Mutual Exclusion in WirelessSensor and Actor Networks

IEEE SECON 2006

Ramanuja Vedantham, Zhenyun Zhuang and Raghupathy Sivakumar

2008. 09. 18

Presented by Jang Chol Soon

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Contents

Introduction

Problem Definition

Context

Different types of Mutual Exclusion

Challenges & Goals

Centralized Approach

Distributed Approach

Performance Evaluation

Conclusions

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Introduction

Wireless Sensor Networks (WSNs)

One type of action: ‘sensing’ the environment

Performance evaluation: read operations

Wireless Sensor and Actor Networks (WSANs)

Two types of action: ‘sensing’ and ‘acting’ on the environment

Performance evaluation: read and write operations

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Introduction

e.g. Automated sprinkler system in WSAN

Sensors (humidity sensors)

Actors (sprinklers)

A minimum subset of sprinklers is activated to cover the entire region

Overall sprinkler resources (water) and energy is minimized.

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Introduction

The outcome of not acting to the appropriate level

depending on the nature of the application in WSANs

Inefficient usage of actor resources

Incorrect operation

A catastrophic situation

Mutual Exclusion

: providing mutually exclusive acting regions to cover an event region

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Introduction

Mutual Exclusion Algorithms

It used in concurrent programming to avoid the simultaneous use

of a common resource, such as a global variable, by piece of

computer code called critical sections.

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Introduction

The challenges to provide Mutual Exclusion

How do we provide mutual exclusion, when there are events of varying

intensities?

Is the approach generic to address different types of events such as

point/multi-point events as well as regional events?

What happens when the event area decreases or increases?Ⅰ. A greedy centralized approach

Ⅱ. A localized and fully distributed approach

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Problem Definition

A. Context

Architectural model

sink

- serves as the coordination entity.

- issues directives to both sensors and actors.

sensor

actor

The problem of mutual exclusion in the context of WSANs

- Given a set of actors in an event region, what is the minimum subset

of actors that covers the entire event region such that there is minimal

overlap in the acting regions?

sink

sensor actor

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Problem Definition

Notations to Define Types of Mutual Exclusion

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Problem Definition

Different Regions based on the Notation

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Problem Definition

B. The different types of Mutual Exclusion in WSANs

Resource Critical Mutual Exclusion

Overlap-type Critical Mutual Exclusion

Overlap-Area Critical Mutual Exclusion

Overlap-Intensity Critical Mutual Exclusion

※ Context

: regional events requiring only one round of execution

with no event dynamics

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Problem Definition

Resource Critical Mutual Exclusion

To maximize the non-overlapped acting regions of each actor within

the event region in order to utilize the actor resources to the least extent.

The minimal overlap in acting regions.

Definition

- To determine the minimum set of actors, M

- Maximizes the overall benefit function by the sum of individual benefit

function

e.g. A fire extinguisher application

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Problem Definition

Overlap-Type Critical Mutual Exclusion

When there is a threshold for the desired level of action and any amount

of action beyond this threshold is perceived as undesirable.

Definition

- To find the minimum set of actors, M

- To maximize the overall benefit function defined by the sum of

individual benefit function

- α is a constant that represents the cost incurred in having new

overlaps in the event region

e.g. An intruder-detection and automated-tranquilizer application

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Problem Definition

Overlap-Area Critical Mutual Exclusion

To maximize the amount of non-overlapped region covered by each actor

To minimize the amount of overlapping regions (both old and new)

Definition

- To determine the minimum set of actors, M

- Maximizes the non-overlapping and minimizes the total overlapping

regions of the actor cover

- β is a constant that represents the cost incurred in having any kind of

overlap in the event region

e.g. A fire extinguisher application

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Problem Definition

Overlap-Intensity Critical Mutual Exclusion

Every overlap beyond a threshold is deemed as undesirable, and

the weight of the function depends on the number of times the overlap

occurs for a particular region (intensity of overlap)

Definition

- To determine the minimum set of actors, M

- Maximizes the non-overlapping and minimizes the total overlapping

regions based on the intensity

- is the weighting factor that represents the cost incurred in having

an overlap with intensity in the event region

e.g. A fire extinguisher application

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Problem Definition

C. Challenges for other types of applications

Differing Event Intensity: (a)

Point/Multi-point Events: (b)

Event Dynamics: (c) (d)

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Problem Definition

D. Goals

Overheads

- small

Correctness

- the percentage of area covered by the actor cover set

in comparison with the total event region

- is able to cover the entire even region

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Centralized Approach

Assumptions

Network Model

- sensors and actors : static, randomly distributed

Location Information

- localization algorithms

Sensing, Acting and Communication Ranges

- same

Routing Model

- an underlying reliable routing protocol

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Centralized Approach

Centralized Approach

A greedy, centralized algorithm

To alleviate the mutual exclusion problem

Actor’s selection criteria : benefit function of actors

Mechanism

- selecting and adding the actor with the maximum benefit function

at each stage

- benefit function : defined by the type of mutual exclusion

- terminates when the selected set of actors cover the complete event

region

Optimality of the approach

- NP-hard (Nondeterministic Polynomial-time hard)

- The upper bound of the competitive ratio:

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Centralized Approach

Mechanism

M

- The set of actors selected as part of

actor cover at any given stage

- Initially, an empty set

MAX_ACTOR

- The actor that has the maximum benefit

function

MAX_BENEFIT

- The non-overlapped region of this actor

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Distributed Approach

Distributed and fully localized approach

Neighborhood Back-off (NB) approach

addresses the challenges for other types of applications

A distributed realization of the centralized strategy

Automatic updates to benefit functions of all entities within each

dependency region

Dependency region for a sensor or an actor (entity)

- The maximum region with which another entity can have an impact

on its execution range

- The dependency region of a sensor : Sensing Range + Acting Range

- The dependency region of a actor : 2 * Acting Range

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Distributed Approach

Distributed and fully localized approach

Basic operations after determining of the dependency region

- The determination of initial benefic function for each actor

: issued by the sensor to the actors in its dependency region

- The emulation of the greedy centralized strategy at each actor

by waiting time for an amount of time

: Benefit function ↑ , waiting time ↓

Benefit function ↓ , waiting time ↑

- The updating of the benefit functions for all actor within the dependency

region of an actor

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Distributed Approach

The Neighborhood Back-off Approach

Construction of Dependency Regions

- The initial set up of the network

- One-time discovery process to determine the set of actors within

the dependency region of a sensor or an actor

Operations at the Sensors

- reports the sensed information to the sink and receives the command

directive from the sink

- every sensor in the event region constructs a shortest path tree within

its dependency region

- sending REQUEST() or CANCEL() directives to all the actors in its

dependency region

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Distributed Approach

The Neighborhood Back-off Approach

Operations at the Actors

- determines the event region in its acting range base on the REQUEST()

directive received from the sensors

- every actor in the event region constructs a shortest path tree within

its dependency region

- receives a REQUEST() directive from a sensor

- determines the additional event area covered by the sensor and add

that region to already existing event area

(virtual metric : used to determine the wait time for a actor)

- NOTIFY() transmission, Flag(), Transmit(), wait()

event

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Distributed Approach

sink

sensor

actorsensed info

command directive

REQUEST(Dir_id, Xsi, Ysi)

IF wait time <= 0 send NOTIFY() IF Flag() checked Transmit() ELSE

wait()ELSE wait()

update benefit function

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Distributed Approach

Mechanism

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Distributed Approach

Mechanism for Addressing Challenges

Handling Varying Event Intensities

- adapting the actor cover algorithm based on the difference in the

intensity across the event region

Handling Point Events

- Selecting a minimum set of actors that covers all the point events

without any overlap

Handling Event Dynamics

- The increasing in the event area : REQUEST()

- The decreasing in the even area : CANCEL()

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Performance Evaluation

Performance evaluation for three approaches

Centralized Set Cover (CSC) : 100% correctness

Minimum Dominating Set (MDS) : 70% correctness

Neighborhood Back-off (NB) : 100% correctness

Context for Performance evaluation

The benefit function : Resource Critical Mutual Exclusion

A custom built, event-driven simulator written in C

- 2000 sensors and 2000 actors are randomly placed on a 3000m * 3000m

square area

- The sensing and communication range of sensors : 30m

- The default event radius : 100m ( ~ 500m)

- The default distance from the event center to the sink : 1500m ( 500 ~ 2500m)

- Bounded delay : 10 sec - packet size : 1KB

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Performance Evaluation

Varying the Event Area Size

NB approach

: the best performance in terms

of communication cost

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Performance Evaluation

Varying the Event Area Size

NB approach : the worst performance in terms of overlapped action areas and

Number of actors, but 100% correctness

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Performance Evaluation

Varying the Distance from the Sink to the Event Center

NB approach

: no increase with increasing

Sink-to-event distance

due to the localized operation

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Performance Evaluation

Varying the Delay Bound

NB approach

: execute the command at

different times

due to the back-off mechanism

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Performance Evaluation

Varying the Delay Bound

NB approach : little effect

CSC and MDS approach : large effect

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Performance Evaluation

Varying the Density of Actors

- increase from 1 * 2000 actors to 3 * 2000 actors

NB and CSC approach : incur a slightly larger communication overhead

(※ NB performs slightly worse due to distributed operations)

MDS approach : almost constant

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Performance Evaluation

Varying the Density of Sensors

- increase from 1 * 2000 actors to 3 * 2000 actors

All three approaches : have a slightly larger overhead due to the marginal increase in communication cost to report sensor data

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Conclusions

Conclusions

The problem of mutual exclusion in the context of WSANs

- Generic different types of Mutual exclusion

1) Resource Critical Mutual Exclusion

2) Overlap-Type Critical Mutual Exclusion

3) Overlap-Area Critical Mutual Exclusion

4) Overlap-Intensity Critical Mutual Exclusion

- Challenges

1) Differing Event Intensity

2) Point/Multi-point Events

3) Event Dynamics

• The solution to address the problem of mutual exclusion

- A greedy centralized approach

- A localized and fully distributed approach (NB approach)

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Discussion

No consideration about

Simultaneous occurrence of the analyzed problems about mutual exclusion

Dedicated specification for assumptions in these approaches

Reality

Computation of Dependency region of a sensor or an actor

Q & A

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