Fig 6 - uploaded by Pal Varga
Content may be subject to copyright.
Source publication
We have reached the point with Internet of Things (IoT) end-devices, when procedures of dismantling have to be discussed and put into practice. The first machine-to-machine (M2M) devices were those sensors and actuators that have been exchanging information over the Internet for over 10 years. The early M2M devices are now reaching their end of lif...
Context in source publication
Context 1
... order to demonstrate the different stages and phases of the MoL from the device point of view, we illustrate the model with the life of active devices around us: the UE within the mobile network. Figure 6 shows the main elements of the LTE network, especially its core. In order to support understanding of the coming subsections, let us provide a brief overview of the elements. ...
Similar publications
Multi-access Edge Computing promises to improve mobile users experience by bringing storage and processing capabilities to the edges of the network. This approach allows third parties to deploy innovative services, like augmented reality, directly in the radio access network paving the way for new business models and new revenue stream for mobile n...
Citations
... For each new idea implemented within an IoT ecosystem, it is essential to consider the tight integration of all physical and digital components [29], [30]. Evaluating a new idea is a laborious task, with simulation tools providing a cost-effective way to assess a complex system or use case. ...
UMBRELLA
<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</sup>
is an open, large-scale IoT ecosystem deployed across South Gloucestershire, UK. It is intended to accelerate innovation across multiple technology domains. UMBRELLA is built to bridge the gap between existing specialised testbeds and address holistically real-world technological challenges in a System-of-Systems (SoS) fashion. UMBRELLA provides open access to real-world devices and infrastructure, enabling researchers and the industry to evaluate solutions for Smart Cities, Robotics, Wireless Communications, Edge Intelligence, and more. Key features include over 200 multi-sensor nodes installed on public infrastructure, a robotics arena with 20 mobile robots, a 5G network-in-a-box solution, and a unified backend platform for management, control and secure user access. The heterogeneity of hardware components, including diverse sensors, communication interfaces, and GPU-enabled edge devices, coupled with tools like digital twins, allows for comprehensive experimentation and benchmarking of innovative solutions not viable in lab environments. This paper provides a comprehensive overview of UMBRELLA’s multi-domain architecture and capabilities, making it an ideal playground for Internet of Things (IoT) and Industrial IoT (IIoT) innovation. It discusses the challenges in designing, developing and operating UMBRELLA as an open, sustainable testbed and shares lessons learned to guide similar future initiatives. With its unique openness, heterogeneity, realism and tools, UMBRELLA aims to continue accelerating cutting-edge technology research, development and translation into real-world progress.
... The focus for addressing them based on a secure development lifecycle is discussed. IoT lifecycle is discussed in [5], where the different phases from plan and design to retirement and the changes are introduced. A taxonomy of IoT vulnerabilities and evaluation of maliciousness is discussed in [6]. ...
In the design of an Internet of Things device, it is impossible to build security features in the firmware without support from the underlying hardware. As it is impossible to build security features in the firmware without support from the underlying hardware, enablers for security must start from the underlying hardware. An IoT device goes through various stages during its lifecycle, from manufacturing to decommissioning. Security requirements of each stage necessitate support from underlying hardware based on the needs of the lifecycle stage. This paper discusses the need for hardware-based mechanisms to realize the security requirements and the use of these in the device life cycle. Mechanisms available in some of the controllers used in IoT are discussed as case studies. The paper highlights the need for hardware-supported mechanisms to realize security requirements at different stages in an IoT device lifecycle and calls for incorporating such mechanisms in the underlying hardware.
... Pusintek sebagai Unit TIK Pusat di Kemenkeu mengelola Data Center (DC) dan Data Recovery 16 Jurnal Teknik Informatika (JUTIF), Vol. 4, No. 1, Februari 2023, hlm. [15][16][17][18][19][20][21][22][23][24] Center (DRC) Kemenkeu yang mencakup aplikasi, basis data, infrastruktur, serta perangkat pendukung. Dalam Dengan banyaknya jumlah perangkat yang dikelola, maka pengolahan data secara manual menggunakan Ms. Excel dapat menimbulkan risiko kesalahan manusia (human error) sehingga dibangun data warehouse manajemen aset yang dapat dimanfaatkan dalam pembuatan dashboard oleh Tim Analisis dan Penyajian Data [4] menjadi dashboard yang menyajikan data aset infrastruktur jaringan yang perlu dilakukan pemeliharaan maupun penggantian untuk perencanaan anggaran. ...
