An Efficient On-Demand Hardware Replacement Platform for Metamorphic Functional Processing in Edge-Centric IoT Applications
| Autor: | Hyeongyun Moon, Daejin Park |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Fixed-function
TK7800-8360 Edge device Computer Networks and Communications business.industry Computer science Process (computing) Control reconfiguration metamorphic platform Telecommunications network Power (physics) edge computing Hardware and Architecture Control and Systems Engineering Signal Processing IoT (Internet of Things) Enhanced Data Rates for GSM Evolution Electronics Electrical and Electronic Engineering hardware reconfiguration business Computer hardware Edge computing |
| Zdroj: | Electronics, Vol 10, Iss 2088, p 2088 (2021) Electronics Volume 10 Issue 17 |
| ISSN: | 2079-9292 |
| DOI: | 10.3390/electronics10172088 |
| Popis: | The paradigm of Internet-of-things (IoT) systems is changing from a cloud-based system to an edge-based system. These changes were able to solve the delay caused by the rapid concentration of data in the communication network, the delay caused by the lack of server computing capacity, and the security issues that occur in the data communication process. However, edge-based IoT systems performance was insufficient to process large numbers of data due to limited power supply, fixed hardware functions, and limited hardware resources. To improve their performance, application-specific hardware can be installed in edge devices, but performance cannot be improved except for specific applications due to a fixed function of an application-specific hardware. This paper introduces an edge-centric metamorphic IoT (mIoT) platform that can use various hardware modules through on-demand partial reconfiguration, despite the limited hardware resources of edge devices. In addition, this paper introduces an RISC-V based metamorphic IoT processor (mIoTP) with reconfigurable peripheral modules. We experimented to prove that the proposed structure can reduce the server access of edges and can be applied to a large-scale IoT system. Experiments were conducted in a single-edge environment and a large-scale environment combining one physical edge and 99 virtual edges. According to the experimental results, the edge-centric mIoT platform that executes the reconfiguration prediction algorithm at the edge was able to reduce the number of server accesses by up to 82.2% compared to our previous study in which the prediction process was executed at the server. Furthermore, we confirmed that there is no additional reconfiguration time overhead even for the large IoT systems. |
| Databáze: | OpenAIRE |
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