Multi-Agent Base Evacuation Support System Considering Altitude

Shohei Taga

, Tomofumi Matsuzawa

, Munehiro Takimoto

and Yasushi Kambayashi

Department of Information Sciences, Tokyo University of Science, 2641 Yamazaki, Noda 278-8510, Japan

Department of Computer and Information Engineering, Nippon Institute of Technology, 4-1 Gakuendai,

Miyashiro-machi, Minamisaitama-gun, Saitama 345-8510, Japan

Keywords: Mobile Ad Hoc Network, Mobile Agent, Multi Agent, Contingency Plan, Risk Management.

Abstract: In this paper, we propose an extension of an evacuation support system that we have previously proposed

(Taga, 2018). The system suggests evacuation routes in cases of disasters. We have confirmed the

usefulness of the system. When a disaster occurs, we anticipate that the current popular wireless

communication based on the Internet may not be very reliable. In order to accommodate such a problem,

our proposed system employs multiple mobile agents and static agents on smartphones that use a mobile ad

hoc network (MANET). The proposed system collects information by mobile agents as well as diffuses

information by mobile agents so that the system provides an optimal evacuation route for each user in a

dynamically changing disaster environment. In simulations, our system successfully guides evacuation users

to safe areas. The system, however, does not consider the altitude of the evacuation routes. Therefore, the

system may not be very useful in cases of flood. When a tsunami or a flood tide occurs, low altitude

location may be under water. Therefore, evacuees need to move along high altitude routes. In this paper, we

also take account of the altitude information for constructing evacuation routes.

1 INTRODUCTION

In this paper, we propose an evacuation support

system that provides evacuation routes in cases of

disasters, and verify the usefulness of the system. In

recent years, with the development of

communication and portable device technologies,

people can collect and spread information using the

Internet regardless of time and place. Current

popular wireless communication infrastructure is

supported by a series of base stations and one

communication equipment in such a base station

handles a lot of communication. Therefore, when

problems occur at equipment in such a

communication base station, it may be difficult, even

if possible, for the smartphones to use the Internet.

In fact, in the 2011 off the Pacific coast of Tohoku

Earthquake in Japan, we have observed a large-scale

communication failure due to corruption of the

communication equipment and traffic congestion.

Paralyzed communication infrastructure made it

difficult for people to collect information about the

conditions of transportation and safety information

about family and friends using smartphones.

Our proposed system addresses this problem of

communication infrastructure by constructing

mobile ad hoc network (MANET) by wireless

communication between users’ smartphones. Then

users can share information in such a network. Since

MANET is a network constructed with only portable

devices, it is possible to avoid problems due to

failure of communication infrastructure. However,

the ever changing topology of MANET makes stable

communication extremely difficult. Therefore, we

propose an information sharing method using mobile

software agents. A mobile agent is a program with

mobility, it has a feature of perceiving environment

and deciding its behaviour depending on the

environment. Our proposed system constructs

optimal evacuation routes by using such mobile

agents to share and collect information necessary for

evacuation.

In previous studies, we proposed basic

configuration of the evacuation support system and

verified its feasibility by simulators (Taga, 2016)

(Taga, 2017). In the previous study, we have

expanded the features of the system and verify its

usefulness with a simulator namely NS-3 (Taga,

2018). In the study, we have introduced new mobile

agents that actively collect information closely

Taga, S., Matsuzawa, T., Takimoto, M. and Kambayashi, Y.

Multi-Agent Base Evacuation Support System Considering Altitude.

DOI: 10.5220/0007693302990306

In Proceedings of the 11th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2019), pages 299-306

ISBN: 978-989-758-350-6

299

related to the users’ potential evacuation routes.

Even though the collected information includes

actual and potential dangerous area, it does not

collect information about altitude of the evacuation

routes. Thus our support system may guide the users

to low altitude area and let the users be drown in a

case of flooding.

