Mid Sweden University

Although Internet-of-Things (IoT) is revolutionizing the IT sector, it is not mature yet as several technologies are  still being offered to be candidates for supporting the backbone of this system. IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) is one of those promising candidate technologies to be adopted by IoT and Industrial IoT (IIoT). Attacks against RPL have shown to be possible, as the attackers utilize the unauthorized parent selection system of the RLP protocol. In this work, we are proposing a methodology and architecture to detect intrusions against IIoT. Especially, we are targeting to detect attacks against RPL by using genetic programming. Our results indicate that the developed framework can successfully (with high accuracy, along with high true positive and low false positive rates) detect routing attacks in RPL-based Industrial IoT networks.

In the rapidly evolving cybersecurity landscape, detecting and preventing network attacks has become crucial within the industrial sector. This study aims to explore the potential of intrusion detection by employing deep learning within edge computing, especially for the Industrial Internet of Things. Specifically, TinyML converted CNN, LSTM, Transformer-LSTM, and GCN models on the UNSW-NB15 dataset. A comprehensive dataset analysis gained insights into the nature of attack behavior data. Subsequently, a comparative analysis in an edge computing setup using Raspberry Pi units revealed that the GCN model, with its accuracy of 97.5%, was the best suited of the compared models for this application. However, the study also explored variables like time consumption, where the CNN model was the fastest out of the compared models. This research also highlights the need for continued exploration, especially in addressing dataset imbalances and enhancing model generalizability. By recognizing each model's strengths and areas of improvement, this research serves as a step toward bolstering digital safety and security in an increasingly interconnected industrial world.

The area of computer and electrical engineering is under constant evolution which leads to lifelong learning being an important aspect for being a long term successful working professional. This work presents an education model for creating customized and flexible courses at advanced level for these working professionals. We have approached this work from the educator's point of view and the focus will be on the teaching model and our results from implementing the model during the last two years. Including how we created these need and trend-based education offerings, the course execution inspired with micro-learning and flipped-classroom pedagogics, and our work with creating win-win possibilities within the courses for the working professionals and the companies they work for. Finally, we will present our experiences and lessons learnt, ending with a plan for our upcoming courses and our refined model in our ongoing future work.

This research report outlines the current research into blockchain technologies as of December 2018. Starting with an overview of the blockchain technology to introduce the theory. To then follow with deep looks into the different aspects of blockchain technologies, investigating the research articles and current state of work in these area, including the forefront of the research frontier. As well as existing blockchain products, an outlook on the future, conclusions that can be drawn from this study. With the intent to both create understand of the technologies, as well as inspire further research work and identifying potential knowledge gaps.

Mobile devices with Internet access and large amounts of sensors, pushes the development of intelligent services towards new forms of pervasive applications. These applications are made context-aware by utilizing information from sensors and hence the context of a situation, in order to provide a better service. Based on this, the focus of this thesis is on the challenge of creating context awareness in mobile applications. That both utilizes dynamic context information from globally available sensors and provides adaptive views of relevant context information to applications.

The first challenge is to identify the properties of an architecture that provides scalable access to information from global sensors within bounded time, because existing systems do not support these properties in a satisfactory manner. The majority of related systems employ a centralized approach with limited support for global sensor information due to poor scalability. Therefore, this thesis proposes a distributed architecture capable of exchanging context between users and entities on a peer-to-peer overlay. Pervasive applications can thus utilize global sensor information in a scalable and manageable way within predictable time bounds.

The second challenge to support continually changing and evolving context information, while providing it as both adaptive and manageable views to applications. To address this particular problem, this thesis proposes the usage of a locally stored evolving context object called a context schema. In detail, this schema contains all context information that is considered as being relevant for a specific user or entity. Furthermore, this thesis proposes an application interface that can provide snapshots of the evolving context schemas as adaptive views. These views can then be used in context-aware mobile applications, without inducing unnecessary delays.

By successfully addressing the challenges, this thesis enables the creation of pervasive and adaptive applications that utilize evolving context in mobile environments. These capabilities are made possible by enabling access to global sensor information based on a distributed context exchange overlay, in combination with evolving context schemas offered as views through an application interface. In support of these claims, this thesis has developed numerous proof-of-concept applications and prototypes to verify the approach. Hence, this thesis concludes that the proposed approach with evolving context information has the ability to scale in a satisfactory manner and also has the ability to dynamically offer relevant views to applications in a manageable way.

