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Traditional service markets enable the efficient trading of a variety of services through a centralized platform, acting as an intermediary between service providers and customers. However, this centralization raises concerns about power abuse, corruption, and scalability. Decentralization of service markets addresses these issues by directly exchanging data between service providers, but it introduces trust challenges to distributed data sharing. This study explores the use of blockchain technology to establish trust and security in decentralized service markets, managing trade-offs between decentralization, scalability, and security. Focusing on applications in transportation, such as Mobility-as-a-Service (MaaS), Unmanned Aerial Vehicles (UAV), and autonomous vehicles, this work investigates the potential of blockchain-based service markets to facilitate collaboration in various sectors. The dissertation studies the core components of distributed ledger technologies: system design, deployment, and confidentiality. This work proposes a blockchain-based MaaS system design for the service market and discusses the potential usage of blockchain, along with associated challenges in distributed data sharing. Additionally, confidentiality in blockchain-based interactive systems is examined through zero-knowledge argument schemes, with a comparison of their advantages and dis-advantages. The performance regarding decentralized autonomous operation based on blockchain technology is considered via various updating capacities. The deployment of a blockchain-based service market is analyzed using two types of blockchain platforms—permissioned and permissionless—to represent access requirements. A performance comparison and proposed technique framework for blockchain platform selection are also provided., Tiivistelmä Perinteiset palvelumarkkinat toimivat keskitetyn alustan varassa. Tällainen alusta toimii linkkinä palveluntarjoajien ja asiakkaiden välillä ja välittää palveluita tehokkaasti. Keskittäminen ei kuitenkaan ole ongelmatonta erityisesti vallan väärinkäytön, korruption ja skaalautuvuusongelmien vuoksi. Palvelumarkkinoiden hajauttaminen tarjoaa ratkaisun näihin huolenaiheisiin mahdollistamalla suoran tiedonvaihdon palveluntarjoajien välillä. Toisaalta se tuo kuitenkin mukanaan haasteita luottamuksellisen datan hajautetussa jakamisessa. Tämä tutkimus keskittyy lohkoketjuteknologian käyttöön luottamuksen ja turvallisuuden luomiseksi hajautetuille palvelumarkkinoille. Tutkimus keskittyy hajauttamisen, skaalautuvuuden ja turvallisuuden tasapainottamiseen liittyviin kompromisseihin, ja tutkii erityisesti lohkoketjupohjaisten palvelumarkkinoiden soveltamista älyliikenteen palveluihin kuten liikkuvuuteen palveluna (MaaS), miehittämättömiin lennokkeihin (UAV) ja autonomisiin ajoneuvoihin, sekä niiden potentiaaliin edistää yhteistyötä eri sektoreilla. Väitöskirjassa keskitytään hajautetun tilikirjateknologian keskeisiin osa-alueisiin, kuten järjestelmän suunnitteluun, käyttöönottoon ja luottamuksellisuuteen. Työ esittelee lohkoketjupohjaisen MaaS-järjestelmän palvelumarkkinan suunnitelman ja käsittelee lohkoketjun mahdollisia käyttötapoja ja siihen liittyviä haasteita hajautetussa datan jakelussa. Lisäksi tutkimuksessa arvioidaan lohkoketjupohjaisten interaktiivisten järjestelmien luottamuksellisuutta nollatiedon argumenttien (zero-knowledge arguments) avulla, vertaillen niiden etuja ja haittoja. Hajautetun autonomisen toiminnan suorituskykyä tarkastellaan lohkoketjuteknologian perusteella erilaisten päivityskapasiteettien näkökulmasta. Tutkimuksessa analysoidaan myös lohkoketjupohjaisen palvelumarkkinan käyttöönottoa ja vertaillaan kahta eri lohkoketjualustaa—luvanvaraista ja luvatonta—, jotka edustavat erilaisia pääsynvaatimuksia (access requirements). Lisäksi verta

Smart spaces, physical spaces that are integrated with sensor-enabled IoT devices, are a powerful paradigm for optimizing the operations of the space and improving its quality for the occupants. Managing the applications and services running in the space is a complex task as the operations of the devices and services are dependent on the physical characteristics of the space, the occupants of the space, and the technologies that are being integrated. Digital twinning, the combination of physical representations with a virtual counterpart, is a potential technology for facilitating the management of smart space devices and services. While digital twins are increasingly adopted in industry, their use in everyday environments remains low due to difficulties in creating and linking the virtual representation with the physical environment. In this paper, we propose our vision for the adoption of digital twinning as a pathway to improve the functions of smart spaces. We derive a generic reference architecture that comprises four layers, covering the physical space, the sensing infrastructure, the network interfaces, and the underlying computational infrastructure. Next, we identify and address key requirements for the uptake of digital twins in smart space and assess their benefits using the ascendancy model of business analytics. Finally, to demonstrate the practicality of digital twinning, we present a proof-of-concept digital twin for the TellUs smart space at the University of Oulu in Finland and use it to highlight the potential benefits of different ascendancy levels.

