. 6. In this figure, the top-left imageis the original 256 � 256 Lenna image while the followingones are the images received with an increasing decompo-sition factor from 0.1 to 0.8 (e.g. the bottom-right image ofLenna has been under QTD with a 0.8 decomposition fac-tor). It should be mentioned that in the presented indica-tive cases, the effect of the packet losses is evident on thereceived images, while due to the adopted QTD decompo-sition, these losses are being displayed as squared blackparts.

The decomposition factor R affects not only the qualityof the image but also its compression, while large/smallnumber of blocks indicate a high/low image quality. InFig. 7 the effect of the decomposition factor R on the overallimage size is depicted, while the same effect on the quality

of Lenna and Cameraman.

na for various QTD factors.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.725

30

35

40

45

50

55

60

Decomposition Factor R

PSN

R (d

b)

256x256 pixels128x128 pixels64x64 pixels32x32 pixels

Fig. 8. Effect of the decomposition factor R on the PSNR measurement vs. transmitted image size (Lenna).

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.80

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2x 105

Decomposition Factor R

Imag

e si

ze (b

ytes

)

256x256 pixels128x128 pixels64x64 pixels32x32 pixels

Fig. 7. Effect of the decomposition factor R on the QTD resulting image size vs. transmitted image size (Lenna).

G. Nikolakopoulos et al. / Ad Hoc Networks 11 (2013) 410–426 417

of the image, based on the Peak Signal to Noise Ration(PSNR) Measurement is presented in Fig. 8, for differentimage sizes.

From Fig. 8 it could be observed that the PSNR measure-ment retains the same characteristics for all the examinedimage sizes with respect to the decomposition factor R. By

418 G. Nikolakopoulos et al. / Ad Hoc Networks 11 (2013) 410–426

integrating the results of Figs. 6, 7 and in 8, it can be ob-served that for values of the QTD factor R up to 0.3 thedecomposed image retains a good PSNR value while theimage sizes (bytes to be transmitted) are being signifi-cantly reduced.

The size of the transmitted images, is the key factor thataffects the transmission frame rates. The effect of adoptingvarious decomposition factors R on the time needed totransmit a full Lenna image frame, is presented in Fig. 9.These experimental measurements have been obtainedby repeating the transmission of an image ten times (eachframe separately) and calculating afterwards the meantime, under the same compression rate and for a WSN con-sisting of 3 nodes (1-hop). In Fig. 9 the transmission timeoverhead is also being compared for various resolutionsof the transmitted Lenna image frame, under 1-hop. As itcan be observed it would be possible to select: (a) lowerimage analysis (32 � 32), for a very fast transmissionspeed, (b) high image resolution (256 � 256), where thetransmission time is not of importance, or (c) a trade-offbetween transmission speed and image quality/PSNR value(e.g. 128 � 128 image analysis and with R ¼ 0:2).

Similar results can be extracting, by repeating the sameprocedure, for the cameraman gray scale image. In Fig. 10,the effect of the decomposition factor on the transmissiontime, for different image analysis, are also depicted. More-over the rate-distortion curve is being depicted in Fig. 11,where rate is the number of bits per compressed imagethat needs to be transmitted and the distortion is the var-iance of the difference between the initial and the com-pressed image, based on the classical PSNR value, withrespect to different decomposition factors.

Another factor of great importance is the consumptionof energy, during transmission. For the same test-case of

0.1 0.2 0.3 0.4 0.0

1

2

3

4

5

6

7

8

9

10

Decomposit

Tota

l Tra

nsfe

r Tim

e (s

ec)

Fig. 9. Effect of the decomposition factor R on the

1-hop and multiple image sizes of Lenna, the measure-ments of the energy consumption are depicted in Fig. 12.The energy consumption calculations have been performedbased on the following formula:

E ¼ V � ðIcpu � tcpu þ Itx � ttx þ Irx � trxÞ ð6Þ

where V is the operating voltage of the Telos motes, Icpu isthe current consumed at the CPU, tcpu is the CPU’s time ofoperation, Itx is the current draw when transmitting a datapacket, ttx the overall transmission time, Irx is the currentdraw for receiving a data packet, and trx is the overallreception time. Moreover, in these energy calculations, itshould be noted that V ; I; Itx; Irx have been defined in [13],tcpu; ttx are measured experimentally, and trx ¼ ttx give anupper bound on the consumed energy.