Vi har under de senaste två åren arbetat med att gespecialanpassade kurser på avancerad nivå för yrkesverksammaingenjörer, specifikt inom områdena datateknik ochelektronik/elektroteknik. Tanken med denna artikel är att berättaom våra utmaningar, lärdomar, vårt upplägg, och våra framtidasatsningar inom vidareutbildning för yrkesverksamma. Dettaprojekt blev extra viktigt nu under COVID-19 pandemin då detblev en våg av permittering, varsel, och svårare arbetsmarknadför ingenjörer och data/elektro-tekniker.

We see a shift from todays Internet-of-Things (IoT)to include more industrial equipment and metrology systems,forming the Industrial Internet of Things (IIoT). However, thisleads to many concerns related to confidentiality, integrity,availability, privacy and non-repudiation. Hence, there is a needto secure the IIoT in order to cater for a future with smart grids,smart metering, smart factories, smart cities, and smart manufacturing.It is therefore important to research IIoT technologiesand to create order in this chaos, especially when it comes tosecuring communication, resilient wireless networks, protectingindustrial data, and safely storing industrial intellectual propertyin cloud systems. This research therefore presents the challenges,needs, and requirements of industrial applications when it comesto securing IIoT systems.

This research presents a solution for collecting indoor environmental sensor values and how the gathered sensor values then could be used for green building certification and in turn also machine learning based smart building control. We have created and implemented a proof of concept system consisting of a sensor collecting device using off the shelf hardware to complement the existing sensor information from buildings, as well as a cloud system for persistently storing this data for later usage. We have measured and evaluated our implemented system for our envisioned scenarios. In which we could observe that our proof-of-concept could scale to handle almost four sensor value updates per second at maximum stress, as well as having a latency for uploading a sensor value from our sensor of about 130 ms. Finally, we present our future and ongoing work based on these results which outlines our work for smart building control, green building certification, and the energy signature of buildings. 

Vi har tagit fram en modern läskurs i seminarieform, där studenterna själva sätter upp ett självstudieprojekt genom att dra nytta av MOOC-kursmaterial. Vår kurs avser att ge civilingenjörsstudenter erfarenhet av denna typ av online-kursmaterial och på så sätt förbereda dem på ett framtida livslångt lärande. Kursen ger även fördjupningsmöjligheter inom många fler områden än lärosätet kan erbjuda konventionella kurser. Vidare syftar kursen till att förmedla både teoretiska kunskaper och praktiska färdigheter som förberedelse inför utbildningens avslutande självständiga arbete, samt att möjliggöra specialisering som start på en framtida karriärväg. Vi har med små lärarinsatser skapat detta kursupplägg, genom att tillämpa studentcentrerat lärande och flipped classroom-tekniker. Studenterna kompletterar ofta MOOC-kurserna med djupare teoriuppgifter, positionerar ämnet vetenskapligt, och reflekterar över sitt lärande i en slutrapport. Vi examinerar hur väl de har planerat och genomfört sina självstudier. Kursen har erbjudits för fyra årskullar. Genomströmning har varit 93% och den har fått goda omdömen i kursutvärderingar. Eftersom studenterna kan välja mellan ett stort antal MOOC kurser som ständigt utvecklas, och studenterna brukar välja aktuella ämnen, så håller kursen sig själv uppdaterad inför framtida trender. Vi lärare behöver endast följa med på resan och får kompetensutveckling inom nya områden på köpet.

I dagens samhälle blir det allt viktigare att fortbilda sig under hela sitt yrkesverksamma liv. För att möta efterfrågan på det livslånga lärandet har Mittuniversitetet utvecklat och genomfört ett antal kurser som riktar sig mot yrkesverksamma ingenjörer. Detta arbete presenterar våra erfarenheter av att ge dessa kurser, med en tyngdpunkt på studenternas upplevelser. Syftet med detta är att bygga upp en vetenskaplig bas för vad vi gör som är bra, men även vad som kan förbättras och förändras. Målsättningen är att göra dessa specialanpassade kurser riktade mot yrkesverksamma ingenjörer så givande och flexibla som möjligt. Våra initiala resultat visar bland annat att studenternas negativa upplevelser ofta var kopplade till antagningsförfarandet och det praktiska genomförandet av kurserna. Man hade svårigheter med att hitta hur man skulle registrera sig på kursen och att tidsramen för registrering kunde vara ett problem. Läroplattformen uppfattades som svår att överblicka och det förekom även viss otydlighet gällande var undervisningen skulle äga rum. Den positiva responsen i utvärderingarna gällde främst det faktiska kursinnehållet, då man ansåg att uppgifter och kursmaterial var givande. Vidare uppskattades kursupplägget, att man kunde kombinera studierna med arbete. Framledes kommer vi att fortsätta med dessa utvärderingar i takt med att kurserna ges, och därefter anpassa vårt mottagande och kommunikationen med studenterna. Även kursupplägget ses över kontinuerligt via den återkoppling vi mottar. 

