ieee transactions on mobile computing 1 road network-aware spatial...

IEEE TRANSACTIONS ON MOBILE COMPUTING 1

Road Network-Aware Spatial AlarmsKisung Lee, Ling Liu, Balaji Palanisamy, and Emre Yigitoglu

Abstract—Road network-aware spatial alarms extend the concept of time-based alarms to spatial dimension and remind us when wetravel on spatially constrained road networks and enter some predefined locations of interest in the future. This paper argues that roadnetwork-aware spatial alarms need to be processed by taking into account spatial constraints on road networks and mobility patternsof mobile subscribers. We show that the Euclidian distance-based spatial alarm processing techniques tend to incur high client energyconsumption due to unnecessarily frequent client wakeups. We design and develop a road network-aware spatial alarm processingsystem, called ROADALARM, with three unique features. First, we introduce the concept of road network-based spatial alarms usingroad network distance measures. Instead of using a rectangular region, a road network-aware spatial alarm is a star-like subgraphwith an alarm target as the center of the star and border points as the scope of the alarm region. Second, we describe a baselineapproach for spatial alarm processing by exploiting two types of filters. We use subscription filter and Euclidean lower bound filter toreduce the amount of shortest path computations required in both computing alarm hibernation time and performing alarm checks atthe server. Last but not the least, we develop a suite of optimization techniques using motion-aware filters, which enable us to furtherincrease the hibernation time of mobile clients and reduce the frequency of wakeups and alarm checks, while ensuring high accuracy ofspatial alarm processing. Our experimental results show that the road network-aware spatial alarm processing significantly outperformsexisting Euclidean space-based approaches, in terms of both the number of wakeups and the hibernation time at mobile clients andthe number of alarm checks at the server.

Index Terms—Spatial alarms, road networks, motion behavior, optimization, scalability.

F

1 INTRODUCTION

MANY of us use time-based alarms daily to remindus the arrival of some predefined time points of

interest in the future, such as getting up in the morningand attending an important business meeting. Spatialalarms extend the concept of time-based alarms to spatialdimension and remind us when we enter some prede-fined locations of interest in the future. An example ofspatial alarms is “alert me when I am within 2 miles ofthe dry clean store in Buckhead”. Spatial alarms are basicbuilding blocks for many location-based services, such aslocation-based advertisements, factory danger zone alertsystem, and sex offender monitoring system. Since thenumber of smart devices including smart-phones andtablets is rising steeply , scalable processing of spatialalarms is becoming increasingly important in mobileapplications and location-aware computing.Characterization of spatial alarms. A spatial alarm isdefined by four components: a focal point representingthe alarm target, a spatial distance representing thealarm region, an owner (or a publisher) of the alarm anda set of alarm subscribers. Spatial alarms are categorizedinto three groups by their ownership: private, shared andpublic. A private alarm has only one subscriber who isalso the publisher of the alarm. A shared alarm has apublisher and several subscribers approved by the pub-lisher. In terms of a public alarm, its publisher does notset any restriction on subscribers and thus anyone canbe a subscriber of the alarm. Public alarms are typicallyclassified by alarm interests, such as traffic alerts andcoupons from grocery stores and restaurants. Spatial

• K. Lee, L. Liu and E. Yigitoglu are with the School of Computer Science,Georgia Institute of Technology, Atlanta, GA, 30332.

• B. Palanisamy is with University of Pittsburgh.

alarms can also be categorized by the motion behaviorof their subscribers and their monitoring targets: movingobjects with static targets, static objects with moving targetsand moving objects with moving targets. Typical examplesof spatial alarms having moving objects with static targetsare “alert me when I am within 5 miles of a Whole FoodsMarket in Buckhead” (private) and “notify anyone en-tering I85 North from Spaghetti Jct. in Atlanta” (public).“The Macy’s store at Lenox Square sends advertisementsto its customers who are within 10 miles of its storelocation” (i.e., Macy’s customers are spatial alarm targetsfor the Macy’s store and the store will be notified whenits customers are within a specified spatial range fromthe store) is an example of spatial alarms having staticobjects with moving targets. “Alert Lucy when her car is1 mile apart from her friends’ vehicles on the way toWalt Disney World in Orlando” is an example of movingobjects with moving targets.Challenges of spatial alarm processing. Spatial alarmsare essential for many location-based services, rang-ing from location-based advertisement to location-basedpersonal reminders. Negligent management of spatialalarms can lead to excessive energy consumption ofmobile devices, especially when spatial alarm processingrelies on continuous tracking of mobile devices, whichis known to be prohibitively expensive. We argue inthis paper that intelligent techniques can be developedfor scalable processing of spatial alarms by minimizingthe amount of continuous monitoring of mobile userslocations. Furthermore, the performance of spatial alarmprocessing can be affected by a number of factors, suchas frequency of wakeups � how often mobile devicesshould wake up because of possible alarm hits andfrequency of alarm checks � how many spatial alarmsshould be evaluated at each wakeup. Since frequent and


c

b1

b2

b3b4

b5

m

(a) Rectangular Spatial Alarm

m12

m11

f1f2

f3

b31

b32

b21b22

b23

b24

b25

b26

b27

b11b12

b13b14m1

m2 m3

m4

m5

(b) Road Network Spatial Alarms

Fig. 1: Spatial alarms

possibly unnecessary wakeups and alarm checks notonly reduce battery life of mobile devices considerablybut also increase the loads of a spatial alarm processingserver, we need efficient spatial alarm processing thatcan reduce the number of unnecessary wakeups andalarm checks at each wakeup. Finally, the spatial alarmprocessing system should scale to a large number ofspatial alarms and mobile users while meeting the highaccuracy goal by minimizing the alarm miss rate.

Existing approaches on spatial alarm processing canbe categorized into two groups by their criteria forcontrolling the frequency of wakeups: time-based ap-proaches (such as periodic wakeups) and distance-basedapproaches (such as safe period [1], [2] and safe region[3], [4]). Most existing techniques define spatial alarmsusing Euclidean distances and thus a rectangular regionis typically used to represent the spatial alarm region.Fig. 1(a) shows an example rectangular alarm, whichhas five intersecting points with the underlying roadnetwork. Such an alarm can be triggered even though itssubscribers’ current location is far away from the alarmtarget c based on road network distance. For example,if a mobile subscriber of the alarm is located on b1,the alarm should be triggered because the subscriberis within the rectangular alarm region. However, eventhough b1 is the nearest intersecting point to the alarmtarget c based on the Euclidean distance, its road net-work location is far away from the alarm target c basedon the road network distance and thus the subscribercan save its battery energy by sleeping for a longer time.This example illustrates the problem of using Euclideandistance to define spatial alarms and highlights thepotential benefit of road network-aware spatial alarmprocessing. To the best of our knowledge, no existingresearch has taken into account spatial constraints onroad networks in optimizing spatial alarm evaluation.Contributions and organization of the paper. In thispaper, we present ROADALARM � a road network-awarespatial alarm processing system. By taking into accountspatial constraints on road networks and mobility pat-terns of mobile subscribers, ROADALARM can reducethe frequency of wakeups and increase hibernation timeof mobile clients and, at the same time, minimize thecomputation cost of alarm checks by filtering out thosespatial alarms that are irrelevant or far away from thecurrent location of their mobile subscribers. Concretely,we define road network-aware spatial alarms usingnetwork distances (e.g., segment length-based or travel

Spatial Alarm Processing System

Road Networks

DB

Moving Object

Moving Object

Station

Station

Processing EngineClient communication

Alarm checksHibernation Computation

Location ServerLocalization

Moving object location mgt.Place management

Location/place DB

Fig. 2: System architecture

time-based). Instead of using a rectangular region, aroad network-aware spatial alarm is defined as a star-like subgraph with an alarm target as the center of thestar. We define the scope of an alarm region by the setof border points of the star. In addition, we formulateour baseline approach to road network-aware spatialalarm processing by exploiting subscription filtering andEuclidean lower bound filtering. The former can filterout those spatial alarms that are clearly irrelevant by con-sidering only subscribed spatial alarms. The latter canreduce the number of the network distance computationswithout loss of accuracy. Furthermore, we develop asuite of motion-aware filters as optimization techniquesto further reduce the frequency of wakeups as well as thefrequency of alarm checks while ensuring high accuracyby considering mobility patterns of mobile subscribers.To the best of our knowledge, ROADALARM is the firstsystematic approach to exploring road network-awareand motion-aware filters to reduce the search spaceand computation cost of road network-aware alarmprocessing. Our experimental results show that ROAD-ALARM outperforms existing Euclidean distance-basedtechniques and can scale to a large and growing numberof spatial alarms as well as mobile subscribers.

