a self-powered bluetooth network for intelligent trafﬁc light junction management ·...

A Self-Powered Bluetooth Network for Intelligent Traffic LightJunction Management

MARIO COLLOTTA, ANTONIO MESSINEO, GIUSEPPINA NICOLOSI, GIOVANNI PAUTelematic Engineering Laboratory - Faculty of Engineering and Architecture

Kore UniversityCittadella Universitaria, 94100, Enna

ITALYmario.collotta, antonio.messineo, giuseppina.nicolosi, [email protected]

Abstract: Wireless Sensor Networks (WSNs) are increasingly used in Intelligent Transportation System (ITS)applications, especially in dynamic management of signalized intersections. This paper proposes a Bluetoothnetwork architecture in order to monitor vehicular traffic flows near to a traffic light. The proposed architecture ischaracterized by a novel algorithm in order to determine green times and phase sequences of traffic lights, basedon measured values of traffic flows. It is known that the wireless sensor networks are characterized by very lowpower devices, so the continuous information transmission reduces the life cycle of the whole network. To thisend, the proposed architecture, provides a technique to power the sensor nodes based on piezoelectric materialswhich allow to produce potential energy taking advantage of the vibration produced by the passage of vehicles onthe road.

Key–Words: Wireless Sensor Networks, Intelligent Transportation System, Traffic Lights, Queues Management,Piezoelectric materials

1 Introduction

1.1 WSNs for Road Monitoring Applications

One of the main targets of Intelligent TransportationSystems is to ensure road safety because the num-ber of vehicles constantly grows up in cities. Roadsafety must be improved in traffic lights intersectionswhere many road accidents occur for various reasons,including a wrong traffic lights management. Cur-rently the most of traffic lights are implemented withfixed cycles or they are manually controlled introduc-ing also human evaluation errors. Whereby severalworks focused on innovative traffic lights manage-ment techniques. Unfortunately, most of proposed so-lutions produce several problems, like wrong green-time balancing or excessive fuel consumption. Theseissues can be overcome thanks to modern technol-ogy and novel methodologies. In recent years, severalstudies have focused on the use of WSNs [1], manyof which refer to road monitoring [2] because theyare capable to make different measures on the mon-itored traffic area. In fact, the sensor nodes of thenetwork can be placed anywhere and they are easyto manage. Through a distributed and clustered net-work infrastructure, capable to evaluate real time traf-

fic flows, WSNs are particularly useful to monitorhighly crowded roads and then they represents a con-crete solution to road congestion problem.

1.2 Energy harvestingIn recent years, several energy harvesting applicationshave been developed in order to produce electric en-ergy from vehicular, train or pedestrian traffic or fromenvironmental vibration in general. Anyhow, there arenot applications in ITS systems which can develop en-ergy from used roads. For this reason, several kinds ofenergy sources have been investigated by researchers.A source of energy can be represented by vibrationsbecause they are the most ubiquitous and can be foundeverywhere [3] [4]. In addition, another source is rep-resented by the mechanical energy that can substitutesolar energy and in several applications obtains morepotentiality. In both cases, the objective regards agreater amount of energy generated as efficiently andeconomically as possible. The use of smart materialssuch as piezoelectric has been studied and developedin different scenarios [5] in order to harvest energy. Infact, an effective way for the electricity production isrepresented by the recovery of mechanical energy pro-duced by vibrations. This can be obtained through the

WSEAS TRANSACTIONS on INFORMATION SCIENCE and APPLICATIONSMario Collotta, Antonio Messineo, Giuseppina Nicolosi, Giovanni Pau

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use of piezoelectric materials because their effect con-sists of the generation of an electric field produced bya mechanical deformation of the same piezoelectricmaterial. In recent works, this technology has beenapplied in several application fields, like self-poweredwireless sensor networks for data acquisition and pro-cessing [6].

1.3 Main AimThis paper presents a novel methodology for intelli-gent traffic light junction management based on real-time information detected by self-powered Bluetooth[7] sensor nodes placed along road sections. The mainaim of the proposed approach is to reduce the wait-ing time in traffic light queues. The paper is orga-nized as follow: section 2 proposes main literaturerelated works while section 3 describes the proposednetwork architecture. Section 4 proposes the experi-mental test- bed showing obtained simulations results.Finally, section 5 reports conclusion and possible fu-ture works.