The same measurements could also be performed forthe utilization of a network consisting of 4 and 6 nodes(cases of 3 and 5 hops respectively). The increase of theconsumed energy is depicted in Fig. 13, while the trans-mission time needed has remained the same due to thevery small time overhead involved in one hop. Similar re-sults, can also be extracted for the case of utilizing thecameraman image. These results are depicted in Fig. 14for the case of 1-hop.

4.3. Comparison of proposed scheme with other imagecompression techniques

In order to provide a comparison between the proposedscheme and established approaches in image compression/transmission, the base grayscale image of Lenna wascompressed using three different techniques. The first com-pression algorithm utilized is JPEG progressive compres-sion. JPEG progressive is a very popular format used

5 0.6 0.7 0.8 0.9

ion Factor R

QTD 32x32QTD 64x64QTD 128x128QTD 256x256

transmission time overhead (Lenna, 1-hop).

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.90

1

2

3

4

5

6

7

8

9

10

Decomposition Factor R

Tota

l Tra

nsfe

r Tim

e (s

ec)

QTD 32x32QTD 64x64QTD 128x128QTD 256x256

Fig. 10. Effect of the decomposition factor R on the transmission time overhead (Cameraman, 1-hop).

0 5 10 15x 105

25

30

35

40

45

50

55

60

Rate (bits)

PSN

R (d

b)

Fig. 11. Rate-distortion curve for the Lenna gray scale image ð256� 256Þ.

G. Nikolakopoulos et al. / Ad Hoc Networks 11 (2013) 410–426 419

especially in web image transmission as it enables the im-age to be rendered progressively, starting with a low qual-ity version of the image and increasing in detail as moreinformation arrives over the communication network. Onaverage, JPEG progressive compressed files have slightly

larger size when compared to the typical baseline JPEGcompression but exhibits more efficient optimization forrelatively large images. The second algorithm used wasLZW lossless compression. LZW uses dictionaries to encodeand decode the byte-stream of the image. The dictionary is

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.90

50

100

150

200

250

300

350

Decomposition Factor R

Tota

l Ene

rgy

Con

sum

ed (m

J)

QTD 32x32QTD 64x64QTD 128x128QTD 256x256

Fig. 12. Effect of the decomposition factor R on the consumed energy (Lenna, 1-hop).

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.90

500

1000

1500

Decomposition Factor R

Tota

l Ene

rgy

Con

sum

ed (m

J)

QTD 32x32 − 3 hopsQTD 64x64 − 3 hopsQTD 128x128 − 3 hopsQTD 256x256 − 3 hopsQTD 32x32 − 5 hopsQTD 64x64 − 5 hopsQTD 128x128 − 5 hopsQTD 256x256 − 5 hops

Fig. 13. Effect of the decomposition factor R on the consumed energy (Lenna, cases of 3 and 5-hops).

420 G. Nikolakopoulos et al. / Ad Hoc Networks 11 (2013) 410–426

constantly updated as the information stream is input tothe encoder and the resulting image file contains the en-coded byte-stream and dictionary used. LZW performs bet-ter when applied to grayscale images and achieves highercompression rates for regions of the image where little to

none textural or geometrical information is available. Thethird algorithm is JPEG2000 lossless compression, whichincorporates the biorthogonal CDF 5/3 wavelet transformand uses integer coefficients to achieve reversibility. Theresulting coefficients are quantized in order to achieve

G. Nikolakopoulos et al. / Ad Hoc Networks 11 (2013) 410–426 421

higher compression rates. Each coefficient represents asub-band of the image and, depending on the compressionrate one wishes to achieve, a selection could be maderegarding which of the sub-bands will be encoded. For loss-less compression, all sub-bands are encoded.

