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Per-layer hidden units in the tested 3L-SDAE and 5L-SDAE architectures. Input L. First L. Second L. Third L. Fourth L. Fifth L.
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In this paper, we characterize the main building blocks and numerically verify the classification accuracy and energy performance of SmartFog, a distributed and virtualized networked Fog technological platform for the support for Stacked Denoising Auto-Encoder (SDAE)-based anomaly detection in data flows generated by Smart-Meters (SMs). In SmartFog...
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... tested two SDAEs with L = 3 (3L-SDAE) and L = 5 (5L-SDAE) hidden layers and four different lengths of the input vector x. The number of units in the hidden layers are detailed in Table 1. We used the sigmoid function as nonlinear function in all layers. ...Similar publications
Nowadays, centralized energy grid systems are transitioning towards more decentralized systems driven by the need for efficient local integration of new deployed small scale renewable energy sources. The high limits for accessing the energy markets and also for the delivery of ancillary services act as a barrier for small scale prosumers participat...
Citations
... Liu et al. designed a distributed fog computing platform for detecting smart meter data anomalies [45]. They used a stacked denoising autoencoder and KNN classifier deployed on the fog nodes. ...
Given its advantages in low latency, fast response, context-aware services, mobility, and privacy preservation, edge computing has emerged as the key support for intelligent applications and 5G/6G Internet of things (IoT) networks. This technology extends the cloud by providing intermediate services at the edge of the network and improving the quality of service for latency-sensitive applications. Many AI-based solutions with machine learning, deep learning, and swarm intelligence have exhibited the high potential to perform intelligent cognitive sensing, intelligent network management, big data analytics, and security enhancement for edge-based smart applications. Despite its many benefits, there are still concerns about the required capabilities of intelligent edge computing to deal with the computational complexity of machine learning techniques for big IoT data analytics. Resource constraints of edge computing, distributed computing, efficient orchestration, and synchronization of resources are all factors that require attention for quality of service improvement and cost-effective development of edge-based smart applications. In this context, this paper aims to explore the confluence of AI and edge in many application domains in order to leverage the potential of the existing research around these factors and identify new perspectives. The confluence of edge computing and AI improves the quality of user experience in emergency situations, such as in the Internet of vehicles, where critical inaccuracies or delays can lead to damage and accidents. These are the same factors that most studies have used to evaluate the success of an edge-based application. In this review, we first provide an in-depth analysis of the state of the art of AI in edge-based applications with a focus on eight application areas: smart agriculture, smart environment, smart grid, smart healthcare, smart industry, smart education, smart transportation, and security and privacy. Then, we present a qualitative comparison that emphasizes the main objective of the confluence, the roles and the use of artificial intelligence at the network edge, and the key enabling technologies for edge analytics. Then, open challenges, future research directions, and perspectives are identified and discussed. Finally, some conclusions are drawn.
... Fog computing platforms: stand for decentralized computational infrastructures, where data pre-processing, computing, storage and analysis are conducted in the layer located between the data collection devices and the cloud [201]. In this line, the computational ability of the anomaly detection solution is carried out close to both the data recording devices and the cloud, in which data are produced and handled [202]. • P3. ...
Enormous amounts of data are being produced everyday by sub-meters and smart sensors installed in residential buildings. If leveraged properly, that data could assist end-users, energy producers and utility companies in detecting anomalous power consumption and understanding the causes of each anomaly. Therefore, anomaly detection could stop a minor problem becoming overwhelming. Moreover, it will aid in better decision-making to reduce wasted energy and promote sustainable and energy efficient behavior. In this regard, this paper is an in-depth review of existing anomaly detection frameworks for building energy consumption based on artificial intelligence. Specifically, an extensive survey is presented, in which a comprehensive taxonomy is introduced to classify existing algorithms based on different modules and parameters adopted, such as machine learning algorithms, feature extraction approaches, anomaly detection levels, computing platforms and application scenarios. To the best of the authors’ knowledge, this is the first review article that discusses anomaly detection in building energy consumption. Moving forward, important findings along with domain-specific problems, difficulties and challenges that remain unresolved are thoroughly discussed, including the absence of: (i) precise definitions of anomalous power consumption, (ii) annotated datasets, (iii) unified metrics to assess the performance of existing solutions, (iv) platforms for reproducibility and (v) privacy-preservation. Following, insights about current research trends are discussed to widen the applications and effectiveness of the anomaly detection technology before deriving future directions attracting significant attention. This article serves as a comprehensive reference to understand the current technological progress in anomaly detection of energy consumption based on artificial intelligence.
... Fog computing is as a promising technology that has been emerged to handle the growth of smart devices as well as the popularity of latency-sensitive and locationawareness Internet of Things (IoT) services [1][2][3][4][5][6][7]. After the emergence of IoT-based services, the industry of internet-based devices has grown. ...
This study proposes a Client-Fog-Cloud (CFC) multilayer data processing and aggregation framework that is designed to promote latency-sensitive applications in an IoT context. The framework is designed to address the current IoT-based challenges: wide distribution, massive uploading, low latency, and real-time interaction. The proposed framework consists of the device gateway, the fog server and the cloud. The device gateway collects data from clients and uploads it to the nearest fog node. Received data will be pre-processed and filtered by the fog server before being transferred to the cloud for further processing or storage. An abduction alert fog-based service was implemented to evaluate the proposed framework. Performance was evaluated by comparing the response time and the delay time of the proposed architecture with the traditional cloud computing architecture. Additionally, the aggregation rate was evaluated by simulating the speed of bike riding as well as the walking speed of young adults and elderly. Results show that comparing with the traditional cloud, our proposal noticeably reduces the average response time and the delay time (i.e., whether the newest data or the historical data are being queried). Results indicate the capability of the proposed framework to reduce the response time by 32% and the data transferred to the cloud by 30%.
... Fog computing platform: stands for decentralized computational infrastructures, where power consumption data pre-processing, computing, storage and analysis are conducted in the layer located between the data collection devices and the cloud [193]. In this line, the computational ability of the anomaly detection solution is carried out close to both the data recording devices and the cloud, in which data are produced and handled [194]. • P3. ...
Enormous amounts of data are being produced everyday by sub-meters and smart sensors installed in residential buildings. If leveraged properly, that data could assist end-users, energy producers and utility companies in detecting anomalous power consumption and understanding the causes of each anomaly. Therefore, anomaly detection could stop a minor problem becoming overwhelming. Moreover, it will aid in better decision-making to reduce wasted energy and promote sustainable and energy efficiency behavior. In this regard, this paper an in-depth review of existing anomaly detection frameworks for building energy consumption based on artificial intelligence. Specifically, an extensive survey is introduced, in which a comprehensive taxonomy is introduced to classify existing algorithms based on different modules and parameters adopted in their implementation, such as machine learning algorithms, feature extraction approaches, anomaly detection levels, computing platforms and application scenarios. To the best of the authors' knowledge, this is the first review article that discusses the anomaly detection in building energy consumption. Moving forward, important findings along with domain-specific problems, difficulties and challenges that remain unresolved are thoroughly discussed, including the absence of: (i) precise definitions of anomalous power consumption, (ii) annotated datasets, (iii) unified metrics to assess the performance of existing solutions, (iv) platforms for reproducibility, and (v) privacy-preservation. Following, insights about current research trends are discussed to widen the application and effectiveness of anomaly detection technology before deriving a set of future directions attracting significant research and development.
