这是一个审稿任务，请问：这篇文章的优点和缺点分别是什么？有什么Recommended changes...

下面基于你给的论文《A Cloud–Edge–Terminal Collaborative Framework with Lightweight WiFi Sensing for Smart Elderly-Care Monitoring》内容（含图1/图2、表1与结论段）给出审稿意见。(1571244243 paper.pdf)

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中文（优点 / 缺点 / Recommended changes）

优点（Strengths）

1. 选题契合应用痛点且动机清晰：面向居家养老监护，强调隐私友好与非穿戴式监测需求，WiFi CSI 作为“无感知”感知手段的定位合理，问题背景与两类传统方案（可穿戴/摄像头）的局限阐述清楚。(1571244243 paper.pdf)
2. 模型层贡献具体且工程导向强：提出 SensLight，通过“知识蒸馏→结构化剪枝→INT8 量化”的三阶段压缩管线，把云端大模型压到可在边缘 CPU 部署的超小模型，整体思路完整，且图1表达直观。(1571244243 paper.pdf)
3. 结果展示了显著的体积-精度权衡：在 UT-HAR 上，量化后模型大小到 0.06MB 仍能维持约 97.29% 准确率，并给出教师模型（ResNet18）与学生模型的对比、混淆矩阵等，有助于读者理解压缩后性能保持情况。(1571244243 paper.pdf)
4. 系统层架构有“端-边-云”闭环意识：LightGuard 框架把实时推理放在边缘、长期分析与模型管理放在云、终端负责交互告警（图2），对落地路径有整体化思考，而非只停留在算法精度。(1571244243 paper.pdf)

缺点/不足（Weaknesses）

1. 验证范围偏窄，泛化与真实部署证据不足：实验主要基于单一数据集 UT-HAR，且作者也承认评估“limited to a single CSI dataset and offline training”，缺少跨场景/跨房型/跨设备等稳健性验证。(1571244243 paper.pdf)
2. “可部署到边缘”的关键指标不够：论文强调 edge CPU-only 部署，但没有给出真实边缘硬件（如路由器/网关/嵌入式板）上的延迟、吞吐、功耗、内存峰值测量；当前描述更多是“尺寸/参数量”层面的可部署性。(1571244243 paper.pdf)
3. 与现有方法的对比不足：表1主要比较教师/学生/剪枝/量化不同版本，但缺少与现有 WiFi CSI HAR 轻量化模型或其他主流基线（传统机器学习、其他压缩策略、其他轻量网络）的横向对照，难以判断相对 SOTA 的位置。(1571244243 paper.pdf)
4. 复现细节仍不够完善：对 CSI 预处理、输入张量构造（窗口长度T、特征维F的具体形成）、幅相融合方式、训练/测试划分策略等描述不够“可复现级”；量化部分提到 dynamic/static、QAT，但缺少具体配置与对比结果。(1571244243 paper.pdf)
5. 系统框架偏概念化：LightGuard 的数据流、隐私策略（上传哪些数据、是否上传 CSI 原始数据）、网络开销、告警逻辑与误报/漏报成本控制等尚未实证，现阶段更像概念架构图。(1571244243 paper.pdf)

Recommended changes（建议修改/补充）

1. 补齐“边缘可部署性”实测：在至少一种代表性边缘设备上报告端到端推理延迟（ms）、CPU占用、峰值内存、功耗/能耗（如每次推理能量或每秒功耗），并说明批量/窗口推理设置。
2. 增加横向基线与SOTA对比：除教师/学生内部对比外，加入 3–5 个公开可复现基线（轻量 CNN、MobileNet 类、传统特征+分类器等）并统一数据划分，给出准确率与资源指标。
3. 做更系统的消融实验：分别报告仅蒸馏、蒸馏+剪枝、蒸馏+量化、三者叠加；并对关键超参（温度T、α、剪枝率、动态/静态量化、是否QAT）给出敏感性分析。
4. 增强泛化与鲁棒性验证：至少补充一种跨场景设置（不同房间/摆位/人员）或跨设备采集；并针对“跌倒”类给出 precision/recall/F1、误报率等更贴近养老监护的指标，而不只给整体 accuracy。
5. 完善可复现细节：补充 CSI 数据获取与预处理流程（去噪、对齐、幅相处理、归一化）、滑窗参数、训练/测试划分策略、随机种子；最好提供代码/配置或伪代码。
6. 把 LightGuard 从“概念图”推进到“最小可行原型”：哪怕是小规模原型，也建议报告通信开销（上传频率/数据量）、云端更新策略（多久更新/如何回滚）、终端告警的人机交互流程与时延。