As a general comment and based on the experimentalresults obtained, it should be stated that the aforemen-tioned image compression schemes, when applied toimages before transmission over WMSNs with typical

0.1 0.2 0.3 0.4 00

2

4

6

8

10

12

14

Decomposit

Tota

l Tra

nsfe

r Tim

e (s

ec)

Fig. 15. Transmission time comparison for JPEG, LZW,

0.1 0.2 0.3 0.4 00

50

100

150

200

250

300

350

Decomposi

Tota

l Ene

rgy

Con

sum

ed (m

J)

Fig. 14. Effect of the decomposition factor R on t

packet losses, result in most of the experimental cases ina total loss of the information contained in the image; incase the image is received correctly, its quality (PSNR va-lue) is almost equal to the quality of the QTD Lenna withan R ¼ 0:1 decomposition factor. One of the major advanta-ges of the proposed QTD scheme, is the fact that irrespec-tive of the packet losses encountered, the received imagewill always contain valuable information and it is possibleto reconstruct the transmitted image by applying

.5 0.6 0.7 0.8 0.9

ion Factor R

QTD 256x256JPF 256x256JPEG 256x256TIFF 256x256

JPEG2000 and QTD compression (Lenna, 1 hop).

.5 0.6 0.7 0.8 0.9

tion Factor R

QTD 32x32QTD 64x64QTD 128x128QTD 256x256

he consumed energy (Cameraman, 1-hop).

422 G. Nikolakopoulos et al. / Ad Hoc Networks 11 (2013) 410–426

inpainting to recover the missing partitions. Apart fromthis significant advantage, multiple comparisons have beenmade, regarding the transmission time and energy con-sumption to highlight the superiority of the proposedscheme.

0.1 0.2 0.3 0.4 00

2

4

6

8

10

12

14

Decomposit

Tota

l Tra

nsfe

r Tim

e (s

ec)

Fig. 16. Transmission time comparison for JPEG, LZW, JPE

0.1 0.2 0.3 0.4 00

200

400

600

800

1000

1200

1400

1600

1800

Decomposi

Tota

l Ene

rgy

Con

sum

ed (m

J)

Fig. 17. Energy comparison for JPEG, LZW, JPEG20

In the following comparisons a Lenna image of resolu-tion 256 � 256 is used. Fig. 15 presents comparative re-sults on the time needed to transmit a full frame ofLenna. Similar results have been obtained when the cam-eraman 256 � 256 gray scale image has been utilized, as

.5 0.6 0.7 0.8 0.9

ion Factor R

QTD 256x256JPF 256x256JPEG 256x256TIFF 256x256

G2000 and QTD compression (Cameraman, 1 hop).

.5 0.6 0.7 0.8 0.9

tion Factor R

QTD 256x256 − 3 hopsJPF 256x256 − 3 hopsJPEG 256x256 − 3 hopsTIFF 256x256 − 3 hopsQTD 256x256 − 5 hopsJPF 256x256 − 5 hopsJPEG 256x256 − 5 hopsTIFF 256x256 − 5 hops

00 and QTD compression (for 3 and 5 hops).

G. Nikolakopoulos et al. / Ad Hoc Networks 11 (2013) 410–426 423

it has been depicted in Fig. 16. In addition Fig. 17 presentscomparative results on energy consumption, under the uti-lization of various image compression techniques, and dif-ferent number of hops.

From the results presented in Figs. 15–17, it is observedthat the QTD transmission scheme, is superior to classiccompression techniques. The QTD scheme is faster thanthe JPEG and TIFF schemes, while it is also faster than JPFcompression, for R > 0:18.

The superiority of the proposed QTD scheme, can alsobe observed in Fig. 17, where QTDs with R greater than0.2, have less energy consumption than other adoptedcompression schemes, both for the cases of 3 and 5hops.

