Node localization in wireless sensor networks based on context awareness

There are several algorithms for node localization in wireless sensor networks with different accuracy level. The accuracy level of localization algorithms is compromised when these are implemented on indoor scenarios. This is due to the high level of interference which is produced by either mobile or fixed objects. Context-aware computing can provide important information about the physical structure of the scenario, such as number of obstacles, type (fixed or mobile) and its obstruction level. We believe that integration of context-aware computing as part of localization algorithms can improve the estimation accuracy. This computing capability can also potentially increase the range of application scenarios. We propose the Dynamic Contextual Bounding Box algorithm which uses connectivity information, radio signal quality, and context-awareness to estimate the obstruction level in order to achieve a more accurate estimation.