diff --git a/docs/plan/paper_output_plan.md b/docs/plan/paper_output_plan.md
new file mode 100644
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--- /dev/null
+++ b/docs/plan/paper_output_plan.md
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+好，这一步的目标已经从 **“继续做系统”** 转为：
+
+```text
+冻结仿真平台 → 产出论文级结果 → 图表化 → 可发表材料
+```
+
+下面给你一个**专门面向论文产出**的执行大纲（给执行 AI 用）。重点不是再改算法，而是把现有 Phase-3.5 数据变成 **审稿人可接受的 evidence**。
+
+---
+
+# `paper_output_plan.md`
+
+---
+
+# 1. 目标（Objective）
+
+将 Phase-3.5 仿真平台输出转换为：
+
+* 可复现实验结果
+* 标准论文图表
+* 对比表格
+* 统计显著性验证
+* 可直接插入论文的 figures/tables
+
+**禁止修改算法逻辑**，只允许：
+
+* 数据采集增强
+* 实验批量运行
+* 可视化
+* 统计分析
+
+---
+
+# 2. 输出目录结构（必须固定）
+
+```
+docs/
+ └── paper/
+     ├── figures/
+     │    ├── fig1_topology.png
+     │    ├── fig2_pdr_vs_airtime.png
+     │    ├── fig3_tx_efficiency.png
+     │    ├── fig4_scalability.png
+     │    └── fig5_tradeoff_curve.png
+     │
+     ├── tables/
+     │    ├── table1_algorithm_compare.csv
+     │    ├── table2_scaling_results.csv
+     │    └── table3_efficiency_metrics.csv
+     │
+     ├── raw_data/
+     └── scripts/
+          ├── generate_figures.py
+          └── aggregate_results.py
+```
+
+---
+
+# 3. 实验冻结规则（CRITICAL）
+
+在 config 中增加：
+
+```
+EXPERIMENT_VERSION = "phase3_5_frozen"
+```
+
+要求：
+
+* RANDOM_SEED 固定
+* 参数写入 metadata.json
+* 每次实验自动记录：
+
+  * git commit hash
+  * 时间
+  * 参数集
+
+否则论文不可复现。
+
+---
+
+# 4. 必须生成的论文图（核心部分）
+
+## Figure 1 — Network Topology
+
+目的：
+
+证明不是 toy example。
+
+内容：
+
+* 节点位置 scatter
+* sink 标记
+* 典型 routing tree（gradient）
+
+输出：
+
+```
+fig1_topology.png
+```
+
+---
+
+## Figure 2 — PDR vs Airtime（最重要）
+
+X轴：
+
+```
+airtime_usage (%)
+```
+
+Y轴：
+
+```
+PDR (%)
+```
+
+曲线：
+
+* Gradient
+* Flooding
+* Random
+
+意义：
+
+证明：
+
+```
+Flooding = resource inefficient
+```
+
+这是整篇论文核心图。
+
+---
+
+## Figure 3 — Energy Efficiency
+
+定义：
+
+```
+TX per Success
+```
+
+柱状图：
+
+```
+algorithm → TX/success
+```
+
+审稿人关注点：
+
+LPWAN energy cost。
+
+---
+
+## Figure 4 — Scalability Test
+
+扫描：
+
+```
+node_count = [6, 9, 12, 15, 18, 24]
+```
+
+Y轴：
+
+* PDR
+* Airtime（双图）
+
+目标：
+
+证明算法随规模变化趋势。
+
+---
+
+## Figure 5 — Tradeoff Frontier（论文加分图）
+
+绘制：
+
+```
+(PDR, Airtime)
+```
+
+散点：
+
+每个实验配置一个点。
+
+形成：
+
+```
+Pareto frontier
+```
+
+这张图非常“论文感”。
+
+---
+
+# 5. 必须生成的表格
+
+---
+
+## Table 1 — Algorithm Comparison（主表）
+
+| Algorithm | PDR | Airtime | Total TX | TX/Success |
+| --------- | --- | ------- | -------- | ---------- |
+
+来自 Phase-3.5。
+
+---
+
+## Table 2 — Scaling Result
+
+| Nodes | Gradient PDR | Flooding PDR | Random PDR |
+| ----- | ------------ | ------------ | ---------- |
+
+---
+
+## Table 3 — Efficiency Gain
+
+计算：
+
+```
+Efficiency Gain =
+(TX_flooding - TX_gradient) / TX_flooding
+```
+
+展示资源节省比例。
+
+---
+
+# 6. 实验批量运行规范
+
+新增：
+
+```
+python run_experiments.py --paper
+```
+
+执行：
+
+```
+for seed in [1..20]:
+    run experiment
+```
+
+输出：
+
+```
+mean
+std
+95% CI
+```
+
+论文必须有误差条。
+
+---
+
+# 7. 统计要求（很多人忽略）
+
+每个指标输出：
+
+```
+mean ± std
+```
+
+并计算：
+
+```
+t-test(gradient, flooding)
+```
+
+输出：
+
+```
+p-value
+```
+
+目标：
+
+```
+p < 0.05
+```
+
+否则结论不够强。
+
+---
+
+# 8. 自动绘图规范
+
+统一：
+
+* matplotlib
+* serif 字体
+* 单色可打印
+* 无背景网格
+* dpi ≥ 300
+
+尺寸：
+
+```
+3.5 inch (single column)
+7 inch (double column)
+```
+
+---
+
+# 9. 一键论文数据生成
+
+新增入口：
+
+```
+python generate_paper_results.py
+```
+
+流程：
+
+```
+run experiments
+→ aggregate
+→ statistics
+→ generate tables
+→ generate figures
+```
+
+输出全部 paper assets。
+
+---
+
+# 10. 自测标准（执行AI必须验证）
+
+生成后检查：
+
+* [ ] figures ≥ 300dpi
+* [ ] 所有图可单独理解
+* [ ] table 数值与 raw_data 一致
+* [ ] 重跑结果误差 <5%
+* [ ] seed 改变趋势不变
+
+---
+
+# 11. 最终产物（你将得到）
+
+完成后你会拥有：
+
+```
+✔ 5 张论文核心图
+✔ 3 个结果表
+✔ 可复现实验脚本
+✔ 冻结baseline数据
+```
+
+这已经足够支撑：
+
+* conference short paper
+* systems workshop
+* embedded networking paper
+
+---
+
+# 下一步（论文视角）
+
+你接下来不需要工程决策，只需要三件事：
+
+1. 跑 **大量 seeds**
+2. 生成 **tradeoff 图**
+3. 写 **Method + Evaluation**
diff --git a/docs/plan/paper_result_need.md b/docs/plan/paper_result_need.md
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+下面直接给**论文产出阶段（Post-Phase-3.5）操作流程**。
+目标不是继续开发系统，而是把**已有平台 → 可发表实验结果**。
+
+你现在的系统已经满足论文最低条件：
+
+* ✅ 可复现实验（seed）
+* ✅ baseline（gradient / flooding / random）
+* ✅ 多指标（PDR + airtime + TX cost）
+* ✅ 自动实验 runner
+
+接下来只做 **科研标准三件事**：
+
+---
+
+# 一、跑大量 seeds（统计显著性）
+
+## 为什么必须做
+
+现在结果：
+
+```
+PDR ≈ 18%
+```
+
+LoRa + 随机信道 → **方差极大**。
+
+单次结果 = 不可发表。
+
+论文要求：
+
+> expectation over randomness
+
+即：
+
+[
+Result = E_{seed}[metric]
+]
+
+---
+
+## 目标规模（直接照做）
+
+| 项目        | 建议值        |
+| --------- | ---------- |
+| seeds 数量  | **30–50**  |
+| 每 seed 时长 | 100s（保持一致） |
+| topology  | 固定         |
+| traffic   | 固定         |
+| 唯一变量      | RNG seed   |
+
+---
+
+## 修改 runner（核心思想）
+
+你已经有：
+
+```
+run_experiments.py
+```
+
+现在只加一层：
+
+```python
+for seed in range(50):
+    config.RANDOM_SEED = seed
+    run_single_experiment(...)
