Self-managing Internet of Things

Weyns, Danny; Ramachandran, Gowri Sankar; Singh, R. A.

doi:10.1007/978-3-319-73117-9_5

Cited by 21 publications

(17 citation statements)

References 32 publications

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“…Uncertainty in sensor measurements may be due to sensor types, inaccurate sampling frequency, data compression, and amplification, or even possible hardware faults. At the network layer, communication between "things" is subject to runtime uncertainties, such as interference that may affect ethernet cables in wired networks and noise, multi-path fading, or even mobility of devices that might affect the communication in wireless networks [34].…”

Section: Case Study: Dealing With Uncertainty In Iotmentioning

confidence: 99%

“…Dynamics in the availability of resources (internal dynamics): Managing dynamics autonomously and correctly is especially important in highly critical IoT applications. For example, because the bandwidth of IoT terminals could vary from Kbps to Mbps from sensing simple value to video stream, requirements on hardware are diverging [12,34,36].…”

Section: Sources Of Uncertainty In Iotmentioning

confidence: 99%

“…The dynamics of these systems introduce uncertainties that may be difficult or even impossible to anticipate before deployment. Hence, these systems need to resolve the uncertainties during system runtime [34].…”

Section: Sources Of Uncertainty In Iotmentioning

confidence: 99%

“…• Transmission technology: Wired and wireless technologies are the main transmission methods of data transfer [39]. Issues related to transmission are considered as an issue regarding transfer speeds and delays in the delivery of data [34]. For instance, in WSN, apart from uncertainty emerging from redundancy in densely deployed neighboring sensors, a significant amount of data can be lost or corrupted during the transmission from sensor nodes to the sink because of intrusion attacks, node failures, or battery depletion in sensor networks.…”

Section: Sources Of Uncertainty In Iotmentioning

confidence: 99%

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Toward Management of Uncertainty in Self-Adaptive Software Systems: IoT Case Study

et al. 2021

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Adaptivity is the ability of the system to change its behavior whenever it does not achieve the system requirements. Self-adaptive software systems (SASS) are considered a milestone in software development in many modern complex scientific and engineering fields. Employing self-adaptation into a system can accomplish better functionality or performance; however, it may lead to unexpected system behavior and consequently to uncertainty. The uncertainty that results from using SASS needs to be tackled from different perspectives. The Internet of Things (IoT) that utilizes the attributes of SASS presents great development opportunities. Because IoT is a relatively new domain, it carries a high level of uncertainty. The goal of this work is to highlight more details about self-adaptivity in software systems, describe all possible sources of uncertainty, and illustrate its effect on the ability of the system to fulfill its objectives. We provide a survey of state-of-the-art approaches coping with uncertainty in SASS and discuss their performance. We classify the different sources of uncertainty based on their location and nature in SASS. Moreover, we present IoT as a case study to define uncertainty at different layers of the IoT stack. We use this case study to identify the sources of uncertainty, categorize the sources according to IoT stack layers, demonstrate the effect of uncertainty on the ability of the system to fulfill its objectives, and discuss the state-of-the-art approaches to mitigate the sources of uncertainty. We conclude with a set of challenges that provide a guide for future study.

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Section: Case Study: Dealing With Uncertainty In Iotmentioning

confidence: 99%

Section: Sources Of Uncertainty In Iotmentioning

confidence: 99%

Section: Sources Of Uncertainty In Iotmentioning

confidence: 99%

Section: Sources Of Uncertainty In Iotmentioning

confidence: 99%

See 2 more Smart Citations

Toward Management of Uncertainty in Self-Adaptive Software Systems: IoT Case Study

et al. 2021

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“…The problem can be tackled by designing self-managing devices that can adapt their state to changes in the system context and environment [10] [11]. However, realizing the IoT devices is challenging because of inherent uncertainties in their operation contexts, such as interferences and dynamic traffic in the network [12]. Often these uncertainties are difficult to predict by architects at development time and often lead to indecisiveness.…”

Section: Related Workmentioning

confidence: 99%

IAS: An IoT Architectural Self-adaptation Framework

Moghaddam

Rutten

Lalanda

et al. 2020

Software Architecture

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This paper develops a generic approach to model control loops and their interaction within the Internet of Things (IoT) environments. We take advantage of MAPE-K loops to enable architectural self-adaptation. The system's architectural setting is aligned with the adaptation goals and the components run-time situation and constraints. We introduce an integrated framework for IoT Architectural Self-adaptation (IAS) where functional control elements are in charge of environmental adaptation and autonomic control elements handle the functional system's architectural adaptation. A Queuing Networks (QN) approach was used for modeling the IAS. The IAS-QN can model control levels and their interaction to perform both architectural and environmental adaptations. The IAS-QN was modeled on a smart grid system for the Melle-Longchamp area (France). Our architectural adaptation approach successfully set the propositions to enhance the performance of the electricity transmission system. This industrial use-case is a part of CPS4EU European industrial innovation project. 1

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