This article contributes a research vision for using edge computing to deliver the computing infrastructure for emerging smart megacities, with use cases, key requirements, and reflections on the state of the art. We also address edge server placements, a key challenge for edge computing adoption.

Edge Intelligence (EI) is an emerging computing and communication paradigm that enables Artificial Intelligence (AI) functionality at the network edge. In this article, we highlight EI as an emerging and important field of research, discuss the state of research, analyze research gaps and highlight important research challenges with the objective of serving as a catalyst for research and innovation in this emerging area. We take a multidisciplinary view to reflect on the current research in AI, edge computing, and communication technologies, and we analyze how EI reflects on existing research in these fields. We also introduce representative examples of application areas that benefit from, or even demand the use of EI.

Efficient resource usage in edge computing requires clever allocation of the workload of application components. In this paper, we show that under certain circumstances, the number of superfluous workload reallocations from one edge server to another may grow to a significant proportion of all user tasks—a phenomenon we present as a reallocation storm. We showcase this phenomenon on a city-scale edge server deployment by simulating the allocation of user task workloads in a number of scenarios capturing likely edge computing deployments and usage patterns. The simulations are based on a large real-world data set of city-wide Wi-Fi network connections, with more than 47M connections over ca. 560 access points. We study the occurrence of reallocation storms in three common edge-based reallocation strategies and compare the latency–workload trade-offs related to each strategy. As a result, we find that the superfluous reallocations vanish when the edge server capacity is increased above a certain threshold, unique for each reallocation strategy, peaking at ca. 35% of the peak ES workload. Further, while a reallocation strategy aiming to minimize latency consistently resulted in the worst reallocation storms, the two other strategies, namely a random reallocation strategy and a bottom-up strategy which always chooses the edge server with the lowest workload as a reallocation target, behave nearly identically in terms of latency as well as the reallocation storm in dense edge deployments. Since the random strategy requires much less coordination, we recommend it over the bottom-up one in dense ES deployments. Moreover, we study the conditions associated with reallocation storms. We discover that edge servers with the very highest workloads are best associated with reallocation storms, with other servers around the few busy nodes thus mirroring their workload. Further, we identify circumstances associated with an elevated risk of reallocation storms, such as summ

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

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.

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.

Well-being in smart environments refers to the mental, physiological and emotional states of people passing through environments where sensors, actuators and computers are intertwined with everyday tasks. In that context, well-being must be measurable and, to some extent, susceptible to external influence within the short time-spans that people spend in those environments. Continuing our previous studies, we evaluate an experiment for well-being measurement and control, introducing EEG observations in the experiment. EEG, as an immediate and objective proxy of one’s mental, physiological and emotional state, provides ground truth for comparisons between sensors in the smart environment. We concentrate on the test subject’s emotional state, observed by way of comparing changes in the alpha frequency power levels in the left and right frontal cortical areas, respectively corresponding to positive and negative emotions. The results show that our experimental set-up induces significant changes in the test subject’s emotional state, paving the way for further studies on influencing personal well-being.

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.

In this white paper, we provide a vision for 6G edge intelligence. Moving toward 5G and beyond future 6G networks, intelligent solutions utilizing data-driven machine learning and artificial intelligence will become crucial for several real-world applications, including but not limited to more efficient manufacturing, novel personal smart device environments and experiences, urban computing, and autonomous traffic settings. We sent edge computing with other 6G enablers as a key component to establish the future 2030 intelligent Internet technologies shown in this series of 6G white papers. In this white paper, we focus on the domains of edge-computing infrastructure and platforms, data and edge network management, software development for edge, and real-time and distributed training of ML/AI algorithms, as well as security, privacy, pricing, and end-user aspects. We discuss the key enablers and challenges, and identify the key research questions for the development of intelligent edge services. As the main outcome of this white paper, we envision a transition from the Internet of Things to the Intelligent Internet of Intelligent Things and provide a roadmap for the development of the 6G intelligent edge.

