WiLAD: Wireless Localisation through Anomaly Detection

TL;DR

WiLAD introduces a novel RSS-based wireless localisation method focused on detecting whether an object is inside or outside a specific area, using anomaly detection and one-class SVMs for efficient and cost-effective deployment.

Contribution

The paper presents a new anomaly detection approach for wireless localisation that requires only positive data for training and provides a mathematical framework for optimal device placement.

Findings

Effective in scenarios with multipath fading and shadowing

Reduces training data collection costs

Validated with simulated and real experiments

Abstract

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.