[email protected] 2015 · technologies, systems, and design principles associated with the emerging...

SMART ECONOMY & INNOVATION DELab UW

DELab UW www.delab.uw.edu.pl [email protected] 2015

ŁUKASZ MIROCHA

WORKING PAPER DELAB UW, No. XX (2/2015) | lipiec 2015 | SMART ECONOMY & INNOVATION

THE INTERNET OF THINGS AT THE

CROSSROADS: SMART HOME AND SMART

CITY IMPLEMENTATION MODELS

http://delab.uw.edu.pl/

Digital Economy Lab | University of Warsaw Warszawa, 2015

http://www.delab.uw.edu.pl/

mailto:[email protected]

http://delab.uw.edu.pl/

TABLE OF CONTENTS

SUMMARY 3

1. DISCLAIMER 3

2. INTRODUCTION 4

3. THE AGE OF THE INTERNET OF THINGS 5

3.1. THE BASIC PROPERTIES OF AND IOT DEVICE 6

3.2. AN IOT IMPLEMENTATION AS AN ECOSYSTEM 7

4. THE SMART HOME – INTRODUCTION & IMPLEMENTATION MODELS 8

5. HOME AUTOMATION SYSTEMS – A CRITICAL ANALYSIS 10

6. THE SMART CITY – INTRODUCTION 13

6.1. THE CORPORATE SMART CITY 13

6.2. THE OPEN SMART CITY – AN OVERVIEW 14

7. CONCLUSION 18

8. BIBLIOGRAPHY 20

8.1. WORKS CITED 20

8.2. ADDITIONAL RESOURCES 21



SUMMARY

The working paper is the result of pilot research conducted at the Digital Economy Lab at the

University of Warsaw in mid-2015. It analyzes current Internet of Things (IoT) implementations in the

domains of smart home automation (micro-scale) and smart-city (macro-scale). The study analyzes

various models of IoT infrastructure implementation in these two areas, focusing on the social

impact of open / open source infrastructure in comparison to proprietary hardware, software and

data management solutions. Specifically, it addresses the question of user / citizen subjectivity, user

/ citizen control over the infrastructure and the data generated within each type of IoT ecosystem, as

well as their potential of fostering local social and economic innovation.

1. DISCLAIMER

This pilot research should not be considered as a complete overview of all available market

implementations of the IoT in the domains of the smart home and the smart city. The study

examines several use cases in order to formulate a model illustrating current trends and directions in

consumer IoT ecosystems with regard to their social impact. The working paper is addressed to

researchers (in social sciences and the humanities), urbanists, designers and social entrepreneurs, as

it contains introductory-level technical information and presents the context of implementation of

IoT ecosystems. ICT industry professionals or advanced practitioners in the field of IoT should turn to

more specific research instead.





2. INTRODUCTION

After the civilizational changes triggered by the invention of the microprocessor, the personal

computer and the World Wide Web, the Internet of Things (IoT) is often considered as another

breakthrough phenomenon that will close the gap between the digital and the physical thanks to the

computing of billions of automated sensors, devices and networks operating worldwide. There are

already more connected devices than people. According to various studies, by the year 2020 dozens

of billions of connected devices will be operating globally (Mukhopadhyay 2014: 4) (Business Insider

2013), (Gartner 2014), (MIT Technology Review 2014).

The Internet of Things is often considered as the next step in ubiquitous computing (Cassimally,

Chichester, McEwen 2014: 10), or as Adam Greenfield calls it: the “everyware” — a socio-

technological condition in which processing power is distributed throughout the environment in such

a way that computers per se effectively disappear (Greenfield 2006: 7, 26). Technology becomes

more pervasive by its miniaturization, but above all, by integrating the computational with everyday

objects and environments. From the consumer / user perspective, connected and autonomous

devices may be perceived as magical (Cassimally, Chichester, McEwen 2014: 17) or enchanted (Rose

2011). The IoT is already considered to be one of the key socio-technological phenomena with which

we are currently dealing. Taking into account the changes which will be introduced by the IoT into

the economy, society, governance and many other areas of contemporary civilization, its societal

impact deserves particular attention.





