Smart Irrigation IoT Solution using Transfer Learning for Neural Networks

• URL: http://arxiv.org/abs/2009.12747v1
• Date: Sun, 27 Sep 2020 05:31:19 GMT
• Title: Smart Irrigation IoT Solution using Transfer Learning for Neural Networks
• Authors: A. Risheh, A. Jalili, E. Nazerfard
• Abstract summary: We show high performance of neural networks compared to existing alternative method of support vector regression. To reduce the processing power of neural network for the IoT edge devices, we propose using transfer learning. Our proposed IoT architecture shows a complete solution for smart irrigation.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper we develop a reliable system for smart irrigation of greenhouses using artificial neural networks, and an IoT architecture. Our solution uses four sensors in different layers of soil to predict future moisture. Using a dataset we collected by running experiments on different soils, we show high performance of neural networks compared to existing alternative method of support vector regression. To reduce the processing power of neural network for the IoT edge devices, we propose using transfer learning. Transfer learning also speeds up training performance with small amount of training data, and allows integrating climate sensors to a pre-trained model, which are the other two challenges of smart irrigation of greenhouses. Our proposed IoT architecture shows a complete solution for smart irrigation.

Related papers

• A Cutting-Edge Deep Learning Method For Enhancing IoT Security [0.0]
  This paper proposes an innovative design of the Internet of Things (IoT) Environment Intrusion Detection System (or IDS) using Deep Learning-integrated Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. Our model, based on the CICIDS 2017 dataset, achieved an accuracy of 99.52% in classifying network traffic as either benign or malicious.
  arXiv  Detail & Related papers  (2024-06-18T08:42:51Z)
• NAS-ASDet: An Adaptive Design Method for Surface Defect Detection Network using Neural Architecture Search [5.640706784987607]
  We propose a new method called NAS-ASDet to adaptively design network for surface defect detection. First, a refined and industry-appropriate search space that can adaptively adjust the feature distribution is designed. Then, a progressive search strategy with a deep supervision mechanism is used to explore the search space faster and better.
  arXiv  Detail & Related papers  (2023-11-18T03:15:45Z)
• Energy-Efficient On-Board Radio Resource Management for Satellite Communications via Neuromorphic Computing [59.40731173370976]
  We investigate the application of energy-efficient brain-inspired machine learning models for on-board radio resource management. For relevant workloads, spiking neural networks (SNNs) implemented on Loihi 2 yield higher accuracy, while reducing power consumption by more than 100$times$ as compared to the CNN-based reference platform.
  arXiv  Detail & Related papers  (2023-08-22T03:13:57Z)
• Solving Large-scale Spatial Problems with Convolutional Neural Networks [88.31876586547848]
  We employ transfer learning to improve training efficiency for large-scale spatial problems. We propose that a convolutional neural network (CNN) can be trained on small windows of signals, but evaluated on arbitrarily large signals with little to no performance degradation.
  arXiv  Detail & Related papers  (2023-06-14T01:24:42Z)
• Learning to Learn with Generative Models of Neural Network Checkpoints [71.06722933442956]
  We construct a dataset of neural network checkpoints and train a generative model on the parameters. We find that our approach successfully generates parameters for a wide range of loss prompts. We apply our method to different neural network architectures and tasks in supervised and reinforcement learning.
  arXiv  Detail & Related papers  (2022-09-26T17:59:58Z)
• Automated Customization of On-Thing Inference for Quality-of-Experience Enhancement [27.16877467047541]
  This paper studies automated customization for DL inference on IoT devices (termed as on-thing inference) We use a novel online learning algorithm, NeuralUCB, that has excellent generalization ability for handling various user QoE patterns. We design feedback solicitation schemes to reduce the number of QoE solicitations while maintaining the learning efficiency of NeuralUCB.
  arXiv  Detail & Related papers  (2021-12-11T07:37:13Z)
• Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach [81.5261621619557]
  We propose an adaptive anomaly detection scheme with hierarchical edge computing (HEC) We first construct multiple anomaly detection DNN models with increasing complexity, and associate each of them to a corresponding HEC layer. Then, we design an adaptive model selection scheme that is formulated as a contextual-bandit problem and solved by using a reinforcement learning policy network.
  arXiv  Detail & Related papers  (2021-08-09T08:45:47Z)
• Wireless Sensing With Deep Spectrogram Network and Primitive Based Autoregressive Hybrid Channel Model [20.670058030653458]
  Human motion recognition (HMR) based on wireless sensing is a low-cost technique for scene understanding. Current HMR systems adopt support vector machines (SVMs) and convolutional neural networks (CNNs) to classify radar signals. This paper proposes a deep spectrogram network (DSN) by leveraging the residual mapping technique to enhance the HMR performance.
  arXiv  Detail & Related papers  (2021-04-21T06:33:01Z)
• Machine Learning and Soil Humidity Sensing: Signal Strength Approach [0.0]
  Existing solutions are based on data received from the power hungry/expensive sensors that are transmitting the sensed data over the wireless channel. This work explores a concept of a novel, low-power, LoRa-based, cost-effective system which achieves humidity sensing using Deep learning techniques.
  arXiv  Detail & Related papers  (2020-11-16T21:00:36Z)
• Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks [84.2155885234293]
  We first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC. To address these open problems, we develop a multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC.
  arXiv  Detail & Related papers  (2020-02-22T14:38:11Z)
• Adaptive Anomaly Detection for IoT Data in Hierarchical Edge Computing [71.86955275376604]
  We propose an adaptive anomaly detection approach for hierarchical edge computing (HEC) systems to solve this problem. We design an adaptive scheme to select one of the models based on the contextual information extracted from input data, to perform anomaly detection. We evaluate our proposed approach using a real IoT dataset, and demonstrate that it reduces detection delay by 84% while maintaining almost the same accuracy as compared to offloading detection tasks to the cloud.
  arXiv  Detail & Related papers  (2020-01-10T05:29:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.