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We present an interdisciplinary survey of the history of loosely coupled systems. We apply the presented concepts in communication networks and suggest hybrid self-organizing networks (SONs) as a universal model for future networks. Self-organizing networks can fulfill the tight requirements of future networks but are challenging to use due to their complexity and immaturity. Moreover, the lack of an externally defined goal and centralized control has resulted in many distributed self-organizing systems failing. This is because the nonlinear relationships between the system parts result in emergence, i.e., we cannot predict the behavior of the whole from the behavior of the parts. Furthermore, a set of local optima does not produce a global optimum. Hybrid SONs tackle these challenges with loose or weak coupling of interacting agents that combine centralized control for global optimization with distributed control for local optimization. In the loose centralized control of almost autonomous agents, decisions are made mostly locally with small delays. This architecture has beneficial properties such as stability, obtained by decoupling the feedback loops: vertically with time-scale separation and horizontally with interference avoidance. Applications of loose coupling include modular electronics and computer design, structured software design, and service-oriented architectures, especially for microservices. Cross-layer design for network optimization is a new reason to use loose coupling in networks to improve stability. We also summarize some recent trends and present a roadmap to the future. We expect that loose coupling will be widely used in self-organizing networks of future wireless systems.

In this paper we present procedures for processing raw data collected with moving vehicles and for fusing this data with digital map data. The goal is to have a better understanding of the city traffic via quantitative research on collected taxi data in relation to digital map properties. Map attributes are provided by Digiroad, which is a database of Finnish road and street network. We define methods to clean up data that has been collected with taxis equipped with on-board vehicle tracking devices from real customer service situations. Consequently, the driving behavior may be inconsistent and sensor data can be limited and contain errors. We explain procedures of preparing data; filtering the most obvious errors from the data set, map-matching moving object data, and fetching map attributes along the routes of the moving vehicles. The fetched properties, as well as other measurement data, are used for deriving statistics and illustrations to study driving behavior in downtown Oulu, Finland.

General-purpose computing on graphics processing units (GPGPU) is a promising way to speed up computationally intensive network functions, such as performing real-time traffic classification based on machine learning. Recent studies have focused on integrated graphics units and various performance optimizations to address bottlenecks such as latency. However, these approaches tend to produce architecture-specific binaries and lack the orchestration of functions. A complementary effort would be a GPGPU architecture based on standard and open components, which allows the creation of interoperable and orchestrable network functions. This study describes and evaluates such open architecture based on the cross-platform Vulkan API, in which we execute hand-written SPIR-V code as a network function. We also demonstrate a multi-node orchestration approach for our proposed architecture using Kubernetes. We validate our architecture by executing SPIR-V code performing traffic classification with random forest inference. We test this application both on discrete and integrated graphics cards and on x86 and ARM. We find that in all cases the GPUs are faster than the baseline Cython code.

The emerging industrial verticals set new challenges for 5G and beyond systems. Indeed, the heterogeneity of the underlying technologies and the challenging and conflicting requirements of the verticals make the orchestration and management of networks complicated and challenging. Recent advances in network automation and artificial intelligence (AI) create enthusiasm from industries and academia toward applying these concepts and techniques to tackle these challenges. With these techniques, the network can be autonomously optimized and configured. This article suggests a collaborative cross-system AI that leverages diverse data from different segments involved in the end-to-end communication of a service, diverse AI techniques, and diverse network automation tools to create a self-optimized and self-orchestrated network that can adapt according to the network state. We align the proposed framework with the ongoing network standardization.

