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The proliferation of the Internet of Things (IoT), artificial intelligence (AI), the adoption of 5G, and progress towards 6G technology have led to the accumulation of massive amounts of real-world data; however, a significant portion of the data generated by smart cities and smart buildings remains unused. A notable problem is the shift of statistical properties in real-world streaming over time caused by unexpected factors, referred to as concept drift, which results in less efficient predictive models. To address this problem, the latest research leverages the cloud–edge continuum paradigm for the deployment of AI and general smart city applications while utilising the available resources optimally. In this article, we propose a computing architecture for different smart city applications in edge micro data centre (EMDC) settings over a hybrid cloud–edge continuum to support the deployment of AI workloads. We implement a feedback-driven automated concept drift detection and adaptation methodology, combining base learner long short-term memory (LSTM) with Page–Hinkley test (PHT), adaptive windowing (ADWIN) and the Kolmogorov–Smirnov windowing (KSWIN). Real-world data streams are utilised to forecast from various environmental sensors installed at the University of Oulu Smart Campus. The feedback-based concept drift detection and adaption process is first evaluated using synthetic datasets with known concept drift points and then employed in the real-world data. Subsequently, the implementation is evaluated using the state-of-the-art MAE, RMSE, and MAPE methods. The results showed a reduction in MAPE from 8.5% to 3.88% when concept drift detection was applied. Additionally, the challenges faced and the effectiveness of the suggested solutions are explored.

Shark depredation is a complex social-ecological issue that affects a range of fisheries worldwide. Increasing concern about the impacts of shark depredation, and how it intersects with the broader context of fisheries management, has driven recent research in this area, especially in Australia and the United States. This review synthesises these recent advances and provides strategic guidance for researchers aiming to characterise the occurrence of depredation, identify the shark species responsible, and test deterrent and management approaches to reduce its impacts. Specifically, the review covers the application of social science approaches, as well as advances in video camera and genetic methods for identifying depredating species. The practicalities and considerations for testing magnetic, electrical, and acoustic deterrent devices are discussed in light of recent research. Key concepts for the management of shark depredation are reviewed, with recommendations made to guide future research and policy development. Specific management responses to address shark depredation are lacking, and this review emphasizes that a “silver bullet” approach for mitigating depredation does not yet exist. Rather, future efforts to manage shark depredation must rely on a diverse range of integrated approaches involving those in the fishery (fishers, scientists and fishery managers), social scientists, educators, and other stakeholders.

