engineering complex computational ecosystems (phd defense)

Engineering Complex Computational Ecosystems

Danilo PianiniSupervisor: prof. Mirko ViroliTutor: prof. Antonio Natali

[email protected]

Alma Mater Studiorum—Universita di Bologna

Final Exam of the ET-IT Program XXVII CycleDipartimento di Ingegneria dell’Energia Elettrica e dell’Informazione

“Guglielmo Marconi”

Danilo Pianini (UniBo) Engineering Computational Ecosystems April 13, 2015 / Bologna 1 / 71

Outline Accepting the limitations of birthform betrays lack of imagination

1 Set the stageTowards a pervasive worldEngineering a pervasive world

2 Alchemist: an integrated toolchain for complex ecosystemsComputational modelEngine, architecture, tools

3 Aggregate programmingParadigm shift: programming space/timeProtelis: practical aggregate programming

4 Future technology applied today

5 Conclusion and future work


Set the stage

Outline







Set the stage Towards a pervasive world

Pervasive Devices

Image courtesy of Alois Ferscha (Pervasive Computing Group, Johannes Kepler Universitat Linz)



Progress look pretty normal when you look back...

Image from Tim Urban’s “Wait but why” blog (http://waitbutwhy.com/)



...but fasten your belt anyway, please

Image from Tim Urban’s “Wait but why” blog (http://waitbutwhy.com/)


Set the stage Engineering a pervasive world

The hard part

Challenges

Interaction can be P2P, centralised, or likely mixed

Devices may or may not be mobile

Desired behaviour may change greatly in response to changes in thelocal environment

Evolution of deployed systems is generally unpredictable



Issues for engineers

Lack of design patterns

The closest thing are nature inspired patterns, that make the aggregate ofdevices expose some emergent behaviour in a robust, adaptive anddistributed fashion [ZV11]. Nature is cool, but not always optimal.

Languages

Few languages explicitly target the aggregate of devices rather than thesingle one.

Verification

Unpredictability widen the spectrum of possible evolutions, which makeunit testing and model checking not feasible: simulation is often the onlyway to go



Contribution of this PhD

A toolchain for software ecosystems [PMV13]

Meta-meta-model supporting very different approaches

Built-in, SSA inspired, novel simulation engine

Probabilistic and distributed model checking [PSV14]

A language for aggregate programming [PVB15]

Based on field calculus: formal and lightweight operational semantics

The same code runs in simulator and real world devices

Interoperability with Java (no need to reinvent the wheel)

Self-organisation patterns and possible today’s applications

Crowd steering [VPMS12, MVR+11, PVZF14, APNF13]

Resource discovery [SYD+13]

Anticipative adaptation [MPV12]Danilo Pianini (UniBo) Engineering Computational Ecosystems April 13, 2015 / Bologna 9 / 71

Alchemist: an integrated toolchain for complex ecosystems

Outline







Alchemist: an integrated toolchain for complex ecosystems

What is it

Idea

A simulator was needed for the European project SAPERE [ZCF+11]

SAPERE adopts a chemical-like metaphor [ZOA+14]

Stochastic chemical simulators (SSA) are extremely efficient[GB00, STP08]

Rather than relying on existing ABMs, very expressive but not asscalable as we desired, let’s extend SSAs to be able to express whatwe want [PMV11]

The simulator evolved beyond its original purposes to the point where itstands now.


