The diversity traffic requirements, reliability communication infrastructure, and the real-time end-to-end (E2E) latencies are some of the major communication challenges to support a diverse set of emerging Internet of Things (IoT) applications include Smart Grid (SG) applications. For instance, using point-to-point fibers between each device and the controller has been reported, previously, as one of the solutions to address the E2E latency requirements. However, even with the fiber capacity, utilizing the technique was limited due to its excessive cost. Hence, using a commercial multiservice cellular network such as Long-Term Evolution (LTE) and Long-Term Evolution-Advanced (LTE-A) is a considerable solution due to the high-performance metrics: high throughput, low latency, higher reliability, and large bandwidth.
In this paper, we propose an uplink LTE Cascaded Priority-based scheduling algorithm (CPb) that supports a diverse set of Smart Grid (SG) applications, and improves the performance metrics compared to other two well-known schedulers, Proportional Fairness (PF) and Round Robin (RR). The proposed CPb algorithm uses a differentiation technique, applying the Time Domain Scheduler (TDS) and the Frequency Domain Scheduler (FDS), to meet the various SG traffic requirements and types for massive Machin-to-Machine (M2M) devices. Four SG traffic types for each M2M device are used in this study: (1) SG delay sensitive event-driven traffic is used as a SG Distribution Automation (DA), (2) Time driven traffic is used for the other SG types of traffic, including video surveillance, (3) Power quality data, and (4) Periodic Advanced Meter Infrastructure (AMI) data. The CPb results show a significant improvement in the performance metrics compared to the PF and RR schedulers, according to the LTE QoS Class Identifier (QCI) parameters.
Interaction between the tropical ocean and atmosphere produces interannual climate variability dominated by the El Ni˜no Southern Oscillation (ENSO). We perform a Fourier analysis of the El Ni˜no events, which are characterized by positive sea-surface temperature (SST) anomalies. We consider an elementary nonlinear model for the ENSO phenomenon: the time rate of change of the SST depends on the existence of a strong positive feedback in the coupled ocean-atmosphere system, and on a nonlinear mechanism that limits the growth of unstable perturbations. A key element in this model is the inclusion of the effects of equatorially trapped oceanic waves propagating in a closed basin through a time delayed term. Numerical solution reveals solutions that are self-sustained oscillations. The model is extended by including external influences such as annual forcing, global warming, and stochastic effects. We investigate the range of the parameters that will cause drastic qualitative changes in the climate system, i.e. bifurcation.
Lidar daylight measurements are limited by sky background noise (BGN). Reducing the BGN is essential to improve Lidar signal-to-noise ratio (SNR). We report on an optimization technique to improve SNR in a monostatic/biaxial and bistatic Lidar systems by redesigning the geometrical scheme of Lidar receiver. A series of simulations to calculate the overlap area between both transmitter and receiver field of view (FOV) is conducted to determine optimal receiver aperture shapes, locations, and sizes within different lidar ranges. Techniques to vary receiver aperture shape, position, and size to accommodate backscattering signals over a given range, to maximize Lidar SNR, is introduced. At the same short range, numerical results show a better GF of the bistatic compared to the monostatic/biaxial configurations. A complete comparison between monostatic/biaxial and bistatic configurations, for low altitude measurements between 0.1km and 2km, is discussed.
We report on the measurements of aerosol and cloud vertical structure in New York City (NYC) using the first polarization Micro pulse Lidar (MPL) located at the City University of New York (CUNY). MPL operation, setup, data collection and correction will be introduced. Preliminary results and comparison analysis between 2015 and 2016 of cloud vertical structure and the Planetary Boundary Layer (PBL) above NYC will be discussed. An investigation analysis of the impact of NYC rush hour pollution on the level of PBL depth will be introduced using the MPL measurements (such as temporal and spatial trends in aerosol and cloud structure). Applications of the MPL tow-polarization channels will be investigated. Potential future studies and collaborations in protecting NYC against environmental disasters by employing more devices along with MPL real-time data will be emphasized. For pedagogical purposes, a lab module was developed to be implemented in the newly developed undergraduate track in Earth System Science and Environmental Engineering (ESE) at LaGuardia Community College of CUNY (LaGCC), more details will be presented.
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