Context Fusion in Location-Aware Pervasive Applications

8/9/2019 Context Fusion in Location-Aware Pervasive Applications

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4th Int. Workshop on Frontiers of Information Technology, 06

Context Fusion in Location-Aware Pervasive Applications

Muhammad Akhlaq

Blekinge Institute of Technology, Ronneby, [email protected]

AbstractLocation-awareness is an important aspect ofcontext-aware applications. These applicationsenhance the functionality of ubiquitous computingservices and applications by using numerouslocation sensing systems with variant precisionand confidence levels. There is a need of seamlessintegration for these systems using a middlewareinfrastructure. In addition to interoperability ofdifferent systems, the proposed middlewaremanages the spatial location information in spatial

database, infers using spatial relationshipfunctions, and predicts the location of an objectbased on its history. The system facilitates theseparation between applications and locationtechnologies to enable dynamic add-on of newtechnologies without changing the existingapplications.

Keywords Middleware, context-fusion, ubiquitous/pervasiveapplications, context-awareness, location-awareness,

1. Introduction

Mark Weisers vision of Ubiquitous Computingemphasized the need of unifying computers andhumans seamlessly in an environment rich withcomputing [15]. A pervasive system has to becontext aware in order to be minimally intrusive [11].Location-based applications are an importantcomponent of context-aware pervasive systems [9].

Weisers vision cannot be realized without thecontribution made by location-aware applicationswhich include seamless interaction of environmentwith user, application adaptation according to userpreferences & expectations, and efficient utilizationof available resources. In other words, location-awareness enables pervasive applications to serve a

user based on location, thus minimizing the need ofuser intrusion to demand appropriate service. Theimportance of location-awareness can be realized byresearchers claim that this would be the first real-world example of ubiquitous computing [17].

2. Challenges Faced by Location Systems inUbiquitous Computing

Many applications have been developed in the lastdecade based on location-awareness, particularlyunder the flag of Mobile Computing [4]. Mostly,

these applications use Global Positioning System(GPS) as location sensing system. In spite of thesedevelopments, location systems for ubiquitouscomputing face many challenges:

2.1 Need for Location-Sensing Techniques

It is important to realize that all the objects (devices,users, sensors) need not be location aware in apervasive environment. Objects, not capable ofsensing their location directly, can use location-sensing techniques based on Triangulation,Proximity or Scene Analysis, to sense theirlocation with respect to nearby location awareobjects [8]. Triangulation uses the geometricproperties of objects to compute objects location,using the process of Lateration if based on distancesfrom reference points, or Angulation if based onintermediate angles. Proximity determines positionof an object from a known location. The objectspresence is sensed by physical phenomena withlimited range. Scene Analysis uses features of sceneobserved from a point to draw conclusions about thelocation of the observer or of the objects in the scene.

2.2 Need for Estimation of Location

Irrespective of location sensing techniques employed,usually, it is not possible to sense all possiblelocation-time coordinates due to technological orcost constraints. Therefore, a location-aware systemmust deal with the techniques of predicting thelocation of an object in between the location-timepairs reported, which may be minutes or hours apart.It is mandatory to predict the location of an object onfiner temporal points, if needed by the application.Prediction leads to uncertainty. Hence managinguncertainty within the tolerable bounds is critical forany decent location-aware pervasive application.

Traditional techniques utilize history of movingobjects to estimate the current location, usingobjects direction, last reported location and speed[6]. But these techniques are heavily criticized andare considered too simplistic to encounter the real-time dynamic requirements. Therefore, it isemphasized to incorporate the objects goals,personalities and tasks into active considerationalong with its history [5]. In other words there is aneed to build User Models to get more preciseestimation of objects location.

paper can be referred as:

Akhlaq, Context Fusion in Location-Aware Pervasive Applications, 4th International Workshop on Frontiers of Info

hnology (FIT06), Islamabad, Pakistan, December 20-21, 2006.


