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ForeSight - Platform Approach for Enabling AI-based Services for Smart Living


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In future, smart home and smart living applications will enrich daily life. These applications are aware of their context, use artificial intelligence (AI) and are therefore able to recognize common use cases reliably and adapt these use cases individually with the current user in mind. This paper describes a concept for such an AI-based platform. The presented platform approach considers different stakeholders, e.g. the housing industry, service providers and tenants.
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ForeSight - Platform Approach
for Enabling AI-based Services
for Smart Living
Jochen Bauer1(B
), Hilko Hoffmann2, Thomas Feld3, Mathias Runge4,
Oliver Hinz5, Andreas Mayr1,KristinaF¨orster1, Franz Teske1,
Franziska Sch¨afer1, Christoph Konrad1,andJ¨org Franke1
1Institute for Factory Automation and Production Systems,
Friedrich-Alexander-University Erlangen-N¨urnberg,
Egerlandstraße 7-9, 91058 Erlangen, Germany
2Deutsches Forschungszentrum f¨ur K¨unstliche Intelligenz GmbH,
Stuhlsatzenhausweg 3, 66123 Saarbr¨ucken, Germany
3Strategion GmbH, Albert-Einstein-Straße 1, 49076 Osnabr¨uck, Germany
4IoT connctd GmbH, Hardenbergstraße 32, 10623 Berlin, Germany
5Faculty of Economics and Business Administration, Goethe University Frankfurt,
Theodor-W.-Adorno-Platz 4, 60323 Frankfurt am Main, Germany
Abstract. In future, smart home and smart living applications will
enrich daily life. These applications are aware of their context, use arti-
ficial intelligence (AI) and are therefore able to recognize common use
cases reliably and adapt these use cases individually with the current
user in mind. This paper describes a concept for such an AI-based plat-
form. The presented platform approach considers different stakeholders,
e.g. the housing industry, service providers and tenants.
Keywords: Artificial intelligence ·Ecosystem ·Platform ·Smart
home ·Smart living
1 Motivation
The term “Smart Living” comprises several areas that are separated today:
energy management, health and home automation [1]. Furthermore, smart home
is a core element in a connected world. There is need for intelligent applications,
which fulfil cross-domain use cases. Smart buildings, which include smart homes
and commercial buildings, will take an important role to enable smart grid [2]
and smart city related approaches. Such approaches can only be realised with
intelligent, situation-adaptive control opportunities and building related services
[3]. This leads to more comfort, better assistance and increased safety and secu-
rity as well as improved resource efficiency and reduced overall costs. The reason
Supported by German Federal Ministry for Economic Affairs and Energy.
The Author(s) 2019
J. Pag´an et al. (Eds.): ICOST 2019, LNCS 11862, pp. 204–211, 2019.
ForeSight 205
for such advanced opportunities is the intense usage of AI. This paper describes
a concept for a platform approach to enable such an intense AI usage in smart
The economic relevance of connected homes or buildings is proven by sev-
eral key figures [4,5]. In Germany there are currently approximately 19 million
residential buildings and around 42 million households. The residential build-
ings have a share of approximately 98% of the total building stock. 57% of all
Germans and 72% of single households lived for rent in 2015. [6] The 23 mil-
lion rental housing units are managed by about 68,000 companies. Due to the
situation described above, it is demanded at government level that the topic
of digitisation and connectivity for the housing industry will be addressed. For
other European countries, the situation seems not exactly the same, but similar.
2 State of the Art
There are currently various stand-alone smart home systems on the market.
Some of these are based on wired or wireless technologies, e.g. KNX [7], Home-
matic [8, S. 231], Z-Wave [8, S. 216], free@home or digitalSTROM [9]. Moreover,
most of the systems offer cloud-based remote access possibilities as an addi-
tional option for controlling the building from abroad. Of course, the biggest
value proposition for the tenant is if he is able to add various devices from dif-
ferent vendors and consume third party services in his system. There are various
middleware systems, which follow this idea like the openHAB system [10]or
Middleware systems usually provide an abstraction layer for devices. Thus,
these systems are able to ensure data transfer between such systems and therefore
achieve interoperability. In smart homes and for assistance systems in general,
privacy and security issues [11] play an important role in Europe. This means
that each data record should be extended with the information, who is allowed
to do what actions at what time with it.
