Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

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.

  • Login to see the comments

  • Be the first to like this

On data model for context–aware services

  1. 1. On data model for context– aware services Dmitry Namiot Lomonosov Moscow State University
  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