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PROJECT TITLE :

High Resolution Beacon-Based Proximity Detection for Dense Deployment - 2018

ABSTRACT:

The emergence of Bluetooth low energy (BLE) beacons has promoted the development of proximity-primarily based service (PBS), which could be a context-aware application delivered subject to the Proximity of Interest (PoI). Most business applications use the sequential proximity detection with a fixed scanning mechanism to spot the target PoI. Such sequential execution, though is in a position to supply reliable detection, suffers severe performance degradation especially when the quantity of deployed beacons in the vicinity will increase. To understand the results of dense deployment, we have a tendency to conduct an empirical investigation and derive the statistical properties of both received signal strength (RSS) and signal inter-arrival time. In lightweight of the statistical insights, this Project proposes a high resolution proximity detection using an adaptive scanning mechanism fusion with a spontaneous Differential Evolution (AS+sDE). This novel approach allows the receiver to adapt its scanning length conditioned on the deployment density and build an nearly spontaneous detection in parallel with the scanning. The feasibility of the proposed approach is verified by both simulations and real-world implementations. For a density of = five beacons = m two , AS+sDE achieves a superior performance with a high accuracy rate, i.e., on average <; 1s is spent to guarantee at least ninety percent accuracy.


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