2 Related works2.1 Wireless Sensor Networks for Traffic

Light Junction ManagementThe knowledge of traffic information represents a keyaspect of intelligent transport systems (ITS). Cur-rently, most of used monitoring systems are based ona wired communication infrastructure that increasesmaintenance costs and reduces architecture scalabil-ity. In order to solve these problems, WSNs canbe used because they introduce a higher informationquality due to a denser sensors placement in the mon-itored area. WSNs have been exhaustively studiedand used in several fields, like agriculture [8], homeautomation [9], health [10] and industrial [11] [12]to mention some. Doubtless, one of the main ap-plication fields of this technology is signalized inter-sections management focused on waiting times andqueue lengths reduction. In fact, real-time traffic de-tection, through a WSN, helps drivers to make deci-sions in order to optimize arrival time and to avoidqueues. For these reasons several works in litera-ture deal with WSNs used for road traffic monitor-ing and management. In [13] a solution based on anew network topology applied to sensor networks forroad monitoring, in order to improve performance interms of throughput and energy savings, is proposed.In [14] authors propose an algorithm that considersseveral values about traffic flow for green times cal-culation and phase sequences determination for eachtraffic light cycle. The proposed approach is an al-

ternative to the traditional road intersections manage-ment system but its real implementation needs longperiods for testing to evaluate its effective function-ing. The approach proposed in [15] shows a tech-nique for vehicles detection using magneto-resistivesensors. The authors show an algorithm that identi-fies vehicles through the analysis of signals receivedfrom sensors deployed along road. Simulations areperformed on a simple phasing plan while it is notanalyzed the possibility of phase changes dependingon run-time traffic detected. A novel architecture inwhich sensor nodes detect road information in orderto determines the flow model of the intersection isproposed in [16]. The same authors propose in [17]results obtained using one sensor and two sensors.Results show that the deployment of sensors close toeach other produces the best performance in terms ofdata quality and, also, it reduces energy consumptionof WSN nodes. A fuzzy logic algorithm used to de-termine green time duration at traffic lights is showedin [18] while authors of [19] propose a fuzzy logiccontroller and an innovative WSN architecture in or-der to dynamically enable or disable cameras, accord-ing to the real need, in a monitored signalized inter-section. The approach proposed in [20] involves theuse of a local fuzzy logic controller installed at eachjunction that derive the green time for each phase ina traffic-light cycle through a dynamic-programmingtechnique. Moreover, the authors of [21] propose afuzzy logic controller in order to dynamically adjustgreen time of traffic lights. According to the pro-posed approach, traffic flow can be detected by thesingle-axis magnetic sensors and transmitted throughwireless sensor network. The time for vehicles pass-ing during the green lights is dynamically adjustedthrough the fuzzy algorithm according to the currentvolume of vehicles. In [22] and [23] the authors pro-pose an adaptive traffic light control algorithm. Inorder to determine the optimal green light duration,the proposed approach adjusts both the sequence andlength of traffic lights in accordance with the real timetraffic detected by WSN. An intelligent traffic signalscontrol system based on a WSN is shown in [24]. Au-thors propose an approach that uses the vehicle queuelength during red cycle in order to perform better con-trol in the next green cycle.

2.2 Techniques for energy harvestingIn the last decade, the increasing cost of energy and re-lated environmental problems have led researchers toinvestigate new energy sources [25]. The authors pro-pose innovative mechanisms in order to reduce green-house gas emissions by increasing energy efficiencyof existing systems. Moreover, other works introduce


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Figure 1: System Architecture

Figure 2: Main System Elements

novel solutions for energy saving and energy recov-ery both in civil and in industrial environments [26][27] [28]. Instead, other authors focus on the en-ergy harvesting using piezoelectric materials as veryinteresting research issues concern the energy recov-ery techniques using environmental mechanical vibra-tions. Recently, in the station of Shibuya (Tokyo),an energy collection system that uses a piezoelectrictransducer [29] has been installed, under a proposal ofthe East Japan Railway Company. Moreover, in recentyears, the Innowatech society has proposed severalapplications based on piezoelectric stack transducer

for the energy production through vehicular traffic.After considering all these research and real-worldapplications aspects, some researchers [30] have re-cently shown a mechanical device for energy harvest-ing using piezoelectric bimorph bender transducer,which can recover energy from road, pedestrian andrail traffic. The use of piezoelectric bender devicesand an innovative configuration that allow to transfermechanical vibrations of the main box to the piezo-electric transducer represent the innovation of thistechnique.

3 System Architecture3.1 WSN requirementsFor a better interpretation of traffic flows, in a road-monitoring context, a fundamental condition is repre-sented by the knowledge of the real-time situation ofroad sections. In order to ensure the timely processingof information coming from the road, it is necessary torealize an appropriate monitoring environment, basedon appropriate technologies. This monitoring envi-ronment must guarantee several requirements includ-ing:

real-time communication among network nodes;

real-time scheduling of system tasks [31];


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2

3

1

4 RA

RB

Figure 3: Road intersection

performance predictability;

transmission reliability [32];

prevention and reaction to critical situations.

