Analysis of LoRaWAN Uplink with Multiple Demodulating Paths and Capture Effect

Low power wide area networks (LPWANs), such as the ones based on the LoRaWAN protocol, are seen as enablers of large number of IoT applications and services. In this work, we assess the scalability of LoRaWAN by analyzing the frame success probability (FSP) of a LoRa frame while taking into account the capture effect and the number of parallel demodulation paths of the receiving gateway. We have based our model on the commonly used {SX1301 gateway chipset}, which is capable of demodulating {up to} eight frames simultaneously; however, the results of the model can be generalized to architectures with arbitrary number of demodulation paths. We have also introduced and investigated {three} policies for Spreading Factor (SF) allocation. Each policy is evaluated in terms of coverage {probability}, {FSP}, and {throughput}. The overall conclusion is that the presence of multiple demodulation paths introduces a significant change in the analysis and performance of the LoRa random access schemes

Data Aggregation and Packet Bundling of Uplink Small Packets for Monitoring Applications in LTE

In cellular massive Machine-Type Communications (MTC), a device can transmit directly to the base station (BS) or through an aggregator (intermediate node). While direct device-BS communication has recently been in the focus of 5G/3GPP research and standardization efforts, the use of aggregators remains a less explored topic. In this paper we analyze the deployment scenarios in which aggregators can perform cellular access on behalf of multiple MTC devices. We study the effect of packet bundling at the aggregator, which alleviates overhead and resource waste when sending small packets. The aggregators give rise to a tradeoff between access congestion and resource starvation and we show that packet bundling can minimize resource starvation, especially for smaller numbers of aggregators. Under the limitations of the considered model, we investigate the optimal settings of the network parameters, in terms of number of aggregators and packet-bundle size. Our results show that, in general, data aggregation can benefit the uplink massive MTC in LTE, by reducing the signalling overhead.Comment: to appear in IEEE Networ

What Can Wireless Cellular Technologies Do about the Upcoming Smart Metering Traffic?

The introduction of smart electricity meters with cellular radio interface puts an additional load on the wireless cellular networks. Currently, these meters are designed for low duty cycle billing and occasional system check, which generates a low-rate sporadic traffic. As the number of distributed energy resources increases, the household power will become more variable and thus unpredictable from the viewpoint of the Distribution System Operator (DSO). It is therefore expected, in the near future, to have an increased number of Wide Area Measurement System (WAMS) devices with Phasor Measurement Unit (PMU)-like capabilities in the distribution grid, thus allowing the utilities to monitor the low voltage grid quality while providing information required for tighter grid control. From a communication standpoint, the traffic profile will change drastically towards higher data volumes and higher rates per device. In this paper, we characterize the current traffic generated by smart electricity meters and supplement it with the potential traffic requirements brought by introducing enhanced Smart Meters, i.e., meters with PMU-like capabilities. Our study shows how GSM/GPRS and LTE cellular system performance behaves with the current and next generation smart meters traffic, where it is clearly seen that the PMU data will seriously challenge these wireless systems. We conclude by highlighting the possible solutions for upgrading the cellular standards, in order to cope with the upcoming smart metering traffic.Comment: Submitted; change: corrected location of eSM box in Fig. 1; May 22, 2015: Major revision after review; v4: revised, accepted for publicatio

Scatter of mass changes estimates at basin scale for Greenland and Antarctica

During the last decade, the GRACE mission has provided valuable data for determining the mass changes of the Greenland and Antarctic ice sheets. Yet, discrepancies still exist in the published mass balance results, and comprehensive analyses on the sources of errors and discrepancies are lacking. Here, we present monthly mass changes together with trends derived from GRACE data at basin scale for both the Greenland and Antarctic ice sheets, and we assess the variability and errors for each of the possible sources of discrepancies, and we do this in an unprecedented systematic way, taking into account mass inference methods, data sets and background models. We find a very good agreement between the monthly mass change results derived from two independent methods, which represents a cross validation. For the monthly solutions, we find that most of the scatter is caused by the use of the two different data sets rather than the two different methods applied. Besides the well-known GIA trend uncertainty, we find that the geocenter motion and the recent de-aliasing corrections significantly impact the trends, with contributions of +13.2 Gt yr⁻¹ and −20 Gt yr⁻¹, respectively, for Antarctica, which is more affected by these than Greenland. We show differences between the use of release RL04 and the new RL05 and confirm a lower noise content in the new release. The overall scatter of the solutions well exceeds the uncertainties propagated from the data errors and the leakage (as done in the past); hence we calculate new sound total errors for the monthly solutions and the trends. We find that the scatter in the monthly solutions caused by applying different estimates of geocenter motion time series (degree-1 corrections) is significant – contributing with up to 40% of the total error. For the whole GRACE period (2003–2011) our trend estimate for Greenland is −234 ± 20 Gt yr⁻¹ and −83 ± 36 Gt yr⁻¹ for Antarctica (−111 ± 15 Gt yr⁻¹ in the western part). We also find a clear (with respect to our errors) increase of mass loss in the last four years

Machine Learning Methods for Monitoring of Quasi-Periodic Traffic in Massive IoT Networks

One of the central problems in massive Internet of Things (IoT) deployments is the monitoring of the status of a massive number of links. The problem is aggravated by the irregularity of the traffic transmitted over the link, as the traffic intermittency can be disguised as a link failure and vice versa. In this work we present a traffic model for IoT devices running quasi-periodic applications and we present both supervised and unsupervised machine learning methods for monitoring the network performance of IoT deployments with quasi-periodic reporting, such as smart-metering, environmental monitoring and agricultural monitoring. The unsupervised methods are based on the Lomb-Scargle periodogram, an approach developed by astronomers for estimating the spectral density of unevenly sampled time series

5G NB-IoT via Low Density LEO Constellations

5G NB-IoT is seen as a key technology for providing truly ubiquitous, global 5G coverage (1.000.000 devices/km2) for machine type communications in the internet of things. A non-terrestrial network (NTN) variant of NB-IoT is being standardized in the 3GPP, which along with inexpensive and non-complex chip-sets enables the production of competitively priced IoT devices with truly global coverage. NB-IoT allows for narrowband single carrier transmissions in the uplink, which improves the uplink link-budget by as much as 16.8 dB over the 180 [kHz] downlink. This allows for a long range sufficient for ground to low earth orbit (LEO) communication without the need for complex and expensive antennas in the IoT devices. In this paper the feasibility of 5G NB-IoT in the context of low-density constellations of small-satellites carrying base-stations in LEO is analyzed and required adaptations to NB-IoT are discussed