## 1 Introduction  
The increasing demand of smartness in our daily appliance and novel applications for more efficient use of the resources have led to a massive boom in the deployment of sensors. Due to technological advances, sensors are getting smaller and more powerful. However, the efficient managing of the small energy resource of the sensor is still a challenging task. Therefore, there have been several energy efficient algorithms and protocols proposed and put in experiments in many research papers and practical applications [1; 2; 3; 4]. Apart from focusing on the efficient use of the limited power resource, harvesting energy from the ambient environment can be a promising solution for the aforementioned challenge. Energy harvesting cannot only extend the network lifetime in a sustainable manner, but it can also reduce the necessity of using batteries that pollute the environment due to leakage or when they are discarded [5]. While energy harvesting (EH) is not a new research topic, it has recently attracted more attention due to the massive implementation of sensors, for example in IoT applications. Imagine applications in which thousands of sensors are deployed to monitor the soil moisture in a massive farm for scheduling the watering, or in a forest for the early detection of massive fires. These massive implementations require sensors to work for a long time without a need of human intervention for maintenance. Thus, many researchers focus on overcoming research challenges in this area. Several methods of EH are developed that are powered by sunlight [6], vibration [7], heat [8], and electromagnetic waves [9; 10; 11], even from human body movement and heat [12]. However, the amount of the energy that can be harvested from these resources can be uncertain and unpredictable, which can cause difficulties in the design of different network stack protocols [13]. Similar to the effort to prolong the network lifetime in battery powered sensor networks, there are huge efforts on efficiently using the harvested power in energy harvesting sensor networks (EH-WSN), to have longer network lifetime and more reliable performance [14]. One of the ways that researchers have tried to achieve better energy efficiency in EH-WSN is to bring the clustering concept from WSN and try to remodel and adjust the idea to unique characteristics of EH-WSN. Basically, the clustering approach is a way to convert distributed decentralized networks into several small virtually centralized networks, and hence, bringing the benefits of a centralized architecture to a decentralized network [15]. In clustering, the network is divided into clusters, which consist of a cluster-head (CH), gateway nodes (GW) and member nodes (MN). CH is responsible for managing the MNs and processing the members' data, and communication between two adjacent clusters happens through GWs. In a cluster-based WSN, CHs, and GWs form the network backbone [16]. Clustering for EH-WSN can bring the similar benefits that it can bring to conventional WSN. However, considering the unique characteristic of EH-WSN such as an unpredictable amount of harvested power, clustering algorithms need to be carefully designed in a way to efficiently use the harvested power and at the same time try to deal with its uncertainty. In this paper, we take a closer look at the effect of clustering for EH-WSN. The contributions of this paper are twofold: First is to study the effect of clustering on the performance of EH-WSN and second is to propose a way to improve the performance of the clustered network considering energy harvesting sensors. Therefore, we consider a decentralized network and study the effect of adjusting transmission power in the performance of the network in both flat (non-clustered) and clustered network.