A Cyberphysical Learning Approach forDigital Smart Citizenship Competence Development

Yacine AtifUniversity of Skövde

Skövde, Swedenyacine.atif@his.se

Stylianos SergisUniversity of Piraeus

Athens, Greecesteliossergis@gmail.com

Demetrios SampsonCurtin UniversityPerth, Australia

demetrios.sampson@curtin.edu.au

Gunnar MathiasonUniversity of Skövde

Skövde, Swedengunnar.mathiason@his.se

ABSTRACTSmart Cities have emerged as a global concept that arguesfor the effective exploitation of digital technologies to drivesustainable innovation and well-being for citizens. Despitethe large investments being placed on Smart City infrastruc-ture, however, there is still very scarce attention on the newlearning approaches that will be needed for cultivating Digi-tal Smart Citizenship competences, namely the competenceswhich will be needed by the citizens and workforce of suchcities for exploiting the digital technologies in creative andinnovative ways for driving financial and societal sustain-ability. In this context, this paper introduces cyberphysi-cal learning as an overarching model of cultivating DigitalSmart Citizenship competences by exploiting the potentialof Internet of Things technologies and social media, in orderto create authentic blended and augmented learning experi-ences.

KeywordsDigital Smart citizenship; smart city; smart citizenship com-petences, cyberphysical systems; learning design; social net-works; learning technology; smart grid; collaborative learn-ing; Internet of Things.

1. INTRODUCTIONSmart Cities refer to cities that use digital technologies ef-

fectively to enhance their overall functioning and to engagemore effectively and actively with their citizens [7, 8]. SmartCities have emerged as a global need and challenge due tothe understanding that more than 54% of the global pop-ulation now resides within cities, with an estimation thatthis ratio will increase to 66% by 2050 [19]. This situa-tion places city administrators against significant and com-

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plex challenges related with ensuring not only the well-beingof the citizens and the environment protection, but also fi-nancial and societal sustainability through innovation. Inthis context, the concept of Smart Cities has emerged as apotential solution, receiving a significant level of attentionglobally and forging an emerging research and innovationfield [5] using their own surrounding environment as a liv-ing laboratory. In this context, education for Smart Citiesneeds to be focusing on such cultivating Digital Smart Cit-izenship competences, including digital literacy, communi-cation and collaboration and creative problem solving, to-wards increased productivity and the capacity for self-drivenbehavioral changes. Subsequently, new levels of instructionbeyond traditional classroom contexts need to be definedto support cultivation of such competences, and commodityofferings are packaged with consumer’s education to adoptsome behavioral changes using emerging digital technologies.

In Smart Cities, the use of emerging technologies is usedto drive sustainability and innovation in diverse aspects ofthe city functioning, such as resource management, trans-portation and governance [9]. This approach also requirescitizens to adopt new lifestyles and attain new competencesthat prompt superior sustainability values and allow themto engage in sustainable innovation capacity to solve ill-defined problems by exploiting digital technologies. SuchDigital Smart citizenship competences provide citizens withthe capacity to not only effectively participate in their SmartCity’s development but also influence it and change theworld around them

The use of emerging technologies are driving companies toshift features of value-added business services from “nice tohave” to “must have”. This imperative brings seed citizensto adopt new lifestyles that prompt superior sustainabilityvalues. The process is facilitated by providing citizens withopportunities through which they learn how to change theworld around them [5] using their own surrounding environ-ment as a living laboratory.

Internet of Things (IoT) is one of these technologies thatare expected to lead to behavioural changes across a rangeof applications of Smart City function. To conceptualize thepreparation for a smarter level of digital smart citizenship,IoT unleashes the instructional power hidden within physicalresources and provide learning interfaces through translat-ing real-time data readings into contextual diagnosis. Data

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Figure 1. User engagement in smart grid

streamed from these resources is visualized to learn aboutsustainable assets hidden within physical phenomena, viapersonalized and context-driven instructional paths. Thisapproach leverages a new dimension to situated learningand aggregates the fluid connectedness between citizens andphysical resources across programmable environments em-bedded with information technologies.

For example, trends such as connected cars, connectedappliances, smart grid etc. require citizens engagement torethink sustainable transitions within the so called“circular-economy” [18] market. Thus, citizens as consumers playa pivotal role towards these transitions through everydayobjects that signal their identity and leverage their digi-tal smart citizenship, but require a learning curve to en-gage with presumably passive and lifeless objects. Con-nected objects are creating unprecedented sources of infor-mation, which could be analyzed to recognize personalizedpatterns and incite behavioural changes, taking humanityto smarter digital era shaped by the prospective circulareconomy. A network of devices connects physical objects todeploy services that immerse learners into a social cyber-physical ecosystem. In this ecosystem, tangible educationis delivered to influence progressive behavioral changes thatare mutually beneficial to learners and service providers.