+```
+
+输出结构：
+
+```
+results/
+  gradient/
+    seed_0.json
+    seed_1.json
+    ...
+  flooding/
+  random/
+```
+
+---
+
+## 每个 json 至少包含
+
+```json
+{
+  "pdr": 0.187,
+  "airtime": 0.368,
+  "tx_total": 217,
+  "tx_per_success": 36.1
+}
+```
+
+---
+
+## 然后做统计汇总
+
+计算：
+
+```
+mean
+std
+95% CI
+```
+
+公式：
+
+[
+CI = 1.96 \cdot \frac{\sigma}{\sqrt{N}}
+]
+
+---
+
+# 二、生成 Tradeoff 图（论文核心）
+
+这是**最关键步骤**。
+
+论文 reviewers 不看日志，只看图。
+
+---
+
+## 图 1（必须）：PDR vs Airtime
+
+### 含义
+
+证明：
+
+> flooding 高 PDR 是用 airtime 换来的
+
+---
+
+### 横纵轴
+
+```
+x: airtime_usage (%)
+y: PDR (%)
+```
+
+每个算法一个点（带 error bar）。
+
+---
+
+### Python 示例
+
+```python
+plt.errorbar(
+    airtime_mean,
+    pdr_mean,
+    xerr=airtime_ci,
+    yerr=pdr_ci,
+    fmt='o',
+    label='gradient'
+)
+```
+
+---
+
+### 论文意义（非常重要）
+
+这是：
+
+> efficiency frontier
+
+审稿人一眼能理解贡献。
+
+---
+
+## 图 2（必须）：TX Cost vs PDR
+
+```
+x: tx_per_success
+y: PDR
+```
+
+解释：
+
+```
+能量效率 ↔ 可靠性 tradeoff
+```
+
+---
+
+## 图 3（强烈建议）：Airtime Budget Curve
+
+固定 airtime 上限：
+
+```
+10%
+20%
+30%
+...
+```
+
+看谁 PDR 更高。
+
+这属于：
+
+> fair resource comparison
+
+非常论文化。
+
+---
+
+## 输出格式
+
+保存：
+
+```
+figures/
+  pdr_vs_airtime.pdf
+  pdr_vs_cost.pdf
+```
+
+⚠️ 必须 PDF（矢量图）。
+
+---
+
+# 三、写 Method + Evaluation（论文主体）
+
+你现在不要写 Introduction。
+
+只写两章：
+
+```
+III. Method
+IV. Evaluation
+```
+
+---
+
+## (1) Method 章节结构（直接按这个写）
+
+### A. Network Model
+
+描述：
+
+* N nodes
+* single gateway
+* LoRa PHY abstraction
+* collision model
+
+不用写代码细节。
+
+---
+
+### B. Routing Algorithms
+
+三个 subsection：
+
+#### 1. Gradient Routing
+
+写：
+
+* hello dissemination
+* distance metric
+* next-hop selection
+
+给一个公式：
+
+[
+Cost_i = w_1 RSSI + w_2 HopCount
+]
+
+（即使当前权重简单也可以）
+
+---
+
+#### 2. Flooding (Baseline)
+
+说明：
+
+```
+each node rebroadcasts once
+```
+
+强调：
+
+> upper-bound reliability baseline
+
+---
+
+#### 3. Random Forwarding
+
+说明：
+
+```
+random neighbor selection
+```
+
+作为 lower baseline。
+
+---
+
+### C. Evaluation Metrics（你 Phase-3.5 的贡献）
+
+定义：
+
+#### Packet Delivery Ratio
+
+[
+PDR = \frac{received}{generated}
+]
+
+#### Airtime Usage
+
+[
+A = \frac{\sum TX_time}{simulation_time}
+]
+
+#### Transmission Cost
+
+[
+C = \frac{total_tx}{successful_packets}
+]
+
+这一节其实已经是论文贡献点。
+
+---
+
+## (2) Evaluation 章节结构
+
+### A. Experimental Setup
+
+写：
+
+| 参数       | 值     |
+| -------- | ----- |
+| Nodes    | 12    |
+| Area     | 800 m |
+| Duration | 100 s |
+| Seeds    | 50    |
+
+---
+
+### B. Reliability Comparison
+
+放表格：
+
+| Algo | PDR | CI |
+| ---- | --- | -- |
+
+---
+
+### C. Efficiency Tradeoff（核心）
+
+插入：
+
+* PDR vs Airtime 图
+
+写一句关键论文句式：
+
+> Gradient achieves comparable reliability while consuming significantly less channel airtime.
+
+---
+
+### D. Discussion
+
+直接写你已经发现的：
+
+```
+Flooding improves reliability by saturating the channel rather than improving routing efficiency.
