Leveraging Fog Computing for Sustainable Smart Farming Using Distributed Simulation

Malik, Asad Waqar; Rahman, Anis Ur; Qayyum, Tariq; Ravana, Sri Devi

doi:10.1109/jiot.2020.2967405

Cited by 37 publications

(16 citation statements)

References 39 publications

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“…To ensure long-term food security, productive, sustainable, and environmentally friendly agricultural systems are required. Technology can play a significant role in farming for these challenges, mainly influenced by environmental factors that require better ways to enhance farming [8,9].…”

Section: Introductionmentioning

confidence: 99%

The Impacts of Agricultural Trade on Economic Growth and Environmental Pollution: Evidence from Bangladesh Using ARDL in the Presence of Structural Breaks

2021

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Agricultural trade significantly promotes the economic boom in developing countries. Extensive traditional agricultural production methods have increased the pressure on the agricultural environment by expanding agricultural trade, which has attracted the attention of many scholars. This study aims to empirically examine the impacts of agricultural trade on economic growth and agricultural environmental pollution in Bangladesh from 1972 to 2019, using an Auto Regressive Distributed Lag (ARDL) model with a structural break to examine the long-run and short-run determinants of agricultural environmental pollution in Bangladesh. The ARDL bounds analysis methodology showed that it does not support the hypothesis that agricultural trade led to environmental pollution in the long-run. The results suggest a relationship between economic growth, energy, and FDI towards agricultural environmental pollution, indicating a positive long-run relationship. Furthermore, in the short run, agricultural trade indicates positive drivers towards agricultural environmental pollution. Therefore, it is recommended that the enhancement of trade liberalization policies should ensure cleaner technologies and products that could help reduce environmental pollution.

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Section: Introductionmentioning

confidence: 99%

The Impacts of Agricultural Trade on Economic Growth and Environmental Pollution: Evidence from Bangladesh Using ARDL in the Presence of Structural Breaks

2021

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“…• Network management for sparsely connected farms using newer networking solutions that are bandwidth-aware and do not require cell towers [6], [7]. • Drones and tractors for data ferrying when needed especially under sparse network connectivity [8], [9]. • Effective sharing of data processing and analytics load between sensors, edge devices [10], [11], and the cloud for maximizing throughput or latency, based on the client (farmer/farm manager) preferences [12], [13].…”

Section: Identified Gaps Motivating Latticementioning

confidence: 99%

Lattice: A Vision for Machine Learning, Data Engineering, and Policy Considerations for Digital Agriculture at Scale

Chaterji

DeLay

Evans

et al. 2021

IEEE Open J. Comput. Soc.

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Digital agriculture, with the incorporation of Internet-of-Things (IoT)-based technologies, presents the ability to observe and control a system at multiple levels (individual, local, regional, and global) and generate tools that allow for improved decision making and higher productivity. Recent advances in IoT hardware, e.g., networks of heterogeneous embedded devices, and software, e.g., lightweight computer vision algorithms and cloud optimization solutions, make it possible to collect data and efficiently process data from diverse sources in a connected (smart) farm. By interconnecting these IoT devices, often across large swaths of farmland, it is possible to collect data from multiple farming and food systems, and at different time scales, including in near real-time (i.e., with delays of only a few tens of seconds). This data can then be used for actionable insights, e.g., precise applications of soil supplements and reduced environmental footprint.Through LATTICE, we present an integrated vision for IoT solutions, data processing, and actionable analytics, with economics and policy considerations for digital agriculture. Our paper starts off with the types of datasets in typical field operations, followed by the lifecycle for the data and storage and fast information-retrieval solutions. It then goes on to describe the most prevalent and promising aspects of machine learning and cloud computing for digital agriculture. IoT devices that are capable of sensing different kinds of information connect with each other in a wireless sensor network setting. These form a rich source for optimizing IoT data collection and subsequent analysis. We discuss what algorithms are proving to be most impactful in this space, e.g., approximate data analytics and on-device/in-network processing. We conclude by discussing analytics for alternative agriculture for generation of biofuels and policy challenges in the implementation of digital agriculture in the wild.

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“…More specifically on precision agriculture, the authors of [16] proposed a framework designed to provide a complete farming ecosystem. The framework facilitates the simulation of custom farming scenarios for users, specifically to identify sensor placement, coverage area, line-of-sight deployment, data gathering through a relay mechanism or airborne systems, mobility models for mobile nodes, energy models for on-ground sensors and airborne vehicles, and back-end computing support using Fog computing paradigm.…”

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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Leveraging Fog Computing for Sustainable Smart Farming Using Distributed Simulation

Cited by 37 publications

References 39 publications

The Impacts of Agricultural Trade on Economic Growth and Environmental Pollution: Evidence from Bangladesh Using ARDL in the Presence of Structural Breaks

The Impacts of Agricultural Trade on Economic Growth and Environmental Pollution: Evidence from Bangladesh Using ARDL in the Presence of Structural Breaks

Lattice: A Vision for Machine Learning, Data Engineering, and Policy Considerations for Digital Agriculture at Scale

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

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