The rest of the paper is organized as follows: We givean overview in Section 2 and present the baseline ap-proach in Section 3. We improve the performance of thebaseline approach by developing a suite of optimizationtechniques in Section 4. We evaluate the performance ofROADALARM in Section 5, outline the related work inSection 6, and conclude the paper in Section 7.

2 OVERVIEW

In this section, we describe the system architecture ofROADALARM and define road network-aware spatialalarms, alarm miss and hibernation time. A spatial alarmsystem typically consists of a spatial alarm processingengine and a location server where the locations ofmoving objects (mobile clients) and the locations ofstatic objects (such as gas stations, restaurants, and soon) are managed. The spatial alarm processing enginecommunicates with the location server to obtain thecurrent road network locations of mobile subscribersas well as the road network locations of alarm targetsfor all alarms maintained in its database. The location


server uses localization techniques (such as GPS, WiFior any hybrid localization technology) to keep track ofthe current positions of moving objects. Fig. 2 presentsa sketch of the ROADALARM system architecture.

We assume that moving objects can be any devices(e.g., smart-phones, tablets, navigation systems) withany localization technology such as GPS and WiFi lo-calization. ROADALARM adopts the client-server archi-tecture for spatial alarm processing. Concretely, mobileobjects may install (publish) their spatial alarms at thelocation server as private, shared or pubic alarms. Inaddition to their own private alarms, mobile objects cansubscribe to any public alarms and a subset of sharedalarms authorized by other alarm owners. Mobile objectsneed to install the thin client of ROADALARM as a mobileapplication on their devices. Each mobile subscriber willobtain an initial hibernation time at the commit of heralarm subscription. Upon the expiration of its old hiber-nation time, the mobile client will automatically contactthe alarm server on behalf of the mobile subscriber toobtain its new hibernation time. We assume that themobile clients are able to communicate with the serverthrough wireless data channel. During the hibernationtime, the ROADALARM application is hibernated at themobile client, and the alarm server consumers zero alarmprocessing cost for this mobile client.Road network model. A road network is representedby a directed graph G = (V, E), composed of the roadjunction nodes V = {n0, n1, . . . , nN

} and directed edgesE = {n

i

n

j

|ni

, n

j

2 V}. We refer to an edge n

i

n

j

as aroad segment connecting the two road junction nodesn

i

and n

j

with direction from n

i

to n

j

. When a roadsegment is bidirectional, we use edge n

i

n

j

and edgen

j

n

i

to denote the two directions of the same road seg-ment. For each road segment, road-related informationcan be maintained, such as segment length (e.g., 1.2miles), speed limit (e.g., 55 mph), current traffic data(e.g., average speed is 35 mph), and direction (e.g., one-way road). The length and speed limit of a road seg-ment n

i

n

j

are denoted by seglength(ni

n

j

) in miles andspeedlimit(n

i

n

j

) in miles per hour respectively. Otherroad-related information such as direction and currenttraffic data, if available, can be easily incorporated toprovide more accurate travel time.

Let n1 and n2 denote two road junction nodes andn1n2 /2 E . We define a path from n1 to n2 as a sequenceof road segment edges, one connected to another,denoted as n1ni1 , n

i1ni2 , . . . , ni

k�1ni

k

, n

i

k

n2 (k > 0).The length of a path h between n1 and n2, denotedby pathlength(h), is computed as seglength(n1ni1) +

seglength(n

i

k

n2) +

k�1X

↵=1

seglength(n

i

↵

n

i

↵+1). Given two

road junctions n1 and n2, since there can be more thanone path from n1 to n2, we use PathSet(n1, n2) to denotethe set of all paths from n1 to n2. We define a segmentlength-based shortest path from n1 to n2, denotedby sl shortestpath(n1, n2), as {h

sl

|pathlength(hsl

) =

min

h2PathSet(n1,n2) pathlength(h)}. The travel time of a

road segment ni

n

j

is defined as seglength(ni

n

j

)speedlimit(n

i

n

j

) and thusthe travel time of a path h, denoted by traveltime(h),is calculated as seglength(n1ni1 )

speedlimit(n1ni1 )+

seglength(ni

k

n2)

speedlimit(ni

k

n2)+

k�1X

↵=1

seglength(n

i

↵

n

i

↵+1)

speedlimit(n

i

↵

n

i

↵+1)

. The travel time-based shortest

path from n1 to n2, denoted by tt shortestpath(n1, n2),is defined as {h

tt

|traveltime(h

tt

) =

min

h2PathSet(n1,n2) traveltime(h)}.A road network location, denoted by L = (n

i

n

j

, p), isa tuple of two elements: a road segment n

i

n

j

and theprogress p along the segment from n

i

to n

j

. The roadnetwork distance between two road network locationsL1 = (n

i1ni2 , p1) and L2 = (n

j1nj2 , p2) is the length of theshortest path between L1 and L2 in terms of either seg-ment length or travel time. The segment length-basedroad network distance, denoted by sldistance(L1, L2),and travel time-based road network distance, denotedby ttdistance(L1, L2), are formally defined respectivelyas follows:

sldistance(L1, L2) = seglength(ni1ni2)� p1 + p2

+pathlength(sl shortestpath(ni2 , nj1))

ttdistance(L1, L2) =seglength(ni1ni2)� p1

speedlimit(ni1ni2)

+p2

speedlimit(nj1nj2)+ traveltime(tt shortestpath(ni2 , nj1))

Even though the segment length-based distance isthe most commonly used distance measure on roadnetworks, it may not provide sufficient and accuratedistance information in terms of actual travel time fromthe current location to the destination, considering thathighway road segments are usually much longer butalso with much higher speed limits and thus may haverelatively shorter travel time compared to some localroad segments. To ensure high accuracy and high per-formance of spatial alarm processing, we use the traveltime-based distance as default road network distancemeasure in ROADALARM.Road Network-Aware Spatial Alarms. In ROADALARM,we define a road network-aware spatial alarm as astar-shaped subgraph centered at the alarm focal point,denoted as SA

RN

(p

f

, r, S) where p

f

is the alarm target orthe alarm focal point (a road network location), r is thealarm monitoring region, represented by a spatial range(segment length or travel time) from p

f

, and S is a setof subscribers. Consider Fig. 1(b) that shows three star-shaped alarms with focal points f1, f2 and f3. The roadnetwork-aware spatial alarm with focal point f1 has arange of 5 miles based on the segment length. The roadnetwork-aware spatial alarm with focal point f2 has arange of 10 minutes based on the travel time. We callthose points on the road network which bound a star-shaped spatial alarm border points. For example, b12 isone of the four border points of the alarm with focalpoint f1.Alarm Miss and Hibernation Time. We define an alarmmiss as a case when a spatial alarm is not triggered


as it should be even though a mobile subscriber ofthe alarm enters or passes through the alarm region.Consider Fig. 1(b): moving objects m1,m2,m3 and m5

should each receive a spatial alarm alert and m4 shouldget two alerts from the spatial alarms with alarm targetsf1 and f2 sequentially. If any one of those alarms isnot triggered during the course of travel for the fivesubscribers, the alarm miss has happened. Opposite toalarm miss, an alarm hit refers to the case when a spatialalarm is triggered when one of its mobile subscribersenters the alarm region.

As mentioned earlier, in ROADALARM we computea hibernation time for each mobile subscriber, dur-ing which the mobile subscriber hibernates the ROAD-ALARM thin client on her device. We define a hibernationtime for each moving object, which is a time intervalduring which the moving object does not need to wakeup and the alarm server does not need to performalarm checks for this mobile subscriber. A hibernationtime of a moving object is specified by a time intervalconsisting of its start time and end time. If the currenttime is between them, the moving object’s current statusis hibernation; otherwise, it is alive. Upon expiration of thecurrent hibernation time, the mobile client wakes up, andcommunicates with the spatial alarm server to obtain anew hibernation time. The alarm server examines thecurrent location of the mobile subscriber and the setof alarms subscribed by this subscriber to determinethe new hibernation time. If the new hibernation timeis smaller than a system-defined threshold �, a spatialalarm is triggered and the mobile subscriber is notified.Otherwise, a new hibernation time will be sent to themobile subscriber.

We would like to note that the timeliness of alarmtriggering is also important, especially when spatialalarms are defined with some quality of service (QoS)guarantee. For example, it is possible that based on thecurrent hibernation time for the moving object m5 inFig. 1(b), m5 may receive the spatial alarm alert whenit approaches the focal point f3 or just before leavingthe spatial alarm through the border point b31. Thus, inROADALARM we use a stronger definition of alarm miss:if a moving object’s current status is hibernation whenit enters alarm region of a spatial alarm by crossing aborder point of the alarm, we treat it as an alarm miss.

