Sasu Tarkoma

University of Helsinki | HY · Department of Computer Science

This paper presents the development and evaluation of a Large Language Model (LLM), also known as foundation models, based multi-agent system framework for complex event processing (CEP) with a focus on video query processing use cases. The primary goal is to create a proof-of-concept (POC) that integrates state-of-the-art LLM orchestration framewo...

Smart spaces are ubiquitous computing environments that integrate diverse sensing and communication technologies to enhance space functionality, optimize energy utilization, and improve user comfort and well-being. The integration of emerging AI methodologies into these environments facilitates the formation of AI-driven smart spaces, which further...

Water covers most of the Earth's surface and is nowhere near a good ecological or recreational state in many areas of the world. Moreover, only a small fraction of the water is potable. As climate change-induced extreme weather events become ever more prevalent, more and more issues arise, such as worsening water quality problems. Therefore, protec...

Forecasting electricity load plays a vital role in the planning and management of sustainable power systems, considering the multifaceted impacts of social, economic, technical, environmental, and cultural factors on electricity consumption. Addressing this complexity requires the development of robust models capable of handling high levels of nonl...

We analyze the impact of poisoning attacks on autonomous drones and demonstrate how explainable artificial intelligence techniques can be employed to detect them. We then delve into the risks, opportunities, and research challenges, ultimately paving the way for city-scale deployments of autonomous drones.

In the envisioned 6G landscape, data sharing is expected to become increasingly prevalent, giving rise to digital marketplaces that foster cooperation among organizations for collecting, sharing, and processing data for analysis. These marketplaces serve as connectors between data producers and consumers, empowering multi-tenancy scenarios for seam...

Our article provides a viewpoint on population digital health - the use of digital health information sourced from Health IoT and wearable devices for population health modeling - as an emerging research initiative for offering an integrated approach for continuous monitoring and profiling of diseases and health conditions at multiple spatial resol...

This paper introduces population digital health (PDH)—the use of digital health information sourced from health internet of things (IoT) and wearable devices for population health modeling—as an emerging research domain that offers an integrated approach for continuous monitoring and profiling of diseases and health conditions at multiple spatial r...

The Sustainable Development Goals (SDGs) aim to resolve societal challenges, such as eradicating poverty and improving the lives of vulnerable populations in impoverished areas. Those areas rely on road infrastructure construction to promote accessibility and economic development. Although publicly available data like OpenStreetMap is available to...

Digital Twins (DTs) are set to become a key enabling technology in future wireless networks, with their use in network management increasing significantly. We developed a DT framework that leverages the heterogeneity of network access technologies as a resource for enhanced network performance and management, enabling smart data handling in the phy...

The Sustainable Development Goals (SDGs) aim to resolve societal challenges, such as eradicating poverty and improving the lives of vulnerable populations in impoverished areas. Those areas rely on road infrastructure construction to promote accessibility and economic development. Although publicly available data like OpenStreetMap is available to...

The coronavirus disease 2019 (COVID-19) pandemic has changed the world deeply. Urban trajectory big data collected by wireless sensing devices provide great assistance for COVID-19 prevention. However, except for contact tracing, trajectory data are rarely employed in other preventative scenarios against the pandemic. In this article, we try to ext...

We develop a portable and affordable solution for estimating personal exposure to black carbon (BC) using low-cost sensors and machine learning. Our approach uses other pollutants and environmental variables as proxies for estimating the concentrations of BC and combines this with machine learning based sensor calibration to improve the quality of...

Weather forecasting plays a vital role in estimating energy generation from variable renewable energy sources (VRES). Current weather forecast methods for estimating energy from VRES are typically at a scale of a few kilometers, which is not the fine spatial resolution needed for distributed energy planning. In this paper, we propose IrMaSet (an in...

Generative AI is regarded as a major disruption to software development. Platforms, repositories, clouds, and the automation of tools and processes have been proven to improve productivity, cost, and quality. Generative AI, with its rapidly expanding capabilities, is a major step forward in this field. As a new key enabling technology, it can be us...

Littering is a significant environmental concern that causes significant damage to the natural ecosystem and contributes adversely to human health. Monitoring litter accumulation is currently labour-intensive and costly, often resulting in action being taken only once the environment has already become polluted. We contribute LIZARD, a novel pervas...

This paper offers an insightful examination of how currently top-trending AI technologies, i.e., generative artificial intelligence (Generative AI) and large language models (LLMs), are reshaping the field of video technology, including video generation , understanding, and streaming. It highlights the innovative use of these technologies in produc...

The growing number of AI-driven applications in mobile devices has led to solutions that integrate deep learning models with the available edge-cloud resources. Due to multiple benefits such as reduction in on-device energy consumption, improved latency, improved network usage, and certain privacy improvements, split learning, where deep learning m...