4.4. Image inpainting evaluation

In the case of utilizing the proposed QTD scheme withan appropriate decomposition factor, the received imageswill contain fewer blocks of valuable image data, and thisis a straightforward consequence of the way that the quadtree decomposes an image. Thus, in the case of a lossytransmission scheme, the effect of the missing packets onthe quality of the received image will be dependant onthe QTD factor. The higher its value, the greater the effectof the missing image block will be. In order to improvethe quality of the received images, even in the case of lost

Fig. 18. The R ¼ 0:1;256� 256, QT-decomposed image (left side), the masked recside).

Fig. 19. The R ¼ 0:4;256� 256, QT-decomposed image (left side), the masked recside).

data blocks, the proposed transmission approach preventsthe receiver from requesting retransmission of lost pack-ets. Instead, the receiver base station performs imageinpainting to indirectly retrieve the missing informationfrom the received image. The execution of the fast imageinpainting algorithm presented in Section 3, requires al-most 1sec for typical packet losses of 10–20% (on256 � 256 Lenna Images); it is evident that the need to re-quest retransmission of lost data packets is reduced thusimproving: (a) speed, (b) transmission time delay, and (c)power consumption.

To experimentally evaluate the performance of the pro-posed inpainting scheme, various images of Lenna havebeen considered, under different decomposition factors R.For example in Fig. 18 the effect of the inpainting algo-rithm on the 256 � 256 image of Lenna, with R ¼ 0:1 ispresented.

In Fig. 18, the original received image is presented onthe left side. The decoded image, in the middle, has beenchecked for missing partitions and these missing blockshave been masked in red. The resulting image after theapplication of the inpainting algorithm (0.9 s executiontime) is presented on the right side of Fig. 18.

The utilized inpainting algorithm would commence therepair process using an iterative multi-resolution ap-proach. For each lower level, the image is down-scaled tohalf the size of the level above and the inpainting

eived lossy image (middle portion) and the resulting repaired image (right

eived lossy image (middle portion) and the resulting repaired image (right

Table 2PSNR Comparisons (before and after the application of inpainting).

Utilized Lennaimage

Lossy image PSNR(dB)

Inpainted image PSNR(dB)

256� 256; R ¼ 0:1 31.77 34.52256� 256; R ¼ 0:4 29.24 30.37128� 128; R ¼ 0:1 21.52 26.48

Fig. 20. The R ¼ 0:1;128� 128, QT-decomposed image (left side), the masked received lossy image (middle portion) and the resulting repaired image (rightside).

424 G. Nikolakopoulos et al. / Ad Hoc Networks 11 (2013) 410–426

algorithm is applied to the image frame. At each level thealgorithm fills in the missing pixels of the masked regionby iteratively applying a maximum of N steps of inpainting(2) and diffusion (5), in every M steps until convergencehas been achieved.

In Fig. 19, the case of applying inpainting to a QTD256 � 256 image of Lenna, with a compression factor ofR ¼ 0:3 is presented (0.3 s execution time).

In Fig. 20, the case of applying the inpainting algorithmto a 128 � 128 QTD image of Lenna, with a compressionfactor of R ¼ 0:1 and under the existence of heavy packetlosses is presented (1.2 s execution time).

Finally, the PSNR is compared for the cases of the lossyreceived images of Lenna, depicted in Figs. 18–20 and theresults are presented in Table 2.

From the above experiments it is evident that the imageinpainting algorithm achieves reasonable reconstruction ofthe missing information for all selected QT-decompositionfactors. Smaller decomposition factors result in smootherrestorations of missing partitions while more complexdecompositions result in rougher restorations. This obser-vation should be expected, as the image inpainting algo-rithm bases its operation on the information surroundinga missing partition and there is no capability of increasingthe level of detail of the reconstructions further than theprovided quality of the image frame.

5. Conclusions

In this article a dual scheme for compression and resto-ration of sequentially transmitted images over WirelessSensor Networks (WSNs) has been presented. The pro-posed novel dual scheme is based on: (a) the Quad-Tree

Decomposition that compresses the volume of the imagedata to be transmitted by clustering the image in sets ofvariable size and of similar type of color information, and(b) on the fast image inpainting algorithm to restore the ef-fect of missing data packets by reconstructing its damagedor missing portions from surrounding information. Theoverall proposed scheme has been applied to multipleexperimental studies that prove the efficacy of the pro-posed algorithm. Future studies will focus on the develop-ment of an adaptive scheme for: (a) adapting thedecomposition factor based on the current traffic load(round trip latency time) and (b) utilizing the inpaintingalgorithm as presented in this article.

