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Author

Milan Milenkovic

Bio: Milan Milenkovic is an academic researcher. The author has contributed to research in topics: Interoperability & Metadata. The author has co-authored 1 publications.

Papers
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Book ChapterDOI
01 Jan 2020
TL;DR: One of the major IoT system design challenges is to address the need for interoperability, i.e., represent information in a form whose meaning is independent of the application generating or using it.
Abstract: One of the major IoT system design challenges is to address the need for interoperability. As mentioned in chapter “ Introduction and Overview”, achieving interoperability is estimated to increase the potential IoT market value by 40% [1]. The usefulness of IoT systems generally increases with their scale, volume, and variety of data. Many complex IoT systems – for instance, smart cities – require coordination of different subsystems that may and often use different data formats. The target is to achieve semantic or, as the Industrial Internet Consortium referred to it [2], conceptual interoperability, i.e., “represent information in a form whose meaning is independent of the application generating or using it.”

5 citations


Cited by
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Proceedings ArticleDOI
16 Nov 2022
TL;DR: In this article , the authors proposed a data product devised must adhere to the discrete feed-forward mechanisms in the architecture and incorporate data governance and quality controls inside the data product's architecture.
Abstract: Having applications across sectors, IoT applications rely heavily on sensory data. Because of its variety, complexity, and dynamic nature, IoT data is susceptible to inaccuracies. Decisions taken on such data are likely to be consequently flawed. To overcome the said pitfalls, data quality and trustworthiness need to be maintained. Since IoT architecture is multi-layered, the data product devised must adhere to the discrete feed-forward mechanisms in the architecture. Further, owing to the presence of both physical & virtual systems, and stringent privacy policies for data sharing in the IoT ecosystem, incorporating data governance and quality controls inside the data product's architecture becomes necessary.
Journal ArticleDOI
TL;DR: The background to the Newcastle Urban Observatory project is provided and the socio-technical and practical challenges of developing and maintaining smart city networks of sensors in the plurality that is a modern city are discussed.
Abstract: The smart city term has been widely used for a number of years and many pilot projects and limited scale, sector independent initiatives have been progressed, but comprehensive, long-term, city wide, multi-sector systems are much less evident. This paper examines one such case study in Newcastle, UK highlighting the challenges and opportunities that realizing “smart city” concepts at scale present. The paper provides the background to the Newcastle Urban Observatory project and discusses the socio-technical and practical challenges of developing and maintaining smart city networks of sensors in the plurality that is a modern city. We discuss the organizational requirements, governance, data quality and volume issues, big data management and discuss the current and future needs of decision makers and other city stakeholders. Finally, we propose areas where smart cities can have a positive impact on public outcomes through the discussion of two case studies related to COVID-19 and pedestrianization initiatives.
Journal ArticleDOI
TL;DR: In this article , the authors proposed a low-layer metadata collection method for device management and control in the Internet of Things (IoT), which utilizes the extended area of the probe request (PRQ) frame of IEEE 802.11.
Abstract: With the rapid spread of Internet of Things (IoT) services, the number of sensor devices has exploded, and the complexity of managing sensor devices has become a problem. To solve this problem, a metadata-based approach that uses the unique environmental information associated with each device for its management is being developed. This paper focuses on metadata collection for device management and control, and proposes a new collection method that uses low-layer communication while not modifying the existing protocol. Our proposal utilizes the extended area of the probe request (PRQ) frame of IEEE 802.11, which is a layer-2 protocol, to collect metadata. This makes it possible to achieve stable operation even on inexpensive and resource-limited IoT devices, and to realize metadata collection with low communication overhead and power consumption. It is shown to reduce the load on the central processing unit (CPU) and reduce power consumption compared to Internet Protocol (IP) -based metadata collection (layer-3 protocol and above). In addition, in terms of time sensitivity, the collection delay at the time of rising from deep sleep is reduced by 89.8% compared to IP-based techniques. Furthermore, as a benefit of low-layer collection, it enables periodic metadata collection in the background regardless of network connection above the IP layer. To demonstrate this benefit, an example of applying metadata collection to device management is prototyped and its feasibility is experimentally confirmed.
Proceedings ArticleDOI
16 Nov 2022
TL;DR: In this article , the authors proposed a data product devised must adhere to the discrete feed-forward mechanisms in the architecture and incorporate data governance and quality controls inside the data product's architecture.
Abstract: Having applications across sectors, IoT applications rely heavily on sensory data. Because of its variety, complexity, and dynamic nature, IoT data is susceptible to inaccuracies. Decisions taken on such data are likely to be consequently flawed. To overcome the said pitfalls, data quality and trustworthiness need to be maintained. Since IoT architecture is multi-layered, the data product devised must adhere to the discrete feed-forward mechanisms in the architecture. Further, owing to the presence of both physical & virtual systems, and stringent privacy policies for data sharing in the IoT ecosystem, incorporating data governance and quality controls inside the data product's architecture becomes necessary.