Indexing and searching in wireless sensor networks
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Demetris Zeinalipour [ ] School of Pure and Applied Sciences Open University of Cyprus. Indexing and Searching in Wireless Sensor Networks. HPCL, University of Cyprus, February 14 th , 2008. Presentation Goals.

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Demetris Zeinalipour

[ ]

School of Pure and Applied Sciences

Open University of Cyprus

Indexing and Searching in Wireless Sensor Networks

HPCL, University of Cyprus, February 14th, 2008

Presentation Goals

  • To provide an overview of recent developments in Wireless Sensor Network Technology

  • To highlight some important indexing and searching challenges that arise in this context


  • This is a joint work with my collaborators at the University of California – Riverside.

  • Our results were presented in the following papers:

    • "MicroHash: An Efficient Index Structure for Flash-Based Sensor Devices",

      D. Zeinalipour-Yazti, S. Lin, V. Kalogeraki, D. Gunopulos and W. Najjar, The 4th USENIX Conference on File and Storage Technologies (FAST’05), San Francisco, USA, December, 2005.

    • " Efficient Indexing Data Structures for Flash-Based Sensor Devices",

      S. Lin, D. Zeinalipour-Yazti, V. Kalogeraki, D. Gunopulos, W. Najjar, ACM Transactions on Storage (TOS), ACM Press, Vol.2, No. 4, pp. 468-503, November 2006.

Main Citations

  • Capsule: an energy-optimized object storage system for memory-constrained sensor devices, ACM Sensys'06 (UMass, Amherst)

  • Ultra-low power data storage for sensor networks, IEEE IPSN'06 (UMass, Amherst)

  • TINX: a tiny index design for flash memory on wireless sensor devices, ACM Sensys'06 (Stanford)

  • Re-thinking data management for storage-centric sensor networks, IEEE CIDR'07(UMass, Amherst)

  • FlashDB: dynamic self-tuning database for NAND flash, IEEE IPSN'07, (Microsoft Research, Redmond)

  • MStore: Enabling Storage-Centric Sensornet Research, IEEE IPSN'07 (Stanford)

  • EnviroMic: Towards Cooperative Storage and Retrieval in Audio Sensor Networks, IEEE ICDCS'07 (Univ. of Illinois at Urbana-Champaign)

  • DALi: A Communication-Centric Data Abstraction Layer for Energy-Constrained Devices in Mobile Sensor Networks, ACM Mobisys'07 (Princeton University)

Presentation Outline

  • Overview of Wireless Sensor Networks

  • Overview of Data Acquisition Models

  • The MicroHash Index Structure.

  • MicroHash Experimental Evaluation

  • Conclusions and Future Work

Wireless Sensor Networks

  • Resource constrained devices utilized for monitoring and studying the physical world at a high fidelity.

Wireless Sensor Device

Wireless Sensor Network


Wireless Sensor Networks

  • Applications have already emerged in:

    • Environmental and habitant monitoring

    • Seismic and Structural monitoring

    • Understanding Animal Migrations & Interactions between species

    • Automation, Tracking, Hazard Monitoring Scenarios, Urban Monitoring etc

Great Duck Island – Maine (Temperature, Humidity etc).

Golden Gate – SF, Vibration and Displacement of the bridge structure

Zebranet (Kenya) GPS trajectory

Wireless Sensor Networks

The Great Duck Island Study (Maine, USA)

  • Large-Scale deployment by Intel Research, Berkeley in 2002-2003 (Maine USA).

  • Focuses on monitoring microclimate in and around the nests of endangered species which are sensitive to disturbance.

  • They deployed more than 166 motes installed in remote locations (such as 1000 feets in the forest)

Wireless Sensor Networks


Wireless Sensor Networks

The James Reserve Project, CA, USA

Available at:

The Anatomy of a Sensor Device

  • Processor, in various(sleep, idle, active) modes

  • Power sourceAA or Coin batteries, Solar Panels

  • SRAM used for the program code and for in-memory buffering.

