Matthew Aitkenhead
email: matt.aitkenhead@hutton.ac.uk

David Donnelly
email: david.donnelly@hutton.ac.uk

Malcolm Coull
email: malcolm.coull@hutton.ac.uk

Helaina Black
email: helaina.black@hutton.ac.uk

E-SMART: Environmental Sensing for Monitoring and Advising in Real-Time

Keywords: Environment, mobile phones, apps, monitoring, soil, models

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

What is needed in the modern world

There are many ways in which information about the environment can assist land managers and policymakers in making decisions about appropriate management actions. Much of the current research in the environmental sciences is focussed not only on what processes and relationships there are within the natural world, but also on how we can improve our ability to observe the environment. Several factors are important in terms of how useful information is in this area, including:

• Accuracy -observations and measurements must be accurate, in order to be of use.

In order to achieve this accuracy, a certain level of expertise is required in either making the observations or in interpreting them.

• Relevance -there are many different characteristics of the environment that a manager or policymaker might be interested in, and observations that do not match the requirements are of little use. Making sure that the user's specific requirements are identified and satisfied is therefore important in terms of utility and costeffectiveness. • Spatial distribution -sometimes, a single observation is all that is required. Often however, multiple observations are needed to gain an insight into the spatial distribution of the parameter of interest. The pattern of observations is very much dependant upon what the user is looking at. • Timeliness -some characteristics of the environment such as topography do not change very much over time, while others such as weather can change very rapidly. More recent observations can be much more useful to the user than older ones, although repeating the observations implies increased cost and effort.

Modern technology has much to offer in the field of environmental monitoring, and mobile phone apps (applications) in particular have the potential to allow land managers access to existing datasets and to make their own observations rapidly and cheaply. The number of different sensors embedded into a modern mobile phone, such as camera, microphone, accelerometer and GPS device, enables it to act as a platform for multiple different types of observations [START_REF] Lane | A survey of mobile phone sensing[END_REF]. Another benefit of mobile phone apps in environmental monitoring is that they allow the user to make the observations where they are most relevant, rather than being forced to rely on maps and datasets that have been acquired for different purposes.

Here we demonstrate a framework for the development of mobile phone-based environmental monitoring apps, and highlight three examples of the way in which this framework has been used to produce apps for land managers in Scotland.

The state of the art

A number of different mobile phone apps are available for environmental monitoring. These fall into two different categories, for observation recording and data access. The manner in which each app operates is different, as is the data that is either recorded or accessed by the user. Examples of 'observation' apps include species identification [START_REF] Graham | What's invasive[END_REF][START_REF]Transects for Environmental Monitoring and Decision Making (TREND) app[END_REF][START_REF]NatureWatch invasive species app[END_REF] and water quality monitoring [5]. This type of app requires a certain level of knowledge on the part of the user, in order for them to produce reliable observation data. It also often requires monitoring equipment to be used, for example pH meters, which can provide measurements that the mobile phone is not equipped to take. Another type of app that allows users to record their own information is the 'mapping' app, which can enable land managers, for example, to record the positions at which samples have been taken for later analysis [START_REF] Farmlogic | Soil Test Pro app[END_REF][START_REF]PrecisionEarth soil sampling & recording app[END_REF]. This type of app is useful for recording and retrieving the spatial distribution of observation points in relation to fields or other landscape features. However, it still requires the user to get additional (often costly and time-consuming) analysis carried out. There are also apps that process user observations and provide decision support for agricultural and environmen-tal management, for example in crop pest monitoring [START_REF]Aphid Speed Scout[END_REF], crop yield prediction [START_REF]Optimizer corn yield app[END_REF][START_REF]Yieldcheck app[END_REF] and farm management [START_REF] Croptrak | iCropTrak farm management app[END_REF][START_REF]Farm Manager app[END_REF].

Examples of apps that allow the user to access existing data include interfaces for soil maps (e.g. [START_REF]Soil Web Survey[END_REF][START_REF]Soil-Mapp for iPad: soil information at your fingertips[END_REF][START_REF]mySoil App[END_REF]) and more general environmental map information (e.g. [START_REF]Natural Environment Map in Taiwan iOS app[END_REF]). These apps use the GPS function of the phone to pass the user's location to a server where the map is stored. This information is then passed back to the user. The value of this kind of app is that it can supply the user with existing information, usually for free, that is from a reliable source. However, the observations that have been recorded do not always match those that are required by the user, and even if they do then they may be several years old. For some applications this is not an issue, but as mentioned above, some soil and landscape characteristics, for example land cover or soil moisture content, can change rapidly.

Data requirements

The data requirements for monitoring are highly parameter-dependent. Some measurements, such as the elevation of a particular location, can be acquired through direct detection of elevation using a mobile phone's GPS functions. More complex examples, such as presence of specific plant species, would required either sophisticated image analysis from photographs of the environment or direct data input from an expert user. There are also many different ways in which each monitoring challenge can be tackled, along a spectrum of reliance upon user expertise. For true environmental monitoring that is consistent, reliable and can be carried out by land managers who are not likely to be experts in the relevant scientific fields, there needs to be a high degree of automation built in. Each case will have specific data requirements, and will also require ways in which this data is processed.

Model requirements

Leading on from the data requirements of automated environmental monitoring is the need to interpret the information acquired through mobile phone sensors and turn it into something relevant for the user. Again, this interpretation will be highly application-specific, and so if an organisation is looking to develop more than one app, or even a suite of apps that can be used for environmental monitoring, then it is more cost-effective to develop a generalisable data interpretation approach that can be used in a range of different ways. This implies the use of modern statistical or artificial intelligence-based approaches that can be applied to a wide range of data types and input/output combinations.

Personnel requirements

The development of an app for environmental monitoring is a highly multidisciplinary task, requiring expertise in several different scientific and technical areas. These include the development of the app itself, which requires specific programming skills, and the development and management of datasets and models that will interpret sen-sor data and produce information for the user. App development also implies a focussed project mentality enabling personnel to work closely together.

Methods

The work described here is not a standard scientific experiment, but rather the development and implementation of a framework for linking mobile phone technology with models for environmental monitoring. As such, the methods give a description of how this was achieved. We have divided the methodology into three main areas. Firstly, the organisation structure of the framework is presented. This includes the staffing and skills required, and the information and workflow arrangements set up to ensure success. Secondly, we cover the data framework used to integrate the various components of a successful system. Three examples are given of different types of monitoring system that we have implemented successfully. Thirdly, we cover the challenges that were overcome during the course of the ESMART project [START_REF] Aitkenhead | The E-SMART project[END_REF], and how they were tackled at the James Hutton Institute [18].