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2.3 Need for Integration of DifferentLocation Systems

The most important challenge is the fusion ofdifferent location-sensing technologies [18]. Manycommercial and experimental location-sensingtechnologies are in use, having their own precision,confidence levels, and underlying technologies.Their variant features make one system moreappropriate for one kind of environment whencompared to others. So, existence of various locationsystems in a pervasive environment is inevitable, sois their seamless integration.

3. Classification of Location Systems

Location systems can be classified based oncharacteristics such as cost, location informationprovided (physical or symbolic), frame of reference(absolute or relative), degree of precision andaccuracy, scale, recognition (can system recognizethe object as well, in addition to locating them?),

limitations (GPS can only be used outdoorseffectively), and computation entity (object orsystem) [4]. Below is the discussion on someprominent location systems, evaluating eachsystems working principle and classification.

3.1 Active Badge

The Active Badge location system, which wasdeveloped at Olivetti Research Laboratory (nowAT&T Cambridge) [8], is the first and typical indoorbadge sensing system. It consists of a cellularproximity system that uses diffuse infraredtechnology. The system can locate any person whowears a small infrared badge. This badge emits a

globally unique identifier every 10 seconds or ondemand. It provides absolute symbolic locationinformation, representing the room or other place inwhich the badge is located, for example. A centralserver collects this data from fixed infrared sensorsaround the building, aggregates it, and provides anapplication programming interface for using the data.Diffuse infrared has an effective range of quite a fewmeters. In large rooms, the system can use multipleinfrared beacons. Active Badges have difficulty inlocations with fluorescent lighting or direct sunlightbecause of the spurious infrared emissions theselight sources generate.

3.2 Active BatAT&T researchers have developed the Active Batlocation system which provides more accuratephysical positioning than Active Badges [2]. It usesan ultrasound time-of-flight lateration technique.Users and objects carry Active Bat tags. Thecontroller sends a request via short-range radio. Atthe same time, it also sends a synchronized resetsignal to the ceiling sensors using a wired serialnetwork. A Bat, in response, emits an ultrasonicpulse to a grid of receivers mounted in the ceiling.

Each ceiling sensor measures the time interval fromreset to ultrasonic pulse arrival and computes itsdistance from the Bat. The local controller thenforwards the distance measurements to a centralcontroller which performs the lateration computation.Ceiling sensors consider only ultrasound pulses thattraveled along the direct path from the Bat to the

sensor and ignore the reflected ones. This systemhad an accuracy of 9 cm for 95 percent of themeasurements as in 1999. The work to improve theaccuracy even further is in progress. This systemrequires a large fixed-sensor infrastructure preciselyplaced throughout the ceiling. This system isdifficult to scale, deploy, and costs high.

3.3 Cricket

Cricket Location Support System [1] infrastructureuses ultrasound emitters while embeds receivers inthe object being located. This system complementsthe Active Bat system and forces the objects to

perform all their own triangulation computations.This system does not require a grid of ceilingsensors with fixed locations because its mobilereceivers perform the timing and computationfunctions. Cricket uses the radio frequency signalnot only for synchronization, but also to define thetime region during which the receiver shouldconsider the sounds it receives. The system canidentify any ultrasound it hears after the end of theradio frequency packet as a reflection and ignore it.Cricket implements both the lateration andproximity techniques. Cricket, in its currentlyimplemented form, is much less precise than ActiveBat while future implementations may compete with

each other on accuracy. Active Bat is accurate to 9cm while Cricket can accurately delineate 4 4square-foot regions within a room. Cricketsadvantages include privacy and decentralizedscalability, while its disadvantages include a lack ofcentralized monitoring and the computational burdenon the mobile receivers.

3.4 Smart Floor

Smart Floor is a proximity location system designedby Georgia Tech [12]. In this system, a grid ofpressure sensors is embedded into floor which cancapture footfalls. This does not require people tocarry a device or wear a tag. However, the system

has the disadvantages of poor scalability and highincremental cost because the floor of each buildingin which Smart Floor is to be deployed must bephysically altered.