The alarm success rate is the percentage of spatial alarmalerts which are not missed, and is defined as follows:

alarm success rate = 1� Total number of alarm missesTotal number of actual alarm hits

For example, if there are 9 alarm hits and 1 alarm miss(actual hit but not triggered), the success rate is 90%.

3 SPATIAL ALARM PROCESSING

In this section we present the design consideration ofthe ROADALARM baseline algorithm for efficient pro-cessing of spatial alarms. We first describe the Euclideandistance-based approach and the conventional network

expansion-based approach to process road network-aware spatial alarms and analyze the problems withthese two approaches. Then we introduce the baselinealgorithm for ROADALARM by incorporating subscrip-tion filter and Euclidean lower bound filter.

3.1 Euclidean Distance-based Approach

The Euclidean distance-based approach is often consid-ered as the most intuitive baseline approach to imple-menting spatial alarm processing. Concretely, for everymobile object m, upon the expiration of its hibernationtime, m wakes up and contacts the spatial alarm serverto obtain its new hibernation time. The alarm serverfirst retrieves the index of all spatial alarms and obtainsthe set of border points for each active spatial alarm.Then the alarm server computes the Euclidean distancebetween the current location of m and each of the borderpoints for all spatial alarms and selects the border pointthat is the nearest to the current location of m, denotedby b

nearest

, and calculates the new hibernation time form based on the Euclidean distance and a velocity met-ric that is representative, such as the global maximumspeed (V

max

) or the expected speed of m (Vexpected

). Forexample, if there are 35 mph, 55 mph and 65 mph roadsegments on the road network, the global maximumspeed is 65 mph. Although using the global maximumspeed is too pessimistic to calculate the hibernation time,it ensures high alarm success rate. The end time of thenew hibernation time for m based on this Euclideandistance-based method using the global maximum speedis defined as current time + eucdistance(m,b

nearest

)V

max

whereeucdistance(m, b

nearest

) is the Euclidean distance be-tween m and b

nearest

. The object m will be in thehibernation status during the above hibernation time.

In general, the Euclidean distance-based method usingthe global maximum speed is the most conservativetechnique since not all mobile objects are traveling at themaximum speed. Thus an alternative metric we adoptedin the first prototype of ROADALARM is the expectedspeed calculated using the current location of m, theprevious location, the previous expected speed and theprevious maximum speed [2]. The end time of the newhibernation time for m based on this Euclidean distance-based method using the expected speed is defined ascurrent time+ eucdistance(m,b

nearest

)V

expected

(m) where V

expected

(m) isthe expected speed of m.

Although the Euclidean distance-based approach issimple to implement, it suffers from a number of fatalweaknesses. First, the hibernation time is computedusing the Euclidean distance rather than road networkdistance, thus the hibernation time is unnecessarilyshort. Consequently, mobile objects need to wake up fre-quently, making ROADALARM consuming higher batteryenergy than necessary. Second, for each mobile object m,the nearest spatial alarm may not be subscribed by m,thus the hibernation time computed using the Euclideandistance to the nearest spatial alarm is misleading. This


is especially true when all the spatial alarms subscribedby m is far away from the current location of m.

3.2 Network Expansion-based ApproachAnother intuitive baseline approach to evaluating roadnetwork-aware spatial alarms is to use Dijkstra’s net-work expansion algorithm [5]. We present two methodsusing different road network distances: one is using thesegment length-based distance (NE-S) and the other isusing the travel time-based distance (NE-T).

When a moving object m wakes up, NE-S and NE-T first retrieve a set of spatial alarms (A

m

) subscribedby m. For each spatial alarm a

i

2 A

m

, the set of borderpoints of a

i

are obtained. NE-S and NE-T take the currentlocation of m and each border point of a

i

as input tocalculate the segment length-based shortest path and thetravel time-based shortest path respectively, using Dijk-stra’s network expansion algorithm. After computing theshortest path from m’s current location to every borderpoint of all alarms in A

m

, NE-S selects the shortestpath with the smallest segment length-based distance,denoted by p

sl

, to compute the new hibernation timefor m. Similarly, NE-T chooses the shortest path havingthe smallest travel time-based distance, denoted by p

tt

,to compute the new hibernation time for m. Thus, wecan compute the end time of the hibernation time for m

based on NE-S and NE-T as follows:HT

NE�S

(m) = current time + traveltime(p

sl

)

HTNE�T

(m) = current time + traveltime(p

tt

)

Recall that traveltime(p) computes the travel time of apath p as described in Section 2.

The network expansion-based approach is simple andstraightforward to implement. However, the computa-tion cost of this approach is extremely high since it exam-ines all border points of every spatial alarm subscribedby a mobile object m at each wakeup. The shortest pathcomputation cost to calculate the hibernation time of mincreases rapidly as the number of alarms subscribedby the mobile object m increases and most of the sub-scribed alarms are far away from the current locationof m. This is because the computation cost of Dijkstra’snetwork expansion algorithm primarily depends on thesize of underlying road network, the distance betweenthe source location and the destination location, and thenumber of shortest path computations to be performed.If the destination is far away from the source, it is highlycostly to compute the shortest path using the Dijkstra’snetwork expansion algorithm because it exhaustivelyexpands too many unnecessary nodes and edges.

3.3 ROADALARM Baseline ApproachBearing in mind the problems with the Euclideandistance-based approach and network expansion-basedapproach, we design the baseline approach of ROAD-ALARM by introducing two simple and yet effectivefilters. We use the subscription filter to scope the com-putation of the hibernation time for each mobile object

to only those alarms that are subscribed by the mobileobject. In addition, we use Euclidean lower bound (ELB)as another type of filter to minimize the number ofshortest path computations required to compute the hi-bernation time upon each wakeup by filtering out someirrelevant border points of subscribed spatial alarms. Theconcept of Euclidean lower bound refers to the fact thatthe segment length-based shortest path distance betweentwo network locations L1 and L2 is at least equal toor longer than the Euclidean distance between L1 andL2. By combining the subscription filter and ELB filter,the ROADALARM baseline approach (BA) can minimizethe number of shortest path computations required forcomputing hibernation time for each mobile subscriberwhile maintaining the accuracy of alarm evaluation.We present two methods using the segment length-based and the travel time-based road network distance,denoted by BA-S and BA-T respectively.

Concretely, instead of computing shortest paths fromthe current location L

m

of the mobile subscriber m

to every border point of all alarms subscribed by m,BA-S computes the new hibernation time of m in fivesteps: First, for every alarm subscribed by m, denotedby a

i

2 A

m

, we find the border point that has theshortest distance from L

m

. Instead of computing shortestpaths from L

m

to every border point of alarm a

i

, wecompute the Euclidean distance between L

m

and everyborder point of a

i

and sort the set of border points basedon their Euclidean distances from L

m

in an ascend-ing order using the Incremental Euclidean Restriction(IER) algorithm [6], [7], [8]. Second, let b

nn

denote theborder point that has the smallest Euclidean distancefrom L

m

. We compute the shortest path from L

m

tob

nn

using the segment length-based distance. Third, weuse a binary search algorithm to examine the sortedlist of border points and remove those border pointswhose Euclidean distance from L

m

is bigger than sld-istance(L

m

,bnn

). Fourth, for each remaining border pointb

j

, BA-S computes the shortest path from L

m

to b

j

. Ifsldistance(L

m

,bj

) < sldistance(Lm

,bnn

) holds, we assign b

j

to be b

nn

. Thus, for a given mobile object and an alarma

i

2 A

m

, the nearest border point bnn

of ai

will be usedas the reference border point of a

i

to compute the hiber-nation time for m. Finally, BA-S examines every alarma

i

2 A

m

and its nearest border point bnn

and chooses theborder point whose segment length-based distance fromL

m

is the smallest. Let b

min

denote this nearest borderpoint and p

min

denote the shortest path from L

m

to b

min

.Thus we compute the end time of the new hibernationtime for m as current time + traveltime(p

min

).Now we illustrate the working of the baseline ap-

proach using the example in Fig. 1(b). We have threespatial alarms a1, a2, a3 with focal points f1, f2, f3 re-spectively and two moving objects m11 and m12. Letus assume that m11 subscribes to a1 and a3 and m12

subscribes to a1 and a2. Let L

m11 and L

m12 denotethe current location of m11 and m12 respectively. Whenm11 and m12 wake up upon the expiration of their


hibernation time, without the subscription filter andELB filter, we will need to compute the shortest pathsfrom L

m11 and L

m12 to all 13 border points and thenchoose the nearest border point which has the shortestnetwork distance (either segment length-based or traveltime-based) from L

m11 and L

m12 respectively. With thesubscription filter, we can filter out alarm a2 for m11 andalarm a3 for m12 when computing the new hibernationtime. By the ELB filter, to find the new hibernation timefor m12, we only need to perform one shortest path com-putation from L

m12 to b13. This is because by Euclideandistance, b13 is the nearest border point of a1 from L

m12

and b26 is the nearest border point of a2 from L

m12 .Given that eucdistance(L

m12 ,b13) < eucdistance(Lm12 ,b26),

b13 is the nearest border point for m12. Now we computethe network distance (either segment length-based ortravel time-based) from L

m12 to b13, denoted by sldis-tance(L

m12 ,b13). By comparing sldistance(Lm12 ,b13) with

the Euclidean distance from L

m12 to all other borderpoints of a1 and a2, we find that the following con-dition eucdistance(L

m12 ,bk

) > sldistance(Lm12 ,b13) holds

(k = 11, 12, 14, 21, 22, 23, 24, 25, 26, 27). Thus the ELBfilter effectively removes the unnecessary shortest pathcomputations when computing the new hibernation timefor m12.

