FIPA-OS AGENTS APPLIED TO PROCESS SCHEDULING IN

REAL TIME MONITORING

Angel Gómez, Diego Cantorna, Carlos Dafonte, Bernardino Arcay

Dept. of Information and Communications Technologies, University of A Coruña, Faculty of Informatics, A Coruña, Spain

Keywords: Intelligent agents, Telemedicine, Control systems, Hospital information systems

Abstract: This work presents a mechanism for the management of network tasks, based on the tech

nology of

Intelligent Agents applied to a project of Telemedicine in Intensive Care Units (ICUs).The telemedicine

system provides the real time acquisition and analysis of physiological data of patients, the graphical

visualisation of these data and their transmission to a central system charged with the collection and control

of all the information concerning the patient, including knowledge based systems for medical reasoning.

The system tasks are managed through the use of intelligent agents, implemented according to the FIPA

standard. Each of the agents disposes of a knowledge-based system for its decision-making.

1 INTRODUCTION

The supervision of Intensive Care patients requires a

complex real-time monitoring of their clinical

evolution. In recent years, new monitoring and

control systems were developed that make full use

of the available human and technical resources. The

progress in telemedicine systems has led to the

appearance and introduction of intelligent

monitoring systems that assist the physician with his

diagnoses (Bird, 1975). Researchers have opted for

the supervision of an ICU patient by a bedside

computer, which gathers, integrates, stores and

visualises in a clear way all the information that is

forwarded by the medical devices. These systems

can also be equipped with an intelligent component

that performs tasks such as the automatic

configuration of acquisition, alarm triggering,

devices control, etc. The generalisation of

communications networks has led to the integration

of the medical systems in data networks under the

supervision of a central system, following a

client/server architecture (Bashshur, 1975).

The here described ICU telemedicine system uses

com

munication networks (LAN, ADSL, ISDN, etc.

over TCP/IP) to connect the bedside acquisition

systems to a remote supervision system that

centralises and shows all the information that is

provided by the medical devices to the specialist,

and provides medical reasoning based on

independent knowledge based system (KBS) for

each patient. At the acquisition level or bedside

level, we have implemented the IEEE P-1073

Medical Information Bus (MIB) standard (Taboada,

1988).

All telemedicine systems, and especially those in

Us, must dispose of multimedia applications that

facilitate as much as possible the collaboration with

the physician during the diagnosis (Bozios, 1995):

complete videoconferencing systems (video, audio

and collaborative whiteboard), the visualisation of

graphic archives such as X-rays, or the transmission

of files with any kind of relevant digital information

(e.g. clinical records), etc.

One of the main problems in monitoring ICUs is

e existence of strong temporal restrictions

(Stankovic, 1993), due to the need to continuously

update the data on vital parameters of the patient

(heart rate, arterial pressure, central veined pressure,

intracranial pressure, etc.). It is essential that we

guarantee acceptable response times for the

execution of tasks (mainly processing tasks). We

therefore try to make good use of the available

resources in the computers that are connected to the

network. It becomes obvious that, especially in

states of alarm, we will have to give transmission

priority to the data that proceed from the acquisition

devices, rather than to video and audio. Another

problem, related to the use of WANs (Wide Area

Network) on TCP/IP, is the limited capacity for the

regulation of bandwidth; this inconvenience was

avoided with compression techniques and an

intelligent queue management scheme in the

application level (Arcay, 2002).

Gómez A., Cantorna D., Dafonte C. and Arcay B. (2004).

FIPA-OS AGENTS APPLIED TO PROCESS SCHEDULING IN REAl TIME MONITORING.

In Proceedings of the First International Conference on Informatics in Control, Automation and Robotics, pages 70-75

DOI: 10.5220/0001141000700075

 SciTePress

This paper shows the included control mechanism

that manages the tasks and their distribution between

the connected systems. We have opted for a control

mechanism based on Intelligent Agents technology,

which has proven to be very adequate for this kind

of tasks (Brenner, 1998) (Ferber, 1999). The

development language, Java, allows us to create a

multiplatform system that can be integrated into the

Hospital Information System (HIS). Moreover, the

bandwidth and the available resources will be better

distributed if we incorporate in each agent a rules

based knowledge system that decides which agent

and which computer are responsible for a specific

task (Gomez, 2000). This enables us to extend and

improve the task management system according to

the available resources of the network.

