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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
Hybrid GA-DQN for opportunistic cluster head selection and routing in resource constraint Internet of Things networks ↗
Abstract Energy efficiency and reliable data transmission are two important challenges in Internet of Things (IoT) networks because of the limited energy resources of sensor nodes and their dynamic nature. Accordingly, this research develops a Genetic Algorithm Deep Q-Network (GA-DQN) based hybrid framework for intelligent cluster head selection and adaptive IoT Network routing to counter these threats. A Genetic Algorithm (GA) selects the best cluster heads based on residual energy, coverage ratio, inter-cluster distance, and node density. In turn, this ensures balanced cluster formation and…
- OpenAlex · Published · First archived
Hardware Acceleration of Stochastic Neural Networks Enabled by Bit‐Cell Level Co‐Design of Magnetic Tunnel Junctions ↗
ABSTRACT Processing‐in‐memory architectures mitigate data shuttling bottlenecks in deterministic AI workloads, but still depend on costly external entropy sources for probabilistic models. Here, we present a magnetic‐tunnel‐junction‐based processing‐in‐memory architecture that unifies data storage and massively parallel probabilistic computation for stochastic and deterministic neural networks. By introducing bit‐paired stochastic and stable magnetic tunnel junctions, we realize a multi‐precision stochastic memory cell that transduces stored binary weights into variance‐tunable stochastic bits…
- OpenAlex · Published · First archived
SecureFresh-WSN: An Energy-Aware and Trust-Based Clustering and Routing Protocol for Cyberattack-Resilient Wireless Sensor Networks ↗
Wireless sensor networks (WSNs) in critical environments need to conserve energy and deliver fresh information despite possible cyberattacks. Meeting these requirements remains a challenge. Classical protocols such as LEACH optimise energy but neglect security and freshness, while recent reinforcement-learning and fuzzy-trust methods address security at the expense of energy balance or data freshness. This paper proposes SecureFresh-WSN, a unified clustering and routing protocol that jointly balances the optimisation of energy consumption, cyberattack resilience, and Age-of-Information (AoI) f…
- OpenAlex · Published · First archived
Contrastive Gradient-Flow Interpretation (CGFI): A Composable Operator for Class-Discriminative Explanation of Convolutional Neural Networks ↗
Growing deployment of Convolutional Neural Networks in safety-critical domains has intensified the need for transparent, discriminative explanations, yet gradient-based methods such as Grad-CAM compute explanations for a single class score, producing saliency maps that activate regions shared with competing alternatives. This limitation is particularly consequential in multi-class and fine-grained recognition settings, where non-discriminative explanations reduce the practical utility of the method and impede reliable model auditing. This research study contributes to the body of knowledge by…
- OpenAlex · Published · First archived
A Novel Density-Based Hybrid Multi-Hop PEGASIS Protocol with Adaptive Packet Transmission for Energy-Efficient Wireless Sensor Networks ↗
Energy imbalance and costly long-distance communication remain limitations of chain-based routing in energy-constrained wireless sensor networks. Energy imbalance is still a problem even though PEGASIS reduces transmission distance through chain-based data aggregation. Energy imbalance can be affected by repeated leader burden, expensive leader-to-sink communication, fixed packet size, and limited adaptation to changes in node-energy distribution. This study proposes Density-Based Hybrid MultiHop PEGASIS, which integrates adaptive DBSCAN clustering localized nearest-neighbor chain construction…
- OpenAlex · Published · First archived
Frequency-Shift Filtering for Interference Mitigation in Sensor Networks: Signal Parameter Impacts and Empirical Performance Benchmark ↗
Frequency-shift (FRESH) filtering is a low-compute technique that is deemed an attractive alternative to successive interference cancellation (SIC) algorithms in communication systems that involve resource-constrained devices. FRESH filters exploit the cyclostationary properties of interfering signals by linearly combining the spectrally redundant components of a signal such that they destructively add. Therefore, the performance in terms of bit error rate or mean squared error achievable by a FRESH filter is dependent on the cyclostationarity features exhibited by a signal. Their computationa…
- OpenAlex · Published · First archived
Energy-efficient clustering and routing protocol for wireless sensor networks based on improved HOA and attention-guided D3QN ↗
Abstract Clustering and routing protocols are widely regarded as effective methods for improving energy efficiency and extending network lifetime in wireless sensor networks. However, existing protocols often insufficiently consider dynamic energy distribution and topology characteristics, which may lead to imbalanced cluster structures, uneven energy consumption, and additional communication overhead. To address these issues, this paper proposes HOADRL, an energy-efficient clustering and routing protocol based on the hiking optimization algorithm (HOA) and deep reinforcement learning. First,…
- OpenAlex · Published · First archived
Energy-efficient AI-enabled wireless sensor networks for health information delivery in rural communities ↗
In the field of healthcare, wireless sensor networks (WSNs) are employed to continuously monitor patients, but they often struggle in resource-limited rural areas due to scarcity of resources, intermittent connectivity and inefficient data transmission. In this paper, an efficient and reliable health data delivery system using a Bio-Inspired Self-Learning Energy-Aware Wireless Sensor Network (BISLE WSN) is introduced. The proposed scheme aims to consume less energy by adjusting the network operation according to residual energy to avoid early battery depletion. A self-learning mechanism predic…
- Hacker News · Posted on Hacker News · First archived
Buzz: Share spare compute and run open models together over P2P networks ↗
Hacker News discussion.
- OpenAlex · Published · First archived
Energy efficient multi sink routing for large scale healthcare Internet of Things wireless sensor networks using reinforcement learning and multi objective optimization ↗
Increasing the network lifetime of healthcare Internet of Things systems deployed in large-scale wireless sensor networks requires energy-efficient, reliable, and secure communications. However, existing approaches do not adequately support shared-path reuse, intelligent forwarding, node fault handling, and adaptive relay deployment, resulting in increased energy consumption, communication overheads, and transmission delays. To address these limitations, this paper proposes an energy-efficient multi-sink routing framework based on the Steiner Routing with Route Memory and Path Cache Mechanism…
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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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