3. THE AGE OF THE INTERNET OF THINGS

There is hardly any universal definition that can includes the complexity and diversity of the

technologies labeled as the Internet of Things. The authors of Machine-to-Machine to the Internet of

Things: Introduction to a New Age of Intelligence use the term “IoT” to describe a set of

technologies, systems, and design principles associated with the emerging wave of Internet-

connected devices that operate in the physical environment. From that one may understand that the

IoT as such is a fundamentally heterogeneous set of technologies (Holler et al. 2014: 14).

In contrast to the personal computer, which is a multi-purpose and thus a far more powerful

computational device, an IoT-logic device is a single-purpose computational device (a sensor, an

actuator, or a controller) (Cassimally, McEwen 2014: 9, 11). The IoT facilitates the connection of

sensors, systems and real-world assets to the broader Internet. It can be then described as an

extension of the existing Internet. Still, today’s Internet is rather a virtual world of content and

services. On the other hand, the IoT relies on the interaction between physical entities (such as

people, devices, resources etc.) through the Internet.

According to the authors of the Machine-to-Machine to the Internet of Things, the socio-

technological factors driving the evolution of networks and devices into Internet of Things

ecosystems can be narrowed down to three main megatrends:

1. An increased need for understanding the physical environment in its various forms, from

industrial installations through public spaces to consumer demands. (This phenomenon is

often fuelled by intended maximization of efficiency, certain sustainability objectives, or the

care for health and safety.)

2. Improvements in technology and networking capabilities.

3. Reduced costs of components and increased cost-effectiveness of the collection and

analysis of data output (Holler et al. 2014: 11).

The potential areas of IoT application are practically limitless. Various models of IoT systems and

architecture will be implemented in any domain where generating contextual knowledge about the

environment or enabling remote control of assets would provide added value. Emerging IoT

applications include, but are not limited to: consumer electronics, automotive transport, retail

banking, agriculture, environmental issues, health and well-being, utilities, process industries.





3.1. THE BASIC PROPERTIES OF AND IOT DEVICE

Devices labeled as IoT are fundamentally heterogenous: in terms of hardware and software

architecture, programming possibilities, power consumption and data management. However, some

key properties of any IoT device can be distinguished. Patrz not. O4: tu można np. zapowiedzieć

klasyfikacje, które zaraz nastąpią – albo na początku każdego nowego zaskakującego akapitu dodać

zdanie wyjaśniające.

Each computational device / chip that is an element of an Internet of Things implementation is

equipped with a unique network identity (MAC, IP), built-in communicational capabilities (WAN,

PAN, LAN – using Wi-Fi, Bluetooth etc.), sensors, DA/AD converters and the possibility to be remotely

controlled and programmed (Holler et al. 2014: 83). The IoT system can be also divided into three

basic layers: the hardware ecosystem (physical devices), the software layer and the user layer

(Corredor Pérez, Bernardos Barbolla 2014: 22-23).

Furthermore, three main types of IoT use cases within the IoT industry can be discerned. These are

sensors, actuators and tags. The use cases determine the final hardware/software configuration of a

device (Holler et al. 2014: 178; Cassimally, Chichester, McEwen 2014: 89).

Sensors: These are simple or complex devices that typically involve a transducer that converts

physical properties (such as temperature) into electrical signals. They are the pathways with

which the device acquires information on its surroundings. They include the necessary

conversion of analog electrical signals into digital signals.

Actuators: These are simple or complex devices that involve a transducer that converts

electrical signals into physical properties (such as the turning on a switch or starting a motor).

Actuators are the outputs for the device (such as motors, lights, etc.), which let the device

perform an action in the outside world.

Tags: Tags in general identify the physical entity to which they are attached. In reality, tags can

be devices or physical entities, but not both. An example of a tag as a device is a Radio

Frequency Identification (RFID) tag, while an example of a tag as a physical entity is a paper-

printed immutable barcode or quick response (QR) code.

In most use cases, an IoT implementation can be visualized as the following equation:

Physical Object + Controllers / Sensors / Actuators + Internet = IoT

(Cassimally, McEwen 2014: 11). As for the degree of complexity, each IoT node can be as simple as an

AVR microcontroller chip or Arduino, or as complex as a PC computer.





The rationale behind implementing an IoT solution, which is at the same time its most important

design principle, is its ability to monitor and control its surrounding environment (e.g. home or city).

This task can be performed as long as a framework for seamless data integration and sharing

between devices can be established – both at a local network level (smart house or smart city case)

and externally (a cloud, remote access). A successful IoT application in any use scenario should be

therefore based on a coherent software and hardware ecosystem, founded on standards and

protocols.