Alchemist: an integrated toolchain for complex ecosystems Computational model

Generalised chemistry

Pure chemistry vs. pervasive computing

Single, static compartment versus multiple, possibly mobile, andinterconnected nodes whose connection may depend on environmentaland technological factors

Molecules are described by concentration (an integer), devices maycarry any kind of data item

Reactions “scheduling” in nature follows a Poisson distribution whoserate equation depends on reagents’ concentration [Gil77]. Events in apervasive computing scenario may be influenced by any of theenvironment components and follow any probability distribution(triggers, timers, and people walking are not Markovian)

Devices live in an environment

Yes, it is a nicely big leap



Close the gap: environment

ReactionA proactive behaviour

Linking RuleA function of the environment

that decides wether or nottwo nodes are connected

Moleculetoken representing a

chunk of data(think of it as a pointer)

ConcentrationActual data associated

with a "molecule"

EnvironmentRiemannian manifold

where nodes live

NodeA container of reactionsand molecules situated

in the environment



Close the gap: reactions

Number of

neighbors<3

Node

contains

something

Any other

condition

about this

environment

Rate equation: how conditions

influence the execution speed

Conditions Probability distribution Actions

Any other

action

on this

environment

Move a node

towards...

Change

concentration

of something

Reaction



((Meta) Meta) Models

Meta levels explained

The simulated instance of the system, or scenario, is the model

The specific concentration type (e.g. integer, tuple, or even JavaObject) along with specific actions and conditions that act upon suchdata type represents the meta-model (in the Alchemist jargon,“incarnation”).

The components of the Alchemist abstract computational model arethe meta-meta model, which is common to all incarnations (and, inturn, to all scenarios).


Alchemist: an integrated toolchain for complex ecosystems Engine, architecture, tools

Architecture

Dynamic Dependency Graph

Dynamic Reaction Manager

Discrete Event Engine

Core

Environment(model)

ReportingGraphical Output

Logging System

Interactive UI

XML Bytecode

Environment Builder

LanguageChain

DSL to XML Compiler

Domain Specific Language



Engine

Full fledged Discrete Event Simulator derived from Gibson/BruckSSA, extended with novel structures

Supports dependency graph among reactions (this really boostsperformance)

Supports execution “contexts”, pruning the dependency graph,strongly boosting the boost

0

50

100

150

200

250

300

350

400

450

500

50 100 150 200 250 300 350 400 450 500

Exe

cuti

on t

ime [

s]

Number of agents

Performance comparison with Repast

Repast AlchemistDanilo Pianini (UniBo) Engineering Computational Ecosystems April 13, 2015 / Bologna 17 / 71


Handle with care

When is Alchemist worth it?

An incarnation exposing the desired level of abstraction is alreadyavailable: faster and closer to the model than a generic ABM

There is a long-term investment on Alchemist, and a new incarnationis developed: performance pay back the investment

Incarnations available

Pure chemistry – Little more than an incubation test, deprecated

SAPERE – Mature incarnation, simulates a network of programmabletuple spaces. Programs are chemical-like tuple re-writing rules

Protelis – Supports the execution of Protelis programs on networksof simulated devices. Usable, but under heavy development.

Biochemistry – Sketched, will replace chemistry, exposing muchbetter support for biological oriented applications (back to the fold)


Aggregate programming

Outline







Aggregate programming Paradigm shift: programming space/time

Classic approach

Local to global

Complex global behaviour

Simple local behaviour

The whole is more than the sum of the parts

Interaction is key

Nature inspiration

Several systems use nature as inspiration:

physical particles [MZ09]

chemical reactions [ZCF+11]

ants, termites and other social insects [TM03]

Very brief list! There are many more.



Nice properties and hard challenges

The beauty

High resilience and fault tolerance

Self adaptation

Openness

Self healing

The beast

Local to global is hard to engineer

The desired functionality happens at the aggregate level

It is very difficult to design the local behaviour in such a way that theinteraction of many of them produces the desired global behaviour

Many attempts, but no good engineering processes to safely designsuch systems

Often, development becomes a try-and-simulate loopDanilo Pianini (UniBo) Engineering Computational Ecosystems April 13, 2015 / Bologna 21 / 71


Collective to local

Desiderata

We want to achieve a collective behaviour

We want to move our focus and abstractions towards the the wholenetwork, rather than focussing on the single devices that compose itand their interaction

