  We propose a new approach towards RSS (Received Signal Strength) based
wireless localisation for scenarios where, instead of absolute positioning of
an object, only the information whether an object is inside or outside of a
specific area is required. This is motivated through a number of applications
including, but not limited to, a) security: detecting whether an object is
removed from a secure location, b) wireless sensor networks: detecting sensor
movements outside of a network area, and c) computational behaviour analytics:
detecting customers leaving a retail store. The result of such detection
systems can naturally be utilised in building a higher level contextual
understanding of a system or user behaviours. We use a supervised learning
method to overcome issues related to RSS based localisation systems including
multipath fading, shadowing, and incorrect model parameters (as in unsupervised
methods). Moreover, to reduce the cost of collecting training data, we employ a
detection method called One-Class SVM (OC-SVM) which requires only one class of
data (positive data, or target class data) for training. We derive a
mathematical approximation of accuracy which utilises the characteristics of
wireless signals as well as OC-SVM. Based on this we then propose a novel
mathematical formula to find optimal placement of devices. This enables us to
optimize the placement without performing any costly experiments or
simulations. We validate our proposed mathematical framework based on simulated
and real experiments.