We below show that the network location of themobile object has significant impact on the effectivenessof the ELB filter. Consider the mobile object m11 and thetwo alarms a1 and a3 subscribed by m11 in Fig. 1(b).For the alarm a3, we do not need to compute theshortest path from L

m11 to the border point b31 sincethe Euclidean distance between L

m11 and b31 is longerthan the segment length-based distance from L

m11 to b32.However, for the alarm a1, the list of border points sortedin ascending order of their Euclidean distance from L

m1

is {b12, b11, b14, b13}. Clearly, the segment length-basednetwork distance from L

m11 to its nearest border pointb12, denoted by sldistance(L

m11 , b12), is longer than theEuclidean distance between L

m11 and each of the lastthree border points in the list and thus none of the threeborder points are filtered out for alarm a1.

BA-T finds the nearest border point of a moving objectm using the travel time-based road network distance.For BA-T, we cannot directly use the ELB filtering asdone in BA-S since the Euclidean lower bound propertydoes not hold for the travel time-based distance. Forexample, when the Euclidean distance and the segmentlength-based distance between a border point and thecurrent location of a mobile object m are 5 miles and 10miles respectively, there could be another border pointin which the Euclidean distance and the segment length-based distance are 12 miles and 15 miles respectively,but it has shorter travel time-based distance since thereis a freeway connecting the border point and the currentlocation of m. Therefore, we extend the ELB filtering forthe travel time-based distance. Instead of using only seg-ment lengths, BA-T defines the travel time-based Euclideanlower bound as the travel time multiplied by the global

maximum speed limit on the entire road network. Forexample, if the travel time from the current location ofm to an alarm border point is 1 hour and the globalmaximum speed limit is 70 mph, the travel time-basedELB in BA-T is 70 miles (1h x 70mph). BA-T excludesborder points whose Euclidean distance is longer than70 miles since the moving object m cannot get to thoseborder points within 1 hour even if it travels at theglobal maximum speed. Since BA-T is using the globalmaximum speed limit to calculate the travel time-basedELB, the search space of BA-T is usually larger thanthat of BA-S, i.e., BA-T considers more border pointsof alarms subscribed by m to find the nearest one. Theremaining steps of BA-T are the same as those in BA-S.In the first prototype of ROADALARM we use the globalmaximum speed limit for travel time-based ELB in orderto ensure the high accuracy of alarm evaluation. It wouldbe interesting to use some less conservative speed ormotion metrics to see if we can further reduce the searchspace needed for computing the hibernation time formobile objects at the cost of a small and affordableaccuracy loss.

4 MOTION-AWARE OPTIMIZATIONSCompared to the Euclidean distance-based approach andthe conventional network expansion-based approach,the ROADALARM baseline approach (BA) improves theefficiency of road network-aware alarm processing alongtwo dimensions. First, it uses the subscription filter tonarrow down the set of spatial alarms to be consideredfor computing the hibernation time upon wakeup ofeach mobile subscriber. Second, it utilizes the ELB filterto cut down the number of border points to be exam-ined for shortest path computation while achieving highalarm success rate.

However, the ELB filter is not always effective. In somecases, the number of border points after applying theELB filter remains to be high. Recall the case of m11 inFig. 1(b) in which the ELB filter can filter out one borderpoint (b31) for alarm a3. For alarm a1, the Euclideandistance from L

m11 to b12 is the shortest and thus theroad network-based distance from L

m11 to b12 is firstcalculated. Because this road network distance is longerthan the Euclidean distances from L

m11 to all the otherborder points (b11, b13, b14), the ELB filter filters out noneof border points for alarm a1.

In this section we introduce a suite of motion-awarefilters to further reduce the search space and the compu-tation time of the ROADALARM baseline approach (BA),especially for those moving objects which subscribemany spatial alarms and their alarms are scattered in alarge geographical area. The main idea of the motion-aware filters comes from the observation that mobileobjects traveling on road networks typically exhibit somedegree of steady motion. First, a moving object travelingon a spatially constrained road network can move onlyby following the predefined road segments connected tothe current road segment it resides. Thus, its movement


cannot be changed drastically. For example, if a movingobject is marching on a road segment, its current movingdirection cannot be changed until it reaches a roadjunction. Furthermore, even if it reaches a road junction,it has high probability to follow the road segment in thesame or similar direction at the junction node. We referto such motion behavior as steady motion.

In this section, we present five types of steady motion-based filters. Our first three optimization techniquesuse steady motion degree ⇥ to capture the constrainedmotion characteristics of moving objects traveling on aroad network. For each mobile object, its steady motiondegree ⇥ models the direction of its movements alongthe road network. If a sharp turn occurs at a junctionnode, a new ⇥ value will be computed for the mobileobject based on the characteristics of underlying roadnetworks and past movement history of the mobileobject. We can also view this ⇥ as a confidence indicator.When a mobile object moves on the road network byfollowing its current direction, a large ⇥ value indicatespossible sharp turns and sudden travel direction changeswhereas a small ⇥ value indicates high probability ofsteady motion along the current direction. When a mo-bile object is traveling on the road network with a cleardestination in mind, this ⇥ angle can be determinedbased on the current location of the mobile object andthe destination location.

4.1 Current Direction-based Motion-aware Filter

The first motion-aware filter is based on the currentdirection of moving objects and their steady motiondegree ⇥. This filter selects only those border pointswhich reside in the ⇥ region anchored at the currentlocation of the mobile object. The ⇥ degree is determinedbased on the current travel direction of the mobile object.One popular way to define the current direction of amoving object is to use the current direction vector inwhich we use the last reported location as the initialpoint and the current location as the terminal point ofthe vector. Let (p1, p2) and (c1, c2) denote the previouslocation and the current location of a moving objectm respectively. The current direction vector of m isdefined as v =<c1 � p1, c2 � p2>. Based on this currentdirection vector, when a mobile object m wakes up, thisfilter limits the search space using the steady motiondegree ⇥ and selects only those border points of m

that reside within this reduced search space, as shownin Fig. 3(a). For example, let (xb1, xb2) denote a borderpoint. To check if the border point is within the ⇥

reduced search space, this filter first defines anothervector w =<xb1 � p1, xb2 � p2> and then calculates thedegree of the border point from the current directionvector v using the following equation:

sm degree(v, w) = arccos(

v · w|v||w| )

If sm degree(v, w) > ⇥, this border point is removedsince it is outside the constrained search space. For

Θ

Current

Previous

(a) Current direction-based

Θ

Current

Destination

(b) Destination-based

Fig. 3: Vector-based motion-aware filters

the remaining unfiltered border points, our approachcalculates the new hibernation time by executing theROADALARM baseline approach.

The current direction-based motion-aware filter isgood when the hibernation time is relatively short andthe time window in which the mobile object movessteadily is relatively low as well, since the current di-rection vector changes each time when the mobile objectwakes up due to the expiration of its current hibernationtime. Furthermore, the current direction-based motion-aware filter may be suitable for some mobile clients whodo not want to disclose their destination information dueto privacy reasons. However, if the destination is given(or can be inferred by using Calendar), then it is moreeffective to use a destination-based motion filter.

