Jamming-aware Routing in Military Wireless Sensor Networks

Joao Tiago Henriques MontezInstituto Superior Tecnico, Lisboa, Portugal

Email: jthmontez91@gmail.com

Abstract—Recently, the use of explosive devices, remotely acti-vated by radio frequency signals, has been intensified, as a formof attack to deployed forces in the combat against terrorism.

Guerrilla, as well as terrorism, is an unpredictable threat,without frontiers, that can happen anywhere at any time.Guerrilla groups often use explosive devices as form of attack.Protection against these attacks is essential in a theater ofoperations.

Jammers appear as a solution to the protection of theforce against radio activated explosives, since these apparatuscan prevent wireless communications within their action range.On the other hand, sensor networks are also a useful elementin the battlefield, since they enable surveillance of large areafor long periods of time. However, it is necessary to have inconsideration that the communication between network nodesmay be affected by the use of jammers.

This project addresses this problem, with the objective ofminimizing the disruption of sensors network owing to theuse of jammers, using a technique based on adapting thetraffic routes. For this purpose, an extension of DestinationSequenced Distance Vector (DSDV) protocol has been used.For route selection, the used algorithm deviates routes, basedon knowledge about the geographic position of the nodes andof the jammer. The objective is to keep those routes far enoughfrom the jammer, so that the routes can remain stable for alonger time interval.

Simulations show that the developed algorithm was effi-cient in the considered scenarios, having lost less 20% thanthe original DSDV for used jammers, in manpack version,and 30% less for vehicles assemblies.Keywords: Wireless Sensor Network, jamming, routing proto-col.

1. Introduction

Guerrilla warfare and terrorism are two threats in thecurrent operation theaters, which become very difficult todefeat due to their stealthiness and unpredictability.

The guerrilla tactic is based on the use of small forcesin order to fray a greater force, using ambushes. This isa resistance technique used by small groups with superiorconcealment and mobility skills, which are able to surprisethe opponent at critical points of their organization, beingthe commanders the priority targets.

When it comes to terrorism, it is considered that ithas the same principle of operation of the guerrilla war-

fare although its aim is to create panic and overwhelmthe affected place and civilian population through fear andterror. Terrorism is practiced on a global scale, it has noboundaries and makes no distinction among military forcesand civilians, which makes it even more unpredictable anddifficult to fight.

The groups responsible for this kind of warfare usuallyuse Improvised Explosive Devices (IEDs) activated by radioas a mean of attack.

Both guerrilla warfare and terrorism are actions carriedout by individuals with light equipment, which makes themvery mobile, allowing them to attack more easily areas thatare difficult to reach and permanently guard.

Using Wireless Sensor networks (WSNs) can be a wayto solve this problem. These consist of a large number ofnodes spread over a specific area, allowing its monitoringfor 24 hours a day.

1.1. Motivation and Problem Definition

WSN are able to detect, classify and locate hostileelements on time, even inside buildings or in rough weatherconditions, as shown in Figure 1. However, maintaining thesecurity of a Sensor Network can be a challenging task.

Figure 1. Example of a Wireless Sensor Network. Soldiers uses the WirelessSensor Network to detect the enemy Jeon et al. [1]

The enemy can easily attack wireless communicationsby sending a high-power signal in the same range of fre-quency (jamming).

The jamming attacks to telecommunications systems arenot new, however, their use in Theaters of Operations hasbecome quite common with the emergence of IED’s bothin guerrilla warfare as in terrorist acts. The jammer is able

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to disrupt the signal that activates the IED within a certainradius of action.

Communication among nodes, which is essential in thesurveillance of defensive areas, can not only be affected bythe enemy, if they want to disrupt it, but also by a jammerused on the protection of friendly forces.

Since the main feature of the armed groups responsiblefor guerrilla warfare is the use of light equipment, it isassumed that they prefer using jammers in the manpackversion (Figure 2).

Figure 2. Example of a jammer (manpack version).

