An Attack-Resilient and Energy-Adaptive Monitoring System for Smart Farms
An Attack-Resilient and Energy-Adaptive Monitoring System for Smart Farms
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DOI：
10.1109/globecom48099.2022.10001060
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Qisheng Zhang;Yashika Mahajan;I. Chen;D. Ha;Jin-Hee Cho
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作者：
Qisheng Zhang;Yashika Mahajan;I. Chen;D. Ha;Jin-Hee Cho
在这项工作中，我们为基于太阳能传感器的智能动物农场（例如牛）提出了一种能量自适应的MONI-MONI-ni-ni-ni-ni-MONI系统。拟议的智能农场系统旨在通过有限和波动的能量来维护高质量的监视服务，以与全套的网络攻击行为，包括虚假数据注入，消息删除或协议违规行为。我们利用主观逻辑（SL）作为信念模型，在对感知数据的意见中考虑不同类型的不确定性。我们开发了两个深入的增强学习（D RL）方案，利用了在网关上运行的DRL代理的SL不确定性最大化的设计概念，以收集低不确定性和高新鲜度的高质量感知数据。我们根据累积的奖励，监视错误，系统超负荷和电池维护水平来评估所提出的能源自适应智能农场系统的性能。我们比较了开发的两个DRL方案的性能（即多代理深度Q-Iearning，MADQN和多代理近端策略优化，Mappo）与贪婪和随机的基线方案，以选择要更新到要更新为以要更新为收集高质量的感应数据以实现针对攻击的弹性。我们的实验表明，不确定性最大化技术的Mappo优于其对应物。
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.