SHASPA: Building Intelligent Smart Spaces to increase the energy efficiency in Buildings Panagiotis Petridisa1 ,Oliver Gohb2, Ian Dunwella, and Sara de Freitasa

a Serious Game Institute, Coventry Innovation Village, Cheetah Road, CV1 2TL

bShaspa Research Ltd, 14 Hannover Street, London, W1S 1YH

Abstract. Due to the progress in sensor and ICT technologies, a diverse range of smart spaces have been developed to monitor various aspects of the everyday life of occupants. Smart spaces are focused on supporting more sophisticated human-environment interfaces, monitoring security and temperature control, delivering therapy or monitoring energy consumption. Shaspa (‘Shared Spaces’) is an innovative service framework and set of services blending the technologies of wireless sensors, social networks, mobile and virtual worlds, enabling the user to visualize, monitor and manage user’s environments, whilst gaining immediate feedback through interactive interfaces. Shaspa provides a smart interface to the most widely used industry-standard protocols for assembling real-world data from different input streams to manage physical spaces. Shaspa simplifies the creation of collective intelligence from industry standard data streams, which are further enhanced by using Shaspa’s em-bedded analysis and modelling frameworks. The results are appropriate for web-based, mobile, immersive 3D environments and as a feed for augmented reality applications. Shaspa Smart Shared Spaces act as hubs to social and business networks, and support their sustainable development both environmentally and commercially. Enacting behavioural change on an individual level is a significant challenge, and the Shaspa suite of products seeks to address this problem by pioneering an innovative approach to closing the feedback loop between users and their carbon footprint. This approach is realised through greater user engagement facilitated by more accessible and transparent remote monitoring.

Keywords: Smart Spaces, Carbon Reduction, ubiquitous computing, Smart Home, Smart Building, Virtual Operations Centers.

1 Email: PPetridis@cad.coventry.ac.uk 2 Email: oliver@shaspa.com

1. Introduction

A smart space is defined as an environment wherein pervasive devices, sensors and actuators, ‘sense ongoing human activities and respond to them’ (e.g. the Aware Home Research Initiative at Georgi-aTech and MavHome, Managing an Intelligent Versatile Home [1]. It represents a limited area such as a home or office where many kinds of autonomous and smart devices are continuously working to make inhabitants ‘live and more comfortably’[2].

Continuous progress in Information Technology and communications technology has lead to a cost reduction in sensor technology development and cost reduction of electronics, making smart houses in-creasingly feasible and cost effective[3]. Current re-search into pervasive environments seeks to merge the material and virtual world by incorporating phys-ical and computing entities into smart spaces [4]. Homes, workplaces, vehicles computers, and mobile devices collect information about user locations and different activities. Applications in such envi-ronments must be context aware in order to adapt to the constantly changing environment [4, 5].However many first generation smart spaces lack the ability to evolve as new technologies emerge or the application domain matures[4].

A fundamental issue in the design of smart spaces is the difficulty of anticipating user behaviour prior to sensor deployment and environment design, and subsequent limitations in efficacy resulting from sen-sors which fail to fully capture the necessary data for a given purpose [6]. Similarly, in an energy conser-vation context, this limitation is also reflected in dif-ficulties in predicting behaviour and controlling en-ergy consumption appropriately, e.g. identifying pat-terns in user behaviour and modifying ambient tem-perature accordingly [7].

The recent 2009 Climate Change Programme Re-view suggested that the UK will fail to meet its target of a 20% reduction in CO2 emissions by the end of the decade. It cited a need for individuals to recog-nize their own responsibility to reduce their personal ‘carbon footprint’, and therefore novel technologies which address the goal of reducing personal and in-stitutional CO2 emissions is a key research objective on both national and global levels. One of the fore-most problems faced when attempting to induce the behavioural and attitudinal changes needed to achieve this objective is the perceived disconnect between personal action and environmental impact. As a consequence of this disconnect, previous ap-

proaches have highlighted difficulties in creating behavioural changes in practice.

