convergence of iot and social networks issues

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http://www.social-iot.org

Convergence of IoT and Social Networks Issues

Antonio Iera

University Mediterranea of Reggio Calabria

2014 IEEE Signal Processing Society Summer School on

“Internet of Things and Machine-to-Machine Systems”

26-29 August 2014 - Taipei, TAIWAN

Taipei– 27 August, 2014 1

This lecture is substantially based on material taken from Tutorials given by Antonio Iera at the IWCMC

2013, WF-IoT 2014, and WCNC 2014 Conferences together with Luigi Atzori (University of Cagliari) and

Giacomo Morabito (University of Catania)

Topics addressed by the Lecture

We will talk about:

� brief introduction to IoT� reasons for a change in the IoT vision� evolution from «smart things» to «social things»� a novel paradigm : Social IoT (SIoT)� relational models applicable to «social things»� the structure of a network of «social things»� projects and researches involving «social things»� new SIoT based communication models� possible SIoT architecture and implementation� a sample application

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3 http://www.social-iot.orgTaipei– 27 August, 2014

IoT: What is it?

The Internet of Things integrates:� Static fixed nodes� Handheld wireless devices� Wireless sensor and actuator nodes� RFID readers/tags

Possible definition (The Internet of Things is...)a world-wide network of interconnected objects uniquelyaddressable, based on standard communication protocols.

Major characteristics:�Scale: The number of nodes will be order of magnitude

higher than the current Internet.�Heterogeneity: Many technologies (very different from one

another) will need to interact with each other.�Pervasivity: Computing and communication technologies

will be embedded in our environments.


IoT: is it important?

• The US National Intelligence Council reports the IoT in the list of the 6 civilian disruptive technologies with impact on US nationalpower.

• The list:� Biogerontechnology� Energy Storage Materials� Biofuels and Bio-Based Chemicals�Clean Coal Technologies� Service Robotics� The Internet of Things.

• There are large investments:� In far east Asia: especially in China and Japan.� In Europe large funding from European Commission.� In US, IBM reports it in the list of the hot topics for two years

in a row.

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IoT: What is it?

http://www.social-iot.org5

one paradigm, many visions …

Major characteristics:

� ‘Fuzziness’: Differences in the visiondepend on interests, finalities andbackgrounds of the major stakeholders

� Scale: The number of nodes will beorder of magnitude higher than thecurrent Internet.

� Heterogeneity: Different technologieswill need to interact with each other.

� Pervasivity: Computing and communication technologies will be embedded in our environments.

Taipei– 27 August, 2014


Things-oriented vision

•It has been the very first proposed vision.•The term “Internet of Things" has been introduced by The AutoID Labs1:

� Their focus: To extend the the Electronic Product Code (EPC) to support the spread use of RFID in world-wide trading networks and create industry-driven global standard for the EPCglobal network.

� Their primary interest: improve object visibility (i.e., traceability of an object and the awareness of its status, current location, etc.).

� Their ultimate objective: To architect the IoT in cooperation with EPCglobal.

•The Unique/Universal/Ubiquitous IDentifier (uID) architecture has mostly the same objectives (in both cases proposed solutions are middleware-based).

•The things-oriented vision should extend the portfolio of technologies to

include2:� Near Field Communications (NFC)� Wireless Sensor and Actuator Networks (WSAN)� Wireless Identication and Sensing Platforms (WISP)

_____________________1 http://www.autoidlabs.org2 M. Presser, A. Gluhak, The Internet of Things: Connecting the Real World with the Digital

World, EURESCOM mess@ge, The Magazine for Telecom Insiders, vol. 2,2009.

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It is based on the fact that currently IP connects a huge amount ofcommunication devices and runs on tiny, battery operated embedded devices.

•IP over Smart Objects (IPSO)3: A forum of 25 founding companies promotingthe use of the Internet Protocol for connecting Smart Objects. Key aspects - in line with 6LoWPAN4:

� a wise adaptation of IP� Integration of IEEE 802.15.4 in the IP architecture

•Internet 05: Complexity of IP should be reduced to route IP over anything.Key aspects:

� Simplification of current IP to be implemented in low capability devices.� Global objects addressability and reachability.

•Web of things6: Web technologies are reused to connect and integrate in the Web every day-life objects equipped with embedded devices or computers.

________________________3 A. Dunkels, et al. “IPSO Alliance White Paper #1", September 2008.4 J. Hui, et al. “6LOWPAN: Incorporating IEEE 802.15.4 into the IP Architecture - IPSO Alliance

White Paper #3", January 2009.5 N. Gershenfeld, et al., “The Internet of Things", Scientic American, 2004.6 D. Guinard, et al., “Towards the Web of Things: Web meshups for Embedded Devices", WWW 2009.

Internet-oriented vision


•The number of nodes generating information will be huge !

•It is critical how to accomplish information representation, storage, interconnection, search, and organization.

•Semantic technologies can exploit modeling solutions for7

� things description� reasoning over generated data� semantic execution of environments� architectures that accommodate IoT requirements and scalable

storing and communication infrastructure

•Therefore, semantic technologies may play a key role.

________________________7 I. Toma et al., “A Joint Roadmap for Semantic Technologies and the Internet of Things", Third STI Roadmapping Workshop, June 2009.

