sensor placement by robot teams qiao li 7353963. outline concepts algorithms simulation analysis ...

Sensor Placementby Robot Teams

Qiao Li

7353963

Outline

ConceptsAlgorithmsSimulation AnalysisConclusionFuture WorkQuestions

Concepts

Sensor PlacementTypical Deterministic Deployments Pattern

s

Sensor Placement Sometimes sensors

are deployed in an unknown and/or hazardous environment, where physical dynamics

and spatio-temporal irregularities prevail.

There are two important

sensor placement methods, which do not need human

to place sensors in person: Carried-Based Deployment

and Sensor Self-Deployment.

R. Falcon, X. Li and A. Nayak, "Carrier-based Coverage Augmentation in Wireless Sensor and Robot Networks," M.S. thesis,Dept. Elect. Eng.,Univ. of Ottawa, Ottawa, Canada, 2011.

Sensor Placement Carried-based deployment involves mobile robots carrying

and dropping (static) sensors for optimal coverage formation. Sensor self-deployment deals with autonomous coverage formatio

n in WSN.

R. Falcon, X. Li and A. Nayak, "Carrier-based Coverage Augmentation in Wireless Sensor and Robot Networks," M.S. thesis,Dept. Elect. Eng.,Univ. of Ottawa, Ottawa, Canada, 2011.X. Li, H. Frey, N. Santoro and I. Stojmenovic, "Focused-Coverage by Mobile Sensor Networks" Univ. of Ottawa, Univ. of Padervorn, Univ. of Carleton, Ottawa and Paderborn, Canada and Germany, 2009.

Sensor Placement

Two requirements should be met in sensor deployment design for an area:

• Full Coverage: each point should be covered by at least one sensor.

• Connectivity: each sensor can be connected

to other sensors directly or through multiple hops

so that the data collected by individual node

can be relayed back to data sinks or controllers.

Z. Liao, J. Wang, S. Zhang and X. Zhang, "A Deterministic Sensor Placement Scheme for Full Coverage and Connectivity without Boundary Effect in Wireless Sensor Networks," Ad Hoc & WSN., vol. 19, pp. 327-351, May, 2012.

*Typical Deterministic Deployments Patterns

Hexagon ( )Square ( )Triangle ( )Srd 3

Srd

Srd 2

Algorithms

Snake-Like Deployment (SLD)Least Recently Visited algorithm (LRV)Back-Tracking Deployment (BTD)

Algorithms——SLD

Principle:• The robot moves step by step along a pre-computed geom

etric graph leading to optimal coverage,

each step to an adjacent empty vertex in the graph according to predefined rules for selecting moving direction, and drops a sensor after each step.

• SLD generates a snake-like S-shape robot trajectory.

X. Li, G. Fletcher, A. Nayak and I. Stojmenovic, "Placing Sensors for Area Coverage in a Complex Environment by a Team of Robots," ACM Transactions on Sensor Networks., to appear.

Algorithms——SLD

Drawbacks:• SLD does not support multiple robots

or tolerate sensor failures.• SLD is very likely to be stuck at dead end

and leave uncovered areas in the environment,

thus it does not provide any guarantees

on the full area coverage even in the ideal failure-free environment.


Algorithms——LRV

Principle:• Sensors store weights for each direction that a robot

can travel in. The weights represent the number of times that a robot has visited each direction for a given sensor.

• The sensors then recommend the direction

with the lowest weight for the robot to travel

or the direction that is the least recently visited.


Algorithms——LRV

Drawbacks:• LRV requires many unnecessary movements

to fully explore a given Region of Interest (ROI).

These extra movements lead to an extremely large number of messages sent from the robot.

• It is unclear under what conditions LRV terminates.• LRV is used in the case of a single robot.

M. Batalin, G. Sukhatme, "Coverage, Exploration and Deployment by a Mobile Robot and Communication Network," Proceeding of the International Workshop on Information Processing in Sensor Networks., Palo Alto., USA, 2003, pp. 376-391.X. Li, G. Fletcher, A. Nayak and I. Stojmenovic, "Placing Sensors for Area Coverage in a Complex Environment by a Team of Robots," ACM Transactions on Sensor Networks., to appear.

Algorithms——BTD

Failure-Free Environment• Single-Robot Scenario• Multi-Robot Scenario

Failure-Prone Environment• Robot Failures• Sensor failures

BTD——Failure-Free Environment

Definition:• BTD was designed by equipping SLD

with an important back-tracking technique

and extent it to support multiple robots.

It was presented over a square grid graph.



Single-Robot Scenario:• Principle: The four geographic directions are pre-ordered

as West, East, North, South. This order defines preference when a robot selects its movement direction.

• Example:



Back-tracking:• A sensor colors itself white if it is adjacent to

an empty point and black otherwise.

It updates its own color dynamically.• The back pointer points to the location

of the first white sensor along the robot's backward path.• The robot moves to the next adjacent sensor

with the lowest sequence number

whose back pointer location is the same as

the robot's destination.


*BTD——Failure-Free Environment

Example:


Benefit:• The shortcut method achieves two goals:

a. It allows the robot to reach its destination

regardless of obstacles, using described retracing method.

b. It is also a more efficient method,

eliminating wasted movement by the robot.• For these reasons the shortcut mehod is employed

by BTD for robot back-tracking.


