Analysis of the energy consumption of 3D

localization algorithms

Santiago F. MiñoDAAD - student

Chair for Telecommunications

25.02.2013

2Overview

• Motivation• Localization sequence• Sensor node’s

characteristics• Methodology• Results• Future work and

conclusions

[1]

3Overview

• Motivation• Localization sequence• Sensor node’s

characteristics• Methodology• Results• Future work and

conclusions

4

Decentralized Calculation + Save traffic + High update rate - Poor Accuracy

Centralized Calculation + High Accuracy - Multihop communication - Scalability problems

Anchor (AN)

Mobile Node (MN)

Motivation

5Overview

• Motivation• Localization sequence• Sensor node’s

characteristics• Methodology• Results• Future work and

conclusions

6Localization sequence- DISCOVERY PHASE- RANGING- CALCULATION

Anchor 1 Anchor 2

Anchor 5Anchor 3

Anchor 4

Mobile node

Transmission range?Yes: No:

7

Anchor 1

Anchor 5

Anchor 4 Mobile noded1

d2

d3

Distance: d1, d2, d3

- DISCOVERY PHASE- RANGING- CALCULATION

Localization sequence

8

In this work, Distributed Calculation is considered. The energy consumption of different Localization Algorithms with different number of anchors is investigated.

Distributed Calculation:+ Save traffic+ High update rate- Poor Accuracy

- DISCOVERY PHASE- RANGING* If Centralized Calculation, send ranging measurements to Central Unit- CALCULATION: Execution of the algorithm

www.dresden-elektronik.de

Localization sequence

Mobile node estimate its own position

9Sensor node’s characteristics

Sensor node (RCB231 v4.4.0 [2])

www.dresden-elektronik.de

- Processing power: 8-bit µC- 16 [MHz] quartz- Support timestamps with resolution of 1 µs

10Overview

• Motivation• Localization sequence• Sensor node’s

characteristics• Methodology• Results• Future work and

conclusions

11Methodology

Energy consumption = Execution time x disipated power

TI

ME

timestamp ‘A’

Localization algorithm

timestamp ‘B’

Execution time = timestamp B – timestamp A

- 500 independent samples were taken

- Standard deviation and average values were calculated

- 95% confidence interval

Generation of random values for distance and Anchors position

12Overview

• Motivation• Background• Methodology• Results• Future work and

conclusions

13Results

Energy consumption of basic operations

14Results

Energy consumption of localization algorithms*

*Example for 5 anchors

15Overview

• Motivation• Background• Methodology• Results• Future work and

conclusions

16Future work

• Investigate the energy consumption of other algorithms.

• Investigate the execution time of algorithms in other nodes

• Know the total energy consumption in a position estimation, it means, considering the communication.

17Conclusions

• There is a direct relationship between the execution time and the number of anchors involved.

• Division and sqrt functions have a longer execution time (about factor two) than multiplications.

• Weighted centroid and Standard Min-Max are very low complexity algorithms.

• Position Extended Kalman Filter has an execution time about 1 second with 5 Anchors. Therefore, it is not recommended to execute this algorithm on a sensor node.

18Bibliography

[1]. Image extracted from: http://imagevuex.com/documentation/

[2]. Dresden Elektronik: “Radio Controller Board RCB231SMA mega256 V4.4.0”. Information webpage available in https://shop.dresden-elektronik.de/referenz-designs/evaluierung-rcb/2-4-ghz-evaluierung-rcb/radio-controller-board-rcb231sma-mega256.html?___store=english&___from_store=deutsch. Last access January 2013.

[3]. Franco Mangili: “Evaluation of energy consumption of sensor nodes in localization applications”. Chair of Telecommunications, Technische Universität Dresden, Germany, 2010.

[4]. Jorge Juan Robles, Sebastián Tromer, Mónica Quiroga, Ralf Lehnert: “Enabling Low-power Localization for Mobile Sensor Nodes”, Published in International Conference on Indoor Positioning and Indoor Navigation (IPIN), Zürich, Switzerland, 2010.