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Fast Operation Mode Selection for Highly Efficient IoT Edge Devices
- Source :
- IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 39:572-584
- Publication Year :
- 2020
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2020.
-
Abstract
- In the emerging paradigm of edge computing (EC) for Internet of Things (IoT), data processing is pushed to the edge of the IoT network (e.g., gateways and embedded IoT devices). IoT devices must support multiple operation modes in order to adapt to varying runtime situations, like preserving energy at low battery, while still maintaining some crucial functionality, etc. Adapting the optimal operation mode is a challenge for edge devices given the limited resources at the edge of the network (both bandwidth and processing power of the shared gateway), various constraints (e.g., battery lifetime), etc. This paper proposes a fast and low-overhead scheme to determine and adapt the operation mode of edge devices at runtime and orchestrate devices in a way that the efficiency of IoT devices is optimized with respect to the gateway’s resource constraints. The proposed scheme breaks the optimization problem into several smaller ones (i.e., subproblems) whose solutions are aggregated to find the final solution. We present a novel memoization technique that determines the solution to a range of subproblems based on subproblems that are already solved. In addition, we present a novel pruning technique that reduces the search space and consequently reduces both memory and execution time overhead. The experimental results show up to 50% reduction in memory overhead and $14 \times $ reduction in execution time overhead compared to the state-of-the-art solution which is a major step toward efficient EC for IoT.
- Subjects :
- Edge device
Computer science
Default gateway
Distributed computing
0202 electrical engineering, electronic engineering, information engineering
Overhead (computing)
02 engineering and technology
Enhanced Data Rates for GSM Evolution
Electrical and Electronic Engineering
Computer Graphics and Computer-Aided Design
Software
Edge computing
020202 computer hardware & architecture
Subjects
Details
- ISSN :
- 19374151 and 02780070
- Volume :
- 39
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
- Accession number :
- edsair.doi...........71857ba78c1547126665bf155ffa4336