Query and Storage in Wireless Sensor Networks. The Problem ◊How to perform efficient query and...

Query and Storage in Wireless Sensor Networks

The Problem

◊ How to perform efficient query and storage in wireless sensor networks?

◊ Design goals: Distributed design that works for hundreds of nodes in a

sensor network

Reduce communication overhead in query and storage

◊ Challenges/Issues: Scale of the network: # of nodes Distributed solution

The Solution

Main ideas:◊ Store locally, access globally

◊ Location-based solution Use the location information of each node Build location-based indexing Exploit geographic routing

◊ Exploit locality Store relevant event information locally

• Search is easier

◊ Hierarchical indexing to scale Divide into multiple tiers/levels Tradeoff between storage overhead and query steps

Overview of DIMs

Reference paper: “Multi-dimensional Range Queries in Sensor Networks”, ACM SenSys 2003.

◊ Events (multi-dimensional space) are mapped onto zones of the network (2-D space)

◊ Data Locality: Events with comparable attribute values stored in same location in network

◊ Locality-preserving geographic hash◊ Events are routed to and stored at that node◊ Queries are routed to and resolved by appropriate

nodes

Zone

◊ Rectangle R on x-y plane (entire network)

◊ Subrectangle Z is a zone if Z is obtained by dividing R k times satisfying the following property: After the i-th division, 1 ≤ i ≤ k, R is partitioned into 2i

equal rectangles. If i is odd (even), the division is parallel to the y-axis (x-axis).

◊ k is the level of the zone, level(Z) = k

Zone Identification

◊ code(Z) Bit string of length level(Z) Starting from left of code string, if zone Z

resides on the left half of R, bit equals 0, else 1. For the next bit, if zone Z resides on the bottom half of R, bit is 0, else 1.

◊ addr(Z) Centroid of zone rectangle

Zone Terminology

◊ Sibling subtree of a zone Left/right subtree rooted at the same parent

zone

◊ Backup zone If the sibling subtree of a node is on the left

(right), its backup zone is the rightmost (leftmost) zone in its sibling subtree

Zones Example

Associating Zones with Nodes

◊ Sensor field divided into zones, which can be of different sizes

◊ Zone ownership A owns ZA → ZA is the largest zone that

contains only node A Some zones may not have node owner →

backup(Z) is the owner

Algorithm for Zone Ownership

◊ Each node maintains its four boundaries Initialize to network boundary

◊ Send messages to learn locations of neighbors If neighbor responds, node will adjust its boundaries

accordingly Else boundary is ‘undecided’

◊ Undecided boundaries resolved during querying or event insertion

Event Insertion

◊ Hashing an event to a zone◊ Routing an event to its owner◊ Resolving undecided zone boundaries

during insertion

Event InsertionHashing an Event to a Zone

◊ Have m attributes A1, A2, …, Am and attribute values have been normalized

◊ To assign a k-bit zone code to an event: For i in [1, m], if Ai < 0.5, the ith bit of the zone

code is 0, else 1. For i in [m+1, 2m], if Ai-m < 0.25 or 0.5 ≤ Ai-m <

0.75, then the (i-m)th bit is 0, else 1. Etc. until all k bits are assigned

Event InsertionHashing an Event to a Zone

◊ Example: Hash event <0.3, 0.8> to a 5-bit zone code First range: 0: [0, 0.5) Second range: 0: [0, 0.25), [0.5, 0.75) Third range: 0: [0, 0.125), [0.25, 0.375), [0.5,

0.625), [0.75, 0.875) Zone code = 01110

Event InsertionRouting an Event to its Owner

◊ GPSR delivers message to node A Message contains: event E, code(E), target location, owner,

location of owner, …◊ A encodes the event to codenew(E)

Updates message if codenew(E) is longer than code in message◊ A checks if code(A) has longer match with code(E) than

previous owner If yes, update message by setting itself as the owner

◊ If code(A) and code(E) identical and A’s boundaries are known, A is the owner of E and stores it

◊ Else A will route E to its owner by invoking GPSR

Event InsertionResolving Undecided Boundaries

◊ Suppose node C receives event E◊ If code(C) = code(E) and all of C’s boundaries are

known, C will store the event◊ If C has undecided boundaries, there may be

zone overlap with another node C sets itself as owner and forwards message using GPSR

perimeter mode

◊ If message not changed, it will come back to C C assumes it is the owner and stores it

