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Abstract
The accessibility of accurate location information for operators in mission-critical scenarios would considerably increase their mission success. In order to obtain precise location information, numerous algorithms and technologies have been suggested. These methods and systems show varying performances under different conditions, and with the help of machine learning techniques, their reliability can be enhanced dramatically. In this paper, we overview the state-of-the-art in emerging algorithms and technologies employing cognitive solutions in mission critical localization applications. We compare these algorithms in terms of different localization parameters such as scalability, power consumption, availability, service quality and accuracy. Consequently, this survey will assist researchers who are working in the area of RF-based localization to achieve better performance in mission critical scenarios that can be experienced in smart city applications.
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