An interference-free and simultaneous molecular transmission model for multi-user nanonetworks

Nano Communication Networks ( ) –

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Nano Communication Networks

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An interference-free and simultaneous moleculartransmission model for multi-user nanonetworksÖzgür Umut Akgül, Berk Canberk ∗Department of Computer Engineering, Istanbul Technical University, Ayazaga 34469, Istanbul, Turkey

a r t i c l e i n f o

Article history:Received 21 July 2014Accepted 8 September 2014Available online xxxx

Keywords:Flow-based communicationInterference modelMolecular transmissionMarkov filter

a b s t r a c t

Recent studies in nanotechnology show that the membrane selection process and distanceadaptation algorithms can beused tomaintain the simultaneousmolecular communicationinmulti-user nanonetworks. However, suchmethods prevent the cooperation betweendif-ferent nanodevices, thus, degrade the performance. Moreover, the existing frameworks arenot suitable for dense nanonetworks containing huge number of nano-devices. To the bestof our knowledge, a simultaneous communication framework that enables cooperation hasnot been covered based on the challenges caused by the diffusion nature of the Concentra-tion Shift Keying (CSK)-based molecular communication. First of all, the transmission ofthe molecular signal through all available channels is one of the most important challengeas it increases the interference between devices. Moreover, the experienced latency andattenuation of the chemical signal complicate the decoding process at the receiver sideand necessitate a more complex nanomachine model. Additionally, synchronization be-tween nanodevices becomes problematic. Having these challenges in mind, we propose aninterference-free, simultaneous and selective communicationmodel for Flow-Based Archi-tecture (FBA)-basedNanonetwork. The proposed framework uses amolecule blocking filterstructure that enables inter-group synchronization and limits the side effects of the diffu-sion nature. The packet-based communication is used in the proposed framework, and wedefined a complete packet architecture that consists of synchronization bits, the address ofthe receiver and the data. The filters transmission control mechanism is also presented as aMarkov chain-based active flow control structure. The performance of the proposed frame-work is evaluated from both user perspective and network perspective. We observed thatthe proposed framework can support up to 8 simultaneous selective molecular communi-cations in a 10-user nanonetwork.

© 2014 Elsevier Ltd. All rights reserved.

1. Introduction

Molecular communication is a promising means forpacket transmission in the nanonetwork where EM wavescannot be used due to the necessary communication fre-quencies [1]. Despite its biocompatibility in single-user

∗ Corresponding author. Tel.: +90 5326836780.E-mail addresses: [email protected] (Ö.U. Akgül), [email protected],

[email protected] (B. Canberk).

network [2], molecular communication is clearly problem-atic for multi-user networks due to molecular diffusionnature. However, selective transmission in peer-to-peercommunication gains an extra importance in molecularcommunication as many of the defined applications ofnanonetworks are intra-body applications, and communi-cation errors can result in serious medical problems [3]. Asan example of the importance of the simultaneous inter-group interference free peer-to-peer communication in anintra-body nanonetwork of a pancreatic cancer patient canbe taken. As a common knowledge, most of the pancreatic

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2 Ö.U. Akgül, B. Canberk / Nano Communication Networks ( ) –

cancer patients are also diabetic. Assume that two differ-ent kinds of nanomachine groups exist in this intra-bodynetwork. One group of them observes the spread of cancercells while the other group observes the blood sugar of thepatient. For such a network different nanomachine groupshave to communicate with different nano-centers that cantrigger a medical intervention or some kind of warningmessaging. For such a case, the simultaneous communi-cation between nanomachine groups has to be immuneto both the interference and other groups communication.For example a communication among blood-sugar obser-vation sensors should not activate the cancer-cell obser-vation centers. If this happens, drugs that are harmful forcancer cells may spread to patient’s body when a rise inthe patient’s blood sugar is detected because of a ruineddiet. Therefore, the communication between two nanoma-chines should not bother other nanomachines. The mul-ticast connection in a nanonetwork cannot resolve theproblem of interference. In the cancer patient case, thecancer-cell observation centers will transmit their pack-ets to the blood-sugar observation centers. Such a trans-mission will disable the simultaneous communicationbetween blood-sugar observation centers. Due to this rea-son, the time slot concept needs to be defined. The increas-ing number of nanomachines decreases the efficiency ofthe multicasting framework as the period of communica-tion will increase. Due to this reason, a simultaneous com-munication framework is necessary in nanonetworks.

However, molecular diffusion causes an uncertaintyin the molecule distribution through the communicationchannel, and this uncertainty causes many challenges, likeinterference. Different channel models are proposed formolecular communications such as Diffusion-Based Chan-nel (DBC), Walk-away-Based Channel (WaBC) and Flow-BasedChannel (FBC) [4]. As themovement of themoleculesin the communication environment has a direct impacton the interference and communication success, there ex-ists studies to model the movement of the molecules [5].However, DBC fails to exactly predict the movement ofmolecules, thus, are unsuitable for multi-user nanonet-works. In WaBC, specific carrier substances, like molecularmotors, are used to transfer the chemical signal from trans-mitter to the receiver [6], creating a selective transmissionand reception structure for multi-user communication.However, [7], each transmitter–receiver pair should have adifferent carrier substance, and this is not possible in mostcases. On the other hand, in the flow-based channel, themovements of molecules are guided and predictable [8].The molecule flow is performed within predefined flowsbetween nanodevices (Fig. 2). The molecules cannot ex-ceed these specific borders. Even though the movementsare guided, users communicate by sending their chemicalsignals through all available channels. Therefore, multipleusers will interfere each other, when they try to commu-nicate simultaneously. Because of this, even though the re-ception of the chemical signal is guaranteed, only one usercan use the channel at a time. An alternative solution to theselective transmission problem is the simultaneous usageof different modulation techniques: Molecular Shift Key-ing (MoSK) and Concentration Shift Keying (CSK). In CSK,the signal is modulated using different molecule concen-trations. As this modulation scheme is open to noise and

