# Performance Analysis of Latency-Aware Data Management in Industrial IoT Networks +
## 2 Literature Review  
A note on distributed data management: Traditionally, industrial application systems tend to be entirely centralized. For this reason, distributed data management has not been studied extensively in the past, and the emphasis has been put on the efficient computer communication within the industrial environment. The reader can find state-of-the-art approaches on relevant typical networks in [5; 15; 16]. In [17], we defined the concept of a data management layer separate from routing functions for industrial wireless networks. The DML considered in the current paper is similar to the one defined in [17], although here, we make it more practical. More specifically, in [17], we focused on a graph-theoretic approach, which might sometimes be unrealistic when real industrial implementations with technological constraints are at hand. Furthermore, in [17], we did not provide an extensive experimental evaluation of our methods, as is done in the current paper, but we verified the methods solely via simulations. Even more importantly, in this paper, we provide (i) a real implementation of the DML, (ii) experimental results on its performance, (iii) a validation of a DML simulation model and (iv) a large-scale performance evaluation of the DML using the validated simulation model. Related works: We now provide some additional interesting related works. In [18], although the authors considered delay and real-time aspects, the main optimization objectives were the energy efficiency and reliability. They presented a centralized routing method, and consequently, they did not use proxies. Furthermore, the model of this paper assumes that the network is operating under different protocols (e.g., 802.11). In [19], the authors addressed a different optimization objective, focusing on minimizing the maximum hop distance, rather than guaranteeing it as a hard constraint. Furthermore, they assumed a bounded number of proxies, and they examined only on the worst-case number of hops. Finally, the presented approach was somewhat graph-theoretic, which made it hard to apply to real industrial IoT networks. In [20], the authors, given the operational parameters required by the industrial applications, provided several algorithmic functions that locally reconfigured the data distribution paths, when a communication link or a network node failed. They avoided continuously recomputing the configuration centrally, by designing an energy-efficient local and distributed path reconfiguration method. However, due to the locality of the computations and the complete absence of a central coordination, this method may result in violating the latency requirements. In [21], the authors presented a cross-layer approach, which combined MAC-layer and cache management techniques for adaptive cache invalidation, cache replacement and cache prefetching. Again, the model is different, as we assume a completely industrially-oriented MAC layer, based on IEEE802.15.4e, and a different problem, focusing on the delay aspects, instead of cache management. In [22], the authors considered a different problem than ours: replacement of locally-cached data items with new ones. As the authors claimed, the significance of this functionality stemmed from the fact that data queried in real applications were not random, but instead exhibited locality characteristics. Therefore, the design of efficient replacement policies, given an underlying caching mechanism, was addressed. In [23], although the authors considered delay aspects and a realistic industrial IoT model (based on WirelessHART), their main objective was to bound the worst-case delay in the network. Furthermore, they did not exploit the potential presence of proxy nodes, and consequently, they stuck to the traditional, centralized industrial IoT setting. In [24], the authors considered a multi-hop network organized into clusters and provided a routing algorithm and cluster partitioning.