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The amount of greenhouse gas emissions from streetlights is equivalent to 2.6 million cars with as many as 26 million streetlights in the United States. The proposed IoT controller integrates sensors to make these streetlights as hubs for smart environment monitoring with effective energy usage. Conservation of energy is one of the main concerns in the modern era, and energy coming from the sun can be utilized efficiently alongside a smart streetlight management system instead of conventional streetlight management techniques. Additionally, with streetlights being present throughout a city, the opportunity to collect city-wide weather data is proposed. To this end, a solar-powered IoT-based smart street lighting and environmental monitoring system is proposed. The proposed energy-efficient IoT-based system uses a microcontroller to control light-emitting diode (LED) streetlights depending on lighting conditions and vehicle detection, ensuring that the streetlights can be turned on when needed.
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An Attack-Resilient and Energy-Adaptive Monitoring System for Smart Farms
In this work, we propose an energy-adaptive moni-toring system for a solar sensor-based smart animal farm (e.g., cattle). The proposed smart farm system aims to maintain high-quality monitoring services by solar sensors with limited and fluctuating energy against a full set of cyberattack behaviors including false data injection, message dropping, or protocol non-compliance. We leverage Subjective Logic (SL) as the belief model to consider different types of uncertainties in opinions about sensed data. We develop two Deep Reinforcement Learning (D RL) schemes leveraging the design concept of uncertainty maximization in SL for DRL agents running on gateways to collect high-quality sensed data with low uncertainty and high freshness. We assess the performance of the proposed energy-adaptive smart farm system in terms of accumulated reward, monitoring error, system overload, and battery maintenance level. We compare the performance of the two DRL schemes developed (i.e., multi-agent deep Q-Iearning, MADQN, and multi-agent proximal policy optimization, MAPPO) with greedy and random baseline schemes in choosing the set of sensed data to be updated to collect high-quality sensed data to achieve resilience against attacks. Our experiments demonstrate that MAPPO with the uncertainty maximization technique outperforms its counterparts.
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- Award ID(s):
- 2107450
- PAR ID:
- 10447131
- Date Published:
- Journal Name:
- 2022 IEEE Global Communications Conference
- Page Range / eLocation ID:
- 2776 to 2781
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/GLOBECOM48099.2022.10001060