Although current systems such as the Stafford iRoom [4], Matilda Smart House [8] and the Gator House [6, 9] provide limited capacity for adaptivity, user interaction and control is essential to modify system behaviour, and this is contrary to the long-term objective of creating smart spaces which are seamless and autonomous. This is a particular issue with the deployment of sensors on a large-scale, where an understanding of crowd flow and behaviour is only possible following an initial physical deploy-ment of sensor networks at substantial cost. Similarly, the feasibility and cost of creating a smart space to address a given scenario is difficult to accurately anticipate. This is an increasing problem as the ca-pacity to gather and interpret greater volumes of data from sensor technology improves, as well as the de-mand for smart spaces which effectively interpret and utilise this information to provide tangible real-world improvements.

A common issue for interface designers is the dif-ficulty in providing a natural and intuitive interaction with the environment. In the case of smart spaces, the interactions are remote and ubiquitous, which means the user is likely to interact with any device, if such interaction is applicable, and the system as a whole must be aware of every interaction. Moreover, such interactions are intended to capture feedback from the user in order to learn and respond more relevantly in time. The second concern is consequently related to managing this distributed architecture of hetero-geneous devices, among which some are storage de-vices, some are sensors and others actuators. Such a sensor network involves dealing with several de-pendencies, most often power supply, wireless net-work strength and security. Finally, a smart space needs to have some capacity to analyse the situation, and more importantly the context, in order to adapt or to anticipate to the user needs. Hence, such systems may be considered as intelligent agents.

The smart houses can be divided into several categories according to their function (e.g. supporting elderly people, monitoring energy supplies).

Table 1: Division of Smart Houses according to Function

Services Equipment Tactile Screens Sensitive Remote control Audible and visible beacons Synthetic Voice Generation

Support

Rehabilitation and Companion Robots

Monitor Infared Sensors

Wearable systems EEGs Heart Rate Sensors Temperature Sensors

Blood pressure Therapeutic devices Drug Delivery Hormone Delivery Robotic Devices for physical therapy Smart Objects (i.e. mailbox, closer, mirror)

To deliver therapy

Intelligent household devices (i.e. dishwasher, cooker, oven, etc) Intelligent house equipment (i.e. motion sensors, magnetic switches, humidity, gas, light sensors) Smart Leisure Equipment (i.e. TV, home cinema programs) Interactive communication systems in case of emergency Intelligent Environment Con-trol equipment (i.e. windows, doors, shutters, heating, light-ing, air condition, ventilation)

Comfort

Fitness Devices The problem faced by businesses at present is one

of how to access these new markets and how to cre-ate an interoperable and easy to use platform for reaching these markets. Fig 2 represents a typicals-mart house architecture. The system is composed of the equipment which consists of different sensor de-vices and actuator (see: Table 1), then information is send to the database by the communication system which can be either hard-wired or wireless. Then the data are processed and optimized for use according to the functions / services of the smart house. The deci-sion about an action or an alert of the system is taken into the Decision centre by using several algorithms.

Fig 1: A typical Smart House Architecture

2. Background

Several projects in the last decade have studied different future-orientated approaches in the devel-opment of Smart Spaces.

The adaptive house uses neural networks in order to control the temperature, heating and lighting. ACHE continuously monitors the environment, and observes the actions taken by occupants (using lights, using the thermostat), and attempts to infer patterns in the home. The ACHE system uses reinforcement learning – a stochastic form of dynamic program-ming that samples trajectories in state space in order predict user actions [10].

The MavHome project (University of Texas) aims to create a smart home that treats the environment as

an intelligent agent [11]. MavHome combines several technologies: databases, multimedia computing, arti-ficial intelligence, mobile computing and robotics. The system aims to maximize the comfort of inhabit-ants and minimize the consumption of resources, whilst maintaining safety and security. Hence the system’s agent must be able to sense and predict the residents’ mobility habits and the residents’ use of electrical appliances. The goal of the MavHome pro-ject is to create a universal predictor of user mobility. The system uses the LeZi method, a technique of information theory, is used to create a probabilistic model predicting the inhabitant’s typical path seg-ments, comfort management scheme, and appliance use. Specifically, the Active LeZi (ALZ) algorithm calculates the probability of every possible action occurring in the currently observed sequence and predicts the action with the highest probability [3].

