SOS: A Safe, Ordered, and Speedy Emergency Navigation Algorithm in Wireless Sensor Networks Andong Zhan ∗ †, Fan Wu ∗, Guihai Chen ∗ ∗ Shanghai Key Laboratory.

SOS: A Safe, Ordered, and Speedy Emergency Navigation Algorithm in Wireless Sensor Networks

Andong Zhan †∗ , Fan Wu∗, Guihai Chen∗

∗Shanghai Key Laboratory of Scalable Computing and Systems,

Department of Computer Science and Engineering, Shanghai Jiao Tong University, China

†Department of Computer Science, Johns Hopkins University, USA

IEEE ICCCN (Accepted rate:29.6%)

Wireless & Mobile Network Laboratory

Outline

Introduction Related Works Problem Specification Design Principles Simulations Conclusions

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Introduction

More people were killed by natural disasters worldwide past. Evacuation techniques are highly needed to navigate the

personnel out of danger quickly. Wireless sensor networks can play an important role in detecting

disasters and navigating the personnel out of dangerous areas.

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Introduction

Objective The objective of a successful navigation is to schedule all the users to

bypass the dangerous areas safely, and finally evacuate to pre-known exits as soon as possible.

Requirements All the escape paths given by the navigation algorithm should be safe

paths. All the users should evacuate from the emergency area orderly without

congestion. The navigation algorithm should minimize the total evacuation time. To lower the cost, sensor nodes do not require geographical location

information.

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Problem Specification

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Assumptions a wireless sensor network can detect dangerous areas

Users carry wireless communication devices• which enable them to “talk” with nearby sensors

• e.g., 802.15.4 compatible PDA or smart phone

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Safe Path: A path P = {s1, s2, . . . , sn} is a safe path if and only if ∀ si ∈ P, di ≥ dΓ

di is the distance between sensor node si and its nearest alarming neighbor node.

dΓ is the safe distance threshold.

Safety Capacity: For each sensor node si on a safe path, its safety capacity is the maximum number of people passing through it safely per unit time.

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Design Principles

Constructing the Medial Axis Graph Formulating the Navigation Schedule Problem Designing the Distributed Algorithm

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Design Principles


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Design Principles

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Constructing the Medial Axis Graph


Design Principles


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Design Principles

Navigating users to the closest gateway, choosing the safest path for every user, or taking both safety and distance into account

When the number of users is large and the capacities of safe paths are low, congestion may occur, and greatly increases the evacuation time resulting in more casualties. Time should be considered. Waiting is inevitable when the number of users is larger than the

maximum safety capacity of the network.

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Formulating the Navigation Schedule Problem

It can not guarantee the optimal scheduling


Design Principles

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Time cost (between two nodes) = 1

Time cost=2

Time cost=3

Safety Capacity


Design Principles

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Time cost (between two nodes) = 1

Time cost=2

The number of users is larger than the safety capacity

Time cost=3


Design Principles

We create a graph G(V, E) and denote vertex it ∈ V, i ∈ N as the state of sensor node si at time t, and directed edge edge (i, j, t) ∈E as the arc from vertex it to vertex jt+1

A sensor node may have several vertices so as to represent the number of users in different time units.

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it

edge (i, j, t) jt+1

it

edge (i, i, t) it+1

Traveling: Waiting:


Design Principles

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Design Principles

The navigation schedule problem can be formulated as a linear program:

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Minimize

Subject to:

the flow from vertex it to jt +1

time cost

the number of users in the whole safe area

safety capacity


Design Principles


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Design Principles

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Designing the Distributed Algorithm


Design Principles

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ihopd=10

u(i)

i hopd=3

u(i)


Design Principles

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S4 S3

di:

S2 S1 gateway

Exitin out in out in out in out

height:

4-hop 3-hop 2-hop 1-hop

8 6 4 27 5 3 1

u(i) u(4)=10 u(3)=5 u(2)=10 u(1)=15


Design Principles

Local Minimum Problem

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S4

S3

S2

S1

height8

6

4

24+1=5

Record the navigation schedule, i.e, the time and the number of users to certain neighbor node


Simulations

Simulate randomly deploying sensor nodes in a square field. The number of nodes is from 1000 to 8000. We randomly create 1 to 5 groups of users in each run. The number of users in a group is created randomly between 1

and 50. In each run, we also randomly setup 1 to 5 exits and 3 to 6

dangerous areas.

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Simulations

Uniform capacity: the capacity of every sensor node is equal. Linear capacity: the capacity of a node is linear with the

number of hops from the node to the closest alarming node.

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Linear capacity:


Simulations

Average evacuation time

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Potential Feld (PF)Skeleton Graph (SG)Road Map (RM)


Simulations

Last evacuation time

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Simulations

Network overhead

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Conclusions

We have proposed SOS emergency navigation algorithm in WSNs.

To minimize users’ evacuation time, we have converted the emergency evacuation problem to a traditional network flow problem and used push-relabel algorithm to solve it.

In simulations, SOS is better than existing approaches in terms of average evacuation time, last evacuation time, and network overhead.

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Thanks for your attention！

SOS: A Safe, Ordered, and Speedy Emergency Navigation Algorithm in Wireless Sensor Networks Andong Zhan ∗ †, Fan Wu ∗, Guihai Chen ∗ ∗ Shanghai Key Laboratory.

Documents

navigation schedule

distributed algorithm

successful navigation

sensor node si

number of users

medial axis graphformulating

sensor nodes

path p