The proposed system, based on a Bluetooth [7]network, is characterized by architecture shown inFigure 1. It is important to underline that Blue-tooth does not natively support real-time communica-tions. As demonstrated by [33] [34], a deadline-awarescheduling should be introduced in order to ensure thesatisfaction of real-time constraints. Anyhow, the ar-chitecture proposed in this paper is based on the stan-dard Bluetooth protocol [7], without hardware or soft-ware changes. According to Bluetooth standard, a pi-conet is formed by a master node and a maximum of 7slaves. If the network requires more than 7 slaves thenit is possible to implement a scatternet, a type of ad-hoc computer network consisting of two or more pi-conets. Scatternets can be formed when a member ofone piconet participates as a slave in another piconet.This slave transmits data among members of both net-works. In our approach, the traffic light junctions aremonitored using appropriate nodes, Sensing Slaves(SS), provided with magnetic sensors in order to de-tect the presence of ferrous objects (vehicles). Thedata gathered by the SS nodes is then forwarded totheir Piconet Master (PM) which collect informationof the piconet and forward them to the Routing Slave(RS) which takes care of data exchanging between pi-conets. The information gathered is forwarded amongpiconets to the Junction Master (JM), which has pro-cessing functions. All modules that characterize theJM are shown in detail in Figure 2:

Bluetooth communication module;

Dynamic traffic light manager module;

Data acquisition module.

ty tg ty tg ty

tg ty tg ty tg ty1

2

3

4T1 T2 T3 T4 T5

...

...

...

Figure 4: Periodic traffic lights task scheduling

3.2 Dynamic traffic light manager moduleThe considered traffic light junction is shown in Fig-ure 3. The traffic lights 1 and 3 have to be consideredas one traffic light because they have the same greenand red times. The same goes for traffic lights 2 and 4.For this reason, we indicate with RA the road contain-ing traffic lights 1 and 3 and RB the road containingtraffic lights 2 and 4. The functioning of each trafficlight can be approximated to a periodic task with pe-riod Ti and duration t coincident with the time of thetraffic-light cycle. As shown in Figure 4, in a stan-dard management in which priorities are not consid-ered, every traffic light after a period of time Ti hasa fixed green time (tg) and a fixed yellow time (ty),while the following period is red. In this case, the traf-fic light does not take into account the dynamic queuebehavior. In fact green and red times will be alwaysfixed although a road has a long queue of vehicles.Due to this issue, it is necessary to realize a dynamicscheduling algorithm that takes into account the realnumber of vehicles waiting near traffic lights. Fig-ure 5 shows a road section of length L subdivided intosmaller sub-sections, each one of length li. Each sub-section li is identified by a Sensing Slave which maintask is to detect vehicles presence. A special hous-ing placed on the sidewalk contains the SS equippedwith a magnetic sensor. It measures the earth’s mag-netic field distortion caused by the presence of metalcomponents of a vehicle near the sensor. Through theBluetooth protocol, the information gathered by theSS is forwarded to the PM that forwards them to theRS. The parameters that must be chosen at system de-sign time are the following:

the length (L) of the entire monitored section;

the length (li) of every single sub-section;

the approximate speed (v) with which sections arecrossed.

Generally, cars represent most of waiting vehiclesat traffic lights. Considering that the average lengthof a car is between 3.5 and 5 meters, the length ofeach subsection (li) can assume values between 4 and8 meters. Each sub-section length must be adapted,according to the communication protocol. Accordingto the specifications of Class 1 Bluetooth devices, for


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s1 s2...

sg

l1 l2...

sn+1 sn+2...

sy

lgln+1 ln+2

... ly

sm+1 sm+2...

lm+1 lm+2...

L

Normal+Queue Medium+Queue Long+Queue

Figure 5: Monitored road section

example, the SS/RS nodes can be placed every 10 me-ters while the PM nodes every 25 meters, while theJM can be placed at the end of the road near the traf-fic lights. We approximate the average speed (v) of ageneric vehicle crossing a traffic light intersection atthe value of 15Km/h. The lg value indicates the max-imum value by which the queue at the traffic light isconsidered normal, while ly indicates the maximumvalue by which the queue at the traffic lights is con-sidered medium; over this value the queue is consid-ered long. The number of sub-sections is calculatedthrough the ratio L/li while the lg value can be deter-mined by the following equation (1):

lg =

(Lli

)3

+ 1 (1)

The total number of sub-sections is divided bythree but another has been added to increase the lengthof the area where the queue, at the traffic lights, is con-sidered normal. The ly value, therefore, is calculatedaccording to the equation (2):

ly =L

li− lg (2)

As shown in Figure 6, in case of vehicles detec-tion in a section li < lg the traffic light works un-der standard conditions, with period Ti, green timetg and yellow time ty. On the contrary, there ismedium queue in case of vehicles detection in a sec-tion lg < li < ly. In this case it is necessary to cal-culate the new green time tgrt based on sub-section inwhich vehicles have been detected; instead for li > lythere is a long queue condition. The crossing time of asingle subsection can be easily estimated through theequation (3):

ti =liv

(3)

where li and v are known parameters, as they rep-resent respectively the length of the i-th section andthe approximate crossing speed of each single route.

l1 l2

1 2 ...

