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In-Network Data Aggregation in Wireless Sensor Networks

In-Network Data Aggregation in Wireless Sensor Networks. Supriyo Chatterjea supriyo@cs.utwente.nl University of Twente The Netherlands. Outline. What are Wireless Sensor Networks (WSNs)? Challenges faced in WSNs What is AmbientRT capable of? In-network processing

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In-Network Data Aggregation in Wireless Sensor Networks

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  1. In-Network Data Aggregation in Wireless Sensor Networks Supriyo Chatterjea supriyo@cs.utwente.nl University of Twente The Netherlands

  2. Outline • What are Wireless Sensor Networks (WSNs)? • Challenges faced in WSNs • What is AmbientRT capable of? • In-network processing • Data aggregation algorithm • Plans for deployment

  3. What are Wireless Sensor Networks (WSNs)? • Tiny, energy efficient, battery-powered sensor nodes • Built-in wireless transceivers

  4. What are Wireless Sensor Networks (WSNs)? • Difference between WSNs and conventional sensors with wireless transmitters • Nodes are able to do MUCH more than simple sampling and transmission of data! CPU & Memory • CPU Texas Instruments MSP430 • - 48 KByte of flash • - 10 KByte of RAM • - Running on 4.6MHz • External EEPROM 4Mbit • Ultra low power radio transceiver: • - 50 KByte/s • Connection points: • - up to 16 general purpose I/O • connections • - up to 8 analog inputs • - up to 2 analog outputs • - I2C / SPI • - Optional RS232 serial • interface uNode 2.0 by Ambient Systems

  5. What are Wireless Sensor Networks (WSNs)? • Able to setup multi-hop wireless ad-hoc networks automatically Data transmission in a single hop scenario Data transmission in a multi-hop hop scenario

  6. Challenges faced in WSNs • Limited energy resources • Maximise network lifetime • Minimise usage of transceiver • Minimise usage of sensors (e.g. Temperature, Humidity, etc.) • Limited bandwidth • Minimise the amount of data that needs to be transmitted

  7. Data flow with in-network data aggregation Conventional communication – Without in-network processing or data aggregation In-Network Processing • Basic idea

  8. In-Network Processing • Why is it required? • Transmitting data is a lot more expensive than processing it • To lengthen network lifetime, minimise the amount of data that needs to be transmitted • Prevent a bottle neck from occurring at nodes closer to the gateway • Equal energy usage through out the network

  9. In-Network Processing • Example: Data aggregation • Suppression of duplicate messages • Used in Directed Diffusion • Only one sensor might report a phenomenon reported by several sensors Without aggregation With aggregation

  10. In-Network Processing • The original model – warehousing approach 3 Query the central database • Historical query:“What is the average rainfall for the year 2002?” • Long-running query:“For the next 5 hours, retrieve the rainfall level in location A every 30 seconds if it is above 60mm” Store data in central database 2 Gather data from sensor network 1

  11. In-Network Processing • What AmbientRT allows • Shifts part of the task of executing a query within the network itself 1 Query the sensor network • Long-running query: “For the next 5 hours, retrieve the rainfall level in location A every 30 seconds if it is above 60mm” Loc A Store data in central database for future processing or aggregation > 60mm > 60mm 3 Gather data from sensor network 2

  12. Data Aggregation Algorithm • Our data aggregation project • Designed for collecting raw data in an energy efficient manner • Minimise sensor samplings and transmissions • We also take advantage of spatial and temporal correlations • Only send data when significant changes occur • Completely distributed & self-organising algorithm • Nodes take autonomous decisions • Which neighbouring nodes are correlated?

  13. Bottleneck! Bottleneck! Data Aggregation Algorithm Normal raw data Collection… Gateway Node Communication Tree

  14. Data Aggregation Algorithm Taking advantage of spatial correlations… Gateway Node Communication Tree Aggregating Node Non-Aggregating Node

  15. Computes readings using correlation info N1 N2 N5 Works out correlation info: N4 N1 +2.0˚C N3 N2 +1.3˚C N3 -2.7˚C N4 +3.3˚C Data Aggregation Algorithm Taking advantage of spatial correlations… Gateway Node Communication Tree Aggregating Node Non-Aggregating Node Sensor Readings

  16. Data Aggregation Algorithm A self-organising and distributed algorithm to decide who will perform aggregation and when… Distributed scheduling algorithm adapts automatically to nodes being added/removed.

  17. Data Aggregation Algorithm • Adaptive Sampling Frequency • Sampling frequency dependent on rate of change of measured parameter Low frequency High frequency Temperature Readings Time

  18. WAKE UP!! Data Aggregation Algorithm • Distributed detection of events • Node informs neighbouring dormant nodes when an event is detected Grrrr!!!

  19. Plans for Deployment • Over the next 3 months • Test 1: Distributed Scheduling Algorithm • How long does it take to setup? • How does it perform when the network topology is dynamic (e.g. coping with new/dead nodes)? • Test 2: Spatial and Temporal Correlation Algorithm • Setup two parallel networks • Measure accuracy of readings

  20. www.ambient-systems.net For more info about our wireless sensor nodes and the AmbientRT platform:

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