Organisational structure

In order to successfully develop apps for environmental monitoring, a number of key staff roles are required. These do not all have to be different members of staff, and it may actually be beneficial if at least one of these roles (the project coordinator) is taken by a member of staff who is also fulfilling one of the other roles. This ensures that two of the main challenges mentioned later, namely keeping project expectations realistic and realising the potential of the project team, are successfully handled. The key roles are as follows:

• App developer -while all of the roles identified for the development of environmental monitoring apps are important, this one is perhaps the most obvious for successful app development. The developer is the person who designs and implements the 'front end' of the app, and who ensures that basic information input/output between the app and any server-side processing is handled. The development of apps for different platforms requires skills in different development environments, and it is possible that more than one developer will be required. The app developer also needs to have a certain level of understanding about the environmental characteristic or characteristics that are being monitored or measured. Without this understanding, it is difficult to produce a user interface and background processing chain that is relevant for the requirements of the project. For many of the apps mentioned in the Introduction, the majority of the relevant data to be passed to the user is actually held on servers, and an understanding of the extraction and handling of this data is also important. • Data manager -as mentioned above, environmental monitoring that is capable of adding value greater than simply data entry or access in the field will require the handling and manipulation of existing environmental data. Unless the user simply wants to record observations that are linked to geographical location, there will be some element of integrating new information with existing datasets, usually with a spatial nature (i.e. digital maps). Therefore, at least one member of the development team needs to be familiar with the nature and handling of digital maps and databases. In addition to digital maps, other forms of data may be required. Tabular databases of records are one example that was also used throughout the ESMART project. The database manager for an app development project therefore needs to be able to be able to set up and handle database schemas that can hold new observations generated either by the user or by models that are generating information automatically. • Modeller -for nearly all of the apps mentioned in the Introduction, there is an emphasis either on data recording for future use, or access to existing datasets, with the main utility for the user in both cases that the app allows these activities to be done in the field. However, while entering or accessing data from the location of interest is no doubt more useful than via a PC from another location, there are two limitations from this situation that are important to consider here: (1) that any information entered by the user does not immediately benefit them, and (2) that any data accessed from existing datasets is dependent for its relevance upon how old it is. What we are adding through the inclusion of a modelling aspect is the ability for the user to accomplish two things at once: add new data to an existing framework and get some new and relevant information back out that is derived from their inputs, while minimising the amount of effort and required expertise on the part of the user. The role of the modeller is therefore to develop software models that can provide the user with new and useful information using a combination of their own input and existing data. A priority here is to simplify the requirements placed on the user and make the computers do the work. • Project coordinator -the development of mobile phone apps that can provide 'added value' in response to user inputs requires a high degree of integration between the visible user interface, the running of customised models (either through serverside processing or within the mobile phone itself), and the mechanisms involved in passing data back and forth between interface, models and any secondary processing/preparation of data. Even in a situation where all of the individual components can be implemented, a coordinating overview of the app framework is required.

Data framework

The data infrastructure of the ESMART project has a conceptually similar appearance to that of the organisational structure. The intention of the project is to provide a framework within which mobile phone apps for environmental monitoring may be developed. Each app has broadly similar data requirements, namely (A) access to spatial datasets, (B) models that take inputs from either the user or from these spatial datasets (or both), and (C) software that links the user interface, the database servers and the models. In order to satisfy these requirements, we have set up project-specific servers and made sure that Institute technical staff members are familiar with, and capable of handling, our server requirements. We have also developed a portfolio of spatial datasets of Scottish environmental characteristics, organised appropriately and split into subsets that allow more rapid access, which can be used to provide information for models. These datasets include soil, topographic, land cover, climatic and geological maps, each at a spatial scale of 100m and using a common coordinate system. For each app that is developed, a schema is generated that contains a copy of these datasets adjusted as required for the specific purposes of the app.

In addition to the servers and the spatial datasets stored on them, we have developed the capacity to produce rapid and flexible environmental models for predicting the outputs of interest. We have found that a neural network modelling architecture is sufficiently flexible, generalizable and rapid to satisfy our requirements in this area. Each neural network model is trained as appropriate, using a common training methodology and architecture. Additional processing of information, for example image analysis of photographs provided by the user, is carried out using custom subroutines designed as required. For communication between the various components of an app (user interface, spatial datasets, server-based models), we have found that the programming language PHP is suitable. PHP can be used to pass information from the user interface to the servers, and also to encode the neural network models and other processing subroutines. Figure 1 shows a schematic of the data infrastructure as it relates to the operation of each app, with the boxes representing specific components of the app and the lines representing the flow of operation between individual components.

2.3

Challenges encountered

Keeping expectations realistic

There is a certain air of urgency about the adoption of mobile phone technology by environmental agencies and research organisations in the UK. This is very definitely a fast-paced field, and there is a feeling that everyone is trying to 'get ahead of the competition'. To a certain extent this is appropriate -the first group that manages to produce useful, cost-effective apps for land managers and environmental scientists is likely to benefit more than those following. However, there are risks involved in simply trying to be first, rather than best. A shoddily-produced or unreliable app is a highly visible target for criticism, and does nothing to improve the reputation of those producing it. Any app that will be used by non-expert stakeholders in the field must be both attractive and simple to use but must also produce something new and useful -farmers and other stakeholder groups often spread information about new developments, both good and bad, by word of mouth.

An understanding of what is realistically possible is also necessary. Many apps that can be downloaded for free have been produced at great expense by expert production teams who intend to recover their costs through advertising revenues. Project funding in the environmental sciences is of an entirely different nature, and it is important for both developers and funders to appreciate this.

Understanding the potential

Running counter to the challenge of keeping expectations realistic is the need to ensure that the full potential of the technology is utilised. There are three main characteristics of modern mobile phone technology that provide avenues for developers to exploit:

• Geolocation -most modern mobile phones have in-built GPS technology almost equal to GPS devices. The inclusion of the user's location as information allows an app potential access to available spatial datasets. However, there is also scope for returning mapped data to the user and to models that can use this to develop new information about the location of interest. • Processing power -it is several years since the 1GHz barrier was passed in terms of mobile phone processor speed, and it is standard for memory capacity to exceed 1GB. This enables the device to handle sophisticated and computationally intensive processes. There is therefore the potential to generate new information through modelling or image/data analysis. • Database access -environmental agencies and research establishments either hold, or have access to, spatial datasets and databases in digital form. The practicalities of accessing this information from a mobile phone are no more complex than accessing them from a website. • Sensor technology -an important aspect of most, if not all modern mobile phones is their function as cameras, which could potentially be augmented by the inclusion of additional 'snap-on' sensor devices.

Institutional silos

While the compartmentalising of research areas has not been as much of an issue within the James Hutton Institute as it could have been, there are potential hurdles that must be overcome for successful development of environmental monitoring apps. A wide number of different areas of expertise are required in the development of any app that does more than simply record information or regurgitate it upon demand. In addition, the development of an app is not the only prerequisite to success; also important are the publicising and dissemination of the app, the provision of long-term support for users and the handling of any legal, administrative and reporting requirements. Successful implementation therefore requires good communication and as staff with the required skills are likely to be working from within a number of different research, technical support and administrative 'branches' of an organisation, there are challenges in bringing them together.

IP issues

When an organisation develops an app for mobile phones, the intellectual property of the app is normally fairly clear-cut -the organisation simply holds the intellectual property rights to the app and allows users to access it under licence. However, this situation is complicated when the app makes use of information from other sources, such as digital maps or remote sensing imagery, which has been acquired by the organisation under licence. If the organisation is constrained to the use of this data in a particular way, for example the licence was provided for purely research purposes, this will likely prevent the organisation from selling the app for profit. And if the project was funded by an external funding body, then the situation becomes even more complicated -the intellectual ownership of the app may be split between the funders and the organisation that produced it, or entirely with one of the two. And what of the outputs from such an app? If a farmer enters information about one of their fields, and this information is processed and produces an output, who owns the results? Is the organisation that funded the app development allowed access to this new information, or the organisation that produced the app? It is advisable for agreement to be sought on these issues prior to the development phase. Separate agreement needs to be reached on the intellectual ownership and usage rights of (A) underlying datasets, (B) the app itself, (C) any information supplied by the user to the app, and (D) outputs generated and passed back to the user.