Edge computing augments cloud computing. While cloud computing is based on far away computing centres, edge computing acknowledges the computing resources in the continuum between local devices and the cloud. The computing resources in edge computing are often heterogeneous, with varying capacity, intermittent connectivity, and opportunistic availability. In contrast, modern artificial intelligence and especially machine learning methods are often deployed in the cloud, and assume the computing resources are homogeneous, abundant, centralized, easily scalable, and always available. This thesis studies edge AI, a nascent field of research combining edge computing and artificial intelligence. A particular focus in the thesis is on spatial dependencies, which quantify the similarity of observations in the spatial dimension. Spatial dependencies are prominent in edge AI due to the local nature of edge service users, the computational resources, as well as many of the observed data-generating processes. The thesis asks three research questions. The first one looks for a method to explicitly consider spatial dependencies in edge AI, while the second and third ones apply the method for edge server placement and environmental sensing. As result, the thesis first proposes a novel spatial clustering method, named PACK, which partitions a set of spatial data points according to configurable attributes and constraints. PACK then provides a basis for edge server placement and workload allocation, where a large-scale edge deployment can be optimized such that user quality of experience and deployment quality of service are maximised. Furthermore, PACK serves a crucial function in environmental sensing with a massive fleet of mobile sensors, providing grounds for distributing computations and data for a novel, edge-native method for interpolation. In both edge server placement and environmental sensing, the proposed methods outperform state-of-the-art. Finally, the thesis, Tiivistelmä Reunalaskenta täydentää pilvilaskentaa. Siinä missä pilvilaskenta perustuu kaukaisiin datakeskuksiin, ottaa reunalaskenta huomioon myös laskentaresurssit matkalla paikallisista laitteista pilveen. Reunalaskennan resurssit ovat ominaisuuksiltaan kirjavia: niiden kapasiteetit saattavat olla hyvin erilaisia, niiden yhteydet saattavat katkeilla ja ne saattavat olla saatavilla vain toisinaan. Toisaalta nykyaikaisia tekoäly- ja koneoppimismenetelmiä käytetään tavallisesti pilvipalveluissa, ja ne olettavat, että laskentaresurssit ovat homogeenisia ja ne ovat saatavilla keskitetysti, skaalautuvasti ja jatkuvasti. Tämä väitöstyö tutkii reunalaskennan tekoälyä. Reunalaskennan tekoäly on uusi tieteenala, jossa tutkitaan, kuinka reunalaskenta sekä tekoäly- ja koneoppimismenetelmät voidaan yhdistää. Erityisesti väitös pureutuu paikkariippuvuuksiin, jotka kuvaavat havaintojen paikallisia samankaltaisuuksia. Paikkariippuvuudet ovat reunalaskennan tekoälyssä tärkeitä, sillä reunapalveluiden käyttäjät, niiden laskentaresurssit, ja myös dataa tuottavat prosessit ovat usein luonteeltaan paikallisia. Väitös kysyy kolme tutkimuskysymystä. Ensimmäinen hakee menetelmää, jolla paikkariippuvuudet voisi valjastaa käyttöön reunalaskennan tekoälyssä. Toinen ja kolmas tutkimuskysymys ovat luonteeltaan soveltavia, ja etsivät menetelmää reunalaskennan palvelimien sijoitteluun ja ympäristön anturointiin. Väitöksen tuloksena on, ensinnä, uusi spatiaalinen klusterointimenetelmä, nimeltään PACK, jonka avulla paikkapisteiden joukko voidaan jakaa osiin erilaisten muokattavien ominaisuuksien ja rajoitteiden mukaisesti. Toiseksi, PACK toimii pohjaratkaisuna reunalaskennan palvelimien sijoittelulle ja kuormanjaolle, kun kaupungin laajuinen reunalaskennan asennus halutaan optimoida siten, että käyttäjäkokemus ja palveluntaso saadaan parhaaksi mahdolliseksi. Kolmanneksi, PACK on jälleen kriittisessä roolissa reunatekoälysovellukses

We propose the subsidiarity and weak coupling principles for developing the sixth generation (6G) self-organizing wireless networks. The principles are common in social sciences and control theory, respectively. This proposal leads to organizing the network as a hierarchy of interacting rational agents with vertical and horizontal weak coupling. The central agent provides a performance goal and constraints to the lower level agents that operate almost autonomously in this multi-agent system. The system has various favorable properties, including stability, reliability, and efficiency. Present self-organizing networks are usually distributed without any centralized controller. The lack of a common externally given goal may lead to low performance, staggering behavior, or even chaotic situations. In communications, each transmitter can be interpreted as a rational lower level agent. A principle resembling subsidiarity, the locality principle, is used, for example, in cellular automata, systolic arrays, and edge computing. Subsidiarity is also a solution for the tragedy of the commons where common resources are overused because the costs are divided equally among the users, often with some significant delay. We also provide a historical review that shows each idea’s origin because different disciplines use different terminology for similar concepts. Understanding the origins can reduce fragmentation and enhance scientific progress.

The deployment of edge computing infrastructure requires a careful placement of the edge servers, with an aim to improve application latencies and reduce data transfer load in opportunistic Internet of Things systems. In the edge server placement, it is important to consider computing capacity, available deployment budget, and hardware requirements for the edge servers and the underlying backbone network topology. In this paper, we thoroughly survey the existing literature in edge server placement, identify gaps and present an extensive set of parameters to be considered. We then develop a novel algorithm, called PACK, for server placement as a capacitated location–allocation problem. PACK minimizes the distances between servers and their associated access points, while taking into account capacity constraints for load balancing and enabling workload sharing between servers. Moreover, PACK considers practical issues such as prioritized locations and reliability. We evaluate the algorithm in two distinct scenarios: one with high capacity servers for edge computing in general, and one with low capacity servers for Fog computing. Evaluations are performed with a data set collected in a real-world network, consisting of both dense and sparse deployments of access points across a city area. The resulting algorithm and related tools are publicly available as open source software.

We propose a vision for directing research and education in the field of information and communications technology (ICT). Our Smart and Sustainable World vision targets prosperity for the people and the planet through better awareness and control of both human-made and natural environments. The needs of society, individuals, and industries are fulfilled with intelligent systems that sense their environment, make proactive decisions on actions advancing their goals, and perform the actions on the environment. We emphasize artificial intelligence, feedback loops, human acceptance and control, intelligent use of basic resources, performance parameters, mission-oriented interdisciplinary research, and a holistic systems view complementing the conventional analytical reductive view as a research paradigm, especially for complex problems. To serve a broad audience, we explain these concepts and list the essential literature. We suggest planning research and education by specifying, in a step-wise manner, scenarios, performance criteria, system models, research problems, and education content, resulting in common goals and a coherent project portfolio as well as education curricula. Research and education produce feedback to support evolutionary development and encourage creativity in research. Finally, we propose concrete actions for realizing this approach.

