Query Processing for the Semantic Sensor Web Antonios Deligiannakis Technical University of Crete.
Query Scoping for the Sensor Internet
description
Transcript of Query Scoping for the Sensor Internet
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25/06/2006
Query Scoping for the Sensor InternetLocating personal items using the mobile network
Christian Frank, Christof Roduner ETH Zurich, Switzerland
Chie Noda, Wolfgang Kellerer NTT DoCoMo EuroLabs, Munich Germany
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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?”
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Object Sensing Infrastructure
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
BTNodes
Nokia Phones
Prototype Impl.:
BT Discovery, 10m range
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Challenge: Query Scoping
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
City, World
?
?
Architecture allows for private… objects (only owner may search) space sensors
Not focus of this talk
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Outline
Motivation: Object search
Query scoping for object search Intuitive heuristics Data model Algorithm
Discussion
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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…
Possible search heuristics
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
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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…“
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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
Required search algorithm
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Example data model
Model of data available in the system
One-to-many relationship types
Cell Profile
Object Sensor Registry
Object Sensor Association
User Assoc.User
History
obj
usrcell
OS
Obj. Location
History
Neighb.
Obj. Assoc.
Inter-net
Inter-net
Server
Mobile Device
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Entity type
OS Assoc.
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
Heuristics are paths in data model
Cell Profile
OS Registry
User Assoc.
Obj. Assoc.
User History
obj
usrcell
OS
Obj. Location
History
Relationship type
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obj
Lightweight relation adaptors
cell
OS
Relevance Dest. Entity Attribute
1 Cell1 12:34
100 Cell2 9:35
… … …
Relevance Dest. Entity Attribute
1 OS78 0.02
2 OS92 0.03
3 OS15 0.04
In Cell1 we currently have….
The sought obj1 was seen…
Obj. Location
History (1)
OS Registry (3)
Neighb. (4)
How relevant is this relation
Represent a one to many relation instances of a relationship type
How relevant is Cell1
Relevance measured from 1 (very relevant)
to ∞ (not relevant)
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Algorithm overview
Unfold data model into a search-graph Add child nodes using relation adaptors
Explore all possible paths that… Start at given entity s (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
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Algorithm example
OSs
Cell1
obj1
Cell2
Cell11 Cell12
10 1000
0
14 18
13 16 queried
activecandidate
17 2019
obj
cell
OS
Obj. Location
History (10)
OS Registry (3)
Neighb (4)
Type graph Search graph
10 1000
relation relevance × dest. entity relevance
entity relevance = costs of shortest path from obj1
4
3 6
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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 relatedness to 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
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25/06/2006
Query Scoping for the Sensor InternetLocating personal items using the mobile network
Christian Frank, Christof Roduner ETH Zurich, Switzerland
Chie Noda, Wolfgang Kellerer NTT DoCoMo EuroLabs, Munich Germany