Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock 43600, Pakistan.
Department of Computer Science and Engineering, Soonchunhyang University, Asan 31538, Korea.
Sensors (Basel). 2019 Mar 6;19(5):1145. doi: 10.3390/s19051145.
Smart ocean is a term broadly used for monitoring the ocean surface, sea habitat monitoring, and mineral exploration to name a few. Development of an efficient routing protocol for smart oceans is a non-trivial task because of various challenges, such as presence of tidal waves, multiple sources of noise, high propagation delay, and low bandwidth. In this paper, we have proposed a routing protocol named adaptive node clustering technique for smart ocean underwater sensor network (SOSNET). SOSNET employs a moth flame optimizer (MFO) based technique for selecting a near optimal number of clusters required for routing. MFO is a bio inspired optimization technique, which takes into account the movement of moths towards light. The SOSNET algorithm is compared with other bio inspired algorithms such as comprehensive learning particle swarm optimization (CLPSO), ant colony optimization (ACO), and gray wolf optimization (GWO). All these algorithms are used for routing optimization. The performance metrics used for this comparison are transmission range of nodes, node density, and grid size. These parameters are varied during the simulation, and the results indicate that SOSNET performed better than other algorithms.
智能海洋是一个广泛用于监测海洋表面、海洋生境监测和矿产勘探等领域的术语。由于存在潮汐、多种噪声源、高传播延迟和低带宽等各种挑战，为智能海洋开发高效的路由协议并非易事。在本文中，我们提出了一种名为智能海洋水下传感器网络（SOSNET）的自适应节点聚类技术的路由协议。SOSNET 采用基于 moth flame optimizer（MFO）的技术来选择路由所需的近似最佳簇数。MFO 是一种受生物启发的优化技术，它考虑了飞蛾向光移动的情况。SOSNET 算法与其他受生物启发的算法（如综合学习粒子群优化（CLPSO）、蚁群优化（ACO）和灰狼优化（GWO））进行了比较，这些算法都用于路由优化。用于比较的性能指标包括节点的传输范围、节点密度和网格大小。在模拟过程中会改变这些参数，结果表明 SOSNET 优于其他算法。