Spatio-temporal interpolation provides estimates of observations in unobserved locations and time slots. In smart cities, interpolation helps to provide a fine-grained contextual and situational understanding of the urban environment, in terms of both short-term (e.g., weather, air quality, traffic) or long term (e.g., crime, demographics) spatio-temporal phenomena. Various initiatives improve spatio-temporal interpolation results by including additional data sources such as vehicle-fitted sensors, mobile phones, or micro weather stations of, for example, smart homes. However, the underlying computing paradigm in such initiatives is predominantly centralized, with all data collected and analyzed in the cloud. This solution is not scalable, as when the spatial and temporal density of sensor data grows, the required transmission bandwidth and computational capacity become unfeasible. To address the scaling problem, we propose EDISON: algorithms for distributed learning and inference, and an edge-native architecture for distributing spatio-temporal interpolation models, their computations, and the observed data vertically and horizontally between device, edge and cloud layers. We demonstrate EDISON functionality in a controlled, simulated spatio-temporal setup with 1 M artificial data points. While the main motivation of EDISON is the distribution of the heavy computations, the results show that EDISON also provides an improvement over alternative approaches, reaching at best a 10% smaller RMSE than a global interpolation and 6% smaller RMSE than a baseline distributed approach.

In this article, we study the scaling up of edge computing deployments. In edge computing, deployments are scaled up by adding more computational capacity atop the initial deployment, as deployment budgets allow. However, without careful consideration, adding new servers may not improve proximity to the mobile users, crucial for the Quality of Experience of users and the Quality of Service of the network operators. In this paper, we propose a novel method for scaling up an edge computing deployment by selecting the optimal number of new edge servers and their placement, and re-allocating access points optimally to the old and new edge servers. The algorithm is evaluated with two scenarios, using data on a real-world large-scale wireless network deployment. The evaluation shows that the proposed method is stable on a real city-scale deployment, resulting in optimized Quality of Service for the network operator.

In this article, we study the scaling up of edge computing deployments. In edge computing, deployments are scaled up by adding more computational capacity atop the initial deployment, as deployment budgets allow. However, without careful consideration, adding new servers may not improve proximity to the mobile users, crucial for the Quality of Experience of users and the Quality of Service of the network operators. In this paper, we propose a novel method for scaling up an edge computing deployment by selecting the optimal number of new edge servers and their placement, and re-allocating access points optimally to the old and new edge servers. The algorithm is evaluated with two scenarios, using data on a real-world large-scale wireless network deployment. The evaluation shows that the proposed method is stable on a real city-scale deployment, resulting in optimized Quality of Service for the network operator.

Mist computing extends the Internet of Things computing infrastructures to the IoT devices at the edges of the networks. The dynamic characteristics of the IoT environments and resource limitations of the devices introduce challenges to the orchestration of the mist platform resources. In this paper, we present a hybrid resource discovery solution for mist, based on the IETF CoRE Resource Directories deployed as containerized Microservices to the supporting edge devices. This way the directory instances can be deployed on-demand as part of the edge platform, where each instance serves a mist network connected to the hosting edge device. This enables low latency resource queries at one-hop distance for the mist applications. At the edge layer, the directories form a distributed discovery infrastructure, connecting resources in disparate mist networks with each other and cloud and edge applications. A real-world prototype of such discovery infrastructure is implemented, based on low resource edge devices hosting the directory instances and low-power embedded devices as the mist resource servers and clients. The prototype is evaluated with latency and power consumption measurements, where the results show that discovery latency is as low as half a millisecond with a low power consumption.

The growing network density and unprecedented increase in network traffic, caused by the massively expanding number of connected devices and online services, require intelligent network operations. Machine Learning (ML) has been applied in this regard in different types of networks and networking technologies to meet the requirements of future communicating devices and services. In this article, we provide a detailed account of current research on the application of ML in communication networks and shed light on future research challenges. Research on the application of ML in communication networks is described in: i) the three layers, i.e., physical, access, and network layers; and ii) novel computing and networking concepts such as Multi-access Edge Computing (MEC), Software Defined Networking (SDN), Network Functions Virtualization (NFV), and a brief overview of ML-based network security. Important future research challenges are identified and presented to help stir further research in key areas in this direction.

The rapid increase of Internet of Things (IoT) applications and services has led to massive amounts of heterogeneous data. Hence, we need to re-think how IoT data influences the network. In this paper, we study the characteristics of IoT data traffic in the context of smart cities. Aiming at analyzing the influence of IoT data traffic on the access and core network, we generate various IoT data traffic according to the characteristics of different IoT applications. Based on the analysis of the inherent features of the aggregated IoT data traffic, we propose a Long Short-Term Memory (LSTM) model combined with autoregressive spectrum analysis to predict the IoT data traffic. In this model, the autoregressive spectrum analysis is used to estimate the minimum length of the historical data needed for predicting the traffic in the future, which alleviates LSTM’s performance deterioration with the increase of sequence length. A sliding window enables predicting the long–term tendency of IoT data traffic while keeping the inherent features of the data traffic. The evaluation results show that the proposed model converges quickly and can predict the variations of IoT traffic more accurately than other methods and the general LSTM model.

