### LEACH协议的Python实现及其全流程可视化 LEACH（Low Energy Adaptive Clustering Hierarchy）是一种用于无线传感器网络的能量高效分簇路由协议。以下是基于Python的一个简化版本LEACH协议实现，并附带了全流程可视化的功能。 #### 协议概述 LEACH的核心思想是通过周期性选举簇头节点来平衡能量消耗，从而延长整个网络的生命周期[^1]。该算法主要包括以下几个阶段：初始化、轮次循环中的簇头选举、簇成员分配以及数据传输和汇聚。 --- #### Python代码实现 以下是一个简化的LEACH协议实现： ```python import random import matplotlib.pyplot as plt class Node: def __init__(self, id, energy): self.id = id self.energy = energy self.is_head = False def set_as_head(self): self.is_head = True def consume_energy(self, amount): if self.energy >= amount: self.energy -= amount else: self.energy = 0 def initialize_network(num_nodes=20, initial_energy=100): nodes = [] for i in range(num_nodes): nodes.append(Node(i, initial_energy)) return nodes def elect_heads(nodes, threshold): heads = [] for node in nodes: if not node.is_head and random.random() < threshold: node.set_as_head() heads.append(node) return heads def assign_clusters(nodes, heads): clusters = {head: [] for head in heads} for node in nodes: if not node.is_head: closest_head = min(heads, key=lambda h: abs(h.id - node.id)) # Simplified distance calculation clusters[closest_head].append(node) return clusters def transmit_data(clusters): data_transmission_cost = 5 # Arbitrary cost value for head, members in clusters.items(): for member in members: member.consume_energy(data_transmission_cost) def visualize_network(nodes, heads): positions = [(node.id % 5, node.id // 5) for node in nodes] colors = ['red' if node.is_head else 'blue' for node in nodes] plt.figure(figsize=(8, 6)) for pos, color in zip(positions, colors): plt.scatter(pos[0], pos[1], c=color) for head in heads: head_pos = (head.id % 5, head.id // 5) plt.annotate(f'H{head.id}', xy=head_pos, textcoords="offset points", xytext=(0,10), ha='center') plt.title('Network Visualization') plt.show() if __name__ == "__main__": num_rounds = 10 network_size = 20 initial_energy = 100 p = 0.2 # Probability of becoming a cluster head nodes = initialize_network(network_size, initial_energy) for round_num in range(num_rounds): print(f"Round {round_num + 1}") # Elect new heads based on probability heads = elect_heads(nodes, p / (1 - (round_num * p))) # Assign non-head nodes to the nearest head clusters = assign_clusters(nodes, heads) # Simulate data transmission within each cluster transmit_data(clusters) # Visualize current state of the network visualize_network(nodes, heads) ``` --- #### 关键点说明 1. **Node类定义**: 表示单个节点的状态，包括ID、剩余能量以及是否为簇头。 2. **initialize_network函数**: 初始化指定数量的节点并赋予初始能量。 3. **elect_heads函数**: 基于概率`p`随机选取部分节点作为本轮的簇头[^2]。 4. **assign_clusters函数**: 将非簇头节点分配到最近的簇头下形成集群。 5. **transmit_data函数**: 模拟数据传输过程中的能耗情况。 6. **visualize_network函数**: 使用Matplotlib库绘制当前网络状态图，红色表示簇头节点，蓝色表示普通节点。 --- #### 可视化效果 每一轮结束后会调用`visualize_network()`方法展示当前网络结构变化，帮助理解LEACH的工作流程及动态特性[^3]。 ---