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On data model for context–aware services

This paper discusses the issues of data representation for one model of context-aware services. This model is based on network proximity ideas. For network proximity, the location information (latitude, longitude) is replaced with information about proximity to network nodes. Network nodes play a role of tags and let position the users within some restricted area. In this paper, we target wireless networks nodes (Wi-Fi access points, Bluetooth nodes). Services for such models provide to mobile subscribers access to certain information, depending on availability (presence) of wireless networks. And another option is a publication by mobile users context-sensitive information associated with wireless networks. In our paper, we discuss key-value data store deployment as a basic persistence mechanism for network proximity. Also, we provide a description for system-wide cache on the base of Bloom filter.

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On data model for context–aware services

  1. 1. On data model for context– aware services Dmitry Namiot Lomonosov Moscow State University dnamiot@gmail.com
  2. 2. 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.
  3. 3. 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
  4. 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. 5. Metrics • The basic element: fingerprint • A list of “visible” nodes: ID, MAC-address, RSSI (signal strength) • Occurrence counting • RSSI-based “distance”
  6. 6. iBeacons
  7. 7. Related projects
  8. 8. Related projects
  9. 9. CityProximus: prototype • The prototype (Fraunhofer, FOCUS):
  10. 10. CityProximus:shortly • A set of rules: • Network fingerprint – > data chunks (information) • Can use existing networks as well as especially created wireless nodes
  11. 11. 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)
  12. 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. 13. CityProximus: NoSQL model • Key-value store • Key: MAC-address • Value: JSON array • Apache Accumulo • Query by index • Bloom filter for cache

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