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

Kalyani, Yogeswaranathan; Collier, Rem W.

doi:10.3390/s21175922

Cited by 93 publications

(47 citation statements)

References 104 publications

(326 reference statements)

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“…What are the most important emerging disruptive digital technologies? They include artificial intelligence (AI), additive manufacturing (AM), big data and analytics (BDA), cloud computing (CC), Internet of Things (IoT), system integration (SI), simulation, autonomous robotic systems (ARS), augmented reality (AR), wireless sensor networks (WSN), cyber-physical system (CPS), and digital twin (DT) (Kalyani and Collier 2021;Abbasi et al 2022). There are several applications of smart farming such as water management (using sensors for crop specific irrigation, crop water requirements), animal care (monitoring of animal health, animal tracking), farmer helpline (solving problems by experts, and farm management (climatic specific suggestion, maintain crop growth till harvest), and crop care (protecting crop from pests and animals, and solving problem of excess water) (Nawandar and Satpute 2019;Azadi et al 2021).…”

Section: Smart Agriculture: and Its Applicationsmentioning

confidence: 99%

Applications and Challenges of Smart Farming for Developing Sustainable Agriculture

Abdalla

El-Ramady

2022

EBSS

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griculture is the backbone of any nation, which supplies with the needed foods, feeds, fiber and fuel. The crucial sector has become more flexible and advanced due to the enormous progress in the bioinformatics, which penetrates all fields in our life. Smart agriculture is an important page in the history of global agriculture, which already used several advanced technologies during different agricultural practices starting from the pre-cultivation, cultivation, seedling, fertilization, weed detection, irrigation, spraying pesticides, till harvesting and also pot-harvest. Many sectors have been involved in smart farming like smart irrigation, and all precision farming practices for improving and management of the agriculture especially in the developing countries, which need more supporting from their governments even at the private sector and the small farms level. This short communication is a call for submitting articles on smart farming and its applications in different agricultural practices including irrigation, fertilization, and etc. Due to smart agriculture mainly depends on the bioinformatics, this technology has several open questions particularly their applications, models, and security, which need further investigations.

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Section: Smart Agriculture: and Its Applicationsmentioning

confidence: 99%

Applications and Challenges of Smart Farming for Developing Sustainable Agriculture

Abdalla

El-Ramady

2022

EBSS

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“…The work of [18] was a review to identify all relevant research on new computing paradigms with smart agriculture. They also proposed a new architecture model with the combinations of Cloud-Fog-Edge.…”

Section: Related Workmentioning

confidence: 99%

Processing Complex Events in Fog-Based Internet of Things Systems for Smart Agriculture

Bezerra

Filho

Delicato

et al. 2021

Sensors

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The recent growth of the Internet of Things’ services and applications has increased data processing and storage requirements. The Edge computing concept aims to leverage the processing capabilities of the IoT and other devices placed at the edge of the network. One embodiment of this paradigm is Fog computing, which provides an intermediate and often hierarchical processing tier between the data sources and the remote Cloud. Among the major benefits of this concept, the end-to-end latency can be decreased, thus favoring time-sensitive applications. Moreover, the data traffic at the network core and the Cloud computing workload can be reduced. Combining the Fog computing paradigm with Complex Event Processing (CEP) and data fusion techniques has excellent potential for generating valuable knowledge and aiding decision-making processes in the Internet of Things’ systems. In this context, we propose a multi-tier complex event processing approach (sensor node, Fog, and Cloud) that promotes fast decision making and is based on information with 98% accuracy. The experiments show a reduction of 77% in the average time of sending messages in the network. In addition, we achieved a reduction of 82% in data traffic.

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“…MEC was defined by the European Telecommunication Standards Institute [21], and it is also recently called Multi-Access Edge Computing. MEC is classified into one of the edge computing, which can support several kinds of characteristics including mobility support, location awareness, low latency, and heterogeneity [22][23][24]. In general, edge computing has more limited resources, limited computation and storage capabilities, and proximity to end devices than fog computing [25].…”

Section: Introductionmentioning

confidence: 99%

Optimal Task Allocation Algorithm Based on Queueing Theory for Future Internet Application in Mobile Edge Computing Platform

Katayama

Tachibana

2022

Sensors

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For 5G and future Internet, in this paper, we propose a task allocation method for future Internet application to reduce the total latency in a mobile edge computing (MEC) platform with three types of servers: a dedicated MEC server, a shared MEC server, and a cloud server. For this platform, we first calculate the delay between sending a task and receiving a response for the dedicated MEC server, shared MEC server, and cloud server by considering the processing time and transmission delay. Here, the transmission delay for the shared MEC server is derived using queueing theory. Then, we formulate an optimization problem for task allocation to minimize the total latency for all tasks. By solving this optimization problem, tasks can be allocated to the MEC servers and cloud server appropriately. In addition, we propose a heuristic algorithm to obtain the approximate optimal solution in a shorter time. This heuristic algorithm consists of four algorithms: a main algorithm and three additional algorithms. In this algorithm, tasks are divided into two groups, and task allocation is executed for each group. We compare the performance of our proposed heuristic algorithm with the solution obtained by three other methods and investigate the effectiveness of our algorithm. Numerical examples are used to demonstrate the effectiveness of our proposed heuristic algorithm. From some results, we observe that our proposed heuristic algorithm can perform task allocation in a short time and can effectively reduce the total latency in a short time. We conclude that our proposed heuristic algorithm is effective for task allocation in a MEC platform with multiple types of MEC servers.

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

Cited by 93 publications

References 104 publications

Applications and Challenges of Smart Farming for Developing Sustainable Agriculture

Applications and Challenges of Smart Farming for Developing Sustainable Agriculture

Processing Complex Events in Fog-Based Internet of Things Systems for Smart Agriculture

Optimal Task Allocation Algorithm Based on Queueing Theory for Future Internet Application in Mobile Edge Computing Platform

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