...

n+1 n+2 ... m+1m+2 ...

n m

lg ly

Figure 6: Sections subdivision

According to the estimated queue length, the greentime must be recalculated in case of medium or longqueue. The standard green time tg and the real-timegreen time tgrt can be calculated through the follow-ing relations:

tg, if li ≤ lg

tgrt = tg +lj+lv , if lg < lj ≤ ly

tgrt = tg + lk+lv , if lk > ly

(4)

with j = n+1, n+2, . . . ,m and k = m+1,m+2, . . . . The terms (lk + l)/v and (lj + l)/v representa space and speed ratio, and then a time that must beadded to the green time of traffic lights in order toslightly increase the total green time and to allow abetter queue draining. This time value takes in ac-count the i-th sub-section in which the sensor detectsa vehicle. The road that has the longest green time hashigher priority. Each traffic light independently calcu-lates, during each period, its green time in order todetermine its priority level according to the equation(5):

pi = tg, if li < lg with i = 1, 2, 3, 4

pi = tgrt, if li > lg with i = 1, 2, 3, 4(5)

where pi is the priority of the i-th traffic light. Aspreviously said, traffic lights 1 and 3 must be consid-ered as unique, since they have the same green and redtime. The same is true for traffic lights 2 and 4. So thepriority must be entirely referred to road RA, contain-ing traffic lights 1 and 3, and the road RB , contain-ing traffic lights 2 and 4. The algorithm determinesthe road with highest priority at the end of the periodTi, determining which traffic lights needs more greentime. During each period, the priority pi of each trafficlight is calculated or one or more times in order to de-termine the priority of the road. At the end of the pe-riod Ti the algorithm evaluates the road with highestpriority determining which traffic lights needs moregreen time. The priority of each road is given by thesum of the priorities of the traffic lights which is di-vided by two in such a way that the actual time is not


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given by the sum of the times of each traffic light, be-cause otherwise the time would be too long, but by anaverage value. The priority of each road is calculatedthrough equation (6) and (7):

PRA=

(p1 + p3)

2+

∆A

2(6)

PRB=

(p2 + p4)

2+

∆B

2(7)

The ∆ value (positive or negative) is calculatedconsidering both the road with highest priority and thetraffic light color at the end of the period. Moreover,∆ is divided by 2 in order to avoid that it may assumea very great value (green time too long).

3.3 Self-power technologyIn order to supply power to Bluetooth sensor nodes,the system configuration is based on a bimorph piezo-electric transducer rectangular clamped to an extreme[35]. Two piezoelectric outside (active layers) and athin metal plate in the middle section (passive layer)form the converter. This converter is made of lead zir-conate titanium (PZT-5A), a material with a high ef-ficiency and flexibility in the charge production. Thepassive layer performs both the connection betweenthe two active layers, and the distancing of the samefrom the neutral axis, resulting in increased deforma-tions and electric potential [35]. To exploit the poten-tial of this technology, parallel and serial configura-tions have been analyzed. The parallel configurationinvolves the construction of three electrical connec-tions for the extraction of the converted charge, whilein the serial configuration it is necessary to adopttwo electrical connections. The Figure 7 shows theconstructive characteristics of the two configurationsadopted, with the electric connections and the polar-ization directions [36]. In order to gather the externalvibrations, caused by the passage of vehicles, and totransform them in energy [37], the Energy HarvestingDevice (EHD) will be installed within a road cavity.The energy harvesting system consists of a piezoelec-tric transducer bonded on the surface of the steel beamsubjected to external impulse [38]. The beam, shownin Figure 8, undergoes a tensile stress and a compres-sion producing, as a consequence, an electrical poten-tial. Through an electrical circuit, this potential differ-ence, generated at the extreme poles, is converted intoelectrical energy.

3.4 Electrical circuit model and batterycharging system

It is necessary to highlight that the energy obtainedfrom the piezoelectric transducers is not directly us-

Figure 7: Bimorph piezoelectric. a) Parallel configu-ration (left); b) Serial configuration (right)