The need for better utilization of resources and faster response times in Internet of Things applications has highlighted the need for more suitable Cloud/Fog/Mist architecture archetypes. Hence, this article presents an implemented testbed for one such architecture, realized with readily available off the shelf hardware. The contribution in this article is the resulting testbed system and our evaluation thereof, in which we evaluate how well it will work for typical IoT application scenarios. Including initial latency measurements of the components within the testbed. Ending with our plan on how to extend the testbed in the future, and an outlook on our future work in the testbed.

The expected monetary costs and response time performance is important information to know before deploying an industrial Internet-of-Things system. The system investigated in this paper is built up by combining the Open Plant Communication Universal Architecture and the Microsoft Azure Internet-of-Things Hub. We consider a real life industrial scenario that remotely monitors a piece of industrial equipment consisting of 1500 sensors, of which 600 update their values every second. The measured results show that the response times for a fiber based Internet connection was on average 770 ms with a standard deviation of 330 ms and the same measurements on a mobile internet connection was on average 1150 ms with a standard deviation of 340 ms. Cost wise, the investigated scenario fits within the most expensive price plan for the IoT hub and ends in a total of just over $5000 per month for the whole cloud service.

This article presents a system for creating an implemented open energy market for future smart grid systems, microgrids, the Internet of Things, and the Industrial Internet of Things. We also investigate and analyze different aspects of blockchain technologies for this purpose in order to choose the appropriate methods, technologies, and approaches for realizing and implementing the system as an open source project. Based on this analysis, we present our resulting functional implementation of this energy market. Including measurements and a quantitative evaluation of the implementation itself. Showing the potential of the approach, where this implantation can be applied, showing its drawbacks, and finally our planned future work.

Many small and medium enterprises have existing products that they want to innovate on and move into the new era of Internet of Things. Because of this, we have developed a plain Internet of Things platform aimed towards businesses who wants to try out these new systems but does not have the resources to develop or maintain a full platform from the traditional providers. We have developed an open source solution that can be run on cheap off the shelf hardware and employs today's de facto standards for Internet of Things systems. Our solution also provides adequate functionality for an initial effort and innovation, including taking security and privacy concerns into consideration. We have also evaluated our implementation in terms of scale, which shows that the system scales well in scenarios with up to 50 sensor values per second. Which should be enough for a small business first effort to try out their products as an Internet of Things service. 

Research and industry invest time and resources in producing Internet of Things-based services, and the concept of Internet of Things platforms is climbing in the hype cycle for emerging technologies. Consequently, there is a vast number of enabling technologies, making it difficult to find the most suitable platform. The aim and goal of this article is to list and identify the currently available communication platforms for the Internet of Things. In this work, we surveyed the area and found over \numTot different platforms for communication of data between things and services, out of which \numProm fulfilled our listed basic requirements for being an IoT communication platform.

This article analyzes the challenges of supportingcontinual changes of context information in Internetof-Things applications. These applications require aconstant flow of continuously changing information fromsensor based sources in order to ensure a high quality-ofexperience.However, an uncontrolled flow between sourcesand sinks on a global scale wastes resources, such ascomputational power, communication bandwidth, andbattery time. In response to these challenges we presenta general approach which focuses on four layers wherewe provide a proposed solution to each layer. We haverealized the general model into a proof-of-concept implementationrunning on devices with limited resources,where we can moderate the information exchange basedon relevance and sought after quality-of-experience bythe applications. In conclusion, we evaluate our solutionand present a summary of our experiences regardingthe impact of continuously changing information on theInternet-of-Things.

Context-aware applications require local access to current and relevant views of context information derived from global sensors. Existing approaches provide only limited support, because they either rely on a network broker service precluding open-ended searches, or they adopt a presence model which has scalability issues. To this end, we propose a fully distributed architecture employing context user-agents co-located with data-mining agents. These agents create and maintain local schemas using ranking of global context information based on context proximity. Continually evolving context information thus provides applications with current and relevant context views derived from global sensors. Furthermore, we present an evaluation model for assessing the effort required to present local applications with current and relevant contextual views. We show in a comparison with earlier work that the approach achieves the provisioning of evolving context information to applications within predictable time bounds, circumventing earlier limitations.