There is an increased interest to integrate STEAM methodologies and digital fabrication processes into formal education. However, teachers have repeatedly reported a set of impediments that hampers them to succeed. This integration requires a set of changes in the school organization, resourcing and a proper teacher training. A Community of Practice formed by different stakeholders of a local educational community might provide the necessary grounds to lead to this transition. In this paper, we report our experience of creating and scaffolding a local Community of Practice for a period of ten months. We present the different activities we carried out during this period, emphasizing a digital fabrication training that we conducted, at our university Fab Lab premises, for teachers and school principals separately. We also explore the influence of this training on scaffolding of the development of the Community of Practice. We expect the training structure, discussion and insights presented in this paper would inspire other researchers and practitioners trying to bring digital fabrication to formal education.

The Internet of Things (IoT) connects smart devices to enable various intelligent services. The deployment of IoT encounters several challenges, such as difficulties in controlling and managing IoT applications and networks, problems in programming existing IoT devices, long service provisioning time, underused resources, as well as complexity, isolation and scalability, among others. One fundamental concern is that current IoT networks lack flexibility and intelligence. A network-wide flexible control and management are missing in IoT networks. In addition, huge numbers of devices and large amounts of data are involved in IoT, but none of them have been tuned for supporting network management and control. In this paper, we argue that Software-defined Networking (SDN) together with the data generated by IoT applications can enhance the control and management of IoT in terms of flexibility and intelligence. We present a review for the evolution of SDN and IoT and analyze the benefits and challenges brought by the integration of SDN and IoT with the help of IoT data. We discuss the perspectives of knowledge-driven SDN for IoT through a new IoT architecture and illustrate how to realize Industry IoT by using the architecture. We also highlight the challenges and future research works toward realizing IoT with the knowledge-driven SDN.

Children provide innovative insights and perspectives when designing and evaluating prospective technologies and interaction methods. However, working with children as design informants requires careful consideration of the methodologies used in different steps of the design process. In this paper, we present our insights on a case-study of a design process with 24 children (9—12-year-olds), aiming for understanding children’s view on new technologies and their perception on tangible interaction. We utilized a lightweight design process model: children first reflected on the use of technologies and participated in an ideation session. Based on the results, researchers built prototypes which were evaluated by the children in the final step of the process. We expect our learnings to help researchers in the field of collaborative design when organizing design activities with children.

Advances in technology and data analysis provide rich opportunities for developing intelligent environments assisting their inhabitants, so-called smart environments or smart spaces. Enhanced with technology, sensors, user interfaces, and various applications, such smart spaces are capable of recognizing users and situations they are in, react accordingly, e.g., by providing certain services or changes to the environment itself. Therefore, smart space solutions are gradually coming to different application domains, each with corresponding specific characteristics. In this article, we discuss our experiences and explore the challenges of a long-term real-world Internet of Things (IoT) deployment at a University campus. We demonstrate the technical implementation and data quality issues. We conduct several studies, from data analysis to interaction with space, utilizing the developed infrastructure, and we also share our actions to open the data for education purposes and discuss their outcomes. With this article, we aim to share our experience and provide real-world lessons learned when building an open, multipurpose, publicly used smart space at a University campus.

Advances in technology equip traffic domain with instruments to gather and analyse data for safe and fuel-efficient traveling. In this article, we elaborate on the effects that taxi drivers’ route selection has on fuel efficiency. For this purpose, we fuse real driving behaviour data from taxi cabs, weather, digital map, and traffic situation information to gain understanding of how the routes are selected and what are the effects in terms of fuel-efficiency. Analysis of actually driven trips and their quickest and shortest counterparts is conducted to find out the fuel-efficiency consequences on route selection. The judgments are used for developing a fuel-consumption model, exploring further the route characteristics and external factors affecting fuel consumption.

Mixed reality (MR) applications are expected to become common when 5G goes mainstream. However, the latency requirements are challenging to meet due to the resources required by video-based remoting of graphics, that is, decoding video codecs. We propose an approach towards tackling this challenge: a client-server implementation for transacting intermediate representation (IR) between a mobile UE and a MEC server instead of video codecs and this way avoiding video decoding. We demonstrate the ability to address latency bottlenecks on edge computing workloads that transact graphics. We select SPIR-V compatible GPU kernels as the intermediate representation. Our approach requires know-how in GPU architecture and GPU domain-specific languages (DSLs), but compared to video-based edge graphics, it decreases UE device delay by sevenfold. Further, we find that due to low cold-start times on both UEs and MEC servers, application migration can happen in milliseconds. We imply that graphics-based location-aware applications, such as MR, can benefit from this kind of approach.

In this paper, we describe how the microservices paradigm can be used to design and implement distributed edge services for Internet of Things applications. As a case study, traditionally monolithic user mobility analysis service is developed, with distributed and extendable microservices, for the standardized ETSI MEC system reference architecture. In each of the edge system three tiers, microservices implement the service logic with components for movement trace analysis, movement prediction and visualization of the results. The distributed service is implemented with Docker containers and evaluated on real-world settings with low capacity edge servers and real user mobility data. The results show that the edge promise of low latency can be met in such as implementation. The integration of a software development technology with a standardized edge system provides solid background for further development.

