AOrtiz Telecommunication Systems 2011_published Version

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Telecommun SystDOI 10.1007/s11235-011-9597-y

Fuzzy-logic based routing for dense wireless sensor networks

Antonio M. Ortiz

Fernando Royo

Teresa Olivares

Jose C. Castillo Luis Orozco-Barbosa

Pedro J. Marron

Springer Science+Business Media, LLC 2011

Abstract The task of routing data from a source to the sink

is a critical issue in ad hoc and wireless sensor networks. Inthis paper, the use of fuzzy logic to perform role assignmentduring route establishment and maintenance is proposed. Anincremental approach is presented and compared with sim-ilar existing routing protocols. Efficient routing approachesprovide network load balance to extend network lifetime,efficiency improvements, and data loss avoidance. Experi-ments show promising results for our proposals and its suit-ability for operating with dense networks, obtaining quickroute creation as well as energy efficiency.

Keywords Wireless sensor networks Routing Fuzzylogic

A.M. Ortiz () F. Royo T. Olivares J.C. Castillo L. Orozco-BarbosaAlbacete Research Institute of Informatics, Universityof Castilla-La Mancha, 02071 Albacete, Spaine-mail: [email protected]

F. Royoe-mail: [email protected]

T. Olivarese-mail: [email protected]

J.C. Castilloe-mail: [email protected]

L. Orozco-Barbosae-mail: [email protected]

P.J. MarronUniversity of Duisburg Essen, Bismarckstr. 90 Building BC,47057 Duisburg, Germanye-mail: [email protected]

1 Introduction

Wireless Sensor Networks (WSNs), as well as other wirelesspersonal area networks, have stirred up the world of wirelesscommunications since they present new challenges in termsof energy efficiency and communication performance.

Sensor nodes are resource constrained in terms of energy,processing capabilities and storage. This kind of networksalso have to deal with problems such as mobility and relia-bility. All these issues make necessary some kind of networkorganization to create and maintain data paths and to ensurereliable and efficient communications among the networknodes.

Routing data in networks composed in many cases of ahigh number of low-resourced nodes, is a difficult task sincethe algorithms and protocols have to save as much energyas possible whilst offering good performance. These proto-cols have to be designed by considering the optimization ofparameters, such as the battery status. Not considering thisinformation leads to problems in the network such as in-terrupted paths, data loss or isolated nodes, among others.These problems are directly related to latency and through-put values. Efficient routing approaches should balance thenetwork load in order to extend the network lifetime, ef-ficiency improvements, and data loss avoidance. Network

monitoring is also necessary to control topology changesand the addition or elimination of nodes in the network.

This work presents an incremental approach. Firstly,NORA (Network rOle-based Routing Algorithm), a role-assignment-based routing algorithm is presented, and sec-ondly its evolution, NORIA (Network rOle-based RoutingIntelligent Algorithm), a novel routing algorithm for wire-less sensor networks which combines different effectivetechniques in order to reduce the energy spending and im-prove data routes. These techniques are role assignment for
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distributing tasks over the network nodes and fuzzy logic formaking decisions.

Role assignment enable nodes with better resources to actas data routers in order to forward information from moredistant nodes up to the base station. The comparison of nodeconditions in NORA is performed by mathematical opera-tions, while NORIA is based on a fuzzy logic system that

improves the decision making process, reducing the num-ber of discovery packets and the number of gateways whilekeeping low the route-creation time, making the algorithmeven more reliable and efficient.

Our proposals are compared with two well-known rout-ing approaches, a simple tree-routing algorithm based onTree Routing protocol implemented in ZigBee [20], anda Connected Dominating Set, CDS-based routing proto-col [1]. Routing using CDS has been widely used to per-form routing over wireless ad hoc and sensor networks andwill serve as a basis for checking the performance of ourproposal. The experiments show the efficiency of NORA

and its suitability for working with dense networks in an ef-fective and reliable manner and, additionally, the improve-ments achieved by NORIA, making the discovery processeven more efficient.

The rest of the paper is organized as follows: Sect. 2 de-tails related work, Sect. 3 presents NORA, and its evolu-tion, NORIA, as well as network problem resolution. Ex-periments, tests and comparisons are detailed in Sect. 4, andfinally, Sect. 5 gives some conclusions and lines for futurework.

