1. Selection and Navigation of Mobile sensor Nodes Using a Sensor Network Atul Verma, Hemjit Sawant and Jindong Tan Department of Electrical and Computer Engineering Michigan Technological University Houghton, USA PerCom 2005 Jenchi

2. Outlines Introduction Introduction Problem Formulation Problem Formulation Credit Field Based Navigation Credit Field Based Navigation Selection of a Mobile Sensor Node Selection of a Mobile Sensor Node Simulation Results Simulation Results Conclusion Conclusion

3. Introductions Hybrid sensor networks Hybrid sensor networks –Military applications battle-field surveillance, reconnaissance and enemy tracking battle-field surveillance, reconnaissance and enemy tracking –Civil applications habitat monitoring, environment observation, health and other commercial applications. habitat monitoring, environment observation, health and other commercial applications.

4. Introductions In a hybrid sensor network In a hybrid sensor network –Upon detection of an event, static sensors around the event may request the mobile sensors navigate to the area of interests –Mobile sensors enhance sensing, communication and computation capabilities in the area of interests

5. Introductions The mobile sensors provides environmental sensing, communication, coordination and navigation to the hybrid sensor systems The mobile sensors provides environmental sensing, communication, coordination and navigation to the hybrid sensor systems –provide required coverage and specified sensing accuracy –collect data from close by static sensors with higher energy efficiency –task the static sensors –repair and maintain the network

6. Introductions Navigation of mobile sensor nodes (MSNs) is a challenging task Navigation of mobile sensor nodes (MSNs) is a challenging task Objectives of this paper Objectives of this paper –We have used sensor networks for guiding MSN towards the goal position

7. Introductions Navigation deals with guiding the mobile sensor from its present location to the desired location by avoiding obstacles Where am I ？ Where am I going ？ How should I get there ？

8. Introduction The basic concept of this paper The basic concept of this paper –A credit-based approach Nodes are assigned credit values according to their distance from the phenomenon Nodes are assigned credit values according to their distance from the phenomenon –The MSN calculates its navigation direction towards the phenomenon using the navigation force from the neighboring static sensor nodes

9. Problem Formulation For example ： the task of detecting a fire Region of phenomenon Alert signal

10. Credit Field Based Navigation Region of phenomenon Cluster leader Determines whether and how many mobile sensors are required to enhance the sensing quality Step1 ： to select a mobile sensor among available MSNs and build up a navigation path between the mobile sensor and the region of phenomenon

11. Credit Field Based Navigation Cluster leader Weight request packet (WREQ) Busy with some events Low in power the Weight of MSN Step2 ： to navigate the mobile sensor through the sensor network

12. Credit Field Based Navigation Step1 ： Building up the Navigation Field M1M1 S1S1 S3S3 S2S2 S4S4 S7S7 S5S5 S6S6 S9S9 S8S8 Hot Node Cold Node

13. Credit Field Based Navigation Step1 ： Building up the Navigation Field M1M1 S1S1 S3S3 S2S2 S4S4 S7S7 S5S5 S6S6 S9S9 S8S8 Advertisement packet (ADV) C1C1 C2C2 C2C2 C 2 (C 2 < C 1 ) Hot Node Cold Node C1C1 C1C1

14. Credit Field Based Navigation Step1 ： Building up the Navigation Field M1M1 S1S1 S3S3 S2S2 S4S4 S7S7 S5S5 S6S6 S9S9 S8S8 Advertisement packet (ADV) C1C1 C2C2 C2C2 C 2 (C 2 < C 1 ) C3C3 C3C3 C 3 (C 3 < C 2 ) Hot Node Cold Node C1C1 C1C1

15. Credit Field Based Navigation Step1 ： Building up the Navigation Field M1M1 S1S1 S3S3 S2S2 S4S4 S7S7 S5S5 S6S6 S9S9 S8S8 Advertisement packet (ADV) C1C1 C2C2 C2C2 C 2 (C 2 < C 1 ) C3C3 C3C3 C 3 (C 3 < C 2 ) C4C4 C4C4 C 4 (C 4 < C 3 ) Hot Node Cold Node C1C1 C1C1

16. Credit Field Based Navigation Step1 ： Building up the Navigation Field M1M1 S1S1 S3S3 S2S2 S4S4 S7S7 S5S5 S6S6 S9S9 S8S8 Hot Node Cold Node Navigation Field C1C1 C2C2 C2C2 C2C2 C3C3 C3C3 C3C3 C4C4 C4C4 C4C4 C1C1 C1C1