3.2. AN IOT IMPLEMENTATION AS AN ECOSYSTEM

A particular IoT implementation (smart grid, smart house, smart city, personalized wearables) can be

conceptualized as an ecosystem – both from the technical perspective (focusing on standards,

protocols or abilities) and from a social perspective (analyzing social relationships of owners and

vendors, or use cases etc.)

According to Jan Bosch and Petra M. Bosch-Sijtsema, a software ecosystem consists of a “set of

software solutions that enable, support and automate the activities and transactions by the actors in

the associated social or business ecosystem and the organizations that provide these solutions”

(Bosch, Bosch-Sijtsema, 2010: 77-92). Furthermore, a software ecosystem enables relationships

among the software, the services and their users. The relationships are based on a common

technological platform or market, and operate through the exchange of information, resources and

artifacts (Jansen, Finkelstein, Brinkkemper 2009: 187-190).

Two areas of IoT ecosystems implementation have been chosen as case study examples for this

working paper: home automation systems (smart homes) and IoT-based solutions for smart cities.

The use cases should be considered as user-oriented IoT implementations. Notably, though, the

smart home is a micro-scale ecosystem, while a smart city is a macro-scale IoT ecosystem.

In both cases, I am going to raise questions concerning the social impact of different models of IoT

infrastructure implementation, namely open IoT infrastructure and closed / proprietary

infrastructure and their variants (i. e. the integrator model). Problems such as user / citizen

subjectivity, user / citizen control over the infrastructure and the data it generates within each type

of the IoT ecosystem will be analyzed. Additionally, each model’s potential for stimulating local

innovation centers (i. e. start-ups, Media Labs, Fab Labs etc) and for developing competences in

designing, implementing and managing user-oriented home / urban infrastructure and services

based on the IoT, will be assessed as well.





4. THE SMART HOME – INTRODUCTION & IMPLEMENTATION MODELS

A smart house is a house equipped with highly advanced automatic systems for lighting, temperature

control, multimedia, security, window and door operations, and many other functions. Electronic and

programmable systems of a smart home can initiate timed and triggered events for lighting, shades /

blinds, heating, ventilation, security, irrigation, entertainment and other systems.

A smart home connects all devices and appliances, allowing them to synchronize and communicate

with each other. It allows for collecting information, so that the technology can anticipate the user’s

actions and habits. The IoT system for a smart house can be therefore characterized as a measuring

and analytical ecosystem composed of sensors and actuators that is designed to automate and

control living spaces. In the future, a smart house can also become one of the “smart spaces” or –

more precisely – “social laboratories” where users’ behavior and preferences (converted into data)

are optimized and modified through various social engineering techniques (i. a. gamification) in order

to maximize sustainability and economic efficiency.

Currently, the IoT market is in flux, both from the technological and economic perspective. On the

one hand, there exist completely open source DIY-friendly solutions based on Arduino, RaspBerry Pi

chips and open source software. On the other hand, there are proprietary, closed solutions, designed

with a black-box approach, based on custom hardware and software (Cassimally, Chichester,

McEwen 2014: 69-70).

The heterogeneity of devices and the multiple application cases have caused an interesting

implementation model to emerge: a middleware integrating software and hardware solution. It

offers a unified user interface to control all home automation devices operating in a building, and is

designed to support as many devices and network standards as possible.

One such software is OpenHab. It enables different home automation systems and technologies to

be integrated into one single solution that allows overarching automation rules and offers uniform

user interfaces. It is designed to be absolutely vendor-neutral as well as hardware/protocol agnostic.

Because the whole solution is fully open source, it can be accessed as a web-app or mobile

application. Another such software, HomeAssistant, offers similar capabilities – a user can control all

IoT devices from an open source integrating platform. This software supports both open source

Arduino-standard devices and proprietary devices delivered by vendors (Nest, Philipps etc.).



OpenRemote is yet another automation middleware solution that integrates various open source and

proprietary devices.

Besides fully open solutions (which are inclusive at the integrator level) described above, there are

also hybrid solutions. They are often based on proprietary hardware and software delivered by one

vendor (custom control unit, lights, thermostat etc). However, the same vendor also offers a custom

control unit or power supply sockets that can integrate third-party devices into the ecosystem, giving

a limited control over them (off-on, status information etc.) This is the case of Webee or more

sophisticated Control 4 and Crestron Home Automation solutions. Nevertheless, this model of an IoT

ecosystem implementation prevents the user from modifying the user interface, freely selecting the

use cases of the devices or using another vendor’s devices as core nodes of the ecosystem (as it is in

the case of Webee).