2 Related work

Sensor nodes are small and energy-constrained devices withlimited computational capabilities and memory resources.Because of that, the development of a new generation ofnetwork protocols with new characteristics (such as auto-discovery, self-organization, adaptation to dynamics, andgenerally a higher degree of distribution) is essential to ful-fill the new requirements of sensor networks.

In [2] routing techniques for WSN are reviewed. This sur-vey serves as a basis for future self-organization algorithmswhich will improve network efficiency. A general definition

and classification of self-organization techniques in ad-hocand sensor networks can be found in [3]. In [4], virtual struc-tures such as backbone and cluster are proposed, but the ne-cessity of wired nodes discards it for wireless sensor net-works. Previously, some general design paradigms for self-organized networking were proposed in [5] and [6]. Theseparadigms propose a new network organization model byintroducing a new concept: the role.

Role assignment is a network organization techniquewhich allows nodes or groups of nodes to have different

functionality in order to improve network performance. Roleassignment can be performed in several ways: rule-basedparadigm [7, 8], application-based approach [9] or nodes-placement-based approach [10]. This work extends the rule-based paradigm in order to make routing decisions.

Another interesting technique is the use of set algebra tocalculate Connected Dominating Sets (CDS) in order to ob-

tain efficient routing, as was proposed in [1]. A set is dom-inating if all the nodes in the system are either in the set, orneighbors of at least one node in the set. This technique hasbeen used for wireless ad hoc and sensor networks [11, 12].In this work, CDS technique is used as theoretical basis tocompare our proposals. Towards this end, we have imple-mented the routing approach appearing in [13], and its per-formance is compared with our proposal.

NORA performs decision making by using a series ofmathematical operations; in order to improve this process,our second proposal, NORIA, follows a fuzzy-rule-basedparadigm. Nodes store and evaluate a set of fuzzy rules,

taking both its own and neighborhood parameters into ac-count. Role assignment and routing decisions are performedaccording to the output of the fuzzy-logic-based system,providing global energy saving and focusing packet load tonodes with a better state.

The idea of using artificial intelligence techniques to sup-port the decision-making process in order to get more ef-ficient algorithms is widely used in the recent literature.Nowadays, there are several algorithms that apply thesetechniques to ad-hoc network organization algorithms androuting protocols. Approaches based on fuzzy-logic [28],machine learning [15], neural networks [16], genetic algo-

rithms or ant colonies [17] can be found. Artificial intelli-gence techniques reinforce the efficiency and performanceof routing protocols, by combining data from nodes andtheir interactions in order to make decisions to improveglobal network performance. Some approaches such as antcolonies require a large number of messages, being automat-ically discarded for its use in WSNs [17]. Neural networksrequires a complete knowledge of the WSN prior to discov-ering, and genetic algorithms and machine learning requireshigh computational capabilities [15, 16].

Since we aim to evaluate node conditions in an efficientmanner, our approach makes use of fuzzy logic, which re-

quires low computation capabilities, and is able to supportthese decision-making processes to improve efficiency whileextending the overall network lifetime.

Fuzzy-logic imitates the logic of human reasoning, whichis much less rigid than the calculations computers generallyperform. Diverse approaches using fuzzy-logic have beenpresented for cluster-based routing approaches improvement[14, 25, 26], and also for directed diffusion routing security[27], endorsing so the use of fuzzy logic in wireless sensornetworks. These approaches take advantage of the use of


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fuzzy information treatment in order to get efficient routing,and will be applied herein to our basic tree-based routingprotocol.

3 Routing proposal

The first proposal of this work is the use of a distributedrouting algorithm that assigns roles to the network nodesusing both local and neighborhood information, and createsenergy-efficient routes to the sink. Discovery and routingprocesses are complemented with mechanisms to maintainroutes and to manage the addition and failure of nodes inthe network.

The different experiments performed have led us to pro-pose an incremental approach. The proposal begins by per-forming role assignment in order to have nodes with differ-ent functionality in the network. To assign this functional-ity, it was necessary to implement an efficient method able

to evaluate node conditions. The second proposal uses fuzzylogic in order to obtain a fast and effective technique to eval-uate node conditions. Furthermore, the fuzzy logic imple-mentation presents low computational requirements [30], soit is adequate to be executed in low-resourced nodes com-posing WSNs.