As an illustration, consider household energy data whichcan be harnessed from smart meters and smart plugs, pro-viding a rich data source of appliances usage. These datasources educate consumers on home’s economics and pro-vide energy retailers with a range of marketing implications:which appliance has been used, for how long, and in whatmode for which period of the week/month/year? All this

data is seamlessly collected to engage energy-aware con-sumers, allowing retailers advocating new marketing strate-gies, induced by incentives in order to balance energy-load,as illustrated in Figure 1. Retailers would rather redistributeenergy-excess (across time) than buying shortages from en-ergy suppliers (at a cost) or building new power distribu-tion infrastructure, in a scheme known as Demand-Responseused to schedule load in a way that accommodates the avail-able capacity. However, this endeavor requires consumers’awareness and engagement which are achieved through anew cycle of education processes. Uneducated consumers re-duce their smart digital citizenship footprint and delay sus-tainability shifts as, for example, evidenced by a pilot studyin the UK done throughout 2014–2015 [9], which found threegroups of households with similar characteristics suggestingsimilar energy profile. Yet, one of the groups had three timesthe energy consumption. Further analysis revealed higherenergy consumption for washing machine usage, when us-ing cotton cycles 100% of the time against 40–50% of thetime in the other groups. IoT induced energy data couldhave identified these patterns to devise appropriate educa-tive schemes, in this case, use of economy cycles, promotedby manufacturers or (retail energy) service providers. Theproblem is that business entities are mostly wary about, andexpert in the product or the service they deliver, and lack theeducation focus in terms of pedagogical models and learn-ing processes. This example further highlights the challengethat apart from the investments on designing and imple-menting Smart Cities, significant effort and attention needsto be placed on cultivating the required Digital Smart Cit-izenship competences that will allow citizens and workforceto exploit digital technologies within their city and activelycontribute to its sustainable development. The combinationof IoT, social media and mobile data are paving the way tosuch new personalized education dimensions, which in turncould be turned into new personalized marketing opportu-nities.

In this context, this paper proposes a cyber-physical con-tinuum for supporting the cultivation of digital smart cit-izenship competences, where a spectrum of blended andaugmented experiences offer new opportunities for nurtur-ing progressive behavioral change cycles. Our approach col-lects information seamlessly from citizens’ behaviors (in thecase study of this work such behaviors will be consumers’use of nondigital appliances) and enables a transformationof learning capacity from clumsy quantitative to judiciousqualitative reasoning, that progressively qualify learners toa higher degree in sustainability dispositions, and digitalsmart citizenship.

2. SMART CITIESEven though a unifying definition of Smart Cities has not

yet been proposed [6], they can be broadly defined as thosecities that make “use of information and communicationtechnology to sense, analyze and integrate the key infor-mation of core systems in running cities” (IBM as cited in[6]). More specifically, Smart Cities exploit a wide range ofdigital technologies [15] to improve their functioning acrossa set of commonly acknowledged dimensions [1]:

• Smart Economy, namely financial competitivenessof a city through innovation, productivity and flexibil-ity of the workforce and the market.

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• Smart People, namely consideration for improvingthe learning, creative and collaboration capacity of hu-man capital.

• Smart Living, namely enhancing social and culturalcohesion and the quality of services to citizens such ashousing, health and education.

• Smart Mobility, namely promoting accessible andinnovative transportation and communication infras-tructure.

• Smart Governance, namely promoting inclusion andparticipation of citizens in the city’s decision making.

• Smart Environment, namely promoting effective andsustainable use of natural resources.

As aforementioned, Smart Cities are currently receiving asignificant level of attention globally and vast investmentsare being allocated on researching and implementing SmartCity initiatives [14]. However, as aforementioned, apart fromthe large public and private investments on Smart Cities in-frastructure, in order to holistically introduce Smart Citiesin the fabric of society, it is also important to invest in ap-propriately preparing the human capital of such cities.

More specifically, focus should be placed on preparing thefuture citizens and workforce of such cities in terms of corecompetences which will be needed for driving innovationand sustainability across the different dimensions of the cityfunctions. The required Digital Smart Citizenship compe-tences, therefore, will need to comprise diverse skillsets in-cluding digital literacy, communication and collaboration,creative problem solving as well as inquiry skills for collect-ing, processing and evaluating information and data (e.g.,[14, 12]).

