Smart Dust in the Industrial Economic Sector – On Application Cases in Product Lifecycle Management

Holler, Manuel; Haarmann, Jens; Giffen, Benjamin van; Frank, Alejandro Germán

doi:10.1007/978-3-030-62807-9_14

Cited by 3 publications

(5 citation statements)

References 27 publications

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“…For the iterative conceptualization, we continuously referred to established concepts (e.g., product-service-continuum (Baines et al, 2007), product/service development (Ansoff, 1957)). In sum, we chose dimension 1 as "Product" and dimension 2 as "Service" because both empirical evidence and knowledge base showed that smart dust affords product and service innovation alike (Holler et al, 2020a(Holler et al, , 2020bIlyas & Mahgoub, 2018). Furthermore, these dimensions were differentiated into the states "No product/service", "Existing product/service" and "Smart dust-enhanced product/service" as industrial enterprises typically start with an existing offering striving to complement and improve it (Lerch & Gotsch, 2015).…”

Section: Three Strategies and Their Applicationmentioning

confidence: 99%

“…Comprising the whole IoT technology stack from sensor to service, these motes can be installed in the application range of industrial assets to expedite sensing to the next level (Ilyas & Mahgoub, 2018). Reverting to related literature, recent empirical works (Holler et al, 2020a(Holler et al, , 2020b explored the economic potentials of smart dust in industrial settings. More precisely, Holler et al (2020aHoller et al ( , 2020b showed opportunities across the whole lifecycle, from beginning-of-life (e.g., advancement of requirements engineering) over middle-of-life (e.g., enhancement of logistics monitoring) to end-oflife (e.g., augmented retirement planning).…”

mentioning

confidence: 99%

“…Reverting to related literature, recent empirical works (Holler et al, 2020a(Holler et al, , 2020b explored the economic potentials of smart dust in industrial settings. More precisely, Holler et al (2020aHoller et al ( , 2020b showed opportunities across the whole lifecycle, from beginning-of-life (e.g., advancement of requirements engineering) over middle-of-life (e.g., enhancement of logistics monitoring) to end-oflife (e.g., augmented retirement planning). While these previous studies have identified smart dust as source for product and service innovation (Holler et al, 2020a(Holler et al, , 2020bIlyas & Mahgoub, 2018), the pathways from an initial offering to smart dust-enhanced product-service-systems have received scant attention.…”

mentioning

confidence: 99%

“…More precisely, Holler et al (2020aHoller et al ( , 2020b showed opportunities across the whole lifecycle, from beginning-of-life (e.g., advancement of requirements engineering) over middle-of-life (e.g., enhancement of logistics monitoring) to end-oflife (e.g., augmented retirement planning). While these previous studies have identified smart dust as source for product and service innovation (Holler et al, 2020a(Holler et al, , 2020bIlyas & Mahgoub, 2018), the pathways from an initial offering to smart dust-enhanced product-service-systems have received scant attention. Accordingly, we express the following research question: [RQ] Which strategies could industrial companies pursue to exploit smart dust-enhanced product-service-systems?…”

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confidence: 99%

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Smart Dust for Smart(er) Industrial Product-Service-Systems: Three Strategies and Their Application

et al. 2021

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The nascent technology of smart dust -miniaturized sensor networks -promises high value to advance industrial product-service-systems. While previous studies have identified smart dust as source for product and service innovation, the pathways from an initial offering to smart dust-enhanced productservice-systems have received scant attention. The present work in the scope of a Swiss National Science Foundation project on the economic potentials of this technology aims to conceptualize and apply strategies for smart dust-enhanced product-service-systems. This conceptual research resulted in the three pathways (1) product-driven strategy, (2) service-driven strategy and (3) holistic strategy which could be successfully mapped to the use case "Monitoring of structures". In closing, this emerging technology helps to make industrial product-servicesystems more customer-and user-oriented as called by recent voices. To science, we introduce smart dust in the field of product-service-systems and offer a first systemization of pathways, thus contribute to the product-service-systems engineering knowledge base. To practice, we provide useful approaches to be applied at a strategy level to push the servitization in manufacturing forward.

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Section: Three Strategies and Their Applicationmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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Smart Dust for Smart(er) Industrial Product-Service-Systems: Three Strategies and Their Application

et al. 2021

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“…In recent years, it has received a considerable amount of attention from the research community because many remote sensing applications can benefit from this technology for monitoring rough terrains such as forests, frozen lands, mountains, sea, space, planets, etc. [22][23][24][25]. For example, environmental researchers can use this technology for monitoring air quality, pollution, carbon, or any chemical substances in the air.…”

Section: Introductionmentioning

confidence: 99%

Accurate Location Estimation of Smart Dusts Using Machine Learning

Bashir¹,

Malik²,

Lai³

2022

Computers, Materials &Amp; Continua

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Traditional wireless sensor networks (WSNs) are not suitable for rough terrains that are difficult or impossible to access by humans. Smart dust is a technology that works with the combination of many tiny sensors which is highly useful for obtaining remote sensing information from rough terrains. The tiny sensors are sprinkled in large numbers on rough terrains using airborne distribution through drones or aircraft without manually setting their locations. Although it is clear that a number of remote sensing applications can benefit from this technology, but the small size of smart dust fundamentally restricts the integration of advanced hardware on tiny sensors. This raises many challenges including how to estimate the location of events sensed by the smart dusts. Existing solutions on estimating the location of events sensed by the smart dusts are not suitable for monitoring rough terrains as these solutions depend on relay sensors and laser patterns which have their own limitations in terms of power constraint and uneven surfaces. The study proposes a novel machine learning based localization algorithm for estimating the location of events. The approach utilizes timestamps (time of arrival) of sensed events received at base stations by assembling them into a multidimensional vector and input to a machine learning classifier for estimating the location. Due to the unavailability of real smart dusts, we built a simulator for analysing the accuracy of the proposed approach for monitoring forest fire. The experiments on the simulator show reasonable accuracy of the approach.

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