Self-recognition Emergency Communications for Mobile Handsets

Sonia Majid and Kazi Ahmed

Telecommunications, Asian Institute of Technology,

Thailand Email: sh_sonia22@yahoo.com

Abstract- Disasters in last decade have rapidly increased the

research activities related to emergency communications. This paper presents a self-recognition model along with various attributes for mobile handsets to switch in emergency mode, without human interaction. A software based recognition module is introduced for the said purpose. This paper also discusses the implementation solutions based on: traditional cellular network features, cognitive radio using game theory and artificial neural networks. At the end of paper we present analysis considering the time taken for the identification of emergency situation and probability of false alarming of such situations.

I. INTRODUCTION

Disasters in last decade have left entire mankind in the state of anxiety. Knowing the fact that emergency situations cannot be avoided, people are looking towards precautionary measures and solutions to reduce the loss of future disasters. Need of robust telecommunications network is recognized as a vital entity to support the disaster response and recovery activities.

In this paper we focus on self-recognition of emergency situation by telecommunications equipment left with victims. We assume that survivors are carrying mobile devices e.g., Mobile handSets (MS) in what follows. According to a discussion held at the ITU/ESCAP Regional workshop on Disaster Communications [1] 50% of the deaths occur within two hours after the disaster. This is the time when no rescue teams or organizations responsible for response activities, reach the affected area. Therefore we propose a model for self-identification of emergency situation by mobile handsets to allow survivors to communicate among them and help each other. We also provide possible technological solutions for implementation of proposed model.

Motive behind this work is to enable victim’s MS to switch to emergency mode and start communicating with other surviving nodes in the absence of a centralized network e.g., Base Station (BS). Communications in emergency mode may include sending of messages or call to nearby survivors or broadcasting the liveliness information of victims (so that on the arrival of rescue teams and/or parties involve in response activities, process of localization of disaster victims may accelerate).

Section II describes the model for self-recognition of emergency situation along with its various attributes. Possible

technologies for implementing the proposed model are presented in section III. Section IV shows some analysis of the proposed solutions. Finally, section V concludes the paper.

II. SELF-RECOGNITION EMERGENCY COMMUNICATIONS MODEL

In this section a high-level model for self-recognition of emergency situation is proposed. We assume that a software-based recognition module is installed in MS of disaster victims. In normal mode MS is performing its routine network operations while recognition module keeps on analyzing the some input parameters. These input parameters vary from technology to technology (used for implementing this model) and are used for decision making within the recognition module. Fig. 1 depicts the proposed model.

Normal Mode

EmergencyMode

RecognitionModule

Switching betweenmodes

Fig.1. Self-recognition emergency communications model

1) Normal Mode: In normal mode, MS is performing its ordinary operations i.e., MS is connected to a centralized network e.g., BS and is part of cellular system.

2) Emergency Mode: In emergency mode MS switches itself from cellular to ad hoc mode and starts overhearing its neighbors instead of broadcasting messages to BS. In this mode MS may also broadcast messages but, theses broadcasted messages are not intended for known base stations. Rather, these messages are intended for stations deployed by parties involve in response activities.

3) Recognition Module: This is a software module introduced in MS for identifying emergency situation. Fig. 2 shows a workflow of proposed recognition module.

Proceedings of Asia-Pacific Conference on Communications 2007

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InputParameters

Analyzing InputParameters

Decision Making

OutputParameters

Normal Modeor

Emergency Mode

Fig.2. Workflow of recognition module

On identifying an emergency situation, recognition module switches MS to emergency mode. Fig. 3 illustrates the attributes of Self-recognition Emergency Communications Model (refers as SEC in rest of the text).

Fig.3. SEC attributes

Where,

0x : Outage measurement level of telecommunications network after disaster,

1x : Self-healing of some network functions e.g., communications in emergency mode,

0t : Time when disaster strikes,

1t : Time when MS switches to emergency mode or time of recognition of emergency situation,

2t : Time to restore network operations from 0x to 0 1x x− ,

3t : Time to restore all network operations to normal mode. We consider that communications network is recovered to

some extend i.e., from outage measurement of 0x to outage

measurement of 0 1x x− during the disaster response phase while, the restoring of communications network to its original functionality is achieved during emergency recovery phase.

III. POSSIBLE SEC IMPLEMENTATIONS

A lot of tools (technological solutions) can be used for implementation of proposed SEC model. We present three

solutions including: features of cellular network, cognitive radio approach using game theory, and artificial neural networks for the proposed SEC model.

