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Efficient Vehicle-to-Everything enabling cooperation and enhanced decision-making for autonomous vehicles is essential for optimized and safe traffic. Real-time decision-making based on vehicle sensor data, other traffic data, and environmental and contextual data becomes imperative. As a part of such Intelligent Traffic Systems, cooperation between different stakeholders needs to be facilitated rapidly, reliably, and securely. The Internet of Things provides the fabric to connect these stakeholders who share their data, refined information, and provided services with each other. However, these cloud-based systems struggle to meet the real-time requirements for smart traffic due to long distances across networks. Here, edge computing systems bring the data and services into the close proximity of fast-moving vehicles, reducing information delivery latencies and improving privacy as sensitive data is processed locally. To solve the issues of trust and latency in data sharing between these stakeholders, we propose a decentralized framework that enables smart contracts between traffic data producers and consumers based on blockchain. Autonomous vehicles connect to a local edge server, share their data, or use services based on agreements, for which the cooperating edge servers across the system provide a platform. We set up proof-of-concept experiments with Hyperledger Fabric and virtual cars to analyze the system throughput with secure unicast and multicast data transmissions. Our results show that multicast transmissions in such a scenario boost the throughput up to 2.5 times where the data packets of different sizes can be transmitted in less than one second.

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.

The use of face masks is an important way to fight the COVID-19 pandemic. In this paper, we envision the Smart Mask, an IoT supported platform and ecosystem aiming to prevent and control the spreading of COVID-19 and other respiratory viruses. The integration of sensing, materials, AI, wireless, IoT, and software will help the gathering of health data and health-related event detection in real time from the user as well as from their environment. In the larger scale, with the help of AI-based analysis for health data it is possible to predict and decrease medical costs with accurate diagnoses and treatment plans, where the comparison of personal data to large-scale public data enables drawing up a personal health trajectory, for example. Key research problems for smart respiratory protective equipment are identified in addition to future research directions. A Smart Mask prototype was developed with accompanying user application, backend and heath AI to study the concept.

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.

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.

The complex and opportunistic environment in which edge computing systems operate, poses a fundamental challenge for online edge system orchestration, resource provisioning and real-time responsiveness in response to user movement. Such a challenge needs to addressed throughout the edge system lifecycle, starting from the software development methodologies. In this paper, we propose a novel development process for modeling opportunistic edge computing services, which rely on (i) ETSI MEC reference architecture and Opportunistic Internet of Things Service modeling for the early stage of system analysis and design, i.e. domain model and service metamodel; and on (ii) feature engineering for evaluating those opportunistic aspects with data analysis. To address the identified opportunistic properties, at the service design phase we construct (both automatically and through domain expertise) Opportunistic Feature Vectors for Edge, containing the numerical representations of those properties. Such vectors enable further data analysis and machine learning techniques in the development of distributed, effective and efficient edge computing systems. Lastly, we exemplify the integrated process with a microservice-based user mobility management service, based on a real-world data set, for online analysis in MEC systems.

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.

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.

In the envisioned ubiquitous world, services will follow users as they move across smart surroundings. Services are instantiated to users through the environment, appearing and disappearing as they move, which reduces the need for personal communication devices such as smartphones or tablets. To facilitate this development, service architectures need to support virtualized, on-demand service composition based on the hardware and software resources available at the current user location. The technical context for this type of user interaction with digital services through smart surroundings is called Internet of Everything (IoE). Today’s service architectures will be too inflexible in this highly decentralized and dynamic environment. Hence, in this article we propose a novel service model called nanoEdge, where nodes collaboratively provide needed functions for virtual services that need to be deployed locally due to performance, efficiency or reliability requirements, for example. The main contributions of this article are the nanoEdge conceptual model and its proof-of-concept (PoC) implementation to show that the model is feasible with regard to performance and resource-efficiency. The successful demonstration of PoC implementation exemplifies future IoE service scenarios with today’s hardware components.

In this paper, the Multi-access Edge Computing (MEC) system architecture, as defined by the ETSI standards, is modeled as a multi-agent system. MEC system management services and application execution components are designed as software agents, facilitating distributed artificial intelligence capabilities in their operation and cooperation. Further, the integration of current agent technologies into the standardized MEC system is discussed. Lastly, a case study is presented on how to integrate an existing Internet of Things agent framework and agent-based edge application seamlessly to the MEC system.

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.

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 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

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.

We introduce a novel interaction method which integrates humans into Internet of Things and advances from the existing client-server paradigm. We take advantage of the shared physical space to facilitate decentralized and seamless human-to-thing, human-to-human and thing-to-thing interactions. We build these interactions upon two core technologies: mobile agents and NFC. Mobile agents realize autonomous execution of user-specific interaction tasks among things while facilitating cooperation and interoperability. Humans initiate and control interactions through physical actions that trigger communications, e.g. mobile agent migration, between NFC devices even over disparate systems. Human social relationships are utilized to disseminate tasks further in the IoT system. We discuss the benefits of this method in comparison with the common smartphone-based control of smart spaces. Real-world evaluation shows that this interaction method is feasible for resource-constrained embedded IoT devices.

We present how NFC transponders can be used for energy efficient programming of closed-loop sensor networks, to update or augment the existing functionality. Use cases include road tunnel inspection, water pipeline monitoring and maintaining safety information on behalf of mine workers. We utilize opportunistic movement of the human operator, the flow of fluid in a pipeline or material in mines, to move the NFC transponder in the system effortlessly and without external network connectivity. Transponders contain mobile agents in their memory, which are injected into the system when transponder comes to the proximity of a node with NFC reader component. Then mobile agents autonomously operate their tasks, i.e. collect and process sensor data in the devices, detect events from data, control physical components and report their results. Mobile agents can adapt to the operational conditions of the system and physical environment, e.g. to save energy or operate in isolated network segments in fault situations. Real-world evaluation shows that this method is energy efficient in comparison with communications atop similar wireless sensor network.

The infrastructure built in the City of Oulu provides rich information about the city environment and objects moving in it. We utilize this infrastructure in building an IoT system for data-intensive smart city services; by collecting data from real city environment and developing analysis methods for these data. We are building Smart City Traffic Pilot on top of the infrastructure to provide the functionality to collect the data and perform the analysis. Based on this experience, we present in this article requirements for data-intensive smart city services. Moreover, we describe four implemented use cases for utilizing rich data sources available in the smart city: situational picture, driving coach, real time reasoning, and mobile code. A lively collaboration between a large number of different actors is essential in realizing these use cases. Finally, we discuss how the use cases fulfill the requirements and the lessons we have learnt.

This study outlines initial steps to define a new framework to measure relatedness, originality and creativity of student projects in FabLab environment. A default project topic provided to students in a 3D fabrication and prototyping class served as a basis to investigate originality on the functional component level. The added components with their input and output methods, along with the control logic, were used to judge the relatedness to a given solution of the generated design ideas. An example set of ideas given by the students was evaluated with the proposed framework. The framework can complement existing measures of originality and creativity in general.