As dawn spread over San Antonio on September 9, 2021, almond-colored smoke began to fill the sky above the city's Far West Side. The plumes were whorling off the top of a 132-foot-tall cell tower that overshadows an office park just north of SeaWorld. At a hotel a mile away, a paramedic snapped a photo of the spectacle and posted it to the r/sanantonio subreddit. "Cell tower on fire around 1604 and Culebra," he wrote. In typical Reddit fashion, the comments section piled up with corny jokes. "Blazing 5G speeds," quipped one user. "I hope no one inhales those fumes, the Covid transmission via 5G will be a lot more potent that way," wrote another, in a swipe at the conspiracy theorists who claim that radiation from 5G towers caused the Covid-19 pandemic. The wisecracks went on: "Can you hear me now?" "Great, some hero trying to save us from 5G." That self-styled hero was actually lurking in the comments. As he followed the thread on his phone, Sean Aaron Smith delighted in the sheer volume of attention the tower fire was receiving, even if most of it dripped with sarcasm. A lean, tattooed--and until recently, entirely apolitical--27-year-old, Smith had come to view 5G as the linchpin of a globalist plot to zombify humanity. To resist that supposed scheme, he'd spent the past five months setting Texas cell towers ablaze. Smith's crude and quixotic campaign against 5G was precisely the sort of security threat that was fast becoming one of the US government's top concerns in 2021.

Cellular Trajectory Map-Matching (CTMM) aims to align cellular location sequences to road networks, which is a necessary preprocessing in location-based services on web platforms like Google Maps, including navigation and route optimization. Current approaches mainly rely on ID-based features and region-specific data to learn correlations between cell towers and roads, limiting their adaptability to unexplored areas. To enable high-accuracy CTMM without additional training in target regions, Zero-shot CTMM requires to extract not only region-adaptive features, but also sequential and location uncertainty to alleviate positioning errors in cellular data. In this paper, we propose a pixel-based trajectory calibration assistant for zero-shot CTMM, which takes advantage of transferable geospatial knowledge to calibrate pixelated trajectory, and then guide the path-finding process at the road network level. To enhance knowledge sharing across similar regions, a Gaussian mixture model is incorporated into VAE, enabling the identification of scenario-adaptive experts through soft clustering. To mitigate high positioning errors, a spatial-temporal awareness module is designed to capture sequential features and location uncertainty, thereby facilitating the inference of approximate user positions. Finally, a constrained path-finding algorithm is employed to reconstruct the road ID sequence, ensuring topological validity within the road network. This process is guided by the calibrated trajectory while optimizing for the shortest feasible path, thus minimizing unnecessary detours. Extensive experiments demonstrate that our model outperforms existing methods in zero-shot CTMM by 16.8\%.

Deep learning-based fingerprinting is one of the current promising technologies for outdoor localization in cellular networks. However, deploying such localization systems for heterogeneous phones affects their accuracy as the cellular received signal strength (RSS) readings vary for different types of phones. In this paper, we introduce a number of techniques for addressing the phones heterogeneity problem in the deep-learning based localization systems. The basic idea is either to approximate a function that maps the cellular RSS measurements between different devices or to transfer the knowledge across them. Evaluation of the proposed techniques using different Android phones on four independent testbeds shows that our techniques can improve the localization accuracy by more than 220% for the four testbeds as compared to the state-of-the-art systems. This highlights the promise of the proposed device heterogeneity handling techniques for enabling a wide deployment of deep learning-based localization systems over different devices.

Community resilience is a complex and muti-faceted phenomenon that emerges from complex and nonlinear interactions among different socio-technical systems and their resilience properties. However, present studies on community resilience focus primarily on vulnerability assessment and utilize index-based approaches, with limited ability to capture heterogeneous features within community socio-technical systems and their nonlinear interactions in shaping robustness, redundancy, and resourcefulness components of resilience. To address this gap, this paper presents an integrated three-layer deep learning model for community resilience rating (called Resili-Net). Twelve measurable resilience features are specified and computed within community socio-technical systems (i.e., facilities, infrastructures, and society) related to three resilience components of robustness, redundancy, and resourcefulness. Using publicly accessible data from multiple metropolitan statistical areas in the United States, Resili-Net characterizes the resilience levels of spatial areas into five distinct levels. The interpretability of the model outcomes enables feature analysis for specifying the determinants of resilience in areas within each resilience level, allowing for the identification of specific resilience enhancement strategies. Changes in community resilience profiles under urban development patterns are further examined by changing the value of related socio-technical systems features. Accordingly, the outcomes provide novel perspectives for community resilience assessment by harnessing machine intelligence and heterogeneous urban big data.

