Reliable Navigation of Mobile Sensors in Wireless Sensor Networks without Localization Service Qingjun Xiao, Bin Xiao, Jiaqing Luo and Guobin Liu Department of Computing The Hong Kong Polytechnic University Hunghom, Kowloon, Hong Kong IEEE IWQoS 2009 acceptance rate :33%

Outline Introduction Algorithm – Distributed Wavefront Algorithm – Robot Navigation guided Simulation Conclusion

Introduction 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 Mobile sensor Static sensor

Introduction The problem: find a path in the physical space from the initial position to the goal position avoiding all collisions with the obstacles Objects: – Static sensors do not know their geometric locations – Provide optimized navigation guidance to the goals – without the collision with obstacles

Assumption Mobile sensor are assumed to be able to detect the Angleof- Arrival (AoA) of incoming radio signals Static sensors do not know their geometric locations

Overview Mobile sensor Static sensor a 5

Phase Path Planning Robot navigation phase

defrule Propagate: Choose a backoff; send myhopcounttovicinity; Choose a backoff, resend my hopcount to vicinity Distributed Wavefront Algorithm Mobile sensor Static sensor hop count = -1 1 hop count = 0 a defrule Push: receive a hopcount at least two hops smaller than mine ⇒ Update my hopcount as the received hopcount plus one ; activate rulePropagate. defrule Initiate: become an event owner ⇒ send −1 hopcount to myself.

Distributed Wavefront Algorithm Mobile sensor Static sensor hop count = 1 2 2

Distributed Wavefront Algorithm Mobile sensor Static sensor hop count = 2 2 2

Distributed Wavefront Algorithm Mobile sensor Static sensor hop count =

Distributed Wavefront Algorithm Mobile sensor Static sensor

Phase Path Planning Robot navigation phase

Robot Navigation guided The equation to estimate the reversed gradient in the discrete scalar field built by static sensors potential value at p 0 or hopcount of the robot potential value at p i or hopcount of neighbor i the angle-of-arrival of radio from neighb or i to the robot exert attractive forces exert repulsive forces

Robot Navigation guided Mobile sensor Static sensor Attractive forces Repulsive forces

Robot Navigation guided Event owner Mobile sensor Static sensor Attractive forces Repulsive forces

Robot Navigation guided The equation to estimate the reversed gradient in the discrete scalar field built by static sensors potential value at p 0 or hopcount of the robot potential value at p i or hopcount of neighbor i the angle-of-arrival of radio from neighbor i to the robot exert attractive forces exert repulsive forces

Robot Navigation guided Mobile sensor Static sensor Attractive forces

Robot Navigation guided Mobile sensor Static sensor Attractive forces

The robot has the ultrasound based obstacle sensing ability Robot Navigation guided Mobile sensor Static sensor Attractive forces

Simulation The path planning algorithm is developed by nesC over TinyOS The robot navigation algorithm is implemented by a mixture of nesC and Tython scripts sensor distribution density0.01sensor/ft 2 average symmetric communication range ≈ 20ft range scaling factork

Simulation

Conclusion Provide optimized navigation guidance Avoiding collision with obstacles Mitigating the motion oscillation problem

Thank you~