Semantic-oriented vision

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• Tagging Things: RFID and related technologies– enable real-time identification and tagging

• Sensing Things: Sensor technologies– enable detection of environmental status and sensory

information• Thinking Things: Smart technologies

- build intelligence at the networks’ border• Shrinking Things: Nanotechnology

– enable the “networking” of smaller and smaller objects

IoT: Key enabling technologies 1

________________________1 classification taken from: L. Srivastrava , «the network of the future : what’s over the horizon» BillingAsia 2006, 13 March 2006 Shangai, China.



Application domains and scenarios

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Emerging tech trends driving IoT



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What is going on…

http://www.social-iot.org

Source: Cisco IBSG, Jim Cicconi, AT&T, Steve Leibson, Computer History Museum, CNN, University of Michigan, Fraunhofer. Available at: http://readwrite.com/2011/07/17/cisco_50_billion_things_on_the_internet_by_2020

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• Current implementations enable the cooperation among objects only if belonging to the same closed group.

• Sort of gateways are needed to allow inter-group communication and cooperation.

• The number of embedded computing and communication devices surrounding each of us will soon become too large: � Scalability problems will emerge � Efficient cooperation between smart

objects creating trusted, dynamic social-like communities might contribute to solve the issue.

Rationals for a change in the vision

The IoT vision can be fully achieved only if objects are able to cooperate in an open way.

Figure: Number of people, Internet users,Internet devices: forecast.



• Mental models are internal images or representations of something which people use all the time to make sense.

• People in general have little clue about how networks actually work, but most of us try to guess anyway, consciously or not.

• If a technology is not completely clear we make a guess on how it works by using a (usually simplified) mental model.

• As an example, technologies such as Wi-Fi, 3G or Bluetooth are simply called «wireless technologies» by using the concept of wire to give an idea on it working behavior

________________________2 this concept of mental model is taken from: A Social Web of Things by Joakim Forno, available at

http://www.ericsson.com/uxblog/2012/04/a-social-web-of-things/

…a matter of mental models 2

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source: www.computerlandbg.it

• A study conducted at the Ericsson User Experience Lab shows that the mental model associated to a network is somethig like “very many point-to-point connections”

________________________2 this concept of mental model is taken from: A Social Web of Things by Joakim Forno, available at http://www.ericsson.com/uxblog/2012/04/a-social-web-of-things/



• The main issue with the Internet of Things will thus not be to understand what is IoT from the technological point of view but how it works (complex dynamics to exchange data , interact with the services offered by objects hidden everywhere, control of our private sphere, etc..)

• The old mental model related to cables is only sufficient to understand the technological side of the Internet of Things.

• But, to avoid confusions (and refusal) and to make interacting users feel comfortable with the novel IoT paradigm it is interesting to resort to a mental model that man has developed for thousands of years: social behavior and social relations that are the basis of other ecosystems, and other types of networks (not technological but human ... for example)


________________________2 this concept of mental model is taken from: A Social Web of Things by Joakim Forno, available at http://www.ericsson.com/uxblog/2012/04/a-social-web-of-things/

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• Concepts of “friendship” and ”social relationship” are very intuitive!

• Ericsson researchers think that a solution to both the practical scalability issues and the mental model/pedagogical issue could be to simply “dress” a network of things as if it was a social network!

• It is enough to envisage a social networks of objects with relevant services that allows them to interact, express in a natural way which data they need, which service they offer to users, collaborate with each other to create collective services to offer to the user.

• Deriving concepts are «Social Web of Objects» 3 and «Social Internet of Things » 4


________________________2 the concept of mental model is taken from: A Social Web of Things by Joakim Forno, available at http://www.ericsson.com/uxblog/2012/04/a-social-web-of-things/

3 Andreas Fasbender, Joakim Formo, Marcus Gårdman, Takeshi Matsumura, U.S. patent, US20110161478 A1

4 SIoT: Giving a Social Structure to the Internet of Things, L. Atzori, A. Iera, G. Morabito, IEEE Communications Letters, vol. 15, November 2011, p. 1193 -1195


• Modern technologies have made “smart objects” available.

• We are now witnessing to a generational leap from objects with “smartness” to objects with “social consciousness”.

• The evolutionary path towards the notion of objects that manifest a social behavior began years ago (around 2000).

The need for social objects: and evolutionary approach 5

“res sapiens”(smart object)

“res agens”(acting object)

“res socialis”(social object)

___________________________5 L. Atzori, A. Iera, G Morabito, “From ‘Smart Objects’ to ‘Social Objects’: The Next Evolutionary Step of the Internet of Things” IEEE Communication Magazine, Networks, vol. 52, no. 1, 2014, pp. 97–105

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Definitions

“an everyday artifact augmented with computing and communication, enabling it to establish and exchange information about itself with other artifacts and/or computer applications.” 6

“Smart objects might be able to not only to communicate with people and other smart objects, but also to discover where they are, which other objects are in the vicinity, and what has happened to them in the past.” 7

… evolution

“spime” (neologism for a currently theoretical object introduced by Sterling), which are space-time objects that are aware of their surroundings and can memorize real-world events 8

___________________________6 M. Beigl, H.-W. Gellersen, and A. Schmidt, “MediaCups: Experience with Design and Use of Computer-Augmented Everyday Objects,” Computer Networks, vol. 35, no. 4, 2001, pp. 401–409 7 F. Mattern, “From Smart Devices to Smart Everyday Objects,” Proc. Smart Objects Conf. (SOC 03), Springer, 2003, pp. 15–16. 8 B. Sterling, Shaping Things, MIT Press, 2005.