Helal and colleagues [6, 9] have developed a smart house named as “Gator Smart House”. The system is based on a number of individual devices such as the mailbox, front door, bed, floor, etc. All these compo-nents are fitted with sensors and actuators and con-nected to the operational platform that improves the comfort and safety of the older people. The Gator House includes ultrasonic location tracking sensors in order to detect occupant’s movements, location and orientation.

Microsoft ‘s EasyLiving Project focuses on an en-vironment that is aware of the users’ presence and can adjust the environment according to the user re-quirements [4]. The system identifies people in real-time using video images in real time, and can track them in the real space.

The Aware Home (Georgia Institute of Technol-ogy) is a 5040 sq ft that functions as a living labora-tory for evaluating and testing new sensor technolo-gies and domestic appliances. The system is com-posed by a smart floor that senses the occupant’s steps, which allows the smart home to build a model regarding the occupant’s movements. The system evaluates this model by using a set of mathematical tools, such as neural networks, simple featured vector averages and Markov models [3, 12] .

The HP CoolTown Project provides all physical entities (people, places and objects) with web pres-ence. Hence the user can move from the physical world to the virtual world by picking links to the web using sensor technologies [Spasojevic et al., 2001]. The Stanford University iRoom provides the user with tools to exchange information and control dif-ferent devices in the environment [13].

Other relevant projects include the MIT’s Oxy-gen5 and Carnegie Mellon University’s Aura6. These projects have greatly contributed to smart space re-search by exploiting different pervasive computing features [4].

House-n group at MIT proposes smart services that will conduct qualitative and quantitative studies in order to investigate the relationships between spaces, behaviours of people [14]. The system is composed of three components: the environmental sensors which collect real-time data from the objects, the Experience Sampling Component (ESM) which can identify routines in activities of the occupant and the pattern recognition and participation component for predicting the occupant’s activities [15].

Researchers at the intelligent home project (IHome) at the University of Massachusetts system lab (UMASS) at Amherst have deployed a set of dis-tributed autonomous control agents in a simulated environment. IHome’s objective is to automate some of the tasks which are currently performed by hu-mans [16]. The UMASS IHome is composed of four rooms: a bedroom, a living room, a bathroom, a kit-chen, all joined by a hallway. Various intelligent agents control the environment and can move objects from one location to another. The home agents rea-son about their assigned tasks and select different actions based on the occupant’s preferences and the availability of the resources [16]. Resource, resource interactions, and the performance characteristics of primitive action are quantified in IHome’s task mod-elling framework so that the agents can reason about the different tradeoffs of different courses of action and adapt their behaviour to the changing envi-ronment [3, 17].

The smart house developed by Matsuoka [17] con-tains 167 sensors and the system can automatically detect unusual events such accidents, etc. The smart house was designed in order to monitor 5 people simultaneously. Identification of the person is carried out by detecting a signal from the Radio Frequency Identification (RFID) generator and a wearable ter-minal installed on each person. Matsuoka developed a method which reduces the amount of data received from the sensor and can detect changes in the behav-iours of the occupants. The system extracts principal components from the sensor data within the specified time framework, and then it identifies statistical clus-ters in the complete sensor data based on the princi-pal components. The system was evaluated using a four person family for a period of a year. The system has detected 73 unusual states linked to the changes in the behaviour of the occupants. 19 of these events

coincided with a real change in the behaviours of the occupants.

The Ubiquitous Home project is a test facility for the creation and evaluation of useful services directed towards sensors, devices and appliances through data networks [18]. The Ubiquitous Home is equipped with various sensors to monitor human activities. Each room has cameras and microphones in the cei-ling to gather audiovisual information, floor pressure sensors, infared sensors and Radio Frequency Identi-fications (RFID) tags are installed / used in order to track the occupants and detect movements of objects into space. Plasma panels and LCD displays, provide the residents with audiovisual information. Robots are introduced for certain home activities and the smart house can be considered as an unconscious robot that controls the environment through the uses of sensors.