ITSM e-Prime is an ICT service management application based on ITSM framework owned by Pusintek that includes service desk, incident management, problem management, change management, release management, and configuration management processes. Currently there is a problem in determining the number of devices that will be included in the device maintenance contract or determining the number of devices that need to be replaced in a given year. The objective of this research is to build an asset management data warehouse so that it can be utilized by the Data Analysis and Presentation Team to produce a dashboard that presents data on network infrastructure assets that need to be maintained or replaced for budget planning needs. This descriptive verification analysis research used nine out of ninety tables from the ITSM e-Prime application and applied dimensional modeling Kimball to build a data warehouse because this methodology offers high query performance and understandable by end-user. The resulting data warehouse were tables in the form of star-schema. The tests were carried out by qualitative methods, namely quality testing by users (user acceptance test and blackbox testing) and quantitative method, namely comparing the number of infrastructure devices included in the maintenance contract in 2022. The final result of this research is a data warehouse consisting of fact table F_infrastructure and dimension table D_Merk, D_Area, D_Kategori, D_EoS, D_Garansi, and D_StatusPemeliharaan with acceptance percentage of 95% based on the test results.
... For further details on the areas briefly presented in this chapter please refer to my earlier works [21], [22], [23] and [16]. ...
... My results related to this research domain are detailed in [26], [23], [27], [28] and [22]. ...
... Life cycle stages[23] ...
My dissertation focuses on data and signaling traffic measurement methods and metrics and on providing methodological elements for managing future networks and services.
In the first Thesis group, I created a general life cycle model for IoT devices and demonstrated its application in a telecommunications example. I have developed a model to be used for security investigation purposes related to IoT devices. One of the interesting findings of mine was that the users who were previously banned from the network pose a threat to the operators’ services with their re-appearance. I defined the predecessors of 5G use cases in pre-5G networks, and measured their footprint requirements in existing networks. I have shown correlation between the data and signaling traffic, based on the transmission characteristics of actively operating Industry 4.0 devices. I discussed that traffic transients needed to be avoided.
In the second Thesis group, I identified the challenges arising from the interconnection of Industry 4.0 and mobile networks. As part of this work, I examined existing standards in detail and prepared a network architecture to meet these 3GPP recommendations. I presented the needs, requirements, and motivations of the field related to private industrial mobile networks. I determined industrial data traffic’s main characteristics and the main parameters that the 5G industrial network must meet. I designed and created a 5G NSA mobile network consisting of 3GPP standardized building blocks and performed complex measurements to determine the KPI values of the network. I created a mathematical model and algorithm, examined the joint needs of specific clients and client groups and the services provided by the network.
... The three main pillars of the systems approach in Industry 4.0 are digital manufacturing, supply chain networks, and product lifecycle management, which interact and influence each other. Real-life cases of this approach are presented in work [10], showing results where digital manufacturing resources, distributed by supply chain network (including warehousing and logistics tasks) [11], and product lifecycle stages can also be monitored through its digital footprint [12]. Within the above pattern, paradigms such as Sustainability, Manufacturing Networks, Smart Manufacturing, the Internet of Things, and Digital Twins have formed [13][14][15][16]. ...
This paper presents an aspect of asset tracking and storage conditions. This paper aims to fill the gap in the development of Industry 4.0 in terms of fully digital asset tracking to be implemented by medium and large-size manufacturing and logistics facilities. The article presents an innovative technology for the remote monitoring of chemical raw materials, including fertilizers, during their storage and transport from the place of manufacture to the local distributor or recipient. Methods: The method assumes the monitoring and identification of special transport bags, so-called “big-bags,” through embedded RFID tags or LEB labels and monitoring the key parameters of their content, i.e., temperature, humidity, insolation, and pressure, using a measuring micro-station that is placed in the transported raw material. Results: The automation of inference based on the collected information about the phenomenon in question (the distribution of parameters: pressure, temperature, and humidity), and expert knowledge, allows the creation of an advisory system prototype indicating how to manage the measuring devices. Conclusions: No similar solution in the field of monitoring environmental parameters has been implemented in the Polish market. The developed system enables the monitoring of 10,000 pieces of big bags in at least 30 locations simultaneously.
... A generic model of an IoT device lifecycle has been presented by Soós et al. in [6]. Starting with the three stages lifecycle model defined in PLM, Soós et al. extend this model (see Fig. 2) and describe each stage in some detail. ...