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English (copy-paste ready)

Strengths

1. Well-motivated application setting: The paper targets privacy-preserving, device-free elderly-care monitoring and clearly positions WiFi CSI sensing as an alternative to wearables and cameras. (1571244243 paper.pdf)
2. Concrete model contribution with an engineering focus: SensLight is produced via a three-stage compression pipeline (knowledge distillation → structured channel pruning → INT8 quantization). The pipeline is clearly illustrated (Fig. 1) and aligns with edge deployment needs. (1571244243 paper.pdf)
3. Strong size–accuracy tradeoff shown on UT-HAR: The quantized model achieves ~97.29% accuracy with only 0.06 MB, and the paper provides teacher/student comparisons and confusion matrices to interpret behavior. (1571244243 paper.pdf)
4. System-level perspective: The LightGuard cloud–edge–terminal architecture (Fig. 2) demonstrates an end-to-end view of real-time edge inference, cloud analytics/model management, and terminal interactions. (1571244243 paper.pdf)

Weaknesses

1. Limited validation and generalization evidence: Experiments rely mainly on a single dataset (UT-HAR) and offline training; the paper explicitly notes this limitation, leaving robustness across environments/devices unclear. (1571244243 paper.pdf)
2. Missing on-device deployment metrics: The work claims CPU-only edge deployment, yet lacks measurements on representative edge hardware (latency/throughput, peak memory, power/energy). Model size alone is insufficient for deployment claims. (1571244243 paper.pdf)
3. Insufficient comparison to external baselines/SOTA: Results primarily compare teacher vs. compressed variants (Table I), with little to no horizontal comparison against existing lightweight CSI-HAR models or conventional baselines. (1571244243 paper.pdf)
4. Reproducibility gaps: CSI preprocessing details (windowing, feature construction, amplitude/phase handling), data splits, and quantization settings (dynamic vs. static, QAT specifics) are not described at a fully reproducible level. (1571244243 paper.pdf)
5. System framework is largely conceptual: LightGuard is presented as an architecture, but dataflow, privacy choices (what is uploaded), network overhead, alert logic, and failure costs are not empirically evaluated. (1571244243 paper.pdf)

Recommended changes

1. Report real edge measurements: Benchmark end-to-end inference latency (ms), CPU utilization, peak memory, and power/energy on at least one edge device class (router/gateway/embedded board).
2. Add external baselines/SOTA: Include several reproducible baselines (lightweight CNNs, MobileNet-style, and/or traditional features+classifiers) with consistent splits and resource metrics.
3. Provide comprehensive ablations: Distillation only vs. +pruning vs. +quantization vs. full pipeline; also sensitivity to temperature T, α, pruning ratio, and dynamic/static quantization (with/without QAT).
4. Evaluate robustness and task-relevant metrics: Add cross-scenario/device tests if possible; report per-class precision/recall/F1 (especially for “fall”) and false alarm/miss rates for elderly-care relevance.
5. Improve reproducibility: Detail CSI acquisition/preprocessing, window parameters, split protocol, random seeds, and quantization configuration; ideally release code/configs.
6. Move LightGuard toward an MVP prototype: Even a small prototype should report communication overhead, update cadence/rollback, end-to-end alert latency, and user interaction flow.

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