3.1 The role-based approach

Our first routing proposal assigns roles to nodes in the net-work in order to establish the tasks for each node or groupof nodes. Role assignment lies in assigning different tasks

to each node or group of nodes in the network in order toglobally improve network performance. This technique usesboth local and neighborhood data to make decisions. Roleassignment paradigm is an efficient manner of optimizingspecific parameters such as network lifetime, path length,QoS. . . , while data routing is performed.

Our role-based approach to create routes in the network,NORA (Network rOle-based Routing Algorithm), evaluatesnode conditions and assigns roles depending on current nodeand neighborhood characteristics. The whole process beginsat the base station (sink node or coordinator), and finishes atthe furthest nodes. Intermediate nodes decide between be-

ing end devices (nodes that just send sensed data) or routers(which, in addition to the formers, also forward data comingfrom end devices). Each router or end device selects the bestrouter to act as parent, inside their radio range, to forwardits data to the coordinator.

Following this scheme, every node in the network is ableto send data to the sink either directly, or through routernodes. The protocol establishes minimum routes in terms ofenergy consumption and efficiency, from every node in thenetwork to the base station, this latter gathers data from all

network nodes. The considered node conditions, in order toselect nodes optimizing this parameters to route data fromtheir neighbors are the remaining battery and the distance tothe sink (number of hops).

To perform role assignment and route selection, NORAuses five kinds of message:

IPM (Information Propagation Message): includes local

information (node ID, number of hops to the sink, remain-ing battery, . . . ).

RDM (Role Decision Message): includes the same infor-mation as IPM, and is interpreted by nodes as a trigger toinitiate the discovery process.

RRM (Router Request Message): this kind of message isused by nodes which do not have any router within theirradio range (no node can forward their data), and urges anend device to become router.

RCM (Role Changing Message): used by router nodeswith low resources to notify role changing (from routerto end device), and urge their dependent end devices to

look for another available router. ACK: unicast message to acknowledge addressed mes-

sages. It is used to control router presence and allowsnodes to look for another router in case of parent failures.

Data are encapsulated in Data Messages (DM) which areindependent of the control messages defined above.

The route creation process followed by NORA is out-lined in Fig. 1. In NORA, the discovery process begins whenthe base station sends an RDM. Nodes receiving this mes-sage send an IPM and start a timer. Along that time inter-val, nodes wait for information messages from neighbor-

ing nodes. Once the timer expires, nodes perform role deci-sion and parent election, that is, selection of the best routeramong the known routers located at a lower level, i.e. lowernumber of hops to the base station. One hop nodes willchoose the base station as parent. If no router is found, anRRM is sent to the best end device neighbor, i.e. the onecharacterized by the lowest number of hops, and highestbattery level. Once nodes have selected role and parent, anRDM message is broadcasted in order to induce the next hop

Fig. 1 NORA organization phase transitions


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neighbors to start the organization process. This RDM alsoinforms the selected router on the new child. This procedureis propagated hop by hop until reaching the furthest nodesand all network nodes have a route to reach the base station.

The pseudo-code corresponding to this phase is shownbelow:

when receive(RDM) then:update_neighbors_table(RDM)

send(IPM, BROADCAST)

wait(temp)

foreach received message during

temp do

update_neighbors_table(message)

end foreach

end wait

temp_coord = select_coordinator()

if get_role(temp_coord)==leaf then

send(RRM, temp_coord)

end ifset_coord(temp_coord)

local_role = calculate_role()

send(RDM, BROADCAST)

Lets formally define the route-creation process: Consid-ering Ha,b as the number of hops between nodes a and b,and as the set of all network nodes. Equation (1) definesthe set of neighbor nodes, n, of a given node n with lowerhops number to the base station, B.

n = n {i}/i, H{i,B} < H{n,B} (1)

Similarly, (2) defines the set of same level neighbors (nodeswith same number of hops to the base station), n.

n = n {i}/iH{i,n} = 1 and H{i,B} = H{n,B} (2)

With these previous definitions, router (parent) selectionprocess as well as role decision can be defined. If we con-sider Li and Ri as the battery level and role of the node i,respectively, parent of node n, P(n,t), at time t is selectedby using (3).

