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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 ↗22 classified signals in the fourteen-day window. These counts include signals awaiting readable evidence.
Recent 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
TA-BI: Reference Implementation in OMNeT++/INET for Reactive Routing Support in Wireless and Mobile Wireless Sensor Networks ↗
This article reports an OMNeT++ 6.3.0/INET 4.5.4 implementation of the TA-BI (trust-aware behavioral intelligence) observation-to-decision contract. Typed records separate current node Behavioral Information (BI), reported NeighborBI, historical node Trust and candidate-specific RouteBI and RouteScore. Evidence eligibility retains availability, age, support, confidence, provenance and version information; missing evidence is not replaced by a neutral score. The Ad hoc On-Demand Distance Vector (AODV) adapter applies native admission, destination-sequence freshness and minimum-hop precedence be…
- OpenAlex · Published · First archived
Multi-Parameter Estimation for κ-μ Shadowed Fading Model in Wireless Sensor Networks ↗
In wireless sensor networks (WSNs), reliable link-quality estimation depends strongly on channel characteristics, which can be flexibly captured by the κ-μ shadowed fading model in complex closed metallic cavity (CMC) environments. However, a fundamental tension arises: conventional numerical methods for estimating the parameters of this model generally rely on the iterative solution of coupled nonlinear moment equations, which may introduce considerable computational cost and sensitivity to initialization and convergence settings, thereby limiting their applicability to rapid channel characte…
- OpenAlex · Published · First archived
An Energy Efficient and Network Lifetime-Aware Routing Protocol for Wireless Sensor Networks ↗
In wireless sensor networks (WSNs), energy management is a major problem because of the sensors' limited battery life, which reduces the network's longevity. The Green Multi-Mechanism-Low Energy Adaptive Clustering Hierarchy (GME-LEACH) routing protocol presented in this paper combines four complementary mechanisms into a single clustering framework: adaptive CH rotation, sleep-awake scheduling, hybrid multi-hop communication, and multi-criteria cluster-head (CH) selection. To find energy-efficient and strategically placed cluster heads, the suggested CH selection method takes into account res…
- OpenAlex · Published · First archived
A Hybrid Deterministic-Stochastic Cross-Layer Energy Model for Hierarchical Wireless Sensor Networks ↗
Accurate current-consumption estimation in IEEE 802.15.4 Wireless Sensor Networks requires linking network operation to the hardware states that determine node energy use. This paper proposes a cross-layer energy-modeling methodology that combines experimentally characterized Current Consumption Profiles (CCPs), protocol-derived state-duration models, topology-dependent workload, and a two-stage probabilistic model for retransmissions. The methodology distinguishes structurally constrained states, empirically modeled state durations, and discrete stochastic communication events. In the evaluat…
- OpenAlex · Published · First archived
TrajGNN-RL: Adaptive Mobile Sink Intelligence for Lifetime-Aware Wireless Sensor Networks ↗
Mobile wireless sensor networks require adaptive routing and sink mobility strategies to reduce energy imbalance, congestion, and communication delay. In this study, we propose a new method called TrajGNN-RL that combines the topology learning method based on a graph neural network with the deep reinforcement learning method to plan the trajectory of the mobile sink and route the packet in a coordinated manner. Dynamic communication graphs model the residual energy, link quality, neighborhood connectivity, and traffic conditions, and reinforcement learning optimizes the sink movement and forwa…
- OpenAlex · Published · First archived
Sports Activity Recognition Using Textile‐Based Sensor Fusion and Complex‐Valued Residual Convolutional Neural Networks ↗
Traditional human activity recognition systems in wearable technologies typically separate sensing modalities, using mechanical sensors for motion detection and biophysical sensors such as electrocardiography for health monitoring. This work challenges that division by demonstrating that electrocardiographic signals captured via textile electrodes inherently encode activity‐specific information, as motion‐induced signal deformations, traditionally dismissed as noise, carry discriminative patterns. A smart textile system was developed integrating textile‐based electrocardiographic electrodes an…
- OpenAlex · Published · First archived
A scalable process aware routing architecture for resilient wireless communication in industrial and control networks ↗
Industrial carbon-capture plants depend on timely and reliable communication among distributed sensors, controllers, and process models. Existing studies commonly examine fault recovery, energy-aware routing, reinforcement learning, or digital-twin monitoring as separate problems, leaving limited evidence on their coordinated use in one reproducible industrial communication framework. This study presents a process-aware MeshStar wireless sensor network (WSN) that combines low-overhead star clusters with redundant mesh links among cluster routers. A tabular Q-learning controller selects recover…
- OpenAlex · Published · First archived
Hybrid Quantum–Classical Convolutional Neural Networks for Medical Image Classification: A Review of Architectures, Applications, and NISQ Challenges ↗
Deep learning techniques have significantly enhanced medical image classification for disease diagnosis. However, traditional deep learning models face several challenges, such as dependence on large annotated datasets, complex architectural layers, and millions of trainable parameters, which can increase computational load and cause overfitting. Although quantum computing offers potential for parallel processing and improved feature representation, its practical use is limited by the current capabilities of Noisy Intermediate-Scale Quantum (NISQ) hardware. To overcome these issues, Hybrid Qua…
- OpenAlex · Published · First archived
A QMIX-driven routing protocol for dynamic Underwater Acoustic Sensor Networks ↗
Abstract Reliable and energy-efficient data collection remains a fundamental challenge in Underwater Acoustic Sensor Networks (UASNs) owing to limited battery capacity, high propagation delay, dynamic network topology and unreliable communication links. To address these challenges, we propose a Cooperative Multi-Agent Reinforcement Learning (CMARL)-based data routing framework for dynamic UASNs. The routing problem is modeled as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP), where each relay node acts as an autonomous routing agent using locally available information…
- OpenAlex · Published · First archived
Vertical NAND Flash Memory‐Based Bayesian Neural Networks With Intrinsic Gaussian Weight Sampling ↗
Bayesian neural networks (BNNs) provide improved robustness and well‐calibrated uncertainty estimates compared with deterministic networks, but their hardware realization remains challenging because Gaussian weight sampling typically requires random number generators, substantial memory overhead, or additional circuitry. Here we experimentally characterize commercial vertical NAND (V‐NAND) flash memory and show that a fully hardware‐compatible BNN can be realized without modifying the native array structure. We demonstrate that although individual ISPP‐programmed V‐NAND cells exhibit non‐Gauss…
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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