Field observation systems are mainly deployed in the harsh natural environment. These systems principally focus on observation and study within the station currently, which leads to problems such as the inability to form combined network observation and quite challenging to answer the scientific questions of wider regions and scales. To form Field Observation Instruments Networks (FOINs) and accelerate the general automation rate as well as in real-time data exchange in field observation, a multi-objective decision-making mehod named Entropy-based TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) Clustering routing algorithm (ETC) for FOIN is proposed in this paper. The ETC algorithm can select the optimal cluster head (Optimal-CH) through multi-objective decision-making and mainly solves the problem that some existing multi-objective optimization algorithms cannot dynamically and objectively allocate weights. The ETC algorithm was compared with some latest work and similar kinds of work from network lifespan, the number of CH and energy consumption in the Matlab simulations experiments. The result shows that the ETC algorithm performs well, enhancing energy conservation and extending the existence of FOIN.

中文翻译：

---

野外观测仪器网络路由算法中簇首选举的多目标决策

野外观测系统主要部署在恶劣的自然环境中。目前，这些系统主要集中在站内观测和研究，导致无法形成联合网络观测等问题，对回答更广泛地区和更广泛尺度的科学问题具有挑战性。为了形成现场观测仪器网络 (FOIN) 并加快现场观测的总体自动化速度以及实时数据交换，一种名为 Entropy-based TOPSIS (Technique for Order Preference by Similarity to an理想解决方案）本文提出了FOIN的聚类路由算法（ETC）。ETC算法可以通过多目标决策选择最优簇头（Optimal-CH），主要解决现有的一些多目标优化算法无法动态、客观地分配权重的问题。在Matlab仿真实验中，将ETC算法与网络寿命、CH数量和能耗等一些最新工作和类似工作进行了比较。结果表明，ETC算法性能良好，增强了能量守恒，延长了FOIN的存在性。