Internet of Things integrates various technologies, including wireless sensor networks, edge computing, and cloud computing, to support a wide range of applications such as environmental monitoring and disaster surveillance. In these types of applications, IoT devices operate using limited resources in terms of battery, communication bandwidth, processing, and memory capacities. In this context, load balancing, fault tolerance, and energy and memory efficiency are among the most important issues related to data dissemination in IoT networks. In order to successfully cope with the abovementioned issues, two main approaches—data-centric storage and distributed data storage—have been proposed in the literature. Both approaches suffer from data loss due to memory and/or energy depletion in the storage nodes. Even though several techniques have been proposed so far to overcome the abovementioned problems, the proposed solutions typically focus on one issue at a time. In this article, we propose a cross-layer optimization approach to increase memory and energy efficiency as well as support load balancing. The optimization problem is a mixed-integer nonlinear programming problem, and we solve it using a genetic algorithm. Moreover, we integrate the data-centric storage features into distributed data storage mechanisms and present a novel heuristic approach, denoted as Collaborative Memory and Energy Management, to solve the underlying optimization problem. We also propose analytical and simulation frameworks for performance evaluation. Our results show that the proposed method outperforms the existing approaches in various IoT scenarios.

中文翻译：

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一种支持物联网网络的节能和内存高效数据丢失预防新方法

物联网融合了无线传感器网络、边缘计算、云计算等多种技术，支持环境监测、灾害监测等广泛应用。在这些类型的应用中，物联网设备在电池、通信带宽、处理和内存容量方面使用有限的资源运行。在这种情况下，负载平衡、容错以及能源和内存效率是与物联网网络中数据传播相关的最重要问题。为了成功应对上述问题，文献中提出了两种主要方法——以数据为中心的存储和分布式数据存储。由于存储节点中的内存和/或能量耗尽，这两种方法都会导致数据丢失。尽管迄今为止已经提出了几种技术来克服上述问题，但所提出的解决方案通常一次只关注一个问题。在本文中，我们提出了一种跨层优化方法来提高内存和能源效率以及支持负载平衡。优化问题是一个混合整数非线性规划问题，我们使用遗传算法来解决它。此外，我们将以数据为中心的存储功能集成到分布式数据存储机制中，并提出了一种新颖的启发式方法，称为协作内存和能量管理，以解决底层优化问题。我们还提出了用于性能评估的分析和模拟框架。我们的结果表明，所提出的方法在各种物联网场景中优于现有方法。