P(n,t) = i n/Ri =ROUTER and

Li = max{Ln} and H{i,B} = min{H{n,B}}

(3)

If no router node is found, then (4) is used and an RRMis sent to the selected node to request role switch from enddevice to coordinator.

P(n,t) = i n/Ri =ENDDEVICE and

Li = max{Ln} and H{i,B} = min{H{n,B}}(4)

Parent node is chosen as the highest battery level routeramong neighbors with lowest hops number. In case of no

routers available, an RRM is sent to the highest battery levelend device to induce it to switch its role from end device torouter.

In order to make own role decision, each node comparesits battery level with same level (number of hops) neighbors.If local battery level is the highest, then the node sets its roleas router. End device role is assigned otherwise. Equation

(5) defines this process.

R(n,t)=

ROUTER, ifLn = max{Ln}

ENDDEVICE, otherwise(5)

If current node has equal number of hops and batterylevel than other(s), the received signal strength is used todecide.

To sum up, using NORA all network nodes are able tofind a path to reach the base station. Furthermore, forward-ing nodes (routers) are those with lower number of hops tothe sink and highest battery level. These assumptions avoid

node failures and ensure a reliable, and energy-efficient net-work operation.Depending on particular application requirements, e.g.

real time, extended network lifetime. . . , it is possible to se-lect the parameters to be considered during route creationprocess such as available sensors, memory and processingcapabilities, number of nodes connected to a coordinator andso on.

Since wireless sensor networks need simple and fastmethods to make decisions, fuzzy logic appears as an appro-priate approach due to its ability to calculate results fast andprecisely. Moreover, the user-friendly nature to define node

conditions provided by this approach and the need of lowprocessing resources make this technique a suitable methodto make decisions in wireless sensor networks. In order toimprove the efficiency and accuracy of the route creationprocess and to speed it up, the evaluation of node condi-tions through fuzzy logic is proposed. We have incorporateda fuzzy-logic engine in the decision process of NORA.

3.2 Fuzzy-logic principles

Fuzzy logic consists of a decision system approach whichworks similarly to human control logic. It provides a sim-ple method to reach a conclusion from imprecise, vague,or ambiguous input information. The execution of a fuzzy-logic system requires less computational power than con-ventional mathematical computational methods [18]. Fur-thermore, only a few data samples are required in order toextract the final accurate result. Besides, fuzzy logic is ahandy technique since it uses human language to describeinputs and outputs [21].

In a fuzzy-logic-based system, calculations are per-formed by an inference engine. In order to select the in-ference engine, we have studied two widespread approaches


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Fig. 2 Mamdani and TSK dataevaluation diagram

present in the literature: Mandani [22], and TSK [23]. Bothof them proceed in a similar way, consisting of four phases:fuzzification, rule evaluation, combination or aggregation ofrules, and deffuzification (see Fig. 2). The main differenceis presented at the deffuzification stage in which TSK ruleconsequents are mathematical functions (not fuzzy), loosingso its interpretability [24]. Moreover, as will be explainedbelow, in our proposal, rule outputs are independent fromeach other. For our implementation, it does not make anysense to aggregate different nature outputs with a weightedaverage as TSK does. In this work, we aim to get the best

match (max-min inference) and for that approach, the use ofthe Mamdani makes perfect sense.Lets now detail the Mamdani inference system. The in-

put of a Mamdani fuzzy-logic system is usually a crispvalue. To allow this value to be processed by the system,it has to be converted to natural language, that is, it hasto be fuzzified. In this way, the fuzzifier method takes nu-meric values and turns them into fuzzy values which can beprocessed by the inference system. These fuzzy values rep-resent the membership values of the input variables to thefuzzy sets.

Once values have been fuzzified, the inference system

processes the fuzzy rules to get a fuzzy output. In the caseof a fuzzy rule having more than one antecedent (conditionalelement), an AND (minimum) or OR (maximum) operatoris used to estimate the output value of rule evaluation.

The third step in the Mamdani inference method is theaggregation of all outputs, where the outputs of each ruleare combined to form a new fuzzy set.