+```
+
+这句话就是论文卖点。
+
+---
+
+# 四、你接下来真实的 TODO（最终版）
+
+直接复制给执行 AI：
+
+```markdown
+# Paper Production TODO
+
+## Phase-Paper-1: Statistical Experiments
+- [ ] run 50 seeds per algorithm
+- [ ] save per-seed JSON results
+- [ ] compute mean/std/95%CI
+
+## Phase-Paper-2: Figure Generation
+- [ ] plot PDR vs Airtime (error bars)
+- [ ] plot PDR vs TX_cost
+- [ ] export PDF figures
+
+## Phase-Paper-3: Method Section
+- [ ] network model description
+- [ ] routing algorithm definitions
+- [ ] metric formalization
+
+## Phase-Paper-4: Evaluation Section
+- [ ] experiment setup table
+- [ ] comparison tables
+- [ ] tradeoff analysis text
+```
+
+---
+
+# 五、一个关键判断（你现在的位置）
+
+你已经从：
+
+```
+工程实现阶段
+```
+
+进入：
+
+```
+科研结果生产阶段
+```
+
+后续成功与否 **不再取决于算法**，而取决于：
+
+```
+统计严谨性 + 图表达能力
+```
diff --git a/docs/result/paper_evaluation_report.md b/docs/result/paper_evaluation_report.md
new file mode 100644
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+# Paper Evaluation Results Report
+
+## 1. 实验数据汇总 (Experimental Data Summary)
+
+### 1.1 实验配置 (Configuration)
+
+| 参数 | 值 |
+|------|-----|
+| 节点数 | 12 (1 sink + 11 sensor) |
+| 部署区域 | 800m × 800m |
+| 仿真时长 | 100s |
+| 随机种子数 | 50 |
+| 算法 | Gradient, Flooding, Random |
+
+### 1.2 统计结果 (Statistical Results)
+
+| 算法 | PDR (%) | Airtime (%) | TX/Success | Collisions |
+|------|---------|-------------|-------------|------------|
+| **Gradient** | 12.13 ± 1.42 | 39.63 ± 2.05 | 62.38 ± 10.35 | 93.6 ± 6.94 |
+| **Flooding** | 23.74 ± 1.30 | 96.52 ± 1.22 | 67.52 ± 3.30 | 327.5 ± 5.66 |
+| **Random** | 11.55 ± 1.39 | 39.03 ± 2.21 | 69.73 ± 10.85 | 91.74 ± 7.59 |
+
+---
+
+## 2. 论文主线叙事 (Paper Narrative)
+
+### 2.1 核心结论 (Key Finding)
+
+> **Flooding achieves higher delivery ratios at the cost of near channel saturation, whereas Gradient provides a substantially more airtime-efficient operating point with comparable collision levels to Random forwarding.**
+
+### 2.2 正确的问题定位 (Correct Framing)
+
+❌ 错误表述:
+> "Gradient achieves comparable reliability"
+
+✅ 正确表述:
+> "Gradient operates in a significantly more efficient region of the reliability–airtime tradeoff space"
+
+---
+
+## 3. 论文结构建议 (Suggested Paper Structure)
+
+### IV. Evaluation
+
+#### A. Experimental Methodology
+
+> Results are averaged over 50 independent simulation seeds to mitigate stochastic channel effects.
+
+| Parameter | Value |
+|----------|-------|
+| Nodes | 12 |
+| Area | 800m × 800m |
+| Duration | 100s |
+| Seeds | 50 |
+| Confidence Interval | 95% |
+
+#### B. Reliability Comparison
+
+观察:
+- Flooding achieves highest PDR (23.74%)
+- 但 reliability 被 channel contention 限制
+
+#### C. Channel Utilization Analysis (核心)
+
+插入: `pdr_vs_airtime.pdf`
+
+> Flooding operates near full channel utilization (96.52%), indicating that reliability gains are achieved through aggressive channel usage.
+
+#### D. Collision Behavior
+
+| Algorithm | Collisions (avg) |
+|----------|------------------|
+| Flooding | 327.5 |
+| Gradient | 93.6 |
+| Random | 91.74 |
+
+> Flooding increases contention by 3.5× rather than improving forwarding efficiency.
+
+#### E. Reliability–Efficiency Tradeoff (论文核心)
+
+插入: `pdr_vs_tx_cost.pdf`
+
+> Gradient achieves significantly lower transmission cost per successful delivery, demonstrating improved energy efficiency in the resource-constrained LoRa environment.
+
+---
+
+## 4. 三图定位 (Figure Roles)
+
+| Figure | Role | Priority |
+|--------|------|----------|
+| pdr_vs_airtime.pdf | 主图 - Efficiency Frontier | ⭐ 必须 |
+| pdr_vs_tx_cost.pdf | 主图 - Energy Tradeoff | ⭐ 必须 |
+| comparison_bar.pdf | 辅助 - Quick Overview | Appendix |
+
+---
+
+## 5. 讨论要点 (Discussion Points)
+
+### 5.1 关键发现
+
+1. **Flooding 不可扩展**: 96% airtime 意味着无法扩展到更多节点
+2. **信道是真正瓶颈**: LoRa 多跳性能受限于 MAC/PHY 而非路由算法
+3. **效率优先**: 智能路由的价值在于找到高效运行区间
+
+### 5.2 论文贡献定位
+
+本论文贡献:
+> **Quantified efficiency–reliability tradeoff characterization in LoRa multi-hop networks**
+
+而非:
+> "提出新路由算法" (审稿人会质疑 novelty)
+
+---
+
+## 6. 客观评估 (Objective Assessment)
+
+| 阶段 | 状态 |
+|------|------|
+| 仿真平台 | ✅ 完成 |
+| Baseline 对照 | ✅ 完成 |
+| 统计有效性 (50 seeds) | ✅ 完成 |
+| Tradeoff 证据 | ✅ 完成 |
+| 可投稿叙事 | ✅ 已形成 |
+
+---
+
+## 7. 后续工作 (Next Steps)
+
+### 7.1 Figure Polish
+- [ ] 检查字体 ≥ 8pt
+- [ ] 确保单位完整
+- [ ] Error bar 可见
+- [ ] PDF 矢量格式
+
+### 7.2 写作
+- [ ] Abstract (最后写)
+- [ ] 1页 Discussion
+
+### 7.3 可选扩展
+- [ ] 不同节点密度实验
+- [ ] 不同区域大小实验
+- [ ] Duty-cycle 建模
+
+---
+
+## 8. 核心数据速查 (Quick Reference)
+
+```
+PDR:
+  Gradient: 12.13% (CI: ±1.42)
+  Flooding: 23.74% (CI: ±1.30)
+  Random: 11.55% (CI: ±1.39)
+
+Airtime:
+  Gradient: 39.63%
+  Flooding: 96.52% (SATURATED!)
+  Random: 39.03%
+
+Collisions:
+  Flooding: 327.5 (3.5× higher)
+
+Key Insight:
+  Flooding PDR is 2× higher but
+  Airtime is 2.4× higher
+  → Efficiency frontier is the right framing
+```
+
+---
+
+*Generated: 2026-02-25*
+*Data: 50 seeds × 3 algorithms = 150 experiments*
+*Status: Ready for paper submission*
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new file mode 100644
index 0000000..3ddb1e9
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diff --git a/figures/pdr_vs_tx_cost.png b/figures/pdr_vs_tx_cost.png
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diff --git a/generate_bars.py b/generate_bars.py
new file mode 100644
index 0000000..309b65d
--- /dev/null
+++ b/generate_bars.py
@@ -0,0 +1,57 @@
+#!/usr/bin/env python
+"""
+Generate comparison bar chart.
+"""
+
+import json
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+
+
+def load_statistics():
+    with open("results/statistics.json", "r") as f:
+        return json.load(f)
+
+
+def main():
+    stats = load_statistics()
+
+    fig, axes = plt.subplots(1, 3, figsize=(12, 4))
+
+    algorithms = ["gradient", "flooding", "random"]
+    colors = ["#2E86AB", "#E94F37", "#7D8491"]
+
+    metrics = [
+        ("pdr", "PDR (%)"),
+        ("airtime_usage_percent", "Airtime (%)"),
+        ("tx_per_success", "TX/Success"),
+    ]
+
+    for idx, (metric, label) in enumerate(metrics):
+        ax = axes[idx]
+        values = [stats[a][metric]["mean"] for a in algorithms]
+        errors = [stats[a][metric]["ci_95"] for a in algorithms]
+
+        bars = ax.bar(
+            algorithms, values, yerr=errors, color=colors, capsize=5, alpha=0.8
+        )
+        ax.set_ylabel(label, fontsize=11)
+        ax.set_title(label, fontsize=12)
+        ax.grid(True, alpha=0.3, axis="y")
+
+        # Add value labels
+        for i, (bar, val, err) in enumerate(zip(bars, values, errors)):
+            ax.text(
+                i, val + err + 0.5, f"{val:.1f}", ha="center", va="bottom", fontsize=10
+            )
+
+    plt.tight_layout()
+    plt.savefig("figures/comparison_bar.pdf", dpi=300)
+    plt.savefig("figures/comparison_bar.png", dpi=150)
+    print("Saved: figures/comparison_bar.pdf")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/generate_figures.py b/generate_figures.py
new file mode 100644
index 0000000..fa95d7d
--- /dev/null
+++ b/generate_figures.py
@@ -0,0 +1,210 @@
+#!/usr/bin/env python
+"""
+Generate paper figures from experimental results.
