Wireless sensor networks (WSNs) boost the development of the Internet of Things by their ability to monitor environments and flexibility. It is desirable to study an efficient configuration of a WSN that balances its energy consumption and its functionality. In this article, we propose to formulate the sensor deployment task as a combinatorial optimization problem and introduce an effective sensor deployment paradigm in which both the randomness and the dynamics of the environment are captured. Following the activity scheduling mechanism, we adopt a powerful non-associative reinforcement learning method, conjugate learning automata (CLA), to learn the optimal sensor deployment strategy. Compared to conventional methods, the proposed CLAbased sensor deployment method yields good performance by activating only a subset of all sensors and does not lean on prior expertise about environments. Meanwhile, the learning process is efficient, and thus energy is saved in multiple aspects. Comprehensive experiments under different settings demonstrate the effectiveness of the proposed method.

中文翻译：

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无线传感器网络的传感器部署：基于自动机的共轭学习节能方法

无线传感器网络（WSN）具有监视环境和灵活性的能力，从而促进了物联网的发展。期望研究一种平衡其能耗和功能的WSN的有效配置。在本文中，我们建议将传感器部署任务表述为组合优化问题，并介绍一种有效的传感器部署范式，其中可以捕获环境的随机性和动态性。遵循活动调度机制，我们采用功能强大的非关联强化学习方法共轭学习自动机（CLA），以学习最佳的传感器部署策略。与传统方法相比，提出的基于CLA的传感器部署方法通过仅激活所有传感器的一个子集而产生良好的性能，并且不依赖于有关环境的现有专业知识。同时，学习过程是有效的，因此在多个方面节省了能量。在不同设置下的综合实验证明了该方法的有效性。