Project administration

As pointed out above, the success of an app depend on more than how well the software is designed. The frameworks within which Apple and Android app developers operate are significantly different from one another, but there are commonalities between the two. One example of these is in relation to contractual obligations and intellectual property, as mentioned above. For an organisation to be able to publish apps, there must be some kind of legal contract between the organisation and those hosting and supplying the app to end-users. This contractual link is more obvious with Apple developers than for Android, but exists for both. The signing of contracts and other legal agreements is almost by definitions a responsibility of senior staff, who need to have the authority to agree that the organisation will be bound by certain legal requirements. However, the role of senior staff is unlikely to also include dayto-day management of an app development project, which needs to be carried out by a tightly focussed group with high levels of internal communication. The development and production of a mobile phone app is generally a focussed, relatively short-term project. In this sense, it is different from a lot of environmental research and can be funded through small, directed project budgets. However, there are two considerations for the long-term, that of user support and software licensing. The intention will undoubtedly be for the app to be used over the long-term, and the development team should expect to have to provide user support and further application development past the timeline of project funding. It is impossible, particularly in the current financial climate, to guarantee even a small funding source for this kind of work in perpetuity, and so the organisation responsible for app development needs to ensure that there is some kind of allocation made from underpinning capacity funding. Addition-ally, further future costs will be incurred due to annual license renewal for software and app developer accounts.

Results

Here we present and discuss three of the apps that have been produced by the James Hutton Institute, either directly under the ESMART project or with its support. Each app has certain common features, but is distinct in the manner of its use and information flow, and the stakeholder groups at which it is targeted.

SIFSS

SIFSS (Soil Indicators for Scottish Soils) is an app that allows the user to find out what soil type is in their area, to explore the characteristics of around 600 different Scottish soils, to discover the differences in soil characteristics between cultivated and uncultivated soils and to examine a range of key indicators of soil quality. It is the only app that gives access to the full dataset held from the Soil Survey of Scotland. SIFSS allows the user to use their device's GPS or to use a place name or postcode, or just browse using the image map on their device to choose a location in Scotland to investigate information about soil. This information includes pH, soil carbon, N, P, K etc. directly from the James Hutton Institute soils database (see Figure 3).

iDee

The iDee project aims to directly involve people and communities in the River Dee catchment in North-East Scotland in environmental recording. Using the iDee app and website, the user can submit their own records of river conditions in the Dee catchment in the form of a photograph of the river together with simple assessments of water clarity, flow speed and algae cover. An interactive map allows the user to choose and view records submitted by other people to see how conditions in the catchment change over time.

The iDee app was created for the Dee Catchment Partnership by the James Hutton Institute, and while it is currently only available for Apple devices (Figure 4), the idee.hutton.ac.uk web site can be used on any device.

SOCiT

SOCiT is a free app aimed at farmers, land managers and other land users who want to know how much carbon is in the soil. It is available for download on iTunes. The app uses information about the user's position to access existing digital maps of environmental characteristics, such as elevation, climate and geology. Combining this information with data extracted automatically from a photograph of the soil of interest, it uses a mathematical model to predict topsoil organic matter and carbon content (Figure 2). The app has been designed to work in any rural agricultural area of Scotland. The user also requires an image colour correction card that is supplied directly from the James Hutton Institute for free upon request. Figure 5 provides a series of screen shots from this app.

Ongoing projects

Each of the above apps has been successfully developed for iPhone and iPad. Current market share of apps is shifted towards Android, and there is an intention to develop each of the apps mentioned here, and any future apps, for this wider set of users. The additional skills required are available within the James Hutton Institute, and development of these apps is in hand. Additional apps are also being proposed, to make use of existing models and data resources developed and held at the James Hutton Institute. Now that we have successfully implemented a framework through which increasingly useful mobile phone apps can be developed for land managers and environmental scientists, the only real obstacle is finding and allocating the resources to allow us to do so.

Discussion

Specific infrastructure and skill requirements exist for the successful implementation of a mobile phone app that can be used for environmental monitoring. Putting this infrastructure in place and ensuring that members of staff have the necessary skills represents a significant effort, both economically and in terms of project management. However, once an organisation has all of the key components in place, it is in a position to repeat initial successes in this area. In the last year, the James Hutton Institute has put in place the technical, data-management and personnel resources to allow it to produce apps that do more than allow the standard map referencing or data entry from the field that is the industry standard. We have demonstrated this potential with three apps for iPhone/iPad, the most sophisticated of which (SOCiT) gives the user an estimate of soil organic matter content based on information about the location and soil imagery. The intention is to develop more apps, for both Apple and Android platforms that are capable of producing new information about the user's environment and that facilitate environmental monitoring.

Conclusions

We have demonstrated a successful framework for the development of useful mobile phone apps for environmental monitoring. This framework depends for its success on the identification of key roles and the creation of a focussed, results-oriented group in relation to the implementation of each app. It also relies on an organisational infrastructure that is sufficiently flexible to accommodate the technical requirements of specific apps, and that contains an appropriate mix of different types of staff expertise.

Fig. 1 .

 1 Fig. 1. Generalised data infrastructure of an app developed within the ESMART project

Fig. 2 .

 2 Fig. 2. Data infrastructure of the SOCiT app developed within the ESMART project

Fig. 3 .

 3 Fig. 3. Screen shots from the SIFSS iPhone/iPad app

Fig. 4 .

 4 Fig. 4. Screen shots from the iDee iPhone/iPad app

Fig. 5 .

 5 Fig. 5. Screen shots from the SOCiT iPhone/iPad app

Acknowledgements. The authors would like to thank Mr Andrew Thorburn, Mr Donald Barrie and Professor Colin Campbell from the James Hutton Institute for their assistance with the work carried out during the ESMART project. We would also like to acknowledge the funding support provided by QMS (Quality Meat Scotland) for the development of the SOCiT app.


Ioannis N Athanasiadis

Despina Anastasiadou

Kostas Koulinas

Fotis Kiourtsis

Identifying smart solutions for fighting illegal logging and timber trade

Keywords: Agile methods, requirements engineering, forestry, illegal logging, illegal timber trade, user stories, participatory methods, inception workshop, smart solutions, RFID, low cost sensors

We investigate how smart Information and Communication Technologies (ICT) solutions can be used for combating illegal logging and timber trade. We put together techniques from agile requirements engineering to propose a methodology for identifying user stories and associated risks and priorities and via a collaborative, participatory, single day workshop, named inception workshop. We present our findings from the first application of the method, with the active involvement of the relevant stakeholders, i.e technical and domain experts, which concluded in seven user stories.

Introduction

Illegal logging and timber trade is currently a major concern among all countries world wide [START_REF] Nellemann | Green Carbon, Black Trade: Illegal Logging, Tax Fraud and Laundering in the Worlds Tropical Forests. A rapid response assessment, United Nations Environment Programme[END_REF], while it is acknowledged as a complex problem that involves not only environmental dimension, but also economical and societal ones. While until very recently illegal logging was not an important issue in Greece [START_REF]National report of Greece concerning the illegal logging and the trade of the illegally produced forest products. Illegal logging and trade of illegallyderived wood products in the UNECE region: Causes and extent[END_REF], nowadays it has been intensified due to the economic crisis [START_REF] Papachristou | Greek austerity fuels illegal logging, ministry says[END_REF][START_REF] Lowen | Greece crisis: Poor turn to illegal logging to survive[END_REF]. In this paper, we investigate how smart Information and Communication Technologies (ICT) solutions can be used for combating illegal logging and timber trade. Following an analysis of the whole logging and timber trade lifecycle, performed with participatory methods and agile techniques, we have identified the weak links, which could potentially be improved by technical solutions.

