1. Providing Locality Information to Smart Sensor Networks Tim Mead Supervisor: Charles Greif

2. Overview Intro / Aim Smart Sensor Networks Ptolemy Smart Sensor Hardware The Problem Multidimensional Scaling Findings Outcomes Future Work Conclusions

3. Introduction and Aim Project involves the task of calculating the relative locations of nodes in a Smart Sensor network, based on detected inter-node distances A full simulation is devised, not just a specific implementation

4. Smart Sensor Networks What is a Smart Sensor network? An array of small, self-powered, processors with the ability to acquire data from a number of sources, as well as communicate with other nodes Roots lie with the early ’90s Intel/Berkeley “Smart Dust” project Recent research focused on efficiency and miniaturization Research involves very recent technologies such as MEMS

5. Deputy Dust!

6. Smart Sensor Networks (con’t) What can they be used for? The monitoring of data over a distributed space: inside a home, over a factory, crops in a field 29-Palms Experiment. Six nodes dropped from a UAV, which were used to detect ground vehicles. UAV then flew past the nodes and queried them for their findings.

7. Ptolemy What is Ptolemy? A modeling and simulation suite, covering a number of domains, including wireless Why Model? Modeling allows for retargeting, reuse and formal validation and verification Why Simulate? Simulation reduces development time, by allowing developers to simulate the entire system, without having to construct prototypes

8. The need for Verified Software Why V&V? Systems are ‘turn key’ – once they’re out in the field, they can’t easily be collected and reprogrammed. Design and Verification of Embedded Systems Thomas A. Henzinger University of California, Berkeley Thomas A. Henzinger Collaboration between two research groups at Berkeley

9. Ptolemy in Use

10. Ptolemy and TinyOS Ptolemy was designed to support TinyOS What is TinyOS? A lightweight, event-driven real-time OS Manages ad-hoc wireless communication Designed for smart sensors TinyOS is implemented on the sensors using gcc/Atmel cross-compiler with nesC language (extension of C)

11. Smart Sensor Hardware Processor: Atmel processor with Flash memory Wireless RF interface: 310 / 916 MHz Sensor acquisition hardware: Light, temperature, pressure, acceleration sensors Real-time operating system: TinyOS For location detection: Audio receiver / transmitter

12. Commercial Smart Sensor Hardware: Crossbow Crossbow ‘mote’ Audio sensor board PC interface and programmer board

13. Local Developments in SSN’s Dr Peter Corke, with Qld CSIRO, has developed the ‘Flecks’ Similar to overseas units Runs TinyOS Uses Atmel processor Cheaper, more readily available

14. The Problem: Positional Determination Why do nodes want to know their position? Much more powerful processing of acquired data by being able to correlate against position Opens the door to smarter network routing algorithms, saving power and reducing errors How can the position be determined? Systems such as GPS are too cost prohibitive Using a combination of high- and low-speed propagating signals allows the inter-node distances to be determined

15. Positional Determination (con’t) What to with do with distances? Inter-node distances can be transformed into relative positions How to transform? Conventional methods utilise triangulation-like systems, but limit themselves to 3 pieces of information Multidimensional scaling utilises a greater body of information, to provide more reliable results, particularly when data is missing or corrupt

16. Multidimensional Scaling Began in the area of psychology, for grouping and correlation Later adapted to statistics, for reducing dimensionality Iteratively, works on minimising a loss function:

17. Multidimensional Scaling (con’t)

18. Findings Ptolemy’s “building block” system inadequate for complex decision-making and iterative logic The wireless building blocks are well designed and extensible Allows for basic terrain simulation to be easily added Proper simulation of audio effects, such as reflection and diffraction requires complex FE methods

19. Findings (con’t) Multidimensional scaling: Requires a minimum of 3 known positions to determine 2D positions Holds up well with missing distance data Handles spurious data with appropriate weights

20. Outcomes Ptolemy simulation and model of nodes in an environment Simulates network communication Produces a matrix of inter-node distances and weights

21. Outcomes (con’t) javaMDS Basic metric, weighted MDS calculator with a simple graphical output

22. Future Work Immediate future: Improvement of the Ptolemy model and synthesis of downloadable code (for Atmel) Improved Data Acquisition More Efficient Communication If we know where surround nodes are, we know how far away they are, so we can attenuate the power output accordingly. More Efficient Packet Sending If we know where nodes are, any how far they can communicate, we can determine the optimal communication pathway between two nodes. Long-term future: Integration of T. Henzinger’s Verification and Validation tools

23. Conclusions A low-cost system for providing locality information to Smart Sensor networks was devised Ptolemy: A visual programming environment in which solutions for Smart Sensor networks can be developed Simulation can be performed prior to production of code V&V will be able to be performed in the same suite Extensions to all sorts of areas, such as Peter Corke’s Fleck nodes

25. Positional Determination (con’t) Calculation of node position using triangulation utilises only three distances

26. Positional Determination (con’t) Calculation of node position using multidimensional scaling utilises all available data