3.5 E911

Many companies have started work on developing avariety of location systems to determine a cellularphones location that makes a 911 emergency call.E911 location systems [16] will also support newconsumer services such as finding the nearest gas


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station, post office, movie-theater, bus, or automatedteller machine. To comply with E911, vendors areexploring several RF techniques, including antennaproximity, angulation using phased antenna arrays,lateration via signal attenuation and time of flight, aswell as GPS-enabled handsets that transmit theircomputed location to the cellular system. The

positioning is less accurate and ranges from 150meters to 300 meters depending on the techniqueused.

3.6 Global Positioning System

Global Positioning System [10] is the most commonlocation finding system that covers the entire earthsurface. GPS uses a collection of low-orbit satellites.These satellites send radio signals to the earth. AnyGPS enabled device can receive these signals andfind its location using these signals. There are somelimitations of GPS systems. The radio signals sentby GPS satellites are too weak to penetrate buildings

etc. GPS functionality is lost in cities becausebuildings absorb GPS signals or scatter them inmulti-path reflections. Consequently, GPS workswell outdoors but poorly indoors where peoplespend most of their time. GPS has been theprevailing location system in way findingapplications and navigations, but has little use inother application domains.

3.7 Other Systems

RADAR [13] is a building-wide tracking systembased on the IEEE 802.11 WaveLAN wirelessnetworking technology developed by MicrosoftResearch group. MotionStar [7] is a tracking system

which can sense precise physical positions relativeto the magnetic transmitting antenna. MicrosoftResearchs Easy Living uses computer visiontechnology to find the location of objects [14].

4. The Middleware

As discussed in the previous section, there arenumerous location sensing systems with variantprecision and confidence levels. There is a need ofintegrating these systems using a middlewareinfrastructure. The middleware can also provide anabstraction layer to location-aware applications,separating them from location detection and sensingtechnologies. The main issues addressed by amiddleware are [3]:

Integrating various location technologies togive a unified view of the location of mobile

objects.

Handling conflicting information fromvarious sensors in the system.

Maintaining an updated model of thephysical arrangement of the environment.

Deducing spatial relationships betweenmobile objects and their physical

environment.

Associating a probability value of accuracywith location information & spatial

relationships.

To address these issues, middleware must possesthe following features:Interoperability: Middleware should fuse

information gathered from variety of systems and

represent it in a way such that conflicting data is

handled appropriately.

Managing Data Confidence: Managing the

confidence level of location data is very important to

ensure its freshness. The quality of data degrades

with the passage of time. Therefore, its confidence

level must be reduced when it becomes older.

Conversion between Location Models: Themiddleware should support both types of location

models i.e. coordinate based, and symbolic. A

coordinate based model shows location data in the

form of (x, y, z) coordinates whereas a symbolic

model assigns names to different locations (such as

buildings, floors or rooms). A middleware should

also support conversion between the two types.

Supporting Push & Pull Modes: Location-aware

applications can interact with the upper most layer ofmiddleware using either push or pull mode. They

can query about the current location of objects or

can receive notification whenever a particularlocation-based condition turns out to be true.

Handling Location References: Location

information can be of two types: Object-based

information is in the form Where is X located?,

and region-based information is in the form Who

else is present where X is present?

Deducing Spatial Relationships: Middleware

should know how to infer spatial relationships

between mobile objects and their physical

environment.

Predicting Future Location: Due to technological

and cost constraints, the location-time pairs provided

by a location system may be minutes or hours apart.

It is, therefore, necessary to predict the location of

an object based on previous information.

Sections 5 to 9 deal with the main components ofmiddleware (see Figure 1) whereas section 10 showshow these components are integrated.


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5. The Location Service Provider

The Location Service Provider layer provideslocation information to all the location-awareapplications. It supports both symbolic as well ascoordinate location information and useshierarchical structure for information management atboth levels.

