# On Hybrid Energy Utilization in Wireless Sensor Networks
## 1 Introduction
The first policy considers a full-information model to find the optimal greedy policy based on dynamic control theory. The second policy considers the partial-information model which gets information when the event occurs only at an active mode of the sensor. With an efficient heuristic clustering policy, the second policy uses partially observable MDP to obtain the optimal solution. On the other hand, hybrid energy utilization techniques were adopted in [16; 17; 18] to guarantee the reliability of the WSN connection. A dynamic programming method was designed in [16] to find the optimal and suboptimal solutions for point-to-point communications with hybrid energy. A new class of hybrid networks is investigated in [17] to evaluate the performance of hybrid energy utilization. This class evaluates the hybrid energy performance based on the outage duration and the transmission duration criteria, where EH, conventional nodes, and channel fading statistics affect the aforementioned criteria. Under another perspective of hybrid energy, two cluster head technologies are proposed [18], which are powered by solar energy and wireless charging to provide high power density without causing any risk to health. To maximize hybrid energy utilization, a polynomial-time scheduling algorithm and a linear-time algorithm are considered to make a trade-off between solar energy and wireless charging for the cluster head. In [19; 20; 21; 22], Quality of Service (QoS) research issues were investigated that focused on different objectives. In order to determine the optimal location of the energy-harvesting sensor nodes (EHSNs) and the battery-powered sensor nodes (BPSNs), an effective location selection protocol was proposed in [19]. Moreover, the proposed routing protocol was studied for different QoS requirements on maximizing the average end-to-end path reliability, cost of communication paths for both EHSNs and BPSNs, and energy consumption for applications running on hybrid WSNs. In [20], the distortion minimization problem for EH in WSN was investigated to optimize the harvested energy utilization. To solve this problem, the MSE of distortion minimization was formulated using a low-complexity sleep-wake scheduling and power control algorithm. In addition, both limited orthogonal channels and the prior knowledge of the channel power gain were proposed for each sensor node. Additionally, a discrete-time Markov chain was applied in [21] to analyze the average packet dropping probability (PDP) under two specific automatic repeat retransmission protocols without optimizing the transmission policy. On the other side, the trade-off between the energy consumption and the packet error probability were investigated in [22] for body sensor networks (BSNs). In order to maximize the quality of coverage for BSNs, an MDP is formulated by using EH, taking into account both the number of dead slots and the average number of not-reported messages. To the best of our knowledge, there have been no works considering the tradeoff between the minimization of the constant energy source utilization and the PDP with the usage of a hybrid energy source through the MDP method in WSN. In this article, hybrid energy utilization consisting of a constant energy source and solar harvested energy is achieved by considering an MDP design framework. Due to the unpredictability of solar harvested energy, a constant energy source is used to ensure reliable data transmissions. In fact, more constant energy source consumption is required for reducing the PDP if the solar harvested energy is insufficient. Our aim is to find the transmission policy that optimizes the trade-off between the minimization of constant energy source consumption and PDP. The rest of this paper is organized as follows. Problem formulation and Markov decision process are clarified in Section 2, including a case study. Simulation results are presented in Section 3 via computer simulations. Finally, conclusions are drawn in Section 4.