In this paper, we consider altitude information

for constructing evacuation route. If a tsunami or

flood tide occurs, evacuating through low altitude

route could be dangerous. In order to evacuate

safely, evacuees should move along high altitude

route. Therefore, we propose an algorithm that

constructs high altitude evacuation route, and

construct simulator for verifying the feasibility of

the algorithm.

The structure of the balance of this paper is as

follows: the second section describes related works.

The third section describes the details the proposed

system. The fourth section describes the numerical

experiments and discusses the results, and the fifth

section discusses the future works and concludes the

discussion.

2 RELATED WORKS

Komatsu et al. proposed an evacuation system that

estimate impassable point automatically by

comparing evacuation route presented by the system

and actual evacuee's trajectories (Komatsu, 2018).

Estimated impassable points are shared between

evacuees by communication between their portable

devices, or server through available communication

infrastructure.

Wang et al. proposed a solution for fire

evacuation routing problems by applying artificial

bee colony optimization (BCO) algorithm (Wang,

2018). The BCO algorithm is a swarm intelligence

algorithm inspired by foraging of bees. They

simulated this solution at situations of evacuating

from buildings with multiple exits so that they

achieve to improve the total evacuation time.

Ikeda et al. proposed safety evacuation route

planning using multi-objective genetic algorithm

(MOGA) (Ikeda, 2016). The MOGA is a method

applying genetic algorithm to the multi-objective

optimization problem. An evacuee's handset records

GPS data and acceleration data, and send to crowd

server. The server calculates an evacuation route

considering evacuation distance, evacuation time

and the safety degree of evacuation route, and then

provide the optimal route to the evacuees.

Kartalidis et al. proposed evacuation routing

method that detects positions of evacuees by using

trilateration technique using wireless access points

and evacuees’ smartphones, and then constructs

evacuation routes based on cellular automata

(Kartalidis, 2018). The states of cellular automata

express the state of a specific position of the

evacuation area that indicates the presence of the

evacuees detected, obstacle and free area and more.

For each step of the exit path exploring process, the

state of each cell is refreshed. Therefore, evacuees

can evacuate avoiding impassable points.

Asakura et al. proposed algorithm to calculate a

simple evacuation route to reduce the burden of a

particular evacuee (Asakura, 2016). If an evacuation

route is complicated and meandering, the evacuees

may be confused. In order to mitigate this problem,

their proposed method calculates a route that has the

fewest number of turns at intersections.

Avilés et al. proposed an evacuation support

system using MANET and the ant colony

optimization (ACO) for indoor environments

(Avilés, 2014). In the study, they implemented ACO

by using mobile agents so that the ACO algorithm

can take the movement trajectory and speed of

evacuees in consideration. Then the evacuation

support system constructs an optimal evacuation

route for each user.

Ohta et al. studied on evacuation support

methods using ACO and MANET (Ohta, 2015). In

the study, they pointed out a problem such as a

conventional ACO may include dangerous locations

when it constructs evacuation routes. The problem is

caused by the dynamic nature of the disaster

environments such as conflagration or tsunami. In

order to mitigate this problem, they proposed an

improved ACO-based evacuation support system

that equips deodorant pheromone which erases ACO

pheromone traces when dangerous locations are

found. Goto et al. applied this proposed method to

real data of tsunami damage (Ohta, 2016). They

showed practical results by using the data of

Rikuzentakata city which suffered great damage due

to the 2011 off the Pacific coast of Tohoku

Earthquake. They verified their method is feasible

based on the real data.

Kambayashi et al. proposed and implemented a

system that collects safety information of evacuees

using mobile agents on MANET (Kambayashi,

2015). In the study, they proposed a method to

reduce the load on transmission by combining

multiple mobile agents into one. Nishiyama et al.

proposed communication system using portable

devices that switch between MANET and Delay

HAMT 2019 - Special Session on Human-centric Applications of Multi-agent Technologies

300

Tolerant Networking (DTN) according to

communication situations (Nishiyama, 2014). DTN

is a method for coping with a network environment

where maintaining stable communication connection

is hard to achieve. When communication is

disconnected, portable devices accumulate data, and

then transmitted when communication is resumed.