The main standpoint of this paper is that conventionallearning approaches may be inefficient to prepare and sus-tain such Digital Smart Citizenship competences if they donot explicitly build on situated learning paradigms that ex-ploit emerging smart technologies (e.g., IoT technologies)that are essentially the backbone of Smart Cities. There-fore, to address this issue, this paper describes the conceptof cyberphysical learning, as a strand of situated learningthat explicitly comprises social aspects supported by smartIoT technologies.

3. THE CONCEPT OF CYBERPHYSICALLEARNING

Cyberphysical learning comprises instructional dimensionsthat can be used to build digital smart citizenship com-petences by rendering situational learning as a function ofan activity and context, across an instructional scaffoldingapproach. This form of learning contrasts with traditionalclassroom instruction where knowledge is abstract and outof context. Social interaction is a critical component of sit-uated learning whereby learners become involved in a ”com-munity of practice” that embodies targeted beliefs and be-haviors to be acquired. As beginners or newcomers movefrom the periphery of this community to its center, theybecome more active and engaged within the culture of thecommunity and progressively adopt the desired behavior.

Cyberphysical learning shares the same properties as situ-ated learning but community participants may include phys-ical resources used as cognitive tools and blended augmented

experiences in an authentic domain activity [3]. Namely,given learner state S, we say that cyberphysical learning oc-curs when a physical stimulus event Ei maximizes the prob-ability that a learner moves its behavioral state to S’. Hence,cyberphysical learning is distinguished by physical events as-sociated with behavioral-impacts likelihood. Formally, thislikelihood is modeled by the following probability equation:

argmaxi

P (S → S′|Ei)

In addition, the probability that the learner adopts thebehavioral state S’ under stimulus Ei is strictly greater thanthe probability that S changes its state independently, namely:

P (S → S′|Ei) > P (S → S′)

The above formulation suggests identifying new physical-stimulus technologies that shift digital citizenship state to asmarter level. Learners are part of a cyber-physical contin-uum for education where a spectrum of blended augmentedexperiences offer new opportunities for instructional prac-tices. In this context, connected devices consist of internet-connected sensors attached to physical phenomena and linkedto web applications that collect and curate data. The successof this educational convergence depends on engaging learn-ers along this cyber-physical continuum, through contempo-rary instructional approaches such as experiential learning,gamification, inquiry-based and problem-based learning tocultivate Digital Smart citizenship competences. Physicalphenomena are visualized via appropriate hardware, inter-faces and applications that develop tangible learning to el-evate digital smart citizenship competence levels. This isachieved through a visual modeling language based on con-cept maps [16], that conveys a qualitative vocabulary as thebasis for learners to abstract quantitative data about phys-ical phenomena into conceptual knowledge [2]. Cyberphysi-cal learning employs this visual structure that link physicalentities to present an interactive learning platform to learnfrom and about physical processes in view of adopting de-sired behaviors and developing new digital smart citizenshipcompetences.

The following section outlines the proposed model andcomplementing system architecture for realizing the poten-tial of cyberphysical learning.

4. MODEL AND SYSTEM ARCHITECTUREIn Smart City contexts, there is a proliferation of embed-

ded IoT devices with sensing capabilities that capture largeamounts of data across the different dimensions of SmartCities, which can reveal citizens’ behavior. For example, ex-isting devices such as power sensors have built-in features tocapture power consumption that could be turned into usagebehavioral patterns. Sensors perform automated measure-ments of environmental variables and control some desiredaspects of the environment through autonomous or directlycontrollable probing and actuating activities. In addition,these entities incorporate wireless connectivity modules al-lowing both collocated devices and citizens (via their smart-phones) to seamlessly interact. In this context, these syner-gies are controlled by a model library, and can be utilizedto represent the incremental understanding of cyberphysicallearning that needs to be attained for achieving the nextlevel in the digital smart citizenship competences of each

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citizen(-learner), and can also be used as the means to builda portfolio of such competences for reflection. Each instanceof this model library is associated to one or more scenar-ios that simulate the model instance to predict the citizen-learner behavior. In such scenarios, citizens-learners achievethe desired level of competence when the simulator generatesthe intended behaviour. Autonomous processing of data, re-sponse and control allow actors of this cyberphysical ecosys-tem to input change into selected models. Each actor isoutfitted with a unique identifier through Internet of Thingstechnologies to get the ability to transmit data within thecyberphysical network and beyond [22]. Examples of this cy-berphysical configuration where the above qualitative mod-eling approach can be applied extend to smart communi-cations, smart energy and resource management, as well assmart transportation. All of which require citizens’ involve-ment through a progressive learning process.

