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Earlier sources: Networks
This hardware thread follows networks through original research, public projects, and source reports. Read the evidence below to see which developments are represented in SYMTRI’s archive.
SYMTRI organizes observations from public sources into a living map. Connections reflect shared subject matter; they do not establish causation or endorse a source.
Open in the map ↗23 classified signals in the fourteen-day window. These counts include signals awaiting readable evidence.
Earlier sources
Up to ten sources with readable evidence, ordered by their feed timestamp. These archive sources can be older than the live fourteen-day window. This list is a sample, not a measure of total activity. Dates distinguish paper publication, repository creation, model/dataset activity, Hacker News posts, and KEV catalog additions. “First archived” records when SYMTRI first stored the signal.
- OpenAlex · Published · First archived
Synthetic false data injection-inspired perturbation framework for power grid attack detection using graph neural networks and deep learning models ↗
Abstract Modern power grids are exposed to False Data Injection (FDI) attacks due to their increasing dependency on digital sensing and measurement infrastructure. The adversaries deliberately manipulate sensor data for compromising control, system monitoring, and overall grid stability. Undetected attacks trigger cascading failures and severe economic disruption and bring challenges for developing a robust, data-driven detection framework for safeguarding critical energy infrastructure. The present study undertakes this challenge by introducing a structured, coordinated, feature-group-based s…
- OpenAlex · Published · First archived
ASDAE-OPF: an adaptive data reduction and optimization model for efficient data handling and routing in wireless sensor networks ↗
Abstract Wireless sensor networks face severe energy limitations, with data transmission consuming the majority of a node’s power. To address this, the proposed framework combines three energy aware mechanisms. First, an Adaptive Sparse Denoising Autoencoder compresses and reconstructs sensor data, transmitting clean and essential information rather than raw redundant readings. Second, cluster head selection is handled through Hierarchical Agglomerative Clustering integrated with the Firefly Algorithm. It’s enabling energy-balanced and adaptive cluster formation. Third, a Cross-layer Opportuni…
- OpenAlex · Published · First archived
Energy-efficient clustering and mobile sink routing in wireless sensor networks using quantum-inspired optimization and deep learning ↗
Wireless sensor networks (WSNs) require energy-efficient clustering and adaptive routing to maintain reliable communication and prolong network lifetime under varying node density, initial energy, communication range, packet size, residual energy, routing distance, latency, and mobile-sink conditions. However, conventional clustering and routing methods generally optimize these aspects separately, resulting in inefficient energy distribution, increased routing overhead, unstable sink mobility, and reduced network sustainability. To address these limitations, this work proposes an integrated fr…
- arXiv · Published · First archived
Sparse Distributed Fiber Optic Sensor Placement in Metropolitan and Datacenter Elastic Optical Networks ↗
Distributed Fiber Optical Sensing (DFOS) leverages optical backscattering signals to predict failure events and enable proactive Just-In-Time (JIT) restoration. Modern network infrastructures have optical components and fibers as key enablers of high throughput and low latency. The integration of sensing features in this optical infrastructure adds features such as network diagnosis, spatial positioning and sensing, and opens up opportunities for the implementation of intelligent network control expected for the next-generation networks. In this paper, we study how the quantity and the placeme…
- arXiv · Published · First archived
Reversing the Clock: Layout-Aware Recovery of Design Intent from Clock Distribution Networks ↗
Hardware reverse engineering supports competitive analysis and hardware assurance by recovering information about an integrated circuit (IC) from its physical implementation. While existing techniques primarily recover the gate-level netlist, which represents logical functionality, they often overlook physical design decisions such as placement, routing, delay insertion, and interconnect optimization. The clock distribution network encapsulates many of these decisions; however, no prior published work has recovered this network from a fabricated IC to infer design intent. We present a layout-a…
- OpenAlex · Published · First archived
Operating Envelopes of Synchronous and Asynchronous Federated Learning Under Communication Impairments in Smart-Grid Sensor Networks ↗
Federated learning (FL) for distributed smart-grid monitoring is shaped jointly by statistical heterogeneity and communication timing. This study evaluates protocol operating envelopes rather than proposing a new aggregator. A frozen confirmatory campaign compares deadline-constrained FedAvg, FedAsync, FedBuff, and FedStaleWeight under ideal, intermittent, and degraded communication using equal client-dispatch and equal simulated-time budgets. In response to peer review, we add a separate physics-backed validation campaign using pandapower AC power flow, five independent physical trajectories,…
- OpenAlex · Published · First archived
Performance Analysis of Clustered Integrated Sensing and Communication Networks ↗
Integrated sensing and communication (ISAC) provides a promising approach for IoT networks by combining data transmission with target sensing over shared hardware. Different from prior studies centered on the performance of ISAC networks with uniformly distributed nodes, this work explores ISAC networks with clustered distributed nodes. An analytical model based on stochastic geometry is established to assess both the probabilities of sensing and communication in clustered ISAC networks. The study highlights the influence of factors such as cluster density, node distribution, and cluster size…
- OpenAlex · Published · First archived
Protecting Ports using Range-Free Triangulated Sensor Networks: A Case Study ↗
Range-free wireless sensor networks rely on sensors that detect only the bearing of an incoming signal, so that localizing a transmitter requires simultaneous detection by at least three sensors, a condition known as triangulated coverage. This paper extends a previously developed geospatial computational framework with a formal treatment of sensor activation and applies it to the protection of the port of Thessaloniki, Greece. Eleven sensors, in the presence of seven known transmitters that induce angular blind zones, monitor four areas of interest that form successive protection layers: the…
- OpenAlex · Published · First archived
Distributed Artificial Intelligence for Agentic Intent-Based Networking in Mobile Networks ↗
Through intent-based networking (IBN), network objectives are specified declaratively, yet in mobile networks their realisation spans administrative domains that compete for coupled radio, transport and compute resources but cannot share local models and telemetry. A distributed artificial intelligence (DAI) controller with a Belief-Desire-Intention-eXecution (BDIX) cycle is presented, centred on shared-space coordination. The local decision of each domain agent is mapped through Gi into a common p-dimensional resource space, so that only resource contributions and a common price vector cross…
- OpenAlex · Published · First archived
QUINN: Quantized Inference and Dataflow Characterization for Neural Networks on RISC-V Near-Memory Architectures ↗
Machine learning inference is moving to the edge due to latency, energy, and privacy constraints. The growing complexity of edge workloads places increasing pressure on memory hierarchies, making data movement a major limitation for both performance and energy efficiency. Fixed-function accelerators inherit the memory wall of the von Neumann model and lack the flexibility to follow evolving models. Programmable Vector Processing Unit (VPU)-based near-memory architectures alleviate this bottleneck using standard Complementary Metal-Oxide Semiconductor (CMOS) while remaining programmable, but st…
Live window recorded . This is the ingestion observation time, not the publication or discovery date of each source.
How to assess evidence about Networks
Check each original source for its methods, release status, limitations, and supporting data. A repository announcement, a preprint, and a published study provide different kinds of evidence. SYMTRI’s subject classification helps discovery; it does not independently validate the source’s claims.
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