778 IEEE SENSORS JOURNAL, VOL. 15, NO. 2, FEBRUARY 2015

Medical Data Compression and Transmissionin Wireless Ad Hoc Networks

Tanima Dutta, Member, IEEE

Abstract— A wireless ad hoc network (WANET) is a type ofwireless network aimed to be deployed in a disaster area in orderto collect data of patients and improve medical facilities. TheWANETs are composed of several small nodes scattered in thedisaster area. The nodes are capable of sending (wirelessly) thecollected medical data to the base stations. The limited batterypower of nodes and the transmission of huge medical data requirean energy efficient approach to preserve the quality of service ofWANETs. To address this issue, we propose an optimization-based medical data compression technique, which is robustto transmission errors. We propose a fuzzy-logic-based routeselection technique to deliver the compressed data that maximizesthe lifetime of WANETs. The technique is fully distributed anddoes not use any geographical/location information. We demon-strate the utility of the proposed work with simulation results.The results show that the proposed work effectively maintainsconnectivity of WANETs and prolongs network lifetime.

Index Terms— Energy efficient, health care, imagecompression, routing, transmission error, wireless ad-HocNetwork.

I. INTRODUCTION

UTTARAKHAND flood in India occurred on June 2013,has caused extensive damage including the cutting of

roads, destruction of bridges, hotels, hospitals, and com-munication network. As a result, around 5700 people werekilled and more than 110,000 pilgrims and tourists trappedin the valleys [1]. This type of disaster has drawn ever-increasing attention to improving rescue efforts. One of thetechniques that can be effectively applied during disasterrecovery is known as telemedicine in the literature [2] and [3].Telemedicine is the combination of information technologyand medical science. Telemedicine is used to provide medicalinformation, medical consultation, and health care services topatients.

Remote medical monitoring (RMM) system is a remote-station based primary health care system which is an appli-cation of the telemedicine. The RMM system collects themedical data of patients (MDPs) such as audio, video, andother information of patients at the primary health care(PHC) station of the disaster area and transmits the MDPs tothe community care (CC) center through wireless networks.

Manuscript received June 26, 2014; accepted August 18, 2014. Date ofpublication September 4, 2014; date of current version November 25, 2014.The associate editor coordinating the review of this paper and approving itfor publication was Prof. Octavian Postolache.

The author is with TCS Innovation Labs, Bangalore 560066, India (e-mail:dutta.tanima@gmail.com).

Color versions of one or more of the figures in this paper are availableonline at http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/JSEN.2014.2354394

Fig. 1. Illustration of the proposed RMM system.

The CC center is a health care center or hospital, wheredoctors, medical equipments, and other facilities are available.

One of the wireless networks that can be effectivelydeployed during disaster recovery is known as wirelessad-hoc network (WANET) in the literature [4]–[6]. A WANETconsists of several nodes which can communicate with eachother, PHC station of the disaster area, and CC center.A WANET is said to be connected if all nodes can reach theCC center via one or multiple hops. Minimizing the energyconsumed, while ensuring the connectivity of the network, isan important factor to be considered to extend the lifetime ofWANETs because the batteries powering the nodes may notbe accessible for replacement often. Fig. 1 illustrates a RMMsystem where a health care professional at PHC station collectsand transmits MDPs to CC center via WANET.

In this paper, we study an energy-efficient connectivityproblem in WANETs. We assume that nodes are deployed ran-domly in disaster area independent of each other. To enhancethe network lifetime, we propose a visually lossless com-pression technique for MDPs and a fuzzy-logic based routeselection technique to transmit compressed MDPs.

Motivation: Many conventional image compression tech-niques [7]–[10] and routing protocols [11]–[16] have beenaddressed widely in the past. The paper is motivated by thefollowing limitations observed in the literature.

JPEG (Joint Photographic Experts Group) compression isthe most popular compression technique for images [17].In lossy JPEG compression, pixels are first transformed usingdiscrete cosine transform (DCT), then quantized using quan-tization tables, and finally encoded using Huffman coding.In lossless JPEG compression, quantization step is not per-formed. The JPEG2000 gains over JPEG are attributed to theuse of discrete wavelet transform (DWT) and bit plane codingscheme [18]. In [7], the preprocessing of tumor images using

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DUTTA: MEDICAL DATA COMPRESSION AND TRANSMISSION IN WANETs 779

Fig. 2. Block diagram of the MDC technique at PHC Station.

principle component analysis is followed by DCT. Vectorquantization is used to code the transformed images. A surveyof compression algorithms for color retinal images is presentedin [8]. The authors in [9] have designed a compressor systemfor endoscopic images, which integrates a CMOS sensor witha JPEG compression engine in a capsule. In [10], endoscopicimages of wireless capsule are compressed using DCT andGolomb-Rice (GR) coding. None of the aforesaid literature formedical image compression are robust to transmission errors.These algorithms are therefore not meaningful for medicaldata transmission applications since in practice transmissionchannels are not completely noiseless.

