Yang Jin, Liu Fagui, Cao Jianneng, Wang Liangming
School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China.
School of Medical Information and Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, China.
Sensors (Basel). 2016 Jul 14;16(7):1081. doi: 10.3390/s16071081.
Mobile sinks can achieve load-balancing and energy-consumption balancing across the wireless sensor networks (WSNs). However, the frequent change of the paths between source nodes and the sinks caused by sink mobility introduces significant overhead in terms of energy and packet delays. To enhance network performance of WSNs with mobile sinks (MWSNs), we present an efficient routing strategy, which is formulated as an optimization problem and employs the particle swarm optimization algorithm (PSO) to build the optimal routing paths. However, the conventional PSO is insufficient to solve discrete routing optimization problems. Therefore, a novel greedy discrete particle swarm optimization with memory (GMDPSO) is put forward to address this problem. In the GMDPSO, particle's position and velocity of traditional PSO are redefined under discrete MWSNs scenario. Particle updating rule is also reconsidered based on the subnetwork topology of MWSNs. Besides, by improving the greedy forwarding routing, a greedy search strategy is designed to drive particles to find a better position quickly. Furthermore, searching history is memorized to accelerate convergence. Simulation results demonstrate that our new protocol significantly improves the robustness and adapts to rapid topological changes with multiple mobile sinks, while efficiently reducing the communication overhead and the energy consumption.
移动汇聚节点能够在无线传感器网络（WSN）中实现负载均衡和能耗均衡。然而，汇聚节点移动导致源节点与汇聚节点之间路径频繁变化，在能量和数据包延迟方面带来了显著开销。为了提高具有移动汇聚节点的无线传感器网络（MWSN）的网络性能，我们提出了一种高效的路由策略，将其表述为一个优化问题，并采用粒子群优化算法（PSO）来构建最优路由路径。然而，传统的PSO不足以解决离散路由优化问题。因此，提出了一种新颖的带记忆的贪婪离散粒子群优化算法（GMDPSO）来解决这一问题。在GMDPSO中，在离散的MWSN场景下重新定义了传统PSO中粒子的位置和速度。还基于MWSN的子网拓扑重新考虑了粒子更新规则。此外，通过改进贪婪转发路由，设计了一种贪婪搜索策略，以驱动粒子快速找到更好的位置。此外，记忆搜索历史以加速收敛。仿真结果表明，我们的新协议显著提高了鲁棒性，适应了多个移动汇聚节点的快速拓扑变化，同时有效降低了通信开销和能耗。