What a “smart object” is?


Smart objects have been considered as building blocks of the Internet of Things 9 and classified according to their Awareness , Representation , Interaction .

Three main categories identified:

Activity-Aware Smart Objects:

can record information about work activities and its own use.

Policy-Aware Smart Objects:

can also interpret events and activities with respect to predefined organizational policies.

Process-Aware Smart Objects:

understand the organizational processes and provides workers with context-aware guidance about tasks, deadlines, and decisions

________________________________9 Gerd Kortuem, Fahim Kawsar, Daniel Fitton, Vasughi Sundramoorthy, Smart Objects as Building Blocks for the Internet of Things, IEEE INTERNET COMPUTING, 2010

“smart object” as building blocks of IoT 15

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• Recent researches addressed the issue of “smart” objects that exhibit pseudo-social behavior.

• An intense experimental activity involved everyday-life objects augmented in their capabilities to interact in modes which were inconceivable in the past

• The leap forward: distinction between a ‘thing’ that is simply connected to the Internet and a ‘thing’ with an active role in the network

… as a result:

• An “acting object” is an object that is able to translate the awareness of causal relationships - which are the basis of knowledge of change and evolution of its environment - into actions.

• a significant evolution in the concept of “spime” :

• the ability to foment action and participate;

• having an assertive voice within the social web.

What an “acting object” is?


• The Smart-Its Friends procedure allows users to set temporary relationships of friendship on Smart-Its10 (smart wireless devices, with sensing, processing, and communication functions) based on the devices’ context.

• The so-called Blog-jects, a synonym for “objects that blog” 11 , are examples of this new attitude to a “tight interaction with the world”.

• The theoretical concept of Embodied Microblogging (EM) 12 , proposes augmented everyday objects to : (i) mediate human-to-human communication and (ii) support additional ways for making noticeable and noticing activities in everyday life.

• Objects become able to develop a spontaneous networking infrastructure based on the information to be disseminated 13.

________________________10 L. E. Holmquist, et al., “Smart-its friends: A technique for users to easily establish connections between

smart artefacts,” Proc. of ACM UbiComp'01.September/October 2001. 11 J. Bleecker, “A Manifesto for Networked Objects ” Proc. of the 13th MobileHCI, September 2006.12 E. Nazz and T. Sokoler, “Walky for Embodied Microblogging:sharing mundane activities through

augmented everyday objects,” Proc. of MobileHCI, September 2011.13 P. Mendes, “Social-driven Internet of Connected Objects,” Proc. of the Interconnecting Smart Objects

with the Internet Workshop, March 2011.

Examples of “acting object”

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What we mean is an object that is part of and acts in a social community of objects and devices (which, in our case, is a Social Internet of Things).

Current open questions in the IoT arena:• what really an object has to say to another object for which you

really need an Internet of Things• how these “conversations” between objects may promote the

development of the human society

A social object for IoT adds further questions:•why objects should have their own social network, separated from that of humans, if they are not supposed to call each other to agree to go clubbing by themselves (at least for the next years)?

A director of science fiction movies has thousands of answers!

….but as pragmatic computer scientists and engineers we must focus on interesting use cases to implement!

What a “social object” will be?


• An example of Ubiquitous IoT architecture that resembles the social organization framework (SOF) model is given in [14].

• The idea of social objects is found in [15]. But no indications on the characteristics the social network structure of IoT should have.

• The Finnish Strategic Research Agenda cites the concept of Social IoT.• In [16], a Social IoT architecture is described wherein social objects are

able to participate to communities and act as bloggers. • In [17], attributes reflecting the social relations of nodes are analyzed

Contribution to the spread of the “social object” idea

________________________14 H. Ning and Z. Wang, “Future Internet of Things Architecture: Like Mankind Neural System or Social Organization Framework?,” IEEE Communications Letters, Vol. 15, No. 4, pp.: 461- 463. April 2011.15 M. Kranz, Luis Roalter, and F. Michahelles, “Things That Twitter: Social Networks and the Internet of Things,”Pervasive 2010, the Citizen Internet of Things 2010 workshop (CIoT 2010), May 2010.16 A. C. Boucouvalas, E. A. Kosmatos, and N. D. Tselikas, “Integrating rfids and smart objects into a unifiedinternet of things architecture,” Advances in Internet of Things, Vol. 1, pp.: 5 - 12. April 2011.17 A. Jian et al., Nodes Social Relations Cognition for Mobility-aware in the Internet of Things, 2011 IEEE International Conferences on Internet of Things, and Cyber, Physical and Social Computing.

The concept of social networks of IoT objects separated from that of human beings but subservient to their needs is supported by recent studies

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Objects establish social relationships with each other. Objectives of such relationships are twofold:

• Give the IoT a structure that can be shaped as required to guarantee network navigability18 so as that service discovery can be performed effectively while guaranteeing scalability.

• Create a level of trustworthiness which could be used to leverage the level of interaction between things that are “friends”.