interference, it is usually applied as an additional method-ology. InMoSK, different nanomachines use different typesof molecules while they modulate the information usingCSK. In this architecture, the membrane structures outsidethe cells selectively accept one kind of communicationmolecules. However, such an architecture needs differ-ent types of communication for each nanogroups, whichleads to the result that for an n-user intra-body nanonet-work, there should be at least n different kind of moleculeswhose molecular density in body changes periodically dueto communication. However, as the number of nanogroupsn increases, realizing such a network becomes impossi-ble. A more practical approach to realize selective com-munication in multi-user nanonetworks can be increasingthe distance between each nanogroup. By this way, the in-terference between nanodevices cannot cause enormouseffects on the received signal. However, such an architec-ture cannot maintain the cooperation between differentnanodevices. Moreover, especially in long-range commu-nication, as the transmitted signal is amplified, [9,10], thenecessary distance between different nanogroups for suchan architecture increases, and the number of possiblenano-groups decreases. Briefly, the existing models canonly support single-user communication. However, formost of the applications, nanomachines have to work to-gether in different groups [11]. These nanogroups can beidentical and use the same molecules for communication.Due to this reason, the interference and noise in a nanonet-work is extremely high. Communications between group’stransmitter and receiver have to be apart from othergroups. FBA separates the communication channels; how-ever, the transmitted chemical signals are combined at thereceiver cell. Consequently, inter-group interference-freesimultaneous communication in a multi-user nanonet-work can only be achieved by implementing a selectivetransmission structuremechanism. However, as stated be-fore, the diffusion nature of the molecules complicates thedesign process as the uncertainty of the received moleculedensity causes an uncertainty in the received molecularpacket structure. These challenges can be investigated un-der three main title.

1.0.1. Intergroup interference

The existing diffusion-based communication throughall available channels is the main challenge in the se-lective transmission. When the user transmits the data,every nanogroup receiver receives the transmitted molec-ular signal. As all nanodevices send their packets at thesame time, inter-group interference increases and makesit impossible to decode the received packets at the re-ceiver side. Due to interference only a single user can usethe communication channel at a time. The existing struc-ture for a multi-user nanonetwork is presented in Fig. 2where a transmitter nanogroup A is trying to communi-cate to A′. While nanogroup A is communicating with A′,the nanogroup C tries to communicate C′. However, as thetransmitters send their packets through every availablechannels, it is not possible to support simultaneous com-munication in multi-user nanonetworks. As presented inFig. 2, transmitter A uses all the available channels and if

Ö.U. Akgül, B. Canberk / Nano Communication Networks ( ) – 3

Fig. 1. Channel utilization and goodput rate change with the number of transmitting users.

transmitter C tries to communicate, it will interfere withA. Even though the FBA channel structure prevents the in-terference in the channel, at the receiver side two chemi-cal signals will interfere each other. As seen in Fig. 1, thechannel utilization is at 100% for every number of users.However, due to the interference between users, the good-put rate is at 25% only for single user case, and for the re-maining cases, it is at 0 stating that none of the users cantransmit its chemical signal. In this study, the goodput rateis measured by the number of useful packages in all re-ceived packages whereas the channel utility is measuredby the rate of channel usage. A solution to this simulta-neous communication challenge can be the combinationof different modulation techniques in molecular commu-nication, similar to the presented framework in [12]. Thenanomachines can use the MoSK modulation technique intheir inter-group communication whereas they use CSKin their intra-group communication. More specifically, ifthere exist two groups in the network, group one uses type1 molecule, M1 while group two uses type 2 molecule, M2in their communication. However, they use CSK modula-tion in their inter-group communication. As group 1 doesnot check the concentration of communication moleculethat belongs to group 2, they will be immune to each oth-ers’ communication. However, if there exist n differentgroups, then there should exist n different molecules. Inmany applications, like intra-body networks, usage of thislarge range ofmolecule types is problematic. Nevertheless,this type of communication is not an actual simultaneouscommunication framework for multi-user nanonetworksas different groups cannot communicate with each other.

1.0.2. Synchronization betweennanotransmitter andnanore-ceiver

Another difficulty of the simultaneous communica-tion in multi-user nanonetworks is the synchronization ofnanodevices. The synchronization challenge should beinvestigated under two different titles: intra-group andinter-group synchronization problems. In long-range com-munication, to amplify the molecular signal, users haveto use nanogroups. Intra-group synchronization describes

Fig. 2. Existing inter-group interference in an FBA channel.

the synchronization of the nanomachines in the samegroup whereas inter-group synchronization defines thesynchronization between different groups. During the re-ception process, receiver nanogroup samples the molecu-lar concentration of the chemical signal and produces theoutput chemical signal, s′(t) [13]. During this samplingprocess, the inter-group synchronization has a critical im-portance especially for the sequential bit stream usage.

1.0.3. Attenuation and latency in the molecular signal

Due to the movement of the chemical signal, the re-ceived chemical signal, c(t), is different from the trans-mitted chemical signal, r(t). This difference is occurredin terms of both attenuation and latency, as presented inFig. 3. As can be seen in Fig. 3, the duration of package in-crease by tx while the amplitude of the chemical signal de-creases byAt−Ar . Based on these explanations the receivedchemical signal will be as presented in (1) [14]. The atten-uation and latency change the received molecular packetstructure. For a sequential bit stream, the changes in themolecular packet structure disables the decode process inreception. To enable successful communication the attenu-ation and latency must be solved. Even though there exist


Fig. 3. Attenuation and latency problem.

some solutions for attenuation, to the best of our knowl-edge no specific solution for latency is proposed.

c(t) =(At − Ar)

Arx

(t − tL)tpackettx + tpacket

. (1)

In this work, we are proposing a selective transmissionframework that proposes solutions to the stated challengesand enables multi-user nanonetworks. More specifically,our contributions can be listed as:

• The address selection is performed by the Markovchain-based active flow control mechanism that is re-alized by the proposed molecule blocking filter struc-ture to implement the simultaneous transmission to thepeer-to-peer molecular communication.• A molecular reject mechanism is applied in the

proposed molecule blocking filter to minimize theinter-group interferance of the nanogroups to the com-municating peers.• Inter-group synchronization is maintained by using

specific synchronization molecules and also imple-menting a specific inter-group synchronization unit tothe molecule blocking filter.• Latency reduction is investigated under two different

approaches, 1-bit beaconmessages and update packets.We conclude that traffic load and the channel changefrequency are important in the method selection.• In order to handle the synchronization problem, we de-

rived simpler analyticalmodels for total reception time,treception, expansion of packet in time, tx and the optimalbit duration, tbit .