With the active involvement of the relevant stakeholders, we identified seven user stories for strengthening modus operandi, each one of which introduces one or more ICT technologies. These include electronic documents; barcode and RFID (Radio-frequency identification) tagging; low-cost, low-power sensor networks for motion and volume detection; remote sensing and satellite imaging; integrated service-oriented systems. We also identified associated risks and priorities for the implementation pilot of these user stories.

The rest of this paper is organized as follows: Section 2 presents methods and tools, i.e. the problem at hand related to illegal logging and timber trade (par. 2.1), agile methods for requirements engineering (par. 2.2), and our methodology for identifying and prioritizing solutions (par. 2.3). Section 3 demonstrates the methodology application and our key findings, while section 4 presents with a discussion of our experiences with the process.

Methods and tools

The problem at hand: AITOLOS project

The AITOLOS project aims to foster cross-border collaboration and coordinate the activities of Greece and the former Yugoslav Republic of Macedonia against illegal logging and timber trade. Specifically, AITOLOS contributions are towards the following directions:

(a) Assess current legal framework, and define specific recommendations for enhancing legal and regulatory framework in order to fully address legal aspects of illegal logging/trade. (b) Develop policy guidelines for addressing illegal logging, with a special focus on crossborder timber trade. (c) Develop an operational plan across participating border regions for implementing policy/guidelines, including the technical specifications, stakeholders roles, monitoring mechanism, timetable and budget. (d) Engage in communication and dissemination activities at regional,national and European level, presenting developed policy guidelines and regional operational plan. (e) Two implementation pilots, one per country, in order to assess project results in real world situations. Pilots will run in forest offices of Goumenissa and Bitola. One of the major challenges of AITOLOS project was to identify smart ICT solutions to be incorporated into the everyday work of national forest protection services, to facilitate and support traditional processes of forest management, and to protect forest ecosystems against illegal logging and trade; proving that modern technologies can be successfully incorporated into everyday forestry service duties. Identification, prioritization, and risk estimation for building a potential solutions portofolio was a major challenge for the AITOLOS project. Also, selecting which of the potential solutions can be demonstrated as the project pilot was further constrained by our limited amount of resources. To tackle these challenges, a bidirectional knowledge shift between forest and computer scientists was required. To develop a common language and achieve consensus among technical and domain scientist, we adopted agile methods originating in software engineering, and put them in action in a single-day, intensive, 'inception' workshop.

Agile methods for identifying key requirements

Capturing user requirements consist a moving target in projects that are ill-defined, or fastchanging, thus the past years agile approaches as Extreme Programming or Scrum have been proposed for capturing requirements informally, i.e. as users stories.

Agile methods for requirements engineering are based on four principles [START_REF] Eberlein | Agile requirements definition: A view from requirements engineering[END_REF]: a) put customers in the center of attention, i.e. involve actively stakeholders through out the process; b) employ early verification and validation techniques, i.e by developing acceptance tests for user stories, or use cases; c) identify early non-functional requirements and analyze them with customers; d) manage change and incorporate it in the process of requirements engineering.

Cao and Ramesh, in [START_REF] Cao | Agile requirements engineering practices: An empirical study[END_REF], presented an empirical study that evaluates agile requirements practices in 16 organizations, and reveals that intensive communication between the developers and customers as the most important one.

Inspired by agile requirements practices, we developed a methodology for engaging end-users early in the process, and applied it in AITOLOS project technical solution identification.

The inception workshop approach

An inception workshop aims to help project participants (i.e. both technology and domain experts) to identify key requirements of the project technical solution. This can be achieved through eliciting initial user stories, which will be subsequently developed through iterations. Domain and technology experts interact in a single day intensive workshop, in order to come to a common understanding as to the scope of the project technical solution.

Participant roles participants are domain and technology experts, and a facilitator. Domain experts participate in order to present their application domain and present problems in their everyday work practice. Technology experts come to the workshop for understanding better the problem domain and specify the technical solution The facilitator is responsible for running the workshop according to schedule and keep discussions focused.

The inception workshop is organized in three sessions. Figure 2.3 presents a typical agenda of an inception workshop.

The first session focuses on domain understanding, in our case forestry and illegal logging. The goal of this session is to detail the current processes followed, and increase the understanding of the technical experts. Domain experts share their knowledge in a storytelling fashion: They communicate their knowledge by sharing experiences and anecdotes. In a collaborative team environment, domain experts stand up to tell the story of their every day work, in a procedural manner, while technology experts intervene with clarifying questions. The facilitator needs to keep the storyline progressing, and prevent the domain experts from diving into details.

The second session focus is on understanding technology opportunities and identifying technology solutions. Through a brainstorming process, technical experts 'put guns on the table', and exemplify how technical solutions might be used for fighting illegal logging. Existing or new solutions are presented informally by technical experts, in order to increase domain experts awareness of the capabilities of ICT systems, and their application in the domain. In a technology roadshow, technical experts present solutions in the form of examples, not only related to the domain at hand, but from other domains as well. Domain experts are free to ask questions, so that they understand what technology tools may offer, and the facilitator is responsible for moderating jargon language and reminding technical experts to use simple examples from known applications to the domain experts.

The final, third, session aim is to produce a synthesis of the two views, the one of the domain experts with that of technology experts. In a collaborative team environment, domain and technology experts identify together initial user stories. Agile requirements methods consider user stories as very high-level definition of requirements, containing just enough information so that the developers can produce a reasonable estimate of the effort to implement it [START_REF] Cobb | Making Sense of Agile Project Management[END_REF]. In our case, initial user stories aim to produce very high-level requirements of technical solutions for the project at hand. While identifying those, we also set priorities in terms of effectiveness, risk and cost.

Demonstration: the AITOLOS inception workshop

For identifying the potential technical solutions of the AITOLOS project (see par. 2.1), we organized the first AITOLOS inception workshop, in Thessaloniki, Greece, on January 25, 2013, with the participation of all Greek project partners. Approximately 12 domain experts from two forest services (Kilkis and Goumenissa), and 6 technology experts from CERTH and TEIS participated with the main goal of answering the question: 'How smart ICT solutions may be applied in forest service practice in order to combat illegal logging and timber trade?'. We had a very vibrant and interesting discussions in the form of an 'inception workshop'. In the following paragraphs, we present our findings from the workshop. Storytelling and Technology roadshow sessions are presented with annotations from bibliography, to strengthen the opinions that have been revealed during the workshop. Synthesis session presents our results in terms of the technical solutions identified, presented here as user stories, annotated with our estimates in terms of risks and costs.

Storytelling

During the storytelling session, forest scientists presented the whole lifecycle of wood, from the forest to the timber market. Illegal logging activities include the harvest, transportation, purchase or sale of timber in violation of national laws. The harvesting procedure itself may be illegal, including using corrupt means to gain access to forests; extraction without permission or from a protected area; the cutting of protected species; or the extraction of timber in excess of agreed limits [START_REF] Marijnissen | Facing Reality: How to halt the import of illegal timber in the EU[END_REF].

We went through five steps in the timber logging and trade value chain: a) Timber premarking, b) Felling and pilling, c) Delivery and transport, d) Storage (and trade) e) Crossborder timber trade. We highlighted differences in the process between fuel wood and technical wood. Our findings are summarized in Figure 2.

