# Adapting Probabilistic Flooding in Energy Harvesting Wireless Sensor Networks
## 1 Introduction  
Advantages in wireless communications and embedded hardware design, have enabled the extensive development of wireless sensor networks [1]. Wireless sensor nodes are of small size, low cost and are (usually) battery operating devices able to sense and transmit environmental information. Therefore, such devices are extensively used in various environments like underwater, vehicular and agricultural where conventional wired approaches are hard or inconceivable to be implemented. However, the resources required for the operation of the wireless nodes (e.g., energy supply) may be absent, resulting in network degradation [2]. The last decade's technological advantages have resulted in the development of high performance, low-powered embedded devices being able to execute complex tasks. Furthermore, small-scale energy harvesting devices (e.g., solar panels, wind turbines, special antennas) may be attached to the power source and prolong their lifetime [3], allowing for the deployment of wireless sensor networks in a wide variety of environments regardless of the power infrastructure. Prolonging the network's lifetime using energy harvesting is essential in the sense of reducing sustainability costs. However, employing energy harvesting technologies in wireless environments results in increased system complexity. When the nodes rely only on the energy stored in batteries, they can efficiently adapt the power consumption according to the remaining energy. However,when solar panels or wind turbines can replenish the stored energy, the adaption of power consumption is a far more complicated task due to the probabilistic nature of the energy harvesting process. Even if the solar radiation is sufficient to recharge nodes' batteries at certain periods of a day, some nodes may be unable to harvest solar energy (e.g., placed in a shady location) and run out of energy. However, these nodes may replenish their batteries at a later time and become operational. Thus, conventional protocols and mechanisms being widely used in wireless sensor networks need to be revisited under the perspective of energy harvesting. Information dissemination plays a crucial role in modern network environments, being an underlying mechanism for various vital processes. This work aims to shed further light on information dissemination and particularly to adapt this mechanism in energy harvesting environments. The stochastic nature of the energy collection process influences the operational state of the network's nodes. Thus, nodes may be operational or non-operational according to their battery level. Therefore, when the energy collecting from the ambient environment is sufficient to recharge the batteries, nodes are expected to be in an operational state; otherwise, the nodes may run out of energy and become non-operational. Consequently, when the nodes belonging to the bottleneck links of the network are not operational, any conventional information dissemination mechanism is unable to proceed and reach all the operational nodes. Considering that the energy collection process may recharge these nodes at a later time, this motivates the revisiting the information dissemination mechanism and in particular _Probabilistic Flooding_ (PF) [4]. In this work, we present a variant of the Probabilistic Flooding mechanism. i.e., _Robust Probabilistic Flooding_ (RPF), capable of dealing with the nodes' operational states and a Markov chain that captures the behavior of the energy harvesting environments. The proposed Markov chain is further analyzed and simplified in order to derive analytical expressions regarding the probability of a node to be in an operational or non-operational state and a charging or discharging state. An earlier version of this work has been presented in [5].