An interesting variation of this model is a subscription-based AT&T Digital Life solution. It is a home

automation system equipped with security (sensors, cameras) and automation (remote control)

packages. Both the hardware (cameras, sensors, controllers) and the software (apps, web interface)

are delivered by AT&T.



5. HOME AUTOMATION SYSTEMS – A CRITICAL ANALYSIS

Previous revolutions in consumer-oriented electronics were based on designing endless iterations of

more powerful general purpose computers – PC, laptops, tablets, smartphones. However, in the era

of ubiquitous computing, a user is also offered multiple interconnected devices which are scattered

across the physical space in order to deliver to her additional information on the environment or

allow her to automate actions and control processes remotely – this is the case of the smart home.

Ultimately, the main benefit of using IoT solutions comes from the integration of both the devices

and the data that they generate into one ecosystem that can be easily controlled by the user. The

heterogeneity of the devices that form such an ecosystem introduces a multitude of standards and

data protocols etc. (Holler et al. 2014: 76). Therefore, implementation models analyzed in this study

are often based on the middleware integration layer which is provided by a software / hardware

solution, offering a unified user interface or central control unit to connect and control all elements

of the home automation ecosystem.

Despite the fact that various home automation system integrators can differ in terms of openness

and degree of control that a user has over these systems, I argue that from the end-user perspective

any home automation system is a typical computational black-box. This situation is characteristic of

the age of distributed computing in which a user must operate within multiple layers of

interconnected devices, networks and services.

According to David M. Berry, black boxes “are the obfuscated technologies that hide what is inside,

sometimes productively, sometimes not, in order to simplify systems by hiding complexity or to

create abstraction layers” (Berry 2014: 183). A broad definition of black boxing considered as an

approach in science and technology has been formulated also by Bruno Latour: “When a machine

runs efficiently, when a matter of fact is settled, one need focus only on its inputs and outputs and

not on its internal complexity. Thus, paradoxically, the more science and technology succeed, the

more opaque and obscure they become” (Latour 1999: 304).

Any home automation system is a complex, networked technology that brings multiple stakeholders

into play (the device vendor, the software producer, third parties interested in the data the device

generates, and last but not least, its user). However, its complexity is often masked by its

obfuscation. Any home automation system, in order to be usable by a common user, is specifically

designed using the black-box approach, which puts the user in a particular socio-economic position.

Nevertheless, there are still some differences between the open and black-box model middleware





integrators that must be addressed before drawing general conclusions in the final section of the

chapter.

The open source based middleware integrator gives the user more control over hardware and

software. The whole home automation system is more flexible (because of its modular design) and

transparent (since the code for the application is publicly available). Both OpenHab and

HomeAssistant explicitly emphasize that their software gives the user total control over the data. As

a result, user-controllable and customizable data management solutions are created. The whole

ecosystem can work in the intranet mode, so no data ought to ever leave the building (as it is with

OpenHab). We can conclude that in this model the user becomes a data subject (as defined by the

Open Internet of Things Assembly, 2012). The open model also creates an interoperable device

ecosystem – the user does not need to rely on a single device provider. Lastly, the open model is

friendly to the DIY approach. A particular automation system can be developed in cooperation or

solely by local actors such as companies, start-ups, but also Fabrication Labs and Hackerspaces, in

order to address the needs of local communities – blocks, neighborhoods, cities – and support local

innovation economy in the process.

The middleware integrator which is designed and implemented following the black-box logic, puts

the user in an even more objectified position. It is based on proprietary hardware and software

which is designed and maintained by its vendors and developers. The user cannot use any hardware

or software that is not supported by the vendor. As a result a vendor-controllable data management

solution is set in motion. This may create a silo value chain where one agent delivers not only the IoT

home automation solution, but also monetizes user-related data through a cooperation with third

parties, such as insurance companies (as it is with Nest, for example) or advertising companies. I

argue that the user becomes a data object through the obfuscation of the whole home automation

ecosystem on its many levels: the hardware, the data management, the interface.