We want to support space-time abstractions

Possible solution

Create a language that allows to express collective properties

Create a runtime that can run such programs

Possibly, also create a tool to test programs prior to deployment



Real languages

Aggregate programming languages

Several attempts to bring existing languages to the aggregateperspective, we tried with Linda [VPB12]

MIT Proto [BB06] is the most known and successful

Developed at MIT and maintained at BBN Technologies

Functional language, LISP-like syntax (I know you hate it too)

All devices run the same program

Computation happens in rounds:

Every device sleeps for some timeProcesses the messages received from the neighboursExecutes its programSends all the neighbours its result

Complex operational semantics



Field Calculus

A “distillate” of Proto

Provides a lightweight operational semantics [VDB13]

((((LISP-like syntax))))

Still a functional language

Simple enough to formally prove properties, powerful enough to beuniversal (proved!)

Theoretical object, no runtime nor simulation tool provided

Key mechanism: alignment

At the end of each cycle, send to your neighbours your annotated AST

At the beginning of each cycle, each device can inspect what washappening in its surroundings

If two devices took different branches of a if, they are no longeraligned until such branch returns


Aggregate programming Protelis: practical aggregate programming

Protelis

Functional language

Same operational semantics of the field calculus

Java-like syntax with infix operators

Java interoperability: static methods imports and calls, methodinvocation with dynamic binding

Higher order functions (functions as arguments, closures,lambdas)

Dynamic code

Eclipse plugin

Integrated with Alchemist

Stand-alone framework for real devices

Write once, run everywhere — evolved :)



Alignment with HOF

How to deal with alignment?

Without HOF, all the devices are forced to run the same code: theonly branching point is if.

With HOF, and in particular with support for dynamic code, instead:1 The same function can be defined in multiple points2 Different functions may have the same name (dynamic injection)3 The same function can be invoked in multiple points4 The same code path may invoke a different function

Alignment

Alignment had been improved to consider HOF support. Two functionsalign only if:

They have the same name and body

They were defined in the exact same point in code

They are invoked in the exact same point in codeDanilo Pianini (UniBo) Engineering Computational Ecosystems April 13, 2015 / Bologna 26 / 71


LISP-like to Java-like syntax

1 (def distance -to (source)2 (rep d infinity (mux source 0 (min-hood (+ (nbr d) (nbr-range))))))3 distance -to(event)

1 def distanceTo(source) {2 rep (d <- Infinity) {3 mux (source) {4 0

5 } else {6 minHood(nbr(d) + nbrRange)7 }

8 }

9 distanceTo(event)



Example with HOF

1 def gBB(source, initial, metric, accumulate) {2 rep(distanceValue <- [Infinity , initial]) {3 mux(source) {4 [0, initial]

5 } else {6 let ndv = nbr(distanceValue);7 minHood([ndv.get(0) + metric.apply(), accumulate.apply(ndv.get(1))])

8 }

9 }.get(1)

10 }

1112 def distanceCompetition(d, lead, uid, grain, metric) {13 mux(d > grain * 1.5) { uid } else {14 let gsize = grain * 0.5;

15 mux (d >= gsize) { Infinity } else {16 minHood(mux(nbr(d) + metric.apply() >= gsize) { Infinity } else { nbr(lead) })17 }

18 }

19 }

2021 def breakUsingUIDs(uid, grain, metric) {22 uid == rep(lead <- uid) {23 distanceCompetition(

24 gBB(uid == lead, 0, metric, (v) -> {v + metric.apply()}), lead, uid, grain, metric)

25 }

26 }

2728 breakUsingUIDs(grain, metric)


Future technology applied today

Outline








Applications for densely populated environments

Crowd steering and evacuation

Provide guidance to prevent dangerous situations, e.g.:

In case of emergency, dynamically balance the crowds to minimize theevacuation timeAvoid crowded paths when providing directionsPrevent dangerous crowd formationAlert users joining a dangerous cluster