  • LEDs used for debugging

  • Radio, used for transmitting the acquired data to some storage site (SINK) (9.6Kbps-250Kbps)


  • Sensors: Numeric readings in a limited range (e.g. temperature -40F..+250F with one decimal point precision) at a high frequency (2-2000Hz)

Sensor Devices & Capabilities

Sensing Capabilities



  • Light

  • Temperature

  • Humidity

  • Pressure

  • Detect Tone

  • Wind Speed

  • Soil Moisture

  • Location (GPS)

  • etc…





Xbow’s i-mote2




  • The Energy Source is limited.

    Energy source: AA batteries, Solar Panels

    2.Local Processing is cheaper than transmitting over the radio.

    Transmitting 1 Byte over the Radio consumes as much energy as ~1200 CPU instructions.

    3.Local Storage is cheaper than transmitting over the radio.

    Transmitting 512B over a single-hop 9.6Kbps (915MHz) radio requires 82,000μJ, while writing to local flash only 760μJ.

The Programming Cycle

  • The Operating System

    TinyOS (UC-Berkeley): Component-based architecture that allows programmers to wire together the minimum required components in order to minimize code size and energy consumption

    (The operating system is really a number of libraries that can be statically linked to the sensor binary at compile time)

  • The Programming Language

    nesC (Intel Research, Berkeley): an event-based C-variant optimized for programming sensor devices

event result_t {

state = !state;

if (state) call Leds.redOn();

else call Leds.redOff();


“Hello World”: Blinking the red LED!

The Programming Cycle

The Testing Environment

  • Debugging code directly on a sensor device is a tedious procedure

  • nesC allows programmers to compile their code to

    • A Binary File that is installed on the sensor

    • A Binary File that runs on a PC

  • TOSSIM (TinyOS Simulation) is the environment which allows programmers to simulate the mote binary directly on a PC.

  • This enables accurate simulations, fine grained energy

    modeling (with PowerTOSSIM) and visualization (TinyViz)

  • The Programming Cycle

    The Pre-deployment Environment

    • Once you have created and debugged you code you can perform a deployment in a laboratory environment.

    • Harvard’s MoteLab uses 190 sensors, powered from wall power interconnected with an Ethernet connection.

    • The Ethernet is just for debugging and reprogramming, while the Radio for actual communication between motes

    • Motes can be reprogrammed through a web interface.

    Available at:

    Presentation Outline

    • Overview of Wireless Sensor Networks (WSN)

    • Overview of Data Acquisition Models

    • The MicroHash Index Structure

    • MicroHash Experimental Evaluation

    • Conclusions and Future Work

    The Centralized Storage Model

    • A Database that collects readings from many Sensors.

    • Centralized: Storage, Indexing, Query Processing, Triggers, etc.

    Centralized Storage I

    • Crossbow’s MoteViewsoftware

    • No in-network Aggregation

    • No in-Network Filtering

    Available at:

    Centralized Storage II

    • TinyDB - A Declarative Interface for Data Acquisition in Sensor Networks.

    • In-Network Aggregation

    • In-Network Filtering (i.e., WHERE clause)

    e.g., SELECT MAX(temp) FROM sensors

    Available at:

    Centralized Storage: Conclusions

    • Frameworks such as TinyDB:

      • -Are suitable for continuous queries.

      • -Push aggregation in the network but keep much of the processing at the sink.

    • New Challenges:

      • Many applications do not require the query to be evaluated continuously (e.g., Average temperature in the last 6 months?)

      • In many applications there is no sink (e.g., remote deployments and mobile sensor nets)

      • -Local Storage on sensors keeps increasing (e.g., RISE and more recently imote2)

    Our Model: In-Situ Data Storage

    • The data remains In-situ (at the generating site) in a sliding window fashion.

    • When required, users conduct on-demand queries to retrieve information of interest.

    A Network of Sensor Databases

    In-Situ Data Storage: Motivation

    Soil-Organism Monitoring

    (Center for Conservation Biology, UCR)

    • A set of sensors monitor the CO2 levels in the soil over a large window of time.

    • Not a real-time application.

    • Many values may not be very interesting.

