localising missing entities using parked vehicles: an rfid
TRANSCRIPT
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Localising Missing Entities using Parked Vehicles:An RFID-Based System
Wynita M. Griggs, Member, IEEE, Rudi Verago, Joe Naoum-Sawaya, Rodrigo H. Ordonez-Hurtado,Robert Gilmore, and Robert N. Shorten, Senior Member, IEEE
Abstract—In this work, we demonstrate a system for locatingmissing entities using RFID-based techniques. A key feature ofour system is that we utilise the large, high-density networksof parked vehicles incident to urban areas for the detectionand reporting process. RIFD readers and antennas are placedwithin the vehicles, while RFID passive tags are carried on theentity of interest via some means, e.g. a wrist band. If an entityis reported as missing, then the application on board each ofthe parked vehicles is awoken by an administrative centre. Thetechnology on board the vehicles enables those participating inthe service to attempt to locate the missing entity, sending usefulinformation back to the administration centre, which could betied to an organisation like the police. We demonstrate oursystem via a use case of a missing Alzheimer’s patient in inner-city Dublin, Ireland. One of the key challenges in validatingour system is being able to replicate a large-scale, real-worldsetting. Our technique for obtaining an early evaluation of oursystem thus employs the use of the microscopic traffic simulationpackage SUMO. SUMO permits multiple emulations of hundredsor thousands of parked vehicles participating in the service tobe carried out, while simulated pedestrians walk random routes.Our results show that a simulated wandering person in need canbe detected within a thirty minute time frame, in the heart ofDublin city centre, during a typical weekday, up to approximately98% of the time, depending on how various parameters of thesystem are set.
I. INTRODUCTION
M ISSING entities are a cause of a great deal of stress ona frequent basis, whether the missing entity be a pet,
material possession, or even a person. For instance, there areseveral groups of people in need that require special care andare at risk of getting lost. These groups include: patients withdementia; children with severe cases of autism; individuals
W. M. Griggs and R. N. Shorten are with the School of Electrical andElectronic Engineering, University College Dublin, Belfield, Dublin 4, Ireland.E-mail: [email protected], [email protected]
R. Verago and R. H. Ordonez-Hurtado are with IBM Research – Ire-land, IBM Dublin Technology Campus, Building 3, Damastown IndustrialEstate, Mulhuddart, Dublin 15, Ireland. E-mail: [email protected],[email protected]
J. Naoum-Sawaya is with the Ivey Business School, Western University,1255 Western Road, London, Ontario, N6G 0N1, Canada. E-mail: [email protected]
R. Gilmore is with Whale Pumps Technology Centre, 2 Enter-prise Road, Bangor, Co. Down, BT19 7TA, Northern Ireland. E-mail:[email protected]
Earlier versions of this work were presented at the 4th InternationalConference on Connected Vehicles and Expo, Shenzhen, China, in October2015, and at the 3rd International Conference on Connected Vehicles andExpo, Vienna, Austria, in December 2014, and published in their respectiveproceedings [1], [2].
This work was partially supported by Science Foundation Ireland grant11/PI/1177.
with attention deficit disorder, schizophrenia, severe clinicaldepression or brain injury; and individuals that are bound towheel-chairs. Among developed nations, an estimated one inten people aged sixty-five or older is affected by some degreeof dementia [3], [4]. Up to 60% of Alzheimer’s disease patientswander, and up to 50% of those who are not found withintwenty-four hours face serious injury or death [5].
Meanwhile, in the context of the Internet of Things (IoT),the vehicles that we drive are becoming more connected toeach other, to the infrastructure, and to the internet [6]. Withtheir on-board sensor complements and computing powers alsoexpanding, our parked cars need no longer be sitting idle,providing no service to us, during extended periods whenthey are not being driven. Instead, we can now take advantageof them for services besides transport given that they can beconsidered as objects in the Internet of Things.
Particularly, we might consider a network of parked vehiclesas a service delivery platform [2] that hosts a whole rangeof applications, from gas detection, to the application thatis considered in this current work. Namely, in this paper,we consider utilising networked vehicles that are parked forextended periods of time, in dense, urban areas, to detectand localise moving, missing entities using RFID technology.What makes a network of parked cars a suitable choice forsuch a task is, first, the sheer number of vehicles that areowned by people, and the fact that, for 95% of the time onaverage, these vehicles are indeed parked [7]. In other words,the network is vast [8]. Second, the network does not requirededicated maintenance, and technology upgrades are easy [8].Moreover, energy infrastructure and planning permissions arenot required to establish the network.
We thus propose to take advantage of these benefits thata network of connected parked vehicles provides, and intro-duce a system for locating missing entities. Our system issummarily described as follows and is illustrated in Fig. 1.First, we have a network of participating parked vehicles, eachwith an RFID reader and antenna on board, and which areeach able to communicate with an administrative centre viavarious broadcasting and receiving stations. Second, we havea missing entity, carrying with it an RFID passive tag via somemeans, e.g. a wrist band. Third, we have an alert source thatis able to identify the entity as missing and then raises analarm with the administrative centre (e.g. the entity’s carer orowner places a phone call with the police). Once the alarm hasbeen raised, the administrative centre prompts the RFID-basedapplication on board the parked vehicles participating in theservice. The RFID technology enables those parked vehicles
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Parked vehicle
participating in the
detection service
Parked vehicle Parked vehicle
RFID
tag
RFID
system
Wandering
person in need
Administrative
Centre
Broadcast &
receiving station
Servers &
databases
Fig. 1. System illustration. (Sub-images obtained from Wikimedia Commons[9] and Openclipart [10].)
participating in the service to attempt to locate the missingentity, sending useful information back to the administrativecentre when the missing entity is found (i.e. when the RFIDequipment on board a parked vehicle detects and processesthe presence of the unique RFID passive tag carried by themissing entity). Useful information sent to the administrativecentre might include a timestamp, a GPS location of the parkedvehicle, and the unique RFID passive tag ID carried by themissing entity that was detected by the equipment on boardthe parked vehicle. Once detected, the administrative centreis able to invoke a procedure aimed at making contact withthe missing entity, e.g. police are able to go to the location atwhich the entity was detected in order to refine the localisationprocess and find the entity, and determine whether the entityneeds assistance, and thus, if required, aid the entity home.