Prevalent weather prediction methods are based on sensor data, collected by satellites and a sparse grid of stationary weather stations. Various initiatives improve the prediction models by including additional data sources such as mobile weather sensors, mobile phones, and wireless sensor networks (WSN) of, for example, smart homes. The underlying computing paradigm is predominantly centralized, with all data collected and analyzed in the cloud. This solution is not scalable. When the spatial and temporal density of weather sensor data grows, the required data transmission capacities and computational resources become unfeasible. We identify the challenges posed by spatial distribution of a weather prediction model, and suggest solutions for those challenges. We propose EDISON: an edge-native interpolation approach based on AI methods, distributed horizontally on edge servers. Finally, we demonstrate EDISON with a simple, simulated setup.

Maker and DIY communities are constantly sharing tutorials, projects documentation, as well as design sketches and model files. Producing documentation of good quality has its challenges, one of them being the amount of time needed to generate it. In this paper, we present a software tool that assists makers and hobbyists in creating reports and tutorials of their projects on-the-go, that is, at the same time they are working on them. A mobile phone application allows taking pictures, grouping them and annotating them with text and audio. All the material collected during the making activity is automatically stored online and presented in a website created for the project. The maker can later modify this site by means of adding additional text, reordering the pictures, or including extra multimedia content or design files. This tool is specially tailored to the documentation needs of the Fab Lab network.

Physical exercise can improve sleep quality. However, how to perform physical exercise to achieve the best possible improvements is not clear. In this article, we build predictive models based on volume real data collected from wearable devices to predict the sleep efficiency related to users’ daily exercise information. As far as we know, this is the first study to investigate insights of prediction of sleep efficiency from volume physical exercise data collected from real world.

Edge computing in Internet of Things enhances application execution by retrieving cloud resources to the close proximity of resource-constrained end devices at the edge and by enabling task offloading from these devices to the edge. In this paper, edge computing platforms are extended into the data producing end devices, including wireless sensor network nodes and smartphones, with mobile agents. Mobile agents operate, as a multi-agent system, on the opportunistic network of heterogeneous end devices. The benefits include autonomous, asynchronous and adaptive execution and relocation of application-specific computational tasks, while taking into account the local resource availability. In addition to the vertical edge connectivity, mobile agents enable horizontal sharing of information between these devices. Use cases are presented where mobile agents address challenges in current edge computing platforms. An edge application is evaluated where mobile agents as a multi-agent system process sensor data in a heterogeneous set of end devices, control the operation of the devices and share their tasks results in the system. The mobile agents operate atop a REST-compliant software agent framework that relies on embedded Web services for interoperability. A real-world evaluation and large-scale simulations show that energy consumption is reduced significantly, up to 60%, in the edge application execution.

Modern connected cities are more and more leveraging advances in ICT to improve their services and the quality of life of their inhabitants. The data generated from different sources, such as environmental sensors, social networking platforms, traffic counters, are harnessed to achieve these end goals. However, collecting, integrating, and analyzing all the heterogeneous data sources available from the cities is a challenge. This article suggests a data lake approach built on Big Data technologies, to gather all the data together for further analysis. The platform, described here, enables data collection, storage, integration, and further analysis and visualization of the results. This solution is the first attempt to integrate a diverse set of data sources from four pilot cities as part of the CUTLER project (Coastal urban development through the lenses of resiliency). The design and implementation details, as well as usage scenarios are presented in this paper.

Edge computing that leverages cloud resources to the proximity of user devices is seen as the future infrastructure for distributed applications. However, developing and deploying edge applications, that rely on cellular networks, is burdensome. Such network infrastructures are often based on proprietary components, each with unique programming abstractions and interfaces. To facilitate straightforward deployment of edge applications, we introduce open-source software (OSS) based radio access network (RAN) on over-the-air (OTA) commercial spectrum with Development Operations (DevOps) capabilities. OSS allows software modifications and integrations of the system components, e.g., Evolved Packet Core (EPC) and edge hosts running applications, required for new data pipelines and optimizations not addressed in standardization. Such an OSS infrastructure enables further research and prototyping of novel end-user applications in an environment familiar to software engineers without telecommunications background. We evaluated the presented infrastructure with end-to-end (E2E) OTA testing, resulting in 7.5MB/s throughput and latency of 21ms, which shows that the presented infrastructure provides low latency for edge applications.

With this poster, we present our goals, methodology and initial findings of the first 7 months of a pilot aiming to integrate STEAM education in formal education by exploiting digital fabrication tools and processes. The success of the pilot is strongly bound to the development of a Community of Practice formed by diverse local stakeholders including teachers, school principals, education administration representatives, researchers and Fab Lab instructors. Six local schools participated in the first stage of the pilot. We claim that this poster will set the ground for a discussion on how to integrate STEAM and digital fabrication procedures in formal learning environments. We expect our experience could serve as an inspiration for similar projects all around Europe.

Maker educations and distributed educations are increasing in quantity and quality. This gives a possibility for academia to tap into interesting sources of knowledge outside the physical parameters of the institution, as well as outside formal education and traditional learning methods. However, academic recognition of such learning can be challenging. We explore Fab Academy in comparison with a current university course with the same topic; the amount of work by university standards and whether the assessment methods of Fab Academy are sufficient for academic recognition. The workload of Fab Academy is calculated based on the European Credit Transfer and Accumulation System (ECTS). The contents are compared based on the range of subjects and the deliverables required to pass the courses. We find Fab Academy to be compatible with the university course. Hence, we consider it possible to include in university curriculum Fab Academy content accredited by different universities.