Finally, at the deffuzification stage, the new aggregatedfuzzy set is converted to a number. Mamdani uses the cen-troid technique which tries to determine the point where avertical line divides the combined set into two equal parts.

3.3 Routing using fuzzy logic

In order to improve NORAs performance, the integrationof fuzzy logic to the decision process is proposed. Here iswhere NORIA (Network rOle-based Routing Intelligent Al-gorithm) comes into play. Thus, parent election and role as-signment are now based on the results of the evaluation of afuzzy rules set.

The input variables to be considered in the experimentsare: number of hops to the base station, and the remaining

Fig. 3 Number of hops fuzzy sets

Fig. 4 Battery level fuzzy sets

Fig. 5 Output fuzzy sets, node suitability

node energy. These parameters are just a subset within thefull set of parameters which can be included in the decisionprocess (delivery probability, delay, or signal strength...among others). Route length and remaining battery havebeen selected herein since they represent an example of twoof the most important optimization parameters in wirelesssensor networks. The energy is a key parameter in WSNssince it represents one of the main problems of this technol-

ogy, and the route length indicates the number of forward-ings necessary to route data from the source to the desti-nation. The output variable represents the suitability of thenode to be a router and to be selected as parent. Figures 3,4, and 5 show fuzzy sets for input and output parameters.

To perform the role assignment and parent selection pro-cess, nodes will compare the evaluation output for eachneighbor node. The variables and fuzzy sets used in thispaper are an example of the multiple possibilities and havebeen selected after several checks for the application and


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Table 1 Fuzzy rule base

N. of hops Bat. level Node suitability

Very low Low Low adequate

Very low Medium Low adequate

Very low High Adequate

Very low Very high Perfect

Low Low Not adequateLow Medium Low adequate

Low High Adequate

Low Very high Perfect

Medium Low Not adequate

Medium Medium Low adequate

Medium High Adequate

Medium Very high Adequate

High Low Not adequate

High Medium Low adequate

High High Low adequate

High Very high Adequate

Very high Low Not adequate

Very high Medium Not adequate

Very high High Low adequate

Very high Very high Adequate

topologies used, in order to have a generic proposal able towork in a wide range of WSN applications. Notice that fuzzysets (input and output) can be customized depending on theapplication, requirement and circumstances of each particu-lar WSN. For example, in a surveillance network would be

interesting the use of variables such as delivery probabilityin order to ensure alarm nodes to receive important data.In our Mamdani-based implementation, the fuzzy rules

base includes rules such us: if the Number of hops is Lowand the Battery Level is High then the node suitability is

Adequate. Here, since there are 4 fuzzy sets for Battery levelinput and 5 for Number of hops input, there exist 20 rules,which are summarized in Table 1.

3.4 Solving problems in the network

During network operation, NORIA is able to control several

situations related to router failures, new nodes coming to thenetwork, and low-resourced routers. The algorithm managesthese situations as follows:

Router failure: if a node does not receive the ACK mes-sage from its router for at least two consecutive times, itwill proceed as if it would have received a CR from itscoordinator.

New node joining the network: the new node listens thechannel. If a message from a router or from the sink nodeis received, it is selected as parent. If no router or sink is

detected during a pre-fixed period of time, the new nodewill send a RRM to the best end device listened and willselect it as parent. The role of the new node will be enddevice (by default) being able to be changed into router ifa RRM is received.

Low-resourced router: when a router realizes that its bat-tery drops under a determined threshold, it sends a CR

(change role). Then, children nodes will look for anotherrouter following the route-creation process described inSect. 3.1. Thus, nodes sending through the low-resourcedrouter are re-organized and network connectivity is pre-served.

4 Experiments

NORA and NORIA efficiency will be evaluated throughits implementation in OMNET++ [19], an extensible andmodular component-based C++ simulation library andframework for network simulations. As aforesaid, our pro-posals are compared to a Simple Tree Routing protocol(STR) [20] and to a routing scheme based on ConnectedDominating Sets (CDS) [1].