In what follows, we demonstrate our system via a use caseof a missing Alzheimer’s patient. We provide a preliminaryvalidation of our system using real parking space locationsmapped from the city of Dublin in Ireland. One of the keychallenges in validating our system is indeed being able toreplicate a large-scale, real-world setting. We thus employthe use of the microscopic traffic simulation package SUMO(Simulation of Urban MObility) [11], which permits multi-ple emulations of hundreds or thousands of parked vehiclesparticipating in the service to be carried out, while simulatedpedestrians walk random routes.
The remainder of our work is structured as follows. InSection II, we review some of the current technologies andprocedures available in regards to aiding people with dementiawhen walking about. In Section III, we discuss, in moredetail, our system’s architecture, as well as some of its designchallenges. In Sections IV and V, we attempt to validate oursystem with a series of large-scale emulations based on realparking space data from the Dublin city centre, and presentour results. Finally, in Section VI, we conclude the paper withremarks regarding our intentions for future work.
II. STATE OF THE ART
While considered of being able to provide beneficial phys-ical and psychological benefits to people with dementia,walking about [12]–[19] can also present risks. For example,the person can get lost, fall, experience emotional distress,be exposed to harsh weather [20], and/or leave the houseduring the night when they are not appropriately dressed. TheAlzheimer’s Society [21] describes at least two types of saferwalking devices:(i) alarm systems, which provide alerts when someone has
moved outside of a set boundary (e.g. the front garden);and
(ii) tracking devices or location monitoring services [22],which use satellite or mobile phone technology to lo-cate and track a person. These types of devices includewearable technologies [23], [24] like watches, shoes [25],smartphone applications, key rings and pendants.
Given that alarm systems do not provide tracking services,tracking devices or location monitoring services are moreuseful when there is a particular risk of a person getting lostor going missing [21]. The location of the person carrying thetracking device can be viewed online on a computer, tablet de-vice or mobile phone. Many tracking devices allow the personto press a panic button if they get lost or frightened [21]. Manynew mobile phones also have location finder technology andthus can be considered as alternatives to stand-alone trackingdevices. Of course, the use of any device must take intoaccount the need to balance the aim of keeping a person safewith restrictions to their privacy [26]. In this current paper, weomit further detailed discussion on the relevant ethical issuesassociated with the location monitoring or tracking of people,as well as on the technicalities of the security of the detectionor location monitoring systems themselves. However, theseare very important issues [27]–[29]. In our proposed RFID-based system, for instance, any data sent to or from the parkedvehicles that are reporting on the detection of a missing personwould require secure communications with the administrativecentre, e.g. encryption, so that miscreants like hackers, or eventhe car owners themselves, cannot read the data or possiblyeven tell that a detection event has taken place.
The Alzheimer’s Society [21] outlines a number of keyfactors to take into account when considering a device thatenables safer walking:(i) How often will the device need charging?
(ii) What areas of coverage will be required for the serviceto function adequately in and are there “patchy signal”zones? Will it work if the person goes indoors?
(iii) Is the device simple to use?(iv) What is the person meant to do when the service issues
an alert or response regarding the whereabouts of theirlocation?
We will attempt to take into account these factors whendeveloping our system later. First, however, we briefly mentionan existing system for finding missing entities, with a focuson persons in need.
MedicAlert® + Alzheimer’s Association’s Safe Return®[30] is a 24-hour nationwide emergency response service
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for individuals with Alzheimer’s or a related dementia whowander or have a medical emergency. The system does notrely on tracking devices, but rather, comprises of a communitysupport network consisting of local Alzheimer’s Associationchapters and law enforcement agencies. The network is acti-vated when a caregiver calls an emergency response line toreport a missing person in need. If a citizen or emergencypersonnel finds the missing person, they can call the toll-freenumber listed on the person’s MedicAlert® + Safe Return®ID jewellery. MedicAlert® + Safe Return® will then notifythe listed contacts with the aim of reuniting the person whowandered with the caregiver or a member of the family.
As we will see, the system that we are proposing sharesa common objective with the MedicAlert® + Safe Return®system: a caregiver raises an initial alert when a person hasgone missing, and a “community” of agents then searches.However, our searching function incorporates autonomy, i.e.it can function without the assistance of human eyes if noneare available, until the missing entity is located. Similar toMedicAlert® + Safe Return®, our system does not requirethe patient to wear a device containing a battery (i.e. a devicethat requires charging). In our particular case, we are thusable to unload the energy requirements of our system onto thecommunity of searching agents instead. This contrasts withsystems that do rely on the entity to carry mobile phonesor other tracking devices that utilise satellite technology anddo require charging. We accomplish this contrast by utilisingRFID technology. In particular, we equip the patient with apassive RFID tag.
We now note the work of [31], in which the use of RFID forpsychiatric patient localisation in care centres was considered.In particular, the study presented a graph colouring withmerging and deletion (GCMD) algorithm. The goal was toeliminate interference from different Field Generators locatednear each other. A time division scheduling scheme wasimplemented such that overlapping Field Generators operatedin mutually exclusive patterns.
The work of [31] focused on the use of RFID in small,indoor environments, i.e. localising a patient moving about in-side a building. Other examples of indoor alert and monitoringsystems include [17], [32]–[34]. In particular, [17] introducedthe Escort system, in which the user patient wears a mesh-networked badge that transmits indoor location informationobtained in real time from a Talking Lights optical locationsetup. The setup utilises ordinary light fixtures and other lightsources as location beacons. A central server then sends real-time pager or cell phone short messaging service (SMS) alertswhen the user might be at risk. In [32], a smart hospital system(SHS) was introduced for the automatic identification andtracking of people and biomedical devices in hospitals. TheSHS utilises RFID, wireless sensor network, and smart mobiletechnologies, interoperating with each other through a Con-strained Application Protocol (CoAP)/IPv6 over low-powerwireless personal area network (6LoWPAN)/representationalstate transfer (REST) network infrastructure. The SHS col-lects, in real time, both environmental conditions, and patients’physiological parameters, via an ultra-low-power hybrid sens-ing network composed of 6LoWPAN nodes integrating UHF
RFID functionalities. Sensed data are delivered to a controlcentre where an application makes them accessible by bothlocal and remote users via a REST web service. In [33],an electronic tagging system for elderly dementia patientsliving in institutions, derived from a prisoner tagging system,was explored. In this system, the patient wears a braceletwhich is a small radio transmitter. Monitoring stations detectsignals from the transmitter, and these confirm the patient’spresence in particular zones. In [34], an RFID-based PatientTracking and Mobile Alert System integrated with informationcommunications technology was designed and developed forenhancing patient safety and comfort in hospitals.
