Sep 2006 - Nov 2015
University of Oulu · Department of Computer Science and Engineering
Project addresses digitalization challenges cities are facing. Objectives are: - Define an open and operator independent data integration platform and a coherent reference architecture. - Specify system interfaces. - Build-up an IoT piloting environment. - Activate SME’s to pilot their solutions and technologies. - Enable exploitation of IoT information. - Support development of new wireless technologies. - Speed-up execution of national and international strategies to enhance digitalization in Finland. What the project brings to companies: - Companies can have influence on data integration platform definition by specifying their needs and requirements of existing IoT solutions and technologies. - Companies can utilize IoT pilot environments for product development and testing of technologies. Companies’ needs are taking into account when selecting and building pilot environments. - Co-operation between companies from different fields, cities and research organisations create new product ideas and bring new business opportunities. - Replacing vendor spesific solutions with open IoT platform enable co-operation and joint product development between companies and enable for small companies to offer larger solutions by partnering.
Research Items (38)
The development of Internet of Things (IoT) applications can be facilitated by encoding the meaning of the data in the messages sent by IoT nodes, but the constrained resources of these nodes challenge the common Semantic Web solutions for doing this. In this article, we examine enabling technologies for adding semantics to the IoT. Especially, we analyze data formats, which enable IoT applications consume semantic IoT data in a straightforward and general fashion, and evaluate resource usage of different alternatives with a sensor system. Our experiment illustrates encoding and decoding of different data formats and shows how big a difference a data format can make in energy consumption.
The forthcoming ambient systems will contain a large amount of sensors. Representing the data produced by these sensors in a format suitable for ambient intelligence applications would enable a large number of useful services. However, such formats tend to require processing power and communication bandwidth not available in many sensors utilizing ultra low-power microcontrollers and radio chip solutions. This paper presents a lightweight data representation, Entity Notation, to tackle this problem. Sensors with limited computation and communication capabilities can use Entity Notation to describe the data they produce. Entity Notation can be transformed into knowledge representations in a straightforward manner, and hence, the data produced by sensor nodes can be utilized with ease by any ambient intelligence system compatible with the common knowledge representations. This paper presents the design of Entity Notation, its implementations on embedded sensors and the evaluation of its performance. KeywordsLightweight knowledge representation–Embedded sensing–Semantic Web technologies–RDF
The development of ambient social applications brings challenges to aggregate information from heterogeneous sources, like users, physical environments, and available services. We propose a framework for aggregating information from different sources, and utilize a novel representation, Entity Notation (EN), as a starting point of connecting all information to knowledge-based systems, which offers good possibilities to support ambient social intelligence. In this paper, we present the framework, our EN representation, and an implementation of a map reminder service to demonstrate the usability of our framework.
Computational offloading can improve user experience of mobile apps through improved responsiveness and reduced energy footprint. Currently, offloading decisions are predominantly based on profiling performed on individual devices. While significant gains have been shown in benchmarks, these gains rarely translate to real-world use due to the complexity of contexts and parameters that affect offloading. We contribute by proposing crowdsensed evidence traces as a novel mechanism for improving the performance of offloading systems. Instead of limiting to profiling individual devices, crowdsensing enables characterising execution contexts across a community of users, providing better generalisation and coverage of contexts. We demonstrate the feasibility of using crowdsensing to characterize offloading contexts through an analysis of two crowdsensing datasets. Motivated by our results, we present the design and development of EMCO toolkit and platform as a novel solution for computational offloading. Experiments carried out on a testbed deployment in Amazon EC2 Ireland demonstrate that EMCO can consistently accelerate app execution while at the same time reduce energy footprint. We demonstrate that EMCO provides better scalability than current cloud platforms, being able to serve a larger number of clients without variations in performance. Our framework, use cases, and tools are available as open source from github.
With the popularization of intelligent transport and mobile internet services, vehicles and people on board generate increasing amounts of data. To match future networks with this use case, tools are needed to analyze the requirements set for the network. In this paper, we study the characteristics of data traffic in the context of networked vehicles. We generate data traffic based on real-world vehicle traces and reported data patterns of end-user applications and vehicles. Based on this, we propose a two-level hidden Markov model to describe both large and small temporal characteristics of data traffic from vehicles aggregated on base stations. We evaluate the proposed model by comparing the original and synthesized data. The results show that the proposed model can well characterize the data traffic from vehicles.
Information-centric networking (ICN) technology is becoming a popular research topic in vehicular networks due to the connectionless and lightweight characteristics of this networking paradigm. Caching plays an essential role in information-centric networks, but current caching techniques for ICN are not ideal for the dynamic and wireless vehicular networks. This paper presents a caching approach for ICN-based vehicular networks that takes into account both the dynamicity of vehicular networks and the popularity of the information being distributed. We introduce an interval metric for selective caching. With this metric and estimates of information popularity and vehicle density, cooperative caching can be realized without exchanging cache management information among the vehicular nodes. Simulation results show that the proposed approach can increase the storage space utilization and has low data response time for vehicular networks.