The remaining of this paper is organized as follows. In Sec-tion 2, the proposed framework is covered. In this sec-tion first the used package concept is presented, and thenthe proposed structure of the address concept is shown.After the definition of the selective transmission frame-work, the proposed molecule blocking filter mechanism isinvestigated. The Markov model of the molecule blockingfilter is presented. In addition to these, the coordinationbetween nanogroups is investigated with the explanationof the used update mechanism and finally the bit durationis derived in Section 2. In Section 3, performance evalua-tion is presented. In this study, the performance evaluationis done in terms of channel utility and interference.

Fig. 4. Proposed selective transmission framework in the nanonetwork.

2. Proposed framework

In this work, an interference-free selective communica-tion framework is proposed for multi-user nanonetworks.The existing systems are using a diffusion-based multicastframework to transmit the molecular signals [4,14]. In thisframework, themolecular signal is transmitted through allpossible channels and the signal moves through the chan-nel according to diffusion. This transmission policy enablesthe transmission of a packet through all available channels.However, this policy is one of the most important chal-lenges in simultaneous communication in the multi-usernanonetworks as it increases the inter-group interference.To introduce a systematic approach to themolecular signaltransmission concept, a Flow-Based Architecture (FBA) isused in the communication channel, as shown in Fig. 4. De-spite the fact that FBA separates the communication chan-nels, the molecular packet is still transmitted through allavailable channels.

In this work, the transmitted molecular signal ismodulated using concentration shift keying (CSK). Eventhough the same molecule, MC , is dominantly used in thecommunication; a different molecule, MG, is used in theinter-group synchronization.

2.1. Selective transmission framework

The proposed framework for a single nanomachinegroup is presented in Fig. 5, where s(t) represents theinputmolecular signal of themolecular group. In the trans-mitter block, the group of nanomachines realize themolec-ular modulation and emit the molecular signal, r(t). Inthis work, we assume that CSK is employed for the com-munication between different nanogroups. So r(t) is themodulated molecular signal according to the input signals(t). c(t) is the received molecular signal at the receiverpart. The densities of molecules are counted and receivedmolecular signal is produced as s′(t). Due to noise, atten-uation and latency, the transmitted molecular signal, s(t)and receivedmolecular signal, s′(t), are different fromeachother. However, the effect of noise is not covered in thiswork for simplicity. The transmitter and receiver struc-tures are presented as the combination of two parts. In


Fig. 5. The proposed framework.

transmitter complex, there exist a transmitter block thatproduces the molecular signal and a filter block to man-age the simultaneous communication and address selec-tion processes. The channel block is the communicationmedium where information carrying molecules move. Inthis work, for the channel block, we are using the flow-based architecture that presents guided channels and pre-dictable movements. In this guided fluid communicationchannel, molecules are following pre-defined lines in themedium. For simplicity, the noisy effects of molecular sig-nals from a previous communication are not considered.

To integrate the packet-based communication into themolecular communication, the ‘‘bit’’ concept has to beinitialized. In many studies in the literature, the mostgeneral versions of bits are defined in terms of moleculardensity. For CSK modulation, the densities of a specificcommunication molecule state the logic 1 and logic 0values. However, as the main objective in this study is tointroduce a packet-based multi-user nanonetwork, the bitstreams are more important than the definition of a singlebit. As previously stated, CSK modulation is used in thisstudy. In this modulation type, if the density of a certainmolecule exceeds a certain threshold, it is accepted as logic1;while if it is lower than that threshold, than it is acceptedas logic 0. To prevent the interference between the bits,the bit length has to be investigated. If the bit duration istoo short and the logic 1 threshold value is higher than thelogic 0 bit after logic 1 will be masked by the logic 1. Dueto this bitwise interference, the package will be wrong. Onthe other hand, if the bit duration is too long, the channelwill be empty for a long time. The channel usage efficiencyis going to be low. Therefore, the bit duration is criticaland has to be investigated. The optimum bit duration isinvestigated in Section 2.4.

In the selective transmission structure, the main prob-lem is the synchronization of nanomachines, more specif-ically the intra-cell and the inter-cell synchronizations. Asthere is not any buffer definition in the nanonetworks,when the s(t) signal is received, the contained nanoma-chines have to make the transmission. During the trans-mission, to amplify the transmitted molecular signal, allthe cells have to transmit the same bit at the same time.Otherwise, the transmitted bitswill conflict each other andthe transmission will fail.