Exponential population growth and urbanisation pose potential challenges to mobility, governance, well-being, the environment, and the safety of modern cities. This demands data-driven predictions and decision-making systems to achieve sustainable societal goals. Smart city data are being employed to improve citizens’ lifestyles, derive climate initiatives, provide quality health care and education, achieve better governance, and design better urban policies. However, the data from smart cities is vast and heterogeneous, requiring efficient and fault-tolerant data platforms supporting continuous data collection, storage, analysis, visualisation, and results delivery in both batch and real-time fashion. In addition, real-world data brings challenges and may come from malfunctioning, replaced or differently calibrated devices. Concept drift is a crucial barrier to relying on predictions from real-world data streams. It emerges due to unforeseen reasons, changes in statistical properties and the context of data while performing predictive modelling. This thesis focuses on the challenges mentioned by investigating and proposing efficient concept drift detection approaches, providing distributed data pipeline architectures, and highlighting the potential challenges of concept drift in terms of real-world applicability. As a result, two different algorithms are proposed to perform predictive modelling using machine learning methods integrated with concept drift detection and adaptation methods. The experiment showed that integrating concept drift detection with predictive models increases the effectiveness of drawn predictions. Secondly, a cloud computing-based distributed data pipeline architecture is provided to support data collection, data analysis, concept drift detection, and others. Similarly, an edge computing-based distributed data pipeline is proposed for edge micro data centres to perform computationally demanding processes. The proposed data pipelines, Tiivistelmä Eksponentiaalinen väestönkasvu ja kaupungistuminen haastavat modernien kaupunkien hallintoa, logistiikkaa, ihmisten sekä ympäristön hyvinvointia ja turvallisuutta. Kestävien yhteiskunnallisten tavoitteiden saavuttamiseksi voidaan hyödyntää datapohjaisia päätöksentekojärjestelmiä. Älykaupunkien dataa käytetään tukemaan kansalaisten elämäntapoja sekä ilmastoaloitteita, tuottamaan laadukasta terveydenhuoltoa ja koulutusta sekä sujuvoittamaan hallintoa ja kaupunkipolitiikkaa. Kaupungeista saatava data on monimuotoista ja laajaa, saattaa sisältää virheitä ja sen käsittely vaatii tehokkaita ja vikasietoisia data-alustoja. Alustojen on tuettava jatkuvaa datan keruuta, tallennusta, analysointia, visualisointia ja tulosten toimittamista sekä erä- että reaaliaikaisesti. Käsitevuoto (concept drift) on keskeinen este luotettavien ennusteiden tekemiselle reaalimaailman datavirroista. Käsitevuoto ilmenee ennakoimattomista syistä, datan tilastollisten ominaisuuksien ja kontekstin muutoksista ennustavaa mallinnusta tehdessä, johtaen epäluotettaviin datapohjaisiin ennusteisiin. Työssä tutkitaan käsitevuotoa erityisesti laboratorion ulkopuolella tosimaailmassa. Tuloksena esitetään kaksi erilaista algoritmia käsitevuon havaitsevaan ennustavaan mallinnukseen. Algoritmit ovat koneoppimenetelmiä, joihin on integroitu käsitevuodon havaitsemis- sekä mukautumismekanismit. Työssä osoitetaan, että kun integroidaan käsitevuodon havaitseminen ennustemallinnukseen, voidaan lisätä ennusteiden tehokkuutta. Toiseksi työssä kehitettiin pilvilaskentaan perustuva hajautettu dataputkiarkkitehtuuri, joka tukee datan keruuta, datan analysointia, käsitevuodon havaitsemista ja muita reaaliaikaiseen mallintamiseen tarvittavia datankäsittelymekanismeja. Lisäksi kehitettiin reunalaskentaan perustuva hajautettu dataputkijärjestelmä mikrodatakeskuksineen laskennallisesti raskaita datakäsittelytehtäviä varten. Ehdotetut dataputket ovat vikasietoisia, ne voidaan skaalata saumattomasti ja ne tukevat er

Shark depredation is a complex social-ecological issue that affects a range of fisheries worldwide. Increasing concern about the impacts of shark depredation, and how it intersects with the broader context of fisheries management, has driven recent research in this area, especially in Australia and the United States. This review synthesises these recent advances and provides strategic guidance for researchers aiming to characterise the occurrence of depredation, identify the shark species responsible, and test deterrent and management approaches to reduce its impacts. Specifically, the review covers the application of social science approaches, as well as advances in video camera and genetic methods for identifying depredating species. The practicalities and considerations for testing magnetic, electrical, and acoustic deterrent devices are discussed in light of recent research. Key concepts for the management of shark depredation are reviewed, with recommendations made to guide future research and policy development. Specific management responses to address shark depredation are lacking, and this review emphasizes that a “silver bullet” approach for mitigating depredation does not yet exist. Rather, future efforts to manage shark depredation must rely on a diverse range of integrated approaches involving those in the fishery (fishers, scientists and fishery managers), social scientists, educators, and other stakeholders.

