367 lines
4.7 KiB
Markdown
367 lines
4.7 KiB
Markdown
下面是**下一阶段（Phase-3）明确执行清单**。
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目标不是继续“修代码”，而是把当前仿真从 **能运行的系统** 升级为 **可发表 / 可证明算法价值的实验平台**。
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你可以直接把下面内容作为 `next_phase.md` 交给执行 AI。
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---
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# `next_phase.md`
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```markdown
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# LoRa Route Py — Phase-3 任务清单
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## 目标
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当前系统已经：
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- ✅ 完整仿真框架
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- ✅ 多跳路由工作
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- ✅ 业务数据成功传输
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- ✅ 指标统计完善
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下一阶段目标：
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> 将仿真升级为 **算法评估平台（Algorithm Evaluation Platform）**
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核心思想：
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不再证明“能跑”
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→ 而是证明“比别人好”
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---
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# Phase-3 总览
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新增三大能力：
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1. Baseline 对照算法
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2. 可重复实验框架（Experiment Runner）
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3. 自动论文级结果输出
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---
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# TASK 1 — Baseline Routing（最高优先级）
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## 目的
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当前只有 Gradient Routing。
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必须加入对照组，否则结果没有科研意义。
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---
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## 1.1 新建目录
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```
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sim/routing/
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├── gradient_routing.py
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├── flooding.py ← NEW
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├── random_forward.py ← NEW
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└── shortest_path.py ← NEW（可选）
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```
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---
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## 1.2 Flooding Routing（必须实现）
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### 行为
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收到 DATA：
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```
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if packet_id 未见过:
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转发给所有邻居
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````
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需要：
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- seen_packet_cache (TTL)
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- 防止无限广播
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---
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### 接口保持一致
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```python
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class FloodingRouting(BaseRouting):
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def next_hop(self, packet):
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return BROADCAST
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````
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Node 不需要修改。
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---
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## 1.3 Random Forward（必须）
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用于验证：
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> gradient 是否优于随机策略
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逻辑：
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```
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随机选择一个邻居转发
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```
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---
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## 验收标准
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新增测试：
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```
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test_baseline_runs.py
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```
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要求：
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* flooding 能运行
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* random 能运行
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* 无死循环
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---
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# TASK 2 — Experiment Runner（核心）
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## 目标
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自动跑：
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```
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节点数 × 区域大小 × 算法
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```
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而不是手动运行。
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---
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## 2.1 新建
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```
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sim/experiments/
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runner.py
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```
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---
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## API
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```python
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run_experiment(
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routing="gradient",
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node_count=12,
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area_size=500,
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sim_time=500
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)
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```
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返回：
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```python
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{
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"pdr": float,
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"avg_latency": float,
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"avg_hop": float,
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"collision_rate": float,
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}
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```
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---
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## 2.2 参数扫描
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自动执行：
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```
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nodes = [6, 9, 12, 15]
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area = [300, 500, 800]
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routing = ["gradient", "flooding", "random"]
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```
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总实验：
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```
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4 × 3 × 3 = 36 runs
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```
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---
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# TASK 3 — 固定随机种子（重要）
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否则实验不可复现。
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---
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修改：
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```
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config.py
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```
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加入：
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```python
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RANDOM_SEED = 42
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```
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在 main 初始化：
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```python
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random.seed(Config.RANDOM_SEED)
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np.random.seed(Config.RANDOM_SEED)
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```
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---
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验收：
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同参数运行两次结果一致。
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---
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# TASK 4 — 新指标（Metrics v2）
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扩展 metrics.py：
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---
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## 必须新增
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### 4.1 End-to-End Latency
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```
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receive_time - create_time
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```
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输出：
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```
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avg_latency
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p95_latency
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```
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---
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### 4.2 Forwarding Overhead
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```
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total_tx / successful_packets
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```
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衡量能量效率。
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---
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### 4.3 Network Load
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```
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total_airtime / sim_time
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```
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---
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# TASK 5 — 自动结果导出
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新增：
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```
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analysis_tools/export.py
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```
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---
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## 输出 CSV
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```
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results.csv
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```
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格式：
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| routing | nodes | area | pdr | latency | hop |
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| ------- | ----- | ---- | --- | ------- | --- |
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---
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# TASK 6 — 自动绘图（必须）
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使用 matplotlib：
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生成：
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```
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results/
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├── pdr_vs_nodes.png
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├── latency_vs_nodes.png
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├── overhead_compare.png
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```
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---
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图要求：
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* 每条 routing 一条曲线
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* 自动 legend
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---
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# TASK 7 — Regression Tests（防退化）
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新增：
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```
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test_algorithm_compare.py
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```
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要求：
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```
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gradient.pdr >= random.pdr
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```
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（允许 small tolerance）
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---
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# TASK 8 — 自动实验入口
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新增：
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```
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python run_experiments.py
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```
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执行后：
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```
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✔ 运行全部实验
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✔ 输出CSV
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✔ 生成图表
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```
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---
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# Phase-3 验收标准
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必须全部满足：
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* [ ] 至少 2 个 baseline 算法
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* [ ] 自动实验 runner
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* [ ] 固定随机种子
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* [ ] CSV 自动生成
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* [ ] 自动绘图
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* [ ] gradient 与 baseline 可比较
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* [ ] 一键运行实验
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---
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# 完成后系统能力
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完成 Phase-3 后，你将拥有：
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✅ LoRa mesh 仿真平台
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✅ 算法对照实验系统
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✅ 自动论文图生成
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✅ 可直接写实验章节
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