This paper presents our research in developing a model for the dynamic generation of indoor maps with crowdsourcing. With approximation of the user traces, we generate a point cloud and develop the topology of the space from time based segmentation of the traces. Moreover, we add semantic information for navigation and localization enabled maps. We discuss motivation, research objectives, and detailed research methods in this paper.
Information-centric networking (ICN) is being applied to the vehicular networks by more and more researchers on account of its lightweight and connectionless networking paradigm and in-network caching characteristics, making it suitable for the dynamic environments of vehicular networks. However, wireless transmission of interest packets to find content in the network may lead to broadcast storms that can affect the performance of information dissemination severely. This paper proposes a distance assisted data dissemination method with broadcast storm suppressing mechanism (DASB) for supporting rapid and efficient information dissemination in ICN-based vehicular ad hoc networks (VANETs). Geo-position data of vehicles are used to accelerate packet forwarding, and vehicular nodes in certain areas are restricted to forward packets in order to suppress the broadcast storm. Simulation results show that the proposed method can greatly reduce the total number of packets transmitted in the network, and the successful information delivery ratio and information delivery time can also be improved.
Edge computing paradigm moves computation from the Cloud to the edge of the network. We study the benefits of computing at the edge with semantic reasoning. We present our experiments on deploying semantic reasoners on edge nodes and perform reasoning latency and scalability analysis with a real-world smart city scenario.
Health applications involve many data sources, individuals, and services that work against guarantees that an individual's personal data will not be used without consent. The proposed privacy-centered architecture integrates data security and semantic descriptions into a trust-query framework, enabling the provision of user consent as a service.
Privacy is a key challenge for continued digitalization of health. The forthcoming European General Data Protection Regulation (GDPR) is transforming this challenge into regulatory directives. User consent provisioning and coordinating across data services will be the keys in addressing this challenge. We suggest a privacy-driven architecture that provides tools for providing user consent as a service. This enables managing and reusing private health information between a large amount of data sources, individuals and services, even when they are not known beforehand. The proposed architecture integrates data security and semantic descriptions into a trust query framework to provide the required interoperability and co-operation support for future health services. This approach provides benefits for all stakeholders through safer data management, cost and process savings, multi-provider services, and services based on emerging new business models.
Advances in ICT are bringing into reality the vision of a large number of uniquely identifiable, interconnected objects and things that gather information from diverse physical environments and deliver the information to a variety of innovative applications and services. These sensing objects and things form the Internet of Things (IoT) that can improve energy and cost efficiency and automation in many different industry fields such as transportation and logistics, health care and manufacturing, and facilitate our everyday lives as well. IoT applications rely on real-time context data and allow sending information for driving the behaviors of users in intelligent environments.
Nowadays, we experience an abundance of Internet-of-Things (IoT) middleware solutions that provide connectivity for sensors and actuators to the Internet. To gain a widespread adoption, these middleware solutions, referred to as platforms, have to meet the expectations of different players in the IoT ecosystem, including device providers, application developers, and end-users, among others. In this article, we evaluate a representative sample of these platforms, both proprietary and open-source, on the basis of their ability to meet the ecosystem expectations. The evaluation is completed by a gap analysis of the current IoT landscape with respect to (i) the support of heterogeneous hardware, (ii) the capabilities of the platform for data management, (iii) the support of application developers, (iv) the extensibility of the different platforms for the formation of ecosystems, as well as (v) the availability of dedicated marketplaces to the IoT. The gap analysis aims to highlight the deficiencies of today's solutions to improve their integration to tomorrow's ecosystem. Based on the result of the analysis, we conclude this article with a list of recommendations for extending these IoT platforms in order to fill in the gaps.
This document regroups a representative, but non-exhaustive, list of contemporary IoT platforms. The platforms are ordered alphabetically. The aim of this document is to provide the a quick review of current IoT platforms, as well as relevant information.
Applying Semantic Web technologies to Internet of Things (IoT) enables smart applications and services in a variety of domains. However, the gap between semantic representations and data formats used in IoT devices introduces a challenge for utilizing semantics in IoT. Sensor Markup Language (SenML) is an emerging solution for representing device parameters and measurements. SenML is replacing proprietary data formats and is being accepted by more and more vendors. In this paper, we suggest a solution to transform SenML data into a standardized semantic model, Resource Description Framework (RDF). Such a transformation facilitates intelligent functions in IoT, including reasoning over sensor data and semantic interoperability among devices. We present a fishery IoT system to illustrate the usability of this approach and compare the resource consumptions of SenML against other alternatives.
