The ability to reliably predict the future quality of a wireless channel, as
seen by the media access control layer, is a key enabler to improve performance
of future industrial networks that do not rely on wires. Knowing in advance how
much channel behavior may change can speed up procedures for adaptively
selecting the best channel, making the network more deterministic, reliable,
and less energy-hungry, possibly improving device roaming capabilities at the
same time.
To this aim, popular approaches based on moving averages and regression were
compared, using multiple key performance indicators, on data captured from a
real Wi-Fi setup. Moreover, a simple technique based on a linear combination of
outcomes from different techniques was presented and analyzed, to further
reduce the prediction error, and some considerations about lower bounds on
achievable errors have been reported. We found that the best model is the
exponential moving average, which managed to predict the frame delivery ratio
with a 2.10\% average error and, at the same time, has lower computational
complexity and memory consumption than the other models we analyzed.

为提高未来工业网络的性能和设备漫游能力，该研究比较了基于移动平均和回归的多个方法，发现指数移动平均是最佳模型，可在预测帧传输比时以平均误差为 2.10% 并具有较低计算复杂度和内存消耗。

利用移动平均和回归模型预测无线信道质量

Predicting Wireless Channel Quality by means of Moving Averages and  Regression Models

Due to the fourth industrial revolution, and the resulting increase in
interconnectivity, industrial networks are more and more opened to publicly
available networks. Apart from the huge benefit in manageability and
flexibility, the openness also results in a larger attack surface for malicious
adversaries. In comparison to office environments, industrial networks have
very high volumes of data. In addition to that, every delay will most likely
lead to loss of revenue. Hence, intrusion detection systems for industrial
applications have different requirements than office-based intrusion detection
systems. On the other hand, industrial networks are able to provide a lot of
contextual information due to manufacturing execution systems and enterprise
resource planning. Additionally, industrial networks tend to be more uniform,
making it easier to determine outliers. In this work, an abstract simulation of
industrial network behaviour is created. Malicious actions are introduced into
a set of sequences of valid behaviour. Finally, a context-based and
context-less intrusion detection system is used to find the attacks. The
results are compared and commented. It can be seen that context information can
help in identifying malicious actions more reliable than intrusion detection
with only one source of information, e.g. the network.

本文通过模拟工业网络行为，引入恶意行为并采用基于上下文和非上下文的入侵检测系统进行比较，发现采用上下文信息可以更可靠地识别恶意行为。