Aquafel-Pso: A Monitoring System for Water Resources Using Autonomous Surface Vehicles Based on Multimodal Pso and Federated Learning

Kathen, Micaela Jara Ten; Samaniego, Federico Peralta; Johnson, Princy; Flores, Isabel Jurado; Reina, Daniel Gutiérrez

doi:10.2139/ssrn.4378269

Cited by 3 publications

(8 citation statements)

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“…In [19], a monitoring system for Ypacaraí Lake is developed that uses a fleet of ASVs controlled by an improved Particle Swarm Optimization (PSO) algorithm based on a GP as surrogate model. With PSO, a population of potential solutions, represented as particles, moves through the search space to find the best solution.…”

Section: Related Workmentioning

confidence: 99%

Informative Deep Reinforcement Path Planning for Heterogeneous Autonomous Surface Vehicles in Large Water Resources

Barrionuevo,

Yanes Luis,

Gutiérrez Reina

et al. 2024

IEEE Access

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Section: Related Workmentioning

confidence: 99%

Informative Deep Reinforcement Path Planning for Heterogeneous Autonomous Surface Vehicles in Large Water Resources

Barrionuevo,

Yanes Luis,

Gutiérrez Reina

et al. 2024

IEEE Access

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“…Parameters are chosen randomly and the execution time is extended [57] Aqua-Fel PSO Detects the pollution in water and also estimates the quality of the water Multiple water quality parameter models are not estimated [58] PSO + FL = PAASO The function that is needed to be optimized by agents can be understood Does not show good performance in heterogeneous environment [59] PSO and FL PSO optimizes the eight of the clients that are sent to the aggregation FL models are not stable and thus can be improvised further [60] FPSO-FS algorithm PSO can search for optimal private subset and FL solves the privacy issues in multi-participants involvement…”

Section: Related Workmentioning

confidence: 99%

“…The combination of PSO and FL helps us to understand the function to be optimized by a set of agents [58]. A water-monitoring system is proposed by the authors in [57], which has two phases, namely exploration and exploitation, using autonomous surface vehicles which are equipped with water quality sensors which are based on PSO and FL techniques. In other work, the authors in [60] fulfilled feature selection and privacy requirements with the combination of PSO and FL.…”

Section: Execution Time Is Very Highmentioning

confidence: 99%

Particle Swarm-Based Federated Learning Approach for Early Detection of Forest Fires

Gadekallu

2023

Sustainability

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Forests are a vital part of the ecological system. Forest fires are a serious issue that may cause significant loss of life and infrastructure. Forest fires may occur due to human or man-made climate effects. Numerous artificial intelligence-based strategies such as machine learning (ML) and deep learning (DL) have helped researchers to predict forest fires. However, ML and DL strategies pose some challenges such as large multidimensional data, communication lags, transmission latency, lack of processing power, and privacy concerns. Federated Learning (FL) is a recent development in ML that enables the collection and process of multidimensional, large volumes of data efficiently, which has the potential to solve the aforementioned challenges. FL can also help in identifying the trends based on the geographical locations that can help the authorities to respond faster to forest fires. However, FL algorithms send and receive large amounts of weights of the client-side trained models, and also it induces significant communication overhead. To overcome this issue, in this paper, we propose a unified framework based on FL with a particle swarm-optimization algorithm (PSO) that enables the authorities to respond faster to forest fires. The proposed PSO-enabled FL framework is evaluated by using multidimensional forest fire image data from Kaggle. In comparison to the state-of-the-art federated average model, the proposed model performed better in situations of data imbalance, incurred lower communication costs, and thus proved to be more network efficient. The results of the proposed framework have been validated and 94.47% prediction accuracy has been recorded. These results obtained by the proposed framework can serve as a useful component in the development of early warning systems for forest fires.

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“…Specifically to monitoring water quality, recent works include [24], [14] and [25]. These works start from the premise that a exploration is needed to obtain good, reliable WQP model.…”

Section: Related Workmentioning

confidence: 99%

“…During the exploitation phase, each ASV covers a distance of 10km. These values were determined according to the study performed in [25]. This study demonstrated that a balanced ratio of 50% exploration distance (exploration_distance) and 50% exploitation distance (exploitation_distance) is optimal.…”

Section: Parameter Settingsmentioning

confidence: 99%

AquaHet-PSO: An Informative Path Planner for a Fleet of Autonomous Surface Vehicles With Heterogeneous Sensing Capabilities Based on Multi-Objective PSO

Kathen,

Samaniego,

Flores

et al. 2023

IEEE Access

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The importance of monitoring and evaluating the quality of water resources has significantly grown over time. To achieve this effectively, an option is to employ an intelligent monitoring system capable of measuring the physical and chemical parameters of water. Surface vehicles equipped with sensors for measuring water quality parameters offer a viable solution for these missions. This work presents a novel approach called AquaHet-PSO, which addresses the challenge of simultaneously monitoring multiple water quality parameters with several peaks of contamination using a heterogeneous fleet of autonomous surface vehicles. Each vehicle in the fleet possesses a different set of sensors, such as number of sensors and sensor types, which is the definition provided by the authors for a heterogeneous fleet. The AquaHet-PSO consists of three main phases. In the initial phase, the vehicles traverse the water resource to obtain preliminary models of water quality parameters. These models are then utilized in the second phase to identify potential contamination areas and assign vehicles to specific action zones. In the final phase, the vehicles focus on a comprehensive characterization of the parameters. The proposed system combines several techniques, including Particle Swarm Optimization and Gaussian Processes, with the integration of genetic algorithm to maximize the distances between the initial positions of vehicles equipped with identical sensors, and a distributed communication system in the final phase of the AquaHet-PSO. Simulation results in the Ypacarai lake demonstrate the effectiveness and efficiency of AquaHet-PSO in generating accurate water quality models and detecting contamination peaks. The proposed method demonstrated improvements compared to the lawnmower approach. It achieved a remarkable 17% improvement, on r-squared data, in generating complete models of water quality parameters throughout the lake. In addition, it achieved a 230% improvement in accurate characterization of high pollution areas and a 24% increase in pollution peak detection specifically for heterogeneous fleets equipped with four or more identical sensors.

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Aquafel-Pso: A Monitoring System for Water Resources Using Autonomous Surface Vehicles Based on Multimodal Pso and Federated Learning

Cited by 3 publications

References 0 publications

Informative Deep Reinforcement Path Planning for Heterogeneous Autonomous Surface Vehicles in Large Water Resources

Informative Deep Reinforcement Path Planning for Heterogeneous Autonomous Surface Vehicles in Large Water Resources

Particle Swarm-Based Federated Learning Approach for Early Detection of Forest Fires

AquaHet-PSO: An Informative Path Planner for a Fleet of Autonomous Surface Vehicles With Heterogeneous Sensing Capabilities Based on Multi-Objective PSO

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