Random Deployment of Data Collectors for Serving Randomly-Located Sensors

TL;DR

This paper analyzes the deployment of randomly placed data collectors to efficiently serve randomly located sensors in machine-type communication, focusing on the number of collectors, power levels, and channel effects using stochastic models and simulations.

Contribution

It introduces a stochastic analysis framework for optimizing data collector deployment and sensor power in random sensor networks, addressing key design challenges.

Findings

Optimal number of data collectors for reliable communication

Impact of wireless channel conditions on network performance

Sensor power levels affecting successful data decoding

Abstract

Recently, wireless communication industries have begun to extend their services to machine-type communication devices as well as to user equipments. Such machine-type communication devices as meters and sensors need intermittent uplink resources to report measured or sensed data to their serving data collector. It is however hard to dedicate limited uplink resources to each of them. Thus, efficient service of a tremendous number of devices with low activities may consider simple random access as a solution. The data collectors receiving the measured data from many sensors simultaneously can successfully decode only signals with signal-to-interference-plus-noise-ratio (SINR) above a certain value. The main design issues for this environment become how many data collectors are needed, how much power sensor nodes transmit with, and how wireless channels affect the performance. This paper provides answers to those questions through a stochastic analysis based on a spatial point process and on simulations.