Semantic technologies could facilitate realizing features like interoperability and reasoning for Internet of Things (IoT). However, the dynamic and heterogeneous nature of IoT data, constrained resources, and real-time requirements set challenges for applying these technologies. In this paper, we study approaches for delivering semantic data from IoT nodes to distributed reasoning engines and reasoning over such data. We perform experiments to evaluate the scalability of these approaches and also study how reasoning is affected by different data aggregation strategies.
In this paper, we identify ambient social interactions as an important direction of research in Ambient Intelligence. To enable intelligence for ambient social interactions, we build knowledge-based systems to aggregate information from heterogeneous sources, including physical environments, users' social tasks and available services. Such knowledge-based systems enable intelligence not only for individuals, but also for user groups. We propose a general framework for aggregating information from different sources. A novel representation, Entity Notation (EN), is suggested as a starting point for the purpose of connecting all information to knowledge-based systems, which, in its turn, offers good possibilities to support Semantic Web technologies for development of smart functions for ambient social interactions. To demonstrate the usability of our framework and representation, we present an implementation of a map reminder service, and report analysis based of it.
Recently, we have been witnessing how various social applications and networking services are being integrated more deeply into our daily lives. Until now, social interaction has been attributed exclusively to humans, while resources and the smart space have supported interaction as passive mediators only. However, the involvement of smart spaces as an active actor in the interaction process facilitates more flexible and user-centered applications for users. This article explores how knowledge-based technologies enable smart spaces to actively take part in the interaction. We argue that smart spaces should be able not only to adapt their behaviour according to the actions of humans and other participants, but also initiate interaction when it is necessary. In order to support this statement, we categorise the types of interaction from the participants' perspective, and review and evaluate the technologies enabling interaction in smart spaces. Furthermore, we present our constructive research on interaction in smart spaces: proof-of-concept prototype applications realizing different architectures and supporting various types of interaction in smart spaces.
Purpose – Context-awareness is an essential property of any pervasive system perceiving its environment. Such a system captures and processes context, i.e. the features describing the relevant aspects of environment state and user behaviour. However, development of these systems still requires solving a number of research and engineering challenges. The purpose of this paper is to propose perception framework, a RESTful middleware which simplifies and accelerates the development of pervasive systems. Perception framework allows constructing services' application logic using rules and context. Moreover, it collects sensor data and produces the context information that is required for the rules. The authors present the architecture, design, complete implementation, and prototype-based verification of perception framework. Design/methodology/approach – Development of context-aware services is achieved with a novel architecture supporting building of the logic of web services using rules which directly manipulate the available elementary context represented with the Web Ontology Language (OWL) ontology. These rules are described using the Rule Interchange Format (RIF) with support for different rule languages. The implementation of this framework is aligned with RESTful principles, providing a lightweight and flexible solution for large-scale context-aware systems. Findings – The fully implemented prototype verifies the feasibility of constructing the logic of context-aware web services with the rules supported by perception framework. Originality/value – The contributions of this paper include: the requirement specification for a generic context-aware pervasive middleware; and the design and implementation of the framework (i.e. perception framework) supporting the development of context-aware web services. The perception framework includes a generic rule-based reasoner allowing developers to use several RIF-compliant rule description languages.
Pervasive Service Computing for Elderly applies service composition and pervasive computing into assisting elderly Activities of Daily Life. Taking advantages of context-awareness and service-oriented computing, Pervasive Service Computing expects to bring brilliant opportunities for pursuing global successful ageing. This paper proposes a Pervasive Service Computing for Elderly (PSC4E) framework for improving Quality of Life of elderly people, through providing being-, becoming-, and belonging-based services in context of population ageing trends, an elderly service provisioning model, and related studies.
With the advancement of mobile devices' capabilities, it is possible to implement knowledge-based systems on the mobile devices. This development introduces a challenge of transferring knowledge between mobile devices and knowledge-based systems on the server side. This paper presents a novel representation, Entity Notation, to tackle this challenge. It can represent ontology knowledge in a straightforward fashion and allows incremental transfer of ontology. This unique feature makes Entity Notation an ideal solution for transferring knowledge in highly dynamic ubiquitous environments. Moreover, Entity Notation has a short format suitable for communication when resources are constrained. We address the design issues of the representation, demonstrate its usability by a small ontology, and evaluate it based on a set of ubiquitous ontologies.
In this paper, we present a two-layer inference framework to enable Semantic Web technology-based intelligent functionalities in ambient environments.The basic idea is that the low level inference is performed on themobile devices capable of utilizing ontology, and only the high level inference is performed at the server side. This paper presents the design of this framework and illustrates its usability by a use case. The framework fully utilizes the computing capabilities of devices in the system and this way minimizes the communication among devices.
