1. Yong Yao Johannes Gehrke Jie Li Nov. 20, 2008 CS662 Paper Presentation

2.  Developments in hardware have enabled the widespread deployment of sensor networks  Sensor networks promise plentiful of future applications, most of which are naturally data-driven

3.  A sensor network consists of a large number of sensor nodes  A sensor node has one or more attached sensors which produce data to be processed  Declarative queries are preferred for data interaction in sensor networks

4.  Communication  Power consumption  Computation  Uncertainty in sensor readings

5.  To support declarative query processing  Limited resources require highly efficient data management  Can help generate query plans with different tradeoffs for different users

6. SELECTAVG(R.concentration) FROMChemicalSensor R WHERER.loc IN region HAVING AVG(R.concentration) > T DURATION(now, now + 3600) EVERY 10 Support long-running, periodic queries: DURATION: specifies the life time of a query EVERY: determines the rate of query submission

7. Query Plan Flow Block Computation Structure Simple Aggregate Query Processing Complex Query Optimization Communication Structure Wireless RoutingCrash Recovery Flow Block … Coordinate Decides how much computation is pushed into the network Specifies the role of each sensor node, as well as the coordination among them Decides how much computation is pushed into the network Specifies the role of each sensor node, as well as the coordination among them Consists a collection of data from a set of sensors Tasks coordinated by the leader node of the flow block Consists a collection of data from a set of sensors Tasks coordinated by the leader node of the flow block Computes the aggregate at the leader node, or computes partial aggregates at intermediate nodes Computes the aggregate at the leader node, or computes partial aggregates at intermediate nodes Sets up suitable communication routes for delivery of sensor records within the network Sets up suitable communication routes for delivery of sensor records within the network

8. Query Plan Flow Block Computation Structure Simple Aggregate Query Processing Complex Query Optimization Communication Structure Wireless RoutingCrash Recovery Flow Block … Coordinate

9.  In-Network Aggregation Basic Idea: Compute partial aggregates at intermediate nodes, rather than compute all sources in a single destination node (leader node) Synchronization required  Mechanisms for simple aggregate Direct delivery Packet merging Partial aggregation Each source sensor sends data to the leader Computation only happens at the leader Each source sensor sends data to the leader Computation only happens at the leader Each single record packet usually small in size Merge several packets into a larger packet Each single record packet usually small in size Merge several packets into a larger packet Compute partial results in intermediate nodes Partial results are then forwarded to the leader Compute partial results in intermediate nodes Partial results are then forwarded to the leader

10.  Required for packet merging and partial aggregation  Task: Determine how many sensor readings to wait for in each query round  Communication Structure: Spanning Tree (duplicate sensitive aggregates) DAG (Not duplicate sensitive aggregates)  Simple Algorithm: Incremental Time Slot Large cost

11.  A more pragmatic approach When a parent node receives a record from a child node, it adds the child node to its waiting list for the next round (Prediction) The parent node sets a timer to recover from false prediction (when actually, the child node doesn’t send a record to the parent in the next round) The child can also generate a notification packet to its parent about a false prediction

12. … Coordinate Query Plan Flow Block Computation Structure Simple Aggregate Query Processing Complex Query Optimization Communication Structure Wireless RoutingCrash Recovery Flow Block

13.  Extension to GROUP BY and HAVING Clauses (Q1)SELECT D.gid, AVG(D.value) FROM SensorData D GROUP BY D.gid HAVING AVG(D.value) > Threshold Two alternative plans: Create a flow block for each group Create a flow block that is shared by multiple groups

14.  Joins (Q2) SELECT oid FROMSnesorData D1, SensorData D2 WHERED1.loc IN R1 AND D2.loc IN R2 AND D1.oid = D2.oid The join operation can reduce or increase the resulting data size (depending on the selectivity) Increased Join: More expensive to compute at the leader node. Vice versa.

15. Query Plan Flow Block Computation Structure Simple Aggregate Query Processing Complex Query Optimization Communication Structure Wireless RoutingCrash Recovery Flow Block … Coordinate

16.  Main tasks of a routing protocol Route discovery Route maintenance  Distributed and adaptive routing protocol Proactive (e.g. DSDV) Reactive (e.g. AODV) Hybrid (ZRP)  Ad hoc On-demand Distance Vector Scale to large-size networks Does not generate duplicate data packets

17.  Packet merging and Partial aggregation require internal nodes to intercept data packets  Not supported by traditional “send and receive” interfaces of the network layer  This capability is provided by the use of filters The network layer will first pass a package through a set of registered functions that can modify the packet

18. Query Plan Flow Block Computation Structure Simple Aggregate Query Processing Complex Query Optimization Communication Structure Wireless RoutingCrash Recovery Flow Block … Coordinate

19.  2 main enhancements to AODV protocol  Route Initialization The leader of the aggregation broadcasts a route initialization message to create all the routes  Route Maintenance Local repair Bunch repair

20. Query Plan Flow Block Computation Structure Simple Aggregate Query Processing Complex Query Optimization Communication Structure Wireless RoutingCrash Recovery Flow Block … Coordinate

21.  A prototype of the query processing layer tested in the ns-2 network simulator  Prototype Characteristics: High degree of precision, including collisions at the MAC layer, detailed energy models, etc. Communication range of each sensor: 50m Assuming bi-directional links Receive power dissipation: 395mW Transmit power dissipation: 660mW Sensor readings size: 30 bytes per tuple

22.  Simple Aggregate Query Computes the average value over all sensors Assuming a fixed density of sensor nodes Average Dissipated Energy vs. Network SizeAverage Delay vs. Network Size

23.  Routing Improved Local Repair AlgorithmEffect of Bunch RepairResult Accuracy

24.  Query Plans (Q1) SELECT D.gid, AVG(D.value) FROM SensorData D GROUP BY D.gid HAVING AVG(D.value) > Threshold Impact of Sensor DistributionsDistributed TopologyOverlap Topology Plan 1: Creates one big flow block to be shared by all groups Plan 2: Creates a separate flow block for each group in aggregation

25.  Query Plans (Q2) SELECT oid FROMSnesorData D1, SensorData D2 WHERED1.loc IN R1 AND D2.loc IN R2 AND D1.oid = D2.oid Plan 1: Sensors send all tuples back to the gateway Plan 2: Creates a flow block for the Join operator inside the query region Join Query

26.  Query Plans (Q3) SELECT AVG(value) FROM Sensor D WHERE D.loc IN [(400,400), (500,500)] HAVING AVG(value) > t Aggregate Query Plan 1: Uses an existing flow block which covers the whole network Plan 2: Creates a new flow block for aggregation inside the query region

27.  Prototype tested in simulation, not in real implementation  Some important mechanisms (such as synchronization) are based on bidirectional links, which the authors themselves claim is not common in practice  Some discussions are rather preliminary and without quantitative, in-depth analysis (such as query optimization )

28.  Data management remains challenging in resource-constraint sensor networks  Query processing is very common in data-driven sensor network applications  Higher power-efficiency can be achieved through in-network aggregation, enhancement on the routing layer, and query optimization, etc.

29. Thank you!