A new vision based on «social objects»: Social Internet of Things

SIoT (Social Internet of Things)a novel paradigm of “social network of intelligent objects”, based

on the notion of social relationships among objectsa,b .________________________________________________a L. Atzori, A. Iera, G. Morabito, “SIoT: Giving a Social Structure to the Internet of Things”, IEEE COMMUNICATIONS LETTERS, Vol. 15, No. 11, pp.: 1193-1195. Nov. 2011.

b L. Atzori, A. Iera, G. Morabito, M. Nitti, “ The Social Internet of Things (SIoT) – When social networks meet the Internet of Things: Concept, architecture and network characterization ”, Computer Networks, Volume 56, Issue 16, 14 Nov. 2012, Elsevier.

________________________18 J. Kleinberg, “The small-world phenomenon: an algorithmic perspective” in Proc. of ACM Symposium on Theory and Computing, 2000.


Why things should socialize?

What a social network allows to do, and why it matters to people and things?

Reason Humans Things

Become visible Increase popularityPublish

information/services

Find resources Find old friendsFind

information/services

Obtain contextinformation

Get filtered information

Get environment characteristics

Discover new resources

Find new friendsFind new

services/updatedinformation


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Defining a kind of social behavioramong objects.

Which social relationships things can be engaged in?

Parental object relationship: defined among similar objects, built in the same period by the same manufacturer.

Co-location object relationship and co-work object relationship: determined whenever objects (e.g., sensors, actuators, RFID Tags, etc.) constantly reside in the same place (home/industrial automation services) or periodically cooperate to provide a common IoT application (emergency response, telemedicine, etc).

Ownership object relationship: defined for objects owned by the same user (mobile phones, game consoles, etc.).

Social object relationship: established when objects come into contact, sporadically or continuously, for reasons purely related to relations among their owners


Possible relational models among “

““

“social objects”””

”.

• A widely accepted classification of social relations (among humans) is proposed by Alan Fiske though his “relational models” theory19.

• It emerges that four basic relational frames or structures are sources for generating social actions

• These derive from the four elementary models that the Frisketheory proposes:

�Communal sharing. � Equality matching.� Authority ranking. �Market pricing.

_____________________________________19 A. P. Fiske, “The four elementary forms of sociality: framework for a unified theory of social relations,” Psychological review, vol. 99, 1992.


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““


”.

Can be associated with the behaviors of objects, which are not relevant individually but, on the contrary, have only a collective relevance. For example, objects that operate in the form of “swarm”, according to which it is not important the service offered by the single object but the service that the entire swarm can provide to users.

Definition (Communal sharing relationships)equivalence and collectivity membership emerge against any form of individual distinctiveness.

Humans/Things



““


”.

•It may represent all forms of information exchange between objects that operate as equals and that request and provide information (or basic services) among them in the perspective of providing IoT services to users while maintaining their individuality. •While with objects in communal sharing relationship the service is associated to the whole group, in the second case every object has associated a service that it advertises.

Humans/Things

Definition (Equality matching relationships)egalitarian relationships characterized by in-kindreciprocity and balanced exchange.


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““


”.

•Established between objects of different complexity and hierarchical levels(such as: RFID reader and Tags, Bluetooth master and slave terminals, etc..) among which information exchange is highly asymmetric. •In this case, the service advertised is usually associated to the whole group of objects (coalition composed of master and slaves) or to the object of highest rank.

Humans/Things

Definition (Authority ranking relationships)relationships that are asymmetrical, based on precedence, hierarchy, status, command, and deference.



““


”.

•These can be associated with interactions that objects have whenever they find themselves having to work together in the view of achieving mutual benefit. •In many IoT applications, cooperation among smart objects is a fundamental principle, which precisely implies the participation in this relationship only when it is worth the while to do so.

Humans/Things

Definition (Market pricing relationships)relationships that are based on proportionality, with interactions organized with reference to a common scale of ratio values.


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Applications

Category of “

““

“objectrelationship”

””

”

Applicable relational modelType of object

interactionApplication examples

Parental object

relationship

Communal Sharing

Equality Matching

Market Pricing

Swarm

Balanced

Cooperative

Best practice sharing

Co-location object

relationship

Communal Sharing

Equality Matching

Autority Ranking

Swarm

Balanced

Unbalanced

Environmental monitoring

Building automation

Industrial automation

Data fusion

Automatic identification of goods

Co-work object

relationship

Communal Sharing

Equality Matching

Autority Ranking

Swarm

Balanced

Unbalanced

Emergency and first responder deployments

Data distribution

Telemedicine

Military applications

Logistics

Ownership object

relationship

Equality Matching

Autority Ranking

Balanced

Unbalanced

Remote control of devices

Personal data storing and distribution

Multimedia content fruition

Infomobility and positioning

Social object

relationship

Equality Matching

Autority Ranking

Market Pricing

Balanced

Unbalanced

Cooperative

Personal data exchange

Cooperative sharing and downloading

Distribute gaming

Cooperative and hybrid positioning


Use-cases


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Use-cases: an example in details


• Giacomo has just bought a new MAC notebook.

• At the beginning, he experiences difficulties in connecting to some network equipments (e.g., printers, faxes, and smartcard readers)

• By exploiting social relationships with other devices, Giacomo's Mac can find a mate that has already addressed the same configuration issues and fix the problems.