Issues related to illegal logging were presented, and verified that 'two types of illegal logging can be clearly distinguished in South-East Europe countries: (a) poverty driven; and (b) commercial. These types of illegal logging generally take place in different forest areas, are carried out by different offenders, and have different impacts. For example, poverty-driven illegal logging is typically carried out in remote locations, out of sight of the state forest management and law enforcement bodies' [START_REF] Markus-Johansson | Illegal Logging in South Eastern Europe[END_REF]. Domain experts provided with evidence from their areas and detailed incidents of illegal logging and timber trade of the two types. We concluded that in Greece, poverty-driven illegal logging typically involves fuel-wood, while commercial illegal logging is related to both fuel-wood and technical wood.

At the same time, we realized that Greece is not an exception to the UN report observation that in South-East Europe many countries have weak forest information systems [START_REF]UN: The European Forest Sector Outlook Study II[END_REF]. Most forest services in Greece are largely doing their business with 'pen and paper', and digitization of processes and documents are still in its infancy. Wood value chain in Greece remains undeveloped and several obstacles remain related to official forest maps, forest data recording and cadastral, adaptation of EU specifications, and producing official forest maps, forest data recording, rapid adaptation to EU specifications [START_REF] Koulelis | Greek timber industries and wood product markets over the last century: Development constraints and future directions[END_REF].

Technology roadshow

During the technology roadshow session, technical scientists with different backgrounds presented relevant technologies that could be potentially used in improving the situation with illegal logging and timber trade. Among those are the followings:

Central systems for issuing permits: A recent Interpol/UNEP report [START_REF] Nellemann | Green Carbon, Black Trade: Illegal Logging, Tax Fraud and Laundering in the Worlds Tropical Forests. A rapid response assessment, United Nations Environment Programme[END_REF] recommends to centralize nationally the issuing of permits for land clearance whether for logging, plantations or ranching, and permits for road transport of timber with strong anti-counterfeit measures. DNA fingerprinting: The use of ambient sonic spray ionization mass spectrometry as phytochemical markers [START_REF] Cabral | Wood typification by Venturi easy ambient sonic spray ionization mass spectrometry: The case of the endangered Mahogany trees[END_REF], and DNA fingerprinting [START_REF] Lowe | A DNA method to verify the integrity of timber supply chains; Confirming the legal sourcing of merbau timber from logging concession to sawmill[END_REF][START_REF] Nowakowska | Application of DNA markers against illegal logging as a new tool for the Forest Guard Service[END_REF][START_REF] Jolivet | Use of DNA fingerprints to control the origin of sapelli timber (Entandrophragma cylindricum) at the forest concession level in Cameroon[END_REF][START_REF] Degen | Verifying the geographic origin of mahogany (Swietenia macrophylla King) with DNA-fingerprints[END_REF][START_REF] Ganopoulos | Taxonomic Identification of Mediterranean Pines and Their Hybrids Based on the High Resolution Melting (HRM) and trnL Approaches: From Cytoplasmic Inheritance to Timber Tracing[END_REF] has been reported for identifying timber origin, and verifying timber supply chains integrity, especially for high-valued timber. Such forestry forensics tools can be proved effective as a means against illegal logging, however the effects of population substructure and inbreeding [START_REF] Tnah | Forensic DNA profiling of tropical timber species in Peninsular Malaysia[END_REF] need to be taken under account. Market control mechanisms include the expansion of market inspection competencies to ensure the complete control of documentation accompanying shipments of wood, including statements of origin and logging permits [START_REF] Markus-Johansson | Illegal Logging in South Eastern Europe[END_REF]. Forest certification: Chain of custody control measures ensures that illegally sourced wood cannot be processed in plants, as some proof of origin certification is required. Forest governance and overall forest preservation can be improved through the introduction of certificates for sustainable forest management and chain of custody certificates [START_REF] Markus-Johansson | Illegal Logging in South Eastern Europe[END_REF]. GIS systems and databases: Field computers, GPS receivers and GIS databases can ensure the constant monitoring of wood flow and so prevent illegal trade [START_REF] Markus-Johansson | Illegal Logging in South Eastern Europe[END_REF]. Marking logs with RFID tags: Pulping compatible, passive UHF transponder made of a wood composite material was developed for marking of logs together with robust RFID readers for the harvester and saw mills [START_REF] Häkli | UHF RFID based tracking of logs in the forest industry[END_REF]. Satellite imagery at multiple temporal and spatial resolutions can effectively increase transparency in the forestry sector by revealing the rate and extent of deforestation on an annual basis and identifying potential areas of illegal logging [START_REF] Fuller | Tropical forest monitoring and remote sensing: A new era of transparency in forest governance?[END_REF]. Machine vision: Wood identification using anatomical characteristics can be automated through machine vision, as reported in [START_REF] Hermanson | A brief review of machine vision in the context of automated wood identification systems[END_REF], that could be used for fraud detection.

Technology experts presented the above-mentioned technologies, giving examples from forestry applications abroad and from scientific literature, along with examples from other domains with which our domain experts were familiar with. These included market retail, taxation and fiscal systems, electronic tickets, parking stations, and fire protection.

Synthesis: User stories, risks, priorities

The previous two sessions increased confidence of our technology experts with illegal logging and timber trade issues, and our domain scientists with modern ICT technologies, and their potential in forest protection. Following a longer break were informal discussions continued, we reconvened in collaborative session where we started shaping solution ideas into the following user stories. The order of presentation is not significant.

S1: Electronic documents with security characteristics

All documents involved in timber transportation (from the log landing areas to the market) becomes digital, to avoid fraudulent documents. This involves both logging permits and timber transportation notes (which accompany trucks from log landing areas to timber warehouses). New document templates incorporate security features in the form of barcode, QR code, or e-signatures, which are instantly verifiable. Using low cost smart devices, security features can be verified online, via accessing web addresses over the web.

Risk assessment and priority: This solution is easy to implement and resembles an electronic ticketing mechanism with several control points. Legal enforcement and information security infrastructure are the main challenges of this solution. S2: Smart tags for timber marking Currently, timber marking is performed with hammers that mark wood with forest office, or forester code. This is a weak point that can be significantly improved by introducing unique tags for all wood marked by hammer. This can be achieved by using plastic barcode or RFID tags, or RFID nails from pulping compatible material. This solution is ideal both for proving technical wood log origin, and for timber certification.

Risk assessment and priority: RFID technology has been extensively applied in market applications, however there is little experience with timber marking. In this respect, reading capacity of equipment needs to be verified in the environmental conditions of log landing areas. At the same time, there are operational challenges related to who applied the tags, who bears the cost of the application and how to check tags when logs are loaded on trucks. Last but not least, information security and cryptographic characteristics of the information encoded in the tags needs to be studied in depth. S3: Cross-border uniform, multilingual reports Forms attached to timber imported (or exported) are standardized, and all texts appear in both local languages, and English. This solution removes language barriers at inspection points and could be achieved by developing a common standardized template to be shared between the neighboring countries.

Risk assessment and priority: While seems simple, due to the different policies and regulations of the two countries, achieving consensus might be a problem. Enforcement and orchestrating all involved authorities in both countries could be troublesome too. Also, when exported in processed form, timber becomes a marketable product. This implies that European common market rules apply, and forest regulations do not apply any longer, rather is a matter of custom inspection and international trade. S4: Truck load detection Manual estimation of timber volume loaded on trucks is another weak point of the value chain. Using an one-stop gate, equipped with low cost, low power sensors to calculate automatically wood volume loaded on a truck. This could be done for example by two depth cameras getting two viewpoints of a truck: top and rear, and detecting the contour of the volume loaded.

