tsar: a two tier sensor storage architecture using interval skip graphs peter desnoyers, deepak...
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TSAR: A Two Tier Sensor Storage Architecture UsingInterval Skip Graphs
Peter Desnoyers, Deepak Ganesan, and Prashant Shenoy
Department of Computer Science University of Massachusetts
The 3rd ACM Conference on Embedded Networked Sensor Systems, SenSys 2005
Speaker: Shin-Wei Ho
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Outline
Introduction Design Considerations Data Structures Data Storage and Summarization Experimental Evaluation Conclusion
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Introduction Typical sensor applications need acce
ss to both Live sensor data
Monitoring Surveillance
Past sensor data Mining of sensor logs to detect unusual patte
rns Analysis of historical trends
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Introduction
Archival storage of past sensor data requires a storage system.
The key attributes are Where the data is stored How the application can access this
data in an energy-efficient manner.
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Introduction
In the simplest, sensors stream data or events to a server for long-term archival storage. Centralized Reads are efficient and cheap Writes are expensive
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Introduction
An alternate approach is to have each sensor store data or events locally. All writes are local and incur no communi
cation overheads. Flooding.
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Introduction
The geographic hash table (GHT) Each data item has a key associated with
it. A distributed or geographic hash table is
used to map keys to nodes.
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Introduction
Most of these approaches assume a flat, homogeneous architecture in
which every sensor node is energy-constrained.
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Introduction
Any sensor storage system should also carefully exploit current technology trends.
Moore’s Law It will soon be feasible to equip each
sensor with 1 GB of flash storage for a few tens of dollars.
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Introduction
A typical sensor network deployment will contain multiple geographically distributed proxies. Building monitoring application
Camera-based Sensing Application One sensor proxy might be placed per
floor or hallway.
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Introduction
Goal Design a storage system
Exploits the relative abundance of resources at proxies to mask the scarcity of resources at the sensors.
Focus on applications that require access to past sensor data.
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Design Considerations-- System Model
Low power sensors with a significant amount of flash memory (e.g., 1GB).
Low power sensors with a significant amount of flash memory (e.g., 1GB).
Power-rich sensor proxies
Power-rich sensor proxies
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Design Considerations-- Usage Models
Queries specify time interval value range spatial region
E.g. “find all instances where the
temperature exceeded 100o F at location R during the month of August.”
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Design Considerations-- Design Principles
Principle 1: Store locally, access globally
A sensor storage system should leverage the flash memory on sensors.
Efficient mechanisms for retrieval.
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Design Considerations-- Design Principles
Principle 2: Distinguish data from metadata
Data must be identified so that it may be retrieved by the application without exhaustive search.
associate metadata with each data record
leverage the presence of resource-rich proxies to index metadata for resource-constrained sensors.
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Design Considerations-- Design Principles
Principle 3: Provide data-centric query support
The authors expect that applications will be best served by a query interface which allows them to locate data by value or attribute (e.g. location and time).
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Design Considerations-- System Design
The key features of the system design are as follows: Writes occur at sensor nodes
consist of both opaque data as well as application-specific metadata.
Each sensor periodically sends a summary of reported metadata to a nearby proxy.
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Design Considerations-- System Design
Summary of metadata The sensor ID. The interval (t1, t2) over which the
summary was generated. A handle identifying the corresponding
data record (e.g. its location in flash memory).
A coarse-grain representation of the metadata associated with the record.
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Design Considerations-- System Design
The proxy uses the summary to construct an index.
Applications see a unified view of distributed data, and can query the index at any proxy to get access to data stored at any sensor.
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Design Considerations-- System Design
The TSAR summarization mechanism guarantees that index lookups will never yield false negatives. I.e. It will never miss summaries which
include the value being searched for.
However, index lookups may yield false positives, wasting network resources.
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Design Considerations-- System Design
The more coarse-grained the summary lower the update overhead greater the fraction of false positives
TSAR employs a novel adaptive technique dynamically varies the granularity of sensor
summaries to balance the metadata overhead the overhead of false positives.
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Data Structures
Interval skip graphs The goal is to index all intervals such
that the set that overlaps a query interval can be located efficiently.
Skip graph Interval Tree
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Data Structures-- Skip Graph Overview
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Data Structures-- Skip Graph Overview
Properties of a skip graph: Ordered index In-place indexing Log n height Redundancy and resiliency
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Data Structures-- Interval Skip Graph
Extends skip graphs to store intervals [low i, high i]
Allows efficient searches for all intervals covering a value v.
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Data Structures-- Interval Skip Graph
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Data Structures-- Sparse Interval Skip Graph
The cost in network messages of an operation may be reduced. most structure traversals occur locally on
a single node.
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Data Structures-- Sparse Interval Skip Graph
Skip graphs have a full search structure rooted at each element.
However, in order to provide load distribution and failure resilience it is only necessary to provide a full search structure for each system. proxies << data elements
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Data Structures-- Sparse Interval Skip Graph
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Data Storage and Summarization
Local Storage at Sensors Each sensor node in TSAR maintains an a
rchival store of sensor data. A temporally ordered store is suitable.
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Data Storage and Summarization
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Data Storage and Summarization
There might be a need to overwrite older data in cache. High data rate applications multi-resolution storage of data
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Data Storage and Summarization -- Adaptive Summarization
There is a trade-off between the energy used in sending summaries. frequency resolution
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Data Storage and Summarization -- Adaptive Summarization
If most queries result in true hits the sensor determines that the summary
can be coarsened further If many queries result in false hits
the sensor makes the granularity of each summary finer
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Experimental Evaluation Proxy tier: Crossbow Stargate nodes
400MHz Intel XScale processor with 64MB RAM. Radio: Cisco Aironet 340-based 802.11b(inter-pr
oxy) radio and a hostmote bridge to the Mica2 sensor nodes using the EmStar transceiver.
Sensor tier: Crossbow Mica2s / Mica2dots 915MHz CC1000 radio, a BMAC protocol stack, a
4 Mb on-board flash memory.
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Experimental Evaluation
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Experimental Evaluation
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Experimental Evaluation
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Conclusion This paper presents TSAR, a novel two-tier s
torage architecture for sensor networks. Multi-Tier Sparse Interval Skip Graph
Our experimental evaluation of TSAR demonstrated the benefits and feasibility.
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Thank you !
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Experimental Evaluation
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Data Structures-- Skip Graph Overview
Maintaining Skip Lists
To insert an element we ascend levels starting at 0.
randomly choosing one of the two possible chains at each level, an stopping when we reach an empty chain.