The ideas of the Internet of Things are widespread, and connected devices are used in a broader range of application domains. Many specific aspects of IoT applications have been analyzed and applied from a technical perspective, such as the product life cycle. This paper presents the concept of a generally applicable life cycle model focused on IoT devices. While many product life cycle models have been made, the handling and interaction with the device itself were rarely in focus. In reference to the IoT device life cycle model to be presented, the main activities of the device existence are presented, as well as the active actors and the resulting impact on the connected services are highlighted. To align with existing life cycle models based on the generic beginning, middle, and end of life phases, these phases are also used for the life cycle model of IoT devices and structured in more detail. Distinctions and interactions between the product life cycle and the device life cycle are pointed out. Since the application scenarios and business models in the IoT environment differ significantly, the model must be able to reflect these differences to be universally applicable. The presented IoT device life cycle model is applicable in many scenarios, primarily due to its differentiation of generic roles and specific actors. Overall, the model provides a general and clear basis for a structured and device-oriented analysis of activities and responsibility distribution along the IoT device life cycle.
... Moreover, it is pointed out that integrated analyses or metaanalyses are of greater utility than point estimates as new products continue to emerge on the market and span formerly distinct product categories (Teehan, 2014). Generic IoT device life-cycle models are proposed in Soós et al. (2018), Rahman et al. (2018) but even though they are helpful for a better understanding of the different key steps in the life-cycle of an IoT device, they do not provide quantitative results to model the direct impacts of IoT devices. Similarly, environmental concerns associated with the development of IoT technologies are stressed in Nižetić et al. (2019Nižetić et al. ( , 2020 although it is not built up on LCA results. ...
... However in practical IoT deployment scenarios, real-life working conditions can introduce the need for replacement (Bonvoisin et al., 2012) due to aging of components leading to the accelerated death of nodes. This is likely to contribute to the already increasing number of abandoned IoT zombie devices due to poor life-cycle management (Soós et al., 2018), especially in outdoor environments with corrosion and varying temperature-humidity conditions. Shortened effective lifetime for consumer IoT edge devices can also arise due to hardware, software or psychological obsolescence (Lehmann and Hamilton, 2010;Kiddee et al., 2013). ...
In upcoming years, the number of Internet-of-Things (IoT) devices is expected to surge up to tens of billions of physical objects. However, while the IoT is often presented as a promising solution to tackle environmental challenges, the direct environmental impacts generated over the life cycle of the physical devices are usually overlooked. It is implicitly assumed that their environmental burden is negligible compared to the positive impacts they can generate. Yet, because IoT calls for a massive deployment of electronic devices in the environment, life-cycle assessment (LCA) is an essential tool to leverage in order to avoid further increasing the stress on our planet. Some general LCA methodologies for IoT already exist but quantitative results are still scarce. In this paper, we present a parametric framework based on hardware profiles to evaluate the cradle-to-gate carbon footprint of IoT edge devices. We exploit our framework in three ways. First, we apply it on four use cases to evaluate their respective production carbon footprint. Then, we show that the heterogeneity inherent to IoT edge devices must be considered as the production carbon footprint between simple and complex devices can vary by a factor of more than 150×. Finally, we estimate the absolute carbon footprint induced by the worldwide production of IoT edge devices through a macroscopic analysis over a 10-year period. Results range from 22 to 562 MtCO2-eq/year in 2027 depending on the deployment scenarios. However, the truncation error acknowledged for LCA bottom-up approaches usually lead to an undershoot of the environmental impacts. We compared the results of our use cases with the few reports available from Google and Apple, which suggest that our estimates could be revised upwards by a factor around 2× to compensate for the truncation error. Worst-case scenarios in 2027 would therefore reach more than 1000 MtCO2-eq/year. This truly stresses the necessity to consider environmental constraints when designing and deploying IoT edge devices.
... Generic IoT device life-cycle models are proposed in [Soós et al., 2018, Rahman et al., 2018 but even though they are helpful for a better understanding of the different key steps in the life-cycle of an IoT device, they do not provide quantitative results to model the direct impacts of IoT devices. ...
... However in practical IoT deployment scenarios, real-life working conditions can introduce the need for replacement [Bonvoisin et al., 2012] due to aging of components leading to the accelerated death of nodes. This is likely to contribute to the already increasing number of abandoned IoT zombie devices due to poor life-cycle management [Soós et al., 2018], especially in outdoor environments with corrosion and varying temperature-humidity conditions. Shortened effective lifetime for consumer IoT edge devices can also arise due to hardware, software or psychological obsolescence [Lehmann and Hamilton, 2010]. ...
