2652 IEEE SYSTEMS JOURNAL, VOL. 11, NO. 4, DECEMBER 2017

K-Means Clustering-Based Data CompressionScheme for Wireless Imaging Sensor Networks

Jeongyeup Paek and JeongGil Ko

Abstract—Image-based embedded wireless sensor networks(WSNs) can be a useful tool in various environmental monitoringapplications to unobtrusively observe biological phenomena. Ourprior deployments of an embedded wireless imaging system at theJames Reserve have already shown its feasibility and usefulness.However, we argue that data compression schemes employed inprior systems can be improved to provide higher image transferrates per node, or lower the energy costs of wireless communi-cation. In this paper, we develop an image compression schemeusing K-means clustering on low-power embedded devices forimage-based WSNs. Specifically, we use the similarity of pixelcolors to group pixels and compress the original image. Using100 000 images collected from our pilot deployments at the JamesReserve, we study the applicability and impact of the proposedK-means clustering-based compression algorithm. Our resultssuggest that the cost of running K-means learning on a wirelesssensor node may outweigh the benefit of data compression, butoffloading the learning step and only performing the compressioncan provide significant energy gains. Specifically, our evaluationswith real-world data sets show that our proposed scheme reducespower usage by ∼49%, when sending image updates from a birdnest periodically every 15 min.

Index Terms—Data compression, embedded software, imagesensors, real-time systems, sensor systems and applications, wire-less sensor networks (WSNs).

I. INTRODUCTION

W ITH improvements in processing power and low-power hardware components, wireless sensor networks

(WSNs) have entered the phase of real deployments with ma-ture system-level protocols. Their flexibility and extendibilityallows new sensors to easily introduce novel applications forpreviously undermonitored environments. As an example, us-ing image sensors (e.g., low-power cameras), WSNs can offervarious environmental monitoring applications, such as vine-yard monitoring [1], agricultural monitoring [2], pitfall traps[3], and bird nest monitoring [4], [5], to unobtrusively observebiological phenomena. For example, biologists use pitfall traparrays to sample the local population of lizards and amphibians.Specifically, biologists deploy arrays of traps in clusters, andwhen a lizard is caught in a trap, it is tagged and freed. Over

Manuscript received January 10, 2015; revised June 23, 2015 and August 30,2015; accepted October 4, 2015. Date of publication October 30, 2015; dateof current version November 22, 2017. This work was supported in part bythe Chung-Ang University Research Grants in 2015 and also in part by theNew Faculty Fund of Ajou University. (Corresponding author: JeongGil Ko.)

J. Paek is with the School of Computer Science and Engineering, Chung-AngUniversity, Seoul 156756, Korea (e-mail: jpaek@cau.ac.kr).

J. Ko is with the Department of Software Convergence Technology, AjouUniversity, Suwon 16499, Korea (e-mail: jgko@ajou.ac.kr).

Digital Object Identifier 10.1109/JSYST.2015.2491359

time, the count of trapped lizards can be used to estimatethe overall population of lizards. Using a low-power wirelessimaging system, biologists can visually inspect images from alarge number of traps remotely. This leads to the reduction oftrap visits and thus reduces the biologists’ efforts and minimizesthe impact that humans introduce to the original habitat.

For such purposes, we performed multiple pilot studies atthe James Reserve using a custom-designed embedded wirelessimaging system [3]. The initial goal of these deployments wasto investigate the feasibility and usefulness of applying WSNsfor monitoring biological events such as pitfall traps or thestatus of bird nests. In this previous work, we devised andimplemented a low-power, scalable, and wireless imaging sys-tem using off-the-shelf hardware and Tenet, a readily availableopen-source software package for programming tiered WSNs[6]. Using this wireless imaging sensor network system, weconducted two real-world deployments, i.e., one for pitfall trapmonitoring and another for bird nest monitoring, both at theJames Reserve. Over a deployment period of three months forthe bird nest monitoring, we collected more than 100 000 im-ages from 19 camera nodes deployed over a region of 0.05 mi2.Using this system, biologists found the online and near-real-time capabilities of obtaining image data useful for answeringvarious biological questions.

On a computer science perspective, apart from noticinginteresting practical issues related to the system management,we noticed inefficiencies in the system when compressing theimages that we collected. Specifically, we realized that the in-crease in compression ratio would lead to higher image transferrates per node and reduce the energy consumption of wirelesslytransmitting large-sized images [7]. We point out that, in ourpilot studies, we tested a number of different preexisting imagecompression schemes but noticed a number of drawbacks. Forexample, the “background subtraction-based object detection”algorithm [8], [9] resulted in significant false positives dueto light intensity changes, while the “PackBits”1 [10] andthe “Run-Length Encoding” (RLE)2 [11], [12] schemes couldonly achieve compression ratios of ∼20% without sacrificingimage quality [3]. If we can improve the image compressionperformance further, a low-power wireless system can signifi-cantly reduce the per-image packet transmissions, leading to areduction in bandwidth usage and power consumption. Roughlyspeaking, if a compression ratio of 50% can be achieved, we can

1PackBits is a fast and simple lossless compression scheme for run-lengthencoding of data originally developed by Apple.

2RLE is a very simple form of data compression, in which runs of data (i.e.,sequences in which the same data value occurs in many consecutive data ele-ments) are stored as a single data value and count, rather than as the original run.

1937-9234 © 2015 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

PAEK AND KO: K-MEANS CLUSTERING DATA COMPRESSION SCHEME FOR WIRELESS IMAGING SENSOR NETWORKS 2653

either reduce the energy consumption by approximately 50%,3

or transmit (roughly) twice as many images with the sameamount of resources.