Their proposed system apply MANET when there

are many portable devices in the surroundings, and

apply DTN when there are few. With such a method,

they achieved to cope with various network

environments.

As mentioned above, there are various works

aimed at supporting disaster evacuation. In order to

carry out safe and quick evacuation, it is necessary

to promptly identify places that are impassable (such

as the place where a fire occurs) and construct

evacuation route. These can be achieved by using

servers or sensors device installed in town.

However, these fixed devices may be damaged when

a disaster occurs and become unusable. Instead, our

proposed system uses only smartphones owned by

evacuees, and identifies places that are impassable

and construct evacuation routes.

3 AGENT BASE EVACUATION

SUPPORT SYSTEM

In this section, we describe our proposed system in

detail. The proposed system aims to provide an

optimal evacuation route for each user (hereafter we

call the evacuation user). Since the proposed system

maintains the map information of the evacuation

area, it is possible to calculate the shortest route to

the destination (i.e. safe place). However, at the time

of a disaster, there should be many occurrences of

unsuitable points for evacuation (hereafter we call

dangerous point) due to fire, building collapse, or

inundation. Since nobody knows these points before

the occurrence of a disaster, it is necessary to collect

the information during evacuation. When an

evacuation user finds a dangerous point, he or she

inputs the position information to the system. Then

the proposed system constructs a new evacuation

route avoiding this dangerous point, and provides it

to the user. At the same time, the system diffuses the

information about the dangerous point and new route

to other users’ smartphones. As a result, evacuation

users other than the discoverer can know the

dangerous point and avoid it in advance. In order to

realize this function, we use multiple mobile agents.

A multi-agent system is a system that consists of

multiple agents and achieves tasks by their

cooperative operations. The agents can be

categorized into two types: mobile agents and static

agents. A mobile agent is generated when it is

needed and executes a task through migrating among

communication sites including smartphones. Every

mobile agent has a unique identifier. A static agent

resides on communication site including, of course,

a smartphone. Unlike mobile agents, static agent has

no unique identifier. We describe the details of each

agent we use in the proposed system below.

3.1 Static Agents

3.1.1 Information Agent

Information Agent is a static agent residing on a

smartphone that interacts with mobile agents and

constructs evacuation routes. When requested from

the system, it creates mobile agent.

The information agent processes the request in

the following order. (i) It generates the requested

mobile agent. (ii) It acquires the information

necessary for the generated mobile agent from the

node management agent and passes it to the mobile

agent. (iii) It stores the mobile agent in a queue. It

periodically checks the queue, and dispatches the

mobile agents to the neighbouring smartphones.

When a mobile agent comes from another

smartphone, the information agent receives

information held by the mobile agent. Then it passes

the requested information to the arrived mobile

agent and store it in the queue in the same way as

the above step (iii). The information agent records

the unique identifier of the mobile agent that visited

the smartphone as well as it created in a list called

visitor list. The information agent requests the

visitor list of other smartphones when it

communicates with them. It then passes the received

visitor list to the mobile agent that needs it in the

queue. The mobile agent decides the next destination

from this visitor list.

The information agent constructs evacuation

routes based on the information it initially has, and

the information collected from the visited mobile

agents. The evacuation route is the route to the

destination avoiding dangerous points that are

currently known by the information agent. The

evacuation route is determined based on the

Dijkstra’s algorithm. The Dijkstra’s algorithm is an

algorithm for solving the shortest path problem

between two nodes in a graph, and was proposed by

Edgar Dijkstra in 1959 (Dijkstra, 1959). In the

proposed system, the graph consists of intersection

as the nodes, and the distances between intersections

Multi-Agent Base Evacuation Support System Considering Altitude

301

as the edge weights. In this proposed system, in

order to construct high altitude evacuation route, this

proposed system calculate edge weight by dividing

distance by altitude of the node. Hence, high altitude

route is preferentially selected as evacuation route.

