ORBIT:%20Location-based%20services

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ORBIT Location-based services

• Henning Schulzrinne
• Columbia University

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Location-based services

• Finding services based on location
• physical services (stores, restaurants, ATMs, )
• electronic services (media I/O, printer, display,
  )
• not covered here
• Using location to improve (network) services
• communication
• incoming communications changes based on where I
  am
• configuration
• devices in room adapt to their current users
• awareness
• others are (selectively) made aware of my
  location
• security
• proximity grants temporary access
• Privacy rules for access to context data

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Location-based services SIP

• Were using SIP (and SIMPLE) as generic protocols
  for
• effecting change (actuators)
• send MESSAGE to devices
• distributing event information (sensors)
• Advantages
• people and rooms identified by URIs
• siphgs_at_cs.columbia.edu
• sipcepsr815_at_cs.columbia.edu
• cross-domain, with extensive security mechanisms
• domains dont need to trust each other
• scalable to global system
• many other systems are mostly local

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Location-based services

• Presence-based approach
• UA publishes location to presence agent (PA)
• becomes part of general user context
• other users (human and machines) subscribe to
  context
• call handling and direction
• location-based anycast (anybody in the room)
• location-based service directory
• Languages for location-based services
• building on experience with our XML-based service
  creation languages
• CPL for user-location services
• LESS for end system services

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Location information

• geospatial
• longitude, latitude, altitude
• civil
• time zone, country, city, street, room,
• categorical
• type of location
• properties of location
• privacy (no audio privacy)
• suitability for different communication media

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Determining location

• GPS may not be practical (cost, power, topology)
• Add location beacons
• extrapolate based on distance moved
• odometer, pedometer, time-since-sighting
• idea meet other mobile location beacons
• estimate location based on third-party
  information

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Example user-adaptive device configuration
all devices that are in the building RFC 3082?
SLP
802.11 signal strength ? location
device controller
REGISTER To 815cepsr Contact alice_at_cs
PA
HTTP
SUBSCRIBE to each room
tftp

1. discover room URI
2. REGISTER as contact for room URI

SIP
SUBSCRIBE to configuration for users currently in
rooms
room 815
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Architectures for (geo) information access

• Claim all using protocols fall into one of these
  categories
• Presence or event notification
• circuit-switched model
• subscription binary decision
• Messaging
• email, SMS
• basically, event notification without (explicit)
  subscription
• but often out-of-band subscription (mailing list)
• Request-response
• RPC, HTTP also DNS, LDAP
• typically, already has session-level access
  control (if any at all)
• Presence is superset of other two

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Presence/Event notification

• Three places for policy enforcement
• subscription ? binary
• only policy, no geo information
• subscriber may provide filter ? could reject
  based on filter (sorry, you only get
  county-level information) ? greatly improves
  scaling since no event-level checks needed
• notification ? content filtering, suppression
• only policy, no geo information
• third-party notification
• e.g., event aggregator
• can convert models gateway subscribes to event
  source, distributes by email
• both policy and geo data

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Presence model
SUBSCRIBE
subscription policy
subscriber (watcher)
for each watcher
event generator policy
subscriber filter rate limiter
change to previous notification?
NOTIFY
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Policy rules

• There is no sharp geospatial boundary
• Presence contains other sensitive data (activity,
  icons, ) and others may be added
• Example future extensions to personal medical
  data
• only my cardiologist may see heart rate, but
  notify everybody in building if heart rate 0
• Thus, generic policies are necessary

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Processing models

• Sequential model
• for each subscriber, apply rules to new data
• doesnt scale well to large groups
• Relational database model
• re-use indexing and other query optimizations
• well-defined query and matching semantics
• e.g., mySQL and PostGres have geo extensions
• At time of subscription
• SELECT address FROM policies WHERE
  personsubscriber (AND now() between(starttime,en
  dtime) OR starttime is null) AND (a3a3 or a3 is
  null)