Nevertheless, in WSN deployments, there are other addi-tional challenges or design consideration factors, e.g., the mem-ory constraint on low-power embedded platforms. With highprocessing power, complex schemes can execute to achievemaximized compression ratios. However, our platforms (whichinclude the camera sensor) have 64 kB of external static randomaccess memory (SRAM), out of which around 5 kB was usedfor infrastructure code, leaving us with only ∼59 kB for imagestorage and processing. Considering that each 200 × 200 gray-scale image consumes 40 kB, this gives us no extra roomfor additional images other than the one that is currently beingprocessed. Furthermore, the mote platforms that we use (e.g.,MicaZ and TelosB) have RAM of 4 kB and 10 kB, respectively.These platforms are even more extreme in the fact that theycannot even store a single 200 × 200 gray-scale image. Forthis reason, even the recently proposed lightweight image com-pression schemes, such as tiny block-size image coding (TiBS)[13] and Optimal Zonal 2 × 2 binary discrete cosine transform(BinDCT) [14], cannot be used. Therefore, image compressionalgorithms for these systems must be simple enough to be im-plemented on such resource-constrained embedded devices. Inaddition to the limitations in computational resources, typicalsensor nodes are embedded devices operating on battery power.This naturally introduces another challenge in the system de-sign. As a result, we need to design an algorithm that takes intoaccount the limited computational and memory resources, thelimited radio bandwidth, and the power constraints under whichthese devices operate.

To address the aforementioned challenges, in this work, wedevelop an image compression scheme using the K-meansclustering algorithm for image-based WSNs with low-powerembedded devices. Specifically, we use the K-means clusteringalgorithm for image compression in the following way: wereduce the number of colors that occurs in an image to onlyk colors that are most common in the image, and we compressall other colors to a color, among those k colors, that isclosest to the original color. Our design goals in proposingsuch an algorithm focuses on minimizing and breaking downthe computational complexity, so that they are suitable forembedded platforms. Our results obtained using real-world datasets collected from two deployments at the James Reserve (i.e.,100 000 images of bird nests in time series)4 show that utilizingK-means clustering-based learning for image compression canachieve a significant compression in size (∼50% with K = 16),while maintaining a low distortion compared to the originalimage. This compression naturally leads to reduction in energyusage on the low-power embedded sensing platforms.

The remainder of this paper is structured as follows: InSection II, we analytically show the compression impact of

3The actual savings will be slightly less due to packet header/transmissionoverheads. However, since the amount of image data is significantly larger thanthe size of the overheads, their impact is negligible, around ∼49% in our case.

4The 100 000 images worked on, in this paper, have been obtained withoutcompression and then have been worked upon independently using the pro-posed algorithm.

utilizing K-means clustering-based learning for image com-pression. Using Sections III and IV, we show our efforts inidentifying proper parameters of utilizing K-means clusteringfor our purposes, specifically the K value, the number of iter-ations for learning, and the number of learning steps required.Section V presents our energy usage analysis, and the followingSection VI presents our scenario and the design of our proposedscheme on a systematic perspective. Finally, we present overallevaluation results in Section VII, discuss the related work inSection VIII, and conclude the work in Section IX.

II. K -MEANS CLUSTERING AND IMAGE COMPRESSION

The K-means algorithm automatically clusters similar dataexamples together. Given a training set x(1), . . . , x(m) (wherex(i) ∈ Rn), the goal is to group the data into a few cohesive“clusters” that are close to a set of centroids. The intuitionbehind K-means clustering is an iterative procedure that startswith a “guess” on the initial centroids. Based on this, theinitial guess is refined by repeatedly assigning examples to theirclosest centroids and then recomputing new centroids based onthe new assignments. Using K-means clustering, we target tocompress images by identifying common colors that occur inan image.

Algorithm 1 K-means algorithm

Input:K (number of clusters), Training set x(1), x(2), . . . , x(m)

Randomly initialize K cluster centroids μ1, μ2, . . . , μK

repeat// cluster assignment step: find closest centroidfor i = 1 to m data points doc(i) := index of cluster centroid closest to x(i)

end for// update centroid step: compute means based onassignmentfor k = 1 to K centroids doμk := average(mean) of points assigned to cluster k

end foruntil N iterations

Output: centroids assignments c(1), c(2), . . . , c(m), and learnedcentroids μ1, μ2, . . . , μK

The inner loop of the algorithm 1 repeatedly carries out twosteps: 1) assigning each training example x(i) to its closestcentroid and 2) recomputing the means of each centroid usingits assigned points. In the cluster assignment phase of theK-means algorithm, the algorithm assigns each training exam-ple x(i) to its closest centroid μj , given the current positions ofthe centroids. Specifically, for every example i, we set

c(i) := j that minimizes∥∥∥x(i) − μj

∥∥∥2

where c(i) is the index of the centroid closest to x(i), and μj isthe value of the j ′th cluster centroid. Then, μc(i) is the clustercentroid of a cluster to which example x(i) is assigned.

2654 IEEE SYSTEMS JOURNAL, VOL. 11, NO. 4, DECEMBER 2017

Given the assignments of each point to a centroid, the secondphase of the algorithm recomputes, for each centroid, the meanof the points that it was assigned. Specifically, for everycentroid k, we set

μk :=1

|Ck|∑i∈Ck

x(i)

where Ck is the set of examples assigned to centroid k.The optimization objective of the K-means algorithm is

given by

J(c(1), . . . , c(m), μ1, . . . , μK

)=

1

m

m∑i=1

∥∥∥x(i) − μc(i)

∥∥∥2

minc(1),...,c(m),μ1,...,μK

J(c(1), . . . , c(m), μ1, . . . , μK

). (1)

The K-means algorithm will always converge to some final setof means for the centroids. However, note that the convergedsolution may not always be ideal and depends heavily on theinitial centroid settings. Therefore, in practice, the K-meansclustering algorithm is usually run a few times with differentrandom initializations. One way to choose between these differ-ent solutions from different random initializations is to choosethe one with the lowest cost function value.

