Combination of Task Allocation and Approximate Computing for Fog-Architecture-Based IoT Article Swipe
Wanli Yu
,
Ardalan Najafi
,
Yanqiu Huang
,
Alberto García-Ortiz
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/jiot.2020.3040892
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/jiot.2020.3040892
Achieving energy efficiency is always a primary concern for fog-architecture-based Internet of Things (IoT) applications. As the IoT devices are typically of small sizes and powered by battery energy, it is essential to address the energy consumption at all levels from the circuit to the system. Two of the promising solutions at circuit and system levels are approximate computing and energy-aware task allocation, respectively. However, the existing task allocation approaches are designed without considering the aspect of approximate computing. In this work, we fill this gap and aim to maximize the network lifetime subject to the accuracy requirements of the applications. By considering both the approximate computing and task allocation simultaneously, a nonlinear problem is obtained to allocate the tasks for the devices (fog nodes and IoT end devices) and to select the corresponding execution modes (tasks in approximate or exact modes). To efficiently solve this problem, a centralized algorithm is first proposed by transferring the above nonlinear problem as a linear programming (LP) problem. As executing the centralized algorithm is a challenge for the resource-limited IoT devices, this work further proposes an optimal distributed algorithm based on Dantzig–Wolfe decomposition to solve the problem of tasks distribution and execution modes selection. The centralized large-scaled LP problem is decomposed into small-scaled subproblems, which can be efficiently solved by each IoT device. The proposed algorithms are tested by extensive simulations. The results demonstrate that the distributed algorithm achieves the same results as the centralized algorithm, and both of them significantly outperform the previous approaches.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/jiot.2020.3040892
- OA Status
- green
- Cited By
- 12
- References
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W3107232227Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/jiot.2020.3040892Digital Object Identifier
- Title
-
Combination of Task Allocation and Approximate Computing for Fog-Architecture-Based IoTWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-11-26Full publication date if available
- Authors
-
Wanli Yu, Ardalan Najafi, Yanqiu Huang, Alberto García-OrtizList of authors in order
- Landing page
-
https://doi.org/10.1109/jiot.2020.3040892Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://research.utwente.nl/en/publications/0bf27f03-0f69-42e7-b850-b27c5cde67e1Direct OA link when available
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Computer science, Distributed computing, Task (project management), Energy consumption, Resource allocation, Efficient energy use, Computer network, Management, Electrical engineering, Biology, Engineering, Economics, EcologyTop concepts (fields/topics) attached by OpenAlex
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12Total citation count in OpenAlex
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2024: 2, 2023: 5, 2022: 3, 2021: 2Per-year citation counts (last 5 years)
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32Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2105560970, https://openalex.org/W2173299665, https://openalex.org/W2811107380, https://openalex.org/W3033190354, https://openalex.org/W3032939875, https://openalex.org/W2980235527, https://openalex.org/W3009804865, https://openalex.org/W2912921810, https://openalex.org/W2996942942, https://openalex.org/W3022500044, https://openalex.org/W2508661730, https://openalex.org/W2919017393, https://openalex.org/W2994654853, https://openalex.org/W2114786478, https://openalex.org/W3022291517, https://openalex.org/W2808667664, https://openalex.org/W2917580194, https://openalex.org/W2801702941, https://openalex.org/W2906242844, https://openalex.org/W2160059864, https://openalex.org/W3091557463, https://openalex.org/W2946729799, https://openalex.org/W2802295390, https://openalex.org/W2905503927, https://openalex.org/W3000480525, https://openalex.org/W2920031528, https://openalex.org/W2991450287, https://openalex.org/W2905287427, https://openalex.org/W2020480126, https://openalex.org/W2801205959, https://openalex.org/W1752175949, https://openalex.org/W3048118227 |
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