Figure 8: a) Road cavity; b) Detail of the EHD in-stalled inside the speed bump with cavity

able by electronic devices due to random variations ofpower and voltage over the time. For this reason, asuitable circuit for the management of gathered poweris required. In fact, the effort made to obtain effi-cient transducers may be lost without the use of properadapters able to convert signals of a few millivolts, oreven less, without substantial losses. Moreover, thesecircuits consume energy and so they must be able toshut down when the source of energy is not enoughto power the device in order to avoid unnecessaryenergy consumption. Anyhow, the power manage-ment circuit must be able to automatically switch-on(self-starting) when the energy increases. The generalstructure of the power manager circuit can be dividedin three interfaces (Figure 9 a). Generally, the outputvoltage of a piezoelectric generator is characterized bya pseudo-periodic behavior and assumes positive andnegative values alternately. For this reason, a recti-fier circuit [40] is always necessary. The circuit usesa small piezoelectric transducer in order to convertthe mechanical vibration into an AC voltage sourcethat is powered inside the bridge rectifier internal ofthe LTC3588 (piezoelectric energy harvesting powersupply). It can recover small vibrations and gener-ate system power instead of using traditional batter-ies. The LTC3588-1 is a very low quiescent currentpower supply, specially designed for energy recoveryapplications or step-down at low current. It can di-rectly interface with a piezoelectric transducer or an-other AC power source, rectifying the voltage wave-


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Figure 9: a) General structure of a power manager circuit [39]; b) Energy harvester simulation model includingmechanical part and bridge diode rectifier power conditioning

form and storing the energy recovered in an externalstorage capacitor. At the same time it dissipates theenergy in excess through an internal derivation regu-lator, maintaining an output voltage regulated througha buck regulator with high efficiency. Two differen-tial inputs, PZ1 and PZ2, which rectify the AC in-puts, can access the full-wave bridge rectifier insidethe LTC3588-1. The rectified output is stored in a ca-pacitor to the VIN pin and can be used as an energyreserve for the buck converter. The low loss bridgerectifier has a total voltage drop of about 400 mVwith typical piezoelectric currents which are normallyequal to about 10 mA. Moreover, this bridge is ableto conduct currents up to 50 mA. On the contrary, thebuck regulator is enabled as soon as an enough volt-age is available on VIN in order to produce a regu-lated output. In order to control the output throughthe internal feedback of the detection pin VOUT, thebuck regulator uses an algorithm for hysteretic volt-age. Furthermore, the buck converter loads an out-put converter through an inductor to a value slightlyhigher than the regulation point. At this point, thecurrent inductor is increased to 260 mA through aninternal PMOS switch and then decreased to 0 mAthrough an internal NMOS switch. In this way, theenergy is efficiently supplied to the output capacitor.It is worthwhile to note that this hysteretic method re-duces losses associated with FET switching and holdslow the output loads. In fact, in the switching pro-cess, the buck converter provides at least 100 mA ofaverage load current. The circuit that manages the

Figure 10: Road A: values obtained with a fixed trafficlight cycle

output AC current from the piezoelectric transducer[41] is graphically represented in Figure 9 b. The ACstandard is the simplest form of power conditioningand directly connects a resistive load between the twoelectrodes of the considered transducer. Whereas, theDC standard rectifies the output transducer, througha full bridge diode rectifier, and connects a resistiveload in parallel with a storage capacitor at the outputof the rectifier.

4 Performance Evaluation4.1 Dynamic traffic light manager evalua-

tionA generic crossroads, shown in Figure 1, has beenchosen as test-bed scenario in order to evaluate ourapproach. Several simulations have been carried out


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Figure 11: Road A: values obtained with a dynamictraffic light cycle

Figure 12: Road A: managed vehicles with fixed traf-fic light cycle

considering both a fixed-cycle traffic light and the ap-proach proposed in this work. Regarding to traffic in-tensity, we considered up to 70 vehicles on each roadfor each traffic light. Up to 140 vehicles can be mea-sured on each road. In both simulation cases, the ref-erence cycle was 60 seconds and measurements havebeen gathered for 1 hour. Approximately, durations offixed traffic light cycles are: Minimum duration: 30 seconds;

Normal duration: 50 - 75 seconds;

Maximum duration: 90 - 120 seconds.The number of vehicles measured and drained in

Road A, in case of fixed and dynamic cycle, are shownin Figure 10, 11, 12 and 13 respectively, while Fig-ure 14, 15, 16 and 17 show values obtained in RoadB. As shown in Figure 10, the queue it is not correctlydrained in case of fixed cycle. An average value of10.95 vehicle/minute is drained compared to an av-erage value of 37.73 vehicles/minute measured nearthe traffic light. In this case 29.02% of the arrivedvehicles has been drained (Figure 12). On the con-trary, in Figure 11 and 13 is clearly shown that ap-proximately 95.44% of the total number of vehiclesis drained. In Figure 14 values measured in Road Busing a fixed cycle are shown. In this case an aver-age value of 31.50 vehicles/minute has been detected

Figure 13: Road A: managed vehicles with dynamictraffic light cycle

Figure 14: Road B: values obtained with a fixed trafficlight cycle

Figure 15: Road B: values obtained with a dynamictraffic light cycle

Figure 16: Road B: managed vehicles with fixed traf-fic light cycle


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Figure 17: Road B: managed vehicles with dynamictraffic light cycle

Figure 18: Load Voltage vs Time plotting for differ-ent resistance R2 values: a) Serial configuration; b)Parallel configuration

but of these just an average value of 11.67 has beendrained. Therefore approximately 37.04% of the ar-rived vehicles has been drained (Figure 16). As shownin Figure 15 and 17, using our approach about 95.91%of total arrived vehicles has been drained.