1.2. Objetives

This work aims to improve the sensor network’s effec-tiveness when communication between the nodes is pre-vented by the use of jammers, whether by friendly forces-when these are used as protection against IEDs-, whetherwhen it is used by the enemy with the intention of disruptingthe communication in the Wireless Sensor Network. In orderto improve the effectiveness of the Wireless Sensor Network,it was decided to create a routing algorithm, at the level ofthe Network layer, which takes into account the presence ofthe jammer and forwards packets through unaffected nodes.In order to achieve the main goal and test its performance,it becomes necessary to:

• Make range tests with the jammer.• Set an appropriate propagation model in order to

insert the jammer on the simulation.• Develop a routing algorithm which minimizes the

disruption of the sensor network when it is affectedby a jammer.

• Test the developed algorithm.• Analyze the results.

At the end of the project, the developed algorithm isexpected to be able to reduce the disruption caused by thejammer in the Wireless Sensor Network.

2. Related Work

In this section it will be introduced some Routing Pro-tocols at section 2.1 and some type of jammers will bedescribed at section 2.2. It will be made an approach tothe Jammer’s Detection, location and tracking subject atsection 2.3 and it will be described some jamming coun-termeasures at section 2.4.

2.1. Routing Proocols

Taking into account the specifications of the proposedwork, it was given special importance to the study oftwo routing algorithms: the Destination Sequenced DistanceVector Routing (DSDV) [2, 3] and the Ripple RoutingProtocol (RPL) [4]]. The routing algorithm that must bedeveloped will be based on one of them.

It should be noted that the DSDV has the ability toprevent loops, using a sequence number to determine themost recent information and discard the obsolete. In thesensor network it’s usually used a simplified version ofDSDV, where only the sink performs the routes update.

On the other hand, the RPL deserves special attentionbecause it is more efficient in static scenarios it is believedthat it forms the basis of the Internet of Things (IoT),being defined by the Internet Engineering Task Force (IETF)(Vasseur et al. [4]).

For the execution of this project, the option was followedof using a proactive routing protocol. Proactive protocolsare advantageous because they have a smaller delay whensending sporadic data.

Among the studied routing protocols, the RPL wouldbe the most advantageous to use. However, there is noimplementation of this protocol in NS-3. This fact led to theuse of a simplified version of DSDV where only the sinkgenerates the routes’ updates. This simplification makes theDSDV more similar to the RPL, even though it still lackssome mechanisms that make the RPL protocol the moreefficient in WSN scenarios.

2.2. Types of Jammers

The jammers are devices which are essentially made todisrupt radio communications.

In the electronic warfare , four types of jammers arecommonly used ([5]): spot jammer, sweep jammer, barragejammer and reactive jammer.

The spot jammer is a jammer that knows exactly thetarget network’s radio frequency, attacking it only on thatfrequency. It requires less power to operate, being the mostefficient and effective jammer. However, it has a flaw: thenetwork can change its frequency in order to avoid jamming(channel surfing / frequency hopping).

The sweep jammer, unlike the spot jammer, does notknow the target’s frequency, only knowing the spectrum ofthe most likely frequencies. Therefore, it performs frequencyhopping in this spectrum periodically or not-periodically,affecting the network temporarily. It is less effective and

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less efficient than the spot jammer although it can attackmore than one network, imposing restrictions on the targetnetwork’s freedom to make frequency hopping.

The barrage jammer covers a very wide band of theradio spectrum at the same time. This type of system leaveslittle room for the target network to prevent jamming and itcan block multiple networks at the same time. However, itrequires a very high power in order to keep the the powerspectral density high enough for jamming.

The three models described so far are models of activejammers which try to block the channel, not allowing datasharing. Active jammers are usually more effective becausethey keep the channel always busy. However, these are easierto detect.

An alternative approach is to use reactive jamming.The reactive jammer aims to affect communications

precisely during the transmission periods. It remains inac-tive for the period in which there is no data transference,only starting to work by transmitting radio signals when itfeels activity in the channel. This type of jammers has theadvantage of being more difficult to detect.

On this project it was used the spot jammer.