Climate change is predicted to alter the distributions of tropical tuna stocks in the Pacific Ocean. Recent modelling projects significant future shifts in tuna biomass from west to east, and from national jurisdictions to high seas areas. As the distributions of these stocks change, the relevant regional fisheries management organisations (RFMOs)—the Western and Central Pacific Fisheries Commission (WCPFC) and the Inter-American Tropical Tuna Commission (IATTC)—will need to develop an expanded framework for cooperation and collaboration to fulfil their conservation and management responsibilities under international law. The key elements of a possible expanded framework for cooperation can be developed, and fundamental areas for collaboration identified, by applying and adapting principles established in the United Nations Convention on the Law of the Sea, the United Nations Fish Stocks Agreement, and the constituent instruments of the RFMOs themselves. Our analysis reveals a wide range of important issues requiring cooperation, and three clear priorities. First, a formal mechanism for cooperation is needed to enable effective and efficient decision-making and action by the two RFMOs on key issues. Second, further cooperation is required in scientific research and modelling to better understand the biology and distributions of Pacific tuna stocks and how they will respond to climate change, and to inform stock assessments and harvest strategies. Third, the RFMOs must cooperate to define appropriate limits on fishing for each stock in a way that ensures they are compatible across the two organisations, taking into account their different members and management regimes.

In a postpandemic world, remaining vigilant and maintaining social distancing are still crucial so societies can contain the virus and the public can avoid disproportionate health impacts. Augmented reality (AR) can visually assist users in understanding the distances in social distancing. However, integrating external sensing and analysis is required for social distancing beyond the users’ local environment. We present DistAR, an android-based application for social distancing leveraging AR and smart sensing using on-device analysis of optical images and environment crowdedness from smart campus data. Our prototype is one of the first efforts to combine AR and smart sensing technologies to create a real-time social distancing application.

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.

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.

Social distancing is a critical tool for mitigating disease transmission, particularly in crowded indoor spaces. In this paper, we contribute by assessing the feasibility of re-purposing existing infrastructure of occupancy monitoring sensors and environmental sensors for the dual purpose of monitoring social distancing and supporting disease transmission risk estimation. We consider 410 continuous days of measurements from CO 2 and PIR (passive infrared) motion detectors collected from a collaborative smart space, prior to the start of the pandemic in 2017–2018. We demonstrate how these sensors can be used to estimate occupancy levels, as well as analyze occupancy patterns within the space. We also consider the use of overall air quality within the space for estimating insights about potential transmission risks. Based on our analysis, we derive insights into how infrastructure-based sensors can be used to detect problematic areas in the space and offer guidelines on how to modify these areas to be more social distancing aware.

Real-world data streams pose a unique challenge to the implementation of machine learning (ML) models and data analysis. A notable problem that has been introduced by the growth of Internet of Things (IoT) deployments across the smart city ecosystem is that the statistical properties of data streams can change over time, resulting in poor prediction performance and ineffective decisions. While concept drift detection methods aim to patch this problem, emerging communication and sensing technologies are generating a massive amount of data, requiring distributed environments to perform computation tasks across smart city administrative domains. In this article, we implement and test a number of state-of-the-art active concept drift detection algorithms for time series analysis within a distributed environment. We use real-world data streams and provide critical analysis of results retrieved. The challenges of implementing concept drift adaptation algorithms, along with their applications in smart cities, are also discussed.

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.

Sentiment analysis, also known as opinion mining, plays a big role in both private and public sector Business Intelligence (BI); it attempts to improve public and customer experience. Nevertheless, de-identified sentiment scores from public social media posts can compromise individual privacy due to their vulnerability to record linkage attacks. Established privacy-preserving methods like k-anonymity, l-diversity and t-closeness are offline models exclusively designed for data at rest. Recently, a number of online anonymization algorithms (CASTLE, SKY, SWAF) have been proposed to complement the functional requirements of streaming applications, but without open-source implementation. In this paper, we present a reusable Apache NiFi dataflow that buffers tweets from multiple edge devices and performs anonymized sentiment analysis in real-time, using randomization. The solution can be easily adapted to suit different scenarios, enabling researchers to deploy custom anonymization algorithms.