4.2 Piezoelectric device evaluationIn this section, simulation results related to thepiezoelectric device for Bluetooth sensors charge areshown. In order to evaluate the response variation,both in terms of electric potential and electric power

Table 1: Serial configuration: average voltage andpower harvested from the system vs. load resistance

R load (Ω) Vm (V) P (mW)

1,00E+02 0,25 0,651,00E+03 2,20 4,848,00E+03 5,80 4,219,00E+03 6,01 4,011,00E+04 6,20 3,842,00E+04 7,03 2,47

generated by electromechanical coupling, two differ-ent simulations have been carried out. The analysishas been conducted through the development of some3D numerical models using the FEM code COMSOLMULTIPHYSICS 4.2 [42] and QUCS 0.0.16 for theelectromechanic coupling and the simulation respec-tively. The FEM model consists of a free tetrahedricmesh made by 14.658 solid elements (90.514 degreeof freedom). Firstly, a static analysis was made byapplying a displacement imposed to the upper surfaceof the box, of 10 mm (Figure 8). Then, two dynam-ical studies with and without the circuit respectivelywere made. Two different piezo polarization config-urations [43] have been used in order to perform thenumerical simulation. The main aim of this choice isto evaluate how the response of the model changesby changing the polarization direction and resistiveload applied [35]. Simulation results show how theincreasing of the R2 resistance value (from 100 ohmto 20.000 ohm) produces a more stable load voltageover the time, both for the serial and the parallel con-figuration (Figure 18). More in detail, as shown inFigure 18 (a) and (b) respectively, in the serial config-uration the load voltage varies from 0,0 V to 10,0 Vwhile in the parallel configuration values varies from0,0 V to 6,0 V. As shown in Figure 19, it is clear howthe serial configuration produces little better perfor-mance than the parallel configuration by comparingthe peak voltages measured using the serial (see Table1) and parallel (see Table 2) polarization respectively.

5 ConclusionsBluetooth networks are used in several applicationsfor their useful features. The possibility to use real-time information in order to dynamically manage thetraffic light cycles is a key aspect of ITS applications.However, Bluetooth sensor devices are battery pow-ered, whereby batteries must be replaced after theirnormal duration (dependent on the use). In order toreduce queues at traffic lights, in this paper a novel


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Table 2: Parallel configuration: average voltage andpower harvested from the system vs. load resistance

R load (Ω) Vm (V) P (mW)

1,00E+02 0,35 1,131,00E+03 2,25 5,068,00E+03 4,49 2,529,00E+03 4,66 2,331,00E+04 5,05 2,172,00E+04 5,45 1,28

Figure 19: Comparison between the average electricalpower harvested from the system depending from theload Resistance

methodology for intelligent traffic light junction man-agement has been presented. The proposed approachis based on real time information provided by a Blue-tooth sensor network powered by energy harvestingdevices inserted within a road cavity in order to catchthe external vibrations, caused by the passage of ve-hicles. Several simulations have been carried out andobtained results are very promising both in terms ofqueues management and in terms of the proposed en-ergy harvesting technique. The obtained positive re-sults lead to further detailed experimental analysis.

References:

[1] J. Norman, Optimized Routing for Sensor Net-works using Wireless Broadcast Advantage,WSEAS TRANSACTIONS on INFORMA-TION SCIENCE and APPLICATIONS, Issue 5,Volume 10, pp. 159-168, 2013.

[2] M. Collotta, V. Conti, G. Scata, G. Pau, S.Vitabile, Smart wireless sensor networks andbiometric authentication for real time trafficlight junctions management, Int. J. of IntelligentInformation and Database Systems, Vol.7, No.5,pp. 454-478, 2013.

[3] Hao Wu, Lihua Tang, Yaowen Yang, CheeKiong Soh, A novel two-degrees-of-freedom

piezoelectric energy harvester, Journal of Intel-ligent Material Systems and Structures, 24: 357,2012.

[4] R. Yang, R. Qin, C. Li, G. Zhu, Z.L. Wang,Converting biomechanical energy into electric-ity by a muscle-movement-driven nanogenera-tor, Nano Lett., 9 (3), pp 1201-1205, 2009.

[5] I. Chopra, Review of State-of-Art of Smart Struc-tures and Integrated Systems, AIAA Journal 40,pp. 2145-2187, 2002.

[6] S. Roundy, P.K. Wright, J. Rabaey, Energy scav-enging for wireless sensor networks with specialfocus on vibrations, Kluwer Academic Publish-ers, XV, 212 p., 2003.