17. Credit Field Based Navigation Step2 ： Navigation of Mobile Sensor Node M1M1 S1S1 S3S3 S2S2 S4S4 S7S7 S5S5 S6S6 S9S9 S8S8 Hot Node Cold Node Navigation Request (NAV) C1C1 C2C2 C2C2 C2C2 C3C3 C3C3 C3C3 C4C4 C4C4 C4C4 C1C1 C1C1

18. Credit Field Based Navigation Step2 ： Navigation of Mobile Sensor Node M1M1 S1S1 S3S3 S2S2 S4S4 S7S7 S5S5 S6S6 S9S9 S8S8 Hot Node Cold Node Credit field value, ID, and Location information C1C1 C2C2 C2C2 C2C2 C3C3 C3C3 C3C3 C4C4 C4C4 C4C4 Select the highest credit value Executes navigation C1C1 C1C1

19. Credit Field Based Navigation Step2 ： Navigation of Mobile Sensor Node S1S1 S2S2 S5S5 S3S3 S4S4 M1M1 C3C3 C3C3 C3C3 C2C2 C2C2 (C 3 < C 2 ) C3C3 C2C2

20. Credit Field Based Navigation Step2 ： Navigation of Mobile Sensor Node The navigation process The navigation process –is suitable for dynamic events C 11 C 12 (C 12 > C 11 ) C 12 S1S1 S3S3 S2S2 S4S4 S5S5 S6S6 S8S8 S7S7 S9S9 C 13 (C 13 > C 12 )

21. Selection of a Mobile Sensor Node Each MSN calculated its weight with its Each MSN calculated its weight with its –Coverage –Power –Distance from the phenomenon Lower is the weight more is the probability of the MSN reaching the phenomenon Lower is the weight more is the probability of the MSN reaching the phenomenon

22. Selection of a Mobile Sensor Node — Coverage Coverage of the MSN Coverage of the MSN –MSN calculates its Voronoi Area Hello message

23. Selection of a Mobile Sensor Node — Coverage Coverage of the MSN Coverage of the MSN –MSN calculates its Voronoi Area Location information

24. Selection of a Mobile Sensor Node — Coverage –MSN calculates its Voronoi Area –Smaller is the Voronoi area of the MSN and less will be its weight

25. Selection of a Mobile Sensor Node — Power Greater the power of the mobile node greater is the distance it can traverse in the network Greater the power of the mobile node greater is the distance it can traverse in the network

26. Selection of a Mobile Sensor Node — Distance from the phenomenon The number of hops The number of hops –The total number of intermediate nodes through which the weight request WREQ packet travels from the phenomenon to the MSN More the number of hops more is the weight of the MSN More the number of hops more is the weight of the MSN

27. Selection of a Mobile Sensor Node — Weight Lower is the weight more is the probability of the MSN reaching the phenomenon Lower is the weight more is the probability of the MSN reaching the phenomenon In case of two or more MSN ’ s having equal weight the one which is closer to the phenomena in is selected for navigation In case of two or more MSN ’ s having equal weight the one which is closer to the phenomena in is selected for navigation

28. Simulation Results The NRL ’ s Sensor Network Extension to ns-2 The NRL ’ s Sensor Network Extension to ns-2 The phenomenon was simulated using a phenom node which transmitted phenom packets The phenomenon was simulated using a phenom node which transmitted phenom packets The environment ： 500m * 500m The environment ： 500m * 500m –A uniformly distributed sensor network –A randomly distributed sensor network –A sensor network with a “ coverage hole ”

29. Simulation Results — Uniformly distributed sensor network Phenom node Mobile sensor 4 MSNs 45 static sensor 1 phenom node

30. Simulation Results — Randomly distributed sensor network 4 MSNs 45 static sensor 1 phenom node

31. Simulation Results — Sensor network with a coverage hole 1 MSNs 80 static sensor 1 phenom node

32. Simulation Results — Navigation with dynamic events

33. Conclusion A credit field for mobile sensor navigation A credit field for mobile sensor navigation –Provide navigation path between the mobile sensor and region of phenomenon A distributed navigation algorithm is presented A distributed navigation algorithm is presented Robust to failures of static sensor Robust to failures of static sensor