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新论文 · v1arXiv:2611.13943

HERO-D3QN: Inter-Cluster Routing with Energy-Aware Soft Constraints in Wireless Sensor Networks

Weihao Wang, Heng Yang, Guangbo Chen, Shanshan Li, Jiulin Guo, Ajay Kaushik, Fangwei Li, Junrui Hao 等 9 位作者

原文摘要

Inter-cluster forwarding in wireless sensor networks can concentrate energy consumption on frequently selected relays, while hard energy thresholds restrict forwarding choices as residual energy declines. We propose Hierarchical Energy-balanced Routing Optimization based on a dueling double deep Q-network (D3QN), termed HERO-D3QN. Given the cluster structure, the method combines direction- and cost-based relay screening with energy-aware soft constraints. Relays below an adaptive energy threshold remain eligible if the other selection criteria are met. An enhanced state representation captures relay energy, progress toward the sink, potential forwarding load, and hotspot risk, and a multi-objective reward guides learning. In the comparison with D3QN using the same node locations, HERO-D3QN delayed first-node death (FND) from time step 629 to 1269 and reduced peak residual energy variance by 55.5%. In the ablation study, the full method achieved the latest FND and half-node death (HND) and the largest cumulative transmitted volume (170.52 Mbit) among the tested variants. Across five seed settings in which node locations and the random processes used in training and evaluation varied jointly, the coefficients of variation of FND, HND, and transmitted volume were 8.26%, 2.75%, and 3.88%, respectively. These results demonstrate the effectiveness of HERO-D3QN in delaying early node failure and reducing peak energy dispersion.

cs.NI

新论文 · v1arXiv:2611.06977

BlockLock: Reproducible LLM Inference with Fixed Batch Geometry

Xiuyuan Qi, Pedro Reviriego, Javier Conde, Shanshan Liu

原文摘要

Reproducible LLM inference requires that a request produces the same output regardless of the other requests in the batch. Existing solutions pay for this in different ways: batch-invariant kernels constrain every operator, and isolating prefill with fixed-shape decode ({our CoRun-style baseline}) gives up shared prefill compute and pads decode. We present BlockLock, which keeps shared batched prefill and pins only the numerical path of the protected request: it fixes the geometry that the request sees, with {two modes: BlockLock-Rows uses fixed-width rows and retains batched decode of all requests; BlockLock-Pack uses terminal-anchored packing with an isolated dummy segment, then extracts the protected request's KV state for target-only decode. Under a stated per-deployment contract, both modes preserve the target's output bitwise when its companions change.} Across five {dense} models (Qwen3-8B/14B, Llama-3.1-8B-Instruct, and Gemma-3-4B/12B-IT) and three single-GPU deployments (NVIDIA A6000, A100, and H100), we evaluate our approach on both tinyBenchmarks and BFCL{; we also include initial tensor-parallel experiments on two NVIDIA A6000 GPUs. Both modes preserve target sequences and Top20-logit bits in the reported tests.} At equal delivered work, {BlockLock-Rows} reduces latency by 53.5--55.0\% and improves throughput by 115.2--122.2\% relative to vLLM Batch-Invariant; compared with batch-invariant kernels, it needs no custom kernel implementations and constrains only the protected requests.

cs.AI · cs.CL