+"""
+
+import json
+import matplotlib
+
+matplotlib.use("Agg")  # Non-interactive backend
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+def load_statistics():
+    """Load statistics from JSON."""
+    with open("results/statistics.json", "r") as f:
+        return json.load(f)
+
+
+def load_all_results():
+    """Load all raw results."""
+    algorithms = ["gradient", "flooding", "random"]
+    all_data = {}
+
+    for algo in algorithms:
+        with open(f"results/{algo}/all_seeds.json", "r") as f:
+            all_data[algo] = json.load(f)
+
+    return all_data
+
+
+def plot_pdr_vs_airtime(all_data, stats):
+    """Generate PDR vs Airtime plot."""
+    fig, ax = plt.subplots(figsize=(8, 6))
+
+    colors = {"gradient": "#2E86AB", "flooding": "#E94F37", "random": "#7D8491"}
+    markers = {"gradient": "o", "flooding": "s", "random": "^"}
+
+    for algo in ["gradient", "flooding", "random"]:
+        algo_stats = stats[algo]
+
+        # Get mean and CI
+        pdr_mean = algo_stats["pdr"]["mean"]
+        pdr_ci = algo_stats["pdr"]["ci_95"]
+        air_mean = algo_stats["airtime_usage_percent"]["mean"]
+        air_ci = algo_stats["airtime_usage_percent"]["ci_95"]
+
+        # Plot with error bars
+        ax.errorbar(
+            air_mean,
+            pdr_mean,
+            xerr=air_ci,
+            yerr=pdr_ci,
+            fmt=markers[algo],
+            color=colors[algo],
+            markersize=12,
+            capsize=5,
+            capthick=2,
+            linewidth=2,
+            label=f"{algo.capitalize()} ({pdr_mean:.1f}%, {air_mean:.1f}%)",
+        )
+
+        # Add individual points (semi-transparent)
+        for r in all_data[algo]:
+            if "error" not in r:
+                ax.scatter(
+                    r["airtime_usage_percent"],
+                    r["pdr"],
+                    alpha=0.15,
+                    color=colors[algo],
+                    s=20,
+                )
+
+    ax.set_xlabel("Airtime Usage (%)", fontsize=12)
+    ax.set_ylabel("Packet Delivery Ratio (%)", fontsize=12)
+    ax.set_title(
+        "PDR vs Channel Airtime Usage\n(50 seeds per algorithm, error bars = 95% CI)",
+        fontsize=14,
+    )
+    ax.legend(loc="lower right", fontsize=10)
+    ax.grid(True, alpha=0.3)
+    ax.set_xlim(0, 110)
+    ax.set_ylim(0, 35)
+
+    # Add annotation for efficiency frontier
+    ax.annotate(
+        "Efficiency\nFrontier",
+        xy=(40, 12),
+        fontsize=10,
+        style="italic",
+        bbox=dict(boxstyle="round", facecolor="wheat", alpha=0.5),
+    )
+
+    plt.tight_layout()
+    plt.savefig("figures/pdr_vs_airtime.pdf", dpi=300)
+    plt.savefig("figures/pdr_vs_airtime.png", dpi=150)
+    print("Saved: figures/pdr_vs_airtime.pdf")
+    plt.close()
+
+
+def plot_pdr_vs_tx_cost(all_data, stats):
+    """Generate PDR vs TX Cost plot."""
+    fig, ax = plt.subplots(figsize=(8, 6))
+
+    colors = {"gradient": "#2E86AB", "flooding": "#E94F37", "random": "#7D8491"}
+    markers = {"gradient": "o", "flooding": "s", "random": "^"}
+
+    for algo in ["gradient", "flooding", "random"]:
+        algo_stats = stats[algo]
+
+        pdr_mean = algo_stats["pdr"]["mean"]
+        pdr_ci = algo_stats["pdr"]["ci_95"]
+        tx_mean = algo_stats["tx_per_success"]["mean"]
+        tx_ci = algo_stats["tx_per_success"]["ci_95"]
+
+        ax.errorbar(
+            tx_mean,
+            pdr_mean,
+            xerr=tx_ci,
+            yerr=pdr_ci,
+            fmt=markers[algo],
+            color=colors[algo],
+            markersize=12,
+            capsize=5,
+            capthick=2,
+            linewidth=2,
+            label=f"{algo.capitalize()} (PDR: {pdr_mean:.1f}%)",
+        )
+
+        # Add individual points
+        for r in all_data[algo]:
+            if "error" not in r and r["tx_per_success"] > 0:
+                ax.scatter(
+                    r["tx_per_success"], r["pdr"], alpha=0.15, color=colors[algo], s=20
+                )
+
+    ax.set_xlabel("Transmission Cost (TX per Successful Delivery)", fontsize=12)
+    ax.set_ylabel("Packet Delivery Ratio (%)", fontsize=12)
+    ax.set_title(
+        "PDR vs Transmission Cost\n(50 seeds per algorithm, error bars = 95% CI)",
+        fontsize=14,
+    )
+    ax.legend(loc="lower right", fontsize=10)
+    ax.grid(True, alpha=0.3)
+
+    plt.tight_layout()
+    plt.savefig("figures/pdr_vs_tx_cost.pdf", dpi=300)
+    plt.savefig("figures/pdr_vs_tx_cost.png", dpi=150)
+    print("Saved: figures/pdr_vs_tx_cost.pdf")
+    plt.close()
+
+
+def plot_comparison_bar(stats):
+    """Generate comparison bar chart."""
+    fig, axes = plt.subplots(1, 3, figsize=(12, 4))
+
+    algorithms = ["gradient", "flooding", "random"]
+    colors = ["#2E86AB", "#E94F37", "#7D8491"]
+
+    metrics = [
+        ("pdr", "PDR (%)"),
+        ("airtime_usage_percent", "Airtime (%)"),
+        ("tx_per_success", "TX/Success"),
+    ]
+
+    for idx, (metric, label) in enumerate(metrics):
+        ax = axes[idx]
+        values = [stats[a][metric]["mean"] for a in algorithms]
+        errors = [stats[a][metric]["ci_95"] for a in algorithms]
+
+        bars = ax.bar(
+            algorithms, values, yerr=errors, color=colors, capsize=5, alpha=0.8
+        )
+        ax.set_ylabel(label, fontsize=11)
+        ax.set_title(label, fontsize=12)
+        ax.grid(True, alpha=0.3, axis="y")
+
+        # Add value labels
+        for bar, val in zip(bars, values):
+            ax.text(
+                bar.get_x() + bar.get_width() / 2,
+                bar.get_height() + errors[algorithms.index(bar.get_x())],
+                f"{val:.1f}",
+                ha="center",
+                va="bottom",
+                fontsize=10,
+            )
+
+    plt.tight_layout()
+    plt.savefig("figures/comparison_bar.pdf", dpi=300)
+    plt.savefig("figures/comparison_bar.png", dpi=150)
+    print("Saved: figures/comparison_bar.pdf")
+    plt.close()
+
+
+def main():
+    print("Loading results...")
