Query Scoping for the Sensor Internet

1. Query Scoping for the Sensor Internet Locating personal items using the mobile network Christian Frank, Christof Roduner ETH Zurich, Switzerland Chie Noda, Wolfgang Kellerer NTT DoCoMo EuroLabs, Munich Germany

2. Motivating Application • Locate lost or misplaced personal items • “I can’t find my wallet, did I leave it in the car?” • “Who has my old MC Hammer tape?” • “Where did I leave my umbrella?”

3. Prototype Impl.: Object Sensing Infrastructure BTNodes BT Discovery, 10m range • Tagged Objects • Objects are tagged with electronic labels • Object sensors • RFID Readers, Mobile Phones • Many object sensors • User may install additional sensors • Home, car, cellar, office • Public sensors • Lost and Found offices, libraries, public transport • Other users’ mobile phones • Friends, colleagues, or unrelated users • Object sensors interconnected by mobile network Nokia Phones

4. Very large system Ubiq. infrastructure for finding misplaced items Interaction scheme? Proactive: Store all sensed data in database Reactive: Send query to all sensors Both do not scale! Query scoping: Send query to a subset of relevant sensors Particular challenge in the Sensor Internet setting Challenge: Query Scoping ? ? City, World • Architecture allows for private… • objects (only owner may search) • space • sensors • Not focus of this talk

5. Outline • Motivation: Object search • Query scoping for object search • Intuitive heuristics • Data model • Algorithm • Discussion

6. Possible search heuristics • Appl.-specific assumption • generally, object does not move • object does not move without user • above + object is mostly lost/left within user space • sensors installed by owner are most promising • objects are with family… • Query sensors which are… • located where the object was seen in the past • at locations recently visited by the user • located where the user spends much time • associated with the object owner • strategies ” “ for a family member

7. Heuristics based on data stored by application services • Association registry stores information on: • Owned objects • Owned object sensors • Past location of sought object is sometimes known: • Object was last seen at X • Provided by user device or other object sensors • Location trace of user sometimes known • Can be recorded by mobile device • Location profile • A list of locations in which the user spends most of his time • Implementation of Laasonen et al.: „Adaptive on-device location recognition…“

8. Required search algorithm • Heuristics cannot guarantee success • Combination of heuristics • Did we list all of them? • Which ones should be used? • Given • All data available in the system • Follow a wide range of heuristics • Consider user preferences • Return • A list of object sensors ordered by relevance • Stop when maximum number of sensors (max. cost) is reached

9. Inter-net Server Example data model • Model of data available in the system • One-to-many relationship types obj Mobile Device Obj. Location History Obj. Assoc. Cell Profile User Assoc. Neighb. usr cell User History Object Sensor Registry Object Sensor Association OS

10. Heuristics are paths in data model Entity type • Query sensors… • where the object was seen in the past. • at locations recently visited by the user • located where the user spends much time • associated with the object owner. • strategies “ “ for a family member Relationship type obj Obj. Location History Obj. Assoc. Cell Profile User Assoc. usr cell User History OS Assoc. OS Registry OS

11. Relevance measured from 1 (very relevant) to ∞ (not relevant) Lightweight relation adaptors How relevant is Cell1 • Represent a one to many relation • instances of a relationship type The sought obj1 was seen… obj Obj. Location History (1) How relevant is this relation Neighb. (4) cell In Cell1 we currently have…. OS Registry (3) OS

12. Algorithm overview • Unfold data model into a search-graph • Add child nodes using relation adaptors • Explore all possible paths that… • Start at given entitys(sought obj). • End at entity of type object sensor (mobile phone with object sensing capability) • Visit entities in order of their relatedness to s • At each step, add edge that leads to entity most related to s • Explore shortest (most related) paths first • Return destination entities (OS) in the order these are visited

13. queried active candidate Algorithm example relation relevance× dest. entity relevance • Type graph • Search graph entity relevance = costs of shortest path from obj1 obj obj1 0 Obj. Location History (10) 1000 10 Neighb (4) 10 1000 Cell1 Cell2 cell 4 Cell11 Cell12 14 18 6 3 OS Registry (3) OSs 13 16 19 17 20 OS

14. Discussion • Search scoping algorithm • Based on uniform cost search • Generic algorithm, parameterized with the application’s data model • Given a start entity • Explores paths of highest relatednessto start entity • Returns a sorted list of destination entities (object sensors) in order of decreasing relatedness • Traditional algorithm – novel parameterization • Explores real-world links between entities • As humans would follow association chains

15. Query Scoping for the Sensor Internet Locating personal items using the mobile network Christian Frank, Christof Roduner ETH Zurich, Switzerland Chie Noda, Wolfgang Kellerer NTT DoCoMo EuroLabs, Munich Germany