• Looking for potential sources of information through its social network (and exchanging best practices) is quite straightforward. Features such as geographical location, class of object, brand, and typology, allow for identifying the right friends in the community.

Use-cases: a further example in details


• Giovanni is a sales representative that frequently moves by car around the city. Unfortunately, increased traffic makes his tour more and more problematic.

• However, by exploiting the social network, his car is able to gather information in advance about traffic congestion along possible routes and to choose the best path.

• Finding the right source of information in the IoT social network is easy for the car by contacting “friend" devices acquired by means of co-location relationships. By this we mean those cars with which Giovanni’s car shared some routes in the past but which belong to drivers that Luigi might not even know.

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The structure of a network of social things

Questions

� How do things move?

� How do the resulting network look like?

� What observations can be drawn?

� Assumption: things that are often in proximity ofeach other establish relationships


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What are the mobility patterns of things?

� Mobility patterns of humans:

− Many datasets (see CRAWDAD)

− Many mobility models (see P. Santi, Mobility models for nextgeneration wireless networks, Wiley 2012)

� Mobility patterns of things: uncovered!

− … almost (L. Galluccio, et al. “On the potential of grouplocalization in the IoT”, IEEE WoWMoM 2011)

� A starting point:

− Usually things do not move by themselves: they are transportedby humans

− Mobility patterns of people have been studied


Things are usually transported by humans...

� … but what are the things that we usually carry with us indifferent situations?

� We carried out a survey involving around 600 people

� Questions:

− What “smart” things do owe?

− Which them you carry out when you go to XXX?

� XXX can be work, leisure, vacation, gym, shopping, etc...

Survey still available at:

http://www.social-iot.org/index.php?p=news&n=15

Please help us and fill the form (it takes less than a couple ofminutes!)


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At home I have

Desktop 329 58%

Laptop 478 85%

Tablet 139 25%

Smartphone 96 17%

MP3 reader 332 59%

TV 505 90%

Networking equipment 99 18%

Sensors deployed in the environment

29 5%

"Arduino" device 17 3%

None of the above 0 0%

Users can select more than

one answer, therefore the

sum of percentage values

could be greater than 100%.



When I am at work (school/university/office) in the environment there are

Desktop 372 68%

Laptop 339 62%

Tablet 130 24%

Smartphone 90 17%

MP3 reader 129 24%

Network printer 101 19%






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When I go to an entertainment place (movie theater/theater/club/pub) I bring

Laptop 12 2%

Tablet 26 5%

Smartphone 97 18%

MP3 reader 50 9%








When I practice sport (examples: gym/swimming pool) I bring

Laptop 7 1%

Tablet 8 2%

Smartphone 145 29%

MP3 reader 168 33%

Sensors to monitor vital parameters

7 1%






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When I go shopping I bring

Laptop 3 1%

Tablet 16 3%

Smartphone 105 19%

MP3 reader 79 14%








When I go on vacation I bring

Laptop 180 33%

Tablet 108 20%

Smartphone 107 19%

MP3 reader 243 44%

Digital camera or videocamera

64 12%





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When I go on a business trip I bring

Laptop 332 62%

Tablet 89 17%

Smartphone 104 19%

MP3 reader 170 32%

Digital camera or videocamera

40 7%








When I drive I keep the following devices switched on

Navigator (example: Tom Tom)

42 8%

Smartphone 92 17%

Tablet 10 2%

MP3 reader 9 2%






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Results of our survey

H T B W S P E C

Desktop 0.62 0.61

NTV Devices 0.74

Laptop 0.82 0.52 0.55 0.01 0.01

Camera 0.76 0.43

MP3 reader 0.67 0.51 0.26 0.26 0.17 0.36 0.10 0.10

Navigator 0.06

Env. sensors 0.15 0.36

Vital par. sensors 0.06

Printer 0.54

Tablet 0.15 0.11 0.10 0.20 0.02 0.01 0.04 0.02

Smartphone 0.94 0.98 0.91 0.79 0.97 0.42 0.96 0.88

TV 0.91

H: home; T: trip; B: business trip; W: work, S: shops, P: sport, E: Entertainment, C: Car


Human mobility patterns & types of places visited

� A few datasets exists which relate the mobility pattern ofhumans to the type of places they are visiting, e.g.,

− P. Meroni, et al.,

http://crawdad.cs.darthmouth.edu/unimi/pmtr

� Very informative, but very limited → Simulations!

� SWIM: a mobility model that takes social behavior intoaccount

− A. Mei et al., “SWIM: A simple model to generate smallmobile worlds”, Infocom 2009


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• The dataset collected by the University of Milan is selected as the most appropriate for our purposes

• it offers the opportunity to recover some information about the type of places where people meet.

• Human mobility real data are collected by sensors (it refers to two types of places, 44 nodes, and 19 days).

• Fixed nodes are considered as places, which are five and indicated by five different IDs.

• Additional information obtained from the authors of the research at the Milan University :

• a description of their sampling nodes, information on the fixed and the moving nodes, description of the type of place where the fixed nodes were installed, etc.