2.3. Jammer Detection, location and tracking

The authors Xu et al. [6, 7], Cakiroglu and Ozcerit[8], Chimankar and Nandedkar [9] propose several jammerdetection techniques, which are mainly based on the impactof interference in the received power, while the authorsYanqiang et al. [10], Liu et al. [11] present methods tolocate the jammer. These are essentially based on the use ofborder nodes of the affected area to estimate the centralposition. After mapping the area, it is assumed that thecentral position of the affected zone is the geographicalposition of the jammer.

For the preparation of this project it is assumed that thereis an algorithm of jammer detection already implemented,therefore this was not developed.

2.4. Jamming Countermeasures

W. Xu [12] presents countermeasures to prevent jam-ming in the physical and MAC layers, such as ChannelSurfing for MAC and Spatial Retreats for both.

However, for the development of this project, the coun-termeasures within the network layer are the most important.

B. Kan et al. [13] presents the Interference ActivityAware Multi-path Routing Protocol (IAMP), which is avariation of the AOMDV protocol.

The IAMP uses metrics of priorities, which are higherfor nodes that suffer less interference from the jammer, tochoose a path. Using this mechanism, the node less affectedby the jammer is more likely to define the critical path.However, not all detected interferences are caused by jam-mers. The simultaneous communication among nodes cancause interference in the neighboring nodes, which wouldincrease the packet delivery time because it would have tobe needlessly diverted to alternative routes.

Muraleedharan et al. [14] present an algorithm based onant systems and swarm intelligence. This algorithm modelsthe behavior of collective social insects.

The system adapts to environmental network changes.The agents move in the direction of the optimal solutionand communicate directly sharing knowledge with theirneighbors.

The first group of agents traverse the nodes ran-domly, and once they reach their destination, they depositpheromones on the paths as a means of indirect communi-cation with the other ants. The number of pheromones leftby preceding ants increases the probability that the sameroute is chosen in the same iteration. Pheromones evaporatewith time preventing not optimized solutions to dominateinitially.

This system is capable of avoiding network problems,for example, if a node loses power, the agents cease topass through and new paths are added to avoid this node.Thus, the communication continues without degraded sen-sor. These agents ensure the optimal route to the destination,using limited resources and learning network environment.

Mpitziopoulos et al. [15] presents a Jamming AvoidanceItinerary Design algorithm (JAID). The JAID algorithmcontains a process of choice based on graph paths. Duringthe process of path choices, each node receives a cost thatis a function of the losses in the connection.

In case of interference caused by a jammer, it is useda field mapping algorithm affected by the jammer [16] inorder to identify trees with range that reaches the nodes atthe perimeter of that area.

The JAID algorithm chooses to link the trees with thelost connection to the nodes in range in order to reduce thetotal cost of training routes.

This routing algorithm does not take into account themovement of the jammer, therefore it would be useful topredict its movement direction, in order to avoid creatingalternative routes which are not about to be affected.

3. Project Description

The project consists essentially in creating a variant ofDSDV protocol, which could achieve an improvement in thenumber of packets received on the sink in the original DSDVprotocol when the Sensor Network is affected by jamming.

3.1. Routing Algorithm

The created routing algorithm was based on the DSDV.Initially the DSDV was simplified to create only the routesto the sink, that is the only one generating updates. Thisallows to reduce the overhead in Wireless Sensor Network.

The implementation is depicted in the diagram of Fig-ure 3.

In the implemented solution, the distance from node tothe jammer is the principal measure to choose the route1. If

1. It was assumed that a jammer detection algorithm was already imple-mented

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Figure 3. Flowchart of the developed routing algorithm.

possible, the algorithm forwards packets for routes where thenodes are farther than the safety margin (m seg). Initially, itchecks if the node distance to the jammer already placed inthe routing table is greater than the safety margin. If verified,the distance from the jammer to the candidate node is testedand the number of hops to the destination is compared withthose already present on the routing table. Finally, afterchecking these two conditions, and if the candidate nodewith the smallest number of hops was found, it replaces theprevious next hop node in the routing table. Furthermore, ifthere was a second condition or reduction in the number ofhops the candidate node is eliminated.