    • D. Zeinalipour-Yazti, S. Neema, D. Gunopulos, V. Kalogeraki and W. Najjar,

    • "Data Acquision in Sensor Networks with Large Memories", IEEE Intl. Workshop on NetworkingMeets Databases NetDB (ICDE'2005), Tokyo, Japan, 2005.

    Presentation Outline

    • Overview of Wireless Sensor Networks

    • Overview of Data Acquisition Models

    • The MicroHash Index Structure

    • MicroHash Experimental Evaluation

    • Conclusions and Future Work

    Flash Memory at a Glance

    • The most prevalent storage medium used for Sensor Devices is Flash Memory (NAND Flash)

    • Fastest growing memory market (‘05 $8.7B, ‘06:$11B)

    • (NAND) Flash Advantages

    • Simple Cell Architecture (high capacity in a small surface)

    • Fast Random Access (50-80 μs) compared to 10-20ms in Disks

    • Economical Reproduction

    • Shock Resistant

    • Power Efficient

    Surface mount NAND flash

    Removable NAND Devices

    Flash Memory at a Glance

    • Write-Constraint: Writing can only be performed at a page granularity (256B~512B), after the respective page (and its respective 8KB~64KB block!) has been deleted

    • Delete-Constraint: Erasure of a page can only be performed at a block granularity (i.e. 8KB~64KB)

    • Wear-Constraint: Each page can only be written a limited number of times (typically 10,000-100,000)

    Measurements using RISE

    Energy(Page Size = 512 B)

    Read = 24 μJ

    Write =763μJ

    Block Erase =425μJ

    Asymmetric Read/Write Energy Cost :

    MicroHash Objectives

    • General Objectives

    • Provide efficient access to any record stored on flash by timestamp or value

    • Execute a wide spectrum of queries based on our index, similarly to generic DB indexes.

    • Design Objectives:

    • Avoid wearing out specific pages.

    • Minimize random access deletions of pages.

    • Minimize SRAM structures

      • SRAM is extremely limited (8KB-64KB).

      • Small memory-footprint => quick initialization.

    Main Structures

    • 4 Page Types: a) Root Page, b) Directory Page, c) Index Page and d) Data Page

    • 4 Phases of Operation: a) Initialization, b) Growing, c) Repartition and d) Garbage Collect.

    Growing the MicroHash Index

    • Collect data in an SRAM buffer page Pwrite

    • When Pwrite is full flush it out to flash media

    • Next create index records for each data record in Pwrite

    • If SRAM gets full, Index pages are forced out to flash media by an LRU policy.





    (ts, 74F)









    Index Pages


    Growing the MicroHash Index

    A populated Flash Media

    Flash Media

    idx: next empty page

    Garbage Collection in MicroHash

    • When the media gets full some pages need to be deleted => delete the oldest pages.

    • Oldest Block? The next block following the idx pointer.

    • Note:

    • This might create invalid index records.

    • This will be handled by our search algorithm

    Directory Repartition in MicroHash

    • MicroHash starts out with a directory that is segmented into equiwidth buckets

      • e.g., divide the temperature range [-40,250] into c buckets)

    • Not efficient as certain buckets will not be utilized

      • Consider the first few or last few buckets below.

    Directory Repartition in MicroHash

    • If bucket A links to more thanτ index pages, evict the least used bucket B and segment bucket A into A and A’

    • We want to avoid bucket reassignments of old records as this would be very expensive

    Example: τ=2

    C: #entries since last split

    S: timestamp of last addition

    Searching in MicroHash

    • Searching by value

      “Find the timestamp (s) on which the temperature was 100F”

      • Simple operation in MicroHash

      • We simply find the right Directory Bucket, from there the respective index page and then data record (page-by-page)

    • Searching by timestamp

      “Find the temperature of some sensor on a given timestamp tq”

      • Problem: Index pages are mixed together with data pages.

      • Solutions:

        1. Binary Search (O(logn), 18 pages for a 128MB media)

        2. LBSearch (less than 10 pages for a 128MB media)

        3. ScaleSearch (better than LBSearch, ~4.5 pages for a 128MB media)

    LBSearch and ScaleSearch

    Solutions to the Search-by-timestamp problem:

    • LBSearch: We recursively create a lower bound on the position of tq until the given timestamp is located.