Notwithstanding, even the open source-based middleware integrator model offers real control over

the system and data it generates only to a handful of users. Performing any action which has not

been previously programmed by the hardware/software of an IoT vendor – modifying software or

user interface, adding various nodes (sensors, actuators) to the system, regulating data management

settings (to control privacy) – is possible only to users with some level of engineering and

programming skills. Therefore, the argument that the open source / open home automation system

gives a user real agency and control over it (because of its apparent transparency and flexibility), is in

most use cases just an illusion.

If a user lacks specific knowledge, there is little difference between using an open source / open

home automation system and a closed system based on proprietary hardware / software.

Consequently, any unskilled user is left “at the interface level” (Berry 2011: 36, 57, 137) of a mobile

application or central control unit, and her control over the system and data management is limited

only to options preprogrammed / made visible by its vendor. In this case, any home automation

implementation may become a source of valuable data on house-related daily activities of the user –





from power consumption to entertainment. What is particularly dangerous is that the user of a home

automation system becomes a source of data too, and has little control over it, or may not even be

aware of this. Any user of a ubiquitous computing ecosystem designed following the black-box

approach can often engage unknowingly or unwillingly with its particular function of the

computational system (Greenfield 2006: 46, 72).

In response to the current black-box design principle in home automation systems, a few interesting

counterinitiatives have been established. In 2015, The IoT Design Manifesto has been formulated by

a group of active professionals and designers working in the IoT industry. It outlines the “10

Principles to Help Create Balanced and Honest Products” (IoT Manifesto 2015). It postulates a win-

win strategy (that takes into account the interests of all stakeholders – the users, the businesses, and

everyone in between), promotes a culture of privacy (a culture of integrity where the norm is to

handle data with care and to conscientiously and deliberately identify what those data points are)

and encourages the actors to make all parties associated with an IoT solution explicit. Finally,

according to the signatories of the manifesto, users of IoT solutions should be empowered to set the

boundaries of how their data is accessed and how they are engaged via the product.

Casa Jasmina is another response to the obfuscation in home automation system solutions. Here, all

interested parties are invited to collaborate and co-design future-oriented home automation

systems. It is a Turin-based mansion / laboratory / FabLab coordinated by Arduino and Bruce Sterling

which focuses on designing and implementing open source solutions for housing spaces. It is

envisioned as a real-world testbed for hacks, experiments and innovative IoT and digital fabrication

projects, a curated space for the public exposure of excellent artifacts and best practices, and a

guest-house for occasional visitors to Toolbox, Officine Arduino and Fablab Torino (Casa Jasmina

2015). The long-term goal of Casa Jasmina is to “encourage industries that will create tomorrow’s

living spaces [to follow the open source mode].”





6. THE SMART CITY – INTRODUCTION

The authors of From Machine-to-Machine to the Internet of Things: Introduction to a New Age of

Intelligence formulated a working definition of a smart city, emphasizing the importance of IoT-based

technologies for its development. Any city is a smart city as long as: it uses data and ICT in order to

manage and optimize existing infrastructure investments and plan for new investments more

effectively; provides more efficient, new, or enhanced services to citizens; reduces organizational

silos in its service delivery and creates new levels of cross-sector collaboration; enables innovative

business models for public and private sector service provision (Holler et al. 2014: 283). Sectors that

have been developing smart city technologies include government services, transport and traffic

management, energy, health care, water and waste. Smart city applications are developed with the

goal of improving the management of urban flows and allowing for real time responses to challenges

(Wikipedia 2015).

However, as Adam Greenfield argues, originally the term described a very small number of

development projects initiated in Korea (Songdo), United Arab Emirates (Masdar City) or Portugal

(PlanIT Valley). They were all designed and built from the ground up by private developers (often on

reclaimed land) with information-processing capabilities based on sensors and actuators embedded

in the objects, surfaces and spaces (Greenfield 2013: loc. 75-78). Therefore, they should be

considered more as laboratories of new urbanism for the elites than actual examples for a successful

technology-based transformations for other cities (Greenfield 2013: loc. 1079).

The main challenge for the smart city idea to be fulfilled is to integrate IoT technologies with existing

cities. So far, two models of equipping urban areas with distributed computing based on the IoT have

emerged.

6.1. THE CORPORATE SMART CITY

For the last decade, corporations from ICT industries that seek new markets and new revenue

streams have advocated the dominant vision of the “city of the future”. Gartner estimates that the

base of connected things installed within smart cities will reach more than 2.5 billion devices by 2017

(Gartner 2015). Navigant Research says that the smart city technology revenue will grow to $27

billion in 2023 (Navigant Research 2015). Forbes estimates that smart cities are a $1.5 trillion global

market opportunity (Singh 2014).