Risk assessment and priority: While today there are several cheap sensors and this solution seems easy to deploy, our estimation was that it is gonna be hard to calibrate with accuracy, and truck type knowledge might be needed. To the best of our knowledge there are no similar applications, at least reported in literature, so a prototype is needed to evaluate the proof of concept. S5: Truck motion detection While effective monitoring of timber transport and access control to forest areas have been identified as a key to combat illegal logging, this is a very difficult task, as forests are not closed and a dense network of dirt roads and paths exist. However, as a forest service we would like to see implemented a typical fleet management scenario, that uses GPS/GSM network to detect truck routes, and report them online. Similar approaches might use RFID tags or automatic vehicle identification (AVI) at "forest gates" to patrol entrance/exit from certain checkpoints. Risk assessment and priority: While this solution could be extremely effective, transporter's consent would be required and privacy issues may arise. Also implementation in forest conditions are not well studied, and poor signal coverage may affect efficiency. S6: Satellite imaging This story considers the application of image processing algorithms on satellite images to detect areas of intervention and protection. Such tools have been applied extensively in forest fire monitoring. In our case, we identified two scenarios.

The operational scenario, where we use images on demand, in order to identify illegal logging at the time it happens, and the strategic scenario, where the same techniques may apply on low-cost, historical images, to estimate forest degradation due to illegal logging and prioritize future intervention actions.

Risk assessment and priority:

We need to keep a balance between image cost and resolution needed. A high-resolution, on demand image cost is extremely expensive, thus the operational scenario seem not viable, at least for fuel wood related illegal activities. S7: Systems integration In the current situation, information doesn't flow across the borders, neither between the various national services involved in the inspection of timber trade value chain (i.e customs, taxation, police and forest authorities). As a forest service we need an online system that links customs with forest services, and can notify forest services on vehicle movements across borders, and report on volume and type of wood, origin and destination, etc. Risk assessment and priority: While this seems from a technical point of view a relatively straightforward solution, we identify risks related to data content, system interoperability and standardization, and the implementation of an appropriate information security infrastructure.

Discussion

The inception workshop helped a lot our project members to develop a common language and understand better each other expertise. It was also proven helpful in building team spirit due to the participatory sessions, where interactions brought our team closer. We also managed to identify early in the project, what is more important to our own users, rather than considering generic literature cases or an average hypothetical user.

Additionally, before and after the workshop we conducted a survey to evaluate participant perceptions. Currently we process the survey results, and in general we identified that there was a knowledge shift in both domain and technical scientists. Also, participant feedback on the process was very positive.

We identified that a main issue during our AITOLOS inception workshop was the large number of participants. This made discussions last longer than expected, but on the other hand, it was an important factor in producing seven user stories in a single day. Future plans include organizing a second inception workshop with rest of the project partners, and we intend to repeat the same process with half of the participants, reducing the total number of participants to ten (i.e 3-4 technical experts, 5-6 domain experts, 1 facilitator).

Last, but not least, the user stories we identified with the inception workshop are epic, in the sense that they can be broken down to smaller, more targeted stories. However, this was rather expected as the goal of the inception workshop was not to identify certain features to be implemented, rather it was to identify systems to be subsequently specified in detail.

Fig. 1 .

 1 Fig. 1. Typical agenda of an inception workshop (activities with durations)

Fig. 2 .

 2 Fig. 2. Workshop notes on timber logging and trade value chain

Acknowledgments

Authors would like to thank the personnel of the Forest Service Offices of Goumenissa and Kilkis, and the technical experts from Centre for Research and Technology Hellas, and Technological Education Institute of Serres for their participation to the inception workshop.

This work have been partially supported by the IPA cross-border programme 'Greece-The former Yugoslav Republic of Macedonia 2007-2013' under grant AITOLOS, which is co-funded by the European Union and by national funds of the participating countries.

More about the project at http://www.aitolos.eu.


Francesca Cellina
email: francesca.cellina@supsi.ch

Anna Förster
email: anna.foerster@supsi.ch

Davide Rivola
email: davide.rivola@supsi.ch

Luca Pampuri
email: luca.pampuri@supsi.ch

Roman Rudel
email: roman.rudel@supsi.ch

Andrea Emilio Rizzoli
email: andrea@idsia.ch

Using smartphones to profile mobility patterns in a living lab for the transition to e-mobility

Keywords: personal mobility tracking, smartphones, mobility living lab, mobility transition

The diffusion of electric vehicles is currently the most promising opportunity to reduce the dependency on fossil fuel in the mobility sector. However, the adoption of the electric vehicles seems to be still hindered by psychological and behavioural barriers. Thus, in order to understand and to foster the transition towards more sustainable mobility styles, it becomes essential to adopt an inter-disciplinary approach. In this framework, the e-mobiliTI project was launched in late 2012 in Southern Switzerland. It aims at understanding the potential for transition in the mobility system at the local level, with a special focus on electric mobility. The project builds upon a small living lab made up of around twenty families, who will be monitored in all their trips through smart mobile devices, in order to get quantitative data, and through focus groups, in order to get qualitative data and perceptions. Here we discuss the major challenge in the initial stage of the e-mobiliTI project, that is the gathering of reliable and high-quality data on users' behaviour. We describe the automatic tracking system, and the data processing and the qualitative assessment approach and comment on the overall performances of the living lab experiment.

Introduction and Motivation

The diffusion of electric vehicles is currently the most promising opportunity to reduce the dependency on fossil fuels in the mobility sector and to pave the way for the transition to a more sustainable society.

In the last few years technology made remarkable progresses. In Switzerland, nowadays more than 80% of the demand of personal mobility activities and patterns could be covered by electric vehicles: the 2010 Swiss Transport and mobility census in fact indicates that 84% of the trips are shorter than 10 kilometers and that 73% of them are shorter than 5 kilometres [START_REF](ARE): La mobilité Suisse -Rèsultats du Microrecensement Mobilité et transports 2010[END_REF]. Currently there are no monetary incentives at the national level for electric vehicles, since the Swiss government believes their introduction should only be driven by the market [START_REF]IA-HEV: Hybrid and electric vehicles annual report[END_REF]. However, in Canton Ticino, located in Southern Switzerland, the government is on the verge to approve an incentive scheme for electric and hybrid vehicles, allowing a reduction of the vehicle sale price up to 10'000 CHF.In addition, the network for public charging stations for electric vehicles is already densely distributed all over the urbanised areas. The charging infrastructure will be furthermore supported by a private initiative, named "EVite", which is planning to install up to 250 public fast-charge stations in Switzerland. Finally, the cost of electric vehicles has worldwide dramatically decreased, up to the point that in a few years it is expected to become equivalent to conventional fossil fuelled vehicles (see for example [START_REF]The China New Energy Vehicles Program: challenges and opportunities[END_REF]).

Therefore, all the conditions seem to favour a transition to electric mobility, but this is going to be a large scale and long-term process. Transitions of this type are determined by a wide set of factors, acting together. In the case of electric vehicles, their adoption seems to be still hindered by psychological and behavioural barriers (e.g. issues such as the breadth of the autonomy range, the availability and diffusion of recharge points, the performance, see [START_REF] Everett | Initial Findings from the Ultra Low Carbon Vehicle Demonstrator Programme: How quickly did users adapt? -A review of preliminary usage and perception data from the first three months of vehicle trials[END_REF], [START_REF] Turrentine | The UC Davis MINI E Consumer Study[END_REF], [START_REF]Deloitte: Gaining traction -A customer view of electric vehicle mass adoption in the U.S. automotive market[END_REF], [START_REF] Cocron | Methods of evaluating electric vehicles from a user's perspective -the MINI E field trial in berlin[END_REF]). Thus, in order to understand and, at a later stage, to foster the transition towards more sustainable mobility styles, it becomes essential to adopt an inter-disciplinary approach, able to integrate psychology, sociology, cultural and spatial planning aspects, alongside the technology and engineering aspects.