Ad-hoc On-Demand Distance Vector Routing (AODV) isan on-demand routing protocol for WANETs [11]. Nodes donot depend on active paths neither store any routing infor-mation nor take part in any periodic routing table exchanges.Saida and Mellouk in [12] proposed a delay-oriented adaptiverouting protocol called adaptive mean delay routing (AMDR)by using the reinforcement learning mechanism. In [13], adistributed routing scheme is designed to offer scalability andextend the network lifetime in a mobile ad hoc network.AMDR uses forward exploration packets (FEP) to gatherdelay information of available paths that will be used bybackward exploration packets (BEP) to update routing tableentries throughout the network. In [14], a model to estimatepath duration in a MANET using the random way pointmobility model is proposed. A multicast algorithm is pre-sented in [15] to increase the lifetime of node and networkin the mobile ad hoc network. Two energy-aware routingalgorithms for wireless ad hoc networks, called reliable min-imum energy cost routing (RMECR) and reliable minimumenergy routing (RMER) are presented in [16] to addressenergy-efficiency, reliability, and network lifetime of ad hocnetworks.

The main limitation of the above protocols is that theyconcentrate on the discovery of routes satisfying certain qualityof service (QoS) requirements such as minimum hop countand delay. However, various issues (e.g., network reliability,low routing delay, long battery lifetime, etc.) that have to takeinto account when routing the compressed MDPs. In summary,there is no work in the literature on routing protocols forWANETs that can work with compressed MDPs.

Contribution: The main contributions of the paper can besummarized are as follows:

• First, we propose a novel medical data compression(MDC) technique that reduces the size of MDPs. TheMDC technique compresses and decompresses MDPs atPHC station and CC center, respectively. In addition,robustness is achieved in the presence of transmissionerrors. The technique addresses the compression of MDPsin the form of color images, such as, endoscopic images,where a robust and efficient compression of color images

is still a challenging problem in the literature [19], [20].Moreover, the MDC technique also effectively works withother forms of the medical data, e.g. electrocardiographyimages, magnetic resonance imaging, etc.

• Various issues (e.g., network reliability, low routing delay,and long battery lifetime) have to take into accountwhen routing the compressed MDPs in WANETs. Next,we propose a fuzzy-logic based route selection (FRS)technique that optimizes among of the issues related tothe routing of the compressed MDPs. The FRS tech-nique estimates an effective route selection metric forrouting the compressed MDPs from PHC station toCC center.

• Finally, we show that MDC and FRS techniques can beeasily integrated with the existing routing protocols forWANETs to form an effective RMM system. We usethe simulation to illustrate that the existing protocols forWANETs with MDC and FRS techniques consume lessenergy and reduces packet-loss during the transmissionof the compressed medical data from the PHC station toCC center.

The rest of the paper is organized as follows: Section II andSection III present MDC and FRS techniques, respectively.We present the results of simulation conducted to evaluate theperformance of the work in Section IV and conclude the paperin Section V.

II. MEDICAL DATA COMPRESSION TECHNIQUE

A WANET is characterized by the frequent occurrenceof transmission errors along with limited battery lifetime.Another aspect which often concerns is the lossless trans-mission of MDPs without any perceptual distortions intro-duced by a compression process and noisy channel. Anyperceptual distortions in MDPs may lead to mis-diagnosis.Lossless compression techniques are mainly used for medicalimage transmission. Such techniques have a constraint ofhaving a higher bit rate. The visually lossless compressiontechniques are therefore widely accepted in medical datatransmission applications. The goal of the MDC techniqueis to achieve a highly compressed MDPs without degradingthe perceptual quality and robust against transmission errors.In this paper, we consider the MDPs in the form of colorimages like endoscopic images. The MDC technique is basedon an optimization approach. The block diagram of theoverall compression technique at PHC Station is presentedin Fig. 2. The compression technique is divided into two steps:image transformation and encoding of coefficients, whichare described in Section II-A and Section II-B, respectively.To reduce artifacts and noises, the decompressed MDPs arefiltered using an adaptive edge-based fuzzy filter, which ispresented in Section II-D.

780 IEEE SENSORS JOURNAL, VOL. 15, NO. 2, FEBRUARY 2015

A. Image TransformationThe endoscopic images of patients are usually raw color

images [21]. Each pixel in the image is represented by red (R),green (G), and blue (B) color planes. Independent compres-sion of the color planes is not efficient as the correlation amongthese planes are very high. An adaptive color-space conversioncan perform a better compression. The process of endoscopicimage transformation and quantization is performed in severalprocessing steps as follows:

1) Color-Space Conversion: The MDC technique performsan adaptive color-space conversion for the image. Since alossless and hardware efficient implementation is required,Y U Vr or Y CgCo-R yields the best decorrelation of theimage by means of low entropy and without data loss [8],where Y U Vr and Y CgCo-R correspond to the reversible colortransform of JPEG2000 and the fidelity range extension of theH.264 video coding standard, respectively. We perform thecolor-space conversion from RG B to Y CbCr by combiningthe aforesaid methods [22] and Structure Conversion [23] forarray squeezing of luminance component.