4.2 Destination-based Motion-aware FilterThe destination-based motion-aware filter utilizes boththe current location and the destination information ofmoving objects. Destination information can be directlygiven by the mobile clients, such as those using carnavigational systems or can be extracted from mobileclients’ calendar applications. In this filter, the degree ⇥

indicates how confident the moving object will marchtoward its destination. The destination-based motion-aware filter chooses only border points which reside inthe ⇥ region defined based on the current location ofmoving objects to their destination. We define a destina-tion vector to represent the direction toward the destina-tion, in which the current location and the destinationlocation are used as the initial and terminal point of thevector respectively. Let (c1, c2) and (d1, d2) denote thecurrent location and the destination of a moving objectm respectively. The destination vector of m is defined asv =<d1�c1, d2�c2>. When m wakes up, the destination-based motion-aware filter restricts the search space usingthe destination vector v and ⇥ and then selects only theborder points within this ⇥ restricted search space, asshown in Fig. 3(b). For example, let (yb1, yb2) denote aborder point. To check if the border point is within thereduced search space, this filter first computes anothervector w =<yb1 � c1, yb2 � c2> and then calculatesthe degree of this border point in terms of this newvector and the destination vector v using the equationsm degree(v, w). We remove those border points whose


Θ

Current

Destination

Alarm miss

(a) Alarm miss

Start

Destination

Recalculate

(b) Cache Invalidation

Start

Destination

(c) New search space

Fig. 4: Caching-based motion-aware filters

sm degree(v, w) values are higher than the specific ⇥

defined by m or calculated by the system in the absenceof user-defined ⇥. Our approach calculates the nearestborder point by examining all the unfiltered borderpoints and then computes the new hibernation time form by invoking our ROADALARM baseline algorithm.

4.3 Caching-based Motion-aware FilterBoth the current direction-based motion-aware filter andthe destination-based motion-aware filter can reduce thecomputation cost of finding the nearest border point foreach mobile object upon its wakeup. This computationreduction is achieved by reducing the number of candi-date border points and thus the search space througha combination of the steady motion degree ⇥ withcurrent direction or destination information. However,those two filters also suffer from a couple of inherentproblems. First, if a mobile object m takes a short detourdue to traffic and moves slightly out of the scopedspatial region defined by ⇥ and the current directionor destination, there could be an alarm miss. ConsiderFig. 4(a): a moving object m has moved slightly outsidethe scoped spatial region and there exists an spatialalarm which resides just outside the scoped region andis very close to the current location of m. Unfortunately,this alarm will be missed if we use the current direction-based motion-aware filter or destination-based motion-aware filter. Another weak point is that those two filtersrecalculate the search space and thus the set of candidateborder points at every wakeup of each moving object.This causes not only unnecessarily frequent and possiblyduplicate computation of the candidate border pointsbut also adds some unnecessary susceptibility to smalldetour-like movements of mobile objects. Concretely,if a moving object changes its direction slightly, forexample, due to traffic directed detour, the selection ofthe candidate border points found at the current wakeupcould be very different from the selection at the previouswakeup. Therefore, the two filters may miss some spatialalarms which have high probability to be a hit due to thisunnecessarily sensitive susceptibility.

To address this problem, we propose another motion-aware filter, called caching-based motion-aware filter, basedon the observation that moving objects will move towardtheir destination constantly and persistently even thoughthey may change their direction opposite to (or deviatequite bit from) the destination for a short period of time.Initially, this filter selects the candidate border points for

each moving object based on its current location, its des-tination location and its ⇥ using the destination-basedmotion-aware filter. This filter then stores the selectedcandidate border points with the calculated destinationvector for each moving object. When a moving object mwakes up the next time, instead of recomputing the ⇥

region and the set of candidate border points as done inthe destination-based motion-aware filter, this caching-based motion-aware filter retrieves the stored candidateborder points of m and then finds the nearest borderpoint to the current location of m by examining thestored border points using our ROADALARM baselineapproach. Finally, this approach calculates the hiberna-tion time using the nearest border point in the same wayas is done in the baseline approach.

Even though the caching-based filter is proposed tohandle the susceptibility to small changes, continuoussmall changes can make a big change as shown in Fig.4(b). To address this problem, this filter has a mechanismto check whether the stored border points are obsoleteand thus to calculate new candidate border points. Whena moving object wakes up, this filter calculates thedegree of the moving object’s current location from thestored destination vector. If the degree is larger than ⇥

(i.e., the object went out of the scoped search space),then this filter recalculates the search space based on theobject’s current location as shown in Fig. 4(c).

4.4 Shortest Path-based Motion-aware FilterEven though the caching-based filter avoids unnecessar-ily frequent filtering of border points, it still needs toexamine too many border points in order to find thenearest one, especially when ⇥ is large and many alarmsare subscribed by moving objects. Consider Fig. 3(b): theborder points on the bottom far left or far right cornerare unlikely to be hit by the moving object since it isfar away from the object’s destination. Motivated by thisobservation, we propose the shortest path-based motion-aware filter based on a natural assumption that movingobjects will follow the shortest path to the destination.Initially, this filter calculates the shortest path (p

min

)from the current location to the destination for each mov-ing object and then selects some border points within aboundary distance d from the shortest path, as shown inFig. 5(a). The distance d indicates the level of steadiness:if a moving object follows the calculated shortest path, asmall value of d is sufficient. For example, let b denote aborder point of the moving object. To check if b is withinthe boundary distance d from the shortest path p

min

,this filter calculates the perpendicular distance from theborder point to all road segments of p

min

and then findsthe minimum value as follows:

min

b,p

min

= min

n

p

i

n

p

i+12p

min

pdistance(b, n

p

i

n

p

i+1)

where n

p

i

n

p

i+1 is a constituent road segment of thepath p

min

and pdistance(b, n

p

i

n

p

i+1) is the perpendiculardistance from border point b to road segment n

p

i

n

p

i+1 . Ifmin

b,p

min

is less than d, the border point is selected as a


Current

Destination

(a) Selection of border points

Current

Destination

Recalculate

(b) Shortest path recalculation

Fig. 5: Shortest path-based motion-aware filter

candidate border point of the moving object. The shortestpath-based motion-aware filter then stores the selectedcandidate border points with the calculated shortest pathfor each moving object. When a moving object m wakesup, this filter retrieves the stored candidate border pointsof m and then finds the nearest border point, amongthe retrieved border points, using the ROADALARMbaseline algorithm. Finally, this approach calculates thehibernation time using the nearest border point in thesame way as is done in the baseline approach.

Like the caching-based filter, the shortest path-basedfilter also has a mechanism to handle moving objectswhich go out of our reduced search space based onthe shortest path as show in Fig. 5(b). When a movingobject wakes up, this filter calculates the distance fromthe stored shortest path of the object and if the distanceis larger than d, recalculates the search space based onthe object’s current shortest path to the destination.

4.5 Selective Expansion-based Motion-aware FilterThe shortest path-based motion-aware filter selects bor-der points which have high probability to be hit basedon the shortest path from the current location of movingobjects to their destination. Even though it reduces thecomputation time to calculate the hibernation time byconsidering fewer (but more relevant) border pointscompared to the ROADALARM baseline approach andthe other steady motion-based approaches, it still needsat least two shortest path computations: one for calcu-lating the shortest path from the current location to thedestination to select candidate border points and theother for choosing the nearest border point among theselected border points. These computations will increasethe server loads when the destination is far away fromthe current location of a moving object and there is nonearby spatial alarm from the current location. To reducethe computation cost, we propose a selective expansion-based motion-aware filter in which an exact shortest pathcomputation is not needed. The selective expansion-based filter expands only road segments which havehigh probability to be passed by a moving object. Toselect target road segments to be expanded, we uti-lize the destination of moving objects and apply theconcept of Simulated Annealing (SA) to the expansion.SA probabilistically finds a good approximation to the

global optimal solution in a large solution space bygiving high randomness in early stages and almost norandomness in ending stages. Using this basic concept ofSA, the selective expansion-based filter expands most ofroad segments in early steps even though some of themhave opposite direction to the destination. In followingsteps, this filter incrementally strengthens the conditionof the expansion and thus only road segments whosedirection points toward the destination are expanded.This expansion is terminated if it satisfies one of threecases: 1) the expansion arrives at the destination, 2) theexpansion meets any spatial alarm of the moving objectand 3) there is no more road segment which satisfies thecondition of the expansion. Since this filter expands onlyrelevant road segments which have high probability tobe hit from the current location to the destination andit terminates the expansion process even though there isno found border point (case 3), it considerably reducesthe computation cost to calculate the hibernation timecompared to other processing methods which requireshortest path computations. In addition to the reducedcomputation cost, since it expands most of road seg-ments in early steps, the selective expansion-based filtercan cover common cases in which moving objects movein the opposite direction from the destination to takefaster roads such as freeways.

