Distributed Connectivity Control in Bio-Hybrid Wireless Sensor Networks

Križmančić, Marko; Rabbel, Tim-Lucas; Buss, Eduard; Wahby, Mostafa; Hamann, Heiko; Bogdan, Stjepan

doi:10.1145/3524458.3547260

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…In our EU-funded project 'Watch-Plant' [16,17] (2021-2024), we use phytosensing to measure environmental features via living plants. We develop a self-powered, energy-efficient sensor network of electronic devices attached to plants [14,18]. This forms a so-called bio-hybrid system of strongly coupled technological devices and living organisms.…”

Section: Introductionmentioning

confidence: 99%

Stimulus classification with electrical potential and impedance of living plants: comparing discriminant analysis and deep-learning methods

et al. 2023

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The physiology of living organisms, such as living plants, is complex and particularly difficult to understand on a macroscopic, organism-holistic level. Among the many options to study plant physiology, electrical potential and tissue impedance are arguably simple measurement techniques to gather plant-level information. Despite the many possible uses, our research is exclusively driven by the idea of phytosensing, that is, interpreting living plants' signals to learn information about surrounding environmental conditions. As ready-to-use plant-level physiological models are not available, we consider the plant as a blackbox and apply statistics and machine learning to automatically interpret measured signals. In simple plant experiments, we expose Zamioculcas zamiifolia and Solanum lycopersicum (tomato) to four different stimuli: wind, heat, red and blue light. We measure electrical potential and tissue impedance signals. Given these signals, we evaluate a large variety of methods from statistical discriminant analysis and from deep learning for the classification problem of determining the correct stimulus to which the plant was exposed. We identify a set of methods that successfully classify stimuli with good accuracy without a clear winner. The statistical approach is competitive, partially depending on data availability for the machine learning approach. Our extensive results show the feasibility of the blackbox approach and can be used in future research to select appropriate classifier techniques for a given use case. In our own future research, we will exploit these methods to drive a phytosensing approach for air pollution monitoring in urban areas.

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Section: Introductionmentioning

confidence: 99%

Stimulus classification with electrical potential and impedance of living plants: comparing discriminant analysis and deep-learning methods

et al. 2023

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Adaptive connectivity control in networked multi-agent systems: A distributed approach

Križmančić,

Bogdan

2024

PLoS ONE

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Effective communication is crucial for the performance and collaboration within cooperative networked multi-agent systems. However, existing literature lacks comprehensive solutions for dynamically monitoring and adjusting communication topologies to balance connectivity and energy efficiency. This study addresses this gap by proposing a distributed approach for estimating and controlling system connectivity over time. We introduce a modified consensus protocol where agents exchange local assessments of communication link quality, enabling the estimation of a global weighted adjacency matrix without requiring centralized information. The system’s connectivity is measured using the second smallest eigenvalue of the communication graph Laplacian, commonly referred to as algebraic connectivity. Additionally, we enhance the consensus protocol with an adaptive mechanism to expedite convergence, irrespective of system size or structure. Furthermore, we present an analytical method for connectivity control based on the Fiedler vector approximation, facilitating the addition or removal of communication links. This method adjusts control parameters to accommodate minor variations in link quality while reconfiguring the network in response to significant changes. Notably, it identifies and eliminates energy-consuming yet non-contributory links, improving long-term connectivity efficiency. Simulation experiments across diverse scenarios and the number of agents validate the efficacy of our proposed algebraic connectivity estimation and tracking strategy. Results demonstrate robust connectivity maintenance against external disturbances and agent failures, underscoring the practical utility of our approach for real-world multi-agent systems.

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Distributed Connectivity Control in Bio-Hybrid Wireless Sensor Networks

Cited by 2 publications

References 22 publications

Stimulus classification with electrical potential and impedance of living plants: comparing discriminant analysis and deep-learning methods

Stimulus classification with electrical potential and impedance of living plants: comparing discriminant analysis and deep-learning methods

Adaptive connectivity control in networked multi-agent systems: A distributed approach

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