2) Block-Wise Pixel Scanning: The detailed characteristicsof the image will be omitted after quantization due to the highcomputational burden for implementing full-image transfor-mation. The image is therefore divided into non-overlappingblocks to decrease the number of operations. The conversionof progressive pixel scan to block-wise order is required tooperate on small non-overlapping image blocks. The advan-tages of small block size are less computational complexityand moderate memory requirement, but the size of compresseddata is large. On the other hand, large block size can attainsignificantly compressed data for the consequent coefficientsof small magnitude but may cause visual distortions. However,to avoid visual artifacts in the case of higher compression ofMDPs, the size of the transformed block was set to 4 × 4. TheMDC technique therefore operates on non-overlapping 4 × 4image blocks for block-wise data access [10].

3) Transformation and Quantization: The signal dependentKarhunen-Love transform (KLT) is the most efficient decor-relating transform for compressing the component planes toa single spectral plane of a color image prior to encodingusing the maximum energy compaction. However, the KLTtransform is not used in practice because it depends on signalstatistics and does not have an efficient implementation [24].DCT comes close to ideal transform KLT and more practicalthan others [10]. Therefore, 2-D DCT can be used in imagecompression process to compact the energy into a few coeffi-cients along the spatial directions. However, DCT coefficientsare real number and processing these real numbers increasesthe computational complexity. We therefore use 2-D integerDCT (IntDCT) instead of 2-D DCT for transformation andquantization of the component planes. The degradation inperceptual quality for using IntDCT is mostly imperceptibleto the human visual system. The MDC technique is thusconsidered as visually lossless. Specially, IntDCT providesgood decorrelation property and instruction throughput ofIntDCT can be increased by performing multiple operationsin parallel. Multiplication in the transform process is avoidedby integrating it with the quantization. Block wise access

decreases rounding operation and decreases the computationaloverhead. Typical DCT devices are used to implement Int-DCT. Therefore, redesigning of the hardware is also notrequired.

The coefficients obtained from the block transformationwill be entropy encoded using an efficient encoder, which ispresented in the next section.

B. Encoding of Coefficients

In this section, we present a hardware efficient encoderto encode the transformed coefficients. The coefficients of ablock are partitioned into DC and AC coefficients. First, DCcoefficients are differentially encoded, i.e., the DC coefficientof the previous block is used as the predicted value for thecurrent coefficient. DC coefficients of the image can be treatedas a smaller image that essentially has the same smoothnessproperties encountered in the image. Next, the differentiallyencoded DC coefficients are further encoded using AdaptiveGolomb Rice (AGR) code that uses adaptive coding andrequires only one pass through the data. AGR encoder with aparameter k is defined by the encoding rule as follows:

G(u, k) = 11 · · ·1︸ ︷︷ ︸

prefix,pbits

0bk−1bk−2 · · · b0︸ ︷︷ ︸

suffixz,kbits

(1)

The AGR encoder encodes the positive integer u as twostrings: a prefix of p + 1 bits, where p = �u/2k� and asuffix of k bits. For example, if u = 10 and k = 2, thenthe code for u is ‘11010’, the prefix is ‘11’ and the suffixis ‘10’ [10]. As the AGR encoder encodes only non-negativeintegers, the following mapping function is used to transformDC coefficients to non-negative integers, which is given by

M1(d) ={

2d if(d ≥ 0)

2|d| − 1 otherwise,(2)

where d and |d| denote a DC coefficient and its absolute value,respectively. AGR decoder will be able to decode correctly thefirst block of consecutive error-free codewords in forward andreverse directions.

Next, the majority of high frequency AC coefficients arequantized to zero because of the energy compaction propertyof IntDCT. The remaining nonzero coefficients in a blockare typically low frequency coefficients clustered around theDC coefficient. The AC coefficients in are scanned along azigzag order. The encoder proposed to encode AC coefficientsuses the principle of the zero run-length encoding. Since ACcoefficients are signed, a mapping function, denoted by M2(e),is used to transform a nonzero AC coefficient e to a positiveinteger for the AGR coding, such that

M2(e) = {M1(e) : e ∈ Z�=0}. (3)

A GR code with adjustable k is inefficient when coding lowentropy distributions, such as, encoding zero AC coefficients.In AGR code, the encoded sequence is therefore rearrange toform (r, q) pairs, where a nonzero AC coefficient r is followedby q numbers of consecutive zero-value AC coefficients.Any zero-value AC coefficient in a block is represented bya single symbol (0, 0). Since the symbol (0, 0) occurs very

DUTTA: MEDICAL DATA COMPRESSION AND TRANSMISSION IN WANETs 781

Fig. 3. Block diagram of the adaptive edge-based fuzzy filtering method.

