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4G, 5G, and smart city networks often rely on microwave and millimeter-wave x-haul links. A major challenge associated with these high frequency links is their susceptibility to weather conditions. In particular, precipitation may cause severe signal attenuation, which significantly degrades the network performance. In this paper, we develop a Predictive Network Reconfiguration (PNR) framework that uses historical data to predict the future condition of each link and then prepares the network ahead of time for imminent disturbances. The PNR framework has two components: (i) an Attenuation Prediction (AP) mechanism; and (ii) a Multi-Step Network Reconfiguration (MSNR) algorithm. The AP mechanism employs an encoder-decoder Long Short-Term Memory (LSTM) model to predict the sequence of future attenuation levels of each link. The MSNR algorithm leverages these predictions to dynamically optimize routing and admission control decisions aiming to maximize network utilization, while preserving max-min fairness among the nodes using the network (e.g., base-stations) and preventing transient congestion that may be caused by switching routes. We train, validate, and evaluate the PNR framework using a dataset containing over 2 million measurements collected from a real-world city-scale backhaul network. The results show that the framework: (i) predicts attenuation with high accuracy, with an RMSE ofmore »less than 0.4 dB for a prediction horizon of 50 seconds; and (ii) can improve the instantaneous network utilization by more than 200% when compared to reactive network reconfiguration algorithms that cannot leverage information about future disturbances.« less

Outdoor-to-indoor (OtI) signal propagation further challenges link budgets at millimeter-wave (mmWave). To gain insight into OtI mmWaveat28GHz, we conducted an extensive measurement campaign consisting of over 2,000 link measurements in West Harlem, NewYorkCity, covering seven highly diverse buildings. A path loss model constructed over all links shows an average of 30dB excess loss over free space at distances beyond 50m. We find the type of glass to be the dominant factor in OtI loss, with 20dB observed difference between clustered scenarios with low- and high-loss glass. Other factors, such as difference in floor height, are found to have an impact between 5ś10dB. We show that for urban buildings with high-loss glass, OtI data rates up to 400Mb/s are supported for 90% of indoor users by a base station (BS) up to 49m away. For buildings with low-loss glass, such as our case study covering multiple classrooms of a public school, data rates over 2.8/1.4Gb/s are possible from a BS 68/175m away when a line-of-sight path is available. We expect these results to be useful for the deployment of OtI mmWave networks in dense urban environments and the development of scheduling and beam management algorithms.

Traffic intersections are the most suitable locations for the deployment of computing, communications, and intelligence services for smart cities of the future. The abundance of data to be collected and processed, in combination with privacy and security concerns, motivates the use of the edgecomputing paradigm which aligns well with physical intersections in metropolises. This paper focuses on high-bandwidth, lowlatency applications, and in that context it describes: (i) system design considerations for smart city intersection intelligence nodes; (ii) key technological components including sensors, networking, edge computing, low latency design, and AI-based intelligence; and (iii) applications such as privacy preservation, cloud-connected vehicles, a real-time ”radar-screen”, traffic management, and monitoring of pedestrian behavior during pandemics. The results of the experimental studies performed on the COSMOS testbed located in New York City are illustrated. Future challenges in designing human-centered smart city intersections are summarized.

Video cameras in smart cities can be used to provide data to improve pedestrian safety and traffic management. Video recordings inherently violate privacy, and technological solutions need to be found to preserve it. Smart city applications deployed on top of the COSMOS research testbed in New York City are envisioned to be privacy friendly. This contribution presents one approach to privacy preservation– a video anonymization pipeline implemented in the form of blurring of pedestrian faces and vehicle license plates. The pipeline utilizes customized deeplearning models based on YOLOv4 for detection of privacysensitive objects in street-level video recordings. To achieve real time inference, the pipeline includes speed improvements via NVIDIA TensorRT optimization. When applied to the video dataset acquired at an intersection within the COSMOS testbed in New York City, the proposed method anonymizes visible faces and license plates with recall of up to 99% and inference speed faster than 100 frames per second. The results of a comprehensive evaluation study are presented. A selection of anonymized videos can be accessed via the COSMOS testbed portal. Index Terms—Smart City, Sensors, Video Surveillance, Privacy Protection, Object Detection, Deep Learning, TensorRT.