The algorithm of the selective expansion-based filteris formally defined as follows. Like other processingmethods we propose, this filter starts when a movingobject m wakes up. Let L

m

and d

m

denote a currentlocation and a current destination of m respectively.We define T (i) which denotes a time-varying parameterat step i and E(n

p

, i) which denotes an energy of anexpansion node on a road junction n

p

at step i. A smallerenergy of a road junction means that the road junctionhas higher probability to be visited by m comparedto other road junctions having a larger energy. A roadsegment n

p

n

q

connected to n

p

is expanded if a newenergy E(n

q

, i+1), defined as follows, is less than T (i).E(n

q

, i+ 1) = E(n

p

, i)⇥ dv(n

p

n

q

, d

m

)

where dv(n

p

n

q

, d

m

) represents a deviability of n

p

n

q

from the destination d

m

. Road segments whose directionpoints toward the destination have a small dv value and,on the other hand, road segments in which their direc-tion is opposite to the destination have a large dv value.Therefore, road segments in which their direction pointstoward the destination will have higher probability to beexpanded since their dv value is small. The deviabilityis defined as follows:

dv(n

p

n

q

, d

m

) =

degree(

��!n

p

n

q

,

��!n

p

d

m

)

180

� ⇥ 1

w(speedlimit(n

p

n

q

))

where w(speedlimit(n

p

n

q

)) is a weight based on thespeed limit of n

p

n

q

. By giving more weights to fasterroads such as freeways, it makes such faster roads havehigher probability to be expanded than slower roads. Ifdegree(

��!n

p

n

q

,

��!n

p

d

m

) is 0

� (i.e. np

n

q

exactly points towardthe destination), we use 1

� instead of 0� to continue the


selective expansion.T value gradually decreases as steps increase to

strengthen the expansion condition and is defined asT (i) =

T0k

i

where k is a parameter that controls the expan-sion rate and T0 is an initial value for the expansion. Fora large k, the T value becomes smaller rapidly as stepsincrease and thus more road segments are excluded fromthe expansion, compared to a small k. A large T0 valuemakes more road segments to be expanded. We use 1 asT0 value to ensure that all road segments are expandedregardless of the k value and their dv value at first step.

The selective expansion-based filter starts with ex-panding the road junction n0, where the current locationL

m

of m is located, with its initial energy 1 (E(n0, 0)).If L

m

is located on the road segment n

p

n

q

, this filtertreats L

m

as a road junction n0 and n0np

and n0nq

asroad segments connected to n0. For each road segmentn0nj

connected to n0, this filter calculates E(n

j

, 1) usingthe above formula and then expands n0nj

if E(n

j

, 1) isless than T (0). While expanding n0nj

, this filter stops thewhole expansion process if there is a border point of m orthe destination d

m

on n0nj

. If n0nj

is expanded withoutencountering any border point or d

m

, n

j

is insertedinto the expansion list for the next step with its energyE(n

j

, 1). After checking all road segments connected ton0, this filter moves to the next step and expands roadjunctions in the expansion list using the above process.This expansion process is terminated if there is no roadjunction for the next step or any border point or d

m

isencountered during the expansion.

To calculate the hibernation time for m, if a borderpoint or d

m

is encountered during the expansion, thisfilter uses the travel time, from L

m

to the encounteredborder point or d

m

, as the hibernation time. Since thisfilter keeps the accumulated travel time from L

m

to eachexpanded road junction, no additional computation isneeded to calculate the hibernation time for m. If theexpansion is terminated because there is no more roadjunction to be expanded, this filter chooses a terminalroad junction (i.e., in which no connected road segmentis expanded) having the smallest travel time amongselected candidate terminal road junctions and then usesthe travel time to the terminal road junction as thehibernation time for m. To select the candidate terminalroad junctions, we introduce another confidence degree⇥

SESM

. The selective expansion-based filter checks onlyterminal road junctions within ⇥

SESM

based on thevector from L

m

to d

m

and then chooses a terminalroad junction having the smallest travel time among thecandidate terminal road junctions. If ⇥

SESM

value is toolarge, it ensures high success rate, but its hibernationtime is unnecessarily short because some terminal roadjunctions which are terminated at earlier steps and thushave short travel time are included in the search space.On the other hand, if ⇥

SESM

value is too small, it cannotensure high success rate because only terminal roadjunctions which survived until last steps are included inthe search space and thus the selected travel time is too

long. Since this filter also keeps and updates the smallesttravel time based on ⇥

SESM

during the expansion,no additional computation is needed to calculate thehibernation time.

One disadvantage of the above synchronous (i.e., alltarget road junctions are expanded at the same step) se-lective expansion on road networks is that, even thougha border point or the destination d

m

is encounteredduring the expansion, the point could be reached byother road segments having shorter travel time at latersteps. Furthermore, nearby border points could not bereached during the expansion if there are many shortroad segments from L

m

to the border points. Therefore,spatial alarms can be missed due to the long travel timecalculated by ignoring some nearby border points orfaster road segments connecting to the border points.To solve this problem, we use an asynchronous versionin which a road junction having the smallest segmentlength (SESM � S) or travel time (SESM � T ) isexpanded first, regardless of its current step, using apriority queue.

In summary, ROADALARM provides five motion-aware filters to reduce the search space and the com-putation time of the ROADALARM baseline approach.The current direction-based and the destination-basedfilters are based on steady motion degree ⇥ to take intoaccount the constrained motion characteristics of movingobjects traveling on a road network. The caching-basedfilter extends the destination-based filter for improvedaccuracy by capturing small detour-like movements ofmobile objects. To further reduce the number of borderpoints evaluated by ROADALARM, we develop the short-est path-based filter to select only border points that areclose to the shortest path to the destination. Last butnot the least, we further reduce the computation costin ROADALARM by introducing the selective expansion-based filter, which avoids exact shortest path computa-tion by expanding only road segments that have highprobability to be passed by a mobile user.

5 EXPERIMENTAL EVALUATIONIn this section we evaluate the performance of ourROADALARM methods through four sets of experiments.We use gt-mobisim simulator [9] to generate mobilitytraces on real road networks downloaded from U.S.Geological Survey (USGS [10]). For the first three setsof experiments, the mobility traces are generated on amap of northwest Atlanta, which covers about 11 km(6.8 miles) by 14 km (8.7 miles), using the random tripmodel [11]. The road networks consist of four differentroad types: residential roads and freeway interchangewith 30 mph speed limit (48 km/h), highway with 55mph limit (89 km/h) and freeway with 70 mph limit(113 km/h). Ranges of spatial alarms are chosen from aGaussian distribution with a mean of 50 m and standarddeviation of 10 m. We use 50 m as the boundary distanced of the shortest path-based motion-aware filter. Forthe selective expansion-based motion-aware filter, we


80%

85%

90%

95%

100%Su

cces

s Rat

e

Segment Length Travel Time

(a) Alarm success rate

050

100150200250300

Avg

Hib

erna

tion

Tim

e (s

)

Segment Length Travel Time

(b) Average Hibernation time

Fig. 6: Segment length-based vs Travel time-based approaches

empirically use 4 as the k value and 90

� as the ⇥

SESM

value to select not too short and not too long traveltime. We give 1, 2 and 3 to 30 mph, 55 mph and 70mph road segments respectively as their speed weightsin order to give faster roads more chances of expansion.All experiments were conducted on a desktop havingone Intel i5-2300 quad-core processor, 6GB of RAM, andone 1TB 7200 rpm hard disk.

5.1 Comparison with existing methodsWe first compare segment length-based approaches andtravel time-based approaches as shown in Fig. 6. Theseexperiments use 15,000 objects (and about 72,000 spatialalarms) and 180� as the ⇥ value of the current direction-based, destination-based and caching-based motion-aware filters. Each object has different number of spatialalarms, given by Zipf distribution with five alarms asthe most common value (i.e., rank 1). We exclude theresults of network expansion-based methods since theycannot scale to 15,000 moving objects. The alarm successrate for travel time-based approaches is higher thanthe corresponding segment length-based approaches asshown in Fig. 6(a). This is primarily because segmentlength-based approaches select the segment length-basedshortest path in which spatial alarms can be missed ifmoving objects follow paths having shorter travel time.On the other hand, the average hibernation time of eachtravel time-based approach is shorter than that of itscorresponding segment length-based approach since thetravel time on the segment length-based shortest path isalways equal to or longer than that on the travel time-based shortest path for the same source and destinationlocation. Without loss of generality, in the rest of theexperiments, we include the results of only travel time-based approaches for simplicity.

