The suitability of WiFi infrastructure for occupancy sensing

Abstract

The focus of this study was to investigate an alternative and more cost effective solution for occupancy sensing in commercial office buildings. The intended purpose of this solution is to aid in efficient energy management. The main requirements were that the proposed solution made use of existing infrastructure only, and provided a means to focus on occupant location. This research was undertaken due to current solutions making use of custom occupancy sensors that are relatively costly and troublesome to implement. These solutions focus mainly on monitoring environmental changes, and not the physical locations of the occupants themselves. Furthermore, current occupancy sensing solutions are unable to provide proximity and timing information that indicate how far an occupant is located from a specific area, or how long the occupant resided there. The research question was answered by conducting a proof of concept study with data simulated in the OMNeT++ environment in conjunction with the MiXiM framework for wireless networks. The proposed solution investigated the fidelity of existing WiFi infrastructure for occupancy sensing, this entailed the creation of a Virtual Occupancy Sensor (VOS) that implemented RSS-based localisation for an occupant’s WiFi devices. Localisation was implemented with three different location estimation techniques; these were trilateration, constrained nearest neighbour RF mapping and unconstrained nearest neighbour RF mapping. The obtained positioning data was interpreted by a developed intelligent agent that was able to transform this regular position data into relevant occupancy information. This information included a distance from office measurement and an occupancy result that can be interpreted by existing energy management systems. The accuracy and operational behaviour of the developed VOS were tested with various scenarios. Sensitivity analysis and extreme condition testing were also conducted. Results showed that the constrained nearest neighbour RF mapping approach is the most accurate, and is best suited for occupancy determination. The created VOS system can function correctly with various tested sensitivities and device loads. Furthermore results indicated that the VOS is very accurate in determining room level occupancy although the accuracy of the position coordinate estimations fluctuated considerably. The operational behaviour of the VOS could be validated for all investigated scenarios. It was determined that the developed VOS can be deemed fit for its intended purpose, and is able to give indication to occupant proximity and movement timing. The conducted research confirmed the fidelity of WiFi infrastructure for occupancy sensing, and that the developed VOS can be considered a viable and cost effective alternative to current occupancy sensing solutions.