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

2Sunday, 25 June 2006 ICPS 2006

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?”

3Sunday, 25 June 2006 ICPS 2006

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

4Sunday, 25 June 2006 ICPS 2006

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

5Sunday, 25 June 2006 ICPS 2006

Outline

Motivation: Object search

Query scoping for object search Intuitive heuristics Data model Algorithm

Discussion

6Sunday, 25 June 2006 ICPS 2006

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

7Sunday, 25 June 2006 ICPS 2006

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…“

8Sunday, 25 June 2006 ICPS 2006

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

9Sunday, 25 June 2006 ICPS 2006

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

10Sunday, 25 June 2006 ICPS 2006

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

11Sunday, 25 June 2006 ICPS 2006

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)

12Sunday, 25 June 2006 ICPS 2006

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

13Sunday, 25 June 2006 ICPS 2006

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

14Sunday, 25 June 2006 ICPS 2006

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

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