2 SYSTEM DESCRIPTION

One of the main problems with real-time monitoring

systems in ICU is the management and execution of

monitoring process tasks that require a great amount

of CPU and memory: mainly signal processing

tasks, image processing tasks, and database access

tasks. These systems have very important temporal

restrictions, and we need to guarantee the response

times of their execution. An adequate tasks manager

can assure the efficient use of the computers with

free resources by making them execute

simultaneously a larger number of specific tasks, or

by indicating the most adequate computer for a

concrete task. Efficient management of the available

resources enhances the performance of the whole

telemedicine system.

On the other hand, during the migration of the

network monitoring system that was originally

designed towards a telemedicine environment, we

noticed that our previous reasoning, based on

intelligent agents (Jennings, 1998), could also be a

valid approach for the bandwidth regulation

problem. The main work was focused on modifying

the rules-based system, so that it could also

efficiently manage the transmissions between all the

involved subsystems (bedside computers and central

system), acting on the state of the different

transmission channels.

The main objectives of this development are the

following:

The design of a communication mechanism, i.e. a

language based on standards, that allows us to cover

our message exchange needs.

The management of a platform of agents that are

distributed in the various devices of which the

network disposes. This platform allows us to know,

at any given moment and through its local agent, all

the agents that are distributed in the multiplatform

network (Windows, Linux, UNIX). Every agent

must know at each moment all the other agents that

are dispersed over the network, and efficiently detect

appearing or disappearing agents. These agents will

collect information on the characteristics of all the

computers that belong to the network, regularly run

tests on the capacity of the available data lines and

compile information on the data channels that are

established in the system. In our implementation, a

running agent resides in each computer.

The efficient management of the execution of the

specific tasks. To this effect, we need to implant a

priorities mechanism that is dynamically variable

and error tolerant (replicating executions whenever

possible). This Tasks manager uses the following

information: free and busy resources of each

computer; network congestion level; characteristics

of the requested and running tasks (required

computing power, amount of memory for the

execution, size of the used data to decide if their

transmission is feasible, evolution of the execution,

etc.).

The management of the data transmission

channels. This includes managing the priorities of all

the transmission threads; establishing delays in

determined transmission queues; regulating the

bandwidth consumed by the collaborative systems;

etc. These actions can be applied to outgoing or

incoming data, in the latter case by means of agent

messages. It is important for the agents to establish

communication with both the knowledge based

systems for the medical analysis (this will allow

them to know specific data needs and alarm

situations) and with the distinct execution threads

dedicated to the data transmission (to reconfigure

them). This Communications Manager uses the

following information: the state of all the active

transmission queues for each patient; the occupation

level of the queues and introduced delays; the

established quality for the collaborative system;

alarm states; available bandwidth; etc.

On the basis of the information provided by the

Tasks manager and the Communications Manager,

and the parameters that define a device (CPU;

memory; hard disk; etc.), we wrote the decision

rules that assign tasks to the devices according to the

resources that are available at each moment.

The graphical interface of each agent

continuously shows the devices (platform of agents)

and the tasks in which an agent is involved and the

parameters that are linked to each device and each

task. The interaction with this interface can be

merely informative (to learn about the functioning of

the agents platform), but it can also consist in

modifying the state of the network by hand (task

cancelling, disconnexion of an agent, etc.).

FIPA-OS AGENTS APPLIED TO PROCESS SCHEDULING IN REALTIME MONITORING

3 MATERIAL AND METHODS

The process control system is implanted through a

system of distributed agents based on the

specifications of the FIPA (FIPA, 2003), which

establishes the basic architecture for the

development of those agents.

The Foundation for Intelligent Physical Agents

(FIPA) is an international organisation dedicated to

promoting the industry of intelligent agents by

openly developing specifications that support

interoperability among agents and agent-based

systems. The primary focus of this FIPA Abstract

Architecture is to create semantically meaningful

message exchanges between agents that may be

using different messaging transports, different Agent

Communication Languages (ACLs), or different

content languages. Agent management provides the

normative framework within which FIPA agents

exist and operate. It establishes the logical reference

model for the creation, registration, location,

communication, migration and retirement of agents.

Each installation disposes of a component that

registers all the agents in the network with the

interface of the agents, Agent Management System.

To this effect, it uses the Localisation Service, which

makes it possible to locate the other agents during

the initiation and to detect the possible decrease or

increase of new agents by means of triggers. In case

of an increase, the new agent is registered; in case of

a decrease, it is eliminated from the Agent

Management System. The agents were implemented

with FIPA-OS components, and the communications

are established with a subset of ACL language.