4.1 Smart Object VirtualizationIntegrating new generation of IP-enabled objects are pro-

pelling IoT applications across a wide spectrum of domains.Embedded 6LoWPAN Wireless sensors with IPv6 perceiveenvironmental parameters and bring objects to Internet. Asmart object is defined with four embedded or augmentedcapabilities, namely: unique identification, processing, stor-age and communication. Figure 2 shows this virtualizationcomposed of sensor/actuator module, CPU module, batterymodule, and communication module (radio). CPU provideslimited storage and processing capabilities. This moduleruns the device interface and control components as well aslightweight RESTful web services. The device interface in-cludes an analog to digital transformers with data encoderfacilities that provide online/offline data access functionali-ties. Data is stored in lightweight SQLite database system.Web service layer defines data access operators, control ac-tivities, event-trigger mechanisms, and QoS parameters. Aphysical sensor/actuator node is abstracted as a RESTfulweb service. Physical devices are thus packaged into ser-vices access via short URLs. These devices may be con-nected directly or indirectly to IoT platform via a gatewaybridge. In the latter case, sensors and actuators communi-cate using wireless protocols such as Zigbee or bluetooth butuse the gateway to perform data transfers in HTTP formatvia built-in light-weight web services within the gateway. Inany case though, communication between physical nodes isabstracted by RESTful web services.

4.2 Cyberphysical CommunityFigure 3 and Figure 4 show a software representation of a

community platform where data is exchanged across REST-ful web services in JSON format. A cyberphysical commu-nity is built to divide citizen-members into model-driven ag-gregation of heterogeneous resources. Interactions are trig-gered by model-defined rules. Learning models within thelibrary define actor interactions in the community as illus-trated in Table 2. The model library is provided by resourcemanufacturers, service-providers or other third-party stake-holders (e.g., education providers) seeking to increase digi-tal smart citizenship competences of their target group base.Our approach is to enable an integrated system whose mainobjective is to stimulate citizens to learn about adopting be-havioral activities in order to raise digital smart citizenshipcompetences, by progressively reflecting on the knowledge

Figure 2. Smart object virtualization

acquired from the deployed models involving surroundingphysical resources. Sensing devices monitor physical envi-ronments and involve citizens in social network-like syner-gies to guide them into activities driven by scenarios thatare associated with each learning model.

4.3 Learner ProfileEach citizen-member of the cyberphysical network is de-

scribed by its updatable and extendable profile. Usage ofthe profiles makes it possible to “individualize” proactivelearning. Model configurations is driven by the analysis ofmembers’ profiles, such as the parameters of smart objectsand behaviors of members. Table 1 and Table 2 providerespectively an overview of smart object and learner nodeswithin a cyberphysical network. Events occurring from net-work members trigger gap analysis to optimize behavioral-learning criteria. Progressive learning is advocated to in-cite behavioral change based on information related to an-tecedents and consequences stored in each network memberprofile.

Table 1. Smart-object member profile

Category Description

Context Perceived phenomenon informationMeasurement Data source patternConsequence Parameter re-configuration

Table 2. Cyberphysical learner profile

Category Description

Current behavior Current competenceTarget behaviour Target competenceLearning path Cyberphysical learning environments

4.4 Progressive LearningBehaviorists such as B. F. Skinner state that complex be-

havior is learned gradually through the modification of sim-pler behaviors [17]. We define a learning path LP as a route,

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Figure 3. Cyberphysical social interactions

across an ordered set of cyberphysical learning environmentsEnvi.

LP = . . . {Env1, Env2, . . . Envp}.

Each cyberphysical learning environment Envi is com-posed of a cyberphysical community represented by the setof data sources DSi generated by the actors of the commu-nity, within a model-based learning structure, as well as aset of scenarios Si and a targeted competency Ci.

Envi =⊔

1≤j≤p

DSj .

Digital smart citizenship competence building proceedsprogressively across cyberphysical environments with increas-ing data set sources. Cyberphysical learning models con-tributing to digital smart citizenship involve interconnectedsmart objects within a progressively-scaled cyberphysicalcommunity where resulting dynamics are dictated by learn-ing scenarios to assert a prescribed competency. A learningmodel Mi is defined as a function of a given cyberphysicalenvironment Envi that is mapped to a targeted competenceCj through instructional scenarios Sij :

Mi : (Envi, Cj)→ Sij

A model is bound to a cyberphysical learning environ-ment. In this context, model-based learning prevents theinformation flow to outpace the capacity of learners in ab-sorbing the next digital smart citizenship competence withina given cyberphysical learning environment. Hence, learningmodels start with basic data sources and proceed to morecomplex ones along a curriculum-like structure, termed as“progressive learning path”. Algorithm 1 shows a processthat leads to inductive learning of concepts from examplesmaterialized by data flows distributed over time.

The following section will present a case study for present-ing and preliminarily evaluating the aforementioned modelfor supporting digital smart citizenship competences.