According to the paragraph before, it becomes obvious that available seman-
tic information is useful to include domain knowledge in suitable data structures.
For this purpose the concept of ontologies exists. Ontologies are semantic orders
of domain related terms and their relationships [12]. Due to ontologies it is pos-
sible to start semantic search operations, combine two knowledge bases or test
data for inconsistencies [13]. In the domain of smart living the SAREF ontology
[14], BRICK [15] and Web of Things [16] are ontologies or approaches which
should be investigated. The world of ontologies and knowledge representations
lost some importance in recent years because solutions for numerous challenges
were delivered by another part of AI, so-called machine learning (ML). In ML,
algorithms and statistical methods are used to find solutions for optimization
problems, e.g. more and more pre-trained neural networks offer robust high-
quality answers to related questions [21]. In the context of smart living, speech
recognition [22], object identification or energy management are relevant exam-
ples, where ML is used successfully.
206 J. Bauer et al.
3 Challenges
Requirements are rapidly increasing in the smart building context, i.e. for achiev-
ing the energy transition flexible control and monitoring mechanisms need to
be offered. Another challenge for German society is to assist elderly people and
enable them to live in their preferred environment as long as possible. To achieve
this, AI-based smart living technologies can help to extend this period of time.
Thus, it is necessary to increase the amount of connected devices and enable
energy consumption measurement and localisation technologies to create indi-
vidual adaptable use cases. Currently, systems lack this functionality and do
not offer such individually profile-based approaches. Future smart home systems
need to have the specific user in mind, detect users’ behaviour and make the
correct conclusions. Smart speaker can help here to locate and identify persons.
Moreover voice user interfaces (VUI) can bring smart home technology to people
who are not used to smartphones. The first numbers of sold smart speakers show
that VUIs are a game changer. Anyway, there are lots of people who are still
reluctant, because they do not know what happens to their data. Summing up,
promising smart home platforms should offer a VUI and address privacy issues
as well.
Existing middleware systems are slowly beginning to ensure interoperabil-
ity [18,19]. In addition to automating tasks, smart home systems begin to offer
authentication options based on OAuth. This is pretty common for social media
websites, i.e. signing in with your Google or Facebook account to another web-
site [20]. On the one hand, this is quite comfortable for the user but on the other
hand it will be even more advanced to sign in with your preferred service and
avoid sharing data to lots of different servers abroad. Current platforms and eco
systems lack organizational interoperability. This interoperability level should
be persuaded, because only then it will be possible to create a flexible eco sys-
tem with different partners, who are able to include their specific services and
corresponding business models. The ideal place to store semantic information is
in an ontology, e.g. the SAREF ontology. Ontologies can be extended [17], hence
this should be considered by almost any platform project in the smart living
Furthermore, concepts are needed which enable the transition from hard-
coded if-else-statements towards context aware, dynamic and situation adap-
tive, self-learning approaches, which consider future trends as well. These new
approaches need to add to current best practice approaches, e.g. plug-and-play
mechanisms like the OSGI-based openHAB system. Changing the perspective
from system to device, it is possible to speak from “Thinking Objects”.
4 Approach
The following ForeSight platform approach was initiated from members of the
German Smart Living Business Initiative, a network of smart home and smart
building experts. The goal of the ForeSight platform is to create a significant
ForeSight 207
contribution to the further development of smart buildings and homes by pro-
viding cross-domain AI-based solutions in combination with established building
automation technologies.
In the past, there have been various platform approaches, most of these were
not able to conquer the market. There are technical, legal and economic chal-
lenges to deal with. In a first step core partners were allowed to found ForeSight.
In this six months lasting project a useful platform concept needs to be created
and relevant partners have to be identified, so that the value added chain is
completed. In addition to that, established research institutes and organisations
are added to the consortium to develop the concept further and further.
Due to literature research and experience, possible partners were detected.
Afterwards, the team decided to start the working phase by founding a matrix
based organisation, composed of several working groups (WG) (see Fig.1). The
figure shows WGs with domain specific focus and groups with cross-sectoral
topics. The WG coordinators need to collect relevant data of their WG and
interconnect with other WGs.
commissioning and
operaƟon WG 4
WG 9
Tes Ɵng and evaluaƟon
WG 5
AI and ML
WG 7
IT infrastructure, data, security and privacy (WG 6)
Interoperability (WG 8)
Use cases,
smart service
concepts and
smart service
Smart data and
AI algorithms
intelligent services
and processes
environments and
WG 1
WG 2
Access control
WG 3
Fig. 1. The consortium created working groups to start an efficient process to push
the ForeSight concept creation.