The advances in communication technologies have made it possible to gather road condition information from moving vehicles in real time. However, data quality must be assessed and its effects on the road weather forecasts analyzed before using the new data as input in forecasting systems. Road surface temperature forecasts assimilating mobile observations in the initialization were verified in this study. In addition to using measured values directly, different statistical corrections were applied to the mobile observations before using them in the road weather model. The verification results are compared to a control run without surface temperature measurements and to a control run that utilized interpolated values from surrounding road weather stations. Simulations were done for the period 12 October 2017–30 April 2018 for stationary road weather station points in southern Finland. Road surface temperature observations from the stations were used in the forecast verification. According to the results, the mobile observations improved the accuracy of road surface temperature forecasts when compared to the first control run. The statistical correction methods had a positive effect on forecast accuracy during the winter, but the effect varied during spring when the daily temperature variation was strong. In the winter season, the forecasts based on the interpolated road surface temperature values and the forecasts utilizing mobile observations with statistical correction had comparable accuracy. However, the tested area has high road weather station density and not much elevation variation, so results might have been different in more varying terrain.

This study discusses measurement of well-being in the context of smart environments. We propose an experimental design which induces variation in an individual’s flow, stress, and affect for testing different measurement methods. Both qualitative and quantitative measuring methods are applied, with a variety of wearable sensors (EEG sensor, smart ring, heart rate monitor) and video monitoring. Preliminary results show significant agreement with the test structure in the readings of wearable stress and heart rate sensors. Self-assessments, on the contrary, fail to show significant evidence of the experiment structure, reflecting the difficulty of subjective estimation of short-term stress, flow and affect.

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.

The Experience Sampling Method is widely used to collect human labelled data in the wild. Using this methodology, study participants repeatedly answer a set of questions, constructing a rich overview of the studied phenomena. One of the methodological decisions faced by researchers is deciding on the question scheduling. The literature defines three distinct schedule types: randomised, interval-based, or event-based (in our case, smartphone unlock). However, little evidence exists regarding the side-effects of these schedules on response rate and recall accuracy, and how they may bias study findings. We evaluate the effect of these three contingency configurations in a 3-week within-subjects study (N=20). Participants answered various objective questions regarding their phone usage, while we simultaneously establish a ground-truth through smartphone instrumentation. We find that scheduling questions on phone unlock yields a higher response rate and accuracy. Our study provides empirical evidence for the effects of notification scheduling on participant responses, and informs researchers who conduct experience sampling studies on smartphones.

Recent technological development offers new possibilities for taking into account peoples’ personal wellness data in adjustment of environment conditions. For example, users’ heartrate, facial expression, room temperature, and CO₂ data could be used for adjustment of lighting, temperature, and air-condition to support people’s wellbeing in smart environment. Using personal wellness data for adaptive smart environment condition controlling seems inviting, however before expensive implementation is started, it is essential to know peoples’ perceptions towards such systems to be able to create environments that people want to use. We conducted an anticipated user experience study of an adaptive wellbeing supporting meeting room concept video with 48 participants. Based on our results we present the four design challenges for future research.

Social media data represent an important resource for behavioral analysis of the ageing population. This paper addresses the problem of age prediction from Twitter dataset, where the prediction issue is viewed as a classification task. For this purpose, an innovative model based on Convolutional Neural Network is devised. To this end, we rely on language-related features and social media specific metadata. More specifically, we introduce two features that have not been previously considered in the literature: the content of URLs and hashtags appearing in tweets. We also employ distributed representations of words and phrases present in tweets, hashtags and URLs, pre-trained on appropriate corpora in order to exploit their semantic information in age prediction. We show that our CNN-based classifier, when compared with an SVM baseline model, yields an improvement of 12.3% and 6.6% in the micro-averaged F1 score on the Dutch and English datasets, respectively.

Target audience analysis (TAA) is an essential part of any psychological operation. In order to convey a change in behaviour, the overall population is systematically segmented into target audiences (TAs) according to their expected responsiveness to different types of influence and messages, as well as their expected ability to behave in a desired way. The cyber domain poses a challenge to traditional TAA methods. Firstly, it is vast and complex, requiring effective algorithms to filter out relevant information within a meaningful timeframe. Secondly, it is constantly changing, representing a meshwork in formation, rather than a stable collection of TAA-specific data. The third challenge is that the target audience (TA) consists not of people, but of digital representations of people, whose true identity and characteristics cannot usually be verified. To address these challenges, the authors of this article suggest that the concept of TAA has to be revised for use in the cyber domain. Instead of trying to analyse physical people through the cyber interface, the authors have conceptualized an abstract entity whose physical identity might not be known, but whose behavioural patterns can be observed in the cyber environment. These cyber personalities, some of which are more or less intelligent algorithms, construct and share their interpretation of reality as well as carefully planned narratives in the digital environment. From the viewpoint of TAA, the only relevant quality of these entities is their potential ability to contribute to the objectives of a psychological operation. As a first step, this article examines the cyber domain through a five-layer structure and looks at what TAA-relevant data is available for analysis. The authors also present ways of analysing cyber personalities and their networks, in order to conduct a TAA that effectively supports psychological influence in the cyber domain. As a way of better utilizing the digital nature of cyber