In contrast to the works of [17], [31]–[34], the objective ofour system is to locate a missing entity outside, in an urbanarea that could have a geographical range spanning over anentire inner city.
III. SYSTEM DESCRIPTION AND DETAILS
The system proposed in this paper shares similarities withthe MedicAlert® + Safe Return® system, albeit with someautomation. What we propose is that, when a caregiver phonesin to an administration centre of our service (e.g. a lawenforcement agency) to report a missing person in need, anapplication is activated in a network of parked vehicles. Thisnetwork encompasses a large enough geographical area inwhich it is assumed that the person in need went missingin and would still be in the vicinity of. Each parked vehicleparticipating in the service is equipped with an RFID readerand antenna (we review RFID technology next). It is assumedthat the person in need would be wearing a medical piece ofjewellery into which passive RFID tags would be embeddedor attached to. If the missing person in need then walks pasta parked vehicle that is actively participating in our service,the RFID antenna and reader in the vehicle may detect thetag on the missing person’s jewellery and report back to theadministration centre. Information reported by the vehicle backto the administration centre would include data such as thepassive RFID tag’s unique ID, a timestamp concerning whenthe tag was read, and the geographical coordinates of theparked car. Furthermore, if the person continues to wanderafter initial localisation, then other parked vehicles in thevicinity that are also participating in the service can attemptto track the person, continuing to send reports back to theadministrative centre concerning the tag ID, timestamps andlocation data, until a member of the law enforcement agencyor emergency personnel can arrive at the geographical positionat which the missing person was last detected, and aid themback to their family or home.
A detailed architecture of our system is presented in Fig.2. Therein, the components discussed above are included,as well as some others that can be of great importance ina more elaborated on manner. For example, by accessingthe CAN bus of the participating vehicle (e.g. by using anOBD-II interface), the state of charge of the car batterycan be obtained in real time and utilised to optimise thepower consumption of the system. Similarly, by accessingthe Vehicle-to-Vehicle/Infrastructure (V2X) communications
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MODEM
RFID
ANTENNA
SMART DEVICE:
- HMI and setup.
V2X
COMMUNICATIONSCAN BUS
ADMINISTRATION CENTRE:
- Monitors triggered alerts.
- Manages detection protocol.
- Deploys contact protocol.
SYSTEM APP:
- Communication.
- RFID-based detection.
- Resource use optimisation.
CPU
PARTICIPATING VEHICLE
LOCAL AUTHORITY:
- Conducts contact process.
ALERT SOURCE:
- Raises an alert.
- Awaits search results.
Alert
Results
Instructions
Results
RFID
TAG
Control signal
& targets
Detection data
(if available)
RFID
READER
Fig. 2. Detailed system architecture of our proposed approach.
system, a better position estimation of the participating vehiclecan be obtained, or a collaborative detection process can beperformed to optimise the use of resources of a cluster ofparticipating cars.
We now describe certain aspects of our system in moredetail.
A. Radio-Frequency Identification
Radio-frequency identification (RFID) [35], [36] is a genericterm for technologies that utilise radio waves to automaticallyidentify entities. Several methods of identification exist, butthe most common is to store a serial number used to identifyan entity on a microchip that is attached to an antenna. Thechip and antenna together are called an RFID transponder, ortag. The antenna enables the chip to transmit the serial numberto a reader. The reader, which emits electromagnetic waves,converts the radio waves reflected back from a passive RFIDtag into digital information that can then be passed on to acomputer to be used for some purpose.
An RFID tag can be active (battery-backed), semi-passive(partially battery-backed), or passive (battery-less). A passiveRFID tag draws power from the field created by the readerand uses it to power the microchip’s circuits. In other words,passive tags do not require a local power source. The energyrequirements of our system, based on the implementationof passive tags, are therefore unloaded onto the parked ve-hicles, which are expected to have large battery capacitieswith respect to the average power consumption of an RFIDreader, especially considering the currently increasing andmore widespread use of hybrid and electric vehicles [37], [38].In terms of our proposed system, a passive RFID tag carriedby a person in need could be worn as a piece of jewellery, thelack of a battery permitting the tag to be of a smaller size andto not require charging. Also, unlike barcodes, RFID tags donot need to be within the line of sight of the reader which, inour system, would be situated securely within a parked vehicle.
RFID tags can be read very rapidly. Some RFID readersare capable of capturing tag IDs at a rate of up to a thousandtags per second [39]. However, having multiple RFID readersand antennas situated in close proximity to each other inneighbouring parked vehicles, and reading constantly (whichwe will subsequently call the Always On polling case), canresult in a polluted spectrum and unnecessarily high energyconsumption in terms of the vehicles’ batteries. Timed readspermit the conservation of energy but at the potential expenseof increasing the chance of failing to detect a tag passing bydue to the readers and antennas not actively scanning whenthe tag is within range.
Typically, passive RFID tags can theoretically be readfrom distances away that range from a few centimetres toapproximately 12m. (In fact, with the use of high-performanceUHF passive tags, the detection range can potentially beextended to up to 35m when using fixed readers [40].) Inreality, however, in addition to the specified read range that isprovided for a tag, read range also depends on a number ofother factors, including antenna orientations and angles, tagplacement, cable lengths, reader settings, and environmentalfactors (e.g. water or competing frequencies, i.e. other radiowaves) [41]. In one of our validation experiments later, wewill vary both the polling rate of the RFID equipment onboard the parked vehicles, as well as the detection range, andalso the percentage of parking spaces inhabited by vehiclesparticipating in our service. We will present our results (i.e.average detection times, population standard deviations fromthese average detection times, and number of failed to detectresults recorded) in regards to changing these parameters.