Many ICT industry leaders are involved in developing technologies or complete solutions for the

smart city – IBM (Smarter Cities initiative), Cisco (Smart+ Connected Communities), Siemens

(Intelligent Infrastructure for a Sustainable Future), Intel (Smart City) and many others. Those





enterprises seek to position themselves both as providers and integrators of key elements of smart

city ecosystems. Although this strategy is similar to the one described in the chapter on home

automation systems, smart city integrators / providers act on a macro-scale.

The rationale of the corporate vision of a smart city is to deliver “package” solutions to municipalities

based on proprietary hardware, software and data management systems that would help to

“manage” and “optimize” the city and the life of its inhabitants. The critics of the corporate vision of

smart cities (as marketed by their PR) have deconstructed the corporate narrative behind the smart

city (Greenfield 2013; Söderström, Paasche, Klauser, Klauser 2014; Sterling 2014), and elaborated on

the societal and political consequences of this model of smart city implementation (Greenfield 2013,

Townsend 2014, Gabrys 2014).

The narrative that advocates “optimization” and “efficiency” is targeted at municipalities and

governments rather than citizens. Generally, the critics of the corporate smart city argue that the

citizens are not taken into account as stakeholders and effective actors, but rather as data generators

and assets that can be governed and optimized by ICT systems (Söderström, Paasche, Klauser,

Klauser 2014: 11) (Greenfield 2013 loc. 836-837, 852-854, 909-911), (Gabrys 2014: 11, 23), (Shelton,

Zook, Wiig 2014: 3,5), (Townsend 2014: 188). The famous Intelligent Operations Center designed by

IBM for Rio De Janeiro is an example of this approach. Adam Greenfield describes the rationale

behind the system: “It fuses data from weather stations, traffic cameras, police patrols, sewer-

mounted sensors and social-media postings into a synoptic, war room-style overview. This Center’s

primary innovation is that it gathers the entire apparatus of computational awareness and response

in a single room, allowing managers to tweak the city’s dynamic performance in (and ideally, ahead

of) real time.” (Greenfield 2013: loc. 134-137)

For its inhabitants, a smart city as envisioned by ICT corporations becomes a place of obfuscation,

filled with black-box technologies that transform the dwellers’ everyday living into data patterns.

However, the inhabitants are offered little agency in the smart city power dynamic. Often these

“package solutions” are designed and manufactured as part of the global strategy of their vendors.

Therefore, local businesses, start-ups, NGOs etc. can hardly benefit from these innovations (both

economically and in terms of knowledge transfer).

6.2. THE OPEN SMART CITY – AN OVERVIEW

Another vision of the smart city would achieve its efficiency and improved management through an

open infrastructure and the cooperation of many stakeholders (including citizens). This model of a

smart city has gained much attention in recent years particularly in Europe – partly because of a

coordinated strategy at the EU level (Digital Agenda for Europe) and partly thanks to the

participatory culture traditions in some European countries (mainly in Scandinavia). This model of the

smart city promotes initiatives and partnerships that are based on a non-proprietary open





infrastructure which allows the data that it generates to be accessible by any interested party.

Furthermore, this approach allows a city or a group of cities and companies to collaborate in building

an open source service platform, on top of which different stakeholders can develop their own

services. Consequently, in contrast to a proprietary infrastructure provided by a single vendor, the

open model offers flexibility and interoperability of networks and devices, as well as prevents the

creation of closed value chains. For instance, it enables a city to change services providers (i. e.

hardware maintenance / upgrade or data management systems) and invite local actors to participate

in IoT solutions implementation, as Adam Greenfield argues:

“The fundamental proprietary / open distinction applies as well to hardware specifications,

interoperability standards and data-exchange protocols. What is at issue in all of these cases is the

degree to which the party offering some technical product or service wishes to extend to its users

the rights to freely use, study, modify, improve and redistribute the system at hand, without

requiring that they pay a licensing fee or seek the manufacturer’s specific approval.” (Greenfield

2013: loc. 577-580)

Anthony Townsend advocates not only that the infrastructure remain open, but also that the city

maintain control over its critical layers such as broadband: “Community owned broadband (or any

other IoT ecosystem) puts the city in control of its own nervous system, giving it tremendous bargain

and power over any private company that wants to sell smart services to the city government or its

businesses and residents” (Townsend 2014: 289). Again, in this model of a smart city, it is much

harder to create closed value chains based on a techno-economic monopoly of one vendor.