Other services

Local communication

Context sensitive applications

Local services and advertisement



Crowd-sensitive user steeringSteering against GPS traces taken at Vienna City Marathon 2013



Context sensitive user steeringGeneralisation of the former, ran in London



Mobile code



Upgradable mobile code




Crowd steeringservice version 2

Crowd steeringservice version 1



Overlapping services

Exhibitiontickets

Foodservice

Public water


Conclusion and future work

Outline







Conclusion and future work Results

Results

A toolchain for software ecosystems

Novel, chemical inspired architecture, developed from scratch

Flexible still retaining high performace

A language for aggregate programming [PVB15]

Based on Field Calculus, lightweight semantics

Support for higher order functions, closures, code injection

Integrated development: write your program, run both in Alchemistand real devices


Novel self-organisation patterns

Application of existing and novel patterns to crowd steering, WSNand other high- density scenarios


Conclusion and future work Future works

Future development

A toolchain for software ecosystems

Stabilise development, strenghten existing features

Include interactivity

Rethink the distributed approximate model checker

Complete biochemistry incarnation

A language for aggregate programming

Are we about to create distributed processes?

Protelis could be a nice language for distributed service scheduling

Enable better modularity

Strong type checking


Create a rich library of reusable building blocks for self-* applicationDanilo Pianini (UniBo) Engineering Computational Ecosystems April 13, 2015 / Bologna 41 / 71

References

References I

Bernhard Anzengruber, Danilo Pianini, Jussi Nieminen, and Alois Ferscha.Predicting social density in mass events to prevent crowd disasters.Lecture Notes in Computer Science (including subseries Lecture Notes in ArtificialIntelligence and Lecture Notes in Bioinformatics), 8238 LNCS:206–215, 2013.cited By 1; Conference of 5th International Conference on Social Informatics, SocInfo 2013; Conference Date: 25 November 2013 Through 27 November 2013; ConferenceCode:101766.

Jacob Beal and Jonathan Bachrach.Infrastructure for engineered emergence on sensor/actuator networks.IEEE Intelligent Systems, 21(2):10–19, 2006.

Michael A. Gibson and Jehoshua Bruck.Efficient exact stochastic simulation of chemical systems with many species and manychannels.J. Phys. Chem. A, 104:1876–1889, 2000.

Daniel T. Gillespie.Exact stochastic simulation of coupled crfreactions.The Journal of Physical Chemistry, 81(25):2340–2361, December 1977.


References

References II

Sara Montagna, Danilo Pianini, and Mirko Viroli.Gradient-based self-organisation patterns of anticipative adaptation.In Proceedings of 6th IEEE International Conference on Self-Adaptive and Self-OrganizingSystems (SASO 2012), pages 169–174, September 2012.

Sara Montagna, Mirko Viroli, Matteo Risoldi, Danilo Pianini, and GiovannaDi Marzo Serugendo.Self-organising pervasive ecosystems: A crowd evacuation example.Lecture Notes in Computer Science (including subseries Lecture Notes in ArtificialIntelligence and Lecture Notes in Bioinformatics), 6968 LNCS:115–129, 2011.cited By 4; Conference of 3rd International Workshop on Software Engineering for ResilientSystems, SERENE 2011 ; Conference Date: 29 September 2011 Through 30 September2011; Conference Code:86796.

Marco Mamei and Franco Zambonelli.Programming pervasive and mobile computing applications: The tota approach.ACM Trans. Softw. Eng. Methodol., 18(4):1–56, 2009.

Danilo Pianini, Sara Montagna, and Mirko Viroli.A chemical inspired simulation framework for pervasive services ecosystems.In Proceedings of the Federated Conference on Computer Science and InformationSystems, pages 667–674. IEEE Computer Society Press, 2011.


