An RFID and Particle Filter based
Indoor Spatial Query Evaluation
System
Jeff Ku
Auburn University
Introduction

Why research on indoor spatial queries?
1. People spend a significant amount of time
daily in indoor spaces.
1. Office buildings, shopping malls, etc.
2. New York City subway system delivered over 1.7
billion rides in 2015
2. Accurate localization techniques based on
RFID are not available.
3. Modeling methods are very different from
outdoor space.
1
Introduction

Characteristics of RFID devices
1. Consist of RFID readers and tags.
2. Tags are very cheap.
3. Challenges include limited sensing range,
false negatives, inability to cover the whole
indoor space.
An example of RFID
reader and tag
Background—Indoor Spatial
Queries
Previous works [1] and [2] proposed
solutions for indoor range queries and
kNN queries, respectively.
 Underlying assumption: an object’s
location is uniformly distributed after
leaving a reader’s detecting range.

Object is uniformly distributed in its
uncertain region, a circle whose radius is
growing with time.
2
Symbolic Model
Symbolic Model (Cont.)
3
Background—RFID Data Cleansing
Due to the inherent unreliability of RFID
raw readings, data cleansing is necessary.
 [3, 4, 5, 6] adopted a sampling based
method—particle filters
 [7, 8] employed a different sampling
method called Markov Chain Monte
Carlo
 Particle filtering is more suitable for our
setting

Preliminary—Particle Filters
Represent the posterior probability by a
set of samples (particles) associated with
weights
 Particles update themselves from parent
particles at previous time step according
to
 Weights are updated according to the
observation
 Particles are resampled to replicate high
weight particles, eliminate low weight
particles

4
Preliminary—Particle Filters based
Location Inference

Suppose an object is detected by d2 at t0 .
It’s particles are initially uniformly
distributed within the detecting range of
d2 with random speeds and directions.
Initial distribution of particles at t0
Preliminary—Particle Filters based
Location Inference

The object is later detected by d3 at t1, when
particles are dispersed around d2. After
resampling most particles will become
replicates of the ones within the detecting
range of d3, which are moving from left to right.
Dispersed particles at time t1 before resampling
5
System Design—Overall system
structure
System Design—Raw Data
Collector
Only store readings of the most recent
two detecting devices for each object.
 Aggregate multiple readings per second
to only one entry per second, since
particle filters update once every second.

6
System Design—Indoor Walking
Graph Model and Anchor Point
Indexing Model
Indoor Walking
Graph: Simplify
particle movement
Anchor Point:
Discretize particles’
locations
An example of Indoor Walking
Graph and Anchor Point
System Design—Query Aware
Optimization

Range Query:
Filter out non-candidate objects for range query
7
System Design—Query Aware
Optimization

kNN Query:
Let f be the k-th minimum of all
objects’ li values
If oi.si>f, oi can be safely pruned.
Filter out non-candidate objects
for kNN query
System Design—Particle Filterbased Preprocessing
Run particle filtering for every object oi in
candidate set until the current time stamp
 Assign oi‘s particles to their nearest
anchor point, calculate
pi(oi.location=ap)=n/Ns,
where n is the number of particles falling
on an anchor point ap, and Ns is the total
number of particles for oi. pi stands for
the probability of oi being at ap.

8
System Design—Particle Filterbased Preprocessing

Update the indexing hashtable
APtoObjHT.
An example of APtoObjHT
key
value
ap1
<o1, 0.12>, <o2, 0.3>,<o3, 0.2>
ap2
<o2, 0.2>, <o4, 0.6>
ap3
<o3, 0.5>
…
…
System Design—Indoor Range
Query


For queries in hallways, identify anchor points covered
along the length of query window, sum up probabilities
for each object, also consider ratio wqh/wh.
For queries in rooms, identify anchor points within the
room, sum up probabilities for each object, also
consider the ratio Areaqr /Arearoom.
9
System Design—Indoor kNN
Query
Expand from query point q, search for anchor
points in ascending order of their distance to q.
 Stop if the accumulated probability is no less
than k.

System Design – Continuous Spatial Queries

Critical device idea
10
Experimental Validation




Conducted on an Ubuntu Linux Server
equipped with an Intel Xeon 2.4GHz
processor and 16GB memory.
The setting includes 30 rooms and 4
hallways on a single floor. A total of 19 RFID
readers are deployed on hallways with
uniform distance to each other.
Compared with the symbolic model based
methods [1, 2].
Metrics include top-k success rate (higher
better) and KL divergence (lower better)
Haley Center Floor Plan
11
The Simulator Structure
Effects of Query Window Size
12
Effects of k
Effects of Number of Particles
13
Effect of Number of Moving Objects
Effect of Activation Range
14
Conclusion



Proposed the particle filter-based location
inference method, the indoor walking graph
model, and the anchor point indexing model
for RFID data cleansing.
Proposed efficient algorithms for evaluate
range and kNN queries on probabilistic data.
In the future, extend the current framework
to support more spatial query types such as
continuous range, continuous kNN, closestpairs, etc.
References
[1] Bin Yang, Hua Lu, and Christian S. Jensen. Scalable continuous range monitoring of
moving objects in symbolic indoor space. In CIKM, pages 671-680, 2009
[2] Bin Yang, Hua Lu, and Christian S. Jensen. Probabilistic threshold k nearest neighbor
queries over moving objects in symbolic indoor space. In EDBT, pages 335–346,
2010.
[3] Thanh T. L. Tran, Charles Sutton, Richard Cocci, Yanming Nie,Yanlei Diao, and
Prashant J. Shenoy. probabilistic Inference over RFID Streams in mobile
Environments. In ICDE, pages 1096–1107, 2009.
[4] Christopher Re, Julie Letchner, Magdalena Balazinska, and Dan Suciu. Event queries
on correlated probabilistic streams. In SIGMOD Conference, pages 715–728, 2008.
[5] Evan Welbourne, Nodira Khoussainova, Julie Letchner, Yang Li, Magdalena Balazinska,
Gaetano Borriello, and Dan Suciu. Cascadia: a system for specifying, detecting, and
managing rfid events. In MobiSys, pages 281–294, 2008.
[6] Julie Letchner, Christopher Re, Magdalena Balazinska, and Matthai Philipose. Access
Methods for Markovian Streams. In ICDE, pages 246–257, 2009.
[7] Haiquan Chen, Wei-Shinn Ku, Haixun Wang, and Min-Te Sun. Leveraging spatiotemporal redundancy for RFID data cleansing. In SIGMOD Conference, pages 51–62,
2010.
[8] Wei-Shinn Ku, Haiquan Chen, Haixun Wang, and Min-Te Sun. A Bayesian InferenceBased Framework for RFID Data Cleansing. IEEE Trans. Knowl. Data Eng.,Vol. 25, Issue
10, pp. 2177-2191, 2013.
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