Algorithm 1 Progressive Learning Path

1: Given data sources DSi, for i = 1..n2: Given a target competence Ck, for k = 1..p3: Env0 = ∅4: for j = 1 to m do5: Envj = Envj−1 ∪DSj

6: Mj ← RetrieveModel(Envj)7: Sjk ← RetrieveScenarios(Mj , Ck)

8: for each s ∈ Sjk do9: Learn(s)10: end for11: AssertCompetence(Ck)

12: end for

5. CASE STUDY AND ANALYSISThis case study focuses on energy consumers, who form an

integral part of the upcoming smart grid within smart cities,as they engage in utilizing new information and adoptingnew behavior to better balance energy supply and demand.The success of this evolution relies on the transformationof passive end-users into actively engaged digital smart cit-izens. We propose the adoption of our proposed cyberphys-ical learning approach to drive this transformation by inte-grating Internet of Things technologies and resulting dataanalytics into household practices. This integration enablesenergy suppliers understand and affect their customers’ be-haviour to embrace an active role into smart grid opera-tions. In an attempt to assist utilities understand how acustomer may act in the future smart grid, this case studyinvestigates the deployment of data intensive customer seg-mentation across a set of increasing levels of digital smartcompetences. Utilities gain customer intimacy through in-centive rewards such as energy tariff models for raising theirdigital smart competence levels.

Discerning how much power is being used across home ap-pliances (called energy disaggregation) is achieved throughnon-intrusive IoT represented by smart plugs as illustratedearlier in Figure 1. Data is streamed over a cloud-platformvia load-monitoring services similar to the one depicted inFigure 3. Thus, energy disaggregation identifies and tracksmajor appliances at home –including air-conditioning, elec-tric heating, pool pumps, refrigerators, electric water heaters,electric car charging, etc – to push personalized learning sce-narios that lead to saving tips or reward incentives, via aclient mobile app or a Web-based portal. A simulation toolevaluates the operations of a home under various hypothet-ical “what if” scenarios to predict insights that drive utilitycustomers to develop smart grid competences.

In Sweden, energy providers offer retail consumers elec-tricity contracts based on real-time pricing following Demand-Response schemes which is a form of supply-demand priceadjustments in energy markets [4]. As electrical energy can-not be stored in large quantities with current technology, thebalance of the electricity supply has to be maintained contin-ually by adjustments in supply to meet an unrestricted cus-tomer demand. Hence, real-time pricing appears a fair andflexible scheme over a rigid time-of-day use scheme whichprojects peak-demand trends. However, real-time tariffshave to be accompanied by appropriate home automationtechnologies on the one hand and the necessary digital smartcompetences in order for consumers to understand and mod-

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Figure 4. Cyberphysical learning workflows

ify their daily consumption behaviour. We have conductedan experiment where smart-plugs augment home applianceswith IoT capabilities, allowing the customer to be informedcontinually of the cost of a kiloWatt hour (kWh) at the timeof use. The home is also fitted with a smart-meter whichdata is monitored via a led-light reader to capture overallreal-time power usage. Next, we illustrate the competence-driven cyberphysical learning model discussed throughoutthis paper in the context of a smart-home energy manage-ment. This envisioned case study illustrates the profoundtransition for energy management patterns from the con-ventional centralized infrastructure towards the autonomousdemand-response and cyber-physical energy systems. Theadvocated learning scenarios discussed next and further il-lustrated in Figure 5 contribute to the progressive learningbuild-up of digital smart-citizenship.

5.1 Competence 1: Energy use dataAs described earlier, a learning path is composed of an

ordered set of cyberphysical learning environments. The ini-tial instance of such environment in this case study consistsin monitoring data from a washing-machine. The targetedoutcome from this situated learning process is an increasedawareness on the value of energy data and an ability to as-sess energy-consumption patterns to gain competence intoembracing energy-savings programs.

Model 1: Washing-machine

This model includes the washing machine as a cyberphysi-cal learning environment and strives to build Competence 1through the following scenarios.

Scenario 1: Visualization of current power data: Displaysthe current power consumption and costs using data fromwashing-machine and energy-retailer. A simulation modelcalculates energy consumption and predicts energy savings,resulting from comparative or model household. The pur-pose of this task is to provide statistically relevant data fromtypical communities with respect to washing-machine powerusage. This scenario introduces household inhabitants to thepower of visual analytics.Scenario 2: “What-if” appliance is started at an alternativetime: Displays measures and schedules to match against

user-specified schedules via a schedule wizard, which pro-vides a graphical view where user inputs defining customschedules are streamlined (and contrasted with schedulesdefining DR behavior of appliances). The scenario wouldsuggest to move laundry task to off-peak hours or along DRprograms to meet some rewarding incentives. This behavioural-shift would raise the competence level in smart digital cit-izenship, if adopted. This scenario develops active digitalfootprints whereby user data is released deliberately throughan enhanced intimacy between users and physical assets.