The Internet of Things (IoT) has revolutionized the connectivity of diverse sensing devices, generating an enormous volume of data. However, applying machine learning algorithms to sensing devices presents substantial challenges due to resource constraints and privacy concerns. Federated learning (FL) emerges as a promising solution allowing for tr...

Monitoring, understand, and being aware of pollutants are key steps in improving environmental sustainability, one of the grand challenges of our time. We contribute by presenting how low-cost sensing improves our understanding and awareness of environmental pollution by increasing coverage and resolution of available information, paving the way...

Industry 4.0, leveraging the Internet of Things (IoT) and Artificial Intelligence (AI), is a key enabler for many automated processes in modernized industrial applications. This paper addresses significant challenges pertaining to sensing and data analytics by connecting a large number of industrial IoT (IIoT) devices and deploying federated learni...

6G networks are envisioned to enable the Internet of Things (IoTs) and foster ubiquitous sensing. Urban opportunistic crowdsensing, which leverages participants carrying mobile sensing units (MSUs) in their daily activities to collect data, enables low-cost and large-scale urban sensing for applications such as air quality monitoring, pothole detec...

Cities play an important role in achieving sustainable development goals (SDGs) to promote economic growth and meet social needs. Especially satellite imagery is a potential data source for studying sustainable urban development. However, a comprehensive dataset in the United States (U.S.) covering multiple cities, multiple years, multiple scales,...

The metaverse aims to blur the boundary between the physical world and digital content. To achieve this goal, the metaverse relies heavily on extended reality (XR), the Internet of Things, and communication technologies. Concurrently, connected vehicles and intelligent transportation systems (ITSs) are envisioned as the future paradigm of driving a...

The Internet of Things (IoT) plays a significant role in the development and future evolution of smart cities by connecting physical devices and systems to the Internet to collect and exchange data, automate processes, and improve overall urban management and quality of life. This chapter presents the requirements and challenges to realize IoT depl...

p>We develop a portable and affordable solution for estimating personal exposure to black carbon (BC) using low- cost sensors and machine learning. Our approach uses other pollutants and environmental variables as proxies for estimating the concentrations of BC and combines this with machine learning based sensor calibration to improve the quality...

p>We develop a portable and affordable solution for estimating personal exposure to black carbon (BC) using low- cost sensors and machine learning. Our approach uses other pollutants and environmental variables as proxies for estimating the concentrations of BC and combines this with machine learning based sensor calibration to improve the quality...

This paper proposes the neural publish/subscribe paradigm, a novel approach to orchestrating AI workflows in large-scale distributed AI systems in the computing continuum. Traditional centralized broker methodologies are increasingly struggling with managing the data surge resulting from the proliferation of 5G systems, connected devices, and ultra...

Cities play an important role in achieving sustainable development goals (SDGs) to promote economic growth and meet social needs. Especially satellite imagery is a potential data source for studying sustainable urban development. However, a comprehensive dataset in the United States (U.S.) covering multiple cities, multiple years, multiple scales,...

Carbon monoxide (CO) and Nitrogen dioxide (NO2) are major air pollutants that have the potential to affect human health adversely. There is a lack of useful information regarding the spatial distribution and temporal variability of CO and NO2 emissions in major metropolitan areas. The primary goal of this research is to provide a geospatial data me...

Unmanned Aerial Vehicles (UAVs) equipped with air quality sensors offer a powerful solution for increasing the spatial and temporal resolution of air quality data, searching and detecting emission sources, and monitoring emissions from fixed and mobile sources. Despite the numerous advantages of using UAVs, their use, however, presents several chal...

Spark is one of the most popular big data analytical platforms. To save time, achieve high resource utilization, and remain cost-effective for Spark jobs, it is challenging but imperative for data scientists to configure suitable resource portions.In this paper, we investigate the proper parameter values that meet workloads’ performance requirement...

The rapid growth of megacities has led to higher levels of air pollution in cities. To supplement fixed air quality monitoring sites, the megacities offer an unprecedented opportunity to deploy air quality sensors on public transportation systems, and thus enable air quality monitoring at different locations in the city within the routes of the tra...

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 c...

Mobile apps have become an indispensable part of people’s daily lives. Users determine what apps to use and when and where to use them based on their tastes, interests, and personal demands, depending on their personality traits. This paper aims to infer user profiles from their spatiotemporal mobile app usage behavior. Specifically, we first trans...

Satellite imagery depicts the earth’s surface remotely and provides comprehensive information for many applications, such as land use monitoring and urban planning. Existing studies on unsupervised representation learning for satellite images only take into account the images’ geographic information, ignoring human activity factors. To bridge this...

The present article contributes a research vision for virtual sensing that combines Artificial Intelligence (AI) and Internet of Things (IoT) to increase the coverage of air quality information. Virtual sensors take advantage of correlations between different pollutants to estimate the concentrations of pollutants for which no affordable sensors ar...

