STDCS: A Spatio-Temporal Data-Centric Storage Scheme For Real-Time Sensornet Applications Mohamed Aly (University of Pittsburgh & Yahoo, Inc.) In collaboration

STDCS: A Spatio-Temporal Data-Centric Storage Scheme For Real-Time Sensornet Applications

Mohamed Aly(University of Pittsburgh & Yahoo, Inc.)

In collaboration with Anandha Gopalan

(University of Pittsburgh, Imperial College) and

Jerry Zhao, Adel Youssef(Google, Inc.)

STDCS: A Spatio-Temporal Data-Centric Storage Scheme For Real-Time Sensornet Applications 2 Mohamed Aly

Motivation: Real-Time Geo-Centric Sensor Network Applications

Globally deployed sensor around the globe. Clusters of sensors forming networks. Mobile users roaming across the networks. Real-time geo-centric ad-hoc queries issued from within

or nearby the queried area. The sensor network is responsible of answering these

queries directly from the sensors rather than from base stations.

Examples: Bronx Zoo cluster. Disaster management cluster.


Motivation: Real-Time Geo-Centric Sensor Network Applications


Data Storage Options in Sensor Networks

Base Station Storage: Events are sent to base stations where queries are

issued and evaluated. Best suited for continuous queries.

In-Network Storage (INS): Events are stored in the sensor nodes. Best suited for ad-hoc queries. All previous INS schemes were Data-Centric Storage

(DCS) schemes.


In-Network Data-Centric Storage (DCS)

Mainly to answer range queries. Quality of Data (QoD) of ad-hoc queries. Assign a value-range of readings for each sensor. Examples:

Distributed Hash Tables (DHT) [Shenker et. al., HotNets’02]

Geographic Hash Tables (GHT) [Ratnasamy et. al., WSNA’02]

Distributed Index for Multi-dimensional data (DIM)[Li et. al., SenSys’03, Aly et. al., DMSN’05, MOBIQUITOUS’06]

K-D Tree based Data-Centric Storage (KDDCS) [Aly et. al., CIKM’06]


STDCS Overview

Motivation: No previous INS schemes adopting geo-centric storage. Expected techniques may be:

Local storage. Spatial storage

Design Goal: Load-Balancing of storage load among sensors

Differences from previous schemes: Temporally evolving spatial indexing scheme to balance

query load among sensors. Dynamic query hotspot detection and decomposition.


Roadmap

Motivation: Real-Time Geo-Centric applications. Background: Data-Centric Storage (DCS). Problem Statement: Real-Time Geo-Centric Storage. Scheme Overview: STDCS. STDCS Components

Local Virtual address assignment Spatio-Temporal data indexing. Point-to-point data delivery. Query processing. Adaptive hotspot decomposition.

Experimental Results Conclusions


STDCS Components: Local Virtual Address Assignment


STDCS Components: Spatio-Temporal Data Indexing


STDCS Components: Reading Delivery and Querying


STDCS Components: Adaptive Hotspot Decomposition

Motivation: Dynamic query hotspots as time progresses.

Observation: Recurrent querying scenarios across the day, the week,

etc. Technique:

Continuously keeping track of hotspots using the Average Querying Frequency (AQF) metric.

Dynamically chaning the switching time to decompose hotspots.


Roadmap

Motivation: Real-Time Geo-Centric applications. Background: Data-Centric Storage (DCS). Problem Statement: Real-Time Geo-Centric Storage. Scheme Overview: STDCS. STDCS Components

Local Virtual address assignment Spatio-Temporal data indexing. Point-to-point data delivery. Query processing. Adaptive hotspot decomposition.

Experimental Results Conclusions


Simulation Description

Compare: STDCS, local storage, spatial indexing. A cluster of stationary sensors (with random locations). Each sensor senses a reading each 10 min. Sensor reading = 1 packet. Sensor capacity = 20 readings (packets) Multiple mobile users. A query: random sensor, radius, and type. Two phases: initialization (3 hours of readings) &

running (1 day of readings and queries). Metrics: throughput, energy level, node deaths.


Experimental Results: STDCS vs. Query Hotspots


Experimental Results: STDCS vs. Query Hotspots


Experimental Results: Switching Time Effect


Experimental Results: Switching Time vs. Node Deaths


Experimental Results: Adaptive Hotspot Decomposition


Conclusions

STDCS: A real-time geo-centric data storage scheme. A new concept of spatio-temporal data indexing. Ability to dynamically cope with dynamic loads and query

hotspots.


Acknowledgment

This work has been partly supported by: Google, Inc. The “Secure CITI: A Secure Critical Information

Technology Infrastructure for Disaster Management (S-CITI)” project funded through the ITR Medium Award ANI-0325353 from the National Science Foundation (NSF). For more information, please visit: http://www.cs.pitt.edu/s-citi/


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STDCS: A Spatio-Temporal Data-Centric Storage Scheme For Real-Time Sensornet Applications Mohamed Aly (University of Pittsburgh & Yahoo, Inc.) In collaboration