References

References III

Danilo Pianini, Sara Montagna, and Mirko Viroli.Chemical-oriented simulation of computational systems with Alchemist.Journal of Simulation, 2013.

Danilo Pianini, Stefano Sebastio, and Andrea Vandin.Distributed statistical analysis of complex systems modeled through a chemical metaphor.pages 416–423. Institute of Electrical and Electronics Engineers Inc., 2014.cited By 0; Conference of 2014 International Conference on High Performance Computingand Simulation, HPCS 2014 ; Conference Date: 21 July 2014 Through 25 July 2014;Conference Code:107210.

Danilo Pianini, Mirko Viroli, and Jacob Beal.Protelis: practical aggregate programming.In SAC, Salamanca, Spain, 2015. Accepted, to appear this year.

Danilo Pianini, Mirko Viroli, Franco Zambonelli, and Alois Ferscha.Hpc from a self-organisation perspective: The case of crowd steering at the urban scale.pages 460–467. Institute of Electrical and Electronics Engineers Inc., 2014.cited By 0; Conference of 2014 International Conference on High Performance Computingand Simulation, HPCS 2014 ; Conference Date: 21 July 2014 Through 25 July 2014;Conference Code:107210.


References

References IV

Alexander Slepoy, Aidan P. Thompson, and Steven J. Plimpton.A constant-time kinetic monte carlo algorithm for simulation of large biochemical reactionnetworks.The Journal of Chemical Physics, 128(20):205101, 2008.

Graeme Stevenson, Juan Ye, Simon Dobson, Danilo Pianini, Sara Montagna, and MirkoViroli.Combining self-organisation, context-awareness and semantic reasoning: The case ofresource discovery in opportunistic networks.pages 1369–1376, Coimbra, 2013.cited By 1; Conference of 28th Annual ACM Symposium on Applied Computing, SAC2013 ; Conference Date: 18 March 2013 Through 22 March 2013; Conference Code:96995.

Robert Tolksdorf and Ronaldo Menezes.Using swarm intelligence in linda systems.In Andrea Omicini, Paolo Petta, and Jeremy Pitt, editors, ESAW, volume 3071 of LectureNotes in Computer Science, pages 49–65. Springer, 2003.


References

References V

Mirko Viroli, Ferruccio Damiani, and Jacob Beal.A calculus of computational fields.In Carlos Canal and Massimo Villari, editors, Advances in Service-Oriented and CloudComputing, volume 393 of Communications in Computer and Information Science, pages114–128. Springer Berlin Heidelberg, 2013.

Mirko Viroli, Danilo Pianini, and Jacob Beal.Linda in space-time: an adaptive coordination model for mobile ad-hoc environments.In Marjan Sirjani, editor, Coordination Languages and Models, volume 7274 of LNCS,pages 212–229. Springer-Verlag, June 2012.Proceedings of the 14th Conference of Coordination Models and Languages (Coordination2012), Stockholm (Sweden), 14-15 June.

Mirko Viroli, Danilo Pianini, Sara Montagna, and Graeme Stevenson.Pervasive ecosystems: a coordination model based on semantic chemistry.In Sascha Ossowski, Paola Lecca, Chih-Cheng Hung, and Jiman Hong, editors, 27thAnnual ACM Symposium on Applied Computing (SAC 2012), Riva del Garda, TN, Italy,26–30 March 2012. ACM.


References

References VI

Franco Zambonelli, Gabriella Castelli, Laura Ferrari, Marco Mamei, Alberto Rosi, GiovannaDi Marzo, Matteo Risoldi, Akla-Esso Tchao, Simon Dobson, Graeme Stevenson, Yuan Ye,Elena Nardini, Andrea Omicini, Sara Montagna, Mirko Viroli, Alois Ferscha, SaschaMaschek, and Bernhard Wally.Self-aware pervasive service ecosystems.Procedia Computer Science, 7:197–199, December 2011.Proceedings of the 2nd European Future Technologies Conference and Exhibition 2011(FET 11).

