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TitleCloud platforms for context-adaptive positioning and localisation in GNSS-denied scenarios-a systematic review
Author(s)Quezada-Gaibor, Darwin
Torres-Sospedra, Joaquín
Nurmi, Jari
Koucheryavy, Yevgeni
Huerta, Joaquin
KeywordsCloud platform
GNSS-denied scenarios
Systematic review
Issue dateJan-2022
CitationQuezada-Gaibor, D.; Torres-Sospedra, J.; Nurmi, J.; Koucheryavy, Y.; Huerta, J. Cloud Platforms for Context-Adaptive Positioning and Localisation in GNSS-Denied Scenarios—A Systematic Review. Sensors 2022, 22, 110.
Abstract(s)Cloud Computing and Cloud Platforms have become an essential resource for businesses, due to their advanced capabilities, performance, and functionalities. Data redundancy, scalability, and security, are among the key features offered by cloud platforms. Location-Based Services (LBS) often exploit cloud platforms to host positioning and localisation systems. This paper introduces a systematic review of current positioning platforms for GNSS-denied scenarios. We have undertaken a comprehensive analysis of each component of the positioning and localisation systems, including techniques, protocols, standards, and cloud services used in the state-of-the-art deployments. Furthermore, this paper identifies the limitations of existing solutions, outlining shortcomings in areas that are rarely subjected to scrutiny in existing reviews of indoor positioning, such as computing paradigms, privacy, and fault tolerance. We then examine contributions in the areas of efficient computation, interoperability, positioning, and localisation. Finally, we provide a brief discussion concerning the challenges for cloud platforms based on GNSS-denied scenarios.
Publisher version
AccessOpen access
Appears in Collections:CAlg - Artigos em revistas internacionais / Papers in international journals

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