As mentioned in [42], the assessment of the reliability ofthe backscattered radio frequency signals for the detectionof moving agents carrying passive RFID tags greatly relieson the characterisation of the curves of the Received SignalStrength Indicator (RSSI) as a function of the measurementdistance. In this context, Fig. 3 illustrates the relationship
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between RSSI measurements and the antenna-tag distance,for different distances and under different scenarios. Themain conclusion from these experimental results is that, inpractice, the maximum detection range can virtually triplicatethe minimum width of sidewalks (which is around 1.5m [43])independently of the shape of each curve, and this is of highrelevance for our proposed system given that we considermissing entities making use of sidewalks.
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0 0.5 1 1.5 2 2.5 3 3.5 4 4.5
RVSSIVValu
e
Distance V(m)
EnclosedVlab
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Fig. 3. RSSI-distance curves for a passive RFID tag attached to a static testagent in different environments and for different distances. Each point is thestandard average of 100 readings collected every 0.2s [42]. [Equipment: AlienALR-9680 RFID Reader, ALR-8696-C Antenna, and ALN-9654-G RFID WetInlay Tags.]
B. Positional Information
The automotive navigation systems found in many vehiclestoday utilise a satellite navigation device, like GPS, to obtaintheir position data. While real-time GPS trackers can beworn directly by people with dementia [44], these devicesrequire batteries, while passive RFID tags do not. In effect,in addition to unloading the energy requirements of oursystem onto the parked vehicles, the position broadcastingresponsibilities are also unloaded onto the vehicles when atag detection event occurs. With the ever-increasing sensorcompliments, and computing and communication capabilities,that exist on present-day vehicles, position data obtained froma satellite navigation device can be improved upon by usingvarious techniques (e.g. cooperative positioning [45]) or bysupplementing it with WiFi, for instance, in the event of GPSsignal loss.
C. Design Challenges
As is likely apparent, our system design presents a numberof interesting challenges. These are described in detail next.
Technical Issues. Passive tags can only be detected ifsufficient power can be obtained from incident radio waves.Thus, a successful identification will strongly depend on twofactors: (i) the material to which the tag is attached; and(ii) the orientation of the tag with respect to the antenna. Inthe former instance, the most problematic situations includeliquids between the tag and antenna (which will refract alarge portion of the radio wave), or the tag being on a metal
surface (which will reflect a large portion of the radio energy).Concerning factor (ii), problems arise from an unsuitable (i.e.not aligned) relative orientation of the tag to the antenna.In order to mitigate the aforementioned technical issues, wepropose the following: (a) passive RFID tags carried by theusers to be on silicone [46] or rubber wristbands worn inclose proximity to the skin, so that they are not attached tometal surfaces; (b) one wristband per wrist so that a usercan potentially be detected by parked cars from either sideof their body (i.e. less blind spots), and thus the probabilityof detection could be increased; and (c) multiple tags perwristband, so that the probability of finding a well-orientedtag is increased. We also remark that, with only a few tags perwristband, interference between tags should be expected to benegligible. In a similar vein, RFID readers and antennas wouldneed to be safely and securely mounted in vehicles in such away that the metal from the cars did not interfere with thereads. This would be a design task for vehicle manufacturersand RFID technologists.
Privacy. Given that we are proposing for a large number ofRFID readers and tags to be introduced into the environment,personal privacy could be compromised if deployment werenot carefully executed. For example, since we are proposingthat unique tags be linked to unique individuals, unauthorisedaccess and tracking could be easily performed if securitymeasures were not implemented, as passive tags are consideredto be “dumb” devices in the sense that they can be accessed bypotentially any RFID reader. Meanwhile, allowing car ownersto have their own (on-board) RFID readers could certainlyincrease the risk of unauthorised access through both directmethods (i.e. by directly reading the tags) or indirect methods(i.e. by eavesdropping on a third-party communication). Thus,protection policies such as encryption and password protectionfor secure data access and transmission must be implementedin order to mitigate these kinds of privacy issues, as wellas some others including spoofing (i.e. rewriting the contentof a tag), denial-of-service (DoS) attacks, tag killing and tagcloning.
Health Issues. Regarding health concerns [47], providedthat the RFID tags would be worn far from the head of apatient (especially the eyes), and that the RFID interrogatorswould be configured so that the read zone would be under theneck, there would likely be no major health risks to address inthe general case (i.e. people maintaining a minimum distanceas they walk past RFID-equipped cars). However, certainsafety measures must be taken into account for particularcases, such as children in close proximity to RFID-equippedcars (i.e. closer than a minimum safe distance), and passengersremaining inside the parked vehicles (which can be easilyaddressed with the use of directional antennas to prevent UHFradio waves from entering the car).
Willingness to Participate. Similar to encouraging ownersof parked vehicles to aid in providing other dedicated services,such as positioning [45], willingness to participate in ourproposed system could be boosted by offering vehicle ownersmonetary incentives, e.g. compensation or free access toprivileged services such as parking spots.
Battery Issues. The use of passive RFID tags translates to
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zero-power sensing [48] on the entity’s side, and thus a patientwould never have to charge their sensing device(s). Concerningeach of the sensing agents (i.e. parked vehicles), the on-boardsystem is expected to operate only if the car’s battery is ata capable enough level for it to do so. For example, the 8Watt Alien ALR-9680 RFID Reader fully operating for around6 hours will only discharge around 10% of a 12V/40Ah carbattery; this, along with e.g. a cooperative energy efficiencymanagement plan among all of the participating parked cars,could easily result in longer operating cycles without affectingeither the integrity of the batteries or the autonomy of theparticipating vehicles.
D. Addressing the Alzheimer’s Society’s Key Factors
In Section II, some key factors that the Alzheimer’s Societyrecommends be taken into account when considering a devicethat enables safer walking were provided. We make thefollowing comments regarding these, in relation to our system.
Comment 1: Provided that the missing entities will carryupon themselves zero-power sensing technology, they willnever need to charge the sensing device, nor interact with it atall while it is being worn (e.g. switch it on or off). Wristbandsand other wearable technologies can be made to be attractiveto wear.