Franco Zambonelli, Andrea Omicini, Bernhard Anzengruber, Gabriella Castelli,Francesco L. DeAngelis, Giovanna Di Marzo Serugendo, Simon Dobson, Jose LuisFernandez-Marquez, Alois Ferscha, Marco Mamei, Stefano Mariani, Ambra Molesini, SaraMontagna, Jussi Nieminen, Danilo Pianini, Matteo Risoldi, Alberto Rosi, GraemeStevenson, Mirko Viroli, and Juan Ye.Developing pervasive multi-agent systems with nature-inspired coordination.Pervasive and Mobile Computing, 2014.Special Issue on “10 years of Pervasive Computing” in honor of Chatschik Bisdikian.

Franco Zambonelli and Mirko Viroli.A survey on nature-inspired metaphors for pervasive service ecosystems.International Journal of Pervasive Computing and Communications, 7(3):186–204, 2011.


References

Engineering Complex Computational Ecosystems

Danilo PianiniSupervisor: prof. Mirko ViroliTutor: prof. Antonio Natali

[email protected]

Alma Mater Studiorum—Universita di Bologna

Final Exam of the ET-IT Program XXVII CycleDipartimento di Ingegneria dell’Energia Elettrica e dell’Informazione

“Guglielmo Marconi”


Alchemist: director’s cut

Model to model

Generic procedure

1 Map your meta model onto Alchemist’s meta-meta model entities

2 Optionally, write a Domain Specific Language (DSL) which generatesan Alchemist-XML compatible file from your domain-specific entities

3 Write whichever model you want to test and simulate

4 Simulate it using Alchemist



2.04 4

3.71

7.32 1

5.51 0

2

8.91 0

4.20 0

9.10 0

10.10 0

inf0 0

A+B→C

B+C→D E+G→A

D+E→E+F F→D+G

Next Reaction efficient structures made dynamic

Dynamic Indexed Priority Queue

Allow to access the next reaction to execute in O(1) timeWorst case update in log2 (N) (average case a lot better)Extended to ensure balancing with insertion and removal

Dynamic Dependency Graph

Allows to smartly update only a subset of all the reactionExtended with the concept of input and output context



Real world maps and GPS traces

Figure: A snapshot of the whole city of Vienna as simulated in Alchemist. Thissnapshot is taken while simulating the city at 10am during the annual marathon,each black point corresponds to a GPS trace.



Approximate Stochastic Model Checker

Problem

Model checking is not feasible with such complexity, but Monte Carlosampling is

How many simulations should you run to in order to get the meanvalue of some property, along with its confidence and approximation?

Alchemist + MultiVeStA

Alchemist has been chained with MultiVeStA

A properties can be written in MultiQuaTeX

MultiVeStA runs the number of experiments required using thechained Alchemist engine

Big plus: MultiVeStA supports distributed execution


Aggregate programming: director’s cut

Example

1 (def distance -to (source)2 (rep d infinity (mux source 0 (min-hood (+ (nbr d) (nbr-range))))))3 distance -to(event)



We want to do more

Dynamic and mobile code with higher order functions

Functions can be arguments and can be returned

Closures

Anonymous functions

Dynamic code

Sugar and utilities

Java interoperability

C family like syntax




1 def upgrade (injection) {2 rep (versioned <- [0, "\"no program installed.\""]) {3 max-hood (nbr (4 if (injection.get(0)) {5 if(versioned < injection) {6 injection

7 } else {8 versioned

9 }

10 } else {11 versioned

12 }

13 ))

14 }

15 }

16 let prog = upgrade([version, code]);17 let code = prog.get(1);18 eval(code);




1 def map(f, t){2 if (t.isEmpty()) {3 t

4 } else {5 [f.apply(t.get(0))].mergeAfter(map(f, t.subTupleEnd(1)))