Isoda and colleagues in the multimedia laborato-ries, NTT DoCoMo have developed a system that uses RFID tags and floor mounted weight sensors in order to detect the spatio-temporal relationship be-tween the occupant and various objects. They have proposed a user activity assistant system that per-forms robust state decisions by identifying which of these data are valid based on information received from the sensors [19].

Nishiba and colleagues have developed an intelli-gent environment system called SELF (Sensorised Environment for Life) [20]. The system can state, store and analyse the resident’s psychological status by comparing it with a predefined human model, based on information received by the sensorized en-vironment located in the occupant’s bedroom. The human model consists of an environmental model, a human physical model and a human psychological model. The sensorized environment consists of a pressure sensor array, a ceiling dome microphone in order to detect breathing sounds, and a wash-stand display which consists of cameras, a monitor and two way mirror, used in order to superimpose health in-formation of the occupant [20].

Noguchi and colleagues have developed a sensing room for accumulating and measuring human daily actions [21]. The goal of the project is to support the occupant’s daily activities. Nogucchi proposed the use of a network system for measurement and accu-mulation of sensor data from an occupant room uses the sensors. Additionally Noguchi and colleagues proposed a summarization algorithm that segments the accumulation data at points where outputs chan-ges drastically and thus the occupant has to interro-gate a small segment of the data [21].

British Telecom and Anchor trust have developed a smart house (called Millennium home) in order monitor the activities of occupant using IR Sensors, magnetic contacts on doors, temperature sensor to measure the ambient temperature. The aim of this project is to monitor the occupant’s activity and cre-ate a behavioural model that will better control alarms when an anomaly in occupant behaviour is detected [22].

The SmartBo house in Norway was designed in order to support the elderly. The house is equipped with sensors that control the house lighting, house door, windows and shutters [23].

The HIS Project in Grenoble is an apartment that uses IR Sensors in order to monitor individual ac-tivity. The system transmits the data acquired from the sensors via network to a personal computer. The system monitors vital signs, and the weight of the occupant and informs the administrator of the system in case of danger [24, 25].

An initial attempt to include smart house in com-mercial properties was made in 1994 in Eindhoven. The model houses were equipped with motion detec-tion sensors, actuators in order to control heating, lighting and home appliances in order to alert the service provides for suspicious inactivity and break-ins [26].

ERGDOM is a smart multi-criteria, management system that minimises the cost of comfort and heat related expenses. Various sensors are distributed throughout the house and collect data on the occu-pants motions. The system uses an automatic learn-ing procedure based on real-time observation of the occupant and integrates any changes made by the occupant. Then the system compares this model with the current situation and adjusts the heating [3].

TERVA is a smart health station that monitors psychological and psychological health of the patient by measuring arterial blood pressure, heart rate, body temperature by using blood pressure monitor, an ECG and an activity monitor device, a static stage sensitive bed, a scale to monitor weight [27]. The system is controlled by a laptop and the data from the sensor devices are taken by using a serial interface (RS232). The research team has tested the system with 14 healthy patients for a period of 2 months. In order to process the data from the sensors they have used artefact filtering and computation of derived parameters, and window in order to compare variable with different sample rates [3, 28].

The aim of PROSAFE was to develop means of continuously monitoring the motor behaviour of older subjects.

PROSAFE supports autonomous living; to inform the participants with an alarm in case of an emer-gency. PROSAFE uses pyroelectric IR Devices for motion sensors. Several hospital rooms in a long-stay setting for the elderly have been equipped with the system. A wireless version of the system using the 868MHz band has also been designed. To access the mobility of the subject, the whole system needs to be switched on. The data are stored in computer, which run the software that checks the subjects’ mobility and activity. The system uses neural networks in order to predict the presence of the subject using simulated data [29, 30]. The system was validated using real subjects in a hospital environment for sev-eral months [3].

Celler and colleagues [31, 32] have designed a system that monitors the user interactions with his home environment. The house is equipped with IR sensors, light sensors, temperature sensors, micro-phone, a central power meters, appliance power me-ters and pressure sensors. The system collects infor-mation about ECG sensors, blood, oxygen and satu-ration.

