In the context of IoT deployments, a multitude of devices concurrently require network access to transmit data over a shared communication channel. Employing symmetric strategies can effectively facilitate the collaborative use of the communication medium among these devices. By adopting such strategies, devices collectively optimize their transmission parameters, resulting in minimized collisions and enhanced overall network throughput. Our primary focus centers on the formulation of symmetric (i.e., identical) strategies for the sensors, aiming to optimize a finite horizon team objective. The imposition of symmetric strategies introduces novel facets and complexities into the team problem. To address this, we embrace the common information approach and adapt it to accommodate the use of symmetric strategies. This adaptation yields a dynamic programming framework grounded in common information, wherein each step entails the minimization of a single function mapping from an agent's private information space to the space of probability distributions over possible actions. Our proposed policy/method incurs a reduced cumulative cost compared to other methods employing symmetric strategies, a point substantiated by our simulation results.

翻译：在物联网部署场景中，大量设备需同时接入网络，通过共享通信信道传输数据。采用对称策略可有效促进这些设备间通信介质的协作使用。通过此类策略，设备可协同优化其传输参数，从而最小化冲突并提升整体网络吞吐量。本文重点研究传感器对称（即同质）策略的制定方法，旨在优化有限时域下的团队目标。对称策略的引入为团队问题带来了新的维度和复杂性。为应对这一挑战，我们采用公共信息方法并加以改造，使其适配对称策略的应用。这一改造形成了基于公共信息的动态规划框架，其中每一步骤均需最小化从智能体私有信息空间到可能动作概率分布空间的单一映射函数。与采用对称策略的其他方法相比，本文提出的策略/方法能降低累积成本，仿真结果验证了该结论。