References

[1] I. Akyildiz, T. Melodia, K. Chowdhury, E. Cayirci, Wireless multimediasensor networks: applications and testbeds, Proceedings of the IEEE96 (10) (2008) 1588–1605.

[2] I.F. Akyildiz, W. Su, Y. Sankarasubramamian, E. Cayirci, Wirelesssensor networks: a survey, Computer Networks 38 (4) (2002) 393–422.

[3] I.T. Almalkawi, M.G. Zapata, J.N. Al-Karaki, J. Morillo-Pozo, Wirelessmultimedia sensor networks: current trends and future directions,Sensors 10 (2010) 6662–6717.

[4] T. Arampatzis, J. Lygeros, S. Manesis. A survey of applications ofwireless sensors and wireless sensor networks, in: Proceedings ofthe 13th IEEE Mediterranean Conference on Control and Automation,Nicosia, Cyprus, June 2005, pp. 719–724.

[5] M.-F. Auclair-Fortier, D. Ziou, A global approach for solving evolutiveheat transfer for image denoising and inpainting, IEEE Transactionson Image Processing 15 (9) (2006) 2558–2574.

[6] M. Bertalmio, Strong-continuation, contrast-invariant inpaintingwith a third order optimal PDE, IEEE Transactions on ImageProcessing 15 (7) (2006) 1934–1938.

[7] M. Bertalmio, A. Bertozzi, G. Sapiro, Navier-stokes, fluid dy-namics,and image and video inpainting, in: Proceedings of the IEEEConference on Computer Vision and Pattern Recognition, vol. 1,2001, pp. 355–362.

[8] T. Chan, J. Shen, Non-texture inpainting by curvature-drivendiffusions (CDD), Journal of Visual Communication and ImageRepresentation 12 (2000) 436–449.

[9] T. Chan, J. Shen, Mathematical models for local deterministicinpaintings, SIAM Journal of Applied Mathematics 62 (3) (2001)1019–1043.

[10] C.Y. Chong, S. Kumar, Sensor networks: evolution, opportunities, andchallenges, Proceedings of IEEE 91 (2003) 1247–1256.

[11] A. Dunkels, Rime a lightweight layered communication stack forsensor networks, in: Proceedings of the European Conference onWireless Sensor Networks (EWSN), Poster/Demo Session, Delft, TheNetherlands, January 2007. <http://www.sics.se/adam/dunkels07rime.pdf>.

G. Nikolakopoulos et al. / Ad Hoc Networks 11 (2013) 410–426 425

[12] A. Dunkels, B. Grnvall, T. Voigt, Contiki – a lightweight and flexibleoperating system for tiny networked sensors, in: Proceedings of theFirst IEEE Workshop on Embedded Networked Sensors (Emnets-I).Tampa, Florida, USA, November 2004. <http://www.sics.se/adam/dunkels04contiki.pdf>.

[13] F. Kerasiotis, A. Prayati, C. Antonopoulos, C. Koulamas, G.Papadopoulos, Battery lifetime prediction model for a WSNplatform, in: Proceedings of the 4th International Conference ofSensor Technologies and Applications, 2010, pp. 525–530.

[14] C. Garcia-Hernandez, P.H. Ibarguengoytia-Gonzalez, J. Garcia-Hernandez, J.A. Perez-Diaz, Wireless sensor networks andapplications: a survey, International Journal of Computer ScienceNetwork Security 7 (2007) 264–273.

[15] D. Kandris, P. Tsioumas, A. Tzes, G. Nikolakopoulos, D. Vergados,Power conservation through energy efficient routing in wirelesssensor networks, Sensors 9 (2009) 7320–7342.

[16] M. Bertalmio, G. Sapiro, V. Caselles, C. Ballester, Image inpainting, in:Proceedings of the 27th Anual Conference on Computer Graphicsand Interactive, Techniques, 2000, pp. 417–424.