As mentioned, we utilize this K-means clustering algorithmfor image compression by reducing the number of colors of animage to only those that are most common in the given image.Specifically, our images are 200 × 200 pixel gray-scale images,where each pixel is represented as an 8-bit unsigned integer.Each 8-bit pixel specifies the intensity of the gray-scale value.Our image contains up to 256 colors, and we would like toreduce this into K colors.

By applying this reduction, images can be represented (orcompressed) efficiently. In other words, if we can reduce thenumber of colors to K , storing an image only requires savingthe pixel values of K colors, and the index of the color for eachpixel location. The K-means clustering is used to identify theseK colors in an image. Specifically, we treat each pixel in theoriginal image as an independent data sample, and K-meansclustering is used to identify K color sets that group the pixels’colors. Once the cluster centroids are computed on the image,the original images are “re-represented” using the selectedK colors.

This naturally leads to the representation of the originalimage using the centroid assignments of each pixel. Notice that,at this point, the number of bits required to describe the imagehas already been reduced. For example, only 4 bits are neededto represent 16 possible colors. Quantitatively speaking, theoriginal image that we obtained required 8 bits for each of the200 × 200 pixel locations. This results in 320 kb (i.e., 40 kB).Once the K-means clustering algorithm is applied, this size canbe significantly reduced to log2 K bits per pixel, with a smalladditional space for storing K colors (with 8 bits per color).For example, if K = 16, the number of bits for representingthe aforementioned image is 16× 8 + 200× 200× log2 4 =160 128 bits. This is a reduction of the original image in almosttwofold.

Fig. 1. Number of colors K versus image quality metrics MSE/PSNR/SSIM.(a) Sample image 1. (b) Sample image 2. (c) Sample image 3. (d) Sample image 4.

III. SELECTING A PROPER K VALUE

In K-means clustering, selecting the properK value is one ofthe most important factors in obtaining an effective performance.Here, we present the selection process for the K value in ourimage-based WSN application. A largerK value means that theclassification leads to a diverse set of colors; thus, it provides ahigher quality image. On the other hand, a smallerK value leadsto less computation and higher compression ratios, resulting inless energy usage while sacrificing image quality. The judgmentof acceptable quality is subjective, and we must rely on humanperception of the images from our domain scientists to determinethe tradeoff between target quality and the compression ratio.

Fig. 1 illustrates four sample images compressed with vary-ing K values. We also present their resulting mean squareerror (MSE), peak signal-to-noise ratio (PSNR), and structuralsimilarity (SSIM) values, which are commonly used metrics tomeasure the quality of reconstruction of lossy compression.

1) MSE measures the average of the squares of the er-rors in color representation for each pixel in the image

PAEK AND KO: K-MEANS CLUSTERING DATA COMPRESSION SCHEME FOR WIRELESS IMAGING SENSOR NETWORKS 2655

Fig. 2. K versus MSE for four different images.

TABLE IK VERSUS IMAGE COMPRESSION RATIO FOR 200 × 200 IMAGE.

COMPRESSION RATIO OF 25% MEANS 75% REDUCTION IN DATA SIZE

(see Fig. 2). This metric is identical to the optimizationobjective of the K-means clustering algorithm in (1).

2) PSNR is the ratio between the maximum possible powerof a signal (original image) and the power of corruptingnoise (error introduced by compression). PSNR can bedefined as a function of MSE.

3) SSIM index is a method for measuring the similaritybetween two images, and it can be used as a measureof image quality based on an initial uncompressed ordistortion-free image as the reference.

All of the three aforementioned metrics are widely used inmeasuring the quality of compressed images.

We make several observations from visual inspection ofthe images in Fig. 1. First, K-means clustering with K = 16shows very slight degradation in image quality, and it is dif-ficult to notice any visual differences between the originaluncompressed image and the resulting compressed images.However, when K-means clustering operates with K = 4, weobserved a number of images that are significantly lower inquality. We note that this does not satisfy the requirements ofour biologists. K = 8 shows varying results depending on theimage type. Images had sufficient quality in most cases, butsome were unacceptable to the domain scientists. Furthermore,using K = 8 has another implementational disadvantage thatthe pixels of the compressed image (i.e., 3 bits) do not alignwell with the byte boundaries within packets, and this mayrequire significantly more bit operations. Of course, using asmaller K results in a better compression ratio. We summarizethe compression ratio for K = 4, 8, and 16 in Table I. However,given that sufficient image quality was our primary applicationrequirement imposed by our domain scientists, we selectedK = 16 as our default value for K-means clustering-basedimage compression.

Fig. 3. SSIM versus MSE from compressed images.