+    stats = load_statistics()
+    all_data = load_all_results()
+
+    print("Generating figures...")
+    plot_pdr_vs_airtime(all_data, stats)
+    plot_pdr_vs_tx_cost(all_data, stats)
+    plot_comparison_bar(stats)
+
+    print("\nAll figures saved to figures/")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/results/all_raw_results.json b/results/all_raw_results.json
new file mode 100644
index 0000000..633bd5b
--- /dev/null
+++ b/results/all_raw_results.json
@@ -0,0 +1,1808 @@
+{
+  "gradient": [
+    {
+      "seed": 0,
+      "pdr": 5.88,
+      "total_sent": 34,
+      "total_received": 2,
+      "max_hop": 4,
+      "avg_hop": 2.33,
+      "total_transmissions": 170,
+      "airtime_usage_percent": 28.11,
+      "tx_per_success": 85.0,
+      "collisions": 56
+    },
+    {
+      "seed": 1,
+      "pdr": 11.76,
+      "total_sent": 34,
+      "total_received": 4,
+      "max_hop": 18,
+      "avg_hop": 9.25,
+      "total_transmissions": 190,
+      "airtime_usage_percent": 31.94,
+      "tx_per_success": 47.5,
+      "collisions": 68
+    },
+    {
+      "seed": 2,
+      "pdr": 7.89,
+      "total_sent": 38,
+      "total_received": 3,
+      "max_hop": 37,
+      "avg_hop": 15.2,
+      "total_transmissions": 218,
+      "airtime_usage_percent": 37.25,
+      "tx_per_success": 72.67,
+      "collisions": 87
+    },
+    {
+      "seed": 3,
+      "pdr": 5.56,
+      "total_sent": 36,
+      "total_received": 2,
+      "max_hop": 13,
+      "avg_hop": 7.0,
+      "total_transmissions": 201,
+      "airtime_usage_percent": 34.18,
+      "tx_per_success": 100.5,
+      "collisions": 73
+    },
+    {
+      "seed": 4,
+      "pdr": 13.89,
+      "total_sent": 36,
+      "total_received": 5,
+      "max_hop": 29,
+      "avg_hop": 12.69,
+      "total_transmissions": 243,
+      "airtime_usage_percent": 41.28,
+      "tx_per_success": 48.6,
+      "collisions": 94
+    },
+    {
+      "seed": 5,
+      "pdr": 5.26,
+      "total_sent": 38,
+      "total_received": 2,
+      "max_hop": 23,
+      "avg_hop": 12.33,
+      "total_transmissions": 202,
+      "airtime_usage_percent": 34.23,
+      "tx_per_success": 101.0,
+      "collisions": 74
+    },
+    {
+      "seed": 6,
+      "pdr": 11.76,
+      "total_sent": 34,
+      "total_received": 4,
+      "max_hop": 105,
+      "avg_hop": 54.61,
+      "total_transmissions": 322,
+      "airtime_usage_percent": 56.35,
+      "tx_per_success": 80.5,
+      "collisions": 150
+    },
+    {
+      "seed": 7,
+      "pdr": 9.38,
+      "total_sent": 32,
+      "total_received": 3,
+      "max_hop": 4,
+      "avg_hop": 2.25,
+      "total_transmissions": 168,
+      "airtime_usage_percent": 27.65,
+      "tx_per_success": 56.0,
+      "collisions": 63
+    },
+    {
+      "seed": 8,
+      "pdr": 7.14,
+      "total_sent": 42,
+      "total_received": 3,
+      "max_hop": 64,
+      "avg_hop": 33.22,
+      "total_transmissions": 262,
+      "airtime_usage_percent": 45.43,
+      "tx_per_success": 87.33,
+      "collisions": 108
+    },
+    {
+      "seed": 9,
+      "pdr": 13.16,
+      "total_sent": 38,
+      "total_received": 5,
+      "max_hop": 78,
+      "avg_hop": 30.33,
+      "total_transmissions": 282,
+      "airtime_usage_percent": 48.94,
+      "tx_per_success": 56.4,
+      "collisions": 124
+    },
+    {
+      "seed": 10,
+      "pdr": 15.62,
+      "total_sent": 32,
+      "total_received": 5,
+      "max_hop": 24,
+      "avg_hop": 7.22,
+      "total_transmissions": 241,
+      "airtime_usage_percent": 41.44,
+      "tx_per_success": 48.2,
+      "collisions": 102
+    },
+    {
+      "seed": 11,
+      "pdr": 8.82,
+      "total_sent": 34,
+      "total_received": 3,
+      "max_hop": 31,
+      "avg_hop": 17.0,
+      "total_transmissions": 221,
+      "airtime_usage_percent": 37.54,
+      "tx_per_success": 73.67,
+      "collisions": 83
+    },
+    {
+      "seed": 12,
+      "pdr": 7.89,
+      "total_sent": 38,
+      "total_received": 3,
+      "max_hop": 57,
+      "avg_hop": 31.18,
+      "total_transmissions": 262,
+      "airtime_usage_percent": 45.4,
+      "tx_per_success": 87.33,
+      "collisions": 117
+    },
+    {
+      "seed": 13,
+      "pdr": 16.67,
+      "total_sent": 42,
+      "total_received": 7,
+      "max_hop": 74,
+      "avg_hop": 26.31,
+      "total_transmissions": 296,
+      "airtime_usage_percent": 51.54,
+      "tx_per_success": 42.29,
+      "collisions": 138
+    },
+    {
+      "seed": 14,
+      "pdr": 2.63,
+      "total_sent": 38,
+      "total_received": 1,
+      "max_hop": 85,
+      "avg_hop": 37.91,
+      "total_transmissions": 270,
+      "airtime_usage_percent": 46.87,
+      "tx_per_success": 270.0,
+      "collisions": 116
+    },
+    {
+      "seed": 15,
+      "pdr": 20.0,
+      "total_sent": 30,
+      "total_received": 6,
+      "max_hop": 51,
+      "avg_hop": 14.33,
+      "total_transmissions": 272,
+      "airtime_usage_percent": 47.22,
+      "tx_per_success": 45.33,
+      "collisions": 113
+    },
+    {
+      "seed": 16,
+      "pdr": 15.79,
+      "total_sent": 38,
+      "total_received": 6,
+      "max_hop": 13,
+      "avg_hop": 5.18,
+      "total_transmissions": 219,
+      "airtime_usage_percent": 36.89,
+      "tx_per_success": 36.5,
+      "collisions": 86
+    },
+    {
+      "seed": 17,
+      "pdr": 15.0,
+      "total_sent": 40,
+      "total_received": 6,
+      "max_hop": 14,
+      "avg_hop": 4.71,
+      "total_transmissions": 190,
+      "airtime_usage_percent": 31.72,
+      "tx_per_success": 31.67,
+      "collisions": 62
+    },
+    {
+      "seed": 18,
+      "pdr": 7.14,
+      "total_sent": 42,
+      "total_received": 3,
+      "max_hop": 45,
+      "avg_hop": 21.0,
+      "total_transmissions": 226,
+      "airtime_usage_percent": 38.58,
+      "tx_per_success": 75.33,
+      "collisions": 90
+    },
+    {
+      "seed": 19,
+      "pdr": 12.5,
+      "total_sent": 32,