6 }

7 }

8 def distanceTo(source) {9 rep (d <- Infinity) {

10 mux (source) { 0 } else { min-hood (nbr(d) + nbr-range) }11 }

12 }

13 def inRange(source, r) { distanceTo(source) < r }14 /* actual program */

15 let services = [service0 , service1 , service2];16 map((s) -> {

17 if(inRange(s)) {18 eval(s)19 } else {20 "out of range"21 }

22 }, services);



Simpler semantics

Proto Field calculus



Key mechanism: Alignment

Abstract syntax trees in Field Calculus

Representation of the abstract syntactic structure of a program

The computation modifies every node of the tree, associating thecorresponding value to it

At the end of the round, it is sent to every neighbour

During the round, each device can read the data every other nodehad at the same point in the code, and use it

If some neighbour has not any data associated with the code path, itmeans that it has chosen a different branch

Such node would be not aligned, and not considered for subsequentcomputation



Example

1 (if (> x 0) (x + 1) (x - 1))

if

>

x 0

+ -

x x1 1



Example

if•4

>•true

x•3 0•0

+•4 -•

x•3 x•1•1 1•

if•-2

>•true

x•-1 0•0

+• -•-2

x• x•-11• 1•1



Conclusion

Aggregate programming

Focus on the whole network

Think in space and time

Much less focus on communication protocols

Paradigm shift: requires training!

Protelis

Solid foundations (field calculus)

Support for higher order functions

Interoperability with Java

Friendly syntax

Simulation framework



Future work

Language side

Sugar, sugar, sugar

Namespacing (done by now, if no disasters happened just before thisdissertation)

Libraries

Tools side

Distributed middleware

Better simulator UI

Better batch executor and logging service

Tons of documentation


Self-org patterns: director’s cut

Why crowds

We are preparing for a world that does not exist yet.

Why we often focus on crowds

Smallest and most diffused devices in today’s world are normallyaccessories or wearables

The higher density of people, the higher density of devices

Challenging issues to tackle, including low reliability of Internetconnection

Crowds of people are real world scenarios available today that closelyresemble our future pervasive world.



Today’s infrastructure

Internet



Big crowds



Big crowds and cloud don’t play well

Internet



Big crowds and cloud don’t play well



Big crowds and dangers

Photo: Sam Panthanky/AFP/Getty Images



Big crowds and dangers

Crowd disasters

July 24, 2010 – Germany, Duisburg – 21 deaths

January 1, 2013 – Ivory Coast, Abidjan – 60 deaths

February 20, 2003 – USA, Rhode Island – 100 deaths

October 13, 2013 – India, Madhya Pradesh, Datia – 115 deaths

May 9, 2001 – Ghana, Accra – 126 deaths

January 23, 2013 – Brazil, Santa Maria – 242 deaths

January 12, 2006 – Saudi Arabia, Mecca – 345 deaths

November 22, 2010 – Cambodia, Phnom Penh – 347 deaths

August 31, 2005 – Iraq, Baghdad – 1000 deaths

This is a small list of stampedes, there are thousands more.



Channel



Crowd sensitive steering benefits: data

0

20

40

60

80

100

0 0.2 0.4 0.6 0.8 1

Num

ber

of

people

wit

hin

10

0 m

ete

rs

Normalised vicinity to target

Benefit of crowd-sensitiveness

Classic steeringCrowd sensitive steering

Number of users surrounding the user (within 100 meters from her). With “normalised distance”, we mean that we divided the distance the user still hasto walk to reach the target by the total length of the suggested path.Danilo Pianini (UniBo) Engineering Computational Ecosystems April 13, 2015 / Bologna 71 / 71

engineering complex computational ecosystems (phd defense)

Technology

pervasive worldengineering

pervasive worldissues

pervasive worldcontribution

pervasive worldprogress

pervasive world2 alchemist

aggregate of devices

aggregate ofdevices

integrated toolchain