FIPA-OS (FIPA-OS, 2003) is a component-

orientated toolkit for the construction of FIPA

compliant Agents through mandatory components

(i.e. components required by all the executable

FIPA-OS Agents), components with switchable

implementations, and optional components (i.e.

components that a FIPA-OS Agent can optionally

use). FIPA-OS tasks generally encapsulate some

functionality that forms a logical work unit (i.e.

search the DF, conduct a negotiation with another

Agent, or wait for a specific lapse of time). Tasks

can also be composed of sub-tasks so as to enable

more complex “units of work” that are sub-divisible

into smaller chunks of functionality. Tasks are based

upon event-based processing, where events are

delivered by the dynamic invocation of various

methods.

Each agent includes a KBS based on the

information that is collected either locally (from the

computer that executes the agent) or by

communicating with the other agents. The rules

system that is integrated into the agents was made

with the JESS expert system shell and scripting

language (JESS, 2003). Jess can be used in two

overlapping ways. Firstly, it can be a rule engine, a

special type of program that very efficiently applies

rules to data. In this case, they are said to constitute

an expert system. Among the newest applications of

expert systems are the reasoning part of intelligent

agents. But the Jess language is also a general-

purpose programming language, and furthermore, it

can directly access all Java classes and libraries

(Friedman-Hill, 2003).

Jess's rule engine uses an improved form of the

Rete algorithm (Forgy, 1982) to match rules against

the knowledge base. Jess is optimised for speed at

the cost of space. Jess is different than some Rete-

based systems in that it includes both a kind of

backwards chaining and a construct called defquery

which allows the user to make direct queries of the

knowledge base.

The implementation of the rules associated to the

task management was based on the characterisation

of the tasks (resources and temporal requirements

that are necessary for their execution) and on the

information that is compiled by the agents network.

3.1 Tasks Control

The Tasks Manager includes a priorities scheme that

is dynamically reconfigurable according to the needs

of medical KBS, patient state, physician requests,

etc. Possibilities like a task re-launch after the

detection of a missing agent or normal

disconnection, duplicated launch in extreme priority

conditions, and others, were implemented for some

concrete image processing tasks. The fact that each

agent is linked to a rules system that determines all

the executable actions, makes this implementation

possible.

The election mechanism of the various candidates

is very much influenced by the priority of the

processes. From less to more priority, the processes

were simplified and divided into 3 levels (0: low, 1:

normal and 2: high), which, according to the tests,

provide sufficient efficiency. We have followed the

general rule that a superior priority process that

surges at a given moment can cancel a lower priority

process that is already being executed. The selection

of the computer for a task of high priority consider

the total capacity of which it disposes, without

considering the current executions except if the alter

are also marked as having high priority. This process

will be re-launched after a new selection between

the remaining candidates. After a first candidates

selection, based on the necessary memory for the

process execution (physical and virtual), we pass on

to a successive refinement of the set of candidates,

ICINCO 2004 - INTELLIGENT CONTROL SYSTEMS AND OPTIMIZATION

until there is only one left. At that moment, the

system is informed about the decision, and the agent

sends the pertinent message for the start of the

execution.

3.2 Communications Control

The implementation of the Communications

Manager is based on the real-time management of

queues, multi-thread and with low TCP/IP

transmissions. This avoids the implementation of a

centralized events trigger (Schmidt, 1997), although

we did define mechanisms for the reconfiguration of

the system and the task handling.

When a new bedside PC (i.e. a new patient)

connects with the central system, initially the two

following connection channels are always

established:

Control and medical Analysis Information (CAI).

This transmission thread allows us to transmit and

receive information on the remote control of the

bedside PC from the central system, send the

information generated by the intelligent module of

the bedside PC (partial states obtained from the

physiological subsystems; analyses of stabilities;

alarms; etc.), and transfer messages between the

physicians.

Signals digitalised in Real Time (SRT). The

signals are compressed and transmitted to the

Central System for each acquisition of an

intermediate storage or buffer in the bedside

communications controller (BCC).

Both execution threads for data transmission are

established with high priority. The SRT and CAI

channels are considered essential for the functioning

of the system, since they allow a minimally

acceptable monitoring, and let the central system

dispose of information on physiological parameters,

alarms in the acquisition system, tendency studies,

etc. In fact, in the worst case, the CAI queue is

always maintained because the supported traffic is

minimal and informs us on the state of the remote

patient.