The increase in sensor-assisted applicationssuch as sensing campaigns and interactions in large-scalepopulations put new demands on seamless sharing ofsensor information on a global scale. Existing support islimited due to simplified information models reflectingnarrow application scenarios and poorly scaling architectures.In response to this, we present an application architecturefor pervasive internet-of-things applications whichcircumvents these limitations. Our architecture enablesapplications to utilize information from sensors and wirelesssensor networks via a peer-to-peer overlay. Theoverlay shares sensor information in an generic informationmodel, which is extensible and thus enables intelligentapplication behavior. Our evaluation of the architectureindicates that it supports the sharing of sensor informationon a global scale in sensor-assisted applications, with lowresponse times. Finally, we evaluate the feasibility ofrunning such applications in end devices with limitedresources in a sensor-assisted application prototype.

Existing sensor-assisted mHealth applications wouldbenefit from large-scale sharing of sensor information in realtime.Existing communication solutions are however limited inthis respect, because of centralized application-level communication.In response to this, we presents a distributed communicationsolution for mHealth applications which circumvents these limitations.Our Internet-of-Things architecture enables mHealth applicationsto utilize information from sensors and wireless sensornetworks via a peer-to-peer overlay, where sensor information isorganized in an information model which is stored in the overlayitself. We present a proof-of-concept application and evaluationresults regarding the architecture’s real-time capabilities. Theresults indicate that a fully distributed architecture can supportreal-time sensing in mHealth applications and the support isavailable as an open source platform, MediaSense. Current workis focused on evaluating scalability in very large scale scenariosusing field trials.

Intelligent artifacts are real-world objects enhancedwith capabilities in order to display relevant behavior in varioustypes of context-aware applications, such as in mHealth,commerce, or pervasive games. This can be achieved by attachingsensors and store associated information on the Internet.Interaction with such artifacts requires secure communication,to protect personal and private information. This mandatesresearch in how to safeguard interactions via heterogeneousmeans of communication involving interconnected local and nonlocalartifacts. In response to these challenges, this paper presentskey schemes to secure interaction via heterogeneous means ofcommunication. In conclusion, the architecture can securelyauthenticate an intelligent artifact as well as securely exchangesensor information with other authenticated artifacts attached inan overlay. Our proof-of-concept application demonstrated in anInternet-of-Things platform validates the approach.

Current context-aware applications often use thelocation of a user as the only indication of the current situation.These existing applications are therefore limited in their situationawareness, because of the poor indoor resolution of the locationsensor and its high resource consumption. In response to theselimitations we present an approach to estimate the contextualsituation of a user without using resource inefficient locationsensors. Our proposed solution utilizes a wide range of lowpowered sensors, together with two modified machine learningtechniques to estimate the situation in a more resource efficientmanner. Simulations and a proof-of-concept application show thatthe situation of a user can be determined within 50 ms at anaccuracy above 90%, when only using the low energy sensorsavailable on a smartphone and its limited processing power.

There is currently a vast amount of sensors, actuators, and other devices intended to create the Internet-of-Things. However, most of these only operate within a particular manufacturer’s ecosystem and with a limited set of compatible hardware. In response to this we present a general sensor and actuator gateway for the Internet-of-Things, which provides the flexibility to easily exchange sensor and actuator hardware. The gateway is implemented as a proof-of-concept prototype that is verified to be compatible with a wide range of commercial off the shelf hardware. It is also shown to be able to run the SensibleThings platform, a fully distributed and open communication platform for Internet-Things applications.

Context-aware applications and services require ubiquitous access to context information of users. The limited scalability of centralized servers used in the provisioning of context information mandates the search for scalable peer-to-peer protocols. Furthermore, unnecessary signaling must be avoided in large-scale context networks, when location-based services only require nodes in a certain area with which to communicate context. To this end, we propose a lightweight model for composing and maintaining unstructured location-scoped networks of peer-to-peer nodes, which gossips in order to ensure quality of service for each user. The model is implemented in a prototype application running in a mobile environment, which is evaluated with respect to real-time properties. This model can also be extended to include more context dimensions, other than location.

The increasing ubiquity of context aware services and systems has been primarily underpinned by the use of centralised servers employing protocols that do no scale well for real time distribution and acquisition of neither sensor data nor dependent services.Any shift from this generic sensor framework mandated a new thinking where sensor data was capable of being propagated in real time using protocols and data models which serve to reduce unnecessary communication overhead. DCXP is proposed as an alternative architecture for the real time distribution of context information to ubiquitous mobile services.As a P2P based distributed protocol, it inherently poses the challenge of user anonymity across the system. In this paper we briefly present DCXP along with further work to enable the anonymised dissemination of sensor information within the architecture.Such a solution would have a negligible impact on the overall scalability and performance of DCXP.

