1. Aggregation In Wireless Sensor Network. Under the guidance of Dr. N. Marchang. By H. Saratchandra MT/11/CSE/04
2. I. Introduction sensor networks composed of small and cost effective sensing devices equipped with wireless radio transceiver for environment monitoring.
3. Advantages does not require infrastructure such as electric mains for power supply wired lines for Internet connections to collect data. human interaction while deploying. These sensor nodes can monitor the environment.
4. Aggregation The intelligent way to combine and compress the data belonging to a single cluster is known as data aggregation in cluster based environment. Clustering process of grouping the sensor nodes in a densely deployed large-scale sensor network
5. Goal of aggregation The main goal of these algorithms is to gather and aggregate data in an energy efficient manner network lifetime is enhanced. WSN offer an increasingly attractive method of data gathering in distributed system architectures and dynamic access via wireless connectivity.
7. 1 2 3 4 5 Time Goal: Count the number of nodes in the network. Number of children is unknown. Scenario: Count
8. 1 2 3 Time Goal: Count the number of nodes in the network. Scenario: Count
9. 1 2 3 Time Goal: Count the number of nodes in the network. Scenario: Count
10. 1 2 3 4 Time Goal: Count the number of nodes in the network. Scenario: Count
11. 1 2 3 4 5 Time Goal: Count the number of nodes in the network. . Scenario: Count
12. 1 2 3 4 5 Time Goal: Count the number of nodes in the network. . Scenario: Count
13. 1 2 3 4 5 Time Goal: Count the number of nodes in the network. . Scenario: Count
14. 1 2 3 4 5 Time Goal: Count the number of nodes in the network. Scenario: Count
15. Issues in clustering how many clusters should be formed that could optimize some performance parameter. how many nodes should be taken into a single cluster. the selection procedure of cluster-head in a cluster.
16. II. Problem Definition Aims of aggregation protocols. eliminating redundant data transmission. improve the lifetime of energy constrained wireless sensor network.
17. Multi-hop fashion . Nodes neighbor nodes close to sink. Not energy efficient. Improvement over the above approach. Clustering. nodes CH(cluster-head) perform aggregation Sink
18. Cont. Performing aggregation function over CH still causes significant energy wastage. homogeneous sensor network CH will soon die out and again re-clustering has to be done which again cause energy consumption.
19. III. DATA AGGREGATION: Data aggregation is a process of aggregating the sensor data using aggregation approaches. Low Energy Adaptive Clustering Hierarchy Tiny Aggregation Fig : General architecture of the data aggregation algorithm
24. uses the base-station to broadcast CH assignment to further spreading out the CHs throughout the network. refines the cluster-head election algorithm that does not require the participation of the base-station and scatters CHs more evenly across the network.
25. node2 node3 node1 Broadcast at the setup stage of each round node4 node5 Fails to conserve energy Highest transmission power
26. LEACH based protocol assumes that BS can be reach by any Node in one hop. Limit the size of Network. Disadvantages Data cannot be aggregated properly. CH has to send many packets to the BS using high transmission power.
27. IV. QUERY PROCESSING Query Models. Query Language in TinyDB. Queries and Aggregates .
28. 1.Query Models. COUGAR approach proposes a query layer to support aggregate queries. the clients can issue queries without knowing how the results are generated, processed and returned by the sensor network to them. TAG also proposes a query model for supporting aggregate queries.
29. 2. Query Language in TinyDB based on SQL TinySQL Supports:- selection projection determining sampling rate group aggregation user defined aggregation event trigger lifetime query setting storing point and simple join
31. 1. Simple queries These are non aggregate queries. E.g. "SELECT temperature FROM sensor WHERE node = z". These are generally mapped into broadcast or point to point queries.
32. 2. Complex queries They may contain sub queries. E.g. "SELECT temperature FROM sensor WHERE room = (SELECT room WHERE floor = ’3’)"
33. 3. Event Driven Queries continuous query that returns the values periodically at specified time intervals. Eg: “SELECT AVG (temperature) FROM sensor where node = z“
34. The Grammar of TinySQL query language is as follows: SELECT select-list [FROM sensors] WHERE predicate 294 [GROUP BY gb-list] [TRIGGER ACTION command-name[(param)]] [EPOCH DURATION time] attribute list of the unlimited virtual relational table Query Condition subordinate clause which defines the trigger Attribute list trigger operation query cycle
35. an example of a TinyDB query, SELECT nodeid, AVG(light), AVG(temp) FROM sensors WHERE AVG(light)=100 GROUP BY nodeid EPOCH DURATION 5min
36. V. SIMULATION Simulation Tools: TOSSIM, NS-2, OPNET, OMNet++, J-Sim, GlomoSim, and Qualnet
37. TOSSIM discrete event simulator for TinyOS sensor networks. Instead of compiling a TinyOS application for a mote, users can compile it into the TOSSIM framework, which runs on a PC. allows users to debug, test, and analyze algorithms in a controlled and repeatable environment.
38. VI. CONCLUSION The two most important parts of data communication in sensor networks- query processing, data aggregation. communication in sensor networks is different from other wireless networks. It is an energy constrained network. The process of data aggregation becomes an important issue and optimization is needed. Efficient data aggregations not only provide energy conservation but also remove redundancy data and hence provide useful data only.
40. Reference:- S. Lindsey and C. Raghavendra, “PEGASIS: Power-efficient gathering in sensor information systems,” in Proceedings of IEEE AerospaceConference, vol. 3, Mar. 2002, pp. 1125–1130. Nandini. S. Patil, Prof. P. R. Patil, “Data Aggregation in Wireless Sensor Network” .