If the first condition is not verified (distance from thenode to the jammer already present in the table is higherthan the safety margin) there are two possible solutions: Ifthe distance from the node to the jammer is greater thanthe safety margin and the number of hops less or equalthan the inserted node in table, the routing table is updated.Otherwise, the routing table is not changed.

Finally, if it is not possible to get a node with a greaterdistance than the safety margin. It has a similar result thanoriginal DSDV: the choice is based on the number of hops.

Figure 4 depicts a simulation of NS-3, where the de-scribed countermeasures are applied. As shown, the routesare deviated farther than the safety margin.

It should be noted that, because the considered jammeris the manpack version, the moving speed is assumed to below, and therefore, the DSDV protocol behaves quite wellwhen the update of the routes takes place in a relatively shorttime. Within a short update time, Wireless Sensor Networkrebuilds the routing tables constantly, giving little time to thejammer to affect the established routes before alternativesare created.

While the reduction of the time between route updatesleads to a faster topology adaptation, it is very costly interms of power consumption and overhead caused by theextra update packets.

Figure 4. Simulation of the developed routing algorithm.

4. Jammer Model

The created routing algorithm was tested in simulationenvironment. In order to accomplish that goal, the jammer’sperformance must be tested, so that a simulation model canbe developed.

During testing, the jammer was configured with a trans-mission power of -25 dB and the measured values wereobtained for various distances with a spectrum analyser. 2

Although -25 dB is not the maximum power of the jammer,it is possible to interpolate to different values.

Figure 5 chart shows the values obtained for the signalpower at various distances when the jammer is configuredwith an transmit power of -25 dB. These data were obtainedusing a spectrum analyser configured between 869.4-869.65MHz. This bandwidth was used because it is the operatingband of XBee868Pro module, which is widely used in sensornetworks.

During the tests, the jammer was not transmitting at itsmaximum power. In military operations, the power outputof the jammer is directly related to the need for forceprotection and battery life and, for long-term operations, itis recommended to use lower power, especially for manpackconfigurations.

The model was obtained based on the equation 1 for theLog-Distance radio propagation path loss model:

PL = PL0 + 10· γ· log( dd0

) +Xg

PL = PTx − PRx (1)

The experiment allowed to obtain a γ value of 4, 33.

2. The Jammer used during the tests cannot be described for reasons ofconfidentiality.

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Figure 5. Results from the jammer tests.

The γ value obtained shows that the signal sufferedvarious attenuations, which was expected because the atmo-spheric conditions were not optimal, and due to the presenceof reflections. Note that the γ = 2 is to model the free spacepropagation.

The data provided by the manufacturer of XBee868Promodule allowed to obtain a value of Signal to Noise Rationecessary to perform communications. On the other hand,with the equation (1) and the obtained γ value, it is possibleto verify if the signal to noise ratio for each distance ishigher than the former, and consequently allows communi-cation between sensors.

In military operations, it is unusual to operate in openareas. Usually they occur by watercourse, dense vegetationareas, valleys and built-up areas. To simulate the irregular-ities on the ground, it was decided to use the Three LogDistance Propagation Loss Model.

The value of γ = 4.33, obtained in the equation ( 1),has been increased to distances exceeding 500 m and 1000m, using γ = 5 and γ = 6, respectively.

4.1. Jammer Implementation in NS-3

It is essential to define all NS-3 settings according tothe defined scenario and configure the nodes of the WirelessSensor Network according to the specifications provided bythe manufacturer of XBeePro868 module.

The modelling of the jammer is intended to be addedto a simulation node without large capabilities. This nodeare only comprises a radio module and mobility module.The node will move across the Wireless Sensor Networksimulating a jammer, carried by a force in the battlefield.

When a transmission on physical layer occurs, the dis-tance between emitter and receiver and the distance betweenthe receiver and the jammer are checked.

With the distance between transmitter and receiver, thedistance between the jammer and receiver and the propaga-tion model obtained in tests with the jammer, it is possibleto calculate the transmission power emitted by jammer thatis received (as noise) in the nodes.