At this point, we ask an important question: “Based onthe data in Fig. 1, which image quality metric should weuse to evaluate the performance of K-means clustering-basedcompression?” To answer this question, we plot the relationshipbetween MSE and SSIM using a subset of our compressedimages in Fig. 3. (Since PSNR is a direct function of MSE, wedo not plot PSNR in this figure.) Here, we notice two majorcharacteristics. When the image distortion is low (MSE �∼10), MSE and SSIM show a high correlation. However, whenMSE is high, the SSIM index and MSE are not as heavilycorrelated. This result suggests that, when MSE shows a lowvalue, the SSIM index will remain high; therefore, as long asMSE is low enough, both MSE and SSIM will suggest thatthe quality of the image is still good. Since we are interestedonly in the images that are of good quality, we conclude that wecan use MSE as our primary evaluation metric for comparisonand select MSE < 10 as our target quality. In addition to this,another advantage of using MSE as the primary metric is thatit fits in naturally to the optimization objective of K-meansclustering [see (1)]. For this reason, we use MSE as the mainperformance metric for the remainder of this paper, and inFig. 2, we detail the MSE results in Fig. 1, for different Kvalues.

IV. NUMBER OF ITERATIONS AND TRIALS

In addition to K , there are two other major parameters thataffect the accuracy of K-means clustering-based algorithms:1) the number of learning “iterations” within a run of K-meansclustering and 2) the number of “trials” that the K-meansclustering algorithm should run. In addition to affecting theaccuracy, these two factors directly give impact to the runningtime of the algorithm, which affects the energy usage of thesystem. Thus, in what follows, we elaborate on our method ofselecting a “good” value for these two parameters. The resultshere will be used for the remainder of this paper, as we developour proposed method. The goal is to choose the minimum num-ber of iterations and trials, so that the application requirementscan be satisfied with tolerable image quality.

2656 IEEE SYSTEMS JOURNAL, VOL. 11, NO. 4, DECEMBER 2017

Fig. 4. “Number of learning iterations” within the K-means algorithm versus“MSE” for bird nest images from 19 different nodes.

A. Identifying the Ideal Iteration Count

As mentioned, the iteration parameter takes the role ofconfiguring the number of learning iterations to perform theclustering process. This iterative process eventually leads to aconvergence toward a set of centroids and cluster assignments.Each iteration includes the find closest centroidassignment phase and the update the centroidbased on assignment phase. More iterations generallyresult in better learning of the centroids and lower optimizationcost; thus, less distortion (e.g., MSE) can be expected in the re-sulting images. However, after a certain point, the optimizationcost will converge to a local minimum, and further increasingthe number of iterations will no longer improve the quality ofthe images. In such cases, the additional iterations will increasethe computational latency and lead to energy waste for smallgains. Therefore, finding an optimal number of iterations for theK-means clustering algorithm is important. We need to makesure that this iteration count is sufficient enough for learningaccurately, while not wasting the computation time and, thus,energy.

Fig. 4 plots the MSE of compressed images from 19 ran-domly selected sensor nodes for varying number of learningiterations. The top and the bottom error bars represent themaximum and minimum values, the upper and lower boxesdepict the 75/median/25 percentile values, and the diamondmarker represents the mean value for each test case. Notice inFig. 4 that the MSE decreases rapidly as the number of itera-tions increases from 1 to 3. However, the rate of this decreasediminishes noticeably after the fifth iteration. Since our targetMSE is < 10, and since the MSE difference of ∼2 shows veryslight visible difference, it would be sufficient to select the num-ber of learning iterations to be near the knee of the curve. Giventhat these observations were made with a data set collected fromour field of interest, we select “4” as the default number oflearning iterations for our K-means clustering algorithm.

B. Identifying the Optimal Trial Count

Another important parameter when using K-meansclustering-based learning is the number of trials of theK-means

Fig. 5. “Number of K-means algorithm trials” (with different random initial-izations) versus “MSE“ for images from a node over a three-month period.

clustering algorithm to run before determining the final setof centroids and the centroid assignments. Note that thenumber of iterations within the K-means clustering algorithm(cf. Algorithm 1) is different from how many trials thesystem executes the entire K-means clustering algorithm.As mentioned in Section II, even a single execution of theK-means clustering algorithm will converge to a solution, con-sisting of a set of centroids and centroid assignments for eachdata sample. However, we cannot assure that this convergedsolution will be the optimal and, by nature, the K-meansclustering algorithm is heavily dependent on the initial settingof the centroids. Therefore, in practice, the K-means clusteringalgorithm usually executes for multiple trials with differentrandom initialization values. Once such process ends, the bestsolution with the lowest cost function (e.g., MSE of the imagesin our case) is selected among the different possible solutions.

Fig. 5 plots the MSE of 100 randomly selected compressedimages spanning a three-month period, for varying number oftrials. It shows that running several trials of the K-means clus-tering algorithm improves the cost function (MSE) of the finalsolution. This is an implicit evidence that the initial settings ofthe centroids do influence the final result. However, while thedecrease in MSE is noticeable from trial values of 1–4, the MSEconverges to a sufficiently low (e.g., < 4) value after 4 trialsand does not improve any further. We note that similar resultswere observed for different sets of randomly chosen images aswell. For the same reasons as the iteration value, in this work,we select “4” as the default number of trials for our K-meansclustering-based algorithm.

Overall, we note that the number of iterations and the numberof trials have direct impact on the running time of the K-meansclustering algorithm. Therefore, the energy consumption fromthe computation phase will also be affected on our embeddedsensor devices. We will explore this further in Section V.

