# Adapting Probabilistic Flooding in Energy Harvesting Wireless Sensor Networks
## 2 Past Related Work
The main objective is to maximize the network's lifetime while meeting the application's requirement regarding time delay. Routing plays a crucial role in wireless sensor network environments, since finding the optimal route among the nodes saves valuable network resources and prolongs the network's lifetime. In the presence of energy harvesting, several routing algorithms have been proposed. In [26], two different routing protocols are proposed that combine geographic routing along with energy-aware routing techniques taking into account the realistic lossy wireless channel condition and the renewal capability of environmental energy supply when making routing decisions. Authors in [27] analyze and compare existing routing algorithms and propose a modified version of the Randomized Minimum Path Recovery Time algorithm [28] that outperforms the evaluated algorithms in scenarios where little energy is harvested from the environment. In [29], data collection for traffic monitoring/surveillance purposes for busy highways is considered. The nondeterministic polynomial time (NP) hardness of the problem is proved and a centralized and a distributed algorithm are proposed which demonstrating their efficiency. Data collection is also studied in [30] for energy harvesting sensor networks and it is shown that, during the process of forwarding, each additional link increases the overall probability of losing the sent message. Two data collection protocols are proposed and it is shown to achieve high delivery ratios. In [31], a cross-layer optimized multipath routing algorithm is proposed where a forwarding node chooses the next-hop node based on 2-hop neighbor information rather than 1-hop. A sleep scheduling scheme is also proposed that aims to maximize the network's lifetime. In [32], an energy-aware opportunistic routing protocol for large-scale energy harvesting wireless sensor networks is proposed, where each node chooses the next-hop neighbor considering the available energy, the distance from the sink node and the amount of data to be transmitted, whereas, in [33], novel solutions are introduced for bounding sink-to-node communication delay in energy-harvesting sensor networks. These solutions are evaluated in a testbed and it is shown that energy consumption is improved compared with the proposed protocol in [34]. Probabilistic flooding has been proposed as a suitable alternative to blind flooding [35] in order to disseminate information in a network and reduce unnecessary transmissions, saving valuable network resources. In [36], probabilistic flooding performance is compared against an adaptive flooding approach that changes the (otherwise fixed) forwarding probability. In [37], suitable values for the threshold probability considering various topologies are derived and, in [38], asymptotic expressions for the case of random graphs are also derived. In [4], probabilistic flooding is studied using algebraic graph theory elements and an algorithm is introduced to estimate the threshold probability. Probabilistic flooding in generalized random graphs is studied in [39], while the case of peer-to-peer is studied in [40, 41]. Gossip-based techniques are also used in wireless sensor network environments (e.g., [42]). Authors in [43] propose an opportunistic flooding algorithm, where each node chooses some of its neighbors to broadcast the information message and, in [44], they study gossip and epidemic algorithms for information broadcasting in geometric random graphs. In the area of mobile ad hoc networks in [45], a probabilistic broadcast for flooding is proposed, whereas, in [46], a balanced probabilistic flooding algorithm for wireless sensor networks is also proposed. A link correlation based probabilistic flooding algorithm in proposed in [47] and a variation of probabilistic flooding for reliable dissemination is proposed in [48]. Authors in [49] combine various flooding schemes, e.g., adaptive probability, in order to ensure reduced overhead while they compare their method against traditional and probabilistic flooding.