Among various factors, consumer behaviour is one of the most influential ones, at least as important as technology ( [START_REF] Geels | Automobility in transition? A sociotechnical analysis of sustainable transport[END_REF]). Adopting a socio-technical perspective ( [START_REF] Schot | Towards new forms of participatory technology development[END_REF], [START_REF] Geels | Typology of sociotechnical transition pathways[END_REF]) and an actor-based approach allows a deeper and more effective understanding of this fundamental phenomenon. The living lab research approach ( [START_REF] Higgins | Introduction to the living lab approach[END_REF]) offers promising perspectives, since it allows real life users to explore and experience new technologies in complex, real-world settings.

In the remainder of this paper, first we present the e-mobiliTI project context (Section 2), then we describe the smart-phone app we developed to track user behaviour and collect user feedback (Section 3). We then quickly describe the approach for profiling user behaviour and understanding the drivers for change (Sections 4), and finally we critically discuss the major challenges we will face during the project (Section 5).

The e-mobiliTI living lab experiment

The e-mobiliTI project is funded and coordinated by the University of Applied Sciences of Southern Switzerland (SUPSI) and it is supported by regional authorities, with the final aim of elaborating policy guidelines, incentives, procedures and strategies for a transition in the mobility sector at the urban level.

Background and approach

The e-mobiliTI project adopts an inter-disciplinary, bottom-up approach, aimed at exploring the potential effects and benefits of innovative mobility technologies and options through experimentation and direct interaction with the end-users. Similar socio-technical experiments were held in the past, in the framework of the Strategic Niche Management (SNM) approach, which was developed in the late Nineties at the European level and was successfully applied in the transportation sector ( [START_REF] Kemp | Regime shifts through processes of niche formation. the approach of strategic niche management[END_REF], [START_REF] Kemp | Strategic niche management for sustainable mobility[END_REF]). More recently, similar approaches started being indicated as Living lab processes, intended as inter-disciplinary and user-centric methodologies, that, involving real users in actual living environments, allow to develop, deploy and test new technologies and strategies capable of responding to the complex and evolving needs of the present complex society 3 .

The SNM and Living lab methodologies are especially effective in systems offering environmental and social benefits which are undervalued on the market [START_REF] Kemp | Strategic niche management for sustainable mobility[END_REF], in particular by single individuals, as in the transportation sector. Successful experiences in this sector were for example gathered in Denmark since 2009, with the TryAnEV/Insero E-mobility project 4 . For less recent experiences, one can for example refer to [START_REF] Hoogma | Experimenting for sustainable transport : the approach of strategic niche management[END_REF].

Research objectives and expected outcome

Project activities are being developed in two main phases (see Figure 1): in Spring 2013 the families involved in the living lab are monitored in order to identify their present mobility patterns and styles, while in Spring 2014 they are monitored while testing and experiencing new mobility options, namely electric bicycles and cars as well as car-sharing (Mobility), bike-sharing (Velopass) and season tickets for public means of transportation (Arcobaleno). Providing the participants with the new mobility options, e-mobiliTI removes the present main barrier to the diffusion of electric cars and bicycles, that is their price, and creates a niche in which we can analyse the participants behaviour with respect to the other barriers, such as: range, recharge and performance for the electric cars; physical effort and safety for e-bikes; status-symbol and rigidity for vehicle sharing; rigidity and punctuality for public transport.

Comparing the mobility patterns and styles observed during the two monitoring phases allows us to test the following hypotheses:

1. barriers affecting the use of electric vehicles are mainly psychological and can be removed through experience -or, in other words: using an electric vehicle or bicycle does not require major sacrifices or reductions in the present single individuals well-being; 2. having an electric vehicle or bicycle at one's disposal acts as a leverage for a general transformation of the individual mobility patterns (slow mobility, public transport and use of shared mobility services), instead of merely acting as a pure substitute of conventional vehicles and bicycles.

In order to identify the mobility patterns and styles of the participants in the living lab, during the two phases we rely on both quantitative automatic data-gathering techniques and on group discussions and single interviews for the analysis and review of qualitative data. Automatic data-gathering is performed by means of a specifically developed smart-phone application (app) that relies on GPS tracks and on Bluetooth sensors placed on vehicles and bikes to identify transport modes [START_REF] Förster | Understanding and optimizing human mobility with smart phones[END_REF]. The app posts GPS and Bluetooth data to a centrally hosted database, where data are post-processed in order to make them available to further processing by using data classification techniques aiming at the identification of mobility patterns and styles. Such users profiling also considers information gathered through qualitative discussion groups, mainly focus-groups: the latter suggests hypotheses on styles and patterns to be verified by means of the data mining classification. From the computer science point of view, thus, the e-mobiliTI project has the further objective of effectively integrating the quantitative information produced by the smart mobile devices to the qualitative information produced by the focus groups.

Selection of the participants

For the setting of the experiment, we selected the Lugano region in the Southern Switzerland Ticino Canton, i.e. an urban conurbation of around 135'000 inhabitants. It is particularly interesting because, even though it is often congested and characterised by frequent traffic jams, it already offers alternatives to traditional private owned fossil fuelled vehicles, such as an efficient public transportation network and car-sharing and bike-sharing services.

Selecting the participating families is a key and delicate activity. We performed it by means of a public open call, promoted via the local mass-media (newspapers, television, radio) and via flyers distributed all over the city: therefore, participants come from a process of self-nomination of the interested people.

The public call was open to generic families, i.e. either single individuals or couples or couples with (older) children. More than 350 candidates answered the call: a much higher number than we initially expected, reflecting a very high interest also in the common people for new mobility technologies and options. The selection process aimed at identifying an homogeneous group of subjects based on the place of living, on the demographic features of the family (single, couple, couple with (older) children) and on the present use of mobility options. This process allowed us to identify a set of sixteen families, for a total number of thirty persons actively involved.

Smart mobile devices

In order to capture as precisely as possible and as many as possible trips from our users, we turned our attention to smartphone based gathering. This approach turned out to be a bless and a curse at the same time: a bless, because we are able to gather really a vast amount of data from different users for long periods of time; a curse, because the usage of the smartphone based application proved to be quite complex for many users. In this section, we detail the general architecture of our solution and present the main functionalities of the application.

General architecture

The general architecture of our system, which we also call the e-mobiliTI app, is presented in Figure 2. It consists of the smartphone based application, which communicates to the central server to send the gathered data and to receive the daily reports. The application gathers GPS and Bluetooth data every 1-3 minutes, depending on how fast the user is moving (1 min when moving fast). If the smartphone is not moving at all, the GPS and Bluetooth sampling is interrupted to save the battery. This data is sent regularly to the central server, which stores the data. Once a day, at a user-defined time, the server evaluates all available data, automatically identifies moving segments of the user together with the used vehicle and sends these segments in the form of a daily report back to the smartphone. There, the user is notified and can review all segments and correct them, where necessary.

Additionally to the smartphone (currently running Android OS), each user's car is equipped with a Bluetooth tag. Any Bluetooth device can be used, but the system requires that the tag is always on. A good option is the stick'n'find Bluetooth tag 5 or the Blue Solar car kit from MrHandsFree6 , which are always on and are very energy efficient.

