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

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

2. Contents • Introduction • Related Work • FDSI-Tree • Assumptions and System Model • The Tree Structure • Energy Efficient & Power-aware Query Processing • Experiment • Conclusion and Future Work

3. Introduction • 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 persist

4. 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

5. 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 environment • 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

6. 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 (SRT) • 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 nodes • The notion is grouping to compute aggregates over partitions of sensor readings

7. 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

8. FDSI-Tree • 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

9. FDSI-Tree • 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 nodes • 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 sink • Hence, queries over streams need to be processed in real time

10. FDSI-Tree • 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

11. FDSI-Tree • 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

12. FDSI-Tree • 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

13. FDSI-Tree • 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

14. FDSI-Tree • 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 list • We use Cougar’s approach in our system under similar semantics

15. FDSI-Tree • 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

16. FDSI-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

17. FDSI-Tree • 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

18. FDSI-Tree • 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

19. FDSI-Tree • 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 factor • 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

20. Experiment (Environment Setup) • Regular tessellation, as like a grid • Each node could transmit data to sensors that were at most one hop away • 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 the scale of the sensor network Fig. 2. Sensor node linkage showing the grid tessellation

21. Experiment (Performance Evaluation) • Parent selection an important issue • Closest-parent as in TinyDB • Benefits of FDSI-tree dependent on quality of MBR of children beneath the parents • FDSI-tree reduces the over network traffic by 20% • Maintenance cost and construction cost is nevertheless foreseeable • Emulation based on AVRORA Fig. 3. Number of nodes participating in window queries of different sizes (20 × 20 grid, 400 nodes)

22. 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

23. 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.

24. 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

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