When a packet transmission is realized between twonanogroups, the receiver nanogroup has to decode thereceived packet correctly. To decode correctly, a globalsynchronization is necessary between these two devices.This global need of synchronization is the most importantchallenge in synchronization. The intra-cell synchroniza-tion challenge is maintained by using the concept of quo-rum sensing (QS) framework proposed in [14,10]. In QS,the cells start emitting a certain molecule discretely andwhen the concentration of this certain molecule exceeds athreshold the cells change their states. This QS frameworkneeds the definition of G cells in our framework. By defini-tion, G cells are nanomachines that exist in transmitter andreceiver block in low density, namely Inner G Cells (IGC).However, the highest density of these cells exist, betweentransmitter or receiver block and the filter, namely OuterG Cells (OGC). Their duty is the production of a specificmolecule, namely MG, that is used in the synchronization.The IGC are always producing this MG molecules and pro-ducing a molecular clock signal. The density changes ofMGmolecule with time are presented in Fig. 6. When the con-centration of thismolecule exceeds a certain threshold, IGCcells stop the production of this molecule. MG moleculesmove according to the diffusion movement. At the trans-mitter region, the high density ofmolecules accelerates thediffusion movement in the transmitter region. The densityof molecule in the transmitter block will decrease slowlyas the number of molecules within the transmitter regiondecreases as the molecules move in the environment. Af-ter time t2 the density will fall under a certain threshold.In this state, IGC starts the production of this molecule andthe concentration increases again. After time t1, the densitywill be again higher than the threshold and IGCs will stopproduction of MG again. The clock periods are accepted tobe the state changes of IGCs as presented in Fig. 6. In theproposed framework, we assume that the bit lengths aret1 = t2 = t . There could be other applications that usethe bit length as the summation of fall and increase timesof MG densities, t1 + t2 = t . The intra-cell synchroniza-tion is maintained using these molecular clock signals inthe transmitter and receiver cell complexes. Inter-cell syn-chronization is like the intra-cell synchronization; how-ever, it mostly interacts with the filter block. It is acceptedthat the filter block is passive towards MG molecules, stat-ing thatMG molecules can pass it freely. The inter-cell syn-chronization is performed by OGC. When the modulationof an input signal, s(t), starts a dummy bit is added. Incommunication the CSK modulation is used, and a specialmolecule, namelyMC , is used. All groups are using this spe-cific molecule. When OGCs receive MC at the beginning ofthe communication, it starts releasingMG molecules to thechannel. As the flow-based architecture is used and thenoise is not taken into consideration, it is obvious that allthe molecules in the channel will face a certain latency, tL.When the concentration ofMG molecules exceeds a thresh-old, the IGCs in the receiver block will start synchroniz-ing. Note that rejecting call request will be explained inthe next section. The selective transmission in this frame-work highly depends on the filter mechanism. To explainthe filter block, the general packet structure has to be de-fined. The proposed packet structure is showed in Fig. 7.


Fig. 6. Production of molecular clock signal from the concentration.

Fig. 7. Proposed molecule-based packet structure.

Fig. 8. The proposed filter structure.

As seen in Fig. 7, in the physical world a packet is a contin-uous change in the density of a specific molecule. In thisstudy, an N bit packet is proposed where the first bit ofthis packet is the dummy bit and the next 3 bits are thereceiver address and finally N − 4 bits are the data bits. Asthe main objective of this work is the realization of simul-taneous communication, the existing transmission policyneeds to be improved. With this motivation, each packageneeds to be matched with the receiver address, and for acertain time interval, the user should only send its pack-ages to this receiver. After the definition of packet struc-ture, the filter block can be initialized.

2.2. Molecule blocking filter structure

The accepted packet structure is presented in Fig. 7. Aspreviously initialized the first bit of a packet, the dummybit has to be 1 to initialize the beginning of a packet, and itis encoded using MG. The given packet structure in Fig. 5is the transmitted packet structure from the transmitterblock and it is different from r(t), i.e., the transmittedchemical signal. As previously stated, in this study thecritical structure of selective transmission framework isthe filter block. The communication is based on moleculestraveling from source to destination, and the transmissionis realized in serial communication. The operations of thefilter can be listed as:• controlling the inter-cell synchronization by producing

the synchronization molecules,MG,

• amplification of the transmittedmolecular signal, espe-cially for long-range communication• establishing the connection (call accept or reject),• blocking wrong addressed packets,• packet distribution for the multi-user containing cell

structures.

All these listed duties have to be fulfilled by the filter block.The last duty defined in the list, the packet distributionfor themulti-user containing cell structures, is not coveredin this study. There could be cases such that multiplenanomachine groups are using the same filter block. Forsuch cases when a packet arrives, the filter’s duty is totransmit that package to the actual receiver. In this pointof view, the filter is analogous to a bridge in the computernetwork. MoSK modulation can be used to distinguishdifferent communications at the same time. However, thiskind of network topology is not covered in this work.Even though the proposed filter in this work is capable ofhandlingmulti-user network, it is applied to a single user inamulti-user network that uses CSKmodulation and a flow-based architecture. The proposed filter structure is shownin Fig. 8.

As can be seen in Fig. 8, the proposed filter structureconsists of three main parts,

• OGC part that realizes the inter-cell synchronization,connection setup process and packet distribution inmulti-user system;


Fig. 9. The Markov model of the filter ports.

• filter ports that are used for selective transmission andblocking the wrong addresses;• amplification part which is used especially in long

-range molecular communication and amplifies theoutputted molecular signal.

The OGC part is used in inter-cell synchronization. Aspreviously stated at the earlier in this section that thereshould be a dummy bit at the beginning of the packetthat starts the communication. When this dummy bit isreceived in the filter, it activates the OGC part. All thenanomachines that producesMG molecule begin to releasemolecules. The state change of the filter structure with thereceived molecule is represented using a Markov model.The Markov model of the filter ports is presented in Fig. 9.

In the initial state, the ports are in the state Q0. Whenthe s(t) signal is received, before the modulation of s(t), adummy bit is produced using MG. After that, s(t) is modu-lated serially and passed to the filter. When the dummy bitis received at the OGC part, all ports pass to Q1 state and allG cells start releasingmolecules while they observe the re-ceivedmolecules of dummybit,MG. The emittedmoleculespass through the ports freely and move towards all possi-ble flows.When the address molecules,MC , are received atthe ports, a selection process is realized. For this specificapplication, there exist 3 ports as there exist only three ad-dress bits. In each port an addressing bit is evaluated andopens this port or blocks it. In Fig. 10, the inside of a 1 portis shown. As is seen in the figure, there exist not only am-plification cells but also IGCs, which count the address bitslocation. They are used to produce themolecular clock sig-nal to test the received bit. As the third port in Fig. 10 is a‘‘1’’ port, it expects MC molecules at the third clock. If thetransmitted third address bit is not 1, then the amplifica-tion cells block the input port and collect all the received

molecules until a new packet arrives. Until the receptionof OGC molecules, the amplification cells in a blocked portonly collects the received communication molecules. If itreceives bit-1 at the clock time, then the input and outputamplification cells only amplify the input molecules. If it isa 0 port instead of a 1 port and themolecules exist at clock,then the amplification cells block the port otherwise theyamplify the inputtedmolecules. From this explanation, it isobvious that an address of 000 can never be usedwithout asynchronization bit to trigger the clock counting. Each ad-dress bit is collected at the correct port and activates theport. r(t) signal only carries N − 4 bits from the originalpacket, more specifically nanomachines’ own address andthe transmitted information. As can be seen in Fig. 9, flowscan be used in the transmission if the transmitted addressis suitable. βi terms show the probability of observing theaddress bit equal to the port bit. It is calculated by dividingthe number of receivers that has the specific address bitequal to the port bit to the number of receivers. The proba-bility of being in transmission state,QTr and being in block-ing state, QBl, can be calculated as

