A common question people ask about indoor location positioning goes something like this: "Why do you need Wi-Fi, or BLE, or other technologies? Why can't it be done based on cellular signals?" The common wisdom, of course, is that cellular signals have so broad a range, so big a coverage area, that they don't differentiate different locations sufficiently. Many mobile companies have implemented indoor positioning based on cellular signals, using multilateration algorithms on cellular signal strengths and antenna locations, and have ended up with 60 to 100 meter inaccuracies on average, often worse. Glopos, based in Helsinki, didn't take "no" for an answer. Instead of relying on cellular signal strength values, Glopos's technology uses a wider variety of cellular signal parameters, models of cell area and shape, and data from other nearby cells, to build self-learning probabilistic models for estimating positions. Their latest tests have achieve
Explaining and predicting technology trends in the mobile industry, especially related to indoor location and Internet of Things technologies.