Cellular traffic prediction is an indispensable part for intelligent telecommunication networks. Nevertheless, due to the frequent user mobility and complex network scheduling mechanisms, cellular traffic often inherits complicated spatial-temporal patterns, making the prediction incredibly challenging. Although recent advanced algorithms such as graph-based prediction approaches have been proposed, they frequently model spatial dependencies based on static or dynamic graphs and neglect the coexisting multiple spatial correlations induced by traffic generation. Meanwhile, some works lack the consideration of the diverse cellular traffic patterns, result in suboptimal prediction results. In this paper, we propose a novel deep learning network architecture, Adaptive Hybrid Spatial-Temporal Graph Neural Network (AHSTGNN), to tackle the cellular traffic prediction problem. First, we apply adaptive hybrid graph learning to learn the compound spatial correlations among cell towers. Second, we implement a Temporal Convolution Module with multi-periodic temporal data input to capture the nonlinear temporal dependencies. In addition, we introduce an extra Spatial-Temporal Adaptive Module to conquer the heterogeneity lying in cell towers. Our experiments on two real-world cellular traffic datasets show AHSTGNN outperforms the state-of-the-art by a significant margin, illustrating the superior scalability of our method for spatial-temporal cellular traffic prediction.

Individuals are also eager to learn about its capabilities and how it differs from previous networks. Many service providers are already rolling out 5G across countries like the U.S., U.K. and China. Since its initial adoption in 2019, 5G has already revolutionized the efficiency and reliability of broadband communication for consumers and enterprises. According to the Columbia Climate School, 5G will create a notable impact in the new year on various fronts, including broadband, sustainability and machine-to-machine communication. While we know this to a certain extent, we still look forward to the features that make it stand out.

A great deal has been written about the technologies fueling 5G, especially how those technologies will improve the experience that users have regarding connectivity. Similarly, much has been said about how ongoing developments in technology will usher in a new generation of network-aware applications. In this article, we discuss one key aspect of 5G technology and how it will impact the development of wireless network capacity. This is one of the more important but often neglected aspects of wireless communication evolution. It represents yet another important reason why the convergence of cloud computing and wireless communications makes so much sense.

The internet is now our nervous system. We are constantly streaming and buying and watching and liking, our brains locked into the global information matrix as one universal and coruscating emanation of thought and emotion. What happens when the machine stops though? It's a question that E.M. Forster was intensely focused on more than a century ago in a short story called, rightly enough, "The Machine Stops," about a human civilization connected entirely through machines that one day just turn off. Those fears of downtime are not just science fiction anymore.

"We just know it's the right thing to do for our customers and--I say this from years of doing risk management-- it's good, basic risk management," says Shannon Carroll, director of global environmental sustainability at AT&T. "If all indications are that something is going to happen in the future, it's our responsibility to be prepared for that." Globally, leaders from government, business, and academia see the urgency. When citing risks with the highest impact, those surveyed listed climate action failure and other environmental risks second only to infectious diseases. AT&T is taking action with its Climate Resilience Project, using spatial data analysis and location information to tackle the complex problem of how increasingly powerful storms could affect infrastructure such as cell towers and the telecom's ability to deliver service to its customers. "Spatial analysis is this way of going beyond what we visually see," explains Lauren Bennett, head of spatial analysis and data science at Esri, a geographic information systems (GIS) company.

In forensic investigations it is often of value to establish whether two phones were used by the same person during a given time period. We present a method that uses time and location of cell tower registrations of mobile phones to assess the strength of evidence that any pair of phones were used by the same person. The method is transparent as it uses logistic regression to discriminate between the hypotheses of same and different user, and a standard kernel density estimation to quantify the weight of evidence in terms of a likelihood ratio. We further add to previous theoretical work by training and validating our method on real world data, paving the way for application in practice. The method shows good performance under different modeling choices and robustness under lower quantity or quality of data. We discuss practical usage in court.