Edge computing can process a large amount of data in the power grid locally, and then transmit the results and knowledge to the cloud platform, which reduces the use of network bandwidth and the processing response time. The process requires secure storage and privacy protection on the power ter
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To solve this problem, Xu et al. (2021) introduced an adaptive evolution energy control algorithm based on edge computing and mutation strategy to trade-off between load, cost, timing, and other metrics aimed at
The rapid expansion of the Internet of Things ecosystem has created an urgent need for efficient data processing and analysis technologies. This review aims to systematically examine and
A heterogeneous computing environment has been widely used with UAVs, edge servers, and cloud servers operating in tandem. Various applications can be allocated and linked to the computing nodes that
This paper will analyze the architecture and application of edge computing in three scenarios of power IoT, including distribution network automation monitoring system, smart energy system, and power metering
The unprecedented progress in artificial intelligence (AI), particularly in deep learning algorithms with ubiquitous internet connected smart devices, has created a high
A tiered computing architecture (as shown in Figure 1) that spans across cloud data centers, edge servers, and local computation capabilities of mobile devices is the de facto paradigm for such
A tiered computing architecture (as shown in Figure 1) that spans across cloud data centers, edge servers, and local computation capabilities of mobile devices is the de facto paradigm for such remote computation.The tiers are typically
In this section, we survey the research work of energy-aware edge computing architecture, including memory system, networking, compiler and programmability & reconfiguration, benchmarking and software defined storage. The surveyed work of energy awareness in edge computing system architecture is listed in Fig. 3 and Table 4. Fig. 3.
Although energy efficiency in cloud data centers has been broadly investigated, energy efficiency in edge computing is largely left uninvestigated due to the complicated interactions between edge devices, edge servers, and cloud data centers.
Networking for energy efficient routing and naming Edge computing brings computing and storage resources closer to the data source, and may perform the computation on the edge nodes of the data source and in the cloud data center. Computing and communications may be invoked back and forth on the edge nodes and cloud data centers.
In edge computing environment, data storage and computation are performed on edge devices. Unlike cloud servers equipped with high storage capacity and stable infrastructure, edge devices are usually constrained by storage capacity and exposed to unstable environments.
In many edge computing scenarios increasing the energy consumption could have a negative impact on the power-constrained IoT device or edge cloud side with limited power sources. Since billions of edge devices are deployed in edge computing environment, their total energy consumptions are immense and as important as those of cloud data centers.
Although the energy aware edge computing is investigated in various aspects and application domain, most of the existing work focuses on a single objective, such as low latency, data privacy, power saving, or energy efficiency.
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