Diegel [33] and colleagues have developed a smart house that can learn the user’s habits and make intel-ligent decisions based on the user patterns. The sys-tem use a health monitor that measures physiological signs such as blood pressure, pulse arrhythmia, weight, temperature, body fat, and lung capacity. Once the data are collected and classified from the sensors the intelligent health system detects whether any reading is outside the threshold and then it in-forms the subject.

3. Future Trends and Technologies

In recent years, due to the technological advances in sensor technologies, ICT and reduced costs of computing, merging physical spaces with real spaces has become a hot topic in the research field of perva-sive computing. The literature review in smart space showed that many projects are in prototype stage, but in the next years they are going to make the transition from research to industrial projects [3]. However in order for these systems to be successful we should not repeat the mistakes that were made with home automation technologies in the 1970s [18]. In order to overcome these problems smart spaces need to focus on the user needs, the reliability and effi-ciency of sensor devices and the standardisation of communication systems. Satisfying the needs of the

user is a major challenge in the development of smart spaces. The developer of a smart house has to avoid using technologies that are not suitable to the user/occupant needs. In recent years several studies have focused on the user satisfaction and usability of the smart houses [18, 34-37], have shown that the development of interface technologies between hu-mans and the smart homes for the detection of human intensions, feelings and situations has to be interac-tive, flexible and adaptable to the changes of human lifestyles and activities. So these technologies have to be cost effective and contribute to the energy savings of the occupant.

One of the major problems encountered in smart homes is the reliability and the efficiency of the sen-sory data and data processing algorithms [3]. In all smart houses, the system monitors the behaviour of the occupant, however the system must be able to consider and anticipate deviation of the user from regular behaviours. Several methods have been ap-plied in order to analyse the data provided by sensors such as neural networks [15, 29], Markov chains [14, 15], machine learning[11], predictive algorithms [11], statistical models [29], probabilistic models [15, 24], etc.

The third major issue that has to be considered for the generation of smart houses is the standardisation of the information and communication systems. Ac-cording to the review of smart spaces the developers are using a wide range of communication protocols. Some of the smart spaces are hard-wired, buses (Pro-tocols like KNX, LON, Modbus and others) or they are using wireless technologies such as ZigBee, Eno-cean and ZWave.

The Shaspa Research provides a scalable, cost-effective solution that allows the user to interconnect devices from the most common protocols found in the industry. It acts as interface to assemble real-world data from stream of different types of sensors by using the current building infrastructure. The oc-cupant of the building can monitor, control the dif-ferent appliances, lights, HVAC systems, multimedia equipment, security systems that are connected to the system.

4. SHASPA Technology

The Shared Spaces (Shaspa) [41] service frame-work offers an innovative platform, a set of products and services blending the emerging technologies of wireless sensors, social networks, mobile devices,

and virtual worlds, enabling the user to visualise, monitor and manage physical environments with minimal interaction. Shaspa provides the user with the means to collect data in real time via a network of sensors, wired or wireless. The main objective of Shaspa Research Ltd is to provide methods and tools which increase the technical and structural perform-ance of properties. The Shaspa service framework creates Smart Shared Spaces that act as hubs for social and business networks, supporting sustainable development, both environmentally and commer-cially. These Shared Spaces are defined as areas wherein people work together to achieve mutually agreed goals or targets, based on the information made available by the Service framework. Hence, Shaspa creates a cloud of live data related to the cur-rent behaviour of the real environment. To access this data cloud, Shaspa provides the user with a full set of easy-to-use features to store and analyse data, and communicates heterogeneously with users on differ-ent platforms via a selection of display technologies (see Fig 2) and social networking systems.

Fig 2:SHASPA Portlets

Shaspa delivers an ‘energy dashboard’ (see Fig 3) so that individual users and decision-makers can see how they meet desired targets on institutional and national levels, as identified by the Carbon, Energy and Environmental Issues in the Higher Education report [42]. This in turn enables a reduction in emis-sions, and quantifies environmental and financial benefits. These benefits will be realised through the ability of the system to accurately benchmark per-formance in terms of energy consumption, and this will in turn enable individual stakeholders to imple-

ment policies and practices that minimise energy expenditure. However, a far more fundamental out-come of the project will be its exploration of the op-timum methods and best-practices for behavioural change, which will enable and motivate individuals to take more responsibility for their own levels of energy consumption and wastage.

