负载均衡的水声传感器网络自适应分簇算法

Adaptive Clustering Algorithm with Load-balanced in UASNs

在水中水声信号传输优化中,针对现有的水声传感器网络分簇算法存在的簇首选举随机性、簇首分布不均及簇首负载不均衡的问题,提出一种负载均衡的自适应分簇算法(LBACA)。上述算法综合考虑节点的剩余能量和位置信息,从初步过滤、簇首候选与竞争阶段入手,使靠近基站的簇的规模小于远离基站的簇;在稳定的数据传输阶段,根据簇首能量、位置和相对距离信息合理地选择中继簇首节点。仿真结果表明,改进算法能够提高网络稳定性、延长网络生命周期、提高能量利用率和减少丢包率。

For the problem of cluster head election randommess, cluster heads uneven distribution and cluster head load imbalance in the clustering algorithm for underwater acoustic sensor networks, a load-balanced adaptive clustering algorithm （LBACA） is proposed. The new algorithm considers the residual energy and location information of nodes, and from initial filtering, cluster candidate and cluster competition make clusters closer to the sink head smaller sizes than those farther away from the sink. Based on energy, location and relative distance information, the cluster head selects the trunking node reasonably in the stable period of data transmission. The simulation results show that this algorithm can improve the network stability, prolong network lifetime, improve energy efficiency and re- duce packet loss rate.