V. COMPUTATION TIME AND ENERGY USAGE

Based on the findings until now, we now analyze the com-putation time and energy usage required by the K-means clus-tering algorithm on embedded sensor devices. We emphasize

PAEK AND KO: K-MEANS CLUSTERING DATA COMPRESSION SCHEME FOR WIRELESS IMAGING SENSOR NETWORKS 2657

TABLE IIPOWER CONSUMPTION OF MICA2/MICAZ/TELOS

MOTES [15] AND CYCLOPS CAMERA [16]

TABLE IIIRUNNING TIME OF K -MEANS ALGORITHM ON TELOSB

(MSP430 PROCESSOR) FOR 200 × 200 GRAY-SCALE

IMAGE AND ITS ESTIMATED ENERGY USAGE

once again that embedded sensor devices have limited com-putational and memory resources, limited radio bandwidth,and also power constraints under which these devices operate.Therefore, the algorithm should be designed to respect these re-source constraints. Specifically, the energy usage benefits fromusing image compression must outweigh the energy cost of per-forming the compression algorithm.

Table II presents the power consumption of Mica2/MicaZ/Telos motes (embedded sensor nodes with radio transceivers)and also the energy usage of the Cyclops camera used in ourpilot deployment system [15], [16]. It shows that wireless com-munication is the highest consumer of power, while the poweruse of microcontroller unit (MCU) processing is nonnegligibleas well. Therefore, we must carefully analyze the energy usageof data compression from our K-means clustering algorithmand use the compression if and only if we can reduce the overallenergy usage compared to transmitting compressionless rawimages.

Table III lists the average running time, for each subblock ofthe K-means clustering algorithm, for processing a 200 × 200gray-scale image on a TelosB mote with an MSP430 microcon-troller. It shows that, finding the closest centroid for each datasample takes ∼7.1 s and computing the updated centroids basedon the centroid assignment takes ∼2.5 s. Overall, this resultsin ∼9.6 s for a single iteration of the K-means clustering-based learning. By assuming that we use four iterations for eachK-means clustering trial, as concluded in Section IV, a singleK-means clustering trial takes ∼38.5 s. This translates to anenergy usage of ∼207.89 mJ. Furthermore, performing fourtrials of K-means clustering algorithm with different randominitialization values to make the final decision corresponds to∼154.0 s of runtime, resulting in ∼831.58 mJ of energy.

These cost measurements are important for satisfying variousapplication requirements provided by the domain scientists.Despite its effectiveness in reducing the image size, the use

of the K-means clustering-based algorithm should be carefullyconsidered and compared with the energy usage of transmittingthe image data. Specifically, transmitting a raw 200 × 200 gray-scale image (i.e., 40 kB) over a single hop using CC2420 radiotakes ∼5.33 s,5 which translates to 311.8 mJ of energy usage.

On the other hand, transmitting a compressed image withK = 16 takes ∼2.67 s, which corresponds to ∼155.9 mJ.Therefore, the energy gain here is 311.8− 155.9 = 155.9 mJ.When summed with the ∼831.58 mJ required for the com-putations, using the K-means clustering-based compressionalgorithm ironically increases the energy usage by an additional∼675.68 mJ. Even if we consider the extra energy gains fromtransmitting compressed data over multiple wireless hops [17],the additional cost is too large to easily justify the use ofthe K-means clustering algorithm. This result is similar inconcept with [18], which found that JPEG energy consumptionis actually higher on low-power platforms due to the longertimes needed for these platforms to perform the computationtasks to the desired precision. Therefore, we need to identifyan alternative method to apply image compression on resource-limited (or sensitive) embedded platforms.

VI. SCENARIO AND PROPOSED DESIGN

As discussed in Section V, using K-means clustering forimage compression can consume even more energy whencompared to transmitting a raw image over a wireless link.However, we make the observation that, if the compressionprocess at the sensor nodes start after the learning phase ofthe centroids, the computation overhead can be reduced andthus lead to a more energy-efficient computing process. For thisreason, we propose a systematic approach to create a two-phasecompression process, in which we split the centroid learningand the image compression processes.

Again, from Table III, we extract the fact that a four-iterationK-means clustering process takes> 153 s of execution time. Onthe other hand, compressing images after the centroid learningphase (e.g., the compress image given centroidsphase in Table III) takes only ∼7.1 s. In other words, withalready computed centroids, the resource-limited sensor nodeonly needs to execute the actual compression phase. For suchcases, the overall energy gain per image would be 311.8−155.9− 38.52 = 117.38 mJ. If this image travels over multiplehops, the energy gain grows significantly larger, given that thecompression takes place only once and the data to send overthe air are reduced to half of the original. This benefit can beachieved if the K-means clustering algorithm’s learning phasetake place “off the sensor nodes.” The result of this learningphase, which will be in the form of centroid points, can beconfigured as an input for the compression component “onthe sensor nodes.” Therefore, the goal of our solution is to

5While the nominal bandwidth of the Texas Instruments CC2420 IEEE802.15.4 radio is 250 kb/s, the effective bandwidth is far less, due to reasonssuch as clear channel assessment (CCA) checks, medium access control/physical (PHY) layer headers, preamble, and acknowledgment (ACK) packets.In terms of energy usage, the radio must be on during CCA and packettransmissions and for the ACK waiting duration; thus, the energy consumedby the transceiver can increase.

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Fig. 6. Image-based WSN scenario.

systematically off-load the K-means clustering learning phasefrom the embedded sensor nodes to a PC-scale device.

Fig. 6 illustrates our scenario. Our system is an image-basedWSN, where a camera sensor node is placed at each bird nestbox and communicates with a server over multihop wirelessnetworks. The embedded sensor node consisted of a Cyclopscamera [16] and a Mica2 [19] mote as the processing core andradio transceiver. Cyclops is an inexpensive low-power visionsensor platform that uses a CMOS camera module (ADCM-1700) as the imager and ATMEL ATmega128L as its 8-bit mi-crocontroller (MCU). It has 128 kB of Flash program memoryand 4 kB of SRAM data memory. It also has 64-kB SRAM ex-ternal memory for image buffering andlocal image processing.As we have seen in Table II in Section V, Cyclops consumesless than 53 mW of energy, when taking and storing images,and less than 28 mW, when processing them. A dedicatedapplication server sends tasks (i.e., commands) to sensor nodes,which enable them to take images or sensor readings, performcomputation, and return the processed data. We point the read-ers to our previous work for the full details on the “task”-“response” architecture [6].