A. Cellular Networks This solution may be considered as a traditional approach

using the basic functionality of centralized communications network e.g., cellular systems. In this approach, we consider that recognition module is periodically calculating an ACCess FLaG _ACC FLG for decision making. MS switches to emergency mode when the average signal level from the connected BS and that of the neighboring base stations falls below the threshold level and this situation sustains more than the time limit defined. Manipulations to these pre-defined threshold levels may lead to the false indication of emergency situation when a mobile node may undergo a deep fade; or MS may miss some emergency situation when switching to emergency mode should be considered.

_ ( ) ( ) (B TH N TH E THACC FLG S S AND S S AND T T )= < < >

(1) Where,

BS : Average signal level from connected base station,

NS : Average signal level from neighboring base stations,

ET : Elapsed time after no communications coverage,

THS : Threshold signal level,

THT : Threshold elapsed time.

Mapping to Recognition Module of SEC: Following is the mapping of above described cellular technique to the workflow of recognition module: 1) Input Parameters: , , . BS NS ET2) Analysis of Input Parameters: involves the calculation

of _ACC FLG according to input parameters and

some pre-defined parameters including and . THS THT3) Decision Making:

NormalModeWorkingelse

EmergencyModeWorkingthenTRUEFLGACCIf

=

===

_

_)_(

4) Output Parameters: send to the switch (which switches between normal and emergency modes).

ModeWorking _

B. Cognitive Radio using Game Theory Cognitive radios are adaptive radios that interact with their

environment and adapt their operating parameters to fulfill specific tasks [2]. On the other hand, game theory is a set of mathematical tools used to model and analyze interactive decision making [3]. In cognition cycle of cognitive radio,

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radios interact with environment to gather information which leads them in adaptive decision making. Therefore, we implement the concept of game theory for cognition cycle of cognitive radios and use this information in recognition module of SEC.

We consider a simple game; single iteration of this game is expressed by a normal form game. The player set (N) consists of all MSs connected to a BS. Player action plan is the set of adjacent and nearby frequency channels at which the MS is operating. Information regarding the access parameters, frequency channels etc. of cellular systems, is available to every MS via BroadCast Channels (BCCH) [4]. Utility function for any player is defined as:

)( ja

j( ) ( THBTHj SSANDSjafu ≤≤= )()( ) (2)

Where, : is the signal threshold level and : is the average signal level from the connected BS. Hence, a MS say ‘x’ switches to emergency mode when set of player action plan i.e., spectrum activity in the nearby and neighboring frequencies of MS (x) and the average signal level from the BS (to which MS (x) is connected) lies below the pre-defined threshold level. Spectrum activities of neighboring and nearby MSs are taking into account to avoid false alarming of emergency situation when the MS (x) is in fade.

THS BS

Mapping to Recognition Module of SEC:

Following is the mapping of above described cognitive radio using game theory technique to the workflow of recognition module: 1) Input Parameters: Player set (N), player action plan

, . )( ja BS2) Analysis of Input Parameters: involves the calculation

of utility function for MS (x) using the input

parameters and pre-defined parameter .

)( fu j

THS3) Decision Making:

NormalModeWorkingelse

EmergencyModeWorking

thenTRUEfuIf j

=

=

==

_

_

))((

4) Output Parameters: send to the switch (which switches between normal and emergency modes).

ModeWorking _

C. Artificial Neural Network Artificial Neural Network (ANN) is a popular field of

Artificial Intelligence (AI) which is used to solve many challenging problems [5, 6]. Here, we use ANN for decision making process within the recognition module of proposed SEC model.

We developed a Multi-Layer Perceptron (MLP); this ANN consists of three layers. Input layer consisting of two neurons indicating and Channel State Information (SCI) as input

parameters. Output layers consists of one neuron indicating in which mode MS should work; in between is one hidden layer consisting of n neurons. Hidden layer is introduced to include effects of various wireless channels effect including slow and fast shadowing etc. As the number of neurons in the hidden layer increases the effectiveness of the network also increases, but it also increases the computational complexities. Computational efficiency is considered to be very vital when introducing ANN for mobile devices like MS. Therefore it’s a tradeoff between computational efficiency and network effectiveness.

BS

Supervised learning algorithm is used for ANN training. Fig. 4 shows ANN to be used in SEC model.