[17] S. Masnou, Disocclusion: a variational approach using level lines,IEEE Transactions on Image Processing 11 (1) (2002) 68–76.

[18] S. Masnou, J. Morel, Level lines based disocclusion, in: Proceedings ofInternational Conference on Image Processing, 1998, pp. 259–263.

[19] S. Misra, M. Reisslein, G. Xue, A survey of multimedia streaming inwireless sensor networks, IEEE Communications Surveys & Tutorials10 (4) (2008) 18–39.

[20] J. Porche, An Architecture for Quad-Tree Based Image Compression,Ph.D. Thesis, North Carolina A&T State University, 2002.

[21] A. Rares, M.J.T. Reinders, J. Biemond, Edge-based image restoration,IEEE Transactions on Image Processing 14 (10) (2005) 1454–1468.

[22] P. Stavrou, G. Nikolakopoulos, N. Fanakis, A. Tzes, T. Theoharis, AnApplication of the Inpainting Algorithm for Re-Covering Packet Lossesfrom Transmitting Sequential Quad Tree Compressed Images OverWireless Sensor Networks, Istanbul, Turkey, September 21–23 2009.

[23] G. Sullivan, I.F. Akyildiz, T. Melodia, K.R. Chowdhury, A survey onwireless multimedia sensor networks, Computer Networks 51 (4)(2007) 921–960.

[24] G. Sullivan, R. Baker, Efficient quadtree coding of images and video,IEEE Transactions on Image Processing 3 (1994) 327–331.

[25] S. Sureshchandran, P. Warter, Algorithms and architectures for realtime image compression using a feature visibility criterion,Proceedings of the International Conference on Systems, Man andCybernetics 2 (2007) 339–344.

[26] Y. Wexler, E. Shechtman, M. Irani, Space-time video completion, IEEETransactions on Pattern Analysis nad Machine Intelligence 29 (3)(2007) 463–476.

[27] J. Yick, B. Mukherjee, D. Ghosal, Wireless sensor network survey,Computational Networks 52 (2008) 2292–2330.

[28] G. Zhai, W. Lin, J. Cai, X. Yang, W. Zhang, Efficient quadtree basedblock-shift filtering for deblocking and deranging, Journal of VisualCommunication and Image Representation 20 (8) (2009) 595–607.

[29] F. Zhao, L. Guibas, Wireless Sensor Networks, Elsevier, 2004. pp. 9–16.

G. Nikolakopoulos is Assistant Professor inthe Faculty of Automatic Control Systems atthe Control Engineering Group at the Divisionof Systems and Interaction, Lulea University oftechnology, Sweden. My main research inter-ests encompass fields, such as: NetworkedControlled Systems, Mechatronics, WirelessSensor Networks & Actuators, AUV, UAVs,Robotics, Adaptive & Robust Control and Sys-tem Identification. In the past I have beenproject manager in several R&D projects fun-ded form EU, ESA, and the Greek National

Ministry of Research. In year 2003, I have received the InformationSocieties Technologies (IST) Prize Award, for the best paper that promotesthe scopes of the European IST (currently known as ICT). My published

scientific work includes more than 80 published International Journalsand Conferences in the fields of my interests. Moreover I served as IPCmember for CASE’2010, ETFA’2010, ECC’09, MED’09, MIC’09, and MIC’10international conferences, and have been Associate Editor and Reviewerof several International Journals and conferences. From 2006 to 2012, Iam a member of the IASTED Control Technical Committee.

Pavlos Stavrou is an Informatics and Tele-communications Engineer. He received hisDegree on Informatics & Telecommunicationsfrom the University of Athens, Greece in 2005.Currently he is completing his Ph.D. at theDepartment of Informatics and Telecommu-nications of the University of Athens in thefield of computer graphics and image pro-cessing. So far, his research interests include3D Reconstruction, Computer Vision, ImageProcessing, Pattern Recognition, WirelessSensor Networks, Industrial Automation and

Embedded Systems. Additionally, he has taught undergraduate andgraduate level courses in Computer Graphics at the Department ofInformatics for both the University of Athens and the Athens University of

Business and Economics. In the past he has worked as a software devel-oper and researcher in several EU-funded R&D projects.