[7] Bluetooth Specification Version 4.0, July 2010.[8] T. Kalaivani, A. Allirani, P. Priya, A survey on

Zigbee based wireless sensor networks in agri-culture, 3rd International Conference on Trendzin Information Sciences and Computing (TISC),pp. 85-89, 2011.

[9] M. Collotta, V. Conti, G. Pau, G. Scata,S. Vitabile, Fuzzy Techniques for Access andData Management in Home Automation Envi-ronments, Journal of Mobile Multimedia, Vol.8No.3, pp. 181-203, 2012.

[10] Changzhan Gu, J.A. Rice, Changzhi Li, Awireless smart sensor network based on multi-function interferometric radar sensors for struc-tural health monitoring, 2012 IEEE TopicalConference on Wireless Sensors and Sensor Net-works (WiSNet), pp. 33-36, 2012.

[11] M. Collotta, G. Pau, V.M. Salerno, G. Scata, Adistributed load balancing approach for indus-trial IEEE 802.11 wireless networks, IEEE 17thConference on Emerging Technologies & Fac-tory Automation (ETFA), pp. 1-7, 2012.

[12] M. Collotta, L. Gentile, G. Pau, G. Scata, A dy-namic algorithm to improve industrial WirelessSensor Networks management, IECON 2012- 38th Annual Conference on IEEE IndustrialElectronics Society, pp. 2802-2807, 2012.

[13] Weihao Xie, Xuefan Zhang, Huimin Chen, Wire-less sensor network topology used for road traf-fic, International Conference on Wireless, Mo-bile and Sensor Networks - CCWMSN07, pp.285-288, 2007.

[14] Binbin Zhou, Jiannong Cao, Xiaoqin Zeng,Hejun Wu, Adaptive Traffic Light Controlin Wireless Sensor Network-based IntelligentTransportation System, 72nd IEEE VehicularTechnology Conference Fall (VTC 2010-Fall),pp. 1-5, 2010.


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[15] D.T. Dissanayake, S.M.R Senanayake,H.K.D.W.M.M.R Divarathne, B.G.L.T. Sama-ranayake, Real-Time Dynamic Traffic LightTiming Adaptation Algorithm and SimulationSoftware, International Conference on Industrialand Information Systems (ICIIS), pp. 563-567,2009.

[16] M. Tubaishat, Yi Shang, Hongchi Shi, Adap-tive Traffic Light Control with Wireless SensorNetworks, 4th IEEE Consumer Communicationsand Networking Conference - CCNC 2007, pp.187-191, 2007.

[17] M. Tubaishat, Yi Shang, Hongchi Shi, Wire-less Sensor-Based Traffic Light Control, 5thIEEE Consumer Communications and Network-ing Conference (CCNC 2008), pp. 702-706,2008.

[18] L.G. Rhung, A. Che Soh, R.Z. Abdul Rahman,M. Khair Hassan, Fuzzy Traffic Light ControllerUsing Sugeno Method for Isolated Intersection,IEEE Student Conference on Research and De-velopment (SCOReD 2009), pp. 501-504, 2009.

[19] M. Collotta, G. Pau, V.M. Salerno, G. Scata, ANovel Road Monitoring Approach Using Wire-less Sensor Networks, Sixth International Con-ference on Complex, Intelligent and SoftwareIntensive Systems (CISIS), pp. 376-381, 2012.

[20] Tsin Hing Heung, Tin Kin Ho, Yu Fai Fung,Coordinated road-junction traffic control by dy-namic programming, IEEE Transactions on In-telligent Transportation Systems, Volume 6, Is-sue 3, pp. 341-350, 2005.

[21] Fuqiang Zou, Bo Yang, Yitao Cao, Traffic LightControl for a single intersection based on Wire-less Sensor Network, 9th International Confer-ence on Electronic Measurement & Instruments- ICEMI, pp. 1040-1044, 2009.

[22] Binbin Zhou, Jiannong Cao, Xiaoqin Zeng,Hejun Wu, Adaptive Traffic Light Controlin Wireless Sensor Network-based IntelligentTransportation System, IEEE 72nd VehicularTechnology Conference Fall - VTC 2010-Fall,pp. 1-5, 2010.

[23] Binbin Zhou, Jiannong Cao, Hejun Wu, Adap-tive Traffic Light Control of Multiple Intersec-tions in WSN-based ITS, IEEE 73rd VehicularTechnology Conference - VTC Spring, pp. 1-5,2011.

[24] F.A. Al-Nasser, H. Rowaihy, Simulation of Dy-namic Traffic Control System Based on WirelessSensor Network, IEEE Symposium on Comput-ers & Informatics (ISCI), pp. 40-45, 2011.