Fig 3: Energy Dashboard

Through the storage of information regarding the individual elements of the world (i.e. appliances, sensors, etc), the knowledge-base contains an internal representation of the world, alongside the knowledge that the system has acquired over time. Combined, this could be represented in form of a building energy flow diagram or a real time energy pass (see Fig 4 and Figure 4a).

The Service framework allows user access via web browser, mobile device or 3D Viewer. Using the 3D Viewer the user can access the 3D engine build into the Shaspa Bridge. It provides a user-optimised 3D interface which allows the user to analyse the data received through the 3D Virtual Operation Centre (VOC). Visualisations of the system state are achieved through a virtual abstraction of the mirror world held internally by the virtual worlds communi-cation interface, conveyed via the Virtual Operation Centre (VOC). The VOC is responsible for organis-ing and presenting the information received from the different sensors in a coherent and user-friendly manner, according to user specification and informa-tion previously learnt regarding user behaviour and preferences. The 3D environment is customized ac-cording to unique requirements, and can be delivered as classic command centres, control rooms, data-centers or hybrid custom solutions. The versatile vir-tual world environment ensures a platform for inno-vation, scalability, performance and growth as the business value it brings is expanded to other areas.

Fig 4: Energy Flow Diagram

This customization is useful when managing live systems, and can also be used to perform simulations for planning such as space, thermal, power or other planning needs. The Shaspa 3D Virtual Operations Centre also extends the business value of previous investments, by leveraging and extending existing systems management and monitoring infrastructure. This includes facilities automation and information systems, datacenters, command center information systems and many other systems that provide in-dustry standard programming interfaces.

The Shaspa 3D Virtual Operations Centres is a suite of multi-user virtual world applications, com-plete with in-world 3D messaging Multiple users can have a shared 3D experience and carry on active dis-cussions in-world. This shared experience allows technical, business, and even partner personnel, to collaborate on elements of the enterprise in real-time. These expert collaborators can be located across the world, yet still meet ‘locally’ in the virtual world to collaborate, decreasing time to resolution, increasing planning effectiveness and reduced travel costs. The key benefit of the virtual world collaborative capa-bility and shared immersive experience is providing enhanced overall communications methods and life-cycle time for any business scenario.

Finally, the Shaspa Bridge (see Fig 5) , the enabler, is an optimised embedded device that runs the ser-vice framework allowing applications to interface to the most widely used industry-standard protocols to assemble real-world data from streams of different types of input to manage physical spaces.

Fig 5: Shaspa Bridge

The Shaspa bridge provides support for the most common wireless sensor such as ZigBee, KNX-RF, Enocean, Zwave and support for the most common building automation standards such as KNX, LON, Modbus, X10, CANOpen, Mbus, SNMP, BACNET. The Shaspa Bridge implements features for configur-ing devices and for the display and manipulation of data (Configuration Manager). It also takes on the responsibility for reliably passing data to outside interfaces such as the Shaspa Portal, SAP, Maximo, Facebook, Twitter, AMEE and a host of other readily available and emerging applications, or you can ac-cess the information in the Bridge in 3D by using the opensim or secondlife (see VOC), or via a mobile device or pocket device (e.g. iPhone).

Fig 6: Shaspa Bridge Architecture

There are usually a number of different facilities

within a building, often affecting each other and cul-minating in a total carbon output. Understanding these interactions and working to cut the emissions is a complex task, as the installations exist in isolation with separate dedicated systems.

Fig 7: Shaspa Server Architecture

When a user switches a device Shaspa will know about the energy consumption of the device, as well as its affiliation to a user group. In this way, the user can get a better perspective regarding his energy consumption by using the Shaspa Energy Monitor (see Fig 8).