The SensibleThings platform is an open source architecturefor enabling Internet-of-Things based applications.During its development, multiple problems have been faced andsolved, for example issues related to networking, information dissemination,sensors, and application access. This paper describesthese problems and the technical solutions that are implementedin the platform. We also present the current progress and aseries of demonstrator applications, which show the wide rangeof possibilities enabled by the platform. Finally, we present futurework and how it will be used in future research endeavors andcommercial interests.

This article explores a solution using fog computing and Internet of Things technologies to address some of the drawbacks with cloud computing. We show the potential in the combination of scalable distributed systems technologies, for enabling a distributed cluster using typical lightweight IoT devices. Answering research questions related to the expected overhead, as well as how work and chunk size affect the computational time and scalability of the system. All in order to evaluate the performance and potential of this technology for Industry 5.0 and future industrial IoT applications. Our results show the great potential of the approach, but we also identify necessary improvements that needs to be made in order to achieve performance superiority over the traditional cloud approaches and for the technology to truly proliferate.

In the realm of intelligent transportation systems, vehicle-to-infrastructure technology is a cornerstone for improving road safety. However, it also highlights the need for effective task offloading strategies across the edge-fog-cloud continuum. This paper presents a testbed for evaluating task allocation algorithms designed to optimize resource utilization while meeting real-time requirements. Therefore, a simulated V2I environment testbed has been developed to demonstrate and evaluate the potential of different algorithms to improve computational efficiency across the continuum. Our results therefore include an implemented testbed system to emulate, test, and evaluate different algorithms, laying a foundational step towards more sophisticated offloading strategies in the future. This will enable future research and further advances in optimizing communication and computation across the edge-fog-cloud continuum.

Next generation context-aware mobile applicationswill require a continuous update of relevant information about auser’s surroundings, in order to create low latency notificationsand high quality of experience. Existing mobile devices alreadycontain a large number of built in sensors which are capableof producing huge amounts of sensor data, exceeding boththe capacity of the local storage and the Internet connection.Therefore, we will in this paper study the limits when sharingcontextual information from mobile devices, as well as finding theimpact of this information overload for the Internet-of-Things.Furthermore, we present an evaluation model for assessing theeffort required to present applications with relevant contextinformation. In conclusion, the model shows that one feasiblesolution for the future Internet-of-Things is a peer-to-peer basedsolution which can control the flow of information without anycentralized authority, to circumvent earlier limitations.

Modern IT systems have been transitioning from traditional on-premises solutions to a dynamic mixture of on-premises and off-premises solutions. This transition has also included a trend to run more systems on software-defined resources. The ease of setting up new software-defined servers and systems has led to an increase in IT system complexity as well as the amount of log data generated. Automatic log analysis has become a subject of interest because of the problems with manual log analysis in case of intrusion detection and root-cause analysis. Therefore, this paper proposes and tests a sequence based anomaly detection method. The work has been done in collaboration with the Swedish Social Insurance Agency's IT department. Real system log data with high privacy requirements and limited available information was generated for training and testing. The generated log data was produced with expected time regions of anomalous behavior. Our proposed anomaly detection model was then able to perform at a state-of-the-art level and could accurately detect certain error types. Showing the potential of the approach when applied directly to a real-world system.

Modern enterprise IT systems generate large amounts of log data to record system state, potential errors, and performance metrics. Manual analysis of log data is becoming more difficult as these systems become more complex. Therefore, machine learning based anomaly detection of system logs is a vital component for the future of system management. Existing log anomaly detection models commonly rely on learning the general normal behavior of the target systems to accurately detect anomalies. They are however limited by the often sparse existing system knowledge. Therefore, this paper proposes a general anomaly detection method which requires little or no knowledge of the target system. This is done by assuming there are semantic similarities in different systems’ log data. Labeled log data from other systems can then be used for training the anomaly detection model. The model uses self-attention transformers and ensemble learning techniques to learn the semantic representation of normal and abnormal log messages. The proposed method achieves a performance comparable to other log anomaly detection methods while requiring little knowledge of the target system.

