  To ensure proper functioning of a Wireless Sensor Network (WSN), it is
crucial that the network is able to detect anomalies in communication quality
(e.g., RSSI), which may cause performance degradation, so that the network can
react accordingly. In this paper, we introduce RADIUS, a lightweight system for
the purpose. The design of RADIUS is aimed at minimizing the detection error
(caused by normal randomness of RSSI) in discriminating good links from weak
links and at reaching high detection accuracy under diverse link conditions and
dynamic environment changes. Central to the design is a threshold-based
decision approach that has its foundation on the Bayes decision theory. In
RADIUS, various techniques are developed to address challenges inherent in
applying this approach. In addition, through extensive experiments, proper
configuration of the parameters involved in these techniques is identified for
an indoor environment. In a prototype implementation of the RADIUS system
deployed in an indoor testbed, the results show that RADIUS is accurate in
detecting anomalous link quality degradation for all links across the network,
maintaining a stable error rate of 6.13% on average.