The COVID-19 pandemic highlighted the need to prioritise mature digital health and data governance at both national and supranational levels to guarantee future health security. The Riyadh Declaration on Digital Health was a call to action to create the infrastructure needed to share effective digital health evidence-based practices and high-qualit...

Air quality low-cost sensors are affordable and can be deployed in massive scale in order to enable high-resolution spatio-temporal air pollution information. However, they often suffer from sensing accuracy, in particular when they are used for capturing extreme events. We propose an intelligent sensors calibration method that facilitates correcti...

Dangers associated with poor air quality are driving deployments of air quality monitoring technology worldwide. Having a comprehensive understanding of the health effects of pollutants requires understanding both the distribution and dispersion of pollutants in the environments, but currently this information is highly difficult to capture. This a...

In a post-pandemic 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 requ...

Recently, artificial intelligence paves the way for the development of smart services for people anytime and anywhere, which poses great challenges on accessing computing resources. Multi-access edge computing complements existing cloud computing infrastructure at the edge of the network, where mobile users can offload computationally intensive tas...

We study user's valuations of smartphone sensing resources and the factors mediating them through a systematic auction study with 108 bids from $N=18$ participants, two resource use conditions [fixed battery (FB) and variable battery (VB)] and three sensors (camera, microphone, and GPS) with differing energy and privacy costs. We use a second-pri...

Underwater environments are emerging as a new frontier for data science thanks to an increase in deployments of underwater sensor technology. Challenges in operating computing underwater combined with a lack of high-speed communication technology covering most aquatic areas means that there is a significant delay between the collection and analysis...

The architectures of mobile networks have seen an unprecedented techno-economic transformation, fusing the telcommunications world within the cloud world, adding the spices of Software Engineering to the overall system design, and ultimately yielding the concept of Telco Cloud. This has brought significant benefits in terms of reducing expenditure...

Air pollution is known to be harmful for human health and environments. The official air quality monitoring stations have been established across many smart cities around the world. Unfortunately, these monitoring stations are sparsely located and consequently do not provide high resolution spatio-temporal air quality information. This paper demons...

The Internet has been experiencing immense growth in multimedia traffic from mobile devices. The increase in traffic presents many challenges to user-centric networks, network operators, and service providers. Foremost among these challenges is the inability of networks to determine the types of encrypted traffic and thus the level of network servi...

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.

p>Autonomous drones are reaching a level of maturity when they can be deployed in cities to support tasks ranging from medicine or food delivery to environmental monitoring. These operations rely on powerful AI models integrated into the drones. Ensuring these models are robust is essential for operating in cities as any errors in the decisions of...

p>Autonomous drones are reaching a level of maturity when they can be deployed in cities to support tasks ranging from medicine or food delivery to environmental monitoring. These operations rely on powerful AI models integrated into the drones. Ensuring these models are robust is essential for operating in cities as any errors in the decisions of...

Context-based authentication has been proposed as a way to enable secure authentication with minimal or even no user interaction requirements by using sensor data to ensure the device being authenticated is in possession of the person initiating the authentication request. A key limitation of practically all context-based authentication systems is...

Covert surveillance devices ranging from miniature cameras to voice recorders are increasingly affordable and accessible on the market, raising concerns about surreptitious and unauthorized observation of people. This article contributes an innovative method for discovering covert surveillance devices using thermal imaging integrated with off-the-s...

Understanding economic development and designing government policies requires accurate and timely measurements of socioeconomic activities. In this paper, we show how to leverage city structural information and urban imagery like satellite images and street view images to accurately predict multi-level socioeconomic indicators. Our framework consis...

Applications based on machine learning (ML) are greatly facilitated by mobile devices and their enormous volume and variety of data. To better safeguard the privacy of user data, traditional ML techniques have transitioned toward new paradigms like federated learning (FL) and split learning (SL). However, existing frameworks have overlooked device...

We quantify and derive a general model for the collaboration stability of human mobility and demonstrate its importance for networking applications. Our results demonstrate that collaboration opportunities are highly dependent on the context where they take place, with diurnal patterns and spatial characteristics being particularly important.

Mobile devices and the immense amount and variety of data they generate are key enablers of machine learning (ML)-based applications. Traditional ML techniques have shifted toward new paradigms such as federated (FL) and split learning (SL) to improve the protection of user's data privacy. However, these paradigms often rely on server(s) located in...

Formaldehyde is a carcinogenic indoor air pollutant emitted from common wood-based materials. Low-cost sensing of formaldehyde is difficult due to inaccuracies in measuring low concentrations and susceptibility of sensors to changing indoor environmental conditions. Currently gas sensors are calibrated by manufacturers using simplistic models which...

The COVID-19 pandemic is posing significant challenges to public transport operators by drastically reducing demand while also requiring them to implement measures that minimize risks to the health of the passengers. While the collective scientific understanding of the SARS-CoV-2 virus and COVID-19 pandemic are rapidly increasing, currently there i...