Given this image-based WSN architecture, our proposedmethod for allowing effective K-means clustering-based imagecompression is explained as follows.

1) In the initialization phase, since neither the server nor thesensor nodes possess images to compute the K-meansclustering centroids, the server sends a “take image andsend” task to the sensor nodes.

2) At this point, each sensor node takes a new image andsends this raw image to the server.

3) Upon receiving an image from each node, the server runsthe K-means clustering algorithm, over each image file,and sends the computed centroids back to the correspond-ing sensor node. The sensor node keeps track of thesecentroids for the following steps.

4) The server now initiates a “take image, compress andsend” task to the sensor nodes. Sensor nodes use thestored centroids, to compress subsequent images, andsend the compressed images to the server.

Fig. 7. “Node-ID” versus “MSE” when a single image was used for K-meanslearning and the same set of centroids (learned from that single image) was usedfor compression of images from other nodes.

5) Phase 4 repeats for a user-defined refresh period T (e.g.,one day or one week).

6) Once period T expires, the server returns to step 1,where the K-means learning is performed again using anupdated set of images.

Using the scheme presented earlier, the embedded sensornodes conserve their limited energy resources by compressingthe large-sized images. We emphasize once again that this “en-ergy gain” will increase as the image data travels over multiplehops since the quantity of data to transmit at each relaying hopis already reduced significantly. Furthermore, we point out thatthis compressed data can be reduced even more by applyingadditional schemes such as RLE or other lightweight compres-sion schemes. The fact that we split the learning process ofthe K-means clustering algorithm to a PC-scale server intro-duces an additional benefit. Specifically, the higher processingpower and unlimited energy resources at the server allowsan increase to the number of iterations or trials for betterclustering when higher accuracy is required at the application.

VII. EVALUATION

In evaluating the proposed image compression scheme usingK-means clustering, we take a multiphase approach. First,we examine the impact of various system parameters on theperformance of our scheme, and second, we make compar-isons with previously proposed schemes that can potentially beimplemented on mote platforms. Given that the compressionratio, or the image size reduction, achieved through our schemecan be easily predictable for a given image size and K value(cf. Table I; here, we use K = 16), our evaluation here focuseson the distortion of the compressed image’s quality comparedto the original image.

A. Performance Impact of Centroid Updates

Here, while focusing on the protocol presented in Section VI,we emphasize the fact that, in reality, the centroids used forK-means clustering should be updated with noticeable changesin the image contents.

PAEK AND KO: K-MEANS CLUSTERING DATA COMPRESSION SCHEME FOR WIRELESS IMAGING SENSOR NETWORKS 2659

Fig. 8. “Days” versus “MSE” when a single image from the first day was usedfor K-means learning and the same set of centroids (learned from that singleimage) was used for compression of following images from the node over aperiod of ∼3.3 months.

To validate the need for such centroid updates, we first plotin Fig. 7 the MSE for each node when a single image from anarbitrarily selected node (e.g., node 1 in the figure) was used forthe K-means learning, and this same set of centroids (learnedfrom that single image) was used for compressing images fromother nodes. The horizontal dashed line in the plot showsthe MSE output of the K-means learning on the server fromthat single image. While images are all captured from similar-looking bird nests, we note that the images from different nodescan be notably different in their content and light intensity.Fig. 7 suggests that, due to such nodes, the average distortion(MSE) of the compressed images can be unacceptably largefor some cases while reasonably small for other nodes. Thisimplies that the gray-scale colors of the sensor nodes’ imagescan be very different; thus, we cannot use a single image froma single node to perform the centroid learning for other nodes,and the same set of centroids should not be used to compressimages from other nodes. For this reason, we conclude herethat images should be taken from each node and the K-meansclustering-based learning should be done on a per-node basisfor effective image size reduction.

Next, we take a deeper look into the image changes “within”a node over time. In Fig. 8, we plot the MSE versus days when asingle image from the first day was used for the K-means learn-ing, and the same set of centroids from this first image is used tocompress images collected during the following ∼3.3 months.We can see here that, while a large portion of the imagesmaintain low MSE values over the entire ∼3.3-month period,there exists a time frame when the MSE varies significantly.In terms of the context, we note that this period was the birds’breeding season, from May to June. During this period, thebirds lay and hatch their eggs, which is followed by a breedingperiod. As a result, there existed a noticeably high amount ofactivities in the bird nests, and therefore, the gray-scale colorshad significant differences from the nonbreeding periods. Wevisually show this change inside the bird nest as a time series inFig. 9. Based on this natural phenomena, using the same set ofcentroids computed on the first day of the deployment for theentire deployment duration can result (and did result) in low-quality compression of the images.

Fig. 9. Time-series images from a single node over a month period.

Fig. 10. “Days” versus “MSE” for various learning rates: when K-meanslearning is ran on 1) every image, 2) one image every day, and 3) one imageonly for the whole 4-month period.

The aforementioned results suggest that using the same setof centroids for a long duration can lead to ineffective compres-sion results, despite the use of empirically found parametersin Sections III and IV. Nevertheless, as long as the imagesensor provides “similar” data, these centroids may still beeffective, and making centroid updates more frequently can leadto system-level inefficiency itself. Therefore, we try to identifya reasonable periodic interval for making centroid updates andobserve the distortion of the compressed images.