Scenario 3: “What-if” appliance uses alternative pro-grams: Provides predictive analytics with various wash/rinsetemperature settings to heat the water for washing clothes,to engage user in energy-saving alternatives by just chang-ing the temperature setting. The scenario displays kWh ofelectricity used for each alternative along with cost per loadand cost per year. Current load is contrasted to positionthe current behaviour of user profile. A target behaviour isproposed to incite user to experience a new energy-savingsetting for the washing activity. This scenario drives usersinto adopting an intervening role in digital smart citizenship.

5.2 Competence 2: Energy use footprintThe consumer is presented with information about his

global home energy consumption. The smart-meter datasource is added to the cyberphysical learning environment,which includes instant power data. This data could be dis-patched periodically or upon request. The smart meter pro-vides its data to the cloud-based model-library through ameter service module where the meter records its measure-ments in the form of index (one per tariff period type). Italso exposes a service interface for delivering information(index, tariff period, etc.) to devices in the cyberphysi-cal community (currently limited to washing-machine andconsumer members). This information may be directly re-ceived, treated, stored and displayed on a dedicated displayor user’s mobile app, either in kWh or in €. It may alsobe collected by a cloud-hosted Web application aggregatingdata from community members to contrast them against in-dividual energy-footprint contribution.

402

Figure 5. Progressive learning-path build-up in the proposed case-study

Model 2: Smart-meter and washing-machine

This model augments the cyberphysical learning environ-ment with a smart meter in addition to the washing machineand strives to build Competence 2 through the following sce-narios.

Scenario 4: Display global and per appliance energy con-sumption and costs, either realized and/or forecasted : Theaim of this scenario is to provide consumer with informa-tion on the current situation of her/his home consumptionand in particular one of the smart appliances. Consumer ispresented with information about the global current homeenergy consumption and the appliance contribution. Infor-mation is provided by a dedicated service, which retrievesdata from the smart-meter and, integrates data measuredby the washing-machine’s smart plug. Energy rates serviceprovided by retailer is composed with data retrieval serviceto display costs under different tariff periods in order to ed-ucate consumers on a more efficient and rationale use ofenergy. The targeted behavioural trend with this scenario isthe reduction of global energy by leveraging smart appliancefootprints. Consumers are educated to select the parame-ters of the appliances (in this case washing-machine cycles)by providing feedback on forecasted costs and energy con-sumption. This scenario introduces users into cyberphysicalsocial communities, which are poised to shape future smartcities.

Scenario 5: Devices monitoring: This scenario expandsconsumer’s abilities to manage connected devices in his cy-berphysical community to monitor their status and func-tionalities. The user can receive remotely on his mobile or a

Web app the status of the devices; in this case his washing-machine to let him perform monitoring operations. Thisscenario works on devices which operations are remotely ac-cessible, such as the current phase of the device (e.g. fora washing machine Heating, Rinse, Spin, etc), the selectedprogram, the selected options (temperature, duration, spin,etc), special functions (pre-wash, extra-rinse, etc), and theremaining time to end the current cycle. On-off options canalways be provided via smart-plugs on devices which do notprovide elaborated remote monitoring services. Similarlyto the way Internet has changed our interaction with eachother, this scenario develops new perceptions to change theway citizens interact with the physical world.

5.3 Competence 3: Energy use controlThis competence empowers self-discipline in maintaining

a threshold of energy use across a range of appliances.

Model 3: Smart-meter, washing-machine and dryer

This model considers a cyberphysical learning environmentwith a combination of three physical elements: washing ma-chine, dryer and smart-meter, and strives to build Compe-tence 3 through the following scenarios.

Scenario 6: “What-if” total power exceeds a preset threshold:In this scenario, user chooses preset thresholds of global en-ergy use. Each smart appliance applies statistical analysison past data to estimate the maximum power that wouldbe consumed during its next cycle, and determines whetherthere exists a risk or threat to exceed the user threshold.This threshold trains consumers on the potential gains from

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Figure 6. Survey results on cyberphysical learning environments support for digital smart citizenship

contracted subscribed power. A warning is emitted and dis-played on the appliance or other consumer interface(s) suchas mobile app. The appliance works in tandem with otherappliances to cooperate on collaborative settings that aredeemed to bring energy use below the preset threshold. Inthis case, dryer and washing machine negotiate their pro-grams to calibrate jointly their energy use. For example,heating temperature is set in both devices accordingly tooptimize washing/drying operations while complying withpower-use threshold. Consumer is presented with optionsto combine both programs and invited to select adequateoptions. This level of energy-aware engagement with appli-ance settings could contribute to driving users to the nextera of digital smart citizenship. This scenario allows citizensto influence cyberphysical communities adopting a desiredpattern that drives values for users.