This evacuation route may not be the shortest route,

but evacuation users can avoid a tsunami or flood

tide and reach safe areas. The information agent

constructs an initial evacuation route at the system

start up time. After that, when a mobile agent arrives

and let the information agent know a dangerous

point exists on the current evacuation route, the

information agent reconstructs a new evacuation

route.

3.1.2 Node Management Agent

Node Management Agent is another static agent

residing on a smartphone for man-aging the

information on the smartphone. The node

management agent stores the dangerous point

information collected from the visited mobile agents

in the information table. At this time, if the same

information already exists in the information table,

the node management agent delete older information.

Also, if the information agent requests information

about dangerous points, the node management agent

passes the requested information.

3.2 Mobile Agents

3.2.1 Information Diffusion Agent

Information Diffusion Agent is a mobile agent that

diffuses the information of the dangerous point

found by the evacuation user to neighbouring

smartphones.

When an evacuation user finds a dangerous point

and input its information into the system, the

information agent generate the information diffusion

agent. The information agent passes the coordinates

of the discovered point to the generated information

diffusion agent. And then, the information diffusion

agent waits until a link with another smartphone is

established. After that the information diffusion

agent act as follows: (i) When communication links

with other smartphones are established, the

information diffusion agent copies itself by the

number of linked smartphones and moves to each

smartphone. However, if the information diffusion

agent finds that it has already visited the smartphone

(i.e. the smartphone’s visitor list has its identifier), it

does not move but commit suicide. (ii) After the

movement, it passes its own information to the

information agent on the destination, and be into

standby state until the next communication link

being established. It repeats this process a constant

number of hops. When the information diffusion

agent copies itself, it copies not only its own

information but also its own unique identifier.

Therefore, it does not move to the smartphone that

its own copy has visited. This mechanism prevents a

smartphone from receiving the same information

multiple times.

3.2.2 Information Collecting Agent

Information Collecting Agent is another mobile

agent that collects information about the events on

the evacuation route and returns to the original

smartphone. The information diffusion agent

diffuses the information discovered by the

evacuation user around the discovery point, but there

is no guarantee that this information can be

conveyed to all evacuation users who need it. In

order to solve this problem, we propose the

information collecting agent that actively collects

information diffused by the information diffusion

agent as shown in Figure. 1.

Figure 1: Information diffusing and information

collecting.

The information collecting agent returns to the

original smartphone after the information collecting

process, but there is a problem with the return

method. In the situation of the proposed system

being used, it is difficult to return through the

movement history of the agent in reverse order due

to the disappearance of the smartphones it has

visited. They may move out of wireless

communication range or their batteries may be

exhausted. For this reason, the proposed system

predicts the current location of the original

smartphone based on the evacuation route, moving

speed and elapsed time of the original smartphone.

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The information agent generates the information

collecting agent at regular time intervals. Then, the

information agent passes the current evacuation

route information, the moving speed of the user, and

the life time of the information collecting agent, to

the generated information collecting agent. And

then, this information collecting agent waits until a

communication link with another smartphone is

established. When it is established, the information

collecting agent moves to the neighbouring

smartphone. The information collecting agent has

two states, the collecting state and the return state,

and the behaviour changes depending on the state.

Initially it is in the collecting state. The collecting

state is a state of collecting information and acts as

follows. (i) The information collecting agent moves

to the smartphones along the current evacuation

route of the user. Though, of course, it excludes the

smartphone that it has already visited. (ii) It acquires

information from the destination smartphone. The

information collecting agent repeats this process

during the collecting state. If the information

collecting agent acquires information that tells there

is a dangerous points on the evacuation route, or

exceeded half of the its own life time, or cannot find

the next migration candidate, the information

collecting agent becomes in the return state. The

return state is a state of returning to the original

smartphone and acts as follows. (i) The information

collecting agent calculates the predicted current

position of the original smartphone based on the

information it has; i.e. evacuation route information

of the original smartphone and the moving speed of

the evacuation user of the smartphone, and the

elapsed time since it was generated as shown in

Figure. 2. (ii) It moves to the smartphone closest to

the calculated expected position.