The first set of experiments compares our approacheswith existing Euclidean space-based methods in Fig. 7.This set of experiments uses a moving object populationwith size ranging from 5,000 to 15,000 and each objecthas different number of spatial alarms, given by Zipfdistribution with five alarms as the most common value.Alarm success rate. The success rates for different ap-proaches are shown in Fig. 7(a). The shortest path-basedand selective expansion-based motion-aware filters havealmost the same success rate as the Euclidean distance-based approach using the global maximum speed andthe ROADALARM baseline approach. The caching-basedfilter has more than 5% better success rate than the

50%

60%

70%

80%

90%

100%

5,000 objects/24,000 alarms



Succ

ess R

ate

Baseline Direction Destination CachingShortest Selective Euc (exp) Euc (max)


0

50

100

150

200

250




Avg

Hib

erna

tion

Tim

e (s

)


(b) Hibernation time per wakeup

020406080

100120140



15,000 objects/72,000 alarmsN

umbe

r of W

akeu

ps (x

100

0)


(c) Number of Wakeups

0100200300400500600700800




Com

puta

tion

Tim

e (s

)


(d) Total Computation Time

02468

101214




Num

ber o

f Bor

der P

oint

s

Baseline Direction DestinationCaching Shortest

(e) Number of Border Points

0

100

200

300

400

500

600



15,000 objects/72,000 alarmsA

larm

Che

ckin

g Ti

me

(ms)


(f) Total Alarm Checking Time

Fig. 7: Comparison with Euclidean space-based approaches

destination-based filter. This confirms our assumptionthat moving objects will move toward their destinationconstantly even though they may change their direc-tion opposite to the destination for a short time. TheEuclidean distance-based approach using the expectedspeed has the lowest success rate since it fails to considerspatial constraints of moving objects.

Hibernation time. Fig. 7(b) shows the average hiberna-tion time of moving objects. The longer the hibernationtime is, the more energy the mobile clients can conserve.The hibernation time of the shortest path-based filter isthree times longer than that of the Euclidean distance-based approach using the global maximum speed and40% longer than that of the ROADALARM baseline ap-proach. This result also shows that the shortest path-based filter ensures high success rate in the same way asthe Euclidean distance-based approach and the ROAD-ALARM baseline approach even though moving objectsof the shortest path-based filter can conserve much moreenergy. The selective expansion-based filter has 45% and25% shorter hibernation time than the shortest path-based filter and the ROADALARM baseline approachrespectively since it calculates the hibernation time eventhough there is no found border point. It, however, stillhas 80% longer hibernation time than the Euclideandistance-based approach using the global maximumspeed. The Euclidean distance-based approach using theglobal maximum speed has the shortest hibernation time


75%

80%

85%

90%

95%

100%

Θ=90º Θ=135º Θ=180º

Succ

ess R

ate

Direction Destination Caching


0

50

100

150

200

250

300

Θ=90º Θ=135º Θ=180º

Hib

erna

tion

Tim

e (s

)

Direction Destination Caching

(b) Hibernation time

Fig. 8: Effect of the steady motion degree ⇥

since it pessimistically utilizes the Euclidean distanceand the global maximum speed to calculate the hiber-nation time.The number of wakeups. Fig. 7(c) shows that thenumber of wakeups is inversely related to the hiber-nation time. The smaller number of wakeups indicatesthe lower server loads since the server computes thehibernation time whenever a moving object wakes up.Computation time. Fig. 7(d) shows the total computa-tion time to calculate the hibernation time. The shortestpath-based filter has 45% faster computation time thanthe ROADALARM baseline approach since it has smallernumber of wakeups of moving objects. The selectiveexpansion-based filter has the smallest computation timeamong the road network-based approaches since it doesnot need shortest path computations to calculate thehibernation time. Its computation time is just 24% and45% of that of the ROADALARM baseline approach andthe shortest path-based filter respectively. The Euclideandistance-based approaches need only a little computa-tion time since the computation cost to calculate theEuclidean distance is negligible, compared to the roadnetwork distance calculation, even though the numberof wakeups is more as shown in Fig. 7(c).The number of border points. Even though there is onlyabout 20% difference between the shortest path-basedfilter and the ROADALARM baseline approach in termsof the number of wakeups, there is about 45% differencein terms of the computation time. Furthermore, eventhough all motion-aware filters have similar number ofwakeups, only the shortest path-based filter has bettercomputation cost than the others. This is because theshortest path-based filter considers the smallest numberof border points to calculate the hibernation time, asshown in Fig. 7(e).Alarm checking time. Fig. 7(f) shows the total pro-cessing time to check whether moving objects hit anyalarms. We use a hash map to store spatial alarms. Theresult confirms that our approach checks spatial alarmsefficiently.

5.2 Effect of the steady motion degreeWe investigate the effect of different settings of thesteady motion degree ⇥ on success rate and hibernationtime with 15,000 moving objects and about 72,000 spatialalarms. The results are shown in Fig. 8 with ⇥ valuesset to 90�, 135� and 180�. The success rate for the

current direction-based, destination-based and caching-based filters increases as ⇥ values increase, because moreborder points are selected as shown in Fig. 8(a). Fig. 8(b)shows that the average hibernation time decreases withgrowing ⇥ values. This is because border points havingshorter travel time are newly selected to calculate thehibernation time as the search space increases.

5.3 Effect of growing number of objects and alarmsFig. 9(a) and Fig. 9(b) evaluate the scalability of ourapproaches by increasing the number of moving objects.Total 300,000 spatial alarms are deployed for this set ofexperiments and the number of moving objects increasesfrom 15,000 to 45,000. Each object has zero to 30 spatialalarms, given by Zipf distribution with 15 alarms as themost common value, and all spatial alarms are private.We think this setting deploying 45,000 moving objectsis realistic on this road network of northwest Atlanta,in which the total length of all road segments is 1384km (865 miles), since there is a mobile user every 31m (10 feet) on average. We include the measurementresults of only the ROADALARM baseline approach, theshortest path-based filter and the selective expansion-based filter as they have high success rate compared toother methods. Fig. 9(a) confirms that our approachesensure the high success rate with growing number ofmoving objects. The selective expansion-based filter hasslightly lower success rate than the others since it doesnot try to find the nearest border point if there is nonearby border point. In terms of the total computationtime, there is no increase from 30,000 to 45,000 objectssince with fixed alarms, many objects have no spatialalarms as shown in Fig. 9(b). The selective expansion-based filter’s computation time is only 23% and 9% ofthat of the shortest path-based filter and the ROAD-ALARM baseline approach respectively while ensuringsimilar success rate.

Fig. 9(c) and Fig. 9(d) show the scalability of ourapproaches by increasing the number of spatial alarmswith 15,000 moving objects. We increase the most com-mon value of Zipf distribution from 10 to 20 and thusthe total number of alarms grows from about 147,000to 297,000. Fig. 9(c) verifies that our approaches ensurethe high success rate with increasing number of spa-tial alarms. Note that the success rate of the selectiveexpansion-based filter increases as the number of spatialalarms grows. This is because, if a moving object hasmore spatial alarms, there is a higher probability that aborder point of the object is found during the selectiveexpansion. Fig. 9(d) shows that the computation timeof the shortest path-based filter increases only slightlywith the growing number of spatial alarms. This isprimarily because the shortest path-based filter selectsonly border points having high probability to be hitand thus the increased number of spatial alarms hasno huge impact on the selected border points by theshortest path-based filter. Even though the computationtime of the ROADALARM baseline approach has more


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increase than that of the shortest path-based filter, it doesnot increase linearly with the growing number of spatialalarms. The computation time of the selective expansion-based filter even decreases as the number of alarmsincreases because the selective expansion of the filter isterminated earlier with the fewer number of expandedroad segments due to the higher probability that a borderpoint is found.

5.4 Effect of different road networksThis set of experiments measures the performance of ourapproaches using different types of road networks. In ad-dition to the map of northwest Atlanta as an urban roadnetwork, we choose the map of Duluth, GA and the mapof Helen, GA as a suburban and a rural road networkrespectively. All three road networks cover almost same

size (i.e., 6.8 miles by 8.7 miles) but have totally differentnumber of road segments and road junctions. The totalnumbers of road segments of the urban, suburban andrural road network are 9,187 (average length is 150.7 m),1,600 (258.3 m) and 765 (356.3 m) respectively. The totalnumbers of road junctions are 6,979, 1,486 and 711 forthe urban, suburban and rural road network respectively.The urban road network has 431 and 681 road segmentshaving 70 mph and 55 mph speed limit respectively. Theother road segments have 30 mph speed limit. 24 and218 road segments of the suburban road network have70 mph and 55 mph as their speed limit respectively. Therural road network has 27 and 66 road segments having70 mph and 55 mph speed limit respectively.