.

.

.

Input

Output

Ith node Oth node

Weights(WIi)

Weights(WiO)

ith nodes

W1

W2

Wn

W1

W2

Wn

ActivationFunction 1

ActivationFunction 2

∑

SB

CSI

Fig.4. Artificial neural network for SEC Model

Where,

thI : Nodes at input layer

thi : Nodes at hidden layer

thO : Node at output layer To train ANN we first used the random weights and taken

activation function 1 as tangent sigmoid function and that activation function 2 as pure linear function. Once the ANN is trained its weights are frozen to be used for future decision making. These frozen weights help in reducing the computational complexity of ANN to some extend.

)(TanSig)(Purelin

Mapping to Recognition Module of SEC:

Following is the mapping of above described ANN technique to the workflow of recognition module: 1) Input Parameters: , CSI and frozen weights. BS2) Analysis of Input Parameters: using back-propagation

algorithm for calculating the output adaptively. 3) Decision Making:

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NormalModeWorkingelse

EmergencyModeWorkingthenTRUEOvalueIf th

=

===

_

_))((

4) Output Parameters: send to the switch (which switches between normal and emergency modes).

ModeWorking _

IV. ANALYSIS

For analysis, we have implemented the above three solutions (assumptions are made wherever necessary). A simple scenario considering the simulation area of 3000m× 3000m is considered. It is assumed that a cellular network is deployed in the simulation area using 5 BSs. A total of 25 MS are roaming randomly within the area (implemented using random waypoint mobility model). At some random time instance, disaster event is triggered, which results in the destruction of all BSs within the considered area.

Time taken for self-recognition of emergency/disaster situation by all the three proposed solutions is recorded. Fig. 5 shows the mapping of these recorded timings to SEC parameters. Time taken for the restoring of communications activities in disaster response and recovery phases is out of scope of this paper. Therefore, we have taken uniform random timings for these activities.

time

Outagemeasurement (x)

X0

x1

t0 3+t0

X0 -X1

Disaster ResponsePhase

Disaster RecoveryPhase

Cellular NetworksCognitive Radio using Game TheoryArtificial Neural Netowrk

5+t08+t0

Fig.5. Mapping of proposed solutions to SEC attributes

Here, is the time when the disaster strikes. The solution proposed using the traditional cellular network properties took less time to recognize the occurrence of emergency situation. While on the other hand ANN solution took the most time. This is due to the computational complexities involved in the proposed solutions.

0t

While observing the probability of false recognition of emergency situation, we found the following relationship among the proposed solutions:

(3) C GP P P≥ ≥ N

Where,

CP : Probability of false recognition of emergency situation using cellular network solution

:GP Probability of false recognition of emergency situation using cognitive radio using game theory solution

NP : Probability of false recognition of emergency situation using artificial neural network solution.

V. CONCLUSION

In this paper we proposed a self-recognition emergency communications model. Three technologies have been discussed for the implementation of proposed mode. It is found that there is a tradeoff between efficiently identifying the emergency situation and the time taken for this identification.

Solution using the traditional cellular network features comes out to be the quickest in determining the occurrence of emergency situation. While, game theory and ANN approaches are not quick enough. Contrary to this, ANN is found to be most reliable in terms of recognizing the emergency situation accurately and avoiding false alarm; this is because of trained frozen weights used in ANN. False alarming is more probable in cellular solution because it is more suspect to deep fades and shadowing. While Cognitive radio using game theory avoid false alarming due to fade by analyzing the spectrum of its near by MSs but, this spectrum analysis may sometimes goes wrong due to problems like hidden terminal.

REFERENCES [1] http://www.itu.int/ITU-D/asp/Events/ITU-ESCAP-BangkokDec2006/ [2] Haykin S.; “Cognitive Radio: Brain-Empowered Wireless

Communications”; IEEE journal on selected areas in communications, vol. 23, no. 2, February 2005.

[3] <.http://quello.msu.edu/conferences/spectrum/papers/neel-reed-menon-mackenzie.pdf >

[4] T. S. Rappaport, Wireless Communications Principles and Practice, Prentice Hall, 2002, ch. 11.

[5] Patrick, Henry, Winston, Artificial Intelligence, Addison Wesley Publishing Company.

[6] George F Luger & William A Stubblefield, Artificial Intelligence Structures and Strategies for Complex Problem Solving, Addison Wesley Logman, Inc.

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