Comment 2: Our proposed approach considers missingentities in an urban setting. In our validation experimentsthat follow, we consider real parking space locations mappedfrom inner-city Dublin, and assume that the person walkingabout utilises the inner-city’s footpath and sidewalk network,and thus has associated with them a chance (depending ontheir route) of coming within close proximity to one or moreparked cars at some point during their walk. We consider thisassumption to be a fair one based on the fact that a network ofparked cars in an urban setting is often dense, and has a widegeographic distribution, and constantly refreshes itself overtime, and that the missing entities are moving agents. Thus,particular cases such as an agent going indoors or otherwisestaying static for a long period of time, in a location at whichthey cannot be detected, are currently not taken into account.
Comment 3: Given that the entities would be wearingpassive sensing technology, they would not have to “operate”the sensing device, as the reading process would be fullyautomated and completely governed by the sensing agents. Infact, the entity would not be expected to perform any actionat any stage (not even on detection) at all. In this regard, theentities would only be in charge of carrying the sensing device,which would be carried on them in the form of non-intrusivewearable items of small size.
E. Parked Vehicles versus Fixed Infrastructure
We complete this section with some general remarks inregards to the advantages of utilising parked vehicles to locatemissing entities in urban environments. One could certainlyuse on-street fixed infrastructure and wearable technology toimplement such a system. Our objective in this paper is,however, to show that parked cars can also be an integral partof such a system, or provide an alternative to it. For example,
parked vehicles may provide an infrastructure in environmentswhere fixed infrastructure is not available. Parked vehiclesalso have some nice properties associated with them, even inenvironments in which a fixed infrastructure exists. Some ofthese properties are:(i) First, power consumption is offloaded to the vehicle
completely. Thus, there is no battery to worry about onthe “missing entity” side. This is a big advantage overactive wearable technology with batteries; for instance,the wearable devices do not need charging, and can besmaller in size and lighter in weight.
(ii) Second, in built-up environments, parked cars often offera denser deployment, even over existing infrastructure.
(iii) Third, on average, the vehicle fleet refreshes itself every8-10 years (depending on the country). Thus, no greateffort is required on the part of cities to keep pace withthe latest technology available. Contrast this to a fixedinfrastructure that must be updated regularly.
(iv) Fourth, due to dense deployment, the parked vehicleinfrastructure is robust; meaning, if some cars do notparticipate, or are unable to participate due to equipmentfailure, there is almost surely a car nearby that is func-tioning properly.
(v) Fifth, using parked cars is a cost effective method of de-ployment, requiring little investment from municipalities;i.e. most of the investment costs are offloaded to thevehicle OEMS and telecommunication companies, whorecoup their costs in the form of service provision.
(vi) Finally, the dense deployment also means that localisationerror can be mitigated quickly using measurements frommultiple vehicles.
IV. VALIDATION
To demonstrate our proposed system, we considered theroad vehicle and pedestrian sidewalk and footpath networks inthe region covered by the Dublin Parking Yellow Zone [49],in Dublin, Ireland; see also Fig. 4. The Dublin Parking YellowZone is located in the centre of Dublin, and thus represents anarea of very high demand for parking spaces. For our completevehicle parking space set, we elected to consider all of theon-street parking administered by Dublin’s Pay-and-Displaymachines in the Dublin Parking Yellow Zone, as well as allof the public disabled parking spaces within this area. We didnot consider in our setup, however, private residential parking,or private car parks and garages, or “off-peak” parking spaces(e.g. parking on roads marked with Single Yellow Lines [50]).Then, from our complete vehicle parking space set, one ofour objectives was to vary the percentage of parking spacesinhabited by vehicles that were participating in the detectionof a missing person service, as we will see below.
To emulate our proposed system, i.e. the parked vehiclesactively participating in the search for the missing personwhile the person takes random walks on the sidewalk andfootpath network, we utilised SUMO. SUMO [11] is an opensource, microscopic traffic simulation package primarily beingdeveloped at the Institute of Transportation Systems at theGerman Aerospace Centre (DLR). SUMO is designed to
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Fig. 4. Dublin Parking Yellow Zone (imported for use in SUMO fromOpenStreetMap.org).
handle large networks, and comes with a “remote control”interface, TraCI (short for Traffic Control Interface) [51], thatallows one to adapt the simulation and to control singularvehicles and pedestrians on the fly. In particular, we usedSUMO Version 0.30.0.
We downloaded a map of the Dublin Parking Yellow Zonefrom OpenStreetMap.org, and edited and cleaned it usingJOSM [52] and XMLStarlet [53], respectively, before we ap-plied SUMO’s NETCONVERT tool; refer again to Fig. 4. Toensure that a sufficient sidewalk network existed on our mapalongside the roads, we set as true the processing options –sidewalks.guess and –sidewalks.guess.from-permissions whenapplying NETCONVERT. We utilised SUMO’s NetEdit toolto fix any errors in our map, including to connect togetherany disconnected adjacent sidewalks or footpaths, so thatcontinuous random pedestrian routes could be constructed, asdescribed next.
We developed a Python script which contained an algorithmthat generated random walks for our person on the fly. For eachsimulation, initially, the person begins on a random edge. Thealgorithm then continues by creating a list of neighbouring“next” edges, with respect to the current edge that the personis on, and randomly picks one of these “next” edges to be thenext link on the person’s route. For simplicity, we disallowedthe person from performing U-turns, and set a maximumwalking speed for the person at 1.25m/s. We elected to useSUMO’s nonInteracting pedestrian model [54] as the modelfor how the person otherwise interacted with the map.
In regards to the vehicle parking spaces, we used GoogleMaps satellite imagery to visually locate the approximatelocations of all of the on-street parking spaces administeredby Dublin’s Pay-and-Display machines in the Dublin ParkingYellow Zone, as well as all of the public disabled parkingspaces within this region. We represented all of these parking
spaces as Points of Interest on our SUMO network. Wespecified the dimensions of each parking space as 5m × 2.5mfor simplicity, using [55], [56] as a guide on recommendedparking space size, and otherwise used the satellite imageryto aid us in determining whether each parking space were tobe placed in parallel or perpendicular to the curb.
