Mobile computing provides storage and computation resources of proximal devices to satisfy the real-time and low-energy communication demands of the Internet of Things (IoT). However, in the areas without terrestrial base station infrastructures, the IoT sensors have trouble implementing reliable and stable connections, which results in the difficulties of data gathering and data caching. In this paper, we consider a space-air-ground integrated mobile edge caching IoT system composed of satellite and unmanned aerial vehicles (UAVs), where LEO satellite broadcasts data, and UAVs collect the data from decentralized ground sensors. Since the sensors’ low-power property leads data loss, fault-tolerant codes are employed for availability protection. We first derive the exact expressions of system availability and communication cost for data repair and collection. Then, to address the problems of the lower availability, we exploit an intelligent optimization to determine the erasure coding parameters. Lastly, we further optimize the system parameters, i.e., communication ranges and unit power costs of UAV and decentralized sensors, to minimize the total communication cost. Simulation results show that, compared to MDS codes and regenerating codes, adaptive minimum storage regenerating (AMSR) codes with optimized parameters can significantly reduce total communication cost and maintain availability of the system.

中文翻译：

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具有容错码的卫星-无人机移动边缘缓存系统可用性和通信成本的智能优化

移动计算提供近端设备的存储和计算资源，以满足物联网（IoT）的实时、低能耗通信需求。然而，在没有地面基站基础设施的地区，物联网传感器难以实现可靠稳定的连接，导致数据采集和数据缓存困难。在本文中，我们考虑了一个由卫星和无人机 (UAV) 组成的天地一体化移动边缘缓存物联网系统，其中 LEO 卫星广播数据，无人机从分散的地面传感器收集数据。由于传感器的低功耗特性会导致数据丢失，因此采用容错代码来保护可用性。我们首先推导出系统可用性和数据修复和收集通信成本的精确表达式。然后，为了解决可用性较低的问题，我们利用智能优化来确定擦除编码参数。最后，我们进一步优化了系统参数，即无人机和分散式传感器的通信范围和单位功率成本，以最小化总通信成本。仿真结果表明，与MDS码和再生码相比，优化参数的自适应最小存储再生（AMSR）码可以显着降低总通信成本并保持系统的可用性。我们进一步优化了系统参数，即无人机和分散式传感器的通信范围和单位功率成本，以最小化总通信成本。仿真结果表明，与MDS码和再生码相比，优化参数的自适应最小存储再生（AMSR）码可以显着降低总通信成本并保持系统的可用性。我们进一步优化了系统参数，即无人机和分散式传感器的通信范围和单位功率成本，以最小化总通信成本。仿真结果表明，与MDS码和再生码相比，优化参数的自适应最小存储再生（AMSR）码可以显着降低总通信成本并保持系统的可用性。