Mobile, vehicle-installed road weather sensors are becoming ubiquitous. While mobile sensors are often capable of making observations on a high frequency, their reliability and accuracy may vary. Large-scale road weather observation and forecasting are still mostly based on stationary road weather stations (RWS). Though expensive, sparsely located and making observations on a relatively low frequency, RWS’ reliability and accuracy are well-known and accommodated for in the road weather forecasting models. Statistical analysis revealed that road weather conditions indeed have a great effect on how the observations of mobile and stationary road weather temperature sensors differ from each other. Consequently, we calibrated the observations of mobile sensors with a linear mixed model. The mixed model was fitted fusing ca. 20 000 pairs of mobile and RWS observations of the same location at the same time, following a rendezvous model of sensor calibration. The calibration nearly halved the MSE between the observations of the mobile and the RWS sensor types. Computationally very light, the calibration can be embedded directly in the sensors.

We present a multidisciplinary historical review of manual remote control, automation, autonomy, and self-organization roughly covering the last century. Some conceptual analysis is given using hierarchical classifications. We show the relationships between control theory, computer science, and communication theory. We observe that the three disciplines have progressed at least partially independently, but we can see also some convergence towards similar system models, often using different terminology. We expect that multidisciplinary studies will turn out to be useful for avoiding overlapping work and for making faster progress. Furthermore, a unified terminology would facilitate communication between disciplines. This review provides a starting point for building such terminology.

Data are crucial to support decision making. If data have low veracity, decisions are not likely to be sound. Internet of Things (IoT) generates big data with inaccuracy, inconsistency, incompleteness, deception, and model approximation. Enhancing data veracity is important to address these challenges. In this article, we summarize the key characteristics and challenges of IoT, which influence data processing and decision making. We review the landscape of measuring and enhancing data veracity and mining uncertain data streams. Moreover, we propose five recommendations for future development of veracious big IoT data analytics that are related to the heterogeneous and distributed nature of IoT data, autonomous decision-making, context-aware and domain-optimized methodologies, data cleaning and processing techniques for IoT edge devices, and privacy preserving, personalized, and secure data management.

The key to win on global markets is not based only on excellence of products and services, but increasingly more on successful business models and value networks, where partnering, smart specialisation and joint solution deliveries are the key elements for sustainable success. There also has to be solid processes and tools for facilitating the transfer from research to business — the creation and evolution of business. Hilla Runway provides a model and a toolset that supports business development and commercialisation.

Semantics associates meaning with Internet of Things (IoT) data and facilitates the development of intelligent IoT applications and services. However, the big volume of the data generated by IoT devices and resource limitations of these devices have given rise to challenges for applying semantic technologies. In this article, we present Cloud and edge based IoT architectures for semantic reasoning. We report three experiments that demonstrate how edge computing can facilitate IoT systems in terms of data transfer and semantic reasoning. We also analyze how distributing reasoning tasks between the Cloud and edge devices affects system performance.

Software agents have been exploited to handle the inherent dynamicity in the Internet of Things (IoT) systems, as agents are capable of autonomous, reactive and proactive operation in response to changes in their local environment. Agents, operating at the network edge, enable leveraging cloud resources into the proximity of the user devices. However, poor interoperability with the existing IoT systems and the lack of a systematic methodology for IoT system development with the agent paradigm have hindered the utilization of software agent technologies in IoT. In this paper, we describe the development process and the system architecture of a mobile crowdsensing service, provided by an agent-based smart object that comprises agents in both edge and user devices. Mobile crowdsensing is an example of such an application that relies on large-scale participatory sensor networks, where participants have active roles in producing information about their environment with their smartphones. This scheme introduces challenges in handling dynamic opportunistic resource availability, due to mobility and unpredicted actions of the participants. We present how ACOSO-Meth (Agent-oriented Cooperative Smart Object-Methodology) guidelines the development process systematically from the analysis to the actual agent-based implementation of a crowdsensing service. The implementation is done with the ROAgent framework that utilizes resource-oriented architecture and REST principles to integrate agent-based smart objects seamlessly with the programmable Web.

Energy efficient personal transportation requires fuel-efficient and route aware driving. Driving coaching systems can provide to drivers all the information and guidance that is needed to learn these skills. However, persuading drivers to change their driving behaviour is a challenging task. The authors identify functional, design, safety, and persuasive features for systems supporting fuel-efficiency. Moreover, they analyse how these features are supported by state-of-the-art systems targeting reduced fuel consumption. Finally, based on their analysis, they discuss open issues and opportunities for future development of fuel-efficiency support systems. The literature and the reviewed research in this study illustrate the needs for overall situation assessment and benefits of careful and multifaceted approach for systems design when it comes to eco-driving: an effective system will make use of a versatile design toolkit in order to obtain enduring behavioural results.

The internet of things (IoT) application domain is evolving continuously and is expected to bring digitalization making everything smart. This paper presents reference architecture for IoT system. Moreover, full implementation of such architecture is demonstrated with the IoT testbed deployed at the University of Oulu. Leveraging MQTT protocol, LoRaWAN is utilized as an IoT enabler technology for our platform, comprising a cloud server as well. In addition, a hybrid server is added to the platform to enable enhanced performance and functionality. To showcase the potential impacts of augmented reality (AR) based applications in an IoT infrastructure, we implemented and tested the maintenance support application. The implemented application visualizes environmental conditions and the sensor node itself as an augmented object on a mobile phone. A user study is carried out to discuss and showcase the potential impacts of using such a visualization approach.