Simple Tree Routing protocol was chosen because itworks similarly to NORA in the sense that STR builds a tree-based routing scheme, but considering different node andneighborhood conditions. STR operation can be summed upas follows: (1) base station announces its presence; (2) nodesthat have received the base station announcement send theirown announcement message and start a timer; (3) once the

timer expires, each node decides its parent node based onthe number of hops and link quality information. This pro-cedure spreads hop by hop until reaching all network nodes.This approach is similar to NORA in the sense that STRbuilds a tree-based routing scheme but with the absence ofroles.

CDS uses roles defining nodes inside and outside the set.But the procedure to calculate roles is different: CDS calcu-late roles before calculating routes, while NORA and NO-RIA perform role assignment at the same time that routes arecreated. The operation of CDS-based routing can be summa-rized up as follows: (1) first it computes a marking process

in which the protocol calculates a connected dominating setamong all network nodes; (2) after that, all nodes in the net-work are in the CDS, or are neighbors of at least one nodein the set. This marking process selects nodes depending ontheir set of neighbors in order to keep all nodes in the net-work connected.

The routing process is divided into three steps:

1. If the source is not a gateway host, it forwards the packetsto a source gateway, which is one of the adjacent gatewayhosts.


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2. This source gateway acts as a new source to route thepackets in the induced graph generated from the CDS.

3. Eventually, the packets reach a destination gateway,which is either the destination host itself or a gatewayof the destination host. In the latter case, the destinationgateway forwards the packets directly to the destinationhost.

The parameters considered in the experiments, for theevaluation of the three schemes, are:

Number of packets: represents the amount of packets sentduring route discovery phase, and measures the necessityof information exchange for each approach. A low num-ber of sent packets indicates that a protocol will be moreefficient in terms of the energy spent during route discov-ery.

Energy spending: due to nodes forming WSNs are energyconstrained, the energy spent during the operation of theprotocols is very important. The lower energy spending,

the higher efficiency, and consequently the longer net-work lifetime.

Network set-up time: represents the time spent by a proto-col to discover routes from every node to the base station.It is the time since the first discovery packet is sent untilall nodes in the network have a route to the base station.

Number of gateway nodes: this variable represents thenumber of nodes that have to forward data from othernodes, and so subsequently spending more energy. For theapplication implemented in this work, we suppose the useof data aggregation [29], an effective technique to reducethe amount of information travelling through the network,

so a lower amount of forwarding nodes implies a lowerglobal energy consumption.

4.1 Set-up and scenarios

In order to evaluate the performance of our proposals and tocompare it with other similar approaches, the designed sce-nario has considered circular network areas with radius fromR (50 m) to 10R (500 m), maintaining constant node den-sity (nodes per unit of area). Nodes are randomly deployedin these areas and Base Station is placed at the center. Inthe largest area (10R), 1959 nodes have been used. Table 2

shows number of nodes for each experiment depending onnetwork radio.

For each scenario and particular combination of parame-ters, we have run 100 simulations.

4.2 Results

The first results show the number of necessary messages tocreate the routes across the network for NORA, NORIA,CDS and STR. The amount of messages used to create the

Table 2 Number of nodes by radio

Radio Number of nodes

R (50 m) 21

2R (100 m) 81

3R (150 m) 177

4R (200 m) 317

5R (250 m) 4896R (300 m) 709

7R (350 m) 973

8R (400 m) 1257

9R (450 m) 1597

10R (500 m) 1959

Fig. 6 Packets sent during route discovery phase

routes is a good variable to evaluate the spent energy in this

process. The least number of messages used, the lowest en-ergy used. Figure 6 shows the average number of packetssent by nodes during the simulations of the route creationphase as a function of the number of network nodes, it is,the number of packets since the discovery process is starteduntil all nodes in the network have found a route to the sink.

Remark that STR uses the lowest number of packetsto organize the network among different experiments (seeFig. 6) closely followed by NORIA. These results prove theenergetic efficiency of our proposal, spending just a littlebit more energy than STR. This metric is very importantwhen working with networks composed by a high number

of nodes (dense networks) and for applications that requirea proactive routing and cannot compute a route each time anode have to send data (reactive routing).