The evolution from local toward virtualized data storage, computing, applications and services – in the forms of Internet of Things (IoT), Everything as a Service (EaaS), and Cloud computing – has changed the way of delivering digital services for consumers and businesses. These technologies have brought clear benefits over traditional systems, such as easy management, universal availability, and decreased hardware requirements for end-user devices. The main challenges of the first-generation IoT services are related to communication latency due to high physical and logical distance between end nodes and server resources and vulnerability for network problems along the long routes. Thanks to the unveiling of the fifth-generation wireless technology (5G) for cellular networks, the last-mile connection performance – communication latency in particular – is taking a huge leap and therefore introducing new possibilities for industrial applications. Edge computing is a key technology to unleash the full potential of the arising industrial wireless communication, since it enables deploying computational tasks to computing nodes near the end devices and therefore opens novel business opportunities around real-time cloud services. In this chapter, we introduce the current state of the art and discuss different challenges the edge computing systems are facing particularly in the Industrial IoT (IIoT) domain, as well as present potential solutions for the identified challenges.

The Internet of Things is predicted to consist of over 50 billion devices aiming to solve problems in most areas of our digital society. A large part of the data communicated is expected to consist of various multimedia contents, such as live audio and video. This article presents a solution for the communication of high definition video in low-delay scenarios (<200 ms) under the constraints of devices with limited hardware resources, such as the Raspberry Pi. We verify that it is possible to enable low delay video streaming between Raspberry Pi devices using a distributed Internet of Things system called the SensibleThings platform. Specifically, our implementation transfers a 6 Mbps H.264 video stream of 1280 × 720 pixels at 25 frames per second between devices with a total delay of 181 ms on the public Internet, of which the overhead of the distributed Internet of Things communication platform only accounts for 18 ms of this delay. We have found that the most significant bottleneck of video transfer on limited Internet of Things devices is the video coding and not the distributed communication platform, since the video coding accounts for 90% of the total delay.

Research in Internet-of-Things infrastructures hasso far mainly been focused on connecting sensors and actuatorsto the Internet, while associating these devices to applicationsvia web services. This has contributed to making the technologyaccessible in areas such as smart-grid, transport, health, etc.These early successes have hidden the lack of support forsensor-based applications to share information and limitationsin support for applications to access sensors and actuatorsglobally. We address these limitations in an novel open-sourceplatform, MediaSense. MediaSense offers scalable, seamless,real-time access to global sensors and actuators via hetero-geneous network infrastructure. This paper presents a setof requirements for Internet-of-Things applications support,an overview of our architecture, and application prototypescreated in order to verify the approach in a test bed withusers connected from heterogeneous networks.

Users require applications and services to be available everywhere, enabling users to focus on what is important to them. Therefore, context information from users (e.g., spatial data, preferences, available connectivity and devices, etc.) needs to be accessible to systems that deliver services via a heterogeneous infrastructure. We present a novel approach to support ubiquitous sensing and availability of context to services and applications. This approach offers a scalable, distributed storage of context derived from sensor networks wirelessly attached to mobile phones and other devices. The support handles frequent updates of sensor information and is interoperable with presence services in 3G mobile systems, thus enabling ubiquitous sensing applications. We demonstrate these concepts and the principle operation in a sample ubiquitous mobile awareness service. The importance of this contribution, in comparison to earlier work, lies in the availability of real-time ubiquitous sensing to both applications on the Internet as well as applications in mobile systems.

This paper describes research issues and work-in-progress concerning ubiquitous sensing. We present scenarios where the current approaches are deficient in addressing the needs for ubiquitous sensing in services and applications on the Future Internet, involving the massive sharing of information from sensors via heterogeneous networks. We propose an information-centric architecture for real-time ubiquitous sensing which capitalizes on the proposed locator/identifier split, thus extending the Network of Information (NetInf) approach. From this we identify the challenges for which we present work-in-progress within the framework of the EU-funded MediaSense project. Firstly, we integrate sensors as addressable objects, exposed by means of sensor gateways and relocatable abstract interfaces. Sensor information is thus made available to applications solely based on identity. Secondly, sensor information is made available in a distributed data model towards searching and browsing. Finally, we evaluate the effectiveness of the architecture in proof-of-concept applications for intelligent commuting, environmental monitoring and seamless media transfer, utilizing two different sensor platforms.

Context-aware applications and services require ubiquitous access to context information about the users or sensors such as preferences, spatial & environmental data, available connectivity, and device capabilities. Systems for the brokering or the provisioning of context data via wireless networks do so with centralized servers or by employing protocols that do not scale well with real-time distribution capabilities. In other cases, such as the extending of presence systems, the data models are limited in expressive capabilities and consequently incur unnecessary signaling overhead. This paper presents a distributed protocol, the Distributed Context eXchange Protocol (DCXP), and an architecture for the real-time distribution of context information to ubiquitous mobile services: We present the architecture and its principle operation in a sample ubiquitous mobile awareness service. Preliminary results indicate that our approach scales well for the ubiquitous provision of context data in real-time to clients on the Internet via 3G wireless systems. Performed measurements show that DCXP can reduce the time to process context data with a factor of 20 compared to similar approaches.