It is necessary to determine the Signal to Noise Ratio ofthe connection. If this ratio is higher than the experimentally

obtained the reception of the packet is possible. Otherwise,the packet is lost.

The flowchart of figure 6 represents the implementationof the jammer. For the calculation of the Signal to NoiseRatio it is applied the correspondent γ for Three LogDistance Propagation Loss Model and the resulted value isadded as noise.

Figure 6. Jammer implementation flowchart

Observing the figure 7, it is possible to check the resultof the inserted code, in which, as expected, the closest nodesto the jammer cannot receive data, they only can send tonodes that are not affected.

Figure 7. Simulation in NS-3 during routes update.

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Figure 8 shows the route disruption caused by the move-ment of the jammer at the lower right corner.

Figure 8. Jammer simulation without countermeasure.

5. Results

All performed simulations were based on the practicaltests performed and the propagation model obtained fromthese tests.

It should be noted that, for this project, it was consideredthat the jammer detection algorithm would have an accuracyof 100%, so, this means that the tests performed were donein optimal conditions. It is possible that the results wouldbe worse if occurs wrong jammer detection, because thealgorithm depends on this information.

For simulation purposes it was chosen to spread 250nodes randomly on an area of 28,09 km2. In a tacticaloperation, nodes could either be released by air or placeddirectly on the ground. For this project it was used only onesink.

The DSDV protocol can be used in circumstances wherethe sensor nodes are mobile, however, in this case it was con-sidered that they were static (typical scenario of a WirelessSensor Network).

Each simulation lasted 4100 s, but the updates of theroutes only occurred after 200 s to give time to the networkto adapt before the start of the transmissions data.

Data transmission starts after those 200 s, however, theresults are only recorded after 500 s, when the jammer startsthe transmission.

We tested the created routing algorithm in two distinctsituations: for a manpack version of the jammer and ajamming device used in vehicle assembly.

During the tests it was observed that the jammer actionradius was usually less than 1000 m, so it was considered asafety margin of 1400 m. The additional 400 m allows thejammer to move in that area without affecting closer routes.This additional value in the distance permits to increase thetime between the update of the routing tables, keeping thelow power consumption and low overhead.

5.1. Results (Jammer Manpack Version)

The following graphs represent comparative figures forten simulations with different seeds that change the randomseeds.

The graph of figure 9 shows the amount of lost packetswhen the sensor network suffers interference from a jammer,for the cases where was not applied any countermeasureand simulations where was used the developed routing al-gorithm.

Figure 9. Lost packets (Jammer Manpack Version).

The improvement shown in the graph corresponds tothe difference of lost packets obtained in both cases. Thisimprovement is positive for all simulations, which showsthat the countermeasure applied gets better results than theoriginal DSDV protocol. The improvement was, in average,of 3169 packages with standard deviation of 1216.

It was obtained an average value of 11.28 % for losseswith standard deviation of 0.86, without any countermeasureand an average of 8.80 % with standard deviation of 0.48,for simulations that used the developed routing algorithm.

On average, it was possible to achieve a reduction of21.57 % of lost packets, with a standard deviation of 0.07.These figures confirm the success of the implemented algo-rithm.

The graph shown in figure 10 represents the averagedelay between the generation of a packet until it is receivedat the sink for each of the simulations.

As shown, the average time for receiving packages ismuch lower in the simulations where there was no interfer-ence. It increases slightly in the simulations no countermea-sures were applied and it increases considerably with theuse of countermeasures.

The increased average time is easily justified by the in-creased number of hops. When the jammer causes disruptionon the links with fewer hops, it is necessary to create newroutes, with more hops, that avoid the jammed area. The

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Figure 10. Average time until packets reception (Jammer Manpack Ver-sion).

countermeasure goal is to avoid the jammed area, even if itresult in a greater number of hops. This explains the increaseof that average time. Despite the delay, this method allowsthe reception of more packages in the sink.