During the course of the monitoring, any of the

two nodes that maintain the communication may

solicit a communication channel of the TDI type

(Transmission of Digitalised Information, a generic

channel for digitalised files), or establish a channel

for the Collaborative System (CS) through the

developed environment. These two transmission

threads will initially be executed with normal

priority. It is important to note that the execution of

the CS will not be allowed if the measurement of the

available bandwidth, made by the agents, indicates a

bandwidth of less than 40Kbps.

Obviously, these different communication

channels do not have the same priority, and they can

change depending on the state of the patient, the

state of the data network, the needs of the physician,

etc. The designed mechanism functions on the

application level, and is the only one that can work

in such a heterogeneous environment, allowing all

types of connections and with no more limitations

than those imposed by the available bandwidth. We

will impose these limitations through the proposed

mechanism, reducing the functionality of the system

Figure 2: Agent control interface.

Figure 1: Tasks control scheme.

FIPA-OS AGENTS APPLIED TO PROCESS SCHEDULING IN REALTIME MONITORING

as much as necessary so as to make a certain level of

medical monitoring possible.

3.3 Visualisation Interface

The agents control interface was implemented with

JFC (Java Foundation Classes) Swing components

and the API Java 3D (JAVA 3D, 2003) component

for the tridimensional representation of the agents

network.

The Java 3D API provides a set of object-oriented

interfaces that support a simple, high-level

programming model that can be used to build,

render, and control the behaviour of 3D objects and

visual environments (Sowrizal, 1998).

The control system that was developed for each

agent possesses a user interface that is equipped with

a series of functionalities, which allow us to interact

and follow the behaviour of the agents network and

the tasks that are being executed at each moment.

This interface consists of a main panel that

visualises the agents network in a 3D graphic.

(Figure 2), shows the output of the local computer,

allows to trigger a task and to see the information of

all the tasks of the agents system, and shows

information on the tasks that were requested by the

telemedicine system or by the user himself.

Some of the parameters that show information on

the selected agent are the following: CPU

performance; CPU usage; Free physical memory;

Free virtual memory; Memory load; Number of

CPUs; Page size; Physical memory; Virtual

memory; Mean bandwidth; etc.

A list inside the interface shows the bandwidth

between the selected agent and all the other agents

of the system.

The agents are represented as circular objects

whose colour indicates the type: blue is the local

agent, green is a remote active agent, and red is a

remote inactive agent.

The bandwidth between two agents is represented

by a cylindric line that connects them. The thickness

of the cylinder depends on the bandwidth between

the agents, estimated in real time: a thicker cylinder

indicates more available bandwidth. These links also

follow a colour classification that clearly indicates

the available bandwidth: red is low bandwidth, blue

is normal bandwidth, green is high bandwidth, and

white is very high bandwidth.

Inside the 3D interface, the agents are positioned

around an invisible sphere to avoid that the links

pass through other agents, and to enhance the

visualisation when a large collection of agents is

active. The user can interact with the virtual world

through certain actions, such as movements,

displacements, zooms, rotations, agents selections,

etc.

When an agent is selected, the system shows his

data (state and characteristics).

Finally, if the user wishes to carry out tests from

the interface, he can directly request the execution of

a task to the agents system and determine the

parameters (e.g.: priority, estimation of CPU use,

estimation of memory use, etc.).

4 CONCLUSIONS

This article describes a telemedicine system that is

designed to acquire, monitor and control the clinical

evolution of ICU patients. Our system follows a

client/server architecture, which means that it

consists of two related but independent subsystems:

the local subsystem and the central remote

subsystem. Both subsystems are equipped with a

series of multimedia options that improve the global

system (videoconferencing communication;

collaborative whiteboard; transmission of files; etc.).

Since our telemedicine monitoring system for

ICUs requires a great quantity of data

communications and processing tasks, with

considerable temporal restrictions (real time), we

have developed a tasks manager (by means of an

intelligent agents technology). This module provides

the following advantages:

The agent network manages and distributes the

tasks by means of a KBS inside each agent. In this

way, concurrence can be increased and execution

time decreased.

The agents system is able to regulate the

bandwidths needed for the medical network

monitoring system, by managing the priorities

dynamically and by adapting to the autonomously

available technologies.

In the global medical monitoring system, the

agents include the capacity to react in case of

asynchronous events such as changes in the task

priorities requested by the physician; cancellation of

tasks; changes in network capacity; medical alarms;

etc. We have foreseen possible concurrence

problems by synchronising the accesses to shared

resources, by creating copies of these resources, and

by determining that certain objects cannot be

modified once they are created and initialised.