Our experiments have also shown that while NORA, NO-RIA, and CDS are able to create routes for all networknodes, STR leaves out a large area of the network unorga-nized. The section varies from 1 to 5% of the total amountof network nodes. This is caused by the decision approachimplemented in STR that leaves unorganized those nodeswhich cannot communicate with a signal power greater than


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Fig. 7 Energy consumption reduction for NORA, NORIA, and STRwith respect to CDS

50%. In contrast, our experiments have shown that nodeswhose signal is received below 50% of power still can send

successfully data to the sink.The energy consumption is highly related to the amount

of packet sending. In order to illustrate the differencesamong the energy consumption of the different proposals,Fig. 7 shows the normalized energy consumption with re-spect to the highest value (CDS) for each approach as a func-tion of the number of nodes.

Notice that the energy consumption for NORA, NORIA,and STR has been normalized to the maximum value ob-tained (reached by CDS). This results not only shows the en-ergy saving achieved by fuzzy-logic-assisted approach, butalso that fuzzy-logic itself does not represent an increment

in the energy consumption as might be thought.Another important metric of the routing algorithms is the

time spent to set up a fully connected network, specially forapplications with real-time requirements. A low route es-tablishment time is also propitious when solving problemsin the networks, to create new routes, and to perform pe-riodical set-ups, necessary for some applications. Figure 8shows the time spent by the different proposals to completethe route-creation phase.

NORA and NORIA show good average results in the ex-periments, proving that both are very efficient in terms ofroute-discovery time, even spending half of the time than

other proposals such as CDS. These results make NORAand NORIA suitable to be used in real time networks, inwhich the user needs a fast response from the network.

It is also interesting to consider the number of routernodes in the network. This gives an idea of the number ofnodes spending energy forwarding data from other nodes.Using data aggregation during data routing, the least num-ber of router nodes, the least global energy consumption.Figure 9 shows the number of router nodes obtained in theexperiments.

Fig. 8 Network self-organization time

Fig. 9 Number of forwarding nodes

For that experiment, STR obtains the best value, closelyfollowed by NORIA. But it is important to consider theproblem of unorganized nodes left by STR. We then con-clude that NORIA obtains the best average results in theexperiments, making it suitable to be implemented in realdense wireless sensor networks and to be used with a widerange of applications. Furthermore, role migration is per-formed by using CR messages to get load balancing, avoid-ing node failure and data loss.

Notice that no loops between nodes has occurred duringsimulations, that is, all created paths flows from any node inthe network to the base station.

5 Conclusions and future work

The desire to improve the existing routing approaches forwireless sensor networks has led us to design and experi-ment with the routing algorithm presented in this paper. NO-


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RIA is a novel role-based routing protocol that makes use offuzzy logic to make decisions.

NORIA has been presented as an incremental approach,starting with a basic approach, NORA, improved with theaddition of a fuzzy-logic-based system to be the basis ofthe decision making process. Simulation results show thecorrect operation of the protocol and its suitability to be

used in a wide range of applications and scenarios. Theperformance of this proposal has been compared with twowell-known routing methods and both NORA and NORIAhave proved its efficiency, by achieving better average re-sults than the other proposals. The combination of fuzzylogic with role assignment to perform routing tasks hasbeen proved to be a good association for working withdense WSNs in an energy-efficient, fast, and effective man-ner.

Furthermore, the design of the fuzzy-logic system is sim-ple and easy, allowing users to define different variables,sets, and rules, depending on each particular application and

node features. The use of fuzzy logic makes node featuredefinition easier, and the accuracy and the low amount of re-sources needed to run the system make this technique appro-priate to be executed in the low-resourced nodes that makewireless sensor networks.

The solution of network problems such as node failures,low-resourced routers and the addition of new nodes in thenetwork is now being implemented and will be tested toevaluate the solutions proposed in Sect. 3. With this peri-odical monitoring, network operation will be extended andthe efficiency and reliability of the network will be im-proved.

Our future work is now focused on the incorporation ofother parameters to the decision system (end to end delayand delivery probability, for example) as well as the incor-poration to the standard ZigBee [20] of the techniques pro-posed herein. The implementation and experimentation withthe full system in a real WSN is also contemplated in our fu-ture plans.

Acknowledgements This work was supported by the Spanish MECand MICINN, as well as European Commission FEDER funds, underGrants CSD2006-00046 and TIN2009-14475-C04.