This paper proposes context modelling and reasoning to enable intelligent services in a ubiquitous campus. An ontology-based modelling includes upper level context modelling and domain-specific modelling for the campus area. Ontological and rule-based inferencing, which facilitate ubiquitous functionality for daily life, are implemented by utilizing the context model developed. A student assistant scenario is presented, demonstrating the usefulness of ontological context modelling and reasoning for highly distributed environments, such as a university campus.
Pervasive Service Composition (PSC) incorporates service composition and pervasive computing into managing user's everyday activities. A generic Reference Model of Pervasive Service Composition (PSC-RM) is needed for guiding PSC architecture design and implementation. To design PSC-RM, we first investigate and present a user's generic activity model. Then we analyze characteristics of PSC and envision PSC applications. Based on these applications we present requirements and initial design of PSC-RM.
In this paper, we present our work towards achieving context-awareness in mobile devices by combining Semantic Web technology with sensory data. Our investigation shows that some context data pertaining to the user, such as location, time, and physical surroundings, is vital for the realization of intelligent maps. Hence, embedding context-awareness into intelligent maps may prompt the usability of mobile map applications. Aiming at this goal, we suggest Semantic Web technology-based solution. We present a data representation, Entity Notation, to connect sensors to Resource Description Framework (RDF), the basis of Semantic Web data, in the data interchange level. At the same time, our data representation is lightweight enough that any resource-constrained sensors can support and process it. Ontology and ontology-based inference engine are developed to reason on the sensory data. Finally, intelligent maps could utilize its inference output to achieve context-awareness. We demonstrate our methods with a simulator and discuss the future work.
Semantic Web technology could offer lots of intelligent functionality to multi-robot systems. But limited processing power and storage capability of unsophisticated robots do not necessary allow them to support and process Semantic Web technology. In this paper, we propose a novel solution to provide semantic support for resource-constrained robots. Entity Notation is a lightweight data representation which can be employed to transfer data between resource-constrained robots and intelligent applications at server side. Resources-constrained robots only need to handle the lightweight Entity Notation while intelligent applications handle the more advanced knowledge representation. When the Entity Notation is used, the transfer between the robots and applications is unambiguous and lossless. In this way, an ontology and ontology-based inference at server side can improve the capabilities of the robot swarm. We present a simulator and discuss the future work.
This paper describes our studies in controlling browser ap-plications with an accelerometer based continuous hand gesture recogni-tion system. The system is based on hidden Markov models and includes novel contributions in the areas of tilt compensation (featurization) and gesture segmentation. The efficacy of the contributions is demonstrated, reaching a cross validated (50% fold) recognition rate of about 99% when training data was gathered from a single user. We report the results of a three-stage user study measuring the intuitivity (how well the system fits user expectations), usability and correctness of the system. Our findings suggest that gesturing is a promising alternative interaction mechanism for non-pointing, intermediate paced applications.
This paper focuses on connecting resource constrained robots to knowledge-based systems. These systems would enable various useful applications but on the other hand they present challenging requirements to the robots. The limited bandwidth, processing power, and storage capabilities do not allow the rich knowledge representations to be communicated, processed, nor stored by the robots. We suggest the Entity Notation for tackling this challenge. This representation is light-weight, so it can be handled by the robots. On the other hand, it can be transformed in an unambiguous fashion into the rich representations used by the knowledge systems. We present some preliminary experiments and discuss the future work.
Aimming at the difficulty in getting semantic information from each problem in problem set archives, We propose a new method of ontology-based semantic annotation for problem set archives, which utilizes programming knowledge domain ontology to add semantic annotations to problems in the Web. The system we developed adds semantic annotation for each problem in the form of extensible Makeup Language. Our method overcomes the difficulty of extracting semantics from problem set archives and the efficiency of this method is demonstrated through a case study. Having semantic annotations of problems, a student can efficiently locate the problems that logically correspond to his knowledge.
For students in computer science, knowledge of programming domain needs to be organized and shared efficiently. In this paper, we propose a method to present programming domain knowledge with ontology. We develop the programming knowledge domain ontology analogous to a dictionary. Therefore, the programming domain knowledge becomes machine-understandable and thus can be shared and reused among different implementations.
In this position paper, we present our work towards design-ing a Semantic Web languages-compatible representation for networked sensors. The representation, Entity Notation, is proposed to connect sen-sors to the Semantic Web. Entity Notation can express RDF and OWL ontology models in a uniform format. Meanwhile, it offers a lightweight alternative for sensors with limited computation and communication ca-pabilities. We present motivation and design issues of Entity Notation in this paper.