Fig. 10 shows the experimental results for the threedifferent road networks. This set of experiments uses15,000 moving objects and total about 72,000 spatialalarms. Each object has different number of spatialalarms, given by Zipf distribution with five alarms asthe most common value. Fig. 10(a) confirms that ourapproaches ensure the high success rate for differenttypes of road network. The selective expansion-basedfilter has slightly lower success rate on the rural roadnetwork since moving objects are more likely to use roadsegments whose direction does not point toward thedestination and thus not expanded, due to the limitednumber of road segments. On the rural road network,the computation times is about 8% of that on the urbanroad network as shown in Fig. 10(b). This is primarilybecause of the high complexity (i.e., 12 time more roadsegments and 10 times more road junctions than therural road network) of the urban road network. Fig. 10(c)shows that moving objects on the suburban and ruralroad networks have longer hibernation time than thoseon the urban road network even though the number ofspatial alarms for each moving object and the focal pointand the range of each spatial alarm are given by samedistributions for all three road networks. Since the urbanroad network has 12 times more segments and 16 timesmore segments having 70 mph speed limit comparedto the rural road network, it has more probability tohave a path having shorter travel time between twolocations. As shown in Fig. 10(d), less border pointsare considered to calculate the hibernation time on thesuburban and rural road networks than on the urbanroad network because spatial alarms on complex roadnetworks usually have more border points.

In summary, our experimental results show that theshortest path-based motion-aware filter outperforms therest in most cases since this approach ensures highsuccess rate while reducing the computation cost ofservers and conserving energy of mobile clients. Sincethe selective expansion-based filter considerably reducesthe computation cost while ensuring high success rate,it is suitable for spatial alarm processing servers whichshould compute the hibernation time quickly for a hugenumber of moving objects while ensuring longer hiber-nation time than the Euclidean space-based approach to


save the energy of moving objects. For those applicationsin which high success rate is required, both the ROAD-ALARM baseline approach and the the shortest path-based motion-aware filter are good options. Especially,for some applications in which the battery power ofmobile clients is not a serious problem, the ROADALARMbaseline algorithm may be a better choice since it hasa slightly higher success rate than the shortest path-based motion-aware filter. The current direction-basedfilter, the destination-based filter and the caching-basedfilter are appropriate for those applications in whichreducing the computation cost of servers and the batteryconsumption of mobile clients are top priorities whilemaintaining the acceptable success rate (about 90%).

6 RELATED WORK

There are many existing studies on continuous spatialqueries to find objects within a predefined range ork nearest objects from a query center point [12], [13],[14], [15], [16], [17]. Some of them are based on roadnetworks [18], [19] or land surface [20]. However, spa-tial alarms are fundamentally different from continuousspatial queries in terms of their purposes as well astarget applications. Continuous queries such as “find 3nearest Starbucks stores while driving to Miami” requirecontinuous query evaluation as I am driving on theUS highway. On the other hand, spatial alarms have apredefined location of interest, such as “alert me whenI am within 5 miles of the public library in Buckhead”and thus require alarm evaluation only when subscribersare in the vicinity of the spatial alarms. Even when themobile subscribers are moving on the road, their spatialalarms may not need to be evaluated if those alarmtargets are located far away from the current locationsof their subscribers. This is the fundamental reason whyspatial alarms deserve to be processed more efficientlyusing a different set of algorithms and optimizations.

Existing research on spatial alarms and location re-minders mainly focus on the Euclidean space. [1] pro-poses an approach to process spatial alarms in theEuclidean space by combining spatial indexes such asR-tree and Voronoi Diagram with the maximum speed-based safe period. [3] develops a safe region-based ap-proach for spatial alarm processing in the Euclideanspace. Different shapes of safe regions are proposed andcompared in [3]. [4] pointed out the high cost of saferegion-based approach and proposed the Mondrian treeindex that can index both spatial alarms and alarm freeregions within a uniformed framework.

7 CONCLUSION

We have presented ROADALARM � an efficient andscalable approach to processing road network-awarespatial alarms. By utilizing spatial constraints on roadnetworks and mobility patterns of mobile objects, theROADALARM approach can provide longer hibernationtime of mobile clients while ensuring high success rate.

To the best of our knowledge, all existing results on spa-tial alarms are based on the Euclidean space. This paperis the first one that develops efficient algorithms andoptimizations for scaling road network-aware spatialalarm processing in ROADALARM, with a preliminaryresult in [21] and a software demo in [22].

Our research on ROADALARM continues along sev-eral directions, including distributed version of ROAD-ALARM, efficient indexing of spatial alarms, and privacypreserving management of spatial alarms.

REFERENCES[1] B. Bamba, L. Liu, P. S. Yu, G. Zhang, and M. Doo, “Scalable

processing of spatial alarms,” in HiPC’08, 2008.[2] A. Murugappan and L. Liu, “An energy efficient middleware

architecture for processing spatial alarms on mobile clients,” Mob.Netw. Appl., vol. 15, pp. 543–561, August 2010.

[3] B. Bamba, L. Liu, A. Iyengar, and P. S. Yu, “Distributed processingof spatial alarms: A safe region-based approach,” in ICDCS’09.

[4] M. Doo, L. Liu, N. Narasimhan, and V. Vasudevan, “Efficientindexing structure for scalable processing of spatial alarms,” inGIS’10, 2010.

[5] E. W. Dijkstra, “A note on two problems in connexion withgraphs.” Numerische Mathematik, vol. 1, pp. 269–271, 1959.

[6] G. R. Hjaltason and H. Samet, “Distance browsing in spatialdatabases,” ACM Trans. Database Syst., vol. 24, no. 2, Jun. 1999.

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[8] D. Papadias, J. Zhang, N. Mamoulis, and Y. Tao, “Query process-ing in spatial network databases,” in VLDB’03, 2003.

[9] “GT-mobisim,” 2009, http://code.google.com/p/gt-mobisim/.[10] “U.S. Geological Survey,” 2012, http://www.usgs.gov/.[11] P. Pesti, L. Liu, B. Bamba, A. Iyengar, and M. Weber, “RoadTrack:

scaling location updates for mobile clients on road networks withquery awareness,” Proc. VLDB Endow., vol. 3, 2010.

[12] Z. Song and N. Roussopoulos, “K-nearest neighbor search formoving query point,” in SSTD ’01, 2001.

[13] S. Prabhakar, Y. Xia, D. V. Kalashnikov, W. G. Aref, and S. E.Hambrusch, “Query indexing and velocity constrained indexing:Scalable techniques for continuous queries on moving objects,”IEEE Trans. Comput., vol. 51, no. 10, pp. 1124–1140, Oct. 2002.

[14] Y. Tao, D. Papadias, and Q. Shen, “Continuous nearest neighborsearch,” in VLDB’02, 2002.

[15] G. S. Iwerks, H. Samet, and K. Smith, “Continuous k-nearestneighbor queries for continuously moving points with updates,”in VLDB’03, 2003.

[16] H. Hu, J. Xu, and D. L. Lee, “A generic framework for monitoringcontinuous spatial queries over moving objects,” in SIGMOD’05.

[17] X. Yu, K. Q. Pu, and N. Koudas, “Monitoring k-nearest neighborqueries over moving objects,” in ICDE’05, 2005.

[18] H.-J. Cho and C.-W. Chung, “An efficient and scalable approachto cnn queries in a road network,” in VLDB’05, 2005.

[19] K. Mouratidis, M. L. Yiu, D. Papadias, and N. Mamoulis, “Contin-uous nearest neighbor monitoring in road networks,” in VLDB’06.

[20] S. Xing, C. Shahabi, and B. Pan, “Continuous monitoring ofnearest neighbors on land surface,” Proc. VLDB Endow., 2009.

[21] K. Lee, L. Liu, S. Meng, and B. Palanisamy, “Scaling spatial alarmservices on road networks,” in ICWS’12, 2012.

[22] K. Lee, E. Yigitoglu, L. Liu, B. Han, B. Palanisamy, and C. Pu,“Roadalarm: A spatial alarm system on road networks,” in ICDE’13, 2013.

Kisung Lee is a Ph.D. candidate in the College of Computing at GeorgiaTech. He is conducting research in big data management in the Cloud,mobile computing and social network analytics.Ling Liu is a full Professor in Computer Science at Georgia Instituteof Technology. She directs the research programs in Distributed DataIntensive Systems Lab (DiSL). Prof. Liu is an IEEE fellow.Balaji Palanisamy is an assistant professor in University of Pittsburgh.He received his M.S and Ph.D. degrees in Computer Science from thecollege of Computing at Georgia Tech in 2009 and 2013 respectively.Emre Yigitoglu is a Ph.D. student in the College of Computing atGeorgia Tech.

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