Action-Oriented Programming Model: Collective Executions and Interactions in the Fog

Mäkitalo, Niko; Aaltonen, T.; Raatikainen, Mikko; Ometov, Aleksandr; Andreev, Sergey; Koucheryavy, Yevgeni; Mikkonen, Tommi

doi:10.1016/j.jss.2019.110391

Cited by 16 publications

(12 citation statements)

References 36 publications

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“…In this article, we demonstrated how abstract sensations can be generated with the Human Data Model framework and how these can be used in a software application. The model builds on collective Human Data Model executions, which simplify developing applications that are run on the Cloud, in the Fog, or at the Edge [43]. At the moment, the framework and the sample code for this are available online for Node.js and iOS/watchOS platforms.…”

Section: Discussionmentioning

confidence: 99%

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Human Data Model

Mäkitalo

Flores-Martín

Flores

et al. 2020

ACM Trans. Comput. Healthcare

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Today, an increasing number of systems produce, process, and store personal and intimate data. Such data has plenty of potential for entirely new types of software applications as well as for improving old applications, in particular in the domain of smart healthcare. However, utilizing this data, especially when it is continuously generated by sensors and other devices, with the current approaches is complex-data is often using proprietary formats and storage, and mixing and matching data of different origin is not easy. Furthermore, many of the systems are such that they should stimulate interactions with humans, which further complicates the systems. In this article, we introduce Human Data Model (HDM)-a new tool and a programming model for programmers as well as end-users with scripting skills that help to combine data from various sources, to perform computations, and to develop and schedule computer-human interactions. Written in JavaScript, the software implementing the model can be run in almost any computer either inside the browser or using Node.js. Its source code can be freely downloaded from GitHub, and the implementation can be used with the existing IoT platforms. As a whole, the paper is inspired by a number of interviews with professionals, and an online survey among healthcare and education professionals, where the results show that the interviewed subjects almost entirely lack ideas on how to benefit the ever-increasing amount of data measured of the humans. We believe that this is because of the missing support for programming models for accessing and handling the data, which can be satisfied with the Human Data Model. CCS Concepts: • Information systems → Data management systems; • Software and its engineering → Distributed systems organizing principles; Software design techniques; • Human-centered computing → Human computer interaction (HCI); Ubiquitous and mobile computing; • Computer systems organization → Distributed architectures; Embedded systems.

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Section: Discussionmentioning

confidence: 99%

“…Section we describe the current implementation of Human Data Model 2 . The underlying programming model builds on our previous work [43], where an action-oriented programming model based on collective executions is introduced.…”

Section: Realization Of Human Data Modelmentioning

confidence: 99%

Human Data Model

Mäkitalo

Flores-Martín

Flores

et al. 2020

ACM Trans. Comput. Healthcare

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“…Moreover, the IoT clouds could be used to store the data more efficiently. Fog computing aims to expand the capabilities of cloud storage as well as provide a functionality of transparent and collective interactions [82]. This combination offers the most efficient way to distribute the load on servers and devices, which will affect the efficiency of the integrated system [14], [48].…”

Section: Future Perspectivementioning

confidence: 99%

Demystifying Blockchain Technology for Resource-Constrained IoT Devices: Parameters, Challenges and Future Perspective

Romashkova¹,

Komarov

Ometov

2021

IEEE Access

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One of the most promising enablers for the secure distributed operation of the Internet of Things (IoT) systems could be based on a mathematical construct widely known as blockchain that aims to neglect the system's centralization and scalability properties. This paper aims to map the requirements and features of both systems, highlight the main integration challenges, and technological candidates for smoother integration of IoT and blockchain, as well as highlight the standartization outlook. This work has identified an architectural approach to an integrated solution based on classic literature review methodology aiming to consider the IoT versus blockchain characteristics mapping and outlining related integration challenges. Potential integration challenges of the proposed integrated approach are also identified and classified. Critical solutions to address the integration bottlenecks include moving from Proof-of-Work (PoW) to Distributed Proof-of-Stake (DPoS) consensus, adding a Fog overlay to the architecture model, and leveraging the synergies combining the benefits of blockchain and IoT technology are highlighted.

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“…Although the centralized cloud has traditionally been utilized to manage, process, and store data, it poses two major problems: (i) processing latency should be as short as milliseconds, but it can be critical; (ii) all of this data creates a significant bandwidth load. In contrast, Edge computing offers a solution to the latency problem by moving critical processing to the Edge network [21,26]. Edge devices can collect and process data in real-time, allowing them to respond faster and more efficiently.…”

Section: Related Challengesmentioning

confidence: 99%

When wearable technology meets computing in future networks

Ometov

Chukhno

et al. 2021

Proceedings of the 18th ACM International Conference on Computing Frontiers

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Rapid technology advancement, economic growth, and industrialization have paved the way for developing a new niche of small body-worn personal devices, gathered together under a wearabletechnology title. The triggers stimulated by end-users interest have introduced the first generation of mass-consumer wearables in just the past decade. Evidently, the trailblazing ones were not designed with strict energy-consumption restrictions in mind. Thus, wearable-computing-related research remained fragmented. Advanced and sophisticated batteries and communication technologies could be already procurable on devices. Additional solutions for efficient utilization of processing power are still a white spot on the wearable technology roadmap. A-WEAR EU project aims to enhance the understanding of how the superimposition of those technologies would improve wearable devices' energy efficiency, with the research area being far from saturation. We foresee enormous room for research as the Edge computing paradigm is emerging towards hand-held devices. CCS CONCEPTS• General and reference → Cross-computing tools and techniques; • Hardware → Networking hardware; Wireless devices; Emerging architectures; • Computer systems organization → Distributed architectures; • Networks → Network topology types; Wireless access networks.

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Action-Oriented Programming Model: Collective Executions and Interactions in the Fog

Cited by 16 publications

References 36 publications

Human Data Model

Human Data Model

Demystifying Blockchain Technology for Resource-Constrained IoT Devices: Parameters, Challenges and Future Perspective

When wearable technology meets computing in future networks

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