It was obtained 5.93 ms as average value for the sim-ulations without jammer with a standard deviation of 0.62.It was obtained 6.22 ms for the simulations without coun-termeasure, with standard deviation of 0.55 and 9.21 msfor simulations with active countermeasure, with standarddeviation 1.02.

The graph of figure 11 shows the maximum recordeddelay between the generation of a packet until it is receivedat the sink for the various simulations.

Figure 11. Maximum time until packets reception (Jammer ManpackVersion).

The average values of maximum delay was 2.01 s,with standard deviation of 0.01, for the simulations with-out jammer, was 11.22 s, with standard deviation of 9.75,for simulations with interference jammer and 5.58 s wasobtained with a standard deviation of 7.20, for simulationsit was applied countermeasure.

The graph in figure 12 is the number of physical layertransmissions during the simulation time. With this values,it was possible to indirectly compare the energetic costbetween simulations.

On average, 9.16× 105 transmissions were made at thephysical level, with a standard deviation of 8.64 × 104 forsimulations without jammer. In simulations without coun-termeasures where made 8.82 × 105 transmissions, withstandard deviation 7.80×104, while for simulations in whichthe countermeasure was applied, it was obtained the valueof 1.25× 106, with standard deviation of 1.30× 105.

Figure 12. Total transmissions at physical layer (Jammer Manpack Version).

These information allows to develop an indirect analysisof energy consumption by successfully received packets atthe sink. This analysis is shown in the graph of figure 13.

Figure 13. Energetic evaluation (Jammer Manpack Version).

As shown in the graph, the countermeasure used is theenergetically more expensive. The average value obtainedwas 7.76 with a standard deviation of 0.68 for the DSDVwithout any countermeasure, it was 7.76, with 0.68 standarddeviation for the DSDV suffering jamming interference and10.67 with a standard deviation of 1.1 for the countermea-sure developed.

The used algorithm is better in terms of received packetswhen the Wireless Sensor Network is jammed, however, thedelay and energy consumption of the network increases. Forthis reason, the algorithm should only be applied when ajammer is actually detected, so its implementation is similarto DSDV protocol when no jammer is detected.

5.2. Results (jammer in vehicle assembly)

As in the previous tests for the jammer manpack version,the created algorithm was tested on ten different situationsfor the jammer on vehicle assembly. Seeds and jammerpositions along the Wireless Sensor Network were similarto those used in the 5.1 section tests.

The graph shown in figure 14, represents the differencebetween packets received from the simulation where it wasnot applied any countermeasure and simulation where it wasimplemented the routing algorithm developed.

It should be noted that the simulation time is shorterin the case where the jammer is used in vehicle assembly,so the total amount of packets sent is smaller so as thelost packets. On average were lost 7394 packets, with 1511

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Figure 14. Lost packets (jammer in vehicle assembly).

standard deviation, when there is no countermeasure forthe simulations in turn, the average was 4948, with 746standard deviation for the simulations where was used thedeveloped routing algorithm. The average improvement was2446 packages with a standard deviation of 1341.

The number of lost packets was higher for jammer on ve-hicle assembly than the values obtained for manpack versionof the jammer. So, as expected, the Wireless Sensor Networkis more affected by the jammer in vehicle assembly, becauseit movement speed and transmission power are higher.

Without any countermeasure, it was obtained an averagevalue of losses of 17.32% with a standard deviation of3.54. For simulations in which was used the developedrouting algorithm, it was obtained an average of 11.59%,with standard deviation of 11.42.

However, although the loss values are higher, the relativeimprovement obtained was highest, which indicates that thedifference between packets received without any counter-measure and the developed algorithm is more pronouncedwhen using a jammer on vehicle assembly. Although thealgorithm has been developed based on the use of the jam-mer in manpack version, their use has proved advantageousto vehicle assemblies, yielding an average of 31.6 % withstandard deviation of 11.42.

The graph shown in figure 15, represents the averagetime between sending a packet by a node and receiving itat the sink, for each of the ten simulation. It representsthe average time to receive a package at the sink node,both when were used the DSDV protocol with jammerinterference and the developed method.