An experiment that identifies users’ believes to employcyberphysical environments for learning for enhancing theirlevel of digital smart citizenship was conducted. The abovecase study and related scenarios were depicted as an illus-tration to respondents of the survey instrument that teststhe hypothesis whereby cyberphysical environments have apositive effect on users’ learning towards digital smart citi-zenship. A four-point Likert scale, with 1 being the negativeend of the scale (strongly disagree) and 4 being the positiveend of the scale (strongly agree), was used to examine par-ticipants’ responses to the items of the survey. The data forthis study was anonymously collected from Computer Sci-ence students who are aware about contemporary smart-citydevelopments, and living in a residential area of a mediumsize city in Sweden. 65 completed questionnaires were col-lected, among which 55 of them were valid questionnaires.The results displayed in Figure 6 show that the hypothe-sis was significantly supported, with a statistically positiveeffect on users’ attitude towards embracing cyberphysicallearning environments to foster digital smart competences.Particularly, the obtained results from the two last questionsof this preliminary survey show the benefits of data analyt-ics to empower future digital smart citizens on one hand;and on the other hand, reveal an awareness on the promi-nent role of IoT to alleviate challenges towards smart-cityadoption.

6. RELATED WORKThere has been recent investigations into raising citizens’

aptitude to meet the digital profile of smart citizens, throughthe development of computational-education environments.A future education has been envisioned in [21] which empha-sizes the idea of ‘smart citizens’. IBM’s Smarter Education[10] program is based on a similar motivation but highlightsthe availability of real-time data enabled by new technologiesfor driving the educational continuum. This trend aims atextending IBM’s global smart cities programs into reimag-ining and reconfiguring education as data-based platformsof real-time monitoring and measurement, where learnersare increasingly treated as ‘data objects’ along with envi-ronmental assets, which actions can be altered through pro-gramming the learning environment. Along the same line,Microsoft has been examining how governmental and civilsociety organizations are working within local smart citiesinitiatives to develop citizen’s capacities as active ‘smart cit-izens’ [11]. Competences include technical data skills facil-itated by computational urbanism such as ‘smart homes’and participation in ‘civic coding’ on behalf of the city. Mi-crosoft CityNext initiative on education connects ‘cloud, BigData, mobile and social’ technology trends to the smart cityand to education [11]. To thrive, “smart cities need to pro-vide access to powerful learning devices, tools, and appsthat empower education” argues Microsoft’s white paper.Other commercial computing firms have launched projectspromoting their products for smart cities, including Cisco,Intel, and Siemens, many are linked to huge urban projectsbuilding within new smart cities, such as Songdo in SouthKorea, PlanIT Valley in Portugal and Masdar in Abu Dhabi,but much more commonly in the ‘upgrading’ of existing ur-ban infrastructure [20], such as Stockholm, Helsinki, Ams-terdam, Seattle and Singapore. To achieve these urbaniza-tion developments, an efficient cooperation between inhabi-tants, industry and technology players is expected, prompt-ing shifts in learning processes which evolved outside tradi-tional classrooms towards data-intensive approaches. Theseprocesses are turning tangible interfaces into learning toolsthrough real-time feedback from physical instructional as-sets that can ‘teach’ how they are to be used and deliverpersonalized educational experience [13].

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7. CONCLUSIONSmart cities raise the expectations for citizen’s engage-

ment and increase their digital footprint. As manufacturersbrainstorm, envision, and plan smart city developments, cit-izens found themselves pruned out and set aside for lateror forgotten altogether. Yet, the success of smart citiesrely on general citizens’ engagement into these spaces en-acted by an assemblage of programmed technologies anddata performed from technical experts. We revealed a com-putational representation of these spaces using advances inInternet of Things (IoT) that shape cyberphysical learningenvironments. We also advocated progressive learning pro-cesses within these environments, following a model-basedlearning scheme that integrates physical assets as sourcesof instruction. The proposed algorithm asserts digital com-petencies within specific smart city contexts. A case studyillustrating the proposed cyberphysical learning approach isdiscussed and analyzed along contemporary smart-grid de-velopments, where smart-homes augmented or fitted withIoT technologies empower inhabitants to feedback on theirenergy usage and adopt behavioral changes that expandtheir digital smart citizenship competences. Ongoing workaims at demonstrating the proposed cyberphysical learningapproach within a prototype implementation of a connectedhome to validate incremental advancements in smart digitalcitizenship competence.