Again, we take an experimental approach with our data setfor identifying this duration. Fig. 10 plots the MSE observedfor 300 different images from a single node, when the K-meanslearning operates 1) only once, 2) once per day using the firstimage of each day, and 3) for each image. The case wherecentroid learning is done for each image (i.e., case 3) is used asthe reference case since it will present the most ideal results interms of achieving minimal distortion. Nevertheless, this case isnot a viable option to practically use, given that it will presentthe worst performance in terms of energy usage. In this idealcase, we see most distortion measures falling below 5. With in-creasing update intervals (i.e., less frequent updates), we noticethat more and more images face higher distortion.

To generally quantify the impact of centroid update rates onthe image distortion, Fig. 11 plots the cumulative distributionfunction (CDF) of the image count, with respect to the MSE,for different learning interval T values. We test for five cases,where the centroids are computed only once (at the beginning),once per day using the first image of each day, once per weekusing the first image of the week, once per month using the firstimage of the month, and on a per-image basis. Results in Fig. 11show that, as long as we perform K-means clustering-based

2660 IEEE SYSTEMS JOURNAL, VOL. 11, NO. 4, DECEMBER 2017

Fig. 11. CDF of MSE for various learning rates: when K-means learning isran on 1) every image, 2) one image every day, 3) one image every week,and 4) one image only for the whole 4-month period.

learning at least once a week, > 90% of the images results inan MSE < 5. With a larger T , however, the MSE exceeds 5for more than 10% of the images. Based on these results, wewere able to conclude that a learning interval T of one week isreasonable for our target data set. By doing so, our image-basedsystem can provide domain scientists with images of the targetenvironment with acceptable quality, while maintaining a lowpower usage profile to lengthen the system lifetime using imagecompression. Likewise, while we are careful in generalizingthe results to a wide range, we believe that our methodologyfor finding the thresholds and parameters will stand for similarimage-based environmental monitoring application scenarios.

Overall, our results suggest a number of interesting points.First, given the time-scale dynamics of the captured imagesfor bird nest monitoring applications, a (relatively) frequent up-date interval should be applied for centroid computation. Thiscentroid update does not impact the compression performanceof our K-means clustering-based scheme, but this results indifferent compression “qualities.” Second, the overhead of per-forming such updates are small but not small enough to ignore.We plan to design a more adaptive scheme to detect distortionlevel changes for on-demand centroid/parameter updates as partof our future work. Finally, our results suggest that, with theseefforts, the compression impact of image data using K-meansclustering-based learning can be highly effective for low-powerembedded platforms, given that the computational overheadof our proposed scheme is minimal, while an efficient com-pression performance with minimal quality loss can be easilyachieved.

B. Comparison

Based on the evaluations until now, we now present compar-ison results for our K-means clustering-based image compres-sion against previously proposed compression schemes, such asPackBits [10] and RLE [11].

Table IV shows the compression rate, image quality (MSE andSSIM), computational complexity (running time on MSP430),energy consumption for the compression process (Ecompression),and overall energy gain per image assuming single-hop

TABLE IVCOMPARISON BETWEEN OUR PROPOSED K -MEANS BASED SCHEME

AND THE PACKBITS AND RLE SCHEMES. Ecompression

IS THE ENERGY USAGE FOR COMPRESSION

transmission. For our proposed K-means clustering-basedscheme, we used K = 16, as discussed in Section III. For thePackBits and RLE schemes, we used a threshold value of 10that gives a balance between the compression ratio and imagequality. That is, if we lower the threshold value, we can achievea higher image quality but a lower compression ratio. On theother hand, if we increase the threshold value, the compressionratio improves by sacrificing the image quality.

As we can see from the table, our proposed scheme sig-nificantly improves both the compression ratio and the imagequality (MSE and SSIM). This improvement comes at thecost of longer running durations, which translates to higherenergy usage for the compression process itself. Neverthe-less, we also compare the overall energy gain per image bycomputing the energy gain achieved by transmitting the “re-duced” image data, and we subtract the energy used for com-pression (Etransmit_whole_image∗ Comp.Ratio−Ecompression).(Etransmit_whole_image is 311.8 mJ from Section V.) The resultshows that our proposed scheme achieves the highest energygain among the three compared schemes, even when assum-ing a single-hop transmission. Furthermore, if the images aretransmitted over multiple hops, the reduced size and its benefitscan be further amplified. We emphasize that this performancein overall energy savings come with a better image quality.

VIII. RELATED WORK

Given their energy constraints and bandwidth limitations,many sensor network systems utilize data compression al-gorithms to maximize system-level efficiency. The goal ofdesigning our scheme was in proposing a simple algorithmthat satisfies the application requirements of our collaboratingenvironmental scientists and also meets the requirements at thehardware/system design level as well. A number of previousworks have examined the possibilities of utilizing other compres-sion schemes for similar purposes, as we list in the following.