Dimitris Tsitsipis is a Ph.D. candidate at theElectrical and Computer Engineer Departmentin the University of Patras (UoP). Dimitris hasreceived his Electrical and Computer EngineerDiploma and his MSC in Embedded Systemsfrom the same Department of UoP in 2007and 2010 respectively. His main researchinterests include Wireless Sensor Networks,Security, Wireless Multimedia Sensor Net-works, and Embedded System Design. Dimi-tris has worked as a research in EU-fundedR&D projects, as well as an independent

Software Developer.

Dionisis Kandris is currently a Lecturer in theDepartment of Electronics at the Technologi-cal Educational Institute (TEI) of Athens,Greece. He received a Diploma Degree and aDoctorate Degree, both in Electrical andComputer Engineering from the University ofPatras, Greece. He also holds a Master of Sci-ence degree in Manufacturing Systems Engi-neering from the University of Bradford,United Kingdom. Dr. Kandris has participatedin international research projects and has co-authored one book, and a number of research

articles presented through international scientific journals and confer-ences. His research interests include Networked Controlled Systems andIndustrial Automation Systems.

Anthony Tzes is Professor and Head (2009–2011) of the Electrical & Computer Engineer-ing Department of the University of Patras(UPAT) in Greece. He is a graduate of UPAT(85) and has received his doctorate from theOhio State University (90). From 1990 till1999 he was with NYU Polytechnic. Hisresearch interests include Networked Con-trolled Systems, MEMs, Robotics, Mechatron-ics, Adaptive Control, Neural Networks andFuzzy Logic Applications for IntelligentTransportation Systems, Adaptive Fuzzy Con-

trol, Instrumentation Embedded Systems, System Identification and Sig-nal Processing. Prof. Tzes has been a committee member of the AdvancedTraffic Management Systems of the ITSAmerica organization, and has

received research funding from various organizations including NAzA, theNational (US) Science Foundation, the European Union (FP6), and theEuropean Space Agency (ESA). He has been the Chairman of IEEE’s ControlSystems Society Greek Chapter, a member of the national (Greek) com-mittee of the European Control Association (EUCA), member at severalcommittees of the International Federation of Automatic Control (IFAC),

426 G. Nikolakopoulos et al. / Ad Hoc Networks 11 (2013) 410–426

and until recently the national representative to EU’s FP7’s thematic area‘‘Regions of Knowledge, Research Potential and Coherent Development ofPolicies’’. He has served in various positions (Program Chairman (MIM’00), Organizing Committee Chairman (ECC’07)), and as IPC-member atseveral international conferences. He has over ten years of experience asthe director of the Instrumentation and Control Laboratory at NYUPolytechnic focusing on smart sensors and self-tuning systems. Concur-rently, he served as the principal investigator of the Urban IntelligentTransportation Systems Center in New York, NY. While in Greece, he isthe leader and principal investigator of the ‘‘Applied Networked microMechatronicsSystems group’’. He has authored more than 50 (125) paperspublished in international technical journals conferences) and has servedin the editorial board of several journals (e.g. IEEE Control SystemsMagazine, Circuits Systems and Computers). His current research is fun-ded by several national (Greek) and EU-projects.

Theoharis Theoharis received the BSc. degreein Computer Science from the University ofLondon in 1984, the MSc. Degree in Compu-tation from the University of Oxford in 1985and the Ph.D. degree in Computer Graphicsand Parallel processing from the University ofOxford in 1988. He served as a Research Fel-low at the University of Cambridge between1987 and 1990 and as a Consultant withAndersen Consulting between 1992 and 1993.He is currently a Professor at the Departmentof Informatics, University of Athens and has

an Adjunct appointment at the University of Houston. His main researchinterests lie in 3D Shape Similarity (with applications in Biometrics,Object Retrieval, Archaeological Reconstruction) and Computer Graphics.