# On Hybrid Energy Utilization in Wireless Sensor Networks
## 1 Introduction  
Notably, many studies are now focusing on green communications for wireless sensor networks (WSNs). WSNs usually rely on a fixed battery to supply energy for data transmissions, and network lifetime is mainly dominated by the battery capacity. Several techniques are investigated to maximize and prolong the lifetime of battery-powered nodes [1; 2; 3] such as design new routing path, a mathematical model, and a novel algorithm. However, the proposed techniques may be inconvenient and costly, and may produce impractical results. Consequently, the lifetime of a battery-powered node is still limited. Therefore, energy harvesting (EH) has recently attracted significant attention in many applications such as military usage, air defense, civilian technology for the environment, and visual sensor networks used for surveillance cameras [4; 5; 6]. Each application has different implementation considerations, such as the environment, the application's design objectives, cost, hardware, and system constraints. For a general introduction to WSNs, we refer the readers to the survey literature in [7]. Another aspect in applications is that the required signal is detected then transmitted through WSNs using limited energy resources. Further, resolving energy supply problems is very significant in wireless networks using hybrid energy to provide a perpetual and sufficient energy supply. To date, there have been many studies focusing on circuit, hardware, and manufacturing for intelligent solar energy systems [8] to enlarge the strength of energy harvesting (EH) utilization. Additionally, various policies and models have been considered to increase the efficiency of EHutilization in WSNs [9; 10; 11; 12]. In [9], a minimum-increment matched marginal method was proposed to maximize solar harvested energy utilization. In [10], the authors attempted to maximize harvested energy utilization by using an expectation-maximization algorithm in parallel with a Markov decision process (MDP). In addition, the energy prediction model for multi-source EH was presented in [11] to expect future energy availability based on the past energy observations. Additionally, the prediction model used real-life solar and wind traces to improve the performance of the prediction accuracy. In [12], a novel policy for energy-efficient transmission in harvesting sensors was adopted to expect the sum of the utility values only for transmitted important messages. This policy used threshold function with MDP and depends on the battery level. Based on numerical experiments, the stochastic approximation algorithm was proposed to find the optimal policy that is more efficient and has a faster convergence speed than the Q-learning algorithm. Various energy management methods were studied in [13; 14; 15] by considering different objectives such as the estimation and prediction of EH based on a full-information or partial-information knowledge of EH. In [13], a switching algorithm for rechargeable tree-based WSN was proposed using MDP. In particular, the proposed algorithm alternatively switches from one sensor node to another based on solar energy levels to guarantee the network sustainability and energy efficiency for sensor nodes. Moreover, a real-time sensor traffic pattern was used to analyze the energy consumption of sensor nodes. In [14], the source estimation for EH of sensor nodes was studied for both deterministic and stochastic modes. For deterministic mode, the amount of EH is known prior to transmission for the deterministic model. Besides, the optimal offline power allocation was considered to minimize the average mean-square-error (MSE) over the estimation periods. For stochastic mode, the online power allocation with Lyapunov optimization was considered to minimize the MSE over the estimation periods, where only the current amount of EH is known. In [15], event capture in rechargeable sensor networks was investigated for two policies.