Outdoor-to-indoor (OtI) signal propagation further challenges the already tight link budgets at millimeter-wave (mmWave). To gain insight into OtI mmWave scenarios at 28GHz, we conducted an extensive measurement campaign consisting of over 2,200 link measurements. In total, 43 OtI scenarios were measured in West Harlem, NewYork City, covering seven highly diverse buildings. The measured OtI path gain can vary by up to40dBforagivenlink distance, and the empirical path gain model for all data shows an average of 30dB excess loss over free space at distances beyond 50m, with an RMSfitting error of 11.7 dB. The type of glass is found to be the single dominant feature for OtI loss, with 20dB observed difference between empirical path gain models for scenarios with low-loss and high-loss glass. The presence of scaffolding, tree foliage, or elevated subwaytracks, as well as difference in floor height are each found to have animpact between 5–10dB. Weshowthatforurbanbuildings with high-loss glass, OtI coverage can support 500Mbps for 90% of indoor user equipment (UEs) with a base station (BS) antenna placed up to 49m away. For buildings with low-loss glass, such as our case study covering multiple classrooms of a public school, data rates over 2.5/1.2 Gbps are possible frommore »a BS 68/175m away from the school building, when a line-of-sight path is available. We expect these results to be useful for the deployment of mmWave networks in dense urban environments as well as the development of relevant scheduling and beam management algorithms.« less

Smart cities around the world are supported by high-capacity wireless communication networks, which are based on millimeter-waves links. The propagating waves are sensitive to hydrometeors, and their signal level is attenuated by rain. However, most of the links in such networks are shorter than 1 km, imposing large errors on the rain estimation results. In this paper we demonstrate, using actual measurements from the city of Rehovot, Israel, how high-resolution rain maps can be generated from the received signal level measurements collected by these links. We first propose a method for reducing the errors in converting signal attenuation to rainfall estimates in short, incity links. The proposed method requires calibration of model parameters using side information from either a rain gauge or a long link in the vicinity of the network. We empirically analyze the results of the calibrating method using either auxiliary measurements and show that the performance is satisfactory for both. Then, we apply a spatial interpolation method on the rainfall resulting estimates, and demonstrate the construction of an high-resolution 2-D map of the accumulated rain in a city, a product with great potential for improving well-being of life in urban areas.

This work proposes an Adaptive Fuzzy Prediction (AFP) method for the attenuation time series in Commercial Microwave links (CMLs). Time-series forecasting models regularly rely on the assumption that the entire data set follows the same Data Generating Process (DGP). However, the signals in wireless microwave links are severely affected by the varying weather conditions in the channel. Consequently, the attenuation time series might change its characteristics significantly at different periods. We suggest an adaptive framework to better employ the training data by grouping sequences with related temporal patterns to consider the non-stationary nature of the signals. The focus in this work is two-folded. The first is to explore the integration of static data of the CMLs as exogenous variables for the attenuation time series models to adopt diverse link characteristics. This extension allows to include various attenuation datasets obtained from additional CMLs in the training process and dramatically increasing available training data. The second is to develop an adaptive framework for short-term attenuation forecasting by employing an unsupervised fuzzy clustering procedure and supervised learning models. We empirically analyzed our framework for model and data-driven approaches with Recurrent Neural Network (RNN) and Autoregressive Integrated Moving Average (ARIMA) variations. We evaluate themore »proposed extensions on real-world measurements collected from 4G backhaul networks, considering dataset availability and the accuracy for 60 seconds prediction. We show that our framework can significantly improve conventional models’ accuracy and that incorporating data from various CMLs is essential to the AFP framework. The proposed methods have been shown to enhance the forecasting model’s performance by 30 − 40%, depending on the specific model and the data availability.« less

This work proposes an Adaptive Fuzzy Prediction (AFP) method for the attenuation time series in Commercial Microwave links (CMLs). Time-series forecasting models regularly rely on the assumption that the entire data set follows the same Data Generating Process (DGP). However, the signals in wireless microwave links are severely affected by the varying weather conditions in the channel. Consequently, the attenuation time series might change its characteristics significantly at different periods. We suggest an adaptive framework to better employ the training data by grouping sequences with related temporal patterns to consider the non-stationary nature of the signals. The focus in this work is two-folded. The first is to explore the integration of static data of the CMLs as exogenous variables for the attenuation time series models to adopt diverse link characteristics. This extension allows to include various attenuation datasets obtained from additional CMLs in the training process and dramatically increasing available training data. The second is to develop an adaptive framework for short-term attenuation forecasting by employing an unsupervised fuzzy clustering procedure and supervised learning models. We empirically analyzed our framework for model and data-driven approaches with Recurrent Neural Network (RNN) and Autoregressive Integrated Moving Average (ARIMA) variations. We evaluate themore »proposed extensions on real-world measurements collected from 4G backhaul networks, considering dataset availability and the accuracy for 60 seconds prediction. We show that our framework can significantly improve conventional models’ accuracy and that incorporating data from various CMLs is essential to the AFP framework. The proposed methods have been shown to enhance the forecasting model’s performance by 30 − 40%, depending on the specific model and the data availability.« less