The Agent-based Cooperating Smart Objects methodology (ACOSO-Meth) fully supports the systematic development of Internet of Things (IoT) systems from analysis to implementation by tackling their manifold requirements (e.g., self-management, distributed smartness, interoperability). At the same time, ACOSO-Meth allows the re-engineering of existing IoT systems, thus enhancing their maintainability, reusability and extensibility. In such direction, this paper (i) first presents the integration of the resource-oriented agent framework complying with the IETF Constrained RESTful Environment (CoRE) framework into ACOSO-Meth; then (ii) reports a case study to exemplify the re-engineering of a resource-constrained agent application through the ACOSO-Meth metamodel-driven approach.

As communication and computation systems become more complex and target at higher performance, the fundamental limits of nature can be expected to constrain their development and optimization. This calls for intelligent use of basic resources, that is, materials, energy, information, time, frequency, and space. We present a multidisciplinary and historical review on the body of knowledge that can be applied in researching such intelligent and resource-efficient systems. We review general system theory, decision theory, control theory, computer science, and communication theory. While multidisciplinarity has been recognized important, there are no earlier reviews covering all these five disciplines. Based on the review, we build a chronology of intelligent systems and identify connections between the disciplines. Optimization, decision-making, open- and closed-loop control, hierarchy, and degree of centralization turn out as recurring themes in these disciplines, which have converged to similar solutions that are based on remote control, automation, autonomy, and self-organization. We use future wireless networks as an example to illustrate the open questions and how they can be addressed by applying multidisciplinary knowledge. This paper can help researchers to use knowledge outside their own field and avoid repeating the work done already. The resulting consolidated view can speed up research and is especially important when the fundamental limits of nature are approached and new insights are required to overcome the challenges. The general, long-standing problem to be tackled is multiobjective optimization with autonomous and distributed decision-making in an uncertain, dynamic, and nonlinear environment where the objectives are mutually conflicting.

The Internet of Things vision proposes a global platform in an unforeseen scale for distributed applications that rely on data provided by interconnected resource-constrained things. In such large-scale systems, centralized control of system operation by a single component through vertical interactions becomes unfeasible. Ideally, decentralized control in the proximity of things enables to take into account the local dynamic resource availability and environmental characteristics that are used to optimize the application execution. To realize decentralization, capabilities for horizontal interactions, that complement the vertical interactions, and for opportunistic participation of things are needed. This thesis explores mobile agent technology to implement distributed Internet of Things applications that benefit from both vertical and horizontal interactions of the application components. First, a resource-oriented reactive mobile agent architecture and a software framework are presented. The framework facilitates RESTful interactions between agents and other system components and provides a REST-based interface to build opportunistic agent-based applications. Two agent platforms are presented that integrate resource-constrained things into the framework as mobile agent hosts. Second, mobile agent based mobile crowdsensing and edge computing applications are evaluated with large-scale simulations and real-world experiments. The results show that energy consumption is decreased in the participating things, in comparison with the existing approaches, by agent-based in-network data processing and control of the thing operation. This thesis makes a valuable contribution by enabling mobile agent operations in a heterogeneous set of resource-constrained things. The presented empirical evidence shows how mobile agent technology increases energy efficiency in distributed application execution. The presented mobile agent architecture and software framework potentia, Tiivistelmä Esineiden Internet liittää resurssirajoitteiset sulautetut laitteet laajamittaisesti Internet-verkkoon, jossa niiden tuottamaa tietoa hyödynnetään hajautettujen järjestelmien sovelluksissa. Esineiden Internetin järjestelmien odotetaan skaalautuvan niin laajoiksi, ettei keskitetty, vertikaaliseen vuorovaikutukseen perustuva järjestelmähallinta ole enää käyttökelpoinen ratkaisu. Hajautettu hallinta lähellä tietoa tuottavia laitteita tuo etuja, kun paikallisesti sovelluksen suorituksessa otetaan huomioon toimintaympäristön tila ja voidaan reagoida dynaamisesti resurssien saatavuuteen. Hajautus Esineiden Internetin sovellusalustoilla edellyttää menetelmiä sekä laitteiden vertikaaliseen ja horisontaaliseen vuorovaikutukseen, että niiden dynaamisen osallistumisen mahdollistamiseksi sovelluksen suorittamisessa. Tässä työssä tutkittiin mobiiliagentti-teknologiaa hajautettujen sovellusten toteuttamiseen Esineiden Internet-ympäristössä. Työssä esitettiin resurssisuuntautunut arkkitehtuuri reaktiivisille mobiiliagenteille sekä ohjelmistokehys, joita käyttäen voidaan toteuttaa agenttipohjaisia hajautettuja sovelluksia. Ohjelmistokehys perustuu REST-arkkitehtuurimalliin, jonka pohjalta esitettiin yhdenmukainen rajapinta agenttien ja järjestelmäkomponenttien väliseen vuorovaikutukseen. Ohjelmistokehyksen osana toteutettiin kaksi mobiiliagentti-ohjelmistoalustaa resurssirajoitteisille sulautetuille laitteille. Työssä tarkasteltiin mobiiliagentti-pohjaisten Esineiden Internet-sovellusten energiatehokkuutta simuloinneilla sekä tosielämän kokeilla. Tarkastelun kohteeksi valittiin joukkoistettu mobiilihavainnointi sekä reunalaskennan ulottaminen resurssirajoitteisiin laitteisiin. Saadut tulokset osoittavat, että laitteiden energiankulutusta voidaan pienentää verrattuna aiemmin esitettyihin ratkaisuihin hyödyntämällä mobiiliagentteja tiedonkäsittelyyn laitteessa sekä laitteen toiminnan ohjaamiseen. Työssä esitetty resurssisuuntautunut mobiiliagenttiarkkitehtuuri sekä ohjelm