+      "total_received": 4,
+      "max_hop": 29,
+      "avg_hop": 16.83,
+      "total_transmissions": 216,
+      "airtime_usage_percent": 36.83,
+      "tx_per_success": 54.0,
+      "collisions": 88
+    },
+    {
+      "seed": 20,
+      "pdr": 9.38,
+      "total_sent": 32,
+      "total_received": 3,
+      "max_hop": 18,
+      "avg_hop": 12.5,
+      "total_transmissions": 199,
+      "airtime_usage_percent": 33.65,
+      "tx_per_success": 66.33,
+      "collisions": 78
+    },
+    {
+      "seed": 21,
+      "pdr": 16.67,
+      "total_sent": 36,
+      "total_received": 6,
+      "max_hop": 20,
+      "avg_hop": 7.14,
+      "total_transmissions": 223,
+      "airtime_usage_percent": 38.03,
+      "tx_per_success": 37.17,
+      "collisions": 89
+    },
+    {
+      "seed": 22,
+      "pdr": 0.0,
+      "total_sent": 36,
+      "total_received": 0,
+      "max_hop": 0,
+      "avg_hop": 0.0,
+      "total_transmissions": 167,
+      "airtime_usage_percent": 27.79,
+      "tx_per_success": -1,
+      "collisions": 53
+    },
+    {
+      "seed": 23,
+      "pdr": 7.89,
+      "total_sent": 38,
+      "total_received": 3,
+      "max_hop": 36,
+      "avg_hop": 15.0,
+      "total_transmissions": 203,
+      "airtime_usage_percent": 34.27,
+      "tx_per_success": 67.67,
+      "collisions": 70
+    },
+    {
+      "seed": 24,
+      "pdr": 8.33,
+      "total_sent": 36,
+      "total_received": 3,
+      "max_hop": 14,
+      "avg_hop": 6.67,
+      "total_transmissions": 187,
+      "airtime_usage_percent": 31.4,
+      "tx_per_success": 62.33,
+      "collisions": 74
+    },
+    {
+      "seed": 25,
+      "pdr": 20.59,
+      "total_sent": 34,
+      "total_received": 7,
+      "max_hop": 75,
+      "avg_hop": 24.29,
+      "total_transmissions": 277,
+      "airtime_usage_percent": 48.01,
+      "tx_per_success": 39.57,
+      "collisions": 136
+    },
+    {
+      "seed": 26,
+      "pdr": 15.62,
+      "total_sent": 32,
+      "total_received": 5,
+      "max_hop": 22,
+      "avg_hop": 7.17,
+      "total_transmissions": 205,
+      "airtime_usage_percent": 34.26,
+      "tx_per_success": 41.0,
+      "collisions": 84
+    },
+    {
+      "seed": 27,
+      "pdr": 11.11,
+      "total_sent": 36,
+      "total_received": 4,
+      "max_hop": 28,
+      "avg_hop": 8.83,
+      "total_transmissions": 215,
+      "airtime_usage_percent": 36.63,
+      "tx_per_success": 53.75,
+      "collisions": 83
+    },
+    {
+      "seed": 28,
+      "pdr": 19.44,
+      "total_sent": 36,
+      "total_received": 7,
+      "max_hop": 38,
+      "avg_hop": 15.82,
+      "total_transmissions": 292,
+      "airtime_usage_percent": 50.69,
+      "tx_per_success": 41.71,
+      "collisions": 130
+    },
+    {
+      "seed": 29,
+      "pdr": 5.56,
+      "total_sent": 36,
+      "total_received": 2,
+      "max_hop": 23,
+      "avg_hop": 14.67,
+      "total_transmissions": 205,
+      "airtime_usage_percent": 34.88,
+      "tx_per_success": 102.5,
+      "collisions": 84
+    },
+    {
+      "seed": 30,
+      "pdr": 13.16,
+      "total_sent": 38,
+      "total_received": 5,
+      "max_hop": 26,
+      "avg_hop": 10.1,
+      "total_transmissions": 206,
+      "airtime_usage_percent": 34.52,
+      "tx_per_success": 41.2,
+      "collisions": 75
+    },
+    {
+      "seed": 31,
+      "pdr": 25.0,
+      "total_sent": 32,
+      "total_received": 8,
+      "max_hop": 19,
+      "avg_hop": 7.15,
+      "total_transmissions": 236,
+      "airtime_usage_percent": 40.29,
+      "tx_per_success": 29.5,
+      "collisions": 91
+    },
+    {
+      "seed": 32,
+      "pdr": 15.79,
+      "total_sent": 38,
+      "total_received": 6,
+      "max_hop": 56,
+      "avg_hop": 19.42,
+      "total_transmissions": 323,
+      "airtime_usage_percent": 57.02,
+      "tx_per_success": 53.83,
+      "collisions": 153
+    },
+    {
+      "seed": 33,
+      "pdr": 13.33,
+      "total_sent": 30,
+      "total_received": 4,
+      "max_hop": 37,
+      "avg_hop": 17.89,
+      "total_transmissions": 230,
+      "airtime_usage_percent": 39.39,
+      "tx_per_success": 57.5,
+      "collisions": 92
+    },
+    {
+      "seed": 34,
+      "pdr": 18.75,
+      "total_sent": 32,
+      "total_received": 6,
+      "max_hop": 11,
+      "avg_hop": 4.33,
+      "total_transmissions": 190,
+      "airtime_usage_percent": 31.68,
+      "tx_per_success": 31.67,
+      "collisions": 65
+    },
+    {
+      "seed": 35,
+      "pdr": 11.11,
+      "total_sent": 36,
+      "total_received": 4,
+      "max_hop": 19,
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+  ]
+}
\ No newline at end of file
diff --git a/results/flooding/all_seeds.json b/results/flooding/all_seeds.json
new file mode 100644
index 0000000..fa31f84
--- /dev/null
+++ b/results/flooding/all_seeds.json
@@ -0,0 +1,602 @@
+[
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\ No newline at end of file
diff --git a/results/gradient/all_seeds.json b/results/gradient/all_seeds.json
new file mode 100644
index 0000000..b18a11b
--- /dev/null
+++ b/results/gradient/all_seeds.json
@@ -0,0 +1,602 @@
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+]
\ No newline at end of file
diff --git a/results/random/all_seeds.json b/results/random/all_seeds.json
new file mode 100644
index 0000000..2c96bc7
--- /dev/null
+++ b/results/random/all_seeds.json
@@ -0,0 +1,602 @@
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+  {
+    "seed": 36,
+    "pdr": 8.33,
+    "total_sent": 36,
+    "total_received": 3,
+    "max_hop": 42,
+    "avg_hop": 19.38,
+    "total_transmissions": 252,
+    "airtime_usage_percent": 43.61,
+    "tx_per_success": 84.0,
+    "collisions": 98
+  },
+  {
+    "seed": 37,
+    "pdr": 7.14,
+    "total_sent": 28,
+    "total_received": 2,
+    "max_hop": 81,
+    "avg_hop": 45.78,
+    "total_transmissions": 249,
+    "airtime_usage_percent": 42.96,
+    "tx_per_success": 124.5,
+    "collisions": 100
+  },
+  {
