On data model for context–
aware services
Dmitry Namiot
Lomonosov Moscow State University
dnamiot@gmail.com
Network proximity
• A special model for context-aware
services
• Context described as a set of wireless
networks (nodes)
• Wi-Fi access points, Bluetooth nodes,
Bluetooth tags
• Data could be directly associated with
network nodes.
Context
• Context is anything we can add to location
• Models for context-aware systems:
• Data exchange depending on the context
• Situational awareness
• Context-aware data discovery and data
search
Network nodes
• Wi-Fi (Bluetooth) devices are everywhere
• Wi-Fi (Bluetooth) is presented in every
mobile phone
• Easy to measure (existing standards)
• We can reuse existing infrastructure
• There is no connection with location (geo-
coordinates)
Metrics
• The basic element: fingerprint
• A list of “visible” nodes: ID, MAC-address,
RSSI (signal strength)
• Occurrence counting
• RSSI-based “distance”
iBeacons
Related projects
Related projects
CityProximus: prototype
• The prototype (Fraunhofer, FOCUS):
CityProximus:shortly
• A set of rules:
• Network fingerprint – > data chunks
(information)
• Can use existing networks as well as
especially created wireless nodes
CityProximus: architecture
• Data base for network proximity rules and
content
• Rules editor
• Application server (API for developers)
• Mobile application for access to content
(context-aware browser)
CityProximus: data model
Rules: productions
If (fingerprint condition) then { present some
content }
RETE algorithm
REST API with JSON output:
[
{ “type”:”some_type”,”data”:”some_data”},
{“type”: ...},...
]
CityProximus: NoSQL model
• Key-value store
• Key: MAC-address
• Value: JSON array
• Apache Accumulo
• Query by index
• Bloom filter for cache

On data model for context–aware services

  • 1.
    On data modelfor context– aware services Dmitry Namiot Lomonosov Moscow State University dnamiot@gmail.com
  • 2.
    Network proximity • Aspecial model for context-aware services • Context described as a set of wireless networks (nodes) • Wi-Fi access points, Bluetooth nodes, Bluetooth tags • Data could be directly associated with network nodes.
  • 3.
    Context • Context isanything we can add to location • Models for context-aware systems: • Data exchange depending on the context • Situational awareness • Context-aware data discovery and data search
  • 4.
    Network nodes • Wi-Fi(Bluetooth) devices are everywhere • Wi-Fi (Bluetooth) is presented in every mobile phone • Easy to measure (existing standards) • We can reuse existing infrastructure • There is no connection with location (geo- coordinates)
  • 5.
    Metrics • The basicelement: fingerprint • A list of “visible” nodes: ID, MAC-address, RSSI (signal strength) • Occurrence counting • RSSI-based “distance”
  • 6.
  • 7.
  • 8.
  • 9.
    CityProximus: prototype • Theprototype (Fraunhofer, FOCUS):
  • 10.
    CityProximus:shortly • A setof rules: • Network fingerprint – > data chunks (information) • Can use existing networks as well as especially created wireless nodes
  • 11.
    CityProximus: architecture • Database for network proximity rules and content • Rules editor • Application server (API for developers) • Mobile application for access to content (context-aware browser)
  • 12.
    CityProximus: data model Rules:productions If (fingerprint condition) then { present some content } RETE algorithm REST API with JSON output: [ { “type”:”some_type”,”data”:”some_data”}, {“type”: ...},... ]
  • 13.
    CityProximus: NoSQL model •Key-value store • Key: MAC-address • Value: JSON array • Apache Accumulo • Query by index • Bloom filter for cache