# Adapting Probabilistic Flooding in Energy Harvesting Wireless Sensor Networks
## 2 Past Related Work  
The energy collection from the ambient environment has been extensively studied in the literature and is proposed as a suitable method for prolonging the systems' lifetime. For example, in a system that is impossible to replace [7; 8] or recharge [9] its batteries using conventional approaches (e.g., placed in a hostile environment), the energy collection from the ambient environment may be the only method to prolong their lifetime. A significant advantage of the ambient energy resources is that they can be free and, many times, even inexhaustible, leading to a significant reduction of maintenance costs. However, the stochastic behavior of ambient energy harvesting results in the collection of different amounts of energy over time. In order to alleviate this problem, energy prediction methods have been proposed in the literature. Authors in [10] present algorithms for determining energy consumption in systems with predictable and stochastic energy inputs and those in [11] propose a novel energy prediction model for multi-source energy harvesting wireless sensor networks, for providing accurate estimations about energy availability. Markov processes have also been proposed in order to model energy harvesting from ambient energy resources. In [12], a model is provided considering micro-solar power sources, whereas, in [13], a Markov-model-based method is proposed that sufficiently captures the energy level of body sensors [14] and predicts the probability of energy depletion. In order to prolong the network's lifetime, authors in [15] present an adaptive duty cycling algorithm with nodes to autonomously adjust their operation cycle according to the energy availability in the environment. In [16], an adaptive duty cycling technique for wireless sensor network nodes is presented, whereas, in [17], a method regarding an oscillating energy harvesting system is presented. Authors in [18] present an introduction to Harvesting Aware Power Management, including topologies of Energy Harvesting Systems, the Energy Neutrality Principle and Power Management Techniques. The energy harvesting techniques have also been proposed in the area of Wireless NanoSensor Networks [19], which requires the use of nanoscale energy harvesting systems. In [20], an energy and spectrum-aware Media Access Control (MAC) protocol in the Terahertz Band is presented, using a scheduling algorithm which allows a large number of nanosensors to transmit their packets without inducing collisions. In the area of wireless energy transfer, authors in [21] propose the concept of energy cooperation where the network nodes exchange their remaining energy wirelessly improving the overall system performance, despite the loss incurred in energy transfer. In [22], a self-powered wireless system with one transmitter and one receiver is considered, in which the transmitter has no fixed power supplies and extracts energy only via wireless energy harvesting from ambient radio signals. The transmitter spends a fraction of time collecting energy and the remaining fraction of time is used for data transmission. The experimental results characterize how the maximal achievable throughput varies with energy harvesting rate. In [23], a general model is presented with non-ideal energy storage efficiency and transmit circuit power and a proposed protocol is considered, in which a fraction of time is devoted to energy harvesting and the remaining fraction is used for data transmission. The system outage performance is optimized by finding the optimal ratio between the time spent on energy harvesting and data transmission. Authors in [24] propose an adaptive energy harvesting management framework, which exploits an application's tolerance to quality degradation based on energy harvesting conditions, whereas, in [25], a novel task allocation scheme for solar-powered sensor nodes that operates in two phases of task scheduling and task mapping is proposed.