Real mobility dataset

Simulation setting & simulated mobility dataset

Cell


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In each cell distribution of places is taken

from data published by New York

municipality



Node mobility in accordance to SWIMEach node has a homeNodes prefer to visit popular places and places near their home.A parameter is used to weight popularity and proximity

http://www.social-iot.org56

Pr(go to X) = a [popularity of X] + (1-a)/[distance(X,home)]


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Once the destination has been chosen, the node carries the things in accordance to the results of our survey


Associating objects to human beings:the social graph

Only strong ties


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real mobility trace and real statistics for things’ displacement

• As expected, the highest values in all kinds of Centralities associated to cell phones; desktops usually occupy the second position in the rank; laptops occupy the majority of fourth positions; while other objects have lower scores.

• Cell phones have strictly higher values than the second objects in the ranks.

Performance evaluationReal dataset: co-presence duration

Things’ social graph for Milan data set with weights > 4000

Node 1 Node 2Weight (s)

36_Cellular_Telephone 37_Cellular_Telephone 2239236_Cellular_Telephone 37_Desktop 2239236_Cellular_Telephone 37_Laptop 2239236_Cellular_Telephone 37_Network_Printer 22392

13_Cellular_Telephone 19_Cellular_Telephone 21291

19_Cellular_Telephone 24_Cellular_Telephone 1899937_Cellular_Telephone 36_Desktop 15967

37_Cellular_Telephone36_Environment_sensors 15967

37_Cellular_Telephone 36_Laptop 1596737_Cellular_Telephone 36_Network_Printer 15967


N°°°

°of Contacts after a reduction of the edge weights by 7 times

•Probability Distribution Function of nr. of contacts in Milan data set

Performance evaluationReal dataset: number of contacts

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• The social graph among objects is richer in links than before.

• In the SWIM case, the nodes which have more powerful links does not refer to one kind of thing only (although still cellular phones have a prominent role, as expected).

• The highest centrality values belong to cellular phones and the second objects with higher centralities are laptops. The third ones are MP3 readers and there are no other kinds of objects in the rank.

• The probability distribution function of the co-presence timeand of the number of contacts between nodes (things) show very similar trends to the Milan case.

Performance evaluationSWIM datasets : differences


• Cell phones confirmed as the most important object in different kinds of centralities.

• they are the most suitable devices for service discovery, service providing, and for routing data across the network.

• Laptops and desktops are in the next steps and can also be suitable alternatives in lack of cellular phones.

• A further interesting aspect is that all the charts on the number of contacts and co-presence have similarities to Gamma distributions.

Next research step is to map rough information, only referring to number of contacts and co-presence durations, onto well-defined

relational categories and types of social relationship

Performance evaluationDiscussions and remarks

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Let things establish relationships autonomously

� In the SIoT different types of relationships have been defined:

� Parental Object Relationships (POR)

� Co-Location Object Relationships (C-LOR)

� Co-Work Object Relationships (C-WOR)

� Social Object Relationships (SOR)

� Ownership Object Relationships (OOR)

� Each type of relationship can be enabled (or not) by the things owner

� Enabled types of relationships can be characterized by simpleparameters

� For each type of relationship the information that can be disclosed isselected


Consequences…

1. Ensure network navigability

� area of interest divided into smaller areas called cells

� user u is assigned a home h(u) ; the next destination selection madebased on a weight w(C) to cell C equal to:

w(C)=alpha*distance(h(u);C)+(1-alpha )*seen(C)

� distance(h(u);C) is a decaying function of the distance from the homeof user u to cell C, while seen(C) accounts for the popularity of cell C(i.e. users observed by u in cell C, according to a perception radius).

� The parameter alpha is in the range [0;1] and determines whether theusers prefer to visit popular sites rather than nearby ones.


Performed simulations by using SWIM: assumptions

___________________________________________

L. Atzori, A. Iera, G. Morabito, M. Nitti, “ The Social Internet of Things (SIoT) – When social networks meetthe Internet of Things: Concept, architecture and network characterization ”, Computer Networks, Vol. 56, Issue 16, 14 Nov. 2012, Elsevier.

Users 2000

User peception radius 0,0067

Simulation time 11 days

alpha 0,8

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1. Ensure network navigability

� Average distance:

− SIoT: 2.85

− Random: 3.03

� Diameter:

− SIoT: 6

− Random: 11


Distribution of the distance between pair of nodes

Performed simulations by using SWIM: some results

Improving network navigability: a Shapley based approach

� Objects would be able to navigate the SIoT network with only localinformation, because:

� Stanley Milgram studied the small-world phenomenon anddemonstrated that people are tied by short chains of acquaintances

� Jon Kleinberg concluded that there are structural clues in a socialnetwork that help people to efficiently find a short path even without aglobal knowledge of the network.

� It is possible to define an efficient strategy for the objects to selectthe right friends for the benefit of the overall network navigability.

� The friend selection policy is distributed and dynamic so that nocentral controllers have to set a-priori static rules to establishsocial ties.

� Navigability of the resulting network structure can be madeindependent from the implemented routing algorithm and theapplication triggering the search operations.


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� Friendship selection can rely on a Shapley-value basedalgorithm

� We just started to study two different utility functionscarefully model the corresponding game to meet the posedconstraints

� average local clustering coefficient (a)

� degree centrality of the nodes in the considered coalition (b)

� We are evaluating the performance of network navigabilityin terms of average number of hops by using local peersearch operations.