QTr =λ

µβ1β2β3Q0 (2)

QBl =λ

µ(1− β1β2β3)Q0. (3)

If the addressing is done using 3 bits, only 8 receiver canexist in the network. The address ‘‘000’’ cannot be usedin the proposed framework. So only 7 receivers can existin the network. For the 7-user network the probabilitiesfor the address ‘‘011’’ will be β1 = 0.43, β2 = 0.57 andβ3 = 0.57. Using these probabilities, the probability of re-ceiving ‘‘011’’ is equal to

Q 011Bl = 0.14×

λ

µQ0 (4)

whereas the probability of the flow to ‘‘111’’ to be used isequal to

Q 111Bl = 0.19×

λ

µQ0 (5)

where λ indicates the traffic rate and µ indicates the ser-vice rate. As explained earlier, IGCs are used to synchro-nize the molecule extension process. They all are using thesame quorum sensing process, and by this way producingthe same molecular clock signal.

Fig. 10. The port structure.


The amplification process is also critical for the fil-ter. The amplification cells (Fig. 10) are capable of doingtwo possible acts,molecule releasing andmolecule collect-ing. The amplification cells are divided into two groups,i.e. left-sided cells and right-sided cells. These two groupsare identical and their only difference is in their duties.While transmitting a molecule, the left-sided amplifica-tion molecules are activated by MG molecules. When theyreceive the communication molecule, MC , they also startreleasing MC , and by this way they increase the densityof this molecules in the medium. The augmented den-sity of this molecule increases the probability of success-fully receiving the molecular signal at the receiver end.In the reception process, the right-sided cells are used toblock the wrong addresses. The reception process is theinverse of the transmission process. The synchronizationmolecules,MG, excite the IGCs and start the reception pro-cess. As in the transmission with in each molecular clocksignal, a received bit has to activate the input port. If thereceived molecule does not fit the expected molecule inthe port, then the port is being blocked and all the receivedmolecules are destroyed by a reverse chemical reaction.The OGC and the amplification cells are activated with theconsumption of a certain molecule. They are being activefor N bit time, N × t , and after that they wait for anothermolecule. As the framework uses a flow-based architec-ture, by the activation of a certain port combination andblocking the rest, the molecule flow will continue on thisspecific channel. Amore general algorithm is presented forthe filter mechanism in Algorithm 1. In Algorithm 1, α rep-resents the last transmitted bit. The nanodevice first checksif the received bit is an MG or MC . An MG molecule at thereception port initiate the beginning of an address. On theother hand, an MC molecule is an interference from a pre-vious communication. After successfully receiving an MGmolecule, the three port is activated according to the re-ceived MC molecules.

Algorithm 1 Filtering AlgorithmRequire: r(t)Ensure: Address Path1: if αϵMG then2: Activate IGC and OGC3: α← Next Bit4: end if5: for α < 3 do6: if α = 0 then7: Block port 18: Activate port 09: else

10: Block port 011: Activate port 112: end if13: α← Next Bit14: end for

2.3. Inter-group coordination

The package concept in molecular communicationis covered in Section 2.1. The address of recipient

nanomachine is considered at the beginning of the pack-age. Because of the used channel architecture, FBA, eachrecipient will receive the transmitted package. They willhave to decode the chemical signal, c(t), and even if thepackage is not transmitted for them, they will have towait for the whole package, and after that they can trans-mit their package or receive another package. It is obviousthat the package is not enough for multi-user commu-nication. The addition of the packet concept can onlysupport a multicast framework. However, for larger nano-networks, the multicasting is not efficient. To supportmulti-user nanonetwork, a filtering mechanism has to bedefined. In Section 2.2, we proposed a filter mechanismthat directly sends the package to the intended receiver,blocks unnecessary packages and amplifies the transmit-ted bits. Each communication block presented in Fig. 5 isrealized separately. In this section, the coordination be-tween different nanogroups (molecular packet transmis-sion and reception) is investigated.

The complete framework is presented in Fig. 5. Thereare two other features that need to be covered during theintegration of these discrete features. First of them is thebitwise latency and attenuation in molecular communica-tion. Even though FBA decreases the noise and attenuation,it does not lower the latency. When the chemical signal isbeing released at the transmitter, the transmitted bit hasan amplitude of At and the duration of tpacket . This trans-mitted molecular signal is supposed to be perfectly codedand transmitted. The transmitted chemical signal, r(t), ex-ceeds the threshold of logic 1, Th1, at tpacket/2. The durationof a single bit from the received molecular signal will beequal to tR and the bit will exceed Th1 threshold at an un-certain time tx that varies according to the channel widthand transmittedmolecule density. In Fig. 3, the transmittedmolecular signal, r(t), and the received molecular signal,c(t), are shown. tL represents the latency and tPacket rep-resents the duration of the packet. The expansion of thepacket at time, tx, is a crucial problem. The channel trans-fer function is presented in [14] and it is also presented in(6). In this study, simpler form of treception is going to be de-rived. tx can be calculated using the duration of r(t), tPacketand t ′Packet . As shown in (7), tPacket is the summation of tPacketand tx.

Ht(l, w) = eu1−

1+ j4wD

u2

(6)

t ′Packet = tPacket + tx. (7)

The total packet size of c(t) can be calculated as,c(t)× dt = Ar × t ′Packet (8)

where Ar is the amplitude of the received chemical signal.Ar can be calculated using (9).

