Wireless Sensor and Actuator Networks for Lighting Energy ...

In practice, fusion of sensor data could generate worse information than that
from a single robust, well-tasked and high fidelity sensor that directly measures the
phenomenon of interest [70]. Poor information results from the attempt to combine
accurate data with biased or corrupted data, which closely resembles the Byzantine
generals problem [71]. The problem discusses the dilemma a system may encounter
when receiving conflicting information from malfunctioning components through the
metaphor of the Byzantine Army Generals camped with their troops preparing to attack
an enemy city [72]. Communicating only by messengers, the generals must agree upon
a common battle plan – to attack or to retreat, while one or more of them may be traitors
trying to confuse others with false messages. It has been proven that an agreement
among all generals can be reached if and only if more than two-thirds of the generals
are loyal. In other words, two-thirds of the sensors must be well-functioning in order to
produce a pertinent data by equally weighing and combining each sensor data through
sensor fusion. Although one may prefer to gather information from a single robust and
high fidelity sensor than perform sensor fusion on multiple sensors, such a sensor could
be unaffordable or may not even exist, or the phenomenon of interest is not directly
measurable in reality.
Popular techniques that have been applied to sensor fusion applications include,
but are not limited to Kalman filters, probabilistic data association filters (PADF),
hidden Markov models (HMM), maximum likelihood, fuzzy logic, neural networks,
Dempster-Shafer theory, etc. Hybrids of the sensor fusion methods have also been
implemented in some complex applications. Alag et al. developed a methodology for
sensor validation, fusion, and fault detection for equipment monitoring and diagnostics
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