Low-power sensing model considering context transition for location-based services

Many previous studies have addressed the provision of sustainable context awareness. However, they did not consider the transition between contexts and instead handled each context individually. In other words, they neglected the relationship between contexts, which can be perceived during the transition of contexts, and instead determined the context using only the value output from a sensor. As a result, although the contexts inferred during the transition are meaningless, the service consumes unnecessary power trying to be aware of these contexts. Individual context awareness for Indoor/Outdoor contexts is a representative example of this. The Indoor/Outdoor contexts should not be inferred concurrently. However, the existing services infer each context independently, so they cannot prevent power wastage when two contexts are inferred at once. For this, there is a need to consider the contexts that could simultaneously occur during context transition in order to increase the power efficiency of a context-aware service. To this end, we propose a low-power sensing model capable of considering context transition for a location-based service. In our method, we generate a context-aware model capable of considering context transition based on the activity of sensors and identify the unstable state in which context-aware services do not infer the context and therefore drain the power inefficiently. Then, by adapting the freezing method proposed in this paper to the UNSTABLE state, we block the activation of the sensors to improve the power efficiency until certain conditions are satisfied. On applying our method to context-aware services for Indoor/Outdoor contexts, we were able to improve the power efficiency by 60% in the UNSTABLE state.