Sensing WiFi Network for Personal Object Analytics

We present the design, implementation and evaluation of an enabling platform to locate and query physical objects using existing WiFi infrastructure. We propose the use of WiFi management probes as a data transport mechanism for physical objects that are tagged with WiFi enabled accelerometers capable of determining state-of-use based on motion signature. A local WiFi gateway captures these management signals with objects' state information embedded, and stores them locally after annotating them with coarse grained location using a proximity ranging algorithm. External applications can query the aggregated views of this local data through a cloud based query server. This privacy preserving cloudlet architecture puts end users at the centre of control of their personal objects data, yet offering opportunities to external developers to build novel applications. We present the technical architecture and algorithms of the proposed platform together with a prototype personal object analytics application and assess the feasibility of our different design decisions.