There may be three kinds of locations: points,lines and polygons. An example of hierarchicalrepresentations of coordinate and symbolic locationsis , which represents asymbolic point location of lightswitch3 which is inroom 4 on floor 2 of building B1. Its coordinateform will be . In the same way, or represents a2D door, whereas a room can be represented as or .Note that the coordinates of room, floor or buildingetc are stored in other data structures. The main tasksperformed by the Location Service Provider layer

are described below.

5.1 Multi-Sensor Fusion

A multi-sensor system may produce location data indifferent formats (coordinate or symbolic),resolution, confidence and freshness. Multi-sensorfusion utilizes this data to estimate the location of anobject and associates a probability value of accuracywith this location information. For example, theprobability that the user is in room 7 is 0.8 (or 80%).

As a first step, sensor data is transformed into acommon format. In the same way, all locations areexpressed as minimum bounding rectangles (MBRs)and these sensor rectangles are then joined to find

more accurate location information. It is important tonote that different sensors strengthen one another iftheir MBRs intersect, but are in conflict if disjoint.

5.2 Push Mode Queries

Location Service Provider deals with two types ofqueries in push mode: object-based queries are in theform Where is X located?? and region-basedqueries are in the form Who else is present where Xis present? Location Service Provider gives themost appropriate location of an object using multi-sensor fusion as described above.

5.3 Pull Mode Notifications

The applications are notified when the probabilitythat an object is within a region of interest exceeds acertain threshold.

5.4 Spatial Relationships

Spatial relationships can be deduced between tworegions, between two objects, or between an objectand a region. The Location Service Provider layercomputes different kinds of commonly used spatialrelationships between objects and regions.

Figure 1: The middleware architecture

6. Spatial Database

The Spatial Database maintains an updated model ofthe physical arrangement of the environment andstores location data from different sensors. It alsoallows operations on geometric data types such asintersection, union, disjoint and so on. Objects arerepresented by points, lines or polygons whereasregions are represented by minimum boundingrectangles (MBRs). Even though MBRs provideapproximate boundaries, they allow quickprocessing of location queries. Once a certaincondition is met by an MBR, more accurate

processing of the operation is carried out by takingactual region boundaries.

7. Reasoning Engine

The Reasoning Engine infers spatial relationshipsbetween mobile objects and regions. It estimateslocation of an object using information in spatialdatabase. Location notifications are generatedwhenever some spatial condition becomes true suchas a mobile object entering a certain region etc.

8. Location Prediction Engine

The Location Prediction Engine is based on user

models and Bayesian learning for neural networks.This engine helps in estimating the location-timecoordinate pairs of moving objects in between thelocations reported by the underlying sensors.

9. Adapter Layer

The Adapter Layer serves as client wrapper fordifferent location technologies. It serves as a devicedriver that allows location sensors to work withmiddleware seamlessly.

Adapter Layer

Physical Layer ( GPS, Desktop, RFID etc)

Provider Interface

Location Service Provider

Mid

dleware

Reasoning

Engine

Location

Prediction Engine

Spatial

Database


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10. The Architecture

The middleware consists of Provider Interface,Spatial Database, Reasoning Engine, LocationPrediction Engine and Location Service Provider.This is shown in Figure 1. The Adapter layerconnects physical layer to the middleware and moldssensor information into a common representation tobe stored in spatial database. Spatial Database servesas a repository of all information about physicallocations and sensors. The Reasoning Enginecombines data from different sensors and estimateslocation of an object. The Location PredictionEngine helps in estimating the location-timecoordinate pairs of moving objects in between thelocations reported by the underlying sensors. TheLocation Service Provider layer provides aninterface between the middleware and all location-sensitive applications which need locationinformation.

11. ConclusionsLocation sensing systems, with variant precision andconfidence levels, need seamless integration using amiddleware infrastructure. A middleware facilitatesseparation between applications and locationtechnologies. It also facilitates dynamic add-on ofnew technologies without changing existingapplications. Such middleware makes location awareapplications more flexible & interoperable whileincorporating extended functionality in themiddleware.

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Context Fusion in Location-Aware Pervasive Applications

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