Sharing Wireless Spectrum in the Forest Ecosystems Using Artificial Intelligence and Machine Learning

Naderi, Sonia; Bundy, Kenneth; Whitney, Thayer; Abedi, Ali; Weiskittel, Aaron R.; Contosta, Alexandra R.

doi:10.1007/s10776-022-00572-9

Cited by 5 publications

(2 citation statements)

References 43 publications

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“…Falkner et al proposed a methodology for parameter estimation that enhances effectiveness, utilizing an automated machine learning technique, within the context of optimizing a travelling wave antenna. An automated machine learning method was developed for the purpose of calculating return loss and gain [15,16]. Moshtaghzadeh et al proposed a methodology rooted in machine learning to optimize the parameter estimation of a foldable origami helical antenna, aiming to enhance its effectiveness.…”

Section: Introductionmentioning

confidence: 99%

Optimizable KNN and ANFIS Algorithms Development for Accurate Antenna Parameter Estimation

Ramasamy,

Anto Bennet

2024

PIER C

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The process of smart antenna synthesis involves the automatic selection of the optimal antenna type and geometry in order to enhance antenna performance. A model for intelligent antenna selection employs an optimizable K-nearest neighbors (KNN) classifier to determine the optimal antenna choice. To optimize the utilization of different learner types, the geometric parameters of the antenna are presented as the final step prior to the construction of the Adaptive Neuro-Fuzzy Inference System (ANFIS) model, which involves the integration of five distinct primary learners. The classification of three distinct types of antennas, namely helical antenna, pyramidal horn antenna, and rectangular patch antenna, is performed using an optimizable K-nearest neighbors (KNN) classifier. Additionally, an ANFIS approach is employed to determine the optimal size parameters for each antenna. The accuracy is used to evaluate the performance of an optimizable KNN classifier, whereas Mean Squared Error and Mean Absolute Percentage Error are used to evaluate the performance of an ANFIS. The proposed technique demonstrates high performance in parameter prediction and antenna categorization, achieving a Mean Absolute Percentage Error of less than 3% and an accuracy exceeding 99.16%. The recommended methodology holds significant potential for widespread application in the development of practical smart antennas.

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Section: Introductionmentioning

confidence: 99%

Optimizable KNN and ANFIS Algorithms Development for Accurate Antenna Parameter Estimation

Ramasamy,

Anto Bennet

2024

PIER C

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“…Hongyu Shi et al used deep reinforcement learning (DRL) to optimize task scheduling at the edge of the IoT and proposed an EC-AIoT CMS based on DRL optimization [ 25 ]. Sonia Naderi et al proposed a low-cost, reliable wireless soil moisture sensing system to enable efficient spatial–temporal data collection, in which a random forest, a Gaussian process, and a support vector regressor were used to calibrate the system [ 26 ]. Arash Heidari et al proposed a new deep Q learning approach that uses a Markov decision process (MDP) to solve the IoT edge offloading-enabled blockchain problem [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

Internet-of-Things-Based Multiple-Sensor Monitoring System for Soil Information Diagnosis Using a Smartphone

2023

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The rise of Internet of Things (IoT) technology has moved the digital world in a new direction and is considered the third wave of the information industry. To meet the current growing demand for food, the agricultural industry should adopt updated technologies and smart agriculture based on the IoT which will strongly enable farmers to reduce waste and increase productivity. This research presents a novel system for the application of IoT technology in agricultural soil measurements, which consists of multiple sensors (temperature and moisture), a micro-processor, a microcomputer, a cloud platform, and a mobile phone application. The wireless sensors can collect and transmit soil information in real time with a high speed, while the mobile phone app uses the cloud platform as a monitoring center. A low power consumption is specified in the hardware and software, and a modular power supply and time-saving algorithm are adopted to improve the energy effectiveness of the nodes. Meanwhile, a novel soil information prediction strategy was explored based on the deep Q network (DQN) reinforcement learning algorithm. Following the weighted combination of a bidirectional long short-term memory, online sequential extreme learning machine, and parallel extreme machine learning, the DQN Bi-OS-P prediction model was obtained. The proposed data acquisition system achieved a long-term stable and reliable collection of time-series soil data with equal intervals and provided an accurate dataset for the precise diagnosis of soil information. The RMSE, MAE, and MAPE of the DQN Bi-OS-P were all reduced, and the R2 was improved by 0.1% when compared to other methods. This research successfully implemented the smart soil system and experimentally showed that the time error between the value displayed on the mobile phone app and its exact acquisition moment was no more than 3 s, proving that mobile applications can be effectively used for the real-time monitoring of soil quality and conditions in wireless multi-sensing based on the Internet of Things.

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Artificial intelligence for sustainability: opportunities and risks of utilizing Earth observation technologies to protect forests

Causevic,

Fielding

et al. 2024

Discov Conserv

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Extreme heat, massive floods, and ever-longer droughts caused by climate change are intensifying in magnitude and intensity across the globe. Forests are threatened by increases in the average global temperature and human-driven economic activity. Artificial intelligence and Earth observation technologies present opportunities to improve environmental sustainability, promoting Sustainable Development Goals through more efficient forest management and stewardship. These technologies can potentially improve the mapping, understanding, and monitoring of forests’ ecosystem services, directly aiding humanity’s efforts to combat climate change. However, increased use of these technologies could also create new risks, making these efforts counterproductive. This perspective article identifies opportunities and risks of using artificial intelligence and Earth observation technologies to protect forests globally. It discusses how risks can be mitigated and what governance mechanisms should be used to design sustainable forest-focused artificial intelligence.

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Sharing Wireless Spectrum in the Forest Ecosystems Using Artificial Intelligence and Machine Learning

Cited by 5 publications

References 43 publications

Optimizable KNN and ANFIS Algorithms Development for Accurate Antenna Parameter Estimation

Optimizable KNN and ANFIS Algorithms Development for Accurate Antenna Parameter Estimation

Internet-of-Things-Based Multiple-Sensor Monitoring System for Soil Information Diagnosis Using a Smartphone

Artificial intelligence for sustainability: opportunities and risks of utilizing Earth observation technologies to protect forests

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