On white-space detection, localization and coverage

Abstract

In a finite area, let one transmitter be deployed according to distribution fT (·) and n sensors be deployed i.i.d. with distribution fS(·). Sensors can detect a transmission only within a distance of rn from their location. Using the sensor observations, we estimate the minimal region within which the transmitter is present, called the uncertainty interval. We obtain statistics and scaling laws on the volume of this uncertainty interval. If n nodes are deployed i.i.d. uniformly, then volume of the uncertainty interval scales as Θ(1/n). We also find the optimal sensor location distribution to maximize the probability that volume of the uncertainty interval is less than a chosen threshold.

A Communication Framework for Image Transmission through LPWAN Technology

“…Test results show that high bandwidth (>500 kHz) results in higher capacity and lower SF values (SF7), resulting in data rates around 50 kbps with good sensitivity at reception, even near noise floor values. This result is adjusted by having a large payload, a short overhead, and using low coding rates [48,[99][100][101][102][103][104][105][106].…”

Agricultural monitoring system using images through a LPWAN network

“…CS for multi-target sparse localization algorithm is used in [50] with cross correlation of the signal readings at several access points. In [51], CS stands out as digital image processing, wireless channel estimation, radar imaging and cognitive radio (CR) communications. The paper is a survey that focused on CR, which is used in radio spectrum allocation and occupancy measurements through sparse signal in multiple domains such as time, frequency and space, because these are mechanisms that allow improving the future generation on wireless networks where CS is highlighted as a theory that help to retrieve any signal from fewer samples with a wide margin than traditional methods.…”