For each simulation, then, our goal was to set the persondown on a random edge, and have them walk until either: (i)they were detected by a parked vehicle that was participatingin the service during that particular simulation; or (ii) thirtyminutes had transpired and no detection event had occurred.The beginning of each simulation was intended to mimic themoment that the service application on board the participatingparked vehicles was activated by an administrative centre, e.g.just after an alert had been raised by a carer to the policethat a person in need and carrying a unique RFID tag wasmissing in the area. We permitted thirty minutes to transpirebefore a fail to detect event was recorded, keeping in mind thatquickly finding a missing and potentially stressed person, andreturning them to their home, for instance, is ideal. Parkingspaces inhabited by vehicles participating in the service foreach unique simulation were chosen at random by means of a‘coin flip’ and a ‘goal participation percentage’ (out of the totalnumber of parking spaces mapped), further details of whichare given below; see Section IV-A.1. We made the assumptionthat vehicles that were participating in the service at thebeginning of each simulation continued to participate for thefull thirty minutes, and that no new vehicles then joined in toparticipate throughout the simulation. The maximum allowedtime to park in a Pay-and-Display space is 3h [57], and wenoted that thirty minutes fell well within this 3h time frame soas to permit our simplified assumption of maintaining constantparticipation throughout a single simulation, even though, inreality, cars would be parking and leaving spaces continually.
All simulations had time step updates of 1s. In total, weperformed 80,000 simulations using a number of differentparameters. These variables are explained next.
A. Variables
The parameters that we examined in our experiment, togenerate our different test scenarios, included: the percentageof parking spaces inhabited by vehicles participating in theservice; the detection range of the RFID equipment on boardthe parked vehicles; and the sampling rate of the RFIDequipment on board the parked vehicles. For each scenario,1,000 simulations were performed, and there were 80 differentscenarios (i.e. ten different participation rates, multiplied byfour different detection ranges, multiplied by two differentsampling rates).
1) Participation Rates: In total, 8,736 vehicle parkingspaces were mapped from Google Maps satellite imagery ontoour SUMO network. Given this total, we examined the effectson our service of having different percentages of these parkingspaces inhabited by vehicles participating in our service. Weranged these percentages from 10% to 100%, in increments of10%, to generate different test cases for our simulations. Atthe beginning of each simulation, with a desired percentage
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set, a ‘weighted coin’ was flipped for each parking space(represented as a Point of Interest on our SUMO network).The result of this ‘coin flip’ was compared to the desiredpercentage value, to determine whether that parking spacewould be inhabited by a vehicle participating in the service ornot over that particular simulation. Participation assignmentsthen remained constant for the duration of a simulation.
2) Detection Ranges: As mentioned in Section III-A, theproperties of the read fields surrounding each RFID readerand antenna vary depending on a number of factors (such asantenna orientations and angles), and the read is also impactedby various factors associated with each tag. However, forsimplicity, in our experiment we assumed uniform, circular,two-dimensional read ranges, with radii of 3m, 6m, 9m and12m, respectively, to generate four different test case scenarios.These circular read ranges (or ‘circles’) were placed aroundthe points that denoted the spatial positions of the reader andantenna on board each participating parked vehicle (i.e. aroundevery Point of Interest on the SUMO map representing aparticipating parking space). A detection event was recorded ifthe person walked randomly onto or into the interior of at leastone circle, and if the participating parked vehicle happened tobe polling for tags at the same time. Our sampling, or polling,rates that were utilised are described next.
3) Sampling Rates: We elected to consider two differentpolling rates to generate test cases: (i) Always On; versus (ii) atimed read, or polling rate, of 0.05Hz. We assumed that, once aperson was within range of an RFID-equipped parked vehiclethat was participating in the service, and that was activelypolling during that time step, then a successful detection eventwould be registered. To set up our simulations, it thus becamea matter of obtaining reasonable probabilities of, per time step,an RFID-equipped parked vehicle actively polling, supposingthat a person were within range and the parked vehicle wereparticipating in the service. The probabilities that we electedto use for our experiment are provided in Table I.
TABLE IPROBABILITIES OF A SUCCESSFUL DETECTION OCCURRING PER TIMESTEP, WHEN PERSON IS WITHIN RANGE OF A PARTICIPATING PARKED
VEHICLE
Read RateRadius [m] Always On 0.05Hz
3 1 0.05566 1 0.06599 1 0.0846
12 1 0.154
B. Results
The data collected for each test case scenario comprisedof: (i) the average time taken (in minutes) until detection ofthe missing entity occurred, provided that detection occurredwithin a thirty minute time frame from the beginning of theemulation, else a fail result was recorded; (ii) the populationstandard deviation (in minutes) from the average time takenuntil detection; and (iii) the total number of fail resultsrecorded per test case scenario. To reiterate, 1,000 simulations
were conducted per test case scenario, and there were 80different scenarios in all (i.e. ten different participation rates,multiplied by four different detection ranges, multiplied by twodifferent sampling rates). Thus, a total of 80,000 emulationswere performed over the course of the whole experiment. Theresults are presented below.
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Fig. 6. Average detection time, in minutes, for sampling rate of 0.05Hz.[r: read range radius in m.]
1) Average Detection Time: Presented in Figs. 5 and 6are the average times taken, in minutes, until detection ofthe missing entity occurred (provided that detection occurredwithin a thirty minute time frame from the beginning of anemulation, else a fail result was recorded), for each of theparticipation rates and detection ranges tested, for the AlwaysOn and 0.05Hz polling rates, respectively. As can be seen, andas would be expected, the shortest average detection time (ofjust under four minutes) was achieved by the scenario in which100% of the parking spaces were inhabited by parked vehiclesparticipating in the search, the detection radii were set at 12m,and the readers were Always On; see Fig. 5. Conversely, thelongest average detection time (of just over twelve minutes)was achieved by the scenario in which only 10% of the parkingspaces were inhabited by parked vehicles participating in the
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Fig. 8. Population standard deviation, in minutes, for sampling rate of 0.05Hz.[r: read range radius in m.]
search, the detection range of the equipment was set at 3m,and the readers were polling at a rate of 0.05Hz; see Fig. 6.
2) Population Standard Deviation: Figs. 7 and 8 illustratethe population standard deviations, in minutes, from the aver-age times taken until detection that were presented above inFigs. 5 and 6.