Figure 15. Average time until packets reception (jammer in vehicle assem-bly).

The average value of the average delay obtained toreceive each package at the sink node for the ten simulations,was 7.33 ms, with a standard deviation of 0.82 for thesimulations where was used the DSDV protocol sufferingjamming interference and 11.6 ms, with a standard deviationof 1.38, for the simulations where it was used the developedalgorithm.

Since only the average delay of packets that are actuallyreceived is recorded, we can explain the increase in thereception time with the increase in distance travelled by thepackage when using the algorithm developed, similar to theconclusion obtained for figure 10.

The graph shown in figure 16 is the maximum recordedtime between the emission of a packet and reception at thesink for the simulations where it was considered the jammeron vehicle assembly. Like the values in the graph shown infigure 11, the values obtained are quite irregular, so, nothingcan be concluded.

Figure 16. Maximum time until packets reception (jammer in vehicleassembly).

The mean values obtained for maximum time was 4.64s, with standard deviation of 3.92, for simulations where wasused the DSDV protocol suffering jamming interference invehicle assembly and was 4.24 s, with standard deviation4.22 to simulations when it was used the routing algorithmdeveloped.

The graph in figure 17 is the number of physical layertransmissions during the simulation time.

Figure 17. Total transmissions at physical layer (jammer in vehicle assem-bly).

In simulations without countermeasures where made3.23×105 transmissions, with standard deviation 2.83×104,while for simulations in which the countermeasure was

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applied, it was obtained the value of 4.45 × 105, withstandard deviation of 3.44× 104.

Figure 18 shows the energetic evaluation obtained withthe values of Figure 17.

Figure 18. Energetic evaluation (jammer in vehicle assembly).

The use of the developed routing algorithm becomesmore expensive in terms of energy, which coincides withthe previously results obtained for the manpack version ofthe jammer. The mean value obtained was 9.17, with 0.97standard deviation for the use of DSDV suffering jamminginterference and 11.79, with a standard deviation of 0.94,for the use of the developed routing algorithm.

The results obtained demonstrate the improvement interms of received packets, when a Wireless Sensor Networksuffers the action of a jammer, having achieved enoughsimulations with results greater than 30 % and reaching53.23 % for the simulation 2 with the jammer in vehicleassembly.

It should be kept in mind that the results were obtained,assuming that the jammer location was accurate. Thesevalues are expected to get worse in proportion to the jammerlocation error. if there is a deviation in the jammer detection.

6. Conclusion

The main objective of this thesis was to develop arouting algorithm to improve the performance of a WirelessSensor Network in terms of received packets when affectedby jamming.

Several routing protocols were considered in order toselect the one to be used as the of further development inorder to and meet the defined objectives. It was concludedthat the best protocol to use was the RPL. However, RPL isnot implemented in NS-3, so, it was used a modified versionof DSDV where only routes to the sink are created. Thisapproach turned DSDV more similar to RPL, since both areproactive routing protocols.

In order created routing algorithm, the jammer’s perfor-mance must be tested, so that a simulation model can bedeveloped.

It was considered that the propagation model to be usedwas the Three Log Distance Propagation Loss Model inwhich γ = 4.33, 5 and 6 were used for the first, second andthird distances respectively. The increase of γ was justifiedby the rough terrain areas or in built-up areas where militaryoperations occurs.

The jammer inserted in simulation was represented bya node without any capabilities that moved through theWSN. It allowed to use its distance to the receiver nodeas a parameter. If the minimum values for communicationin accordance with the model were not met, the packagewould be lost. The developed algorithm was tested in boththe jammer manpack version and vehicle assembly, and theresults obtained compared with the DSDV protocol withoutany countermeasure. This algorithm essentially consists indefining a safety distance to the jammer, which packagescan be avoided. In the studied case, this margin was set upas 1400 m.

I was obtained about 20 % and 30 % of improvementfor the jammer in manpack version and in vehicle assembly,respectively. However, there was an increase in networkoverhead as well as the energy spent by packet receivedat the sink.

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