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  • pg 1
									FDSI-tree: A Fully Distributed Spatial Index Tree
for Efficient & Power-Aware Range Queries in
Sensor Networks

        Sanghun Eo, Suraj Pandey, Myungkeun Kim,
            YoungHwan Oh and Haeyoung Bae
• Introduction

• Related Work

• FDSI-Tree
   • Assumptions and System Model

   • The Tree Structure

   • Energy Efficient & Power-aware Query Processing

• Experiment

• Conclusion and Future Work

• A sensor network consists of many spatially distributed sensors, which
  are used to monitor or detect phenomena at different locations, such as
  temperature changes or pollutant level

• Sensor nodes, such as the Berkeley MICA Mote which already support
  temperature sensors, a magnetometer, an accelerometer, a microphone,
  and also several actuators, are getting smaller, cheaper, and able to
  perform more complex operations, including having mini embedded
  operating systems

• While these advances are improving the capabilities of sensor nodes,
  there are still many crucial problems with deploying sensor networks
    • Limited storage, limited network bandwidth, poor inter-node
      communication, limited computational ability, and limited power still

                            Related Work
• We have laid out the importance to the need of spatial indexing
   schemes in sensor networks

• Traditionally, the database community has focused mostly on
   centralized indices and our approach essentially is to embed them into
   sensor nodes

• But, the index structure is decided not just upon the data, but also
   considering the performance metrics and power measurements of
   collective sensors

                          Related Work
• The Cougar project at Cornell discusses queries over sensor networks,
  which has a central administration that is aware of the location of all
  the sensors

• Madden et.al., introduced Fjord architecture for management of
  multiple queries focusing on the query processing in the sensor

• The TinyOS group at UC Berkeley has published a number of papers
  describing the design of motes, the design of TinyOS, and the
  implementation of the networking protocols used to conduct ad-hoc
  sensor networks

                            Related Work
• TAG was proposed for an aggregation service as a part of TinyDB,
  which is a query processing system for a network of Berkeley motes

• They also described a distributed index, called Semantic Routing Trees
    • SRTs are based on single attributes, historical sensor reading and fixed
      node query originations, as contrasting to our design over these aspects

• The work on directed diffusion, which is a data centric framework, uses
  flooding to find paths from the query originator node to the data source
    • The notion is grouping to compute aggregates over partitions of sensor

                             Related Work
• Pre-computed indices are used to facilitate range queries in traditional
   database systems, and have been adopted by the above mentioned work
    • Indices trade-off some initial pre-computation cost to achieve a
       significantly more efficient querying capability
    • For sensor networks, we emphasize that a centralized index for range
       queries are not feasible for energy-efficiency as the energy cost of
       transmitting 1Kb a distance of 100m is approximately the same as that for
       executing 3 million instructions by a 100 (MIPS)/W processor

• All the schemes reviewed earlier are based on grouping of the sensor
  nodes either by event/attribute, which are data centric demanding
  communication that is redundant.
• The FDSI-Tree overcomes these inherent deficiencies

• Assumptions and System Model
    • Wireless Sensor networks have the following physical resource constraints
      and unique characteristics
        • Communication: The wireless network connecting the sensor nodes is
          usually limited, with only a very limited quality of service, with high
          variance in latency, and high packet loss rates

• Assumptions and System Model
       • Power consumption: Sensor nodes have limited supply of energy, most
         commonly from a battery source
       • Computation: Sensor nodes have limited computing power and
         memory sizes that restrict the types of data processing algorithms that
         can be used and intermediate results that can be stored on the sensor
       • Streaming data: Sensor nodes produce data continuously without
         being explicitly asked for that data
       • Real-time processing: Sensor data usually represent real-time events
            • Moreover, it is often expensive to save raw sensor streams to disk at the
            • Hence, queries over streams need to be processed in real time

• Assumptions and System Model
       • Uncertainty: The information gathered by the sensors contains noise
         from environment
            • Moreover, factors such as sensor malfunction, and sensor placement
              might bias individual readings

   • We consider a static sensor network distributed over a large area
       • All sensors are aware of their geographical position
       • Each sensor could be equipped with GPS device or use location
         estimation techniques

• The Tree Structure
    • A FDSI-tree is an index designed to allow each node to efficiently
      determine if any of the nodes below it will need to participate in a given
      query over some queried range

    • To accommodate the spatial query in the network we need additional
      parameters to be stored by individual nodes.
         • Each node must store the calculated MBR of its children along with
           the aggregate values
         • The parent node of each region in the tree has a structure in the form
           <child-pointers, child-MBRs, overall-MBR, location-info>.
         • The child-pointers helps traverse the node structure

• The Tree Structure
    • We have added the MBR in each node which confines the children into a
      box over which a query can be made.
         • The confinement algorithm is responsible to analyze and distribute the sensor
           nodes into the appropriate MBR
    • This classification is largely based on their proximity to their respective
      parent and the contribution factor to the dead space of the resulting MBR

• The network structure, which is common to both Cougar and TinyDB,
  consists of nodes connected as a tree (tree-based routing)

• As it’s evident that nodes within the same level do not communicate
  with each other, the communication boundary is constrained within
  children and their respective parent

• This communication relationship is viable to changes due to moving
  nodes, the power shortage of the nodes, or when new nodes appear