3) Total No. of ‘Failed to Detect’ Results: Finally, Figs.9 and 10 illustrate the absolute number of times out of onethousand that a fail result was recorded per test case scenario;i.e. per 1,000 simulations that had the same parameters set.The figures yield exponential-like curves, with relatively stablefailed to detect totals per test case scenario, beginning fromthe 100% participation rates on the right-hand sides of theillustrations, until a lower “threshold” participation rate isreached, and then the fail totals increase sharply. In general,the Always On polling rate performed better than the 0.05Hzpolling rate, as expected. Furthermore, on the whole, thedetection range of 3m performed the worst. In particular, a3m detection range and a 10% participation rate, polling at0.05Hz, resulted in an almost 50% chance of the servicefailing to detect the person within thirty minutes on any givensimulation (see the top, purple line in Fig. 10). For our system,such a result is likely deemed unacceptable. In contrast, the
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Fig. 9. Total number of failed detections (i.e. failures to detect the missingperson within a thirty minute time period), per test case scenario, for theAlways On sampling rate. [r: read range radius in m.]
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Fig. 10. Total number of failed detections (i.e. failures to detect the missingperson within a thirty minute time period), per test case scenario, for samplingrate of 0.05Hz. [r: read range radius in m.]
detection ranges of 9m and 12m, with otherwise the sameparameters set, yielded a less than 10% chance of failing todetect, which is a much better result.
V. EXPLORING DYNAMIC PARKING
In the preceding validation experiment, we had made anassumption that vehicles participating in the service at thebeginning of each simulation, continued to participate for thefull thirty minutes, and that no new vehicles joined in toparticipate throughout the course of the simulation. In otherwords, we assumed that parking was constant for the durationof each simulation, given that our simulation run time intervalof thirty minutes was relatively short. In reality, however, carswould be parking in and leaving spaces continually; that is,parking is dynamic with respect to time. A more realisticappraisal of our system would thus take dynamic parking intoaccount.
The following additional experiment that we performed istherefore a step towards considering a more realistic sce-nario, in that a preliminary model of dynamic parking wasincorporated into our setup. Everything else regarding ourexperimental setup remained exactly the same, as described
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in Section IV, unless elaborated on specifically further below.The algorithm that we used for our dynamic parking modelwas inserted into our existing Python script, such that vehicleswould now arrive in, and depart from, our Point of Interestparking spaces in a stochastic manner. The key constituentelements of our algorithm are described next.
For the simulations performed, the following variables werepreset: (i) the probability of a vehicle occupying a parkingspace at the simulation’s commencement, denoted by pIO;and (ii) the probability of an arriving vehicle participating inour service, denoted by γ.
At the beginning of each simulation, a ‘weighted coin flip’was performed for each ‘Point of Interest’ parking space. Theresult of each ‘coin flip’ was compared to pIO, to determinewhether the corresponding parking space would be inhabitedby a vehicle or not at the commencement of the simulation. Ifthe parking space were to be occupied at the beginning of thesimulation, then a second ‘weighted coin flip’ was performedand, this time, the result of the ‘coin flip’ was compared toγ, in order to determine whether the vehicle occupying theparking space would be participating in our service or not.
Throughout the course of each simulation then, for sim-plicity, we treated our entire parking space set in the DublinParking Yellow Zone as a single server with an M/M/1 queue[58, page 182]. After the commencement of each simulation,vehicles would arrive according to a homogeneous Poissonpoint process, and have exponentially distributed service times.We set the arrival rate to be linearly proportional to the totalnumber, N , of Point of Interest parking spaces (specifically, to0.36N vehicles per hour for all simulations), while the meanservice completion time (i.e. the average time that a vehiclewould spend in a parking space) was set to thirty minutes.Our buffer had infinite capacity and the queue was dealt within a first come, first served manner. For each vehicle arriving,a ‘weighted coin flip’ was performed, and the result of the‘coin flip’ was compared to γ in order to determine whetherthe vehicle would be participating in the service or not.
One hundred simulations were performed for each test casescenario. To generate different test case scenarios, we setdifferent values for pIO and γ; namely, pIO = 0, 0.5 and1, and γ = 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 and 1.Doing so generated 30 different test case scenarios, and thus3,000 different simulations being performed in total for thedynamic parking experiment. For the whole experiment, weelected to set the sampling rate to Always On, and the readrange to a radius of 6m.
A. ResultsSimilar to the experiment described in Section IV, the data
collected for each test case scenario in the dynamic parkingexperiment consisted of: (i) the average time taken (in minutes)until detection of the missing entity occurred, provided thatdetection occurred within a thirty minute time frame from thebeginning of the emulation, else a fail result was recorded;(ii) the population standard deviation (in minutes) from theaverage time taken until detection; and (iii) the total numberof fail results recorded per test case scenario. The results arepresented next.
Fig. 11 illustrates the average times taken, in minutes,until detection of the missing entity occurred (provided thatdetection occurred within a thirty minute time frame from thebeginning of an emulation, else a fail result was recorded), foreach of the values of γ, and each of the values of pIO, tested,for an Always On polling rate and 6m read range radius. Fig.12 shows the population standard deviations, in minutes, fromthe average times taken until detection, that were presentedin Fig. 11. Fig. 13 illustrates the total number of times thata fail result was recorded per test case scenario (i.e. per 100simulations which had the same values for γ and pIO set). Thedata shows, for example, that under the parameters used forthe experiment, a service participation rate of 50% or aboveresulted in an average detection time of under seven minutesin 90% of cases, when pIO was set at either 0.5 or 1.
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Fig. 12. Population standard deviation, in minutes. [pIO: probability of avehicle occupying a parking space at a simulation’s commencement.]
Figs. 14, 15 and 16 illustrate (each, for three differentrepresentative values of γ), the number of participating parkedvehicles at detection time, versus detection time, in minutes,for pIO equal to 0, 0.5 and 1, respectively. (Note that, inthese figures, each simulation ‘run time end’ corresponds to adetection time.) The slopes of the scatter plots give indicationsof the equilibriums towards which each system was evolving,in terms of number of parked participating vehicles, over time.
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Fig. 13. Total number of failed detections (i.e. failures to detect themissing person within a thirty minute time period), per test case scenario.[pIO: probability of a vehicle occupying a parking space at a simulation’scommencement.]
Figs. 15 and 16 also reveal denser clusters of points closerto the y-axes. This is because, for these systems, there wererelatively more parked participant vehicles at the beginningof each simulation (compared to systems that had pIO equalto 0), and thus there was relatively more potential for earlydetections (as also conveyed in Fig. 11).