Users require applications and services to be available everywhere,enabling them to focus on what is important to them. Therefore, contextinformation (e.g., spatial data, user preferences, available connectivity anddevices, etc.) has to be accessible to applications that run in end systems closeto users. In response to this, we present a novel architecture for ubiquitoussensing and sharing of context in mobile services and applications. Thearchitecture offers distributed storage of context derived from sensor networkswirelessly attached to mobile phones and other devices. The architecture alsohandles frequent updates of sensor information and is interoperable withpresence in 3G mobile systems, thus enabling ubiquitous sensing applications.We demonstrate these concepts and the principle operation in a sampleubiquitous Mobile Awareness service.

Mobile telecommunication is evolving rapidly. People no longer only communicate with each other regardless of time and place, but also share other information that is important for tasks with which they are involved. In response to this growing trend the MediaSense framework addresses the intelligent delivery of any information to any host, anywhere, based on context-aware information regarding personal preferences, presence information, and sensor values. This includes another challenge to achieve seamless delivery, especially of multimedia content, and multimodal services via heterogeneous connections.

Mobile telecommunication is evolving rapidly. People no longer only communicate with each other regardless of time and place, but also share other information that is important for the tasks with which they are involved. In response to this growing trend, the Media Sense framework addresses the intelligent delivery of any information to any host, anywhere, based on context-aware information regarding personal preferences, presence information, and sensor values. The focus of this paper is the overall framework, the handling of user profiles, management of context information, real-time distribution and proof of concepts.

Mobile telecommunication is evolving rapidly. People no longer only communicate with each other regardless of time and place, but also share other information that is important for the tasks with which they are involved. In response to this growing trend, the MediaSense framework addresses the intelligent delivery of any information to any host, anywhere, based on context-aware information regarding personal preferences, presence information, and sensor values. The focus of this paper is the overall framework, the handling of user profiles, management of context information, real-time distribution and proof of concepts. © 2010 IEEE.

The Internet-of-Things will require ubiquitous informationsharing between connected things on a global scale, whichexisting systems do not offer. Most current efforts focus on solutionsfor information dissemination, which induce single pointsof failure and introduce unnecessary communication delays. Tothis end we propose the SensibleThings platform, which is afully distributed open source architecture for Internet-of-Thingsbased applications. This article describes the major problems thatInternet-of-Things platforms must address, our technical solutionto these problems, and an evaluation thereof. We also present thecurrent progress and a series of demonstrators, which show thewide range of applications enabled by the platform. Finally, wepresent how the platform will be used in our future research andpotential spin off companies.

Blockchain, främst känd som teknologin bakom Bitcoin, kan användas till mycket mer än kryptovalutor. World Economic Forum har nyligen listat 65 exempel på hur tekniken kan bidra till att lösa olika miljöutmaningar och MIT:s tidskrift Technology Review har beskrivit hur blockchain används i ett flyktingläger för att människor ska kunna köpa förnödenheter utan att behöva vare sig identitetshandlingar eller kontanter. Med nya tekniker för att hantera mänskliga aktiviteter följer också nya former för dokumentation av dessa. I denna artikel kommer vi därför ge en översiktlig bild av hur blockchain kan användas inom arkivsektorn.

Digitalization is a global trend becoming ever more important to our connected and sustainable society. This trend also affects industry where the Industrial Internet of Things is an important part, and there is a need to conserve spectrum as well as energy when communicating data to a fog or cloud back-end system. In this paper we investigate the benefits of fog computing by proposing a novel distributed learning model on the sensor device and simulating the data stream in the fog, instead of transmitting all raw sensor values to the cloud back-end. To save energy and to communicate as few packets as possible, the updated parameters of the learned model at the sensor device are communicated in longer time intervals to a fog computing system. The proposed framework is implemented and tested in a real world testbed in order to make quantitative measurements and evaluate the system. Our results show that the proposed model can achieve a 98% decrease in the number of packets sent over the wireless link, and the fog node can still simulate the data stream with an acceptable accuracy of 97%. We also observe an end-to-end delay of 180 ms in our proposed three-layer framework. Hence, the framework shows that a combination of fog and cloud computing with a distributed data modeling at the sensor device for wireless sensor networks can be beneficial for Industrial Internet of Things applications.