Ar =At

dD(9)

where d represents the distance, D is the diffusion coeffi-cient and At is the amplitude of the transmitted chemicalsignal. At can be calculated by

At =

r(t)× dttPacket

. (10)


Using (8)–(10), the structural equation of the received andtransmitted packets can be found as follows:

c(t)× dtt ′Packet

=

r(t)× dt

dD × tPacket. (11)

In (11), the numerator parts are the real density of mole-cules at the transmitter and receiver, respectively. In thiswork, as FBA is used and the noise effects in the channelare ignored, these two terms will be equal to each other,and thus, (11) can be simplified as

t ′Packet = tPacket × dD. (12)

From 1 and 6, the tx term can be calculated as

tx = tPacket(dD − 1). (13)

As presented in Fig. 3, the total reception process is thesummation of the latency term, tL, and packet duration,t ′Packet . The latency term can be calculated by

tL =1u

D2

u2+ l2 −

Du2

(14)

D =Kb × T

6π × µ× r(15)

where Kb is the Boltzmann constant, T is the absolute tem-perature, µ is the viscosity and r is the particle radius. Thetotal reception duration can be calculated as,

treception = tPacket + tx + tL. (16)

By combining (13) and (14), treception can be found as givenin (17).

treception = tPacket × dD +λ

√4× π × t3

× e−λ24t . (17)

Because of tx, the received bit stream will change and willbe different from the expectedmolecular packetmodel. Forsuch a scenario, the decoding process could fail and thewhole package will be lost. To avoid these decoding errors,the decoding process should be updated. This update pro-cess can be realized using two different approaches. First,we can use an additional molecular bit stream at the be-ginning of the molecular package to update the Th1 valueand the sampling time, tsample. However, for communica-tion channels that are frequently used, the update processwill be unnecessary, as the characteristic of the channelcannot change so rapidly. But such an approach is valid ifthe paths are used very rare. Because the communicationchannel characteristics could possibly change between thearrivals of two successive packages. A second approachfor the update process is to use dummy packages. Basedon this fact, the update process can be achieved by us-ing dummy packages that are only applied to measure theTh1 parameter and tx parameter. However, this approachis not efficient for rarely used communication channels.Two approaches are good for different traffic loads, the us-age percentage of the communication path. The decisionof the update technique needs to be given according to thetraffic load in the channel. The second subject that needsto be covered is the call-reject mechanisms in filter. In amulti-user nanonetwork, more than one user may wish to

communicate with the same receiver at the same time. Forsuch cases the receiver filter has to block the second userspackage and the second user may try to send the pack-age at a different time. Again two different strategies canbe used in this process. First of them is the call requestmodel (CRM). In the CRM model, the package in Fig. 7 isdivided into two different parts, first part, the call requestconsists of the dummy bit, the destination address and thetransmitter address. The second part, i.e., n−7 bit, containsthe information that needs to be transmitted. The dummybit and the transmitter address are received at the receiverside. If the receiver is not communicating with anothertransmitter, the OGC part of its filter triggers a call acceptpackage that contains a dummy bit, the transmitters ad-dress and the receivers address. Only the dummy bit andthe receivers address are received at the transmitter side.After this call accept package the OGC part of the trans-mitter continues the transmission of the second package,i.e., the data package. If the receiver is communicatingwithanother transmitter than as the OGC cells of the receiverare blocked, they are not going to replay the call-request,and because of this, the second transmitter will not receivethe call accept message. By this method the interferencebetweenusers is prevented. However, thismethod also un-derlines the necessity of a bufferingmethod for the s(t) sig-nal. In thiswork, buffering is not covered. It is assumed thatdevices are capable of buffering.

Algorithm 2 CRM AlgorithmRequire: c(t)Ensure: Accept or Reject1: if αϵMG then2: if OGC is passive then3: Activate OGC and IGC (Accept)4: α← Next Bit5: for α < 3 do6: if α = 0 then7: Block port 18: Activate port 09: else

10: Block port 011: Activate port 112: end if13: α← Next Bit14: end for15: else16: Block Input Port (Reject)17: end if18: end if

The proposed CRM structure is presented in Fig. 11.The CRM process is given in Algorithm 2. The secondmethod to exclude the second user is a perpetualmethod. As the packet accepting process highly dependson the amplification cells in the filter structure, someamplification cells at certain ports can be destroyed. By thisway even though a call request or a package arrive fromthis port, the filter will automatically block this package.After the definitions of CRM process and tx parameter,the utilization concept in the proposed framework can beinvestigated. In this framework the utilization concept is


Fig. 11. Proposed CRM structure.

Fig. 12. Molecular bits.

divided into twomain parts, User -Based Utilization (UBU)and Network-Based Utilization (NBU). The user-basedutilizationmeasures howwell the framework is in terms ofgoodput rate and the successful communication rate (SCR).The goodput rate is a user-based coefficient that measuresthe fraction of the number of correct transmitted packetsin the overall transmitted packets. On the other hand, SCRis a more general coefficient that measures the fractionof a number of successfully communicating users in theoverall communicating users. Network-based utilizationmeasures the complete frameworks utilization in termsof channel utilization and inter-group interference. If theith user transmits a package to the jth user then thechannel between these two users, Cij, is used. Inter-groupinterference is ameasure of howmuch the groups interferewith each other. In this work, it is measured in terms of theratio of the interfered bits to the overall number of bits.

2.4. Derivation of bit duration, tb

In the proposed simultaneous transmission framework,as complete packet structures are being used, the timelinesof each packet have an additional importance. In the pre-vious section, expansion of packet in time, tx, and the to-tal reception time treception are derived. In this section, theduration of a bit is presented. The molecular form of a re-ceived data is presented in Fig. 12. In the reception processthe concentration of the molecules is calculated, and themolecular bits are decided based on thismolecular concen-trations. The molecular concentration is changed during

the emission and collection processes at the transmitter.The transmitter uses one of these processes, emission orcollection, based on the transmitted data. These processes,emission and collection, can be modeled as functions f (t)and g(t) respectively. Themolecular concentration is eval-uated at the sampling time, tsample. As a general rule, sam-pling time is always lower than the bit time, tsample < tbit .From Fig. 12, it can be derived that,

f (tsample) = Th1. (18)

In the transmission phase if the molecular concentra-tion does not fall to 0 or Amax keeps increasing with thenumber of 1s, it is not possible to decode the received data.To overcome these problems, the f (t) function can reachup to a maximum Amax molecular concentration while theg(t) function decreases themolecular concentration downto 0, as shown in Fig. 12. Based on this, the design con-straints are presented in (19)–(21).