The created ForeSight platform concept should be evaluated and verified in
laboratories and real world environments. Therefore, possible user stories and
use cases need to be identified and implemented. There are several user story
catalogues but usually there is no direct combination with AI, which is one of
the core elements of this platform approach. Therefore, a new template for user
stories was created and given to the members of the different WGs. This template
considers relations to AI services, which are necessary for the specific use case.
With reference to the challenges section, ForeSight wants to reach a high level
of interoperability, i.e. that service providers with different business models can
replace their services without much effort. The resident focuses on one use case
and the platform takes care of necessary processes in the background. To iden-
tify users is one of ForeSight’s basic features and the key to enable user specific
208 J. Bauer et al.
use case adaptions. Horizontal interoperability ensures that the manufacturers
and cross-domain interchangeability of devices works. Vertical interoperability
considers that interoperability between two services is available, i.e. energy man-
agement use cases are interchangeable, although tenants are clients from different
energy providers. Following the thinking objects approach it is necessary to offer
the most appropriate AI method in relation to a client’s use case. It makes sense
to offer three subsystems: first, the AI method platform module; second, an IoT
platform module to make sure that all commands can be transported to sev-
eral vendor-independent devices and third, service-related apps which can be
built and ran on the IoT platform module and are allowed to connect to the
AI module. Several AI base services are necessary and will be made available in
Service for activity recognition of tenants
Service for object identification
Service for predictive maintenance related to Thinking Objects
Service for self-configuration of Thinking Objects
Service for position detection of tenants and Thinking Objects
Service for identity and access management
Service for optimized energy management in a group of Thinking Objects
Service for technical-based health analysis of a building
Service for privacy and security issues in a group of Thinking Objects
The AI platform connects with ForeSight service apps and the IoT platform
component. This service-related app offers the AI module data and a preferred
use case. Now the AI method finder needs to check the quality of the data, pick
the most appropriate AI tool and start the function. Afterwards, the answer
of the AI based platform module will be sent back. To generate high quality
answers for accessing apps, several tasks need to be addressed (see Fig. 2).
To get an impression of the time schedule, an excerpt of the ForeSight
roadmap is shown below.
2019, 2020: creation of the ForeSight platform concept and identification of
all project partners
– 2020, 2021: implementation of reference architecture and AI-based context
sensitive services
2021, 2022: realization of service layer to achieve interoperability on business
model layer
2023, ... : ForeSight entering the market for third party service providers
In addition to the time schedule we defined some milestones and evaluation
2019: Partners are able to contribute to all steps of the value added chain.
2020: Partners are willing and able to run the platform from 2020 to 2030.
ForeSight 209
ML systemsInteroperability
IoT, Middleware-
Learning on
Compared to
Mapping of
Quality of
Basic services TesƟng environments
SemanƟc building
AI and ML
Data Method development TesƟngImplementaƟon Use cases /
AI on embedded
Example services for:
Comfort assistant
InstallaƟon, integraƟon
and planning
Building management,
Housing industry
Intelligent digital
twins, ML
Thinking objects
Agent systems
Generate, exchangeand extend plaƞormrelated know-how
Method improvement, update policies, business models, sustainability, proposalsfor standards
Fig. 2. The AI method platform offers different components to fulfil the needs of access-
ing service apps.
2021: Tests in labs and real world scenarios achieve satisfactory results and
2022: 10 third-party companies connect to the ForeSight platform with their
own services.
2023: More than 80% of existing smart living systems are using a ForeSight
platform service.
5 Discussion
When designing a platform, one of the first questions which arises is who wants to
operate such a platform. The ForeSight consortium consists of several companies,
which are experienced in running platforms. A flexible service-based approach
for running the platform seems to outperform a monolithic approach, as first
interviews showed.
Numerous use cases from the ML area require high quality data in order to
train the ML models, e.g. neural networks. Such data is often not available and
must be generated, which usually takes an enormous amount of time. It will be
hard to consider such soft facts in the AI method finder function to answer the
service-based apps.