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Fig. 14. Resultant scatter plot when the probability of a parking space beingoccupied by a vehicle at the commencement of simulation (pIO) is 0.
VI. CONCLUSIONS AND FUTURE WORK
In this work, we described a system for locating missingentities using RFID-based techniques. Our system is based onthe notion of using networks of connected parked vehicles asa service delivery platform, and as such these vehicles can beconsidered as part of the Internet of Things. We demonstratedour system via a use case of a missing Alzheimer’s patient.In regards to the features of our system, in comparison toexisting procedures for finding and tracking missing peoplein need, we highlighted the fact that the energy requirementsof our system are unloaded onto the parked vehicles. Thismeans that a battery running low on a tracking device, whichis being carried with the person in need, is not an issue withour system. Furthermore, we demonstrated that our system is
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Fig. 15. Resultant scatter plot when the probability of a parking space beingoccupied by a vehicle at the commencement of simulation (pIO) is 0.5.
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Fig. 16. Resultant scatter plot when the probability of a parking space beingoccupied by a vehicle at the commencement of simulation (pIO) is 1.0.
highly automated once the application on board the parkedvehicles is activated.
Our results from a preliminary validation experiment, asdescribed in Section IV, showed that a simulated wanderingperson in need can be detected within a thirty minute timeframe in the heart of Dublin city centre, Ireland, during a typ-ical weekday, up to approximately 98% of the time, dependingon how various parameters of the system are set. This resultintroduces a topic that we would like to consider in moredetail in future research: namely, the notion of guaranteeinga certain level of “Quality of Service” with our system, andhow to define such a measure(s). Of interest are questionslike, “What are the optimal parameters for finding a missingperson in need within an x time frame, with y constraints, andgiven a required z detection success rate?” Of importance,also, are the battery considerations of the parked vehicles.As such, energy management algorithms are also a topic ofinterest for future research. A simple, preliminary algorithmwas given in [1]. In a future work, we intend to explore energymanagement in regards to the parked vehicle component ofour system in more detail, by utilising the notion of parkedvehicles communicating with each other in addition to withthe administration centres.
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Another avenue for future work involves not only validatingmore test case scenarios (e.g. validating our system in asuburban setting rather than in a city centre one), but alsoevaluating our system with an enhanced level of realism. Astep towards this was considered in Section V, where we in-troduced a simple dynamic parking model to our experimentalsetup, treating our parking space set as a single server withan M/M/1 queue. In the future, we will develop the dynamicparking model further, by treating each street as an individualserver and experimenting with different arrival rates and meanservice completion times for each street, given that, in reality,different streets experience different demands on their parkingspaces at different times of the day, and on different days.That is, our dynamic parking model can be improved.
Vehicle-in-the-loop platforms, like the one described in[59], would allow us to “embed” real parked vehicles andpedestrians into large-scale SUMO emulations, such that thesereal entities could interact with simulated ones. The effects ofusing real RFID equipment, and of the walking behavioursof real people, within the system could be examined. Finally,we mention that, in the future, we would also like to furtherinvestigate the notion of tracking a missing entity once theyhave been initially detected. Questions relating to whether itis suitable to maintain the same system parameter set, or toswitch to a new parameter set once an initial detection occurs,can be explored.
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Wynita Griggs received a Ph.D. degree in en-gineering from the Australian National University,Canberra, Australia in 2007. Between 2008 and2015, she was a Postdoctoral Research Fellow atthe Hamilton Institute, National University of IrelandMaynooth, Ireland. She is currently a Research Sci-entist at University College Dublin, Dublin, Ireland.Her research interests include stability theory withapplications to feedback control systems, and smarttransportation.
Rudi Verago has over 10 years of experienceworking on research and development. His careerbegan as a researcher and lecturer at the Universityof Padua in Italy, then it continued in one of themost important Italian incubators where he wasresponsible for the engineering design of innovativetechnology software/hardware projects. In 2013, hejoined the IBM Research laboratory in Dublin asa research engineer where he focused on Internetof Vehicles, Smart Cities, Optimisation and Control.Currently, he is working on the Dash/IoT Platform in
Amazon. He has founded a couple of startups developing a novel automationframework targeted for Internet of Things and is helping to build a socialgame platform.
Joe Naoum-Sawaya is currently an Assistant Pro-fessor in Management Science at Ivey BusinessSchool. He received a B.E. in Computer Engineer-ing from the American University of Beirut and aM.A.Sc. and Ph.D. in Operations Research from theUniversity of Waterloo. His research interests in-clude large scale optimisation methods for practicalproblems arising in the industry.
Rodrigo Ordonez-Hurtado received his Ph.D. de-gree from the University of Chile in 2012. He held apostdoctoral position at Maynooth University from2012 to 2015 and at University College Dublin from2015 to 2017, working with Professor R. Shortenand his research group on smart mobility applica-tions and stability theory. In September, 2017, Ro-drigo joined IBM Research – Ireland as a ResearchFellow. Rodrigo’s interests include intelligent trans-portation systems with applications to smart cities,robust adaptive systems (control and identification),
stability of switched systems, swarm intelligence, and large-scale systems.
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Robert Gilmore received the M.E. degree in elec-tronic engineering from University College Dublin,Ireland, in 2017. He is currently working in industrywith interests lying mostly in PCB layout and designfor EMC.
Robert Shorten received a Ph.D. degree from Uni-versity College Dublin (UCD), Dublin, Ireland, in1996. From 1993 to 1996, he was the holder of aMarie Curie Fellowship at Daimler-Benz Research,Berlin, Germany to conduct research in the area ofsmart gearbox systems. Following a brief spell at theCentre for Systems Science, Yale University, work-ing with Prof. K. S. Narendra, he returned to Irelandas the holder of a European Presidency Fellowship in1997. He is a co-founder of the Hamilton Institute,National University of Ireland Maynooth, Ireland,
where he was a full Professor until March 2013. From 2013-2015 he wasa Senior Research Manager at IBM Research Ireland, Dublin, leading theControl and Optimisation activities at IBM Research in Dublin. He currentlyholds a dual appointment with IBM Research and University College Dublin,where he is Professor of Control Engineering and Decision Science.