8. ACKNOWLEDGMENTSThe first author’s contribution in this work has been

partially funded by Vastra Gotaland Region, as a part ofthe research project on smart grid Kraftsamling Smarta Nat2015-2016 (dnr MN 39-2015), and partially supported by SPSveriges Tekniska Forskningsinstitut AB as well as ELIQ AB(energy management company). The second author’s contri-bution in this work has been partially funded by the GreekGeneral Secretariat for Research and Technology, under theMatching Funds 2014-2016 for the EU project ”Inspiring Sci-ence: Large Scale Experimentation Scenarios to MainstreameLearning in Science, Mathematics and Technology in Pri-mary and Secondary Schools” (Project Number: 325123).

9. REFERENCES[1] L. Anthopoulos. Understanding the smart city

domain: A literature review. In Transforming CityGovernments for Successful Smart Cities, pages 9–21.Jan. 2015.

[2] B. Bredeweg and K. D. Forbus. Qualitative Modelingin Education. AI Magazine, 24(4):35, Dec. 2003.

[3] J. S. Brown, A. Collins, and P. Duguid. SituatedCognition and the Culture of Learning. EducationalResearcher, 18(1):32, 1989.

[4] J. Campillo, F. Wallin, and I. Vassileva. Economicimpact of dynamic electricity pricing mechanismsadoption for households in Sweden. In Proceeding ofthe World Renewable Energy Congress, 2013.

[5] G. Clemitshaw. Children, citizenship andenvironment: nurturing a democratic imagination in achanging world. by bronwyn hayward. British Journalof Educational Studies, 62(1):85–86, 2014.

[6] A. Cocchia. Smart and digital city: a systematicliterature review. Smart city. Springer InternationalPublishing, pages 13–43, 2014.

[7] E. Commission. Integrated sustainable urbandevelopment, cohesion policy 2014–2020. EuropeanCommission: Brussels, Belgium, 2012.

[8] R. P. Dameri. Searching for Smart City definition: acomprehensive proposal. International Journal ofComputers & Technology, 2013.

[9] S. Das. Personalise through the internet of things.Kantar TNS. Retrieved from:http://www.tnsglobal.co.uk/thought-leadership/personalise-through-internet-of-things,2016., 2016.

[10] I. S. Education. Education fora Smarter Planet: The Future of Learning. Available at:http://www.redbooks.ibm.com/redpapers/pdfs/redp4564.pdf(accessed 5 January 2016).

[11] M. Education. Educated Cities. Available athttps://enterprise.microsoft.com/en-us/industries/citynext/educated-cities/ (accessedJanaury 5, 2016).

[12] European Commission. The european digitalcompetence framework for citizens. Publications Officeof the European Union, 2016.

[13] F. Gianni, S. Mora, and M. Divitini. IoT for SmartCity Learning - Towards Requirements for anAuthoring Tool. Proceedings of the First InternationalWorkshop on Smart Ecosystems cReation by VisualdEsign co-located with the International WorkingConference on Advanced Visual Interfaces, 2016.

[14] J. R. Gil-Garcıa, T. A. Pardo, and T. Nam. Whatmakes a city smart? Identifying core components andproposing an integrative and comprehensiveconceptualization. Information Policy, 20(1):61–87,2015.

[15] C. Kyriazopoulou. Smart City Technologies andArchitectures - A Literature Review.SMARTGREENS, 2015.

[16] J. D. Novak and D. B. Gowin. Learning how to learn.Cambridge University Press, 1984.

[17] B. F. Skinner. Teaching machines. Science, 1958.

[18] A. N. special issue. Circular economy: Getting thecirculation going. Nature, 531(7595):443–446, Mar.2016.

[19] U. N. (UN). Press release of the world urbanizationprospects 2014. United Nations - Department ofEconomic and Social Affairs. Retrieved fromhttp://tinyurl.com/j3azkyw., 2014.

[20] A. Wiig, T. Shelton, and M. Zook. ”The ‘actuallyexisting smart city’” by Alan Wiig. Cambridge Journalof Region. Available athttp://cjres.oxfordjournals.org/content/8/1/13(accessed Janaury 5, 2017), 2015.

[21] B. Williamson. Educating the smart city: Schoolingsmart citizens through computational urbanism. BigData & Society, 2(2), 2015.

[22] L. Yang, W. Li, Y. Ge, X. Fu, R. Gravina, andG. Fortino. People-Centric Service for mHealth ofWheelchair Users in Smart Cities. Internet of ThingsBased on Smart Objects, Technology, Middleware andApplications, 2014.

405