Amax ≤ g(tbit) (19)Amax = f (tbit) (20)g(tbit) ≥ f (tbit). (21)

As previously stated g(t) represents the molecule collect-ing function. For bit stream ‘‘10’’ the molecular concen-tration has to fall below Th1 after tsample time. This can bewritten as (22).

Amax − g(tsample) < Th1. (22)


If (22) is rewritten using (20), (23) is reached.

f (tbit)− g(tsample) < f (tsample) (23)

e−kt and ln(nt) functions can model the characteristicsof collection and emission functions, g(t) and f (t). Thedefinitions of k and n parameters can be given as thenumber of used nanomachines in the receiver nanogroupand the number of used nanomachines in the transmitternanogroup. By using e−kt and ln(nt) functions in (21) and(23), we can derive (24). During tsample selection both (24)and (25) have to be considered.

ln(ntbit)− e−ktsample < ln(ntsample) (24)tsample ≤ tbit . (25)

Using ln(nt) and e−kt as g(t) and f (t), (19) and (20) can berewritten as (26) and (27) respectively.

tbit ≤e

1Amax

n(26)

tbit =ln(Th1)

k. (27)

Substituting (27) into (26), we derive (28).

ln(Th1)

k≤

e1

Amax

n. (28)

As can be seen in (28), there exist a tradeoff betweenn, k, the maximum concentration, Amax, and the thresholdvalue, Th1. Using (28), the system parameters can becalculated.When the systemparameters, k, n, Amax and Th1are calculated, the duration of a bit can be calculated using(27). After the calculation of tbit , using (24), the optimalsampling time tsample can be calculated.

2.5. The implementation of update process

Twomodels are presented for the update process in theprevious sections. In this study, the bitwise update processis used. An additional bit is added at the beginning ofthe molecular packet. As previously stated, tsample and Th1need to be updated. However, as the additional bit in themolecular packet is still modulated based on themolecularconcentration, it depends on tsample and Th1. To overcomethe dependency problem, a dummy 0 is added after thedummy bit at the beginning of the packet. The algorithmis presented in Algorithm 3 where [D1D2] represents thedummy bits and Density(x) function returns the density ofmolecule x.

The time when the firstMG molecule is first founded atthe receiver side is denoted by t1. After t1, the molecularconcentration of MG keeps increasing until t2. t2 initializesthe beginning of the second bit, dummy 0. Bit duration canbe calculated as tbit = t2 − t1. As design constant, k, is alsoknown in the receiver, Th1 can be calculated using (27).Amax parameter is the measured molecular concentrationat time t2. Using these parameters tsample can be calculatedby the (24). tsample can only be changed by changing thenumber of active IGCs or the threshold value of the IGCs.Between these two methods, the first one, changing thenumber of active IGCs is applied in this study. As thethreshold value of the IGCs is a constant, by using less

Algorithm 3 Inter-cell Synchronization AlgorithmRequire: [D1D2]

Ensure: ClockSignal1: if MG exist in medium then2: t1 ← t3: Amax = 04: while Amax < Density(MG)t do5: t2 ← t6: Amax = Density(MG)t7: end while8: tbit = t2 − t19: Th1 = e(tbit×k)

10: Calculate minimum tsample11: end if

active IGCs, tsample is going to be higher. Activating moreIGCs will lower tsample.

3. Performance evaluation

The proposed framework has explained in Section 2.The performance of the proposed framework is evaluatedin terms of goodput rate, channel availability, channel util-ity and interference. In this section, packets consist of 16bits. The first bit is the synchronization bit and the follow-ing 6 bits of this packet are the address bits. The remaining9 bits are accepted to be the data. The performance evalua-tion is presented under twomain parts, namelyUser-BasedUtility (UBU) and Channel-Based Utility (CBU).

3.1. User-based utility

The interference between nanogroups affects themolecular signals of different nanogroups and decreasesthe goodput ratio. Due to this, the user-based utilizationhas a great importance to determine the success of the pro-posed framework.Wemeasured the UBU in terms of good-put rate and successful communication rate.

3.1.1. Goodput rateThe goodput rate is used to measure the number

of correctly transmitted packages in overall transmittedpackages. The normalized goodput rate (Υ ) calculation isperformed by

Υ =tr1

tr1 + tr0(29)

where tr1 represents the successfully transmitted packetsand tr0 denotes the failed packets. The summation ofsuccessfully transmitted packets and failed packets, tr1 +tr0, produces all the transmitted packages. The change ofgoodput rate with time is presented in Fig. 13.

Fig. 13 demonstrates that even though the rate changesbased on the time, it has an average of 0.8. As presentedin Fig. 1, the goodput rate of the existing molecular com-munication framework is around 0 for a multi-user sys-tem. Compared to the existing communication framework,the proposed framework increases the goodput rate in themulti-user nanonetwork by 80%. In addition to this in-crease, the minimum goodput rates in Fig. 13 also show


Fig. 13. Goodput rate.

Fig. 14. Successful communication rate.

that the increase in goodput rate has a non-zero minimumlevel. The decrease of goodput rate in Fig. 13 is because ofthe lost packages due to interference. However, by eval-uating the knowledge of interference and the minimumgoodput rate together, it can be argued that the proposedframework lowers the effects of interference on the otherusers communication.

3.1.2. Communication rateThe Successful Communication Rate (ϑ) is calculated

using (30). It gives the information of how many user cancommunicate in the proposed nanonetwork.