News companies have a need to automate and make the process of writing about popular and new events more effective. Current technologies involve robotic programs that fill in values in templates and website listeners that notify editors when changes are made so that the editor can read up on the source change on the actual website. Editors can provide news faster and better if directly provided with abstracts of the external sources and categorical meta-data that supports what the text is about. In this article, the focus is on the importance of evaluating critical parameter modifications of the four classification algorithms Decisiontree, Randomforest, Multi Layer perceptron and Long-Short-Term-Memory in a combination with the paragraph vector algorithms Distributed Memory and Distributed Bag of Words, with an aim to categorise news articles. The result shows that Decisiontree and Multi Layer perceptron are stable within a short interval, while Randomforest is more dependent on the parameters best split and number of trees. The most accurate model is Long-Short-Term-Memory model that achieves an accuracy of 71%.

Multi linguistic problems such as auto-categorization is not an easy task. It is possible to train different models for each language, another way to do auto-categorization is to build the model in one base language and use automatic translation from other languages to that base language. Different languages have a bias to a language specific grammar and syntax and will therefore pose problems to be expressed in other languages. Translating from one language into a non-verbal language could potentially have a positive impact of the categorization results. A non-verbal language could for example be pure information in form of a knowledge graph relation extraction from the text. In this article a comparison is conducted between Chinese and Swedish languages. Two categorization models are developed and validated on each dataset. The purpose is to make an auto-categorization model that works for n'importe quel langage. One model is built upon LSTM and optimized for Swedish and the other is an improved Bidirectional-LSTM Convolution model optimized for Chinese. The improved algorithm is trained on both languages and compared with the LSTM algorithm. The Bidirectional-LSTM algorithm performs approximately 20% units better than the LSTM algorithm, which is significant.

 News companies have a need to automate and make the process of writing about popular and new events more effective. Current technologies involve robotic programs that fill in values in templates and website listeners that notify editors when changes are made so that the editor can read up on the source change on the actual website. Editors can provide news faster and better if directly provided with abstracts of the external sources and categorical meta-data that supports what the text is about. To make categorical meta-data a reality an auto-categorization model was created and optimized for Swedish articles written by local news journalists. The problem was that it was not scale-able enough to use out of the box. Instead of having this local model that could make good predictions of the text documents, the model is to be deployed in the cloud and an API interface is created. The API can be accessed from the tools where the articles is being written and therefore these services can automatically assign categories to the articles once the journalist is done writing it. To allow scale-ability to several thousands of simultaneously categorized articles and at the same time improving the workflow of deploying new models easier the API is uploaded to Sagemaker where several models are trained and once an improved model is found that model will be used in production in such a way that the system organically adapts to new written articles. An evaluation of Sagemaker API was done and it was concluded that the complexity of this solution was polynomial. 

Information extraction is a task that can extract meta-data information from text. The research in this article proposes a new information extraction algorithm called GenerateIE. The proposed algorithm identifies pairs of entities and relations described in a piece of text. The extracted meta-data is useful in many areas, but within this research the focus is to use them in news-media contexts to provide the gist of the written articles for analytics and paraphrasing of news information. GenerateIE algorithm is compared with existing state of the art algorithms with two benefits. Firstly, the GenerateIE provides the co-referenced word as the entity instead of using he, she, it, etc. which is more beneficial for knowledge graphs. Secondly GenerateIE can be applied on multiple languages without changing the algorithm itself apart from the underlying natural language text-parsing. Furthermore, the performance of GenerateIE compared with state-of-the-art algorithms is not significantly better, but it offers competitive results. 

In this paper we present an overview of the variousproximity based authentication mechanisms that can be usedin the Industrial Internet of Things (IIoT). We seek to identifyand highlight from a holistic point of view which mechanismscan enable proximity based authentication for the IndustrialInternet of Things. In addition, we identify which upcomingproximity authentication mechanisms are most important for theproliferation of the Industrial Internet of Things, and highlightmajor obstacles that remain unsolved with regard to authen-tication. In answering this, we present seven mechanisms forproximity based authentication (i.e. wire, radio, acoustics, light,image, gesture and biometrics) and discuss each mechanism inperspective of their vulnerability to different kind of attacks (suchas eavesdropping, impersonation and denial of service attacksetc.) and their usability (such as proximity range, hardwarerequirement and ease of use) in terms of the practicality in IIoTenvironment in the light of which we present two typical IIoTuse cases that require proximity based authentication.