Global Navigation Satellite Systems (GNSS) are crucial for modern infrastructure but are vulnerable to jamming due to their open structure, limited security, narrow bandwidth, and weakened signal strength over long distances. Jamming attacks exploit these vulnerabilities, disrupting both civilian and military operations by degrading or blocking positioning, navigation, and timing (PNT) services. Existing jamming detection methods rely on power-hungry Deep Learning (DL) models built with Artificial Neural Networks (ANNs) and pre-trained architectures, making them unsuitable for power- and resource-constrained edge devices used in GNSS systems and are prone to errors in noisy environments. To address these challenges, we propose leveraging Convolutional Spiking Neural Networks (CSNNs) for jamming classification, taking advantage of the energy-efficient, asynchronous, and sparse nature of SNNs. We integrate attention mechanisms and multi-axis data augmentation to enhance classification accuracy and mitigate overfitting. Our approach improves radio environment awareness in wireless communication systems by providing superior jamming classification accuracy and robust physical-layer security, particularly in noisy environments. Experimental results show that CSNN-based models achieve competitive accuracies of up to 98%, comparable to CNN-CBAM and ResNet, while maintaining accuracies as high as 97% under multipath noise with superior computational efficiency, where traditional models degrade to as low as 33%.

Recent advances in satellite technology and the introduction of the Space-Data-as-a-Service business models are attracting interest for using the platform in a wide range of applications that include earth observations, 5G integration, aeronautical and maritime tracking and communications, asset tracking, sensor data collection, among others. In this new scenario, it becomes of interest not only selecting a ground station provider based on the monetary cost, but also the download features with prevailing atmospheric conditions to achieve high reliability and low data delivery latency. To this end, the Cognitive Space Gateway approach is used to route data bundles in a Ground Station-as-a-Service context with multiple providers to find a suitable balance between data latency and the ground station monetary costs, exploiting as possible site-diversity when available. The approach is tested experimentally using NASA's High-Rate Delay-Tolerant Networking (HDTN).

Real-time electricity prices and volatile workload patterns have a major impact on the operational costs of distributed data centers. A smart grid enables large consumers to regulate their usage patterns with the knowledge of real-time prices at different locations, and their energy consumption through the demand response (DR) program. However, the price inclined workload assignment may lead to an increase in peak demand, and decrease the benefits of workload offloading. Moreover, energy-efficient service management is challenged by unpredictable network conditions that can cause service performance degradation. Using an actor-critic approach, we developed a system that distributes a service's load among distributed sites to take advantage of the spatial variations in energy pricing. Our experimental results on the CloudLab testbed have proved with 45% reduction in running costs without impacting the response time using the Wikipedia request traces considering peak-based pricing.

The web infrastructure continues to grow in both size and diversity and is quickly approaching 2 billion sites that serve an equally massive number of users. Content replication is a common technique that aims to improve reliability and performance. It consists of the use of multiple server mirrors that operate across geographically distributed regions. Several factors impair the static system optimization or even a periodic optimization. Final energy costs depend on the level of workload handled by each region given local variations in energy pricing. To accentuate the problem, energy pricing may dynamically change for some sites. Also, network state fluctuations, that are produced by congestion and failures, create time-varying performance to different users. These factors contribute to affect user experience and service costs. We propose an energy-delay aware web routing method that dynamically directs user requests to a set of replicated sites. The method relies on learning to implement a customized performance-cost load-balancing. We present experimental results from a network testbed using actual power measurements and simulated spatial variations in the energy prices according to the U.S. electricity market. The results show that the method can help to achieve the desired balance of energy cost and average response time, while reducing energy costs up to 11% without introducing a major impact to service quality.

Hadoop is a popular application process big data problems in a networked dist computers. Investigations of performance for networking have been of interest with the networking paradigm through on-demand an enforcements. Network usage characterization can further help understand what policy info needed during application use cases. At scale e Hadoop jobs will help facilitate such char report how Hadoop networking usage can be chi experimentation environment using the Environment for Network Innovation). Further distributed switch framework that may help alleviate the fault tolerance schemes in Hadoop application in the forwarding plane. Delay in recovery from failures has been reduced by almost 99% through such a distributed switch architecture deployed on the GENT experimentation environment.