# Centralized Energy Prediction in Wireless Sensor Networks
## 2 Related Work  
In this section, we analyze the literature about energy consumption prediction in WSN using Markov chain models. We emphasize the analysis of works studying energy consumption prediction on SDN-based networks. Main results presented by the works implementing Markov chain for energy consumption prediction in WSN have shown a reduction of the traffic of packets containing energy information and the energy consumption itself [17; 22; 23]. Moreover, the energy models used in these works did not include important elements that affect the accuracy, e.g., the sensing module or the processing module energy consumption [24]. More accurate models provide more accurate and precise network lifetime estimation but require more memory and processing resources. Thus, the implementation of accurate energy models in resource-constrained devices is a current challenge. Recent works propose different strategies to improve energy efficiency and network performance based on the prediction of energy-wise information. In [25], authors propose to predict link quality metric based on an energy density factor using a fuzzy interference system. Then, in [26] authors propose a black widow optimization based algorithm to predict sinks location in mobile WSNs. Their results showed an improvement in energy efficiency; moreover, these works did not address the problem of centralization of energy information and its impact on the network performance. In traditional WSN networks, it is common that individual nodes run the energy consumption prediction algorithm and inform the result to a central node. The SDN paradigm has open the opportunity to migrate processes from individual nodes to the SDN controller, such as communication channel assignment [27], traffic analysis [28], intrusion detection [29; 30], or energy consumption prediction. In a previous work [20], we implemented a centralized energy consumption prediction for SDWSNs. Similar approaches were implemented by [PERSON] et al. [18] and by [PERSON] et al. [19]. In [18; 20], the SDN controller receives energy consumption information from the nodes in the network; the main difference is that in [18] the nodes do the prediction and send an energy consumption rate to the controller, while in [20] the nodes send to the controller the information required to do the prediction using Markov chain. Unlike the works in [18; 20], in [19] the WSN nodes communicate the energy information to a specific sink instead of to the controller. Moreover, they propose a centralized detection as well. From the results in [18; 20; 19], we know that the energy prediction strategy can reduce the average energy consumption of the nodes and delay of the packets containing energy information. However, these works do not investigate the impact of their proposals in performance metrics such as control and data packets overhead, delivery rate, and delay. Moreover, the energy models used are not \"complete\". According to the works in [31; 32], processing and sensing processes represent around 15% of the total consumption. Furthermore, the authors of [24] showed that the consumption by states transitions could have a significant part in the final result. Thus, accurate energy consumption models should consider processing, sensing, communication and transitions energy consumption. From previous works, we noticed that (i) energy prediction proposals for WSN using Markov chain are based on energy models that omit the processing and/or sensing energy consumption, (ii) the energy consumption prediction proposals have been tested through simulations but there is a lack of experiments in real devices, and (iii) the centralized prediction works based on Markov chain have shown promising results, but there are not enough results to measure the impact of its implementation in the network performance.