Localization protocols for wireless sensor networks

1. Localization protocols for wirelesssensor networks Stefan DulmanEmail: s.o.dulman@utwente.nl

2. Presentation Overview • Introduction and motivation • Lateration – a simple approach • Classification • Centralized methods • One hop positioning • Distributed methods • Relative positioning • Mobility schemes • Conclusions

3. Wireless Sensor Networks • Nodes with VERY limited resources • 16 bit processor, 2KB RAM, 60KB FLASH • Low data-rate radio (115.200 bits/sec) • Limited energy available (1-2 small batteries) • Small physical size • Networks characteristics • Distributed network, mobile unreliable nodes • Deployed in a harsh environment • Self-organizing and self-healing

4. Ideal Sensor Networks • “Smart Dust” • Tens of thousands of sensor nodes • Node lifetime longer than 2 years • Node size smaller than 1 mm3 • Node price smaller than 5 cents

5. Typical Applications • A broad range of applications, all possibilities definitely not explored yet! • Environment and wildlife monitoring • Remote study of the birds on Great Duck Island • Zebra monitoring in Kenya • … and even more monitoring • Industry • Agriculture • Disaster control • Surveillance and security systems

6. Is positioning necessary? • YES! • Can be the mean or the goal of a WSN application • Application examples: • Meteorological and environmental monitoring • Data has no meaning if not stamped with time + location • Package tracking • Library archiving • Position tracking (e.g. military applications) • Is used as building block in: • Routing protocols • Data dissemination protocols • Localization as application of WSN

7. Example: geographic routing • Allows development of algorithms with better scalability • Position centric addressing first proposed in 1970’s • Recent growing interest for it • Nodes are addressed by their location instead of ID • No additional job required to support routing • State of the packet (position) and destination position are sufficient • Simplest algorithm: Cartesian routing • Stojmenovic (IEEE Commun.Magazine 2002) presents several strategies for geographical routing

8. Problem statement • Regular assumptions for WSN protocol test scenarios: • Large number of nodes • Random deployment in a (known shape) given area • Known (identical) transmission range for all nodes • Static/not very dynamic networks • Question: • What are the geographical positions of the nodes? • Absolute positioning • Relative positioning

9. A possible solution? • Usage of Global Positioning System (GPS) devices • Not a feasible solution for WSN: • High cost of the device (value/energy/computation power/space) • Unavailability/poor precision of the service in special environments (indoors, underground, etc.) • Conclusion: other approaches need to be developed and deployed

10. Lateration - A simple solution

11. Lateration description • Example: 2D space • Given: • Three points with known positions • Distances to all three of them • Position can be determined by intersecting three circle centered in the points with radius the known distances

12. Lateration as localization technique

13. Lateration’ • The concept can be easily applied to multihop networks • The method as such is not too useful: • Imprecise position information • Imprecise distance estimates • The three circles usually do not intersect in a point (or at all!) • Several algorithms developed on this simple idea (e.g. APS schemes)

14. Classification

15. Classification • Different aspects of localization studied in vision, robotics, signal processing, networking, etc. • Solutions can be classified in several manners: • One-hop or multi-hop schemes • Range free or range based schemes • Absolute, relative or local coordinates • Centralized, distributed or localized algorithms

16. Centralized methods

17. Centralized methods • All the data is collected at a central point and a global map is computed at once • Advantages: • High quality solutions (in terms of the average distance error) • Global maps available • Disadvantages: • Data needs to travel to a central point • High computation power required • Methods usually do not scale with the network size

18. Centralized methods • Convex optimization • One of the first schemes available • Treats the localization problem from the point of view of linear programming and semi-definite programming • Various constraints are represented as linear matrix inequalities

19. Convex optimization’ • Advantages: • It is simple to model the distance and angle information • The solutions provided are optimal • Efficient computational methods have already been developed • Disadvantages: • All the disadvantages of the centralized methods class • Computation complexities: • Linear programming is quadratic in the number of connections • Semi-definite is cubic in the number of connections

20. Convex optimization’ • Conclusions: • Increase in connectivity results in increase in accuracy^2 but also traffic overhead • Precision of distance and angle information have a direct influence on the output • Bad data (missing/false connections) leads to algorithm failure

21. Centralized methods • Multidimensional scaling • MDS-MAP method, makes use of connectivity information (and also distance information) to compute a relative (global) map • Finds an embedding in a lower dimensional space for a set of objects characterized by pair-wise distances between them • Recently the centralized method has been extended to a localized algorithm

23. Multidimensional scaling • Disadvantages • The regular ones for centralized methods • Advantages • It is a quite precise method (one of the best so far) • Can work only with connectivity (and distance) information • Only 3 anchors are needed for a global map (in 2D) • Theoretical bound on the complexity of the method(SVD is cubic in the number of nodes) • A recent paper describes a localized scheme based on MDS • Can deal with topologies containing holes • Eases the complexity of the computation