+    "seed": 38,
+    "pdr": 5.88,
+    "total_sent": 34,
+    "total_received": 2,
+    "max_hop": 31,
+    "avg_hop": 16.38,
+    "total_transmissions": 228,
+    "airtime_usage_percent": 39.04,
+    "tx_per_success": 114.0,
+    "collisions": 94
+  },
+  {
+    "seed": 39,
+    "pdr": 10.0,
+    "total_sent": 30,
+    "total_received": 3,
+    "max_hop": 3,
+    "avg_hop": 2.0,
+    "total_transmissions": 185,
+    "airtime_usage_percent": 30.97,
+    "tx_per_success": 61.67,
+    "collisions": 64
+  },
+  {
+    "seed": 40,
+    "pdr": 11.11,
+    "total_sent": 36,
+    "total_received": 4,
+    "max_hop": 60,
+    "avg_hop": 18.33,
+    "total_transmissions": 236,
+    "airtime_usage_percent": 40.44,
+    "tx_per_success": 59.0,
+    "collisions": 87
+  },
+  {
+    "seed": 41,
+    "pdr": 11.76,
+    "total_sent": 34,
+    "total_received": 4,
+    "max_hop": 58,
+    "avg_hop": 27.64,
+    "total_transmissions": 238,
+    "airtime_usage_percent": 40.68,
+    "tx_per_success": 59.5,
+    "collisions": 94
+  },
+  {
+    "seed": 42,
+    "pdr": 17.65,
+    "total_sent": 34,
+    "total_received": 6,
+    "max_hop": 30,
+    "avg_hop": 12.3,
+    "total_transmissions": 203,
+    "airtime_usage_percent": 33.94,
+    "tx_per_success": 33.83,
+    "collisions": 79
+  },
+  {
+    "seed": 43,
+    "pdr": 8.82,
+    "total_sent": 34,
+    "total_received": 3,
+    "max_hop": 16,
+    "avg_hop": 6.25,
+    "total_transmissions": 179,
+    "airtime_usage_percent": 29.78,
+    "tx_per_success": 59.67,
+    "collisions": 60
+  },
+  {
+    "seed": 44,
+    "pdr": 13.16,
+    "total_sent": 38,
+    "total_received": 5,
+    "max_hop": 44,
+    "avg_hop": 17.42,
+    "total_transmissions": 273,
+    "airtime_usage_percent": 47.42,
+    "tx_per_success": 54.6,
+    "collisions": 122
+  },
+  {
+    "seed": 45,
+    "pdr": 25.0,
+    "total_sent": 28,
+    "total_received": 7,
+    "max_hop": 64,
+    "avg_hop": 14.06,
+    "total_transmissions": 271,
+    "airtime_usage_percent": 46.7,
+    "tx_per_success": 38.71,
+    "collisions": 120
+  },
+  {
+    "seed": 46,
+    "pdr": 12.5,
+    "total_sent": 40,
+    "total_received": 5,
+    "max_hop": 5,
+    "avg_hop": 2.67,
+    "total_transmissions": 213,
+    "airtime_usage_percent": 36.21,
+    "tx_per_success": 42.6,
+    "collisions": 82
+  },
+  {
+    "seed": 47,
+    "pdr": 5.88,
+    "total_sent": 34,
+    "total_received": 2,
+    "max_hop": 6,
+    "avg_hop": 3.0,
+    "total_transmissions": 183,
+    "airtime_usage_percent": 30.59,
+    "tx_per_success": 91.5,
+    "collisions": 63
+  },
+  {
+    "seed": 48,
+    "pdr": 8.82,
+    "total_sent": 34,
+    "total_received": 3,
+    "max_hop": 14,
+    "avg_hop": 4.33,
+    "total_transmissions": 189,
+    "airtime_usage_percent": 31.45,
+    "tx_per_success": 63.0,
+    "collisions": 67
+  },
+  {
+    "seed": 49,
+    "pdr": 15.79,
+    "total_sent": 38,
+    "total_received": 6,
+    "max_hop": 66,
+    "avg_hop": 26.31,
+    "total_transmissions": 266,
+    "airtime_usage_percent": 45.77,
+    "tx_per_success": 44.33,
+    "collisions": 109
+  }
+]
\ No newline at end of file
diff --git a/results/statistics.json b/results/statistics.json
new file mode 100644
index 0000000..9b7a040
--- /dev/null
+++ b/results/statistics.json
@@ -0,0 +1,128 @@
+{
+  "gradient": {
+    "pdr": {
+      "mean": 12.1252,
+      "std": 5.1213,
+      "ci_95": 1.4195,
+      "min": 0.0,
+      "max": 25.0,
+      "n": 50
+    },
+    "airtime_usage_percent": {
+      "mean": 39.6318,
+      "std": 7.3928,
+      "ci_95": 2.0492,
+      "min": 27.65,
+      "max": 57.02,
+      "n": 50
+    },
+    "tx_per_success": {
+      "mean": 62.3804,
+      "std": 37.3461,
+      "ci_95": 10.3518,
+      "min": -1,
+      "max": 270.0,
+      "n": 50
+    },
+    "max_hop": {
+      "mean": 36.12,
+      "std": 26.0154,
+      "ci_95": 7.2111,
+      "min": 0,
+      "max": 113,
+      "n": 50
+    },
+    "collisions": {
+      "mean": 93.6,
+      "std": 25.0347,
+      "ci_95": 6.9393,
+      "min": 51,
+      "max": 153,
+      "n": 50
+    }
+  },
+  "flooding": {
+    "pdr": {
+      "mean": 23.739,
+      "std": 4.705,
+      "ci_95": 1.3042,
+      "min": 14.71,
+      "max": 36.67,
+      "n": 50
+    },
+    "airtime_usage_percent": {
+      "mean": 96.5222,
+      "std": 4.3925,
+      "ci_95": 1.2175,
+      "min": 81.46,
+      "max": 106.38,
+      "n": 50
+    },
+    "tx_per_success": {
+      "mean": 67.5176,
+      "std": 11.8936,
+      "ci_95": 3.2967,
+      "min": 46.73,
+      "max": 96.8,
+      "n": 50
+    },
+    "max_hop": {
+      "mean": 62.48,
+      "std": 6.0449,
+      "ci_95": 1.6756,
+      "min": 50,
+      "max": 76,
+      "n": 50
+    },
+    "collisions": {
+      "mean": 327.5,
+      "std": 20.4333,
+      "ci_95": 5.6638,
+      "min": 265,
+      "max": 368,
+      "n": 50
+    }
+  },
+  "random": {
+    "pdr": {
+      "mean": 11.5456,
+      "std": 5.0199,
+      "ci_95": 1.3914,
+      "min": 2.78,
+      "max": 27.78,
+      "n": 50
+    },
+    "airtime_usage_percent": {
+      "mean": 39.0258,
+      "std": 7.969,
+      "ci_95": 2.2089,
+      "min": 25.01,
+      "max": 62.48,
+      "n": 50
+    },
+    "tx_per_success": {
+      "mean": 69.7302,
+      "std": 39.1544,
+      "ci_95": 10.853,
+      "min": 30.7,
+      "max": 210.0,
+      "n": 50
+    },
+    "max_hop": {
+      "mean": 36.28,
+      "std": 30.5861,
+      "ci_95": 8.478,
+      "min": 3,
+      "max": 134,
+      "n": 50
+    },
+    "collisions": {
+      "mean": 91.74,
+      "std": 27.3897,
+      "ci_95": 7.592,
+      "min": 46,
+      "max": 172,
+      "n": 50
+    }
+  }
+}
\ No newline at end of file
diff --git a/run_statistical_experiments.py b/run_statistical_experiments.py
new file mode 100644
index 0000000..7a93fbb
--- /dev/null
+++ b/run_statistical_experiments.py
@@ -0,0 +1,207 @@
+#!/usr/bin/env python
+"""
+Statistical Experiment Runner for Paper Results.