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fuzzy systems association (IFSA 2005).

Antonio M. Ortiz was born inMadrid, Spain in 1981. He is aComputer Science Engineer fromthe University of Castilla-La Man-cha. He is currently a Ph.D. Can-didate in the Albacete Research In-stitute of Informatics (University ofCastilla-La Mancha). His researchinterests is centered in the area ofwireless sensor networks includ-ing self-organization and integra-tion of cross-layer protocols and al-gorithms. Rule and role-based rout-ing protocols and the incorporationof Artificial Intelligence for WSNs

are some of his main topics. At present, he is involved in severalprojects including the specification, and implementation of an intel-ligent routing algorithm for WSNs and its integration with MAC andphysical protocols as well as the experimentation with real motes.

Fernando Royo was born in Al-bacete, Spain in 1983. He holds aComputer Science Engineer degreefrom the University of Castilla-LaMancha. He is currently a Ph.D.

Candidate at the Department ofComputing Systems. His main sci-entific research interests include thedesign and implementation of MACprotocols for wireless sensor net-works, and the specification and de-sign of architectures for wirelesssensor networks. At present, he isinvolved in several projects deal-ing with power-aware management

techniques for wireless sensor nodes. He is student member of theIEEE.

Teresa Olivares was born in Grana-da, Spain, in 1970. She received thePh.D. degree in computer sciencefrom the University of Castilla-LaMancha, Spain, in 2003. She is anAssociate Professor in the Comput-ing Systems Department of the Uni-versity of Castilla-La Mancha, Al-bacete, Spain, and teaches computer

networks at the Polytechnic School.Her main scientific research inter-est includes environmental sensornetworks, wireless communicationsand network architecture and proto-cols for sensor and actor networks.

She has very interesting publication in these areas and she participateson interesting sensor-based research projects.

Jose C. Castillo holds a ComputerScience Engineer degree from theUniversity of Castilla-La Mancha.He is currently a Ph.D. Candidate

in the Albacete Research Institute ofInformatics. His research interestsinclude the fields of routing proto-cols for wireless sensor networksand multisensory intelligents sys-tems for monitoring and interpreta-tion of behaviors. At present, he isinvolved in several research projectsincluding the combination of differ-ent kinds of heterogeneous sensornetworks for intelligent surveillance

purposes, from the network data acquisition to the detection of objectsand the interpretation of their behaviours.

Luis Orozco-Barbosa received theB.Sc. degree in electrical and com-puter engineering from UniversidadAutonoma Metropolitana, Mexico,in 1979, the Diplome dtudes Ap-profondies from cole NationaleSuprieure dInformatique et deMathmatiques Appliques (EN-SIMAG), France, in 1984 and theDoctorat de lUniversit from Uni-versit Pierre et Marie Curie, France,in 1987, both in computer science.From 1991 to 2002, he was a Fac-ulty member at the School of Infor-

mation Technology and Engineer-ing (SITE), University of Ottawa, Canada. In 2002, he joined theDepartment of Computer Engineering at Universidad de Castilla LaMancha (Spain). He has also been appointed Director of the AlbaceteResearch Institute of Informatics, a National Centre of Excellence. Hehas conducted numerous research projects with the private sector andserved as Technical Advisor for the Canadian International Develop-ment Agency (CIDA). His current research interests include Internetprotocols, network planning, wireless communications, traffic model-ing and performance evaluation. He is a member of the IEEE.


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PedroJ. Marron received his bach-elor and masters degree in com-puter engineering from the Uni-versity of Michigan in Ann Arbor,USA in 1996 and 1998 respectively.At the end of 1999, he moved tothe University of Freiburg in Ger-many to work on his Ph.D., whichhe received in 2001. In 2003, he

started working on his habilitationat the University of Stuttgart, whichhe finished in December 2005. InStuttgart, he lead the mobile datamanagement and sensor networkgroup. Since 2007, he is the head

of the Sensor Network and Pervasive Computing group at the Uni-versity of Bonn. His current research interests are distributed systems,mobile data management, location-aware computing, sensor networksand pervasive systems. He is a member of ACM and GI.

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AOrtiz Telecommunication Systems 2011_published Version