26. One hop positioning

27. One hop positioning • Nodes can directly contact the landmarks (e.g. GPS) • Advantages • Elegant solutions with precise results • Disadvantages • Line of sight is needed between the nodes and the landmarks • Landmarks need to be powerful devices

28. One hop positioning’ • The lighthouse system • Positioning of an entire field of sensors may be achieved with only a single lighthouse device capable of “seeing” all the nodes • This device is able to collect all the data and at the same time help nodes localize themselves • The system requires each node to be equipped with a photo detector and a clock

30. Light House System’ • Advantages: • Simple theoretical method • The prototype used a single lighthouse device • For the 2D experiments: nodes situated at 14 meters positioned themselves with a relative accuracy of 2.2% and relative standard deviation of 0.68%. • Disadvantages: • The line of sight assumption is a very strong one • Solution specific to the hardware

31. Distributed methods

32. Distributed methods • These methods allow nodes to compute their position by communicating to their neighbors only • Advantages: • No need of global knowledge • Simple methods, majority of algorithms fit the hardware • Lower communication overhead • Disadvantages • High number of anchors needed • Not all the nodes can compute their position • The resulting positions are less precise

33. Distributed methods • Ad hoc localization system (AhLOS) • Defines and combines several types of multilateration • Its main strong point is that it is a completely distributed protocol • Its weakest point is that the number of needed anchors should be large for a good result

34. AhLOS’ • Initial phase • Some nodes can compute their position directly using lateration • These nodes behave as anchors for all the others, algorithms goes on iteratively • The position precisions degrade with the number of steps • Additional phases • Some nodes cannot find the position Collaboration groups are identified • Position is identified in a collaborative manner

35. AhLOS’ • Algorithm might not have a convergence point • In general it fails for collaboration groups different than the presented one

36. AhLOS’ • Advantages: • AhLOS may produce very good results if accurate distance measurement hardware is present • Major disadvantage: very large number of beacons is required • Example: av. connectivity ~6.28 • 90% of regular nodes positions to be resolved • 45% landmarks required

37. Distributed methods • Ad-Hoc positioning systems (APS or DV…) • It is a combination between two major ideas: • Distance vector routing (DV)(information is forwarded hop by hop from each anchor in the network) • Global positioning system (GPS)(eventually each node will compute its position based on anchors positions and distance estimates) • The schemes adapt connectivity, distance, angle of arrival and compass information(6 possible combinations make sense and were studied)

38. APS’ • DVHop • The simplest protocol available • Makes use only of connectivity information • Basic ideas: • Number of hops between anchors and nodes are computed • Average hop distance is estimated • Position is computed via lateration - (W)LS method

39. DVHop practice - theory

40. APS’ • DVDistance • Identical with DVHop, but shortest path distance is propagated instead of hopcount • A new parameter has to be taken into consideration: the time to live (TTL) of the messages • There is a close connection between TTL and the final precision

41. APS’ • Euclidean • If accurate distance measurements are available, nodes can estimate exact distance to anchors • DVBearing • Angle estimates are used in order to determine the relative positions • In both methods, lateration is applied as the last step independently at each node

43. APS conclusions • Advantages: • Distributed and localized protocols • Support some limited mobility (periodic schemes) • Can deal with various combinations of connectivity, distance estimates, angle estimates, compass information • Disadvantages • Uniform distribution of anchors required • The DV component will ask for a high cost in case mobile scenarios

44. Relative positioning

45. Relative positioning • Relative positioning schemes generate a relative map, in a local coordinate system • Obtained positions are coherent all over the network (position based services are able to work)

46. Relative positioning’ • The self positioning algorithm (SPA) • The coordinate system is determined by a location reference group (LRG) • Nodes exchange info with neighbors to determine second neighborhood information (connectivity + distances) • Local maps are constructed • LRG helps orienting all the maps by aligning all the coordinate systems

47. SPA’ • The approach is quite similar to the distributed version of multidimensional scaling algorithm • Advantages: • Network-wide coherence is provided • No landmarks (anchors) are needed • Disadvantages: • Existence of a location reference group is expensive if mobility is taken into account (even limited mobility and even if particular conditions apply)

48. Mobility schemes

49. Localization and mobility • All the presented schemes might work in presence of limited mobility • The basic mechanism would be to run periodically the algorithms • The computed positions will almost never reflect reality (computation takes time) but trajectories could be estimated, etc.

50. Distinct approach • Sequential Monte Carlo localization (Mobicom 2004) • Adaptation of Monte Carlo localization used in robotics • Discrete time model • Posterior distributions are computed based on a set of weighted samples • Each step is divided in two: • Prediction phase (new position estimates are computed) • Filtering phase (position estimates are filtered and space is resampled) • SMC already applied to target tracking, robot localization and computer vision