Event InsertionResolving Undecided Boundaries

◊ An intermediate node X marks itself as the owner but code(E) is unchanged X recognizes zone overlap with C and adjusts its

boundaries and messages C to update its boundaries

◊ An intermediate node D refines code(E) D will try to deliver the message to the new zone Another node X may overlap with C X will shrink its zone and send C messages to do the

same C will update its undecided boundary

Event Insertion Example

◊ Nodes A and B have claimed the same zone 0◊ Node A generates event E = <0.4, 0.8, 0.9>,

code(E) = 0◊ Perimeter mode forwarding of event to B◊ B and A engage in message exchange to shrink

zones

QueriesRouting and Resolving

◊ Routing for point queries same as event◊ Range queries

query initially routed to zone corresponding to the entire range

progressively split into smaller sub-queries so each sub-query can be resolved by a single node

QueriesSplitting Queries

◊ If the range of Q’s first attribute contains value 0.5, A divides Q into two sub-queries, one with range 0 to 0.5, and the other 0.5 to 1

◊ If QA , part of zone A that overlaps with query area, is empty, A stops splitting

◊ Else A continues splitting the query using successive attribute ranges and recomputing QA until it is small enough to fit entirely in zone(A)

QueriesSplitting Queries Example

◊ Suppose there is a node A with code(A) = 0110

◊ Split a query Q = <0.3→0.8, 0.6→0.9>

QueryResolution

◊ Once a sub-query falls into a zone, the node owner resolves the query and sends the reply to the querier

◊ The other sub-queries are forwarded to other nodes

Analysis on DIMs

◊ Metrics Average insertion cost – average number of messages

required to insert an event into the network Average query delivery cost – average number of

messages required to route a query message to all the relevant nodes

◊ Compared against alternatives GHT-R and flooding

Average Insertion Cost

Summary

◊ DIM builds a flat indexing structure in sensor networks Store similar events in local nodes Use geographic routing to query events

◊ Use location information Determine the zone code Hashing an event to a zone code

◊ Limitations: Flat structure can incur large overhead in a large

network

Why do we need archival storage?

Applications need historical sensor information. Why? Trigger events:

• Traffic monitoring - crash• Surveillance - break-in• Environmental monitoring - natural disaster

lead to requests for past information.

This requires archival storage.

Reference paper: TSAR*: A Two Tier Sensor Storage Architecture Using Interval Skip Graphs, ACM SenSys 2005.

Limited by lack of sufficient, energy-efficient storage and of communication and computation resources on current sensor platforms.

Optimized for continuous queries. High energy cost if used for archival - data must be transmitted to central data store.

Existing storage and indexing approaches

◊Streaming query systems TinyDB (Madden 2005), etc. Data storage and indexing is performed outside of network.

◊In-network storage and indexing DCS, GHT (Ratnasamy 2002) Dimensions (Ganesan 2003) Directed Diffusion (Intangonwiwat 2000)

Technology Trends

RadioJ/byte

Flash J/byte

Max Flash size

Mica2 30 4.5 0.5MB

MicaZ 3.4 4.5 0.5MB

Telos 3.4 1 1MB

UMassNAND

0.01 >1GB

1000x

100xNew flash technologies enable large storage systems on small energy-constrained sensors.

Hierarchical Storage and Indexing

Hierarchical deployments are being used to provide scaling:

• James Reserve (CENS)

Higher powered micro-servers are deployed alongside resource constrained sensor nodes.

Key challenge:• Exploit proxy resources to

perform intelligent search across data on resource-constrained nodes.

Sensors

Proxies

Application

Key Ideas in TSAR

◊ Exploit storage trends for archival. Use cheap, low-power, high capacity flash memory in

preference to communication.

◊ Index at proxies and store at sensors. Exploit proxy resources to conserve sensor resources

and improve system performance.

◊ Extract key searchable attributes. Distill sensor data into concise attributes such as ranges

of time or value that may be used for location and retrieval but require less energy to transmit.

TSAR Architecture

1. Interval Skip Graph-based index between proxies.

• Exploit proxy resources to locate data stored on sensors in response to queries.

2. Summarization process

• Extracts identifying information: e.g. time period during which events were detected, range of event values, etc.

3. Local sensor data archive

• Stores detailed sensor information: e.g. images, events. Sensor node archive

TSAR Architecture

1. Interval Skip Graph-based index between proxies.



• Stores detailed sensor information, e.g. images, events.



Summarization function

TSAR Architecture




• Stores detailed sensor information, e.g. images, events.

Distributed index1. Interval Skip Graph-based

index between proxies


Example - Camera Sensing

storage

Cyclops camera

summarize

image

Sensor archives information and transmits summary to proxy.

Sensor node<id>

Summary

handle

Birds(t1,t2)=1

<id>

Example - Indexing

Index Network of proxies

Summary and location information are stored and indexed at proxy.

proxy

Birds(t1,t2)=1

<id> Birds t1,t2 1 <id>

Example - Querying and Retrieval

Birds in interval (t1,t2)?

proxy

Cyclops camera

summarize

Cyclops camera

summarize

Query is sent to any proxy.

