Location-Based Analytics in 5G and Beyond

Bartoletti, Stefania; Chiaraviglio, Luca; Fortes, Sergio; Kennouche, Takai Eddine; Solmaz, Gürkan; Bernini, Giacomo; Giustinlano, Domenico; Widmer, Joerg; Barco, Raquel; Siracusano, Giuseppe; Conti, Andrea; Melazzi, Nicola Blefari

doi:10.1109/mcom.001.2001096

Cited by 25 publications

(8 citation statements)

References 12 publications

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“…Furthermore, the fusion of 5G and IoT paves the way for new services and functionalities in smart buildings, such as location-based services (LBS). LBS can guide occupants through complex facilities, find available meeting rooms, or even help conserve energy by adjusting the environment in real-time based on occupancy patterns [19]. Despite the many benefits, the deployment of 5G within smart buildings also presents challenges, particularly regarding infrastructure requirements and investment costs.…”

Section: The Role Of 5g In Smart Buildingsmentioning

confidence: 99%

A Survey on Smart Buildings Integration with 5G and Cloud Technologies towards Energy Efficiency

Markogiannaki,

Stefanidou,

Kourtis

et al. 2023

Preprint

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As urbanization continues to accelerate, the concept of smart buildings has transitioned from a futuristic vision to an integral component of modern cities. These buildings, often seen as the building blocks of smart cities, are increasingly relying on cutting-edge technologies like 5G and cloud computing to reach unprecedented levels of energy efficiency, sustainability, and occupant comfort. This paper offers a comprehensive exploration into the marriage of smart building technologies with 5G and cloud computing. It scrutinizes the individual and combined impacts of these technologies, presents various case studies showcasing real-world applications, and anticipates future trends. Moreover, it delves into technical challenges, policy implications, and offers concrete recommendations for stakeholders.

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Section: The Role Of 5g In Smart Buildingsmentioning

confidence: 99%

A Survey on Smart Buildings Integration with 5G and Cloud Technologies towards Energy Efficiency

Markogiannaki,

Stefanidou,

Kourtis

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…First, the 5G core network is radically different from previous generations' core networks and Mobile Network Operators (MNO) are likely not willing to make a significant capital expenditure for a new change. Second, the 5G core network was designed to adhere to cloud-native and service-based architecture principles, which make it easy to extend it to support new functionalities like location-based analytics [23].…”

Section: Mobile Networkmentioning

confidence: 99%

Toward native explainable and robust AI in 6G networks: Current state, challenges and road ahead

Fiandrino

Attanasio

Fiore

et al. 2022

Computer Communications

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“…e release of location big data is conducive to providing various location-based services (LBSs) [3,4], so LBS has gradually become an indispensable tool in people's lives. Among them, location data range query is a commonly used technique in spatial data analysis.…”

Section: Introductionmentioning

confidence: 99%

Grid Adaptive Bucketing Algorithm Based on Differential Privacy

Zhao

Zhang

et al. 2022

Mobile Information Systems

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With the popularity of location-based services, the restricted relationship between the availability of big location data and user’s privacy security has become a challenging issue. The spatial division is an effective measure for statistical location data characteristics. This paper proposes a grid adaptive bucketing algorithm based on differential privacy (GAB) to solve the problem that the existing differential privacy location data division method does not fully consider the distribution characteristics of spatial data points and noise superimposition. The algorithm first divides the mobile app location spatial data set into two layers of grids. It then performs adaptive binning judgment according to the square sum error value in the divided area. It puts areas with similar distributions into the same bucket, reducing the uniform assumption error and the noise error caused by many blank areas. Finally, a noise allocation strategy based on Hopkins statistic is adopted to achieve a reasonable noise allocation. The experimental results on two real large-scale location data sets, Checkin and Beijing, show that, compared with other classical algorithms based on differential privacy space partitioning, the GAB algorithm performs better in common evaluation indicators such as relative error and absolute error, which means GAB can obtain better range query effects and privacy protection effects.

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Location-Based Analytics in 5G and Beyond

Cited by 25 publications

References 12 publications

A Survey on Smart Buildings Integration with 5G and Cloud Technologies towards Energy Efficiency

A Survey on Smart Buildings Integration with 5G and Cloud Technologies towards Energy Efficiency

Toward native explainable and robust AI in 6G networks: Current state, challenges and road ahead

Grid Adaptive Bucketing Algorithm Based on Differential Privacy

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