The system disposes of a graphic user interface

that allows us to monitor the state of the agents

network, and to easily execute and eliminate tasks.

The decision mechanism (rules system) was

developed in the knowledge that certain hardware

elements (CPU; memory; etc.) evolve very rapidly,

which inevitably requires changes in the rules. We

ICINCO 2004 - INTELLIGENT CONTROL SYSTEMS AND OPTIMIZATION

therefore include the possibility to modify the basic

system configuration and to specify changes in the

value ranges of the semantic labels without having

to change the rules nor the classes.

REFERENCES

Arcay, B., Dafonte, C., Taboada, J.A., 2002. Database

Based Reasoning for Real Time Monitoring and Data

Analysis, Encyclopedia of Microcomputers. Marcel

Dekker Inc, 26(1):73-103.

Bashshur, R.L., Armstrong, P.A., Youssef, Z.I., 1975.

Telemedicine: Explorations in the use of

telecommunications in health care. Telemedicine:

Explorations in the use of telecommunications in

health care, Charles C. Thomas, Springfield, Illinois.

Bird, K.T., 1975. Telemedicine; concept and practice. In:

R.L. Bashur, P.A. Armstrong and Z.I. Youssef (eds.),

Telemedicine; explorations in the use of

telecommunication in health care, Springfield, Illinois.

Bozios, E., Pangalos, G., Pomportsis, A., 1995. Evolution

of hospital communication systems towards

multimedia applications, Medical Informatics,

20(1):53-66.

Brenner, W., Zarnekow, R., Wittig, H., 1998. Intelligent

Software Agents, Foundations and Applications,

Springer Verlag, Berlin, Germany.

Dafonte, C., Gómez, A., Arcay, B., Taboada, J.A., 2000.

Intelligent Management of Processes in a ICU

Telemedicine System, Chicago 2000 World Congress

on Medical Physics and Biomedical Engineering,

Chicago, U.S.A.

FIPA Standard, 2003. Available in: http://www.fipa.org

FIPA-OS toolkit, 2003. http://fipa-os.sourceforge.net

Ferber, J., 1999. Multi-Agent Systems, An Introduction to

Distributed Artificial Intelligence, Addison Wesley.

Forgy, C.L., 1982. Rete: A fast algorithm for the many

pattern/many object pattern match problem. Artificial

Intelligence, 19(1):17-37.

Friedman-Hill, E.J., 2003. Jess, the Expert System Shell

for the Java Platform, Distributed Computing

Systems, Sandia National Laboratories, Livermore,

California, U.S.A..

Gomez, A., Dafonte, C., Arcay, B., Santos, A., 2000.

Intelligent system for the acquisition and

telemonitoring in intensive care units, Proc. 18th

IASTED International Conference: Applied

Informatics, 397-401.

Gomez, A., Dafonte, C., Arcay, B., Castro, A., Pereira, J.,

2002. Real-Time 3D Monitoring for Telemedicine in

Critical Units, 2nd European Medical & Biological E

ngineering Conference (EMBEC 2002).

JAVA 3D Specification v.1.3, 2003. Available in:

http://java.sun.com/products/java-media/3D/index.jsp

JESS v.6.1RC1, 2003. http://herzberg.ca.sandia.gov/jess

Jennings, N.R, Wooldridge, M., 1998. Applications of

Intelligent Agents, Agent Technology Foundations,

Applications, and Markets. Springer-Verlag.

Rezazadeh, M., Evans, N.E., 1988. Remote vital signs

monitor using a dial-up telephone line, Med. & Biol.

Eng. & Computing, 26 (5):557-561.

Schmidt, D.C, Fayal, M.E., 1997. Lessons learned

building reusable OO frameworks for distributed

software. Communications of the ACM, 40(10):85-87.

Shoham, Y., 1993. Agent-oriented programming. Artificial

Intelligence, 60(1):51-92.

Sowrizal, H., Rushforth, K., Deering, M., 1998. The Java

3D API Specification, Addison Wesley.

Stankovic, J.A., Ramamritham, 1993. Hard Real-Time

Systems: A Tutorial, Computer Society Press of IEEE,

1730, Massachussets Avenue, Washington D.C.

Taboada, J.A, Arcay, B, Arias, J.E., 1988. Real time

monitoring and analysis via the medical information

bus. Med. & Biol. Eng. & Comp., 35(1):528-534.

FIPA-OS AGENTS APPLIED TO PROCESS SCHEDULING IN REALTIME MONITORING