Another key principle in the open smart city model is to make its citizens effective actors. They are

encouraged to cooperate within the developing systems and services for the city in which they live.

This approach can foster local economic and social innovation. Instead of corporate-advocated

“efficiency” and “control”, ideas of open participation and engagement are promoted. The authors of

a report published in the UK in 2014 entitles What are future cities? Origins, meanings and uses,

argue that this trend became particularly visible in the last years.

“Formal and informal citizenship engagement with future cities has become more inventive and

collaborative. Citizens adapt future city language to their aspirations for quality of life, safety, design,

culture and vibrancy, and pursue them through crowd-funding, crowdsourcing, DIY solutions and

political campaigns, among other means.” (Moir, Moonen, Clark 2014: 5)

One of the most successful implementations of the open model has taken place in Helsinki, Finland,

under the Forum Virium Helsinki and Six City Strategy initiatives. The project is based on three

principles: open innovation platforms, open data and interfaces, and open participation and

customership. In the information provided by Forum Virium Helsinki, we read that “the principle of

open participation and customership is to invite the entire city community to design and communally

develop service innovations and customer processes. In addition to this, the principle promotes

employment and participation, especially among people with low employment prospects”. As a





result, among many implementations, CitySDK and OpenAhjo were created. They are APIs and an

interface that open up data systems and offer support for private developers interested in

developing city services. City services established in Helsinki are also in use in Amsterdam and Rome.

Over a thousand data sets created with public funds have already been given free, open access.

Today, dozens of mobile apps developed by start-ups and citizens are making use of open data

(Forum Virium Helsinki 2015).

Aarhus, Denmark, is another successful implementation of the open smart city idea. The strategy

behind SmartAarhus is based on citizen participation and collaboration with local actors. Smart

Aarhus involves citizens in the development of projects such as the Digital Neighborhood project,

where the traditional communication model is flipped upside down as issues raised by citizens form

the basis of new city initiatives. Open Data Aarhus (ODAA) gives all interested stakeholders access to

data that they can use to create services and initiatives that meet the needs of the citizens. Based on

that, project RADICAL was developed, wherein a digital chip for bicycles makes traffic lights turn

green and helps cyclists pass through the city easily. Smart Aarhus uses participatory models for

funding as when companies, educational institutions, networks, and individuals make Internet Week

Denmark possible by crowdsourcing events.

Barcelona, Spain, became a Global Smart City 2015 included in the smart city ranking by Juniper

Research. The city has introduced dozens of initiatives in the areas of public and social services,

environment, mobility, communications, infrastructures and citizen cooperation. Among many other

projects, Fab Ateneus were introduced. They are publicly available fabrication labs focusing on

educating local communities in methods of digital fabrication (3D printing, basic engineering etc.).

The Barcelona City Council also provides data to the public so that a range of individuals and

collective entities can access and reuse the data with ease. Additionally, a Smart City Campus was

established to provide a space for forming synergies and co-creation initiatives between public and

private actors.

Open city advocates and municipalities in cities which follow the open model (Aarhus, Helsinki,

Barcelona and others) argue that this approach is crucial for fostering local innovation that can be

based on private-public sector collaboration, but also on promoting the development of local

innovation centers such as Media Labs, FabLabs etc. Therefore, the open model introduces not only

political and social benefits for citizens (who become an active party in city development – on the

political level – and a data subject – on the technological level), but is also a trigger for local

economy. Anthony Townsend argues that “extended public ownership of the data exhaust of cities

could potentially drive new business models to pay for investments in smart systems” (Townsend

2014: 293). Therefore, a key task would be to support local innovator centers and active citizens:

“every civic laboratory needs a physical and social support system for hackers and entrepreneurs to

experiment within. Contests, contracts for specific apps, and networking events are critical”

(Townsend 2014: 301).





