Sentence examples for sensing infrastructure from inspiring English sources

Smartphone-based human indoor localization was previously implemented using wireless sensor networks at the cost of sensing infrastructure deployment.

Moreover, the framework demonstrates considerable robustness in the tests, retaining a room-level accuracy of 70% or higher when the majority of sensing infrastructure is damaged.

The advance in information and telecommunication technologies such as personal mobile phones has opened up new exciting opportunities for behavior studies among which is the opportunistic behavioral sensing that incorporates human being as part of sensing infrastructure via cellular network.

This study has taken an opportunistic sensing approach for real-life behavior observation, as human subjects are also part of the sensing infrastructure.

Comparing to existing indoor smartphone localization methods, the proposed system does not rely on wireless sensing infrastructure, and has comparable localization accuracy with increased flexibility and scalability due to the mobility of the robot.

To address this challenge, the authors have developed a radio frequency based indoor localization framework, for which novel algorithms were designed for two different situations: one where an existing sensing infrastructure exists in buildings and one where an ad-hoc sensing infrastructure must be deployed.

CEP effectively supports the implementation of "sense and respond" behaviours, as it enables to extract meaningful events from raw data streams originated by sensing infrastructures, for enterprise processes and applications consumption.

This paper proposes a mechanism design based approach for public parking slot assignment in an environment empowered by recent advances in parking sensing, infrastructure-to-vehicle, and vehicle-to-infrastructure communications.

An algorithmic architecture for kernel-based modelling of data streams from city sensing infrastructures is introduced.

Research shows that co-operative sensing methods can aid in significant data reduction in large-scale sensing infrastructures [58].

In this article, we present a new architecture that provides to Internet users the capability to obtain any type of data acquired from different heterogeneous sensing infrastructures (SIs), exposed in a uniform way.