We present our Zero Energy Network'' (ZEN) protocol stack for energy harvesting wireless sensor networks applications. The novelty in our work is @math fold: (1) Energy harvesting aware fully featured MAC layer. Carrier sensing, Backoff algorithms, ARQ, RTS CTS mechanisms, Adaptive Duty Cycling are either auto configurable or available as tunable parameters to match the available energy (b) Energy harvesting aware Routing Protocol. The multi-hop network establishes routes to the base station using a modified version of AODVjr routing protocol assisted by energy predictions. (c) Application of a time series called Holt-Winters'' for predicting the incoming energy. (d) A distributed smart application running over the ZEN stack which utilizes a multi parameter optimized perturbation technique to optimally use the available energy. The application is capable of programming the ZEN stack in an energy efficient manner. The energy harvested distributed smart application runs on a realistic solar energy trace with a three year seasonality database. We implement a smart application, capable of modifying itself to suit its own as well as the network's energy level. Our analytical results show a close match with the measurements conducted over EHWSN testbed.
Several optimization techniques have been discussed in literature for battery driven multihop WSN's in the past where the objective is to maximize the network lifetime by minimizing energy consumption @cite_21 , @cite_7 . With a view on utilizing the harvested energy in an optimized manner, adapting the performance of an application while respecting the limited and time-varying amount of available power in EHWSN is discussed in @cite_23 . A formal model that actively adapts application parameters such as rate of sensing'' is used to optimize the performance. The simulation study does not consider network related parameters or network wide energy levels. A Lazy Scheduling Algorithm'' is proposed and tested for its effectiveness by assigning several power values to the system in @cite_11 . Task admittance and future energy prediction is carried out with energy variability characterization curves generated by modeling the power source.
@cite_21 @cite_23 @cite_7 @cite_11
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Recently, there has been a substantial interest in the design of systems that receive their energy from regenerative sources such as solar cells. In contrast to approaches that minimize the power consumption subject to performance constraints, we are concerned with optimizing the performance of an application while respecting the limited and time-varying amount of available power. In this paper, we address power management of, e.g., wireless sensor nodes which receive their energy from solar cells. Based on a prediction of the future available energy, we adapt parameters of the application in order to maximize the utility in a long-term perspective. The paper presents a formal model of the corresponding optimization problem including constraints concerning buffer sizes, timing, and rates. Instead of solving the optimization problem online which may be prohibitively complex in terms of running time and energy consumption, we apply multiparametric programming to precompute the application parameters offline for different environmental conditions and system states. In order to guarantee sustainable operation, we propose a hierarchical software design which comprises a worst-case prediction of the incoming energy. As a further contribution, we suggest a new method for approximate multiparametric linear programming which substantially lowers the computational demand and memory requirement of the embedded software. Our approaches are evaluated using long-term measurements of solar energy in an outdoor environment. Energy harvesting wireless sensor network (EHWSNs) are being actively studied in order to solve the problems faced by battery-operated WSNs