University of Pisa

MSc in Computer Engineering

System Software for Ambient Intelligence

Using Stigmergy to enable Pervasive Computing

Mario G. Cimino, Department of Information Engineering, Center for Logistics Systems

Pisa, May 14-15, 2013, 08.30-10.30, Room: Lab Blu

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1st

Scenario: product recall

A product recall in a simplified supply chain

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Requirements

Heterogeneous structure of data: common semantics;

Confidentiality and Control of Data: pull/push model;

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Interconnectivity Level: RFID, a key sensing technology in the vision of

the Internet of Things (EPCglobal)

Integration level: from Service-Oriented composition to Multi-Agent

cooperation

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Service Oriented Architecture

Dynamic composite application (consumer-centric);

Services do not say ‘no’;

From Integration (technical) to Interoperability (semantic);

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Deployment Diagram of the Traceability System. Self-management.

Self-organization. Self-configuration. Autonomic Computing (IBM).

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A BPMN Business process flow representation, for the production of a

bag

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Communication Diagram of the Traceability System

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Collaborative Agent: an example of query translation from XSLT to

SQL

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User Agent: context-aware interfaces of a Terminal Unit for different

situations.

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Simplified State Diagram of a Multi-Choice context Terminal Unit

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Software products used for the system implementation

SYSTEM COMPNENT SOFTWARE PRODUCT REFERENCES

Storing Unit,

Context Unit,

Register Unit

Java SE 1.6 http://dev.mysql.com

MySql 5 http://java.sun.com/javase

Apache Tomcat 6 http://tomcat.apache.org

ISO 15000-2 Interface Hermes Message Service

Handler 2

http://www.freebxml.org

Terminal Unit Microsoft Windows Mobile 5 http://www.microsoft.com

.NET Compact Framework 2 http://www.microsoft.com

Analysis Unit Compiere ERP 2 http://www.compiere.com

Oracle 10g http://www.oracle.com

Hardware products used for the MOAT system implementation

SYSTEM

COMPONENT

HARDWARE PRODUCT REFERENCES

Terminal Unit

(for indoor use)

HP Ipaq HX2490 PDA http://www.hp.com

Compact Flash Reader: CFR-F13-56 http://www.rf-id.it

Terminal Unit

(for outdoor use)

Psion TekLogix, with OEM

COM3 HF Reader: CPRM02

http://www.psion.com

http://www.rf-id.it

RFID HF tag Work-Tag Film labels http://www.rf-id.it

Wireless

Gateway

Linksys WAG300N http://www.linksysbycisco.com

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1st

Conclusion

Agent-Oriented Computing vs Object/Service-Oriented Computing

Agents as a new paradigm for software engineering

Networked systems and organizations

Dynamic interdependency

Limited knowledge and control

Openness and uncertainties

Cooperation paradigms

Agents with Intentionality

Agents with Autonomy

Agents with Sociality

Agents with Rational Self-interest

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2nd

Scenario: service recommender

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Upper Context Ontology

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A SWRL rule in: (a) human readable syntax and (b) natural language

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Component diagram of a situation reasoner

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ECAA, an extension of the Event-Control-Action pattern for situation

awareness

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Communication diagram for the situation reasoning process.

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Linguistic variable partitions for the pharmaceutical consultant case

study: (a) defined by a domain expert and (b) tuned by the GA

Adaptable systems are customizedby the users themselves, whereas with

adaptive systems this process works automatically

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From Cognitivist to emergent approach

Cognitivist approach: representation, concept formation, reasoning,

functionalism

Emergent approach: intrinsic embodiment with data/physical

instantiation.

From algorithms to data.

Stigmergy

Grassé (1959), biologist, proposed the theory of stigmergy while

observing termites.

Meaning: “incite to work by product of work”

stigma, wound from a pointed object;

ergon, work, product of labour

stimulating product of labour

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Stigmergy: Indirect communication among components of self-organizing

system

Occurs through modifications brought by individual components to their

local environment

Pheromone, a chemical volatile substance, deposited into environment by

individual, and retrieved (sensed) from other individual

Stigmergy allows coordination without central control

Attractive effect, positive feedback

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Examples of stigmergy

Ants’ Trails

Termites Mounds

Pheromone description:

Life time, e.g. 30 min – 60 min

Frequency of marking, e.g. 5 marks / 20 cm

Quantity of pheromone deposited

Type of pheromone: alarm pheromones, food trail pheromones

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Termites

The termites follow a set of simple rules.

Each termite starts wandering randomly.

If it bumps into a wood chip, it picks the chip up, and continues to

wander randomly.

When it bumps into another wood chip, it finds a nearby empty space

and puts its wood chip down.

With these simple rules, the wood chips eventually end up in a single

pile.

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http://ccl.northwestern.edu/netlogo/models/Termites

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Ant foraging

A colony of ants forages for food. Though each ant follows a set of

simple rules, the colony as a whole acts in a sophisticated way.

When an ant finds a piece of food, it carries the food back to the nest,

dropping a chemical as it moves.

When other ants "sniff" the chemical, they follow the chemical toward

the food. As more ants carry food to the nest, they reinforce the chemical

trail.

The ant colony generally exploits the food source in order, starting with

the food closest to the nest, and finishing with the food most distant from

the nest.

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It is more difficult for the ants to form a stable trail to the more distant

food, since the chemical trail has more time to evaporate and diffuse

before being reinforced.

Once the colony finishes collecting the closest food, the chemical trail to

that food naturally disappears, freeing up ants to help collect the other

food sources.

The more distant food sources require a larger "critical number" of ants

to form a stable trail.

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http://ccl.northwestern.edu/netlogo/models/run.cgi?Ants.790.569

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Other models

http://ccl.northwestern.edu/netlogo/models/

Exercises: run a new model of the page, and discuss the emergent

phenomena.

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3rd

Scenario: Collaborative situation awareness via position-based stigmergy

An ontological view of the collaborative situation-aware scheme for mobile

service recommendation (no user calendar)

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Deployment diagram of the architecture

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Rules of the Situation Agent

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Architecture of the adopted neuro-fuzzy network for adjusting rules

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Communication diagram for the situation reasoning process

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GPS data for a scenario involving t participants to a meeting

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Tracks of the participants for scenario

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Marking intensities in a pre-meeting situations

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Marking intensities in a meeting situations

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4th

Scenario: from position-based to motion-based stigmergy, from mobility/proximity to synchrony

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Communication diagram of the main modules

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A pilot scenario of vertical acceleration magnitude against time, with 4

sensored oars: raw samples

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A pilot scenario of vertical acceleration magnitude against time, with 4

sensored oars: (b) after a moving average filter with a span of 5.

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A pilot scenario of vertical acceleration magnitude against time, with 4

sensored oars: (c) after a low-pass filtering with a cutoff frequency of 20 Hz.

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A single triangular mark released in the sensing space by a marking agent

(solid line), together with the same mark after a step of decay (dashed line).

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An example of four accumulated marks (coloured lines) and of collective

mark (black line) created in the sensing space at the instant t=3.22 s by

the signal of Fig. 4

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An example of close-to-optimal accumulated and collective marks,

belonging to the pilot scenario

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An example of marks produced by poorly synchronized rowing strokes

(solid lines) with the reference marks (dashed lines)

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A visual representation of the Similarity between two marks

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Similarity of the accumulated marks and collective mark with their

references, in the pilot scenario

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A scenario of about 12 seconds, with individual (in colour) and collective (in

black) asynchrony, as a result of a micro-granulation with ∆=800.

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The same scenario after s-shape activation

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overall system architecture (boat)

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