Birds t1,t2 1 <id>


Birds in interval (t1,t2)?

proxy

Cyclops camera

summarize

Cyclops camera

summarize

Index is used to locate sensors holding matching records.

Birds t1,t2 1 <id>

<id>

Record is retrieved from storage and returned to application.


proxy

Cyclops camera

summarize

Cyclops camera

Birds t1,t2 1 <id>

<id>

Outline

◊ Introduction and Motivation◊ Architecture◊ Example◊ Design

Skip Graph Interval Search Interval and Sparse Interval Skip Graph

◊ Experimental Results◊ Related Work◊ Conclusion and Future Directions

The index should:

• support range queries over time or value,

• be fully distributed among proxies, and

• Support interval keys indicating a range in time or value.

Goals of Index Structure

insert(| |)

Distributed index

(| |)?

What is a Skip Graph?

2 3 5 6 9 12 18 19

Single key and associated pointers

Distributed extension of Skip Lists (Pugh ‘90):

Probabilistically balanced - no global rebalancing needed.

Ordered by key - provides efficient range queries.

Fully distributed - data is indexed in place.

(Aspnes & Shah, 2003, Harvey et al. 2003)

Log(N) search and insert

No single root - load balancing, robustness

Properties:

Interval search

Given intervals [low,high] and query X:1 - order by low2 - find first interval with high <= X3 - search until low > X

0 1 2 3 4 5 6 7 8 9 10

0 3

5 8

6 10

8 9

42

2 3

1 5

Query: x=4

Simple Interval Skip Graph

0-3 0-1 1-5 2-4 5-8 6-10 8-9 9-12

Derived from Interval Tree,

Cormen et al. 1990

3 3 5 5 8 10 10 12

Method:

Index two increasing values: low, maxSearch on either as needed.

Interval keys: YESlogN search: YESlogN update: NO - (worst case O(N))

Adaptive Summarization

updates

queries

How accurately should the summary information represent the original data?

Detailed summaries =more summaries,

precise index

Precise index =fewer wasted queries


updates

queries

How accurately should the summary information represent the original data?

Approximate summaries =fewer summaries,imprecise index

imprecise index =more wasted queries? ?

= summarization (summaries / data) r = EWMA( wasted queries / data )

Target range: r0

Decrease if: r > r0Increase if: r < r0


updates

queries

Goal: balance update and query cost.

Approach: adaptation.

Prototype and Experiments

◊ Software: Em* (proxy),TinyOS (sensor)

◊ Hardware: StargateMica2 mote

◊ Network: 802.11 ad-hoc,multihop BMAC 11%

◊ Data:James Reserve [CENS] dataset30s temperature readings34 days

For physical experiments, data stream was stored on sensor node and replayed.

Index performance

Queries

Sensor data

How does the index performance scale with the number of proxies and size of dataset?

Tested in: Em* emulation

Tasks: insert, query

Variables: number of proxies (1-48)size of dataset

Metric: proxy-to-proxy messages

Interval skip graph index

Index results

Sparse skip graph provides >2x decrease in message traffic for small numbers of proxies.

Sparse skip graph shows virtually flat message cost for larger index sizes.

Tested on: 4 Stargate proxies12 Mica2 sensors in tree configuration

Task: query

Variables: size of dataset

Metric: query latency (ms)

Query performance

data

queriesWhat is the query performance on real hardware and real data?

4-proxynetwork

3-level multi-hop

sensor field

Validates the approach of using proxy resources to minimize the number of expensive sensor operations.

Query results

Sensor link latency dominates

Proxy linkdelay is negligible

The sensor communication consists only of a query and a response - the minimal communication needed to retrieve the data.

Summary algorithm adapts to data and query dynamics.

Tested in: Em*, EMTOSSIMemulation

Task: data and queries

Variables: query/data ratio

Metric: summarization factor

Query/data = 0.2

Query/data=0.03

Query/data = 0.1

Adaptive SummarizationVaried query

rate

Summary rate adapts

How well does the adaptation mechanism track changes in conditions?

1/

Related Work

◊ In-network Storage: DCS (Ratnasamy 2002) Dimensions (Ganesan 2003) …

◊ In-network Indexing: GHT (Ratnasamy 2002) DIFS (Greenstein 2003) DIM (Li 2003) …

◊ Hierarchical Sensor Systems: Tenet (CENS, USC)

◊ Sensor Flash File Systems: ELF (Dai 2004) Matchbox (Hill et al. 2000)

Summary

◊ Proposed novel Interval Skip Graph-based index structure and adaptive summarization mechanism for multi-tier sensor archival storage.

◊ Implemented these ideas in the TSAR system.◊ Demonstrated index scalability, query

performance, and adaptation of summarization factor, both in emulation and running on real hardware.

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