Fig 8: Shaspa Energy Monitor

Much research and discussion in the area of energy conservation, including both well-established [43] and more recent work [44], suggests that the fundamental difficulties in enacting behavioural change arise from the human propensity to focus on activities with immediate, short-term personal gains, as opposed to long-term and abstract results which have societal rather than individual benefits. Hence, an individual who reduces their energy consumption will not typically observe benefits of their activities on an individual basis.

5. Applications of Shaspa technologies

Shaspa can be used as smart home were the occu-pant can use the system to monitor power consump-tion per appliance, HVAC settings, the envi-ronmental conditions (i.e temperature, humidity, light, CO2 emission) and alert the user when an anomaly of the system occurs). Additionally the occupant can control using Shaspa Smart Plugs the light, blinds, the independent appliance monitor, HVAC and the security system. Smart plugs are sensors that are connected behind the power outlets in order to moni-toring and controlling remotely the connected de-vices. Shaspa system combines the Home Enter-tainment systems with home automation, and can enable TVs as Interface for the system by using CE-HTML (see Figure 9).

Fig 9: Shaspa Smart Home

Shaspa can be integrated into large data centres and can monitor the power consumption per PDU, the environmental condition, the equipment states, and the network and IT equipment. Additionally it can control the Interaction between IT Equipment - HVAC Systems and the Emergency Power devices (UPS, Generator). Additionally it can provide infor-mation about the cost distribution of the datacenters.

Figure 10: Shaspa Smart Datacenter

Other typical applications could be for hotels, mu-seums, schools and other physical spaces where a community with shared interests benefits from addi-tional data. Here the aim is to create a shared phys-ical space which uses technology to enhance the ex-perience of visitors, customers or students. The sys-tem can provide instant feedback for the users, clos-ing the feedback loop.

6. Conclusion and Future Work

Smart homes are evolving from a proven concept to a practical reality. Smart houses are used in order to support, control, monitor and deliver therapy to occupants. However the adaption of the smart houses in commercial projects and public residencies is hap-pening more slowly due to costs. In order for the smart house to be broadly adapted by the public, we need to:

• integrate their computing infrastructure, and services

• match or improve the standard of living of the user

• respect the habits and intentions of the user

• focus on the user needs • focus on the standardization of the com-

munication systems This paper presents the Shared Space (Shaspa)

Service Framework which tries to address the issues mentioned above. Shaspa provides the user with the means to collect data in real-time via a network of sensors, fixed or mobile networks, video camera sys-tem and GPS. The Shaspa main objective is to in-

crease the technical and structural performance of properties. Therefore Shaspa can create a cloud of current data which are related to the behaviour of real environment. Additionally Shaspa provides the user with a full set of easy-to-use features to store and analyze your data and communicate with a variety of users via a selection of display technologies and social networking systems. Additionally it supports all the available industry standards. Future research will, therefore, analyse how the information generated by Shaspa technology on an individual and collective level can be used to overcome this challenge, alongside a range of approaches aimed at encouraging environmentally conscious behaviour and thus reduction in individual carbon levels. These approaches will be developed through participatory design, and evaluated using both social science methodologies (interviews, focus groups, and case studies) and empirical methods (quantitative measures of energy consumption across control and experimental groups). The approaches the project will consider include: • A behaviourist approach, wherein operant

conditioning is used alongside tracking of individual power consumption through smart devices and systems. Users will be rewarded for reducing consumption. This strand of research will use qualitative and quantitative techniques, whether the behaviour engendered through such an approach results in effective conservation, or whether individuals refuse to accept, or attempt to work around, the Shaspa architecture. In turn, this will provide discussion and output on the issues surrounding such approaches and their long-term viability.

• A participatory approach, which encourages individuals to take responsibility in identifying and addressing factors that affect their actions (or inactions) using Shaspa data. Complemented by a range of learning activities which demonstrate to individuals that value of their own contribution to both their organisation and the global community, this approach will focus on enabling users to identify, consolidate, and reinforce positive behaviours whilst collectively generating new behaviours. This approach will build social support systems that reinforce and support desired behaviours.

• A sociocultural approach, seeking to build communities of practice who interact and thus motivation will arise from a social context. These communities will be supported and

allowed to develop; demonstrating their own evolved best-practices and behaviours.

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