Figure 2: Predict the position of original smartphone.

Unlike the collecting state, in the return state, it may

moves to the smartphone it has visited before. It

repeats this process until returning to the original

smartphone. When the information collecting agent

return to the original smartphone, it passes the

collected information to the information agent on the

smartphone and disappears. If the information

collecting agent cannot returns even exceed the own

life time, or if it cannot find the original smartphone

in the vicinity of the calculated predicted current

position, it commits suicide.

One of the disadvantages of the information

collecting agent is that, since it is generated from

each smartphone, the network load tends to increase.

In order to mitigate this problem, the proposed

system controls the generation of the information

collecting agents by broadcasting messages to the

neighbouring smartphones that request to stop

generating the information collecting agent for

certain period. This message contains the address

and the evacuation route of the sender, and the life

time of the information collecting agent. The

smartphones that receive this message stop

generating information collecting agents if its own

evacuation route is the same as described one in the

message. If the own evacuation route is different

from what described in the message, it ignores and

discards the message. When the information

collecting agent returns to the sender smartphone of

the message, this smartphone broadcasts messages

that permit generating information collecting agents

and the collected information to the neighbouring

smartphones. The smartphones that have stopped

generating information collecting agent resume

generating of the agents when they receive this

message. The smartphones also resume generating

information collecting agents when the stopping

period in the message elapses.

4 NUMERICAL EXPERIMENT

This section describes the numerical experiment of

the proposed system. We have verified by

simulation in situations that people use the proposed

system at the disaster area. We used NS-3 for

simulation. NS-3 is a discrete-event network

simulator which is open for research and educational

use. The model of the communication environments

and processes in a form close to the real world, and

the simulator makes it possible to perform various

verification experiments.

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4.1 Experimental Conditions

We have created a simulation map that represents

the evacuation area (Figure. 3). This simulation map

shows a part of downtown Tokyo, and is modelled

of the real geography. This area consists of upland,

lowlands and landfill sites, so it is rough terrain. In

this experiment, we need to verify the algorithm

considering altitude information, therefore such a

terrain is suitable as a model. We use the map

images acquired and edited from Google MAP

(Google, 2018). We also obtained the altitude

information from the map published by the

Geospatial Information Authority of Japan on the

Web (Geospatial Information Authority of Japan,

2018). This simulation map consists of nodes and

edges. The edge is a straight sidewalk, evacuees

move along the edge. The node is an intersection

point of edges. As destination of evacuees, five safe

areas provided in the map. This map is about 4.5

square kilometre in the real map, and includes

altitude information. The number of node is 1430,

the lowest altitude of node is 5.4 metre, and the

highest altitude of node is 32.0 metre. In order to

express tsunami or flood tide disasters, nodes

become the dangerous point in order from the lowest

altitude node, i.e. along the river. The dangerous

point increases every 10 seconds.

Figure 3: Simulation maps.

Evacuation users are randomly placed and move

toward nearest safe area from current position. The

moving speed of the evacuation user is set to 1

meters per second. The proposed system has the map

information. All evacuees moves through the

evacuation route constructed by the information

agent in his or her smartphone. The proposed system

does not have dangerous point information in

advance, and they will know for the first time when

evacuation user actually touches to the dangerous

point or be notified from other evacuation users

through mobile agents. When an evacuation user

knows a new dangerous point, the information agent

reconstructs the evacuation route avoiding this

dangerous point. At this time, the previous nearest

safe area from current position of evacuation user

may change. If such a case occurs, information agent

changes the destination to a new nearest safe area.