• TinyDB has a list of parent candidates

• The parent changes if link quality degrades sufficiently

• The Cougar has a similar mechanism: a parent sensor node will keep a
  list of all its children, which is called the waiting list, and will not
  report its reading until it hears from all the sensor nodes on its waiting

• We use Cougar’s approach in our system under similar semantics

• The Tree Structure

             (a)                                              (b)

         Fig. 1. Node positions in one section of our sensor test bed.
                 (a) Simulated Physical Environment showing region of interest.
                 (b) The MBR under each parent node of a sub tree

• The Tree Structure
    • For the construction of FDSI-tree, in the descending stage, a bounded box
      which overlaps the children and the parent itself should be stored by each
      parent in that region
    • Each descent correspondingly stores the MBR of the region where link
      exists until the leaf node is reached
    • At the end of the descent, when all the nodes have been traversed, the
      parent node of each region is notified about their child nodes’ MBR
    • Hence, in the ascending stage the parent of each region gets updated the
      new MBR of their children which now should include the sub-tree under
      that node, and a distributed R-tree like structure is formed among the
      sensor nodes

• Energy Efficient & Power-aware Query Processing
    • One critical operation of FDSI-tree, called energy efficient forwarding, is
      to isolate the regions containing the sensor nodes that can contribute to the
      range query
    • Our prime objective is to maintain the minimum count of nodes taking part
      in the query
    • A range query returns all the relevant data collected/relayed that is
      associated with regions within a given query window W (e.g., a rectangle
      in a two-dimensional space)
    • To process a range query with FDSI-tree, at first the root node receives the
      query; originating at any node
    • The disseminating of this request to the children node now is based on the
      calculation of the child node/s whose overall-MBR overlaps W

• Energy Efficient & Power-aware Query Processing
    • Each parent under that overlapping region receives this query and based on
      the overlapping regions of its children, the corresponding network (sub-
      tree) is flooded
    • It is here that the child-MBR is used to decide the particular regions which
      need precise selection in-order to limit unnecessary node traversal
    • These child-MBRs are comparatively small regions that cover only the
      perimeter of the children including their parent
    • So the selection operation needs minimum traversal to include the nodes in
      the list needed for range query

• Energy Efficient & Power-aware Query Processing
    • The optional parameter location-info should help to get accurate result for
      overlapping, independent regions
    • Its inclusion is based on the type of sensor network and its scalability
    • In addition to the geographic information it may include additional values
      e.g., time t, location attributes etc., that should act as a filter, which again
      is largely dependent on the computational power of each sensor node

           Experiment (Environment Setup)

• Regular tessellation, as like a grid
• Each node could transmit data to
  sensors that were at most one hop
• Experiments based upon TinyDB
  setup and attributes
• Best-case and closest parent
  approach of TinyDB used as the
  base for comparison
• The overall cost highly depends on
  the size of the window query and       Fig. 2. Sensor node linkage showing the grid
  the scale of the sensor network

      Experiment (Performance Evaluation)

• Parent selection an important
                                                                                                  Query Range Versus Nodes in Query
  issue                                                                     1.2

• Closest-parent as in TinyDB

                                      Fraction of Nodes Involved in Query

• Benefits of FDSI-tree dependent                                           0.8

  on quality of MBR of children                                             0.6

  beneath the parents                                                       0.4                                                    No FDSI-tree
                                                                                                                                   TinyDB (Closest Parent)

• FDSI-tree reduces the over                                                0.2                                                    FDSI-tree

  network traffic by 20%                                                     0
                                                                                  0       0.1   0.2      0.3    0.4   0.5    0.6       0.7        0.8   0.9    1

• Maintenance cost and                                                                                Query Size as Percent of Vaue Range

  construction cost is nevertheless
                                                                                      Fig. 3. Number of nodes participating in window
  foreseeable                                                                         queries of different sizes (20 × 20 grid, 400 nodes)

• Emulation based on AVRORA

              Conclusion and Future Work
• We contribute a new technique to group the sensors in a region for
  spatial range queries

• FDSI-tree can reduce the number of nodes that disseminate queries by
  nearly an order of magnitude

• Isolating the overlapping regions of sensor nodes with the range query,
  non-relevant nodes can be avoided in the communication

• Only the sensor nodes leading to the path of the requested region are
  communicated, and hence substantial reduction in power is achieved
  due to reduced number of sub-trees involved

              Conclusion and Future Work

• In addition, the aggregate values for the region of interest is collected,
   following the in-network aggregation paradigm which has an
   advantage over the centralized index structure in that it does not require
   complete topology and sensor value information to be collected at the
   root of the network

• Since data transmission is the biggest energy-consuming activity in
   sensor nodes, using FDSI-tree results in significant energy savings.

              Conclusion and Future Work

• FDSI-tree provides a scalable solution to facilitate range queries
  adopting similar protocols and query processing used so far, making it
  highly portable

• Currently, we are expanding our scheme to consider moving objects
  trying to achieve moreover the same throughput as in static networks

• Adoption of distributed redundant architecture for efficient processing
  of concurrent queries and for supporting join operations, are challenges
  which are under scrutiny as the capabilities of sensor nodes reaches
  higher levels

          Thank you
Email: eosanghun@dblab.inha.ac.kr


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