Automatic Recognition of Trip Segments

One of the most important functionalities of the system is its ability to recognise the movements of the users automatically and to break them down into segments. According to the commonly used terminology, we define: -Trip: a movement of the user starting at her home and ending also there.

There are also other possible definitions of a trip, however this one is the most well defined and non-ambiguous one. -Segment: part of a trip, which can be associated with a single vehicle used (a bus, a car, on foot, etc.), separated by stop points. A trip can consist of a single segment, but usually it consists of more than one segment. -Stop: it is a humanly defined stop point on a trip, corresponding to some short or long interrupt to the trip. For example, stopping at the gas station, to chat with a friend, at the bakery etc. However, it also includes long stops, such as for whole day work in the office.

Thus, a trip is a sequence of alternating stops and segments, where both the start and the end places are at home. Our system is able to recognise all there levels of the trip: the stops, the segments and the trips themselves. The algorithm is based on the following steps:

-Recognize stop points by identifying GPS points very close to each other (at most 100 meters apart) for at least 2 minutes. -Segment the data into stops (as identified above) and segments.

-Validate the segments with some minimum requirements for distance and velocity. Delete all failed segments by merging them together. -Validate the stops between the segments again by deleting them first and analysing the newly formed segments. If the segment is a valid one (see above), the stop remains deleted. If the segment does not fulfil the requirements any more, put back the stop.

The vehicle is recognised by thresholds on the mean and maximum speeds in a segment. The Bluetooth data is used to recognise the own cars of the users and to differentiate them from public transport or other cars.

The developed algorithm is quite flexible and is able to identify short stops (such as changing the bus at a bus stop), while at the same removing artefacts such as stopping at the traffic light. Of course, not all artefacts are removed and there remain a certain number of segments, which are shorter than expected. The app graphical user interface for the review of the trips by the users is presented in Figure 3. The left-most picture (a) shows the daily survey with the general daily satisfaction level and note and all identified segments for that day. (b) presents the details of one segment, with start and end times, total distance, a map with start and end places and the recognised vehicle. Also, the start and end places can be associated with labels (e.g. home, office, school, bus stop, etc.) and the user can also enter her motivation for the trip, the number of people she is travelling with and the satisfaction for the segment only. Once the user completes the report, the updated data is sent back to the server for final storage. Of course, the user can change also old surveys and segments or complete the survey later than the predefined time.

Graphical User Interface and Functionalities

Practical notes

The implementation on the smartphone is straightforward, but requires a lot of additional functionalities to make it user-friendly at one side and robust on the other side. The main challenge comes from the very different technological skills of the users and their attitude towards the experiment.

In the implementation we also considered the following requirements: when powering on the phone the app had to be automatically started; GPS and Bluetooth had to be also turned on at start-up and kept active; the notification of the alarm signal for the daily report had to remain loud even when the smartphone was silenced; failed data transmission to the servers because of lack of signal had to be queued up for later re-send; all entered data needed to be saved without additional user confirmation; the phone settings were corrected not to allow data roaming, but to always allow 3G data connection (for cost efficiency), and not to use any energy saving options (as they are faulty on many Android phones).

User profiling and evaluation

The collected data on the various mobility choices of all participants in the experiment will then be processed in order to discover and understand how they make their daily personal mobility choices. Given the limited size of the sample, this part of our study is aimed at understanding how a specific person chooses a specific mobility option as a function of various possible influencing factors, such as distance, tightness of schedule, reason for the trip, number of intermediate stops, the weather conditions and so on. For each user, her daily trips will be analysed, looking for patterns, such as the choice of a given mean of transport, or a combination of means, to a specific purpose. We will therefore build a mobility profile for each user, and we will then also look for similarities among all users, on the basis of their typology.

During the second phase of the project, the same users will be monitored, but this time their travel options will be wider, as they will have the choice of opting for public transport, shared mobility and electric mobility options. The data collected during this part of the experiment will also be used to create the users' profiles. The new profiles will be then compared with the old ones, i.e. those obtained before the introduction of new mobility options, and the objective of the comparison will be the identification of the most relevant factors influencing a transport choice change, provided a given degree of profile similarity among the users.

Discussion and conclusions

The e-mobiliTI project relies on the strong involvement of the users, the key components of the living lab, in order to evaluate how a transition to sustainable mobility styles could happen. Such a project is therefore fraught with problems which are common to this type of projects, namely: users' motivation to actively participate in the project can dwindle; automated data collection covers only quantitative data, while qualitative data collection presents some challenges; finally, the size of the sample is small, and thus the general validity of the output of the project, the policy guidelines, can be criticised. The users's attitude during the project is probably the most important challenge, since it cannot be tackled from a purely technical point of view. Literature on the living lab approach in fact implicitly assumes that the users are cheap or unpaid contributors, motivated by the hope that their participation will solve their problems or lead to better policy-making [START_REF] Mulder | Co-creating in practice: results and challenges[END_REF]. Will our living labs achieve and sustain the necessary level of user mobilisation and cooperation in the absence of stronger incentives [START_REF] Dutilleul | Unpacking european living labs: analysing innovation's social dimensions[END_REF]? Will we be able enough in motivating the participants as active co-creators of the transition towards a new mobility system? This challenge is particularly relevant during the first e-mobiliTI monitoring phase, when they are asked to use their own mobility options, thus being little interested in getting reports of their trips. We expect that probably, when in the second monitoring phase they will be allowed to try new mobility options, their curiosity respect to the daily survey will increase.

Provided that users keep an high involvement in the project and keep the e-mobiliTI app active at all times and regularly collect data, certain types of information, such as the reason for the trip, the number of people travelling together and the overall satisfaction for the trip, can only come from personal information enrichments, voluntarily entered by the participants to the e-mobiliTI living lab. For this reason, the app provides the users with a daily report of their segments, which, besides offering them the possibility to correct erroneous trips, allows them to insert the cited qualitative information. However, this is not enough to grasp the full reasons for their behaviour. That is why we believe that physical, personal contact during focus groups and single interviews is essential to fully understand their mobility patterns and styles.

Finally, different users may face different "real-world" situations, or even interpret them in different ways, according to their personal perception and value systems [START_REF] Bergvall-Kåreborn | A milieu for innovation-defining living labs[END_REF]. Thus, in order to get enough different perspectives, it is important to involve a large enough number of users, which, in some cases, might be burdensome and costly. In our experiment, due to limits in the available budget, the number of participating families is very small: therefore, no general representativeness can be attributed to the mobility patterns and lifestyle observed within the e-mobiliTI living lab. The project in fact cannot provide results capable of representing the whole Lugano region inhabitants behaviour and perceptions. While we acknowledge this limitation, on the other hand the information gathered during the project lifetime can provide a starting point for the niche characterisation and suggest the initial actions to be taken to favour the transition towards different mobility lifestyles.

Fig. 1 .

 1 Fig. 1. The two phases of the e-mobiliTI experiment.

Fig. 2 .

 2 Fig.2. The general architecture of our smartphone based solution. The e-mobiliTI app gathers GPS and Bluetooth data and sends them to the central server. The server analyses the data daily and sends to the user all identified trip segments. The user can thus make corrections to the properties of the trip and give her overall satisfaction level, which are again sent to the server for backup.

Fig. 3 .

 3 Fig. 3. The graphical user interface to review the movements of the day and to insert details and satisfaction level.

MIT living lab website http://livinglabs.mit.edu/, retrieved on April 24th, 2013

Nordic Sustainable Logistics Network website http://www.noslone.com/News/ Details.aspx?id=70, retrieved on May 13rd, 2013

http://www.sticknfind.com

http://www.mrhandsfree.com