ϑi =C1i

C1i + C0i∀i = 1, . . . , 10 (30)

where C1i represents the accepted and transmitted pack-ets of user i, and C0i denotes the rejected or untransmittedpackets of user i. If the receiver is communicating with adifferent user, than it rejects the communication requestfrom user i. Untransmitted packets are the packets thatis interfered by other users packets. The successful com-munication rate is presented in Fig. 14. From this figure,

it is revealed that the proposed system can contain nineusers with the successful communication rate of 0.17. Italso shows that for a 10-user network, only four of theseusers can have a communication rate equal to or higherthan 0.5. The existing molecular communication frame-work can only support 1 user. Therefore, it can be con-cluded that the proposed framework can actually supportmultiple users in a nanonetwork.

3.2. Channel-based utility

Aswell as increasing the communication rate, providingan efficient communication environment is another im-portant parameter. The communication environment is in-vestigated in terms of channel utilization and inter-groupinterference.

3.2.1. Channel utilizationThe channel utilization is measured in terms of the

number of used channels in overall channels. In thiswork, simulations are performed for 10 transmitter and 10receiver. As previously stated by FBA, a different channelis defined between each user. As can be understood fromthis perspective there exist 100 channels in the simulatednetwork. Channel Utilization (ζ ) can be calculated by

ζ =Σi(Σ

1j=0Σ

1k=0Σ

1l=0Q

jklTr )i

Σi(Σ1j=0Σ

1k=0Σ

1l=0Q

jklTr + Q jkl

Bl )i∀i = 1, . . . , 10 (31)

where Q jklTr is 1 if the flow channel jkl is used in the given

time instance, and Q jklBl is 1 if the flow channel jkl is blocked

in the given time instance. For this simulation 100 channelsis created as 10× 10 users are defined. The simulation re-sults are presented in Fig. 15 where the channel utilizationchange over time and the average channel utilization arepresented. From Fig. 15, it can be seen that channel utiliza-tion varies a lot over time. However, the highest channelutilization is 0.08 ≪ 1. This degradation in the channelutilization implies that the nanonetwork is using selectivetransmission instead of transmitting through all availablechannels. Fig. 15 also shows that 4.5% of the overall chan-nels are busy in the steady state network.


Fig. 15. Channel utilization.

Fig. 16. Inter-group interferance rate.

3.2.2. Inter-group interferenceFinally, the intergroup interference is measured in

terms of the number of interfered bits in overall transmit-ted bits. Even though their characteristics are the same, in-stead of a packet-based interference evaluationwe presenta bitwise interference evaluation. This is because each usercan interfere to each other with exactly 3 bits (the addressbits). Formost of the cases, it would be possible to calculatethe interfered bits in the receiver side using the previousbits and the address of the interfered user. The inter-groupinterference (Ψ ) is measured using the (32).

Ψ =Σi(α01 + α10)i

ΣiΣ1j=0(α1j + α0j)i

∀i = 1, . . . , 10 (32)

where αkj represents the molecular bit. k term representsthe transmitted bit whereas j represents the received bit.α01 and α10 denote the interfered bits of user i, and α11and α00 represent the successfully transmitted bits. Theintra-group interference is not covered in thiswork and ac-cepted as 0. The simulation result of inter-group interfer-ence is presented in Fig. 16 which demonstrates that eventhough the interferance rate varies over time, it has amean

of 0.27. The inter-group interferance rate for themulti-usernanonetworks using the existing molecular communica-tion framework is equal to 1 as they try to send their pack-ages constantly through all channels. Even for the highestinterferance rate of the proposed framework it is still bet-ter than the existing framework as 0.67 < 1.

4. Conclusion

In this work, we present an interferance free simul-taneous communication framework for FBA-based multi-user nanonetworks. A molecule blocking filter model ispresented to decrease the inter-group interference at thereceiver side by changing the generic transmission frame-work from broadcasting to selective transmission frame-work. Addition to these, two different methodologies arementioned to overcome the synchronization problem. Theperformance of the proposed framework is measured interms of goodput rate, communication rate, channel uti-lization and inter-group interference. The result of thesimulations shows that proposed selective transmittingframework can support up to 90% of the nanonetworkuserswith the degradation of goodput rate. The application


of simultaneous communication enables the design of het-erogeneous nanonetworks in terms of nanodevice types.As each device type communicates with a certain nanode-vice group, different operations can be performed simulta-neously. Following this study, the concepts of buffering inmolecular communication and molecular packet distribu-tion in multi-user containing cell structures will be inves-tigated. In addition to these, the success of the proposedframework in different environments with different noiselevels will be investigated.

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Özgür Umut Akgül received his B.Sc. in Elec-tronics Engineering and Electrical Engineeringfrom Istanbul Technical University in 2011. Hereceived his M.Sc. in the Department of Com-puter Engineering in the same university in2014 and started studying Ph.D. in the same de-partment in 2014. His current research inter-ests include molecular communications, EnergyEfficiency in Wireless Networks, Internet ofThings and Energy Harvesting.

Berk Canberk received his Ph.D. degree in Com-puter Science from Istanbul Technical Univer-sity, Turkey, and his M.Sc. degree in DigitalCommunications Engineering from ChalmersUniversity of Technology, Sweden, in 2011 and2005 respectively. He was a postdoc scholarin Broadband Wireless Networking Laboratory(BWNLab) at the School of Electrical and Com-puter Engineering, Georgia Institute of Tech-nology, Atlanta, USA, during 2011–2012. Hewas also a research scholar in BWNLab during

2008–2009. He is currently an assistant professor in the Computer En-gineering Department of Istanbul Technical University. He is an editor inIEEE Transactions in Vehicular Technology, area editor in ELSEVIER Com-puter Networks and associate editor in WILEY International Journal onCommunication Systems. Dr. Canberk has been involved in several inter-national conferences as technical program co-chair, regional chair, pub-licity chair, tutorial chair and TPC member. His current research interestsinclude molecular communications, software defined wireless network-ing and next-generation green LTE networks.

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An interference-free and simultaneous molecular transmission model for multi-user nanonetworks