___________________________________________________________________________________(a) Duncan J Watts and Steven H Strogatz, “Collective dynamics of small world networks”, Nature, vol. 393, no. 6684, pp. 440–442, 1998.(b) Martin G Everett and Stephen P Borgatti, “The centrality of groups and classes,” The Journal of mathematical sociology, vol. 23, no. 3, pp.181–201, 1999.



� The value of the maximum number of friendship Nmax

� can be fixed a-priori

� can vary during the network lifetime to keep under control thenumber of hubs in the network, which we observed thatinfluences the network navigability. In particular we increaseNmax when there are x% of N nodes in the network with atleast y% of Nmax friends, so that x represents the maximumpercentage of hubs in the network, while y represents thethreshold for a node to become a hub

� Early results

� By reducing the number of hubs, the performance of thenetwork increases as suggested by Kleinberg

� The choice of an efficient strategy is still an important issue:the model based on the coalition degree outperforms the onebased on local clustering even with a higher threshold y

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2. Differentiate the level of trust

� Use social relationshipas an indication of trustbetween things

� Use notions like:

� Reputation

� Centrality

� Opinion

� Security = cost

� Smaller distance in thesocial network ↔

Higher level of trust


3. Answer the need for a new communication model

� IoT requires new communication models.For example,

� IoT nodes may not be aware of the addresses of the nodesoffering the services or contents they need

� Often they rely on an appropriate context server

� Nodes offering a service/content notify the server

� Nodes interested in a service/content query the server

→ Feasible but impractical because of

� Problems related to the deployment and selection of theappropriate server (how to define a context?)

� Lack of autonomy and autonomicity (antidotes tocomplexity)*

_____________* R. Sterrit et al. Autonomicity – an antidote to complexity? IEEE CSBC 2005


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SIoT-Cast

� A node can publish a new service/content or send aninterest in a service/content similarly

− Like in Information Centric Networks (ICN)

� When the corresponding primitives are invoked anotification is propagated throughout the social network(up to a certain distance)

� Such a paradigm

− Can work at the overlay layer

− Can be integrated in ICNs


SioT-Cast in ICNs

� u1 becomes friend of u2 (type of relationship:r1)

� u1 creates an interest n1.n2

− n1 encodes u2

− n2 encodes u2 and the type of relationship


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SioT-Cast in ICNs



− n1 encodes u2

− n3 encodes u2 and the type of relationship r2


SioT-Cast in ICNs



− n1 encodes u2

− n2 encodes u2 and the type of relationship


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Not just a researchers' toy...

Ongoing activities

� Forerunner: Pachube

� Toyota Friends Network

� Nike+

� Paraimpu

� Ericsson

� Evrythng


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Forerunners... Pachube (now Xively) – Bought by LogMeIn in July 2011–


How does it work?


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An industry project: let cars interact in the social networks

� Toyota: 442 million yen ($5.5 million)

� Microsoft Corp.: 335 million yen ($4.1 million)

� Salesforce.com: 223 million yen ($2.8 million)


Toyota Friend


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Nike+

NikeFuel


Nike+ Social Ecosystem


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Ericsson: Social Web of Things


A Facebook for things: Evrythng


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Evrythng


Paraimpu

� Paraimpu is a social tool that allow people to connect, use, share and compose smart things & services

� Built around three blocks


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Paraimpu




Paraimpu




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Paraimpu




Comparison

Project/Company

Interactionbetweenthings

Autonomousestablishment of social relationships

Open to the development of new applications

Clear application/ business case

Nike+ Yes Yes No Yes

Web of Things – Ericsson

Yes Unspecified Yes Yes

Toyota Friend Minimal No No Yes

Evrythng Minimal Yes Yes Use cases

Paraimpu Yes No Yes Unspecified


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Going further...

� Up to now:

� Relationships between things explicitly established by objectowners

� social-like interactions between smart things utilized to buildWeb of Things applications

� What's next?

− Design a platform in which things are given the policies andare let free to interact with each others

− As we have seen before, exploiting social-like relationshipsbetween smart things at the networking layer allows to:

� Ensure network navigability in the IoT

� Support differentiated trust levels for communications in the IoT

� Provide new networking primitives


SIOT: ARCHITECTURE AND APPLICATIONS

- SIoT architecture and implementation

- Exemplary application


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ARCHITECTURE AND APPLICATION





� Recall the major types of relationships

Extending the ARM towards a SIoT model

Co-local

Co-work

Parental Social Co-owenership

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Analysis of SIoT components

Profiling

Social graph

Social presence

Partici-pationmodel

Relation control

Service Service APIs ID Mngt

Profiling

Owner control

Rela-tionship

mngt

Service disco-very

Service compo-sition

Trust-worthi-

nessmngt

Service APIs

Social Network

for Humans*

Social Networks

for Things

*D. M. Boyd and N. B. Ellison, “Social network sites: Definition, history, and scholarship,” Journal

of Computer-Mediated Communication, vol. 1, no. 13, 2007.