Multi-Robot Scenario:• Each robot follows the BTD algorithm as if it was the only robot

in the ROI. In a dead-end situation, if a robot can not find a back pointer on its current sensor, it will select a back pointer (if any exists)

stored in the neighborhood.• When a robot is back tracking to a white sensor, other robots

should not follow the same back-track path for that white sensor. Therefore during back tracking, the robot informs its current sensor

to erase back pointers along the forward path to the first encountered white sensor or the first sensor that stores no back pointer.



Example:

BTD——Failure-Prone Environment

Robot Failures (Easily Handled):• Before every movement step, a robot transmits

a ‘beacon’ message carrying its ID. If later

within a time window, its one-hop neighbors do not receive from it, these sensor nodes will consider that

the robot has failed and run a service discovery algorithm to find a nearby functioning robot to take over

the failed robot’s task.



Sensor Failures (Difficultly Handled):• There are two steps in the fault-tolerance technique

to deal with their resultant sensing holes:

a. Find a search agent which is used to search

for the back pointer in a more efficient way.

b. Search for the back pointer and then move

to that back pointer to resume deployment.


Evaluation

Performance Metrics:• Coverage Ratio (CR): The average ratio of the number

occupied grid points to the total number of grid points.• Robot Moves (RV): The average number of movements

made by each robot during simulation.• Robot Messages (RM): The average number

of messages generated by each robot during simulation.• Sensor Messages (SM): The total number of messages

transmitted by sensors during simulation.


Evaluation

Modification on LRV:• For a fair comparison, LRV was slightly modified as follows:• Each sensor periodically transmits 'hello' message

as it does in BTD.• LRV was also extended to multi-robot scenarios.• As LRV does not terminate itself, it was also modified

to terminate as soon as each grid point has been visited

by a robot.


Evaluation

Simulation Setup:• A sensing hole is generated as follows:

the location of the first sensor to fail is selected randomly, then chose a random adjacent sensor to fail

until h sensors have failed.• Simulation environment:


Simulation Analysis

Coverage Ratio (CR):


Simulation Analysis

Impact of m on Movement and

Message Cost (RV/ RM/ SM): m refers to the number of robots


Simulation Analysis

Impact of h on Movement and

Message Cost (RV/ RM/ SM): h refers to the size of sensing

hole


Conclusion

BTD was demonstrated with both single-robot case

and multi-robot case in a failure-free environment

and a failure-prone environment. Simulation results indicate that BTD far outperforms

the only competing algorithms SLD and LRV

in various metrics.


Future Work

There are some parts in BTD algorithm can be improved in my future work:

• Sensing holes that do not affect robot back tracking

are not known by the robots and thus left untreated area in the failure prone situation.

• Cooperation between robots can be improved.

Questions

Question1: Please draw the square and triangle sensor deployment patterns. Hexagon is already given

as an example. (Tips: hexagon , square , triangle )

Srd Srd 2Srd 3

Questions Question2: Here is a brief description of the algorithm:

• The preference when a robot selects its movement direction is West>East>North>South.

• A sensor colors itself white if it is adjacent to an empty point.

• When the robot reaches a dead end,

it will back track to the nearest back pointer

(white sensor) along the backward path.

• The robot moves to the next adjacent sensor

with the lowest sequence number

whose back pointer location is the same as

the robot's destination. So could you please draw the possible path

for a single robot to deploy sensors

in the given area? (Start from A

and the black rectangle is an obstacle)

Questions

Answer:

Questions

Question3: Please try to proof the correctness of the following viewpoint:

• BTD terminates within finite time. Answer:• BTD terminates once all robots stop moving permanently.

A robot can visit a sensor no more than 4 times.

The number of sensor n is bounded, equal to the number of grid points contained in the ROI. Hence,

the maximum number of movements that a robot

can perform is 4n, implying each robot will make

finite number of moves between grid points.

This completes the proof.

Thank you!


Find a Search Agent:• To handle the sensing hole problem, we introduce

a new color, gray. Sensors adjacent to a sensing hole color themselves gray.

• A robot identifies a sensing hole locally, as soon as

it finds that its back-tracking shortcut is broken due to loss of track of the back pointer and its current sensor

is a gray sensor.



Example:


Find a Search Agent:• The replacement sensors on the outer boundary of the pa

tch network learn back pointer information from adjacent gray sensors by listening to their 'hello' message.

• In order to resume back-tracking, the robot then sends

a search message in an arbitrary direction.• It finally stops, after a traversal of the outer boundary

of the patch network, at a directional extreme node.

This node is called search agent.



Example:


Search a Back Pointer:• The search agent sends a search message carrying

the back pointer along the border of the patch network.

This search message will erase the same back pointer

with a larger sequence number in the adjacent gray sensors; meanwhile, it will pick the location of the gray sensor

with smallest sequence number.



Example:

sensor placement by robot teams qiao li 7353963. outline concepts algorithms simulation analysis ...

Documents

given sensor

robot networks

wireless sensor networks

mobile sensor networks

sensor deployment design

area coverage

single robot

sensor selfdeployment