In upcoming years, the number of Internet-of-Things (IoT) devices is expected to surge up to tens of billions of physical objects. However, while the IoT is often presented as a promising solution to tackle environmental challenges, the direct environmental impacts generated over the life cycle of the physical devices are usually overlooked. It is implicitly assumed that their environmental burden is negligible compared to the positive impacts they can generate. In this paper, we present a parametric framework based on hardware profiles to evaluate the cradle-to-gate carbon footprint of IoT edge devices. We exploit our framework in three ways. First, we apply it on four use cases to evaluate their respective production carbon footprint. Then, we show that the heterogeneity inherent to IoT edge devices must be considered as the production carbon footprint between simple and complex devices can vary by a factor of more than 150x. Finally, we estimate the absolute carbon footprint induced by the worldwide production of IoT edge devices through a macroscopic analysis over a 10-year period. Results range from 22 to 562 MtCO2-eq/year in 2027 depending on the deployment scenarios. However, the truncation error acknowledged for LCA bottom-up approaches usually lead to an undershoot of the environmental impacts. We compared the results of our use cases with the few reports available from Google and Apple, which suggest that our estimates could be revised upwards by a factor around 2x to compensate for the truncation error. Worst-case scenarios in 2027 would therefore reach more than 1000 MtCO2-eq/year. This truly stresses the necessity to consider environmental constraints when designing and deploying IoT edge devices.
... The starting point of a product's lifecycle is named the Beginning of Life (BoL). This main stage usually includes the basic phases of the product to be developed, from the idea to its deployment [95]. In the COBIT 5 terminology, the "Information" and "Plan and Organise" domains deal with the initialization phase, where the emphasis is on the best usage of information and technology reaching the company's goals and objectives. ...
In order to tackle interoperability issues of large-scale automation systems, SOA (Service-Oriented Architecture) principles, where information exchange is manifested by systems providing and consuming services, have already been introduced. However, the deployment, operation, and maintenance of an extensive SoS (System of Systems) mean enormous challenges for system integrators as well as network and service operators. The existing lifecycle management approaches do not cover all aspects of SoS management; therefore, an integrated solution is required. The purpose of this paper is to introduce a new lifecycle approach, namely the SoSLM (System of Systems Lifecycle Management). This paper first provides an in-depth description and comparison of the most relevant process engineering methodologies and ITSM (Information Technology Service Management) frameworks, and how they affect various lifecycle management strategies. The paper’s novelty strives to introduce an Industry 4.0-compatible PLM (Product Lifecycle Management) model and to extend it to cover SoS management-related issues on well-known process engineering methodologies. The presented methodologies are adapted to the PLM model, thus creating the recommended SoSLM model. This is supported by demonstrations of how the IIoT (Industrial Internet of Things) applications and services can be developed and handled. Accordingly, complete implementation and integration are presented based on the proposed SoSLM model, using the Arrowhead framework that is available for IIoT SoS.
... This current paper reports on an actual use-case of this approach. In this, the results of the digital production (assets) [6] are distributed through the supply chain network (including tasks of warehousing and logistics) [7], and the lifecycle steps [8] of the product can also be monitored through its digital footprint (status and ownership changes get noted at its digital twin). The current paper focuses on the asset tracking aspect of this use-case. ...
Revolutionizing logistics and supply chain management in smart manufacturing is one of the main goals of the Industry 4.0 movement. Emerging technologies such as autonomous vehicles, Cyber-Physical Systems and digital twins enable highly automated and optimized solutions in these fields to achieve full traceability of individual products. Tracking various assets within shop-floors and the warehouse is a focal point of asset management; its aim is to enhance the efficiency of logistical tasks. Global players implement their own solutions based on the state of the art technologies. Small and medium companies, however, are still skeptic toward identification based tracking methods, because of the lack of low-cost and reliable solutions. This paper presents a novel, working, reliable, low-cost, scalable solution for asset tracking, supporting global asset management for Industry4.0. The solution uses high accuracy indoor positioning—based on Ultra-Wideband (UWB) radio technology—combined with RFID-based tracking features. Identifying assets is one of the most challenging parts of this work, so this paper focuses on how different identification approaches can be combined to facilitate an efficient and reliable identification scheme.