    • ScaleSearch: Quite similar to LBSearch, however in the first step we proceed more aggressively (by exploiting data distribution)








    Presentation Outline

    • Overview of Wireless Sensor Networks

    • Overview of Data Acquisition Models

    • The MicroHash Index Structure

    • MicroHash Experimental Evaluation

    • Conclusions and Future Work

    Experimental Evaluation

    • Implemented MicroHash in nesC.

    • We tested it using TinyOS along with a trace-driven experimental methodology.

    • Datasets:

      • Washington State Climate

        • 268MB dataset contains readings in 2000-2005.

      • Great Duck Island

        • 97,000 readings between October and November 2002.

    • Evaluation Parameters: i) Space Overhead, ii) Energy Overhead, iii) Search Performance

    Space Overhead of Index

    • Index page overhead Φ = IndexPages/(DataPages+IndexPages)

    • Two Index page layouts

      • Offset, an index record has the following form {datapageid,offset}

      • NoOffset, in which an index record has the form {datapageid}

    • 128 MB flash media (256,000 pages)

    Space Overhead of Index

    Increasing the Buffer Decreases the Index Overhead

    Black denotes the index pages

    Search Performance

    • 128 MB flash media (256,000 pages), varied SRAM (buffer) size

    • 2 Index page layouts

      • Anchor: Index Pages store the last known timestamp

      • No Anchor: Timestamp is only stored in Data Pages

    Indexing the Great Duck Island Trace

    • Used 3KB index buffer and a 4MB flash card to store all the 97,000 20-byte data readings.

      • The index pages never require more than 28% additional space

      • Indexing the records has only a small increase in energy demand: the energy cost of storing the records on flash without an index is 3042mJ

      • We were able to find any record by its timestamp with 4.75 page reads on average

    Presentation Outline

    • Overview of Wireless Sensor Networks

    • Overview of Data Acquisition Models

    • The MicroHash Index Structure

    • MicroHash Experimental Evaluation

    • Conclusions and Future Work

    Conclusions & Future Work

    • We proposed the MicroHash index, which is an efficient external memory hash index that addresses the distinct characteristics of flash memory

    • Our experimental evaluation shows that the structure we propose is both efficient and practical

    • Future work:

      • Develop a complete library of indexes and data structures (stacks, queues, b+trees, etc.)

      • Buffer optimizations and Online Compression

      • Support Range Queries

    • Demetris Zeinalipour

    • Thank you!

    • Questions?

    • Related Publications

    • "MicroHash: An Efficient Index Structure for Flash-Based Sensor Devices", D. Zeinalipour,S. Lin, V. Kalogeraki, D. Gunopulos, W. Najjar, In USENIX FAST’05.

    • " Efficient Indexing Data Structures for Flash-Based Sensor Devices",

    • ACM Transactions on Storage (TOS), November 2006.

    Indexing and Searching in Wireless Sensor Networks

    Presentation and publications available at:

    Searching Bottlenecks

    • Index Pages written on flash might not be fully occupied

    • When we access these pages we transfer a lot of emptybytes (padding) between the flash media and SRAM.

    • Proposed Solutions:

      • Solution 1: Two-Phase Page Reads

      • Solution 2: ELF-like Chaining of Index Pages

    Improving Search Performance

    • Solution 1: Utilize Two-Phase Page Reads.

      • Reads the 8B header from the flash media.

      • Then read the correct payload in the next phase.

    Improving Search Performance

    • Solution 2: Avoid non-full index pages using ELF*.

      • ELF: a linked list in which each page, other than the last page, is completely full.

      • keeps copying the last non-full page into a newer page, when new records are requested to be added.

    *Dai et. al.,Efficient Log Structured Flash File System, SenSys 2004

    Search Performance

    • We compared MicroHash vs. ELF Index Page Chaining by searching all values in the range [20,100]

    • Keeping full index pages increases search performance but decreasesinsertion performance.

    Decreasing indexing performance using ELF (15% more writes)

    Increasing search performance using ELF (10% less reads)

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