FabLabs and Media Labs as well as local less formalized groups of actors may be the key element in

making the vision of the open IoT ecosystem a reality. FabLabs were started by the Center for Bits

and Atoms at MIT, and are defined as a global network of local labs, enabling invention by providing

access to tools for digital fabrication (The Fab Charter 20120). Currently, there are more than 260

FabLabs operating worldwide. According to Cameron Guthrie, the rapid development of these

initiatives can be explained by the availability of affordable digital manufacturing technologies, the

mutation of the DIY community into a wider “maker” movement, as well as the increasing pressure

from social movements for new forms of organization, production and consumption (Guthrie 2014:

2). FabLabs have been recognized by the World Bank as a way for transforming service delivery,

developing local industry, boosting entrepreneurship, providing hands-on learning experience, and

increasing interest in STEM education (World Bank 2014). There is no coincidence that FabLabs

actively operate in leading European smart cities (Barcelona, Helsinki, Copenhagen etc.).

Open smart city initiatives are often coordinated on a national level (Smart Cities Partnership in the

UK, or The Six City Strategy in Finland) or on a pan-European level (Connected Smart Cities Network,

Open & Agile Smart Cities, The European Innovation Partnership on Smart Cities and Communities).

The aim of this approach is to integrate open technologies by using common standards for

infrastructure and data management across many cities as well as to coordinate their political and

legislative agenda towards the smart city.





7. CONCLUSION

This pilot research aimed to describe and analyze current IoT ecosystems in relation to a wider social

context of their implementation. The smart home and smart city concepts were chosen as case

studies. Even though both use cases should be considered as user-oriented IoT implementations, it

must be noted that the smart home is a micro-scale ecosystem, while the smart city is a macro-scale

IoT ecosystem.

Initially, I assumed that there are two basic models of IoT infrastructure implementation, which are

based either on open / open-source infrastructure or on proprietary hardware and data

management solutions. However, the study of home automation systems revealed that in fact there

is a third model that should be taken into account – particularly if we analyze the IoT ecosystem from

the end user perspective. We have concluded that middleware integrator systems play an important

role in everyday user-IoT ecosystem interaction.

Furthermore, the analysis has also proved that if we raise questions about the degree of user agency

and control over the system (both over the infrastructure and the data that it generates) there is

surprisingly little difference between open / open-source and proprietary, black-box solutions. The

current technological heterogeneity and the layered architecture of any IoT ecosystem, together with

the lack of common standards of data sharing and management, prevent a casual user from building

/ modifying any home automation ecosystem, even if the open source approach allows for such a

possibility. In order to fully embrace the social benefits coming from the open philosophy of

implementing any IoT ecosystem, large-scale education in the area of programming and basic

engineering is needed. However, this strategy must take into account the fact that some users /

citizens will simply not be interested in developing such skills, and will choose black-box solutions

due to their user-friendliness and predesigned operating models.

Similar conclusions can be drawn from the study on macro-scale IoT implementation within a smart

city. A smart city which is based on open infrastructure and open collaboration of local actors that

co-design, customize and maintain is an interesting but still a distant vision. Large-scale education

actions are needed to raise awareness on the possibilities coming from this particular approach in

smart city implementation.

Other interesting questions arise from the analysis of human and non-human agency in the IoT

ecosystem, where both human-related and computational-based data are generated and analyzed.

Data harvesting technologies (sensors, tags etc.) make it possible to convert human behavior and

preferences into data patterns in order to address not only the need for the automation of living

spaces, but also in order to answer the “optimization” and “sustainability” trend at a macro scale.





Additionally, through automation based on machine learning, predictive analytics and the likes,

smart city or smart home devices are granted a significant degree of agency and self-control as nodes

of an IoT ecosystem. We could therefore ask whether in the analysis of any such ecosystem we

should follow traditional humanist perspectives (based on a clear subject/object distinction) or rather

turn into transhumanist / object-oriented ontology perspectives, where classic distinctions between

humans, machines and things are redefined or even no longer adequate.

The information provided by open smart city advocates which I have encountered in my desk

research as well as the close analysis of several smart city examples (Barcelona, Helsinki, Aarhus etc.)

suggests that FabLabs and MediaLabs seem to play an important role both in educating citizens and

in providing them with the space and infrastructure for collaboration on IoT technology

implementation. Municipalities, governments and other relevant stakeholders should therefore

support these bottom-up and citizen participatory-based initiatives.

All things considered, the analyzed use cases suggest that the general innovation policy should

encourage the whole ICT industry (both worldwide corporations and local enterprises) to participate

in designing and implementing IoT ecosystems based on open standards with regard to local

community needs.



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