In the past, several critical factors for smart home platforms were identified,
i.e. IT security, privacy and economic beneficial business models for all stake-
holders. The appropriate treatment of these topics needs to be considered from
the very beginning to avoid major concept changes later, which results in high
210 J. Bauer et al.
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Assistance systems equipped with artificial intelligence are present in many Internet of Things applications to address information overload and provide automation. These assistant systems blur the boundaries between classical and individual information systems; display behavior classically associated with humans, and aggregate a wide range of information sources. While this development certainly promises a lot of utility for individuals interacting with assistant systems, it is also evident that those systems process a significant amount of personal information. This contribution investigates in how far the usage of assistance systems leads to privacy concerns and explores users’ privacy preferences for assistant systems on the Internet of Things and ultimately quantifies the willingness to pay for various privacy functions of such assistance system.
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Commercial buildings have long since been a primary target for applications from a number of areas: from cyber-physical systems to building energy use to improved human interactions in built environments. While technological advances have been made in these areas, such solutions rarely experience widespread adoption due to the lack of a common descriptive schema which would reduce the now-prohibitive cost of porting these applications and systems to different buildings. Recent attempts have sought to address this issue through data standards and metadata schemes, but fail to capture the set of relationships and entities required by real applications. Building upon these works, this paper describes Brick, a uniform schema for representing metadata in buildings. Our schema defines a concrete ontology for sensors, subsystems and relationships among them, which enables portable applications. We demonstrate the completeness and effectiveness of Brick by using it to represent the entire vendor-specific sensor metadata of six diverse buildings across different campuses, comprising 17,700 data points, and running eight complex unmodified applications on these buildings.
The process of engineering cyber-physical production systems (CPPS) relies on the collaborative work of multiple and diverse teams of engineers who need to exchange and synchronize data created by their domain-specific tools. Building applications that support the CPPS engineering process requires technologies that enable integrating and making sense of heterogeneous datasets produced by various engineering disciplines. Are Semantic Web technologies suitable to fulfill this task? This chapter aims to answer this question by introducing the reader to key Semantic Web concepts in general and core Semantic Web technologies (SWT) in particular, including ontologies, Semantic Web knowledge representation languages, and Linked Data. The chapter concludes this technology overview with a specific focus on core SWT capabilities that qualify them for intelligent engineering applications. These capabilities include (i) formal and flexible semantic modeling, (ii) intelligent, web-scale knowledge integration, (iii) browsing and exploration of distributed data sets, (iv) knowledge quality assurance and (v) knowledge reuse.
Conference Paper
Household appliances are set to become highly intelligent, smart and networked devices in the near future. Systematically deployed on the Internet of Things (IoT), they would be able to form complete energy consuming, producing, and managing ecosystems. Smart systems are technically very heterogeneous, and standardized interfaces on a sensor and device level are therefore needed. However, standardization in IoT has largely focused at the technical communication level, leading to a large number of different solutions based on various standards and protocols, with limited attention to the common semantics contained in the message data structures exchanged at the technical level. The Smart Appliance REFerence ontology (SAREF) is a shared model of consensus developed in close interaction with the industry and with the support of the European Commission. It is published as a technical specification by ETSI and provides an important contribution to achieve semantic interoperability for smart appliances. This paper builds on the success achieved in standardizing SAREF and presents SAREF4EE, an extension of SAREF created in collaboration with the EEBus and Energy@Home industry associations to interconnect their (different) data models. By using SAREF4EE, smart appliances from different manufacturers that support the EEBus or Energy@Home standards can easily communicate with each other using any energy management system at home or in the cloud.
Speech Emotion Recognition (SER) is a hot research topic in the field of Human Computer Interaction (HCI). In this paper, we recognize three emotional states: happy, sad and neutral. The explored features include: energy, pitch, linear predictive spectrum coding (LPCC), mel-frequency spectrum coefficients (MFCC), and mel-energy spectrum dynamic coefficients (MEDC). A German Corpus (Berlin Database of Emotional Speech) and our self-built Chinese emotional databases are used for training the Support Vector Machine (SVM) classifier. Finally results for different combination of the features and on different databases are compared and explained. The overall experimental results reveal that the feature combination of MFCC+MEDC+ Energy has the highest accuracy rate on both Chinese emotional database (91.3%) and Berlin emotional database (95.1%).