[25] A. Messineo, D. Panno, Potential applicationsusing LNG cold energy in Sicily, InternationalJournal of Energy Research, Vol. 32, Issue 11,pp. 1058-1064, 2008.

[26] D. Panno, A. Messineo, A. Dispenza, Cogener-ation plant in a pasta factory, energy saving andenvironmental benefit, Energy, Vol. 32, Issue 5,pp. 746-754, 2007.

[27] A. Messineo, F. Marchese, Performance evalu-ation of hybrid RO/MEE systems powered by aWTE plant, Desalination, Vol. 229, Issues 1-3,pp. 82-93, 2008.

[28] A. Marvuglia, A. Messineo, G. Nicolosi, Cou-pling a neural network temperature predictorand a fuzzy logic controller to perform ther-mal comfort regulation in an office building,Building and Environment, Vol. 72, pp. 287-299,2014.

[29] H. Abramovich, C. Milgrom, E. Harash, L.E.Azulay, U. Amit, Multi-layer modular en-ergy harvesting apparatus, system and method,Patent, Application Number: 2010/0045111,Publication Number: 12509875, Assignee Nameat Issue: INNOWATTECH LTD, 2010.

[30] A. Messineo, A. Alaimo, M. Denaro, D. Ticali,Piezoelectric bender transducers for energy har-vesting applications, Energy Procedia, Vol. 14,pp. 39-44, 2012.

[31] M. Collotta, G. Pau, G. Scata, Deadline-AwareScheduling Perspectives in Industrial Wire-less Networks: A Comparison between IEEE802.15.4 and Bluetooth, International Journal ofDistributed Sensor Networks, vol. 2013, ArticleID 602923, 11 pages, 2013.

[32] L. Lo Bello, M. Collotta, O. Mirabella, An in-novative frequency hopping management mech-anism for Bluetooth-based industrial networks,International Symposium on Industrial Embed-ded Systems (SIES), pp. 45 -50, 2010.

[33] M. Collotta, L. Lo Bello, O. Mirabella,Deadline-Aware Scheduling Policies for Blue-tooth Networks in Industrial Communications,International Symposium on Industrial Embed-ded Systems (SIES), pp. 156-163, 2007.

[34] O. Mirabella, M. Collotta, L. Lo Bello, Compar-ison between RT scheduling techniques for Blue-tooth Networks in DPCSs, International Sympo-sium on Industrial Embedded Systems (SIES),pp. 320-323, 2007.

[35] D. Ticali, M. Denaro, A. Barracco, M. Guar-rieri, ElectroMechanical Characterization of aBimorph Piezo for Energy Harvesting Applica-tions in Road Infrastructures, European Journalof Scientific Research, Vol. 90, No. 2, pp. 212-217, 2012.


E-ISSN: 2224-3402 22 Volume 11, 2014

[36] P.D. Mitcheson, T.C. Green, E.M. Yeatman,A.S. Holmes, Architectures for Vibration-DrivenMicropower Generators, Journal of Microelec-tromechanical Systems, Vol. 13, No. 3, pp. 429-440, 2004.

[37] C. Scholer, J. Ikeler, J. Ramirez, S. Jen, G. Fal-con, Piezoelectric Harvesting. A sustainable ap-proach to clean energy generation in airport ter-minal, San Jose State University, 2009.

[38] H.A. Sodano, D.J. Inman, G. Park, Comparisonof Piezoelectric Energy Harvesting devices forrecharging batteries, Journal of Intelligent Ma-terial System and Structures, Vol. 16, No. 10, pp.799-807, 2005.

[39] D. Guyomar, M. Lallart, Recent Progress inPiezoelectric Conversion and Energy Harvest-ing Using Nonlinear Electronic Interfaces andIssues in Small Scale Implementation, JournalMicromachines, Vol. 2, Issue 2, pp. 274-294,2011.

[40] N.J. Guilar, R. Amirtharajah, P.J. Hurst, AFullWave Rectifier With Integrated Peak Selec-tion for Multiple Electrode Piezoelectric EnergyHarvesters, IEEE Journal of Solid State Circuits,Vol. 44, No. 1, pp. 240-246, 2009.

[41] M. Lallart, G. Lauric, C. Richard, D. Guy-omar, Double Synchronized Switch Harvesting(DSSH): A New Energy Harvesting Scheme forEfficient Energy Extraction, IEEE Transactionon Ultrasonic, Ferroelectric and Frequency Con-trol, Vol. 55, No. 10, pp. 2119-2130, 2008.

[42] COMSOL 4.2 User manual, COMSOL MULTI-PHYSICS.

[43] E.S. Leland, J. Baker, E. Carleton, E. Reilly,E. Lai, B. Otis, J.M. Rabaey, P.K. Wright, Im-proving Power output for vibration based energyscavengers, IEEE Pervasive Computing, Vol. 4,Issue 1, pp. 28-36, 2005.


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