+
+Runs N seeds per algorithm and saves results for statistical analysis.
+"""
+
+import json
+import os
+import sys
+from datetime import datetime
+
+# Add sim to path
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+
+from sim.main import run_simulation
+
+
+def run_statistical_experiments(
+    num_seeds=50,
+    num_nodes=12,
+    area_size=800,
+    sim_time=100,
+    algorithms=None,
+):
+    """
+    Run experiments with multiple seeds for statistical significance.
+
+    Args:
+        num_seeds: Number of random seeds to run
+        num_nodes: Number of nodes
+        area_size: Area size in meters
+        sim_time: Simulation time
+        algorithms: List of algorithms to test
+
+    Returns:
+        Dictionary with all results
+    """
+    if algorithms is None:
+        algorithms = ["gradient", "flooding", "random"]
+
+    all_results = {}
+
+    total_runs = num_seeds * len(algorithms)
+    current = 0
+
+    print(
+        f"Running {num_seeds} seeds × {len(algorithms)} algorithms = {total_runs} experiments"
+    )
+    print("=" * 60)
+
+    for algo in algorithms:
+        print(f"\n>>> Algorithm: {algo}")
+        algo_results = []
+
+        for seed in range(num_seeds):
+            current += 1
+            print(f"  [{current}/{total_runs}] Seed {seed}...", end=" ")
+
+            try:
+                results = run_simulation(
+                    num_nodes=num_nodes,
+                    area_size=area_size,
+                    sim_time=sim_time,
+                    seed=seed,
+                    routing_type=algo,
+                )
+
+                m = results["metrics"]
+                e = results["efficiency"]
+
+                # Extract key metrics
+                result = {
+                    "seed": seed,
+                    "pdr": m["pdr"],
+                    "total_sent": m["total_sent"],
+                    "total_received": m["total_received"],
+                    "max_hop": m["max_hop"],
+                    "avg_hop": m["avg_hop"],
+                    "total_transmissions": e["total_transmissions"],
+                    "airtime_usage_percent": e["airtime_usage_percent"],
+                    "tx_per_success": e["tx_per_success"],
+                    "collisions": m["collisions"],
+                }
+
+                algo_results.append(result)
+                print(f"PDR={result['pdr']:.2f}%, TX={result['total_transmissions']}")
+
+            except Exception as ex:
+                print(f"ERROR: {ex}")
+                algo_results.append({"seed": seed, "error": str(ex)})
+
+        all_results[algo] = algo_results
+
+        # Save per-algorithm results
+        algo_dir = f"results/{algo}"
+        os.makedirs(algo_dir, exist_ok=True)
+
+        with open(f"{algo_dir}/all_seeds.json", "w") as f:
+            json.dump(algo_results, f, indent=2)
+
+        print(f"  Saved {len(algo_results)} results to {algo_dir}/")
+
+    return all_results
+
+
+def compute_statistics(results):
+    """Compute mean, std, and 95% CI for each metric."""
+    import math
+
+    metrics = [
+        "pdr",
+        "airtime_usage_percent",
+        "tx_per_success",
+        "max_hop",
+        "collisions",
+    ]
+
+    stats = {}
+
+    for algo, algo_results in results.items():
+        valid_results = [r for r in algo_results if "error" not in r]
+
+        stats[algo] = {}
+
+        for metric in metrics:
+            values = [r[metric] for r in valid_results if metric in r]
+
+            if not values:
+                continue
+
+            n = len(values)
+            mean = sum(values) / n
+            variance = sum((x - mean) ** 2 for x in values) / (n - 1) if n > 1 else 0
+            std = math.sqrt(variance)
+
+            # 95% CI = 1.96 * std / sqrt(n)
+            ci_95 = 1.96 * std / math.sqrt(n) if n > 0 else 0
+
+            stats[algo][metric] = {
+                "mean": round(mean, 4),
+                "std": round(std, 4),
+                "ci_95": round(ci_95, 4),
+                "min": round(min(values), 4),
+                "max": round(max(values), 4),
+                "n": n,
+            }
+
+    return stats
+
+
+def save_statistics(all_results, stats):
+    """Save statistics to JSON."""
+    # Save combined raw results
+    with open("results/all_raw_results.json", "w") as f:
+        json.dump(all_results, f, indent=2)
+
+    # Save statistics
+    with open("results/statistics.json", "w") as f:
+        json.dump(stats, f, indent=2)
+
+    print("\n" + "=" * 60)
+    print("STATISTICS SUMMARY")
+    print("=" * 60)
+
+    for algo, algo_stats in stats.items():
+        print(f"\n{algo.upper()}:")
+        for metric, s in algo_stats.items():
+            print(f"  {metric:30s}: {s['mean']:8.4f} ± {s['ci_95']:.4f} (95% CI)")
+
+
+def main():
+    print("=" * 60)
+    print("Statistical Experiment Runner for Paper Results")
+    print("=" * 60)
+    print(f"Start time: {datetime.now()}")
+
+    # Configuration
+    NUM_SEEDS = 50
+    NUM_NODES = 12
+    AREA_SIZE = 800
+    SIM_TIME = 100
+
+    # Run experiments
+    all_results = run_statistical_experiments(
+        num_seeds=NUM_SEEDS,
+        num_nodes=NUM_NODES,
+        area_size=AREA_SIZE,
+        sim_time=SIM_TIME,
+    )
+
+    # Compute statistics
+    stats = compute_statistics(all_results)
+
+    # Save results
+    save_statistics(all_results, stats)
+
+    print(f"\nEnd time: {datetime.now()}")
+    print("\nResults saved to:")
+    print("  results/gradient/all_seeds.json")
+    print("  results/flooding/all_seeds.json")
+    print("  results/random/all_seeds.json")
+    print("  results/statistics.json")
+
+
+if __name__ == "__main__":
+    main()