The communication distance of the smartphone is

50m. The evacuation user who arrives at the

destination (hereafter we call the safe evacuation

user), or who it is impossible that arrive the

destination due to be surrounded by dangerous

points (hereafter we call the dead evacuation user)

terminates the communication and stop. When all

evacuation users stop, the simulation ends.

In this verification experiment, we measured (1)

the number of the touches to the dangerous points of

all evacuees, (2) the number of the safe evacuation

users and the dead evacuation users. We have

verified the effect of considering the altitude

information. We measured the above numbers in the

case of not considering it, and in the case of not

using the proposed system (i.e. evacuation user's

smartphone calculates an evacuation route to shelter,

but does not share information of dangerous points,

and does not consider altitude information).

The number of hops of an information diffusion

agent is 1, and the life time and the generation

interval of an information collecting agent is set to

120 seconds. In all the cases, we further divided the

cases that the number of evacuees 50, 100, 150, 200.

We have carried out all the cases 50 times each, and

taken the averages as the results.

4.2 Results and Discussion

Figure. 4 shows the results of the number of the

touches to the dangerous points. "no altitude" is the

case of not considering altitude, and "altitude" is the

case of considering altitude. "not use" is the case of

not using the proposed system. In the case of the

number of the evacuees is 50 to 150, there is no

significant difference between the "not use" and the

"no altitude", but in the case of the number of

evacuees is 200, the "no altitude" is smaller than the

"no use". In all the cases, the "altitude" shows good

results.

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Figure. 5 shows the results of the number of the

safe evacuation users and the dead evacuation users.

Unlike the result shown in Figure. 4, in the case of

the number of the evacuees is 200, there is no

significant difference between result of "no altitude"

and "altitude". On the other hand, "altitude" shows

Figure 4: Results of the number of the touches to the

dangerous points.

good result in all the cases. If the number of

evacuation users who participate the MANET is

few, evacuees can only know information in the

nearby position because the network is only partially

constructed. Therefore, an evacuee nearby

dangerous point may be surrounded by other

dangerous points before escaping, even if he or she

could know the information of that point in advance.

Therefore, selecting higher evacuation routes by

avoiding places with low altitude in advance

contribute evacuees’ safe evacuations.

Figure 5: Results of the number of the safe evacuation

users and the dead evacuation users.

5 CONCLUSION

In this paper, we proposed an evacuation support

system that enables information sharing under

environments where people cannot use the Internet

communication due to the disaster. In the system, we

also took account of the altitude information when

constructing evacuation route. In the experiment, we

showed that evacuees can grasp dangerous points in

advance and avoid them by using the proposed

system. We also showed that evacuees can safely

evacuate by choosing high altitude routes as

evacuation routes when they are hit by tsunamis and

flood tides.

As a future work, it is necessary to address the

increase of the load in the relay smartphones. Since

the information collecting agent moves along the

evacuation route of the evacuation user, they

frequently move in a busy street such as main streets

and in front of evacuation centres. As a result, the

network load drastically increases in particular

places. Therefore, it is necessary to develop a

mechanism for controlling the flow amount of the

agent in such places. In addition, we need to

consider an evacuation time when choosing high

altitude route. By preferentially selecting high

altitude routes, evacuees can safely escape from the

tsunami and flood tides. However, since this route is

not the shortest route to the safe place, the

evacuation time will increase. As a method to solve

this problem, it is conceivable to change the priority

of selecting a route with a high altitude according to

the altitude of the current position of the evacuee. If

the current position of the evacuee is a place with a

low altitude, it is necessary to move quickly to a

high place. But if an evacuee is in a sufficiently high

place from the beginning, the necessity of selecting a

higher place is low. In this case, it may be safer to

move a route with shorter distance than to move a

route with a longer but higher altitude.

ACKNOWLEDGEMENTS

This work is partially supported by Japan Society for

Promotion of Science (JSPS), with the basic

research program (C) (No. 17K01304 and

17K01342), Grant-in-Aid for Scientific Research

(KAKENHI).

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