Advances in technology equip traffic domain with instruments to gather and analyze data for safe and fuel efficient traveling. This article elaborates on the effects from taxi drivers route selection on fuel efficiency. We fuse real driving behavior data from taxi cabs, weather, and digital map information for fuel consumption prediction. That way we compare actually driven trips and their quickest and shortest counterparts for fuel efficiency.

Edge computing paradigm allows computation to be moved from the central high powered Cloud or data center to the edge of the network. This paradigm often enables more efficient data processing near its source and sends only the data and knowledge that have value over the network. Our study focuses on performing semantic reasoning at the edge computing devices, which requires transferring ontologies to the edge devices. This paper presents different representations for transferring Web Ontology language (OWL) version 2 ontologies to the edge. We evaluate different representations in an experimental IoT system with edge nodes and compare lengths of different syntaxes and their computation effort of building models in Cloud and edge computing devices in terms of processing time.

Promoted by sensor, big data and mobile computing technologies, the number of Internet of Things (IoT) applications and services is increasing rapidly. The massive amounts of heterogeneous data produced by a large variety of IoT devices require us to re-think its influence on the network. In this paper, we study the characteristics of IoT data traffic in the context of smart city. We generate data traffic according to the characteristics of different IoT applications. We propose a Gamma modulated wavelet method for statistical characterization of both IoT data and the aggregated traffic, aiming at analyzing the influence of IoT data traffic on the access and core network. By using Gamma function to modulate the coefficients of the wavelet, both the long range and short range dependency of the IoT data traffic can be described through fewer parameters. The Gamma modulation also reduces the independency of the coefficients and improves the accuracy of the Wavelet model.

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.

We introduce mobile agents for mobile crowdsensing. Crowdsensing campaigns are designed through different roles that are implemented as mobile agents. The role-based tasks of mobile agents include collecting data, analyzing data and sharing data in the campaign. Mobile agents execute and control the campaign autonomously as a multi-agent system and migrate in the opportunistic network of participants’ devices. Mobile agents take into account the available resources in the devices and match participants’ privacy requirements to the campaign requirements. Sharing of task results in real-time facilitates cooperation towards the campaign goal while maintaining a selected global measure, such as energy efficiency. We discuss current challenges in crowdsensing and propose mobile agent based solutions for campaign execution and monitoring, addressing data collection and participant-related issues. We present a software framework for mobile agents-based crowdsensing that is seamlessly integrated into the Web. A set of simulations are conducted to compare mobile agent-based campaigns with existing crowdsensing approaches. We implemented and evaluated a small-scale real-world mobile agent based campaign for pedestrian flock detection. The simulation and evaluation results show that mobile agent based campaigns produce comparable results with less energy consumption when the number of agents is relatively small and enables in-network data processing with sharing of data and task results with insignificant overhead.

Acquiring knowledge from continuous and heterogeneous data streams is a prerequisite for Internet of Things (IoT) applications. Semantic technologies provide comprehensive tools and applicable methods for representing, integrating, and acquiring knowledge. However, resource-constraints, dynamics, mobility, scalability, and real-time requirements introduce challenges for applying these methods in IoT environments. We study how to utilize semantic IoT data for reasoning of actionable knowledge by applying state-of-the-art semantic technologies. For performing these studies, we have developed a semantic reasoning system operating in a realistic IoT environment. We evaluate the scalability of different reasoning approaches, including a single reasoner, distributed reasoners, mobile reasoners, and a hybrid of them. We evaluate latencies of reasoning introduced by different semantic data formats. We verify the capabilities of promising semantic technologies for IoT applications through comparing the scalability and real-time response of different reasoning approaches with various semantic data formats. Moreover, we evaluate different data aggregation strategies for integrating distributed IoT data for reasoning processes.

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.

We contribute in this study a first step in theory-based understanding on how creativity in collaborative design sessions relates to the elements that are present in a creative act. These elements include group composition, objects present, practices used, and previous knowledge of the participants. The context of this study was our search for lightweight methods for technology design with children, which can be used in a school context with large groups, will require as little amount of training as possible, and can be set up quickly. We formed a mixed group, consisting of young children, an older child and an adult, with the aim of involving children in creative collaborative brainstorming during the very early phases of design, so as to come up with fruitful ideas for technology development. We report our process and examine the implications of our results in relation to different elements that trigger and affect creativity in the collaborative design process. Use of Vygotsky’s cycle of creativity as our theoretical lens together with timeline analysis method presented in the paper were essential for seeing beneath the surface of what happened in this complex, collaborative creative process. Our results can be used for further methodological development of creative collaborative sessions, both with children and adults.