Lee et al. [18] explored energy tradeoffs involved in JPEGcompression on energy-constrained platforms. Their findingsshow that the energy usage of JPEG image compression isdramatically high on low-power platforms, due to the longerlatencies of the resource-limited platforms. The fact that JPEGrequires high-precision computation makes such latency is-sues even worse, thus leading to high energy consumption.JPEG 2000 [20], [21] is an image compression standard andcoding system based on raster image compression techniquesusing wavelet transforms of the raw images. In addition to thewavelet transform phase, the “image tiles” from this phase are

PAEK AND KO: K-MEANS CLUSTERING DATA COMPRESSION SCHEME FOR WIRELESS IMAGING SENSOR NETWORKS 2661

passed through a quantization algorithm, in which the coeffi-cients of the transform phase are scalar quantized to reduce thenumber of bits of representation. As a result of the entire JPEG2000 coding process, the resulting image size is typically∼20%smaller than that of JPEG. While JPEG 2000 shows impressivecoding efficiency, the computational overhead that it causes toachieve such efficiency is very high. Therefore, processing ourimages through JPEG 2000 coding is impossible when usingtypical resource-limited platforms in WSNs [15]. Even if thesoftware containing the operations were to fit on our targetplatform, the computational power limitations can cause longlatencies for the image processing to successfully take place.As a result, it does not meet our goal of identifying a plausiblemethod that is applicable to low-power embedded platformswidely used in WSNs.

Furthermore, TiBS [13] and Optimal Zonal 2 × 2 BinDCT[14] are two recently proposed image compression schemes forsensor network systems, which were potential candidates forour deployments. TiBS operates on blocks of 2 × 2 pixels, andits compression is based on pixel removal. Furthermore, TiBScombines itself with a chaotic pixel mixing scheme to reinforcethe robustness of image communication against packet losses.While TiBS being an effective compression scheme, in ourdeployments, the primary challenge in implementing theseimproved algorithms was the practical memory constraint onour mote platforms. The Mica2 and MicaZ motes used in ourdeployments consist of only 4 kB of RAM, and the Tenet soft-ware, which includes the multihop networking stack (e.g., reli-able transport protocol [22]) and the task libraries, consumedover 3.6 kB already. However, TiBS requires 22 946 bytesof ROM and 1820 bytes of RAM [13] to implement on ourtarget platforms. As a result, there was very little room for thesesophisticated image processing algorithms to be implementedon our devices. We agree that newer platforms with more mem-ory hold the resources to support such complex algorithms[23]. Nevertheless, these platforms were not suitable for ourdeployment purposes, due to energy usage constraints.

Alternatively, we note that hardware-based image compres-sion, such as the one proposed by Kaddachi et al. [24], can alsobe a solution if the device cost limitations permit. Nevertheless,our compression algorithm focuses on providing a lightweightsoftware-based solution, which is applicable for most resource-limited low-power platforms.

IX. CONCLUSION

Low-power embedded computing platforms are connectedto various sensors for capturing data from the physical worldto design a number of interesting applications. With the di-versity in such applications, the types of sensors that connectto these low-power embedded platforms have increased aswell. This work focuses on the efficient processing of imagedata generated from low-power camera sensors that operate onresource-limited embedded computing platforms. Specifically,we apply the K-means clustering-based learning algorithm tocompress the image data collected on these platforms. By doingso, the transmission overhead of these images can be reduced

significantly. We show that, despite the reduced size, the dis-tortion of the compressed images are minimal when comparedwith the original larger sized images. We acknowledge thatthere are various compression methods that are lightweight andcan be adopted to low-power embedded platforms. This worktargets to explore the possibilities of applying the K-meansclustering-based learning algorithm for image compression. Wehope that this work can open new possibilities for algorithms onfuture embedded sensing platforms.

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2662 IEEE SYSTEMS JOURNAL, VOL. 11, NO. 4, DECEMBER 2017

Jeongyeup Paek received the B.S. degree in elec-trical engineering from Seoul National University,Seoul, Korea, in 2003 and the M.S. degree in elec-trical engineering and the Ph.D. degree in computerscience from the University of Southern California(USC), Los Angeles, CA, USA, in 2005 and 2010,respectively.

In 2010, he was a Postdoctoral Research Asso-ciate with USC and also a Research Intern withDeutsche Telekom Inc. R&D Laboratories USA. In2011, he then joined Cisco Systems Inc., where he

was a Technical Leader in the Internet of Things Group, Connected EnergyNetworks Business Unit. Until recently, he was an Assistant Professor withthe Department of Computer Information Communication Engineering, HongikUniversity, Sejong, Korea. He is currently an Assistant Professor with theSchool of Computer Science and Engineering, Chung-Ang University, Seoul,Korea. His prior research has focused on topics in wireless sensor networksystems, including network and transport protocols, architecture for tieredembedded networks, and real-world sensornet applications, and also in the areasof mobile smartphone systems. His recent research interests are in low-powerlossy networks for the Internet of Things, with focus on protocols, architecture,software, and performance enhancements.

JeongGil Ko received the B.Eng. degree in com-puter science and engineering from Korea Univer-sity, Seoul, Korea, in 2007 and the M.S.E. and Ph.D.degrees in computer science from the Johns HopkinsUniversity, Baltimore, MD, USA, in 2009 and 2012,respectively.

At Johns Hopkins, he was a member of theHopkins interNetworking Research Group (HiNRG)led by Dr. A. Terzis. In 2010, he was a VisitingResearcher with the Stanford Information Network-ing Group, Stanford University, Stanford, CA, USA,

with Dr. P. Levis. From June 2012 to June 2014, he was a Senior Re-searcher with the Electronics and Telecommunications Research Institute. As ofSeptember 2015, he has been with the Department of Software ConvergenceTechnology, Ajou University, Suwon, Korea, as an Assistant Professor. Hisresearch interests are in the general area of developing web- and cloud-basedsensing systems with ambient intelligence for the Internet of Things andcyberphysical systems.

Dr. Ko was a recipient of the Abel Wolman Fellowship awarded by theWhiting School of Engineering at the Johns Hopkins University in 2007.