IN-NETWORK VS CENTRALIZED PROCESSINGFORLIGHT DETECTION SYSTEMUSINGWIRELESS SENSOR NETWORKS

Presentation by,

Desai, Bhairav

Solanki, Arpan

Outline

Introduction Algorithm and Methodology

Formation of routing topology In-network aggregation Centralized aggregation

Experiments and Results Conclusion References

Introduction

Databases Vs Sensor Networks

Range Queries – much better idea for sensor networks

Additional operators have to be added for Query Language e.g. epoch and duration

Continuous long running Queries

Data Centric Networking

Combination of Querying, storage and routing techniques

Works efficiently if we use the combination as application specific rather than generalized like traditional IP based techniques.

Challenges

Volatile System Append Only Streams High Energy cost of communication Variable data arrival rate at different nodes Limited Storage on nodes

Centralized Processing

In Network Processing

Objective

Implementing In-network aggregation in real environment for a Data-centric application

Comparing In-network and Centralized aggregation approach

Algorithm and Methodology

Topology Formation

Collection Tree Protocol Base Station – Root of the Collection Tree EXTnode = EXTparent + EXTlink to parent

where EXT root = 0 Detecting Routing Loops

In-network Aggregation

Data aggregation at in-network nodes

Steps required to overcome change in topology

Network Behavior

Two phases

Node discovery phaseDiscovery of topologyAssigning time interval

Aggregation phaseSenseAggregateForward

Assigning time interval

Calculate time interval

Where

Tnode – Time duration of a node

D – Total depth of the tree

Lnode – Level of the node in the routing tree

T – Total epoch duration

Processing Plans

(b) Non-sensing intermediate node(a) Sensing leaf node

(c) Sensing intermediate node

Node Operation (Sensing leaf nodes)

Node Operation (Sensing intermediate nodes)

Node Operation (Non-sensing intermediate nodes)

Nodes divided in groups

Change in topology

Consequences

NodeBefore After

Parent Level Parent Level20 11 3 1 230 2 2 3 232 31 3 33 4

Causes change in depth of the tree

That’s why topology reformation is required

Centralized Aggregation

No discovery of topology

No assignment of time interval

No steps to overcome change in topology

Aggregation of data at the base-station

Node Operation (Sensing leaf nodes)

Node Operation (Sensing intermediate nodes)

Node Operation (Non-sensing intermediate nodes)

Job of the base station

Collect data from all the nodes

Perform aggregation

ExperimentsandResults

In-network aggregation

In-network aggregation

In-network aggregation

In-network aggregation

In-network aggregation

In-network aggregation

Centralized aggregation

Comparing both approaches

Comparing Bytes Transmitted

Conclusion

Lesser number of Hop counts

Low amount of bytes transmitted

Lower energy consumption

References

C. Intanagonwiwat, R. Govindan, and D. Estrin, Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks, In Proceedings of the Sixth Annual International Conference on Mobile Computing and Networks (MobiCO, August 2000)

David Gay, Phil Levis, Rob Von Behren, Matt Welsh, Eric Brewer, and David Culler, “The nesC

language: A holistic approach to networked embedded systems,” in SIGPLAN Conference on

Programming Language Design and Implementation (PLDI’03), June 2003. J. Heidemann, F. Silva, C. Intanagonwiwat, R. Govindan, D. Estrin, and D. Ganesan, “Building

Efficient Wireless Sensor Networks with Low-Level Naming,” Proceedings of the ACM

Symposium on Operating Systems Principles (SOSP), October 2001. Wendi Heinzelman, Anantha Chandrakasan, and Hari Balakrishnan, Energy-Efficient

Communication Protocols for Wireless Microsensor Networks, Proc. Hawaaian Int'l Conf. on

Systems Science, January 2000. Z. Cheng and W. Heinzelman, “Flooding Strategy for Target Discovery in Wireless Networks,”

Proceedings of the Sixth ACM International Workshop on Modeling, Analysis and Simulation of

Wireless and Mobile Systems (MSWiM), September 2003. D. Braginsky and D. Estrin, “Rumor Routing Algorithm for Sensor Networks,” Proceedings of

ACM WSNA, September 2002.

References J. Bonfils and P. Bonnet, Adaptive and Decentralized Operator Placement for In-Network Query Processing, Telecommunication Systems - Special Issue on Wireless Sensor Networks, January 2004 S. Madden, M.J. Franklin, J.M. Hellerstein, and W. Hong, TAG: a Tiny AGgregation Service for Ad-Hoc Sensor Networks, 5th Symposium on Operating System Design and Implementation (OSDI 2002), December 2002 Y. Yao and J. Gehrke, The cougar Approach to In-Network Query Processing in Sensor Networks, SIGMOD, March 2002 S. Madden, R. Szewczyk, M.J. Franklin, and D. Culler, Supporting Aggregate Queries Over Ad- Hoc Wireless Sensor Networks, Mobile Computing Systems and Applications, June 2002 S. Ganeriwal, R. Kumar, and M. B. Srivastava, Timing-Sync Protocol for Sensor Networks, Proceedings of ACM SenSys’03, November 2003 TinyOS Mailing list, http://www.tinyos.net/ TinyOS Naming Conventions, http://www.tinyos.net/tinyos-1.x/doc/tutorial/naming.html (TinyOS Introduction 2003) Getting Started with TinyOS and nesC, http://www.tinyos.net/tinyos-1.x/doc/tutorial/lesson1.html (Dissemination Protocol 2004) Dissemination, http://www.tinyos.net/tinyos-2.x/doc/html/tep118.html

References

(Collection Protocol 2004)

Collection, http://www.tinyos.net/tinyos-2.x/doc/html/tep119.html (The Collection Tree Protocol 2004)

CTP-Collection Tree Protocol, http://www.tinyos.net/tinyos-2.x/doc/html/tep123.html “Networking Wireless Sensors” by Bhaskar Krishnamachari. Cambridge University Press, 2005 “Wireless Sensor Networks – An Information Processing Approach” by Feng Zhao, Leonidas

Guibas. Morgan Kaufmann Publishers, 2004