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 … Show more
“… 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.…”
Section: Biological Synapses and Neuronsmentioning
“… 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.…”
Section: Biological Synapses and Neuronsmentioning
“…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 ].…”
Section: Discussionmentioning
confidence: 99%
“…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.…”
Section: Discussionmentioning
confidence: 99%
“…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 ],…”
Cloud Computing is a well-established paradigm for building service-centric systems. However, ultra-low latency, high bandwidth, security, and real-time analytics are limitations in Cloud Computing when analysing and providing results for a large amount of data. Fog and Edge Computing offer solutions to the limitations of Cloud Computing. The number of agricultural domain applications that use the combination of Cloud, Fog, and Edge is increasing in the last few decades. This article aims to provide a systematic literature review of current works that have been done in Cloud, Fog, and Edge Computing applications in the smart agriculture domain between 2015 and up-to-date. The key objective of this review is to identify all relevant research on new computing paradigms with smart agriculture and propose a new architecture model with the combinations of Cloud–Fog–Edge. Furthermore, it also analyses and examines the agricultural application domains, research approaches, and the application of used combinations. Moreover, this survey discusses the components used in the architecture models and briefly explores the communication protocols used to interact from one layer to another. Finally, the challenges of smart agriculture and future research directions are briefly pointed out in this article.
“…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.…”
Internet of Things (IoT) is a system that integrates different devices and technologies, removing the necessity of human intervention. This enables the capacity of having smart (or smarter) cities around the world. By hosting different technologies and allowing interactions between them, the internet of things has spearheaded the development of smart city systems for sustainable living, increased comfort and productivity for citizens. The IoT for Smart Cities has many different domains and draws upon various underlying systems for its operation. In this paper, we provide a holistic coverage of the Internet of Things in Smart Cities. We start by discussing the fundamental components that make up the IoT based Smart City landscape followed by the technologies that enable these domains to exist in terms of architectures utilized, networking technologies used as well as the Artificial Algorithms deployed in IoT based Smart City systems. This is then followed up by a review of the most prevalent practices and applications in various Smart City domains. Lastly, the challenges that deployment of IoT systems for smart cities encounter along with mitigation measures.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
