Deep Learning Entrusted to Fog Nodes (DLEFN) Based Smart Agriculture

Abstract

Colossal amounts of unstructured multimedia data are generated in the modern Internet of Things (IoT) environment. Nowadays, deep learning (DL) techniques are utilized to extract useful information from the data that are generated constantly. Nevertheless, integrating DL methods with IoT devices is a challenging issue due to their restricted computational capacity. Although cloud computing solves this issue, it has some problems such as service delay and network congestion. Hence, fog computing has emerged as a breakthrough way to solve the problems of using cloud computing. In this article, we propose a strategy that assigns a portion of the DL layers to fog nodes in a fog-computing-based smart agriculture environment. The proposed deep learning entrusted to fog nodes (DLEFN) algorithm decides the optimal layers of DL model to execute on each fog node, considering their available computing capacity and bandwidth. The DLEFN individually calculates the optimal layers for each fog node with dissimilar computational capacities and bandwidth. In a similar experimental environment, comparison results clearly showed that proposed method accommodated more DL application than other existing assignment methods and utilized resources efficiently while reducing network congestion and processing burden on the cloud.

Emerging Materials for Neuromorphic Devices and Systems

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Figure 1 Biological Neurons and Functions (A) Schematic illustration of neuron in a biological system. Reproduced with permission ( Lee et al., 2020 ). Copyright 2020, MDPI AG.

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A Systematic Survey on the Role of Cloud, Fog, and Edge Computing Combination in Smart Agriculture

“…First of all, we have grouped the components of architecture into Edge, Fog, and Cloud layers according to the existing models. Edge layer: We observed that, in most of the applications, sensors [ 55 , 58 , 61 , 63 , 64 , 67 , 69 , 79 , 81 , 98 ], actuators [ 67 , 82 , 85 , 97 ], or IoT devices [ 60 ] were used as a bottom layer. The most common sensors used in the applications are wearable sensors, environmental sensors such as temperature, humidity, light, soil moisture, pH, and satellite sensors [ 56 ].…”

“…In some papers, authors used different components in the Fog layer. For instance, Fog gateways [ 82 ], Fog nodes [ 63 , 97 ], farm controller [ 64 ], Fog node, and gateway [ 60 , 80 , 98 ]. Cloud layer: Ref.…”

“…In some other papers, authors used different components in the Cloud layer. For instance, Cloud Servers and its service [ 82 ], Cloud (data storage, data analytics, data visualization, APIs) [ 85 ], Data processing center [ 56 , 60 , 67 , 75 ], Cloud database [ 63 ], Cloud services (Cloud server and KB) [ 80 ], Cloud application [ 64 , 81 ], central Cloud [ 79 ], Cloud server [ 58 , 98 ], Cloud API [ 55 ], and Cloud KB and Resource pool [ 59 ],…”

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

“…There have been some suggested methodologies for bringing fog processing for AI in smart agriculture, for, e.g., in [103] a deep learning entrusted to fog nodes (DLEFN) algorithm is described to support efficient use of resources and reduce cloud resource usage. However, as noted in [104], who use an edge system for temperature prediction using an LSTM, edge device performance still lacks that of similar cloud systems but the inclusion of DL capable hardware does provide opportunities for further innovations.…”