The IoT ARM –Architectural Reference Model

from the IoT-A fp7 project

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IoT – Service and VE levels


IoT – Service and VE levels

SIo

Tco

mp

on

en

ts in

to t

he A

RM

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IoT platforms comparison


SIoT architecture - basic concepts

Place

Channel

SIoT Server

Feed

ThingSpeakInterface

User

Group

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SIoT server – Channel registration


SIoT server – Channel setting

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SIoT server – Channel summary


SIoT Server Architecture

SIoT server

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Service discovery

Relationship mngt


Service discovery

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Client architecture…with reference to an Android device


Client architectureobjectives and functions

• Task of the Client module is to act as an intermediary between platform and SIoT android applications written by third party developers.

• Designed to facilitate the work of developers

• By means of targeted API, they can send/retrieve information from the platform, without having to worry about implementing additional code.

• Further task is to detect the devices around it and consequently send such data to the platform with the purpose to create the friendship between channels.

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Client architecturemain functional blocks

• API INTERFACE: implements the communication interface between Client and Platform SIOT

• RELATIONSHIP MANAGEMENT: implements the functionality for the management of relations.

• FEED MANAGEMENT: deals with the management of the acquired data

• INBOX MANAGEMENT: module for management and exchange of data between channels.

• SERVICE API CONNECTION: service for the management of the communication between clients and applications developed by third parties.

• UTILITY MANAGER: container of utilities.• LOCAL DATABASE: local database for the optimization

of resources.

ARCHITECTURE AND APPLICATIONS




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Application devices

User Device: students’ and teachers’ smartphones.

Room Device: microcomputer located in classrooms.

Information server: application server. It is seen as a SIoT object as well.

SIot ServerSIoT


� Class schedule and relevant changes

� Car pooling among students

� Sharing of exercises and notes

� Meeting during breaks

Applications

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Devices location

University

Classroom

Classroom

Classroom

Classroom

Classroom

Classroom

SIoT


Devices location

University

Classroom

My Location is…….

Classroom

Classroom

Classroom

Classroom

Classroom

SIoT

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Co-location relationship

University

Classroom

Classroom

Classroom

Classroom

Classroom

Classroom

SIoT


CoWork Relationship Creation

Classroom

The Room Device is constantly in Bluetooth «discoverable mode». It always

sends beacon packets visible to any other device working in «scan mode»

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CoWork relationship creation

The User Device periodically scans the Bluetooth frequency bands to discover

new devices; it then «sees» the Room Device and every other device in

«discoverable mode»

Classroom



If a User Device finds a new device nearby, it sends to the SIot Server the

relevant mac-address

I see:

00:12:23:AC:3D:12 Classroom

00:12:23:AC:3D:12

SIoT

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Classroom

Now the SIot Server knows that the user and the room devices are neighbors. If

this event occurs at least for half an hour and at least for two different days,

the condition for the CoWork relation creation is verified and the devices

become friends


Social relationship creation

If one or more user devices are in Bluetooth "discoverable mode” like the

Room Device, the other User Devices can see it and sends the nearby mac-

address to the SIoT Server

ClassroomI see:

AC:A2:90:DE:ED:34

AC:A2:90:DE:ED:34

SIoT

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Social relationship creation

Thus the SIot Server knows that two User Devices are neighbors. As for the

CoWork relation, if this event occurs at least for half an hour and at least for

two different days, the condition for the Social Relation creation is achieved

and the User Devices become friends

Classroom


Social relation creation

Another way followed to create the Social Relation is based on the location. All

the devices that see the Room Device (that is a static device) send the mac

address to the SIot Server. If the server receives the Room Device mac address

from two or more user devices, at the same moment, it understands that the

User Devices are in the same room.

Classroom

I see:

00:12:23:AC:3D:12 00:12:23:AC:3D:12

I see:

00:12:23:AC:3D:12

SIoT

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Application: class schedule distribution

The devices are interested in the class schedule and relevant changes.

Classroom

Distribution of class

schedule

Application installed in the

application server and objects

SIoT middleware installed in

any device


The class schedule is initially

uploaded at the Information

Server


SIoT

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The Information Server has a

CoLocation friendship with the

Room Devices

SIoT



Every Room Device finds and

downloads the lesson timetable

related to the classroom where is

located using CoLocation links

Room X class schedule

Room Y class schedule

SIoT

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During the classes, the Room Device

continuously sends to the SIot Server, a

feed containing the lesson name that is

taking place and the URL to download

the lesson timetable

#Lesson=Mathematics #URL=www.infomation_server.com…….

SIoT


SIoT

Application: the teacher updates the timetable

Teacher

smartphone

Feeds on schedule updates

Retrieve updates

if the source is

trustworthy

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Application: check for changes

The User Devices look for new

feeds related to the class

schedules and relevant changes

using CoWork and Social links

There are new

feeds for my

classes?

SIoT


Application: download of the link to the timetable

The SIot Server responds with

the feeds of the CoWork friends

#Lesson=Mathematics

#URL=www.infomation_server.com…….

#GROUP=MATH

SIoT

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SIoT

Application: download of the timetable

Now, the User Devices know

where to download the lesson

timetable and proceed

downloading it from the

Information ServerMath lesson timetable


Application: notification to the user

If the device that receives the

lesson timetable change is a User

Device, it sends a notification to

the (human) user

The lesson timetable is changed

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www.social-iot.org

convergence of iot and social networks issues

Documents