Algorithm 1 Medical Data Compression Technique1: The color-space conversion from RG B to Y CbCr ofan input image is performed to generate less correlatedcomponent planes.2: Each component plane is transformed and quantizedto reduce inter pixel correlation and packing pixel energyinto few transform coefficients.2.1: The plane is divided into non-overlapping N × Nblocks.2.2: The blocks are transformed and quantized by blockedIntDCT.3: The quantized coefficients of each component plane areencoded using a hardware efficient encoder.3.1: The coefficients of a block are partitioned into DCand AC coefficients.3.2: The DC coefficients are differentially encoded andmapped to non-negative integers using Eq. 2.3.3: The resultant coefficients are further encoded usingAGR coding shown in Eq. 1.3.4: The AC coefficients are scanned along a zigzagorder and the nonzero AC coefficients are also mappedto nonzero positive integers using Eq. 3.3.5: A pair consisting of a nonzero AC coefficient and therun-length of the succeeding zero-value AC coefficients isencoded using the AGR encoder.3.6: Each zero-value AC coefficient is assigned thevalue ‘0’.

frequently, the value ‘0’ is assigned to the symbol (0, 0) formaximizing the compression efficiency.

C. Medical Data Compression Algorithm

The formal algorithm of MDC technique at PHC Station isfacilitated by Algorithm 1.

In summary, the output of the MDC technique is compressedMDPs. These compressed MDPs is transmitted from PHCstation of the disaster area to CC center over WANETsusing the FRS technique as described in Section III. At CCcenter, the compressed MDPs are decompressed using AGRdecoder followed by the inverse transform and finally inversecolor-space conversion is performed. The AGR decoding andthe inverse color-space conversion are just the reverse processof AGR encoding (Eq. 1). Due to block transformation,quantization, and transmission errors due to transmission ofcompressed MDPs over noisy channels, blocking artifacts anddifferent additive and multiplicative noises may add.Therefore, we introduce an adaptive fuzzy filter during

decompression to reduce such artifacts and noises, which isdescribed in the next section.

D. Reduction of Artifacts and Noises

The presence of any artifacts in medical images may lead tomis-diagnosis. Blocking effects result in discontinuities acrossblock boundaries. In this section, we therefore propose anadaptive edge-based fuzzy filtering method to reduce blockingartifacts based on the intensity gradient (slope) of the pixelsclose to the boundary of two blocks during decompressionat CC Center. The blocking artifacts are more in lossy com-pression techniques due to quantization. If the coefficients ofthe adjacent blocks are coarsely quantized, a difference in theintensity gradient across the block boundary is expected. Thestrongest filtering that can be applied in medical images arein the direction perpendicular to the edge. The block diagramof the adaptive fuzzy filtering method is shown in Fig. 3.

The gradient G of an image I (of size M × N pixels)is obtained by applying the Sobel kernels, which is acomputational less expensive edge detector, in horizontaland vertical directions. A global edge map Mg and alocal edge map Ml are obtained by using the edge detec-tor, such that Mg = 2

√∑ ∑

G A/M × N and Ml =(1 − ρ[m, n]/τ [m, n]) × Mg , where ρ[m, n] and τ [m, n]are the standard deviation and the mean of a block of sizem × n in the gradient image and G A is the amplitudeof the gradient. To be adaptive for different areas havingdifferent activity levels, ρ[m, n] in a window centered onI (m, n) the spread parameter of the input is defined in [25] asσ(x[m+m′, n+n′], x[m, n]) = S[m+m′, n+n′] × σA[m, n],where value of the amplitude of spread parameter (σA[m, n])is σ0((1−γ )(ρ(I [m, n]) − ρmin/ρmax − ρmin)+γ ), σ0 is themaximum spread parameter value, and S is the scaling func-tion controlled by the direction of x[m+m′, n+n′] to x[m, n].

The pixels are first classified into edge pixels and non-edge pixels by comparing the amplitude of the intensitygradient to a threshold, denoted by th. Edge pixels are notfurther filtered because any smoothing may blur the edges.For non-edge pixels, if there are no edge pixel in the sameblock, a 2-D adaptive low-pass filtering is performed using a2 × 2 filter over the complete image to remove additive andmultiplicative noises. For non-edge pixels with staircase noise,the tangent angle of their nearest edge pixel is used to controlthe directional spread parameter σ(x[m+m′, n+n′], x[m, n]).For remaining non-edge pixels, the ringing artifacts are filteredwith an isotropic fuzzy filter because such artifacts are notconsidered to be oriented in any particular direction [26].

The resultant MDPs obtained at CC center are visuallylossless and ready for diagnosis.

782 IEEE SENSORS JOURNAL, VOL. 15, NO. 2, FEBRUARY 2015

Fig. 4. Block diagram of the FRS Technique.

III. FUZZY-LOGIC BASED ROUTE SELECTION TECHNIQUE

The existing work in the literature on routing in WANETsdoes not have any special treatment for the compressed MDPs.The path generated by the existing routing protocols candeviate far from the QoS requirements of the compressedMDPs. We propose FRS technique that reduces the energyconsumption of the network. The FRS technique works withdistance vector routing protocols such as AODV, but it can beworked with any underlying routing protocol for WANETs.Our goal is to find a better route selection metric which willenhance the lifetime of the network.

In this section, we first state the assumptions made aboutthe network, define the terms and the notations used in thiswork, and introduce an energy consumption model. Next, wepropose FRS technique and its application in RMM system.

A. Assumptions

We assume that a large number of nodes, say n, are deployedin a two-dimensional field of interest (FoI), uniformly at ran-dom independent of each other. This is a common assumptionin the existing literature, both for theoretical analysis [27] andon real applications [28]. The uniform random deploymentis favored in a situation where the geographical region to berouted is hostile and inimical [29]. In an ad-hoc network, ifsome nodes go down or in case of node failure, it is alsoassumed that the nodes can still send data to the CC center.We assume the binary disc communication model in which anode s, can communicate perfectly with other nodes within thedisc of radius Rc centered at s, where Rc is the communicationrange of s. The area of the communication region denoted byA(s, Rc), is nothing but the area of a disk of radius Rc centerat s, i.e., ‖A(s, Rc)‖ = π R2

c . The network is modeled as adirected graph G = (V , E), where V , |V | = n, is the set ofnodes in the network and E is the edge set. There is a directededge (u, v) ∈ E between node u and node v iff single-hoptransmissions from u to v and v to u are possible.

B. Energy Consumption Model

We employ the energy consumption model given in [30].When a node transmits k bit message directly to a receivernode over a distance d , the energy consumed for transmission(Et (k, d)) and reception (Er (k)) can be calculated as follows:

Er (k) = k × Eelect and Et (k, d) = k × (Eelect + εampd2),

(4)

where the radio dissipates Eelect = 50J/bi t to run thetransceiver and receiver circuit and εamp = 100 pJ/bit/m2 forthe transmitter amplifier.

TABLE I

FUZZY RULE SET. I1 , I2 , I3 , LV, VH, H, M, L, AND VL ARE ENERGY

CONSUMPTION, RESIDUAL ENERGY, ROUTING DELAY, LINGUISTIC

VARIABLE, VERY HIGH, HIGH, MEDIUM, LOW, AND

VERY LOW, RESPECTIVELY

C. Overview of FRS Technique

AODV protocol uses hop count as a route selection metricto find an optimal path in WANETs. However, only thevalue of hop count as a route selection metric does notfulfill the requirements of WANETs. The FL system allowsto combine and evaluate diverse issues of WANETs such asenergy consumption, residual energy, and routing delay in anefficient manner. The use of FL system is therefore a promisingtechnique to optimize the route selection metric. The FL sys-tem performs its work with the help of following four steps:fuzzifier, fuzzy inference engine, rule base, and defuzzifier.When an input is applied to a FL system, the fuzzy inferenceengine computes the output set corresponding to each rulebase and various methods for inferring the rules. All rules inthe rule base are processed in a parallel manner by the fuzzyinference engine. The defuzzifier performs defuzzification tofind a single crisp output value. The proposed work considersm-input 1-output FL system using singleton fuzzifica-tion, center-of-sets defuzzification, and IF-THEN rule. TheFL system uses the following IF-THEN rule: IF input1→ x1 and input2 → x2 and · · · inputm → xm , THENoutput → y.

The FRS technique uses a FL system to compute theroute selection metric as shown in Fig. 4. The inputs areenergy consumption, residual energy, and routing delay andthe output, denoted by select_routing_metric, is the probabilityof a path to be selected for routing.

A path having higher value of select_routing_metric willhave more chances to be a part of the routing path. TheFL system expresses numeric data in word language calledlinguistic variable (LV). The values of LVs are words orsentences in a natural or artificial language providing a meansof systematic manipulation of vague and imprecise concepts.Table I illustrates the LVs for inputs of FL system.Inputs of the FL system are described as follows:

1) Energy Consumption: An energy-efficient path in routeselection phase can prolong the lifetime of WANETs. Let Eab

ik

DUTTA: MEDICAL DATA COMPRESSION AND TRANSMISSION IN WANETs 783

Fig. 5. An example of FRS technique. Label on an edge ad and value atnode a are Ead

il and the residual energy of a, respectively.

denotes energy required to transmit k bit message of com-pressed MDPs between nodes a and b via i th path, wherei ≤ t and t is the number of possible paths between a and b.From Eq. 4, Eab

ik is therefore given by

Eabik =

hi∑

j=1

(

k × (Eelect + εamp∥

∥hi j∥

∥2) + k × Eelect

)

, (5)

where hi and∥

∥hi j∥

∥ are the hop count and length of the j th

hop in the i th path, respectively. Fig. 5 illustrates a scenariowhere s and t are source and destination nodes, respectively.A relay node c receives two RREQ messages from nodes band d . By using Eq. 5, the actual energy required to transmitdata between s and c can be written as Esc

1k = {2 + 2 + 2}and Esc

2k = {2 + 3}. The energy required in an energy-efficient path between s and c, denoted by Esc

k , is expressed asEsc

k = min{Esc1k, Esc

2k, . . . , Esctk }. The LVs for energy consump-

tion are high, medium, and low. The values of LVs are nothingbut the energy required to transmit the compressed MDPsbetween nodes a and b via i th path, i.e., Eab

ik .2) Residual Energy: Considering the residual energy of

nodes in route selection metric can avoid nodes from beingoverused and eventually lead to an increase in the operationallifetime of WANETs. Let eab

i j denotes the energy of the j th

node in i th path between nodes a and b, where i ≤ t ,j ≤ hi , t is total paths between a and b, and hi is thehop count in i th path. The residual energy in the i th path isgiven by

eabi = min{eab

i1 , eabi2 , . . . , eab

ihi +1}. (6)

Let us continue the previous example, as illustrated inFig. 5 to emphasize the residual energy fuzzy set. By usingEq. 6, the residual energy between nodes s and c are esc

1 =min{5, 6} and esc

2 = {2}. The LVs for residual energy are high,medium, and low, where high ≥ Einit − Eth , medium ≥(Einit ± Eth)/2, low ≥ Eth , Einit is the initial energy ofnodes, and Eth is the minimum residual energy required totransmit compressed MDPs. Let k is the size of the compressedMDPs. By using Eq. 4, the value of Eth can be written asEth = k × (Eelect + εamp × C2) + k × Eelect .

3) Routing Delay: WANETs in case of an emergency situa-tion will not be delayed beyond a predefined delay threshold.Let dab

i denotes the routing delay to transmit data betweennodes a and b via i th path, where i ≤ t and t is the number ofpossible paths between a and b. The value of dab

i depends onpropagation delay, transmission delay, and processing delaydenoted by dpn , dtr , and dpg , respectively. We assume thatdpn+dtr +dpg= f , where f is a constant value for equal lengthhop in the routing path. Let hi be the hop count between nodes

Algorithm 2 Fuzzy-Logic Based Route SelectionTechnique1: When a node d wants to find a route to destinationnode t , it broadcasts RREQ messages {s,t ,d , Esd

k , esd1 , dsd

1 }.2: If an intermediate node c receives RREQ messages for t:2.1: Node c uses the fuzzy rule set of Table I and calculatesthe select_routing_metric for each RREQ message.2.2: Node c selects RREQ message having leastselect_routing_metric.2.3: Node c updates its routing table and broadcasts aRREQ message.3: When t receives RREQ messages:3.1: Node t uses fuzzy rule set (Table I), calcu-lates select_routing_metric for each RREQ message, andupdates its routing table.3.2: t waits for a fixed time interval to receive more routerequest messages.3.3: Node t unicasts a RREP message back toits neighbor from which it has received the leastselect_routing_metric.4: Each node, after receiving a RREP message, unicaststhe RREP message towards source node s.

a and b via i th path. The routing delay can be expressed asdab

i = hi ×(dpn+dtr +dpg) = hi × f . Fig. 5 illustrates that thevalues of dsc

1 and dsc2 are 3 f and 2 f , respectively. The LVs

for routing delay are high, medium, and low. The values ofLVs are nothing but the delay to transmit compressed MDPsbetween nodes a and b via i th path.

Next, the value of select_routing_metric is calculated usingthe following if-then rule: IF energy consumption → Lowand residual energy → High and delay → Low, THENthe select_routing_metric → Highest. The fuzzy mappingrules are obtained based on three fuzzy inputs and theircorresponding LV as illustrated in Table I.

D. Application of the Proposed FRS Technique

The proposed FRS technique can be integrated with anyunderlying routing protocol. In this section, we describe theAODV_FLT algorithm that uses select_routing_metric as routeselection metric to find an effective routing path for WANETs.An example of the AODV_FLT algorithm is shown in Fig. 5.The network consists of seven backbone nodes {s, a, b, c, d ,e, t}, where s and t are the source and the destination, respec-tively. The label on an edge

−→ab shows the energy consumption

to transmit k bit message of compressed MDPs between nodesa and b. An entry in the routing table at a node d shows theroute information i.e., [s,t ,a, Esa

k + Et (k, ‖ad‖), min{esa1 , ea},

dsa1 +1], where a and ea are the predecessor neighbor of d

and residual energy of a, respectively. Fig 5 illustrates thatthe routing table at a node d is therefore {s, t, a, 4, 5, 2}. TheRREQ message broadcasts from d is denoted by {s,t ,d , Esd

k ,esd

1 , dsd1 }. Fig 5 illustrates that the RREQ message broadcasts

from d is {s, t, d, 4, 5, 2}. A relay node c receives RREQmessages from nodes d and b. Node c uses Table I to selectan optimal route request.

784 IEEE SENSORS JOURNAL, VOL. 15, NO. 2, FEBRUARY 2015

Fig. 6. Original test endoscopy images (first row) of the dataset [33], respective histograms (second row), their decompressed counterpart (third row) andtheir respective decompressed histograms (last row).

E. Fuzzy-Logic Based Route Selection Algorithm

The formal algorithm of AODV_FRS technique is facilitatedby Algorithm 2.

F. Complexity Analysis of Algorithm 2

1) Message Complexity: Each node broadcasts a RREQmessage to its neighbors and sends a RREP message toits predecessor neighbor node. Each node receives RREQmessages from its neighbors and one RREP message fromits successor node. Let x is the number of neighborsof a node. Since x � N , the message complexity ofAlgorithm 2 is O(N × (x + 1 + x + 1)) = O(N) which isnearly optimal.

2) Space Complexity: We assume a routing table consistsup to three best possible path information. This is an assump-tion widely used in the literature [11]. A routing table atnode d has the following fields: s, t , b, Esb

k + Et (k, ‖bd‖),min{esb

1 , eb}, and dsb1 + 1. The space complexity is therefore

O(N × 3 × 6) = O(N) which is nearly optimal.

IV. SIMULATION RESULTS

In this section, we discuss the results from a simulationstudy of the proposed MDC and FRS techniques imple-mented in MATLAB 7.1 [31] and NS 2.34 simulation [32],respectively. The MDC technique generates compressed MDPsat PHC station from raw endoscopic images of patients. Thecompressed MDPs are decompressed and artifacts and noiseare removed before diagnosis at CC Center. These compressedMDPs are then transmitted to CC Center via WANETs. ThePHC station and CC Center are therefore working as sourceand destination nodes in the FRS technique.

A. Performance Evaluation of the MDC

This section evaluates the performance of the proposedMDC technique and compares to JPEG, JPEG2000, and [10]compression schemes. An image dataset of 500 endoscopic

images with a 512 × 512 resolution [33] are used for compres-sion and are tested against transmission errors, such as leastsignificant bit error. Unlike [9], [10], the MDC technique canbe effectively used for compression of other forms of medicalimages, e.g. electrocardiography images, magnetic resonanceimaging, etc.

1) Visual Quality of MDPs: Fig. 6 illustrates that thedegradation in visual quality of the compressed MDPs isnot perceived by the human visual system. In addition, thehistograms of original and decompressed images are same withhigh probability. Peak Signal-to-Noise Ratio (PSNR) is usedto evaluate the quality of image reconstruction, i.e., PSNR(d B) = 10log(2552/ (ai − ai )2), where ai and ai are valuesof a pixel in original and decompressed images, respectivelyand · is the averaging operator. Mean Opinion Score (MOS) isthe average quality rating over a number of human observersthat have been asked to score an image, often on the scalefrom 1 (worst) to 5 (best) [34]. An objective MOS predictionuses perceptual metrics that correlate with human perceptionof image quality using blockiness and blur in combination witha few others to estimate the perceived quality of an image.

2) Compression Ratio: Compression Ratio (CR) is the ratioof the size of an original image to the size of its compressedcounterpart. As the CR increases, the bit rate will decreaseto achieve highly compressed data. However, visual artifactsmay occur when CR is very high in case of lossy compressiontechniques. Fig. 7(a) illustrates the efficiency of Algorithm 1in terms of MOS for endoscopic images of the dataset [33] inthe noiseless transmission channel. DCT based compressionmethods, such as JPEG, MDC, and [10], provide betterperceptual quality when CR is less compared to DWTbased methods, like JPEG2000. Blockiness effect increasesin DCT based method with the increase in CR which in turndegrade the perceptual quality of the compressed images [34].No such blocking artifacts occurs in DWT based meth-ods. Therefore, JPEG2000 provides better perceptual qual-ity then JPEG and [10] at higher CR. Furthermore, AGR

DUTTA: MEDICAL DATA COMPRESSION AND TRANSMISSION IN WANETs 785

Fig. 7. Illustration of Perceptual quality of compressed MDPs. (a) Perceptualquality of MDPs degrades with compression ratio. (b) Perceptual qualityod MDPs in a noisy channel. (c) Visual quality of MDPs in presence oftransmission errors.

(MDC and [10]) encoder provides better perceptual qualitywith higher compression than Huffman (JPEG) and Bit planeencoder (JPEG2000) as illustrated in literature [10]. Theproposed adaptive edge-based filtering in MDC takes careof blocking artifacts and quantization noise. Therefore, theproposed MDC technique is less sensitive to changes in CRin the noiseless channel.

3) Robustness Against Transmission Errors: The bit errorrate (BER) is the ratio of the number of error bits to thetotal number of transferred bits during a studied time inter-val. BER against transmission errors can be calculated asBER = transmission errors/ total number of bits. Robust-ness are estimated based on BER, such that robustness =(1-BER)×100%. Figs. 7(b) and 7(c) justify that Algorithm 1maintains higher MOS and PSNR, respectively, even whenthe BER is high. MOS and PSNR of the MDPs drop withthe increase in BER due to increase in noise addition, whichmay introduce artifacts. DWT based method (JPEG2000)provides better visual quality than DCT based techniques(JPEG and [10]) when the BER is high. In [10], when the BERis greater than 0.15, the PSNR drops drastically degradingthe visual quality of MDPs. However, JPEG2000 is alsosensitive to transmission errors. The perceptual quality of theJPEG2000 encoded MDPs degrades when the BER is greaterthan 0.1. An adaptive edge-based fuzzy filter is describedin Section II-D, which relatively reduce the effect of the distor-tion on the block borders, will protect the detailed edges fromblurring, and minimizes the impact of other multiplicativeand additive noise, such as transmission errors. In short, theimpact of increasing BER is less in case of the MDC, whicheventually provides better perceptual quality even when theMDPs are transmitted through noisy channels.

Fig. 8. Illustration of the lifetime of network. (a) Relationship betweennetwork lifetime and remaining energy. (b) Relationship between number ofsensors and network lifetime. (c) Relationship between network lifetime anddelivery ratio of MDPs.

B. Performance Evaluation of the FRS

We deployed 400 sensors uniformly at random independentof each other in a square-shaped FoI of 1000m × 1000m.The destination is located at the position (990, 990). Theinitial energy and the communication range of the sensorsare assumed to be 25J and 100m, respectively. We haveimplemented FRS technique on AODV routing protocol withand without using MDC technique. The enhanced versions ofAODV are referred to as AODV_FRS, AODV_FRS_MDC,and AODV_MDC. The goal of this simulation is to showhow the FRS technique prolongs the network lifetime withoutcompromising the QoS of WANETs.

1) Energy Consumption of Network: First, we study theimpact of the proposed work on the energy consumption ofthe network. We measure the total energy consumption ofnetwork with the simulation time. Fig. 8(a) shows the totalenergy consumption of the network for the entire duration ofthe simulation. It can be concluded from the result in Fig. 8(a)that when proposed MDC technique is not used, all theuncompressed MDPs are routed to the destination and theenergy consumption is then very high. Fig. 8(a) also shows thatAODV_FRS consumes less energy than simple AODV. This isbecause AODV routing without the proposed FRS techniquerequires more number of hop count for routing MDPs.

2) Lifetime of Network: Next, we study the impact of theproposed technique on the lifetime of the network. We measurethe network lifetime with the number of rounds of simulationtime till all the nodes drain their energy completely. Fig. 8(b)shows the relationship between the network lifetime andthe number of nodes. It can be observed from the resultsin Fig. 8(b) that when the MDC technique is not used,

786 IEEE SENSORS JOURNAL, VOL. 15, NO. 2, FEBRUARY 2015

uncompressed MDPs are routed to the destination and thenthe energy of the nodes drain out rapidly. It also shows thatwhen the proposed FRS technique is used, the energy of thenodes is equally consumed, because residual energy becomesa part of route selection metric.

3) Delivery Ratio of MDPs: Finally, we show the impact ofthe proposed work on MDPs delivery ratio. The MDPs deliv-ery ratio of a flow is the ratio of the number of MDPs that arereceived by the destination over the number of MDPs sentby the source. Fig. 8(c) shows the overall MDPs deliveryratio for the entire duration of the simulation. AODV_FRS,AODV_FRS_MDC, and AODV_MDC achieve a stable perfor-mance at the entire duration of the simulation, because routelength becomes a part of route selection metric. It illustratesthat the delivery rate is inversely proportional to the routelength (hop count).

V. CONCLUSION

In this paper, we proposed the RMM system for routingthe medical data of patients in the disaster area. The proposedsystem comprises a set of components which collects, com-presses, and transmits compressed medical data to the basestation using WANETs. The coding technique in RMM systemallows to decode correctly even in the presence of transmissionerrors. RMM system exploits the attributes of WANETs tomaintain QoS of WANETs. We simulated the performanceof the RMM system for different network scenarios anddemonstrated that the lifetime of the network is increaseddue to the routing of compressed MDPs and hence maintainsQoS of WANETs. In our future research, we intend to makehardware implementations. We believe that this work alsomotivates further research in the security problem in WANETs.

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Tanima Dutta received the B.Tech. degree fromthe Haldia Institute of Technology, India, in 2005,and the M.Tech. degree from Calcutta University,Kolkata, India, in 2010. She is currently pursing thePh.D. degree in computer science and engineeringfrom Indian Institute of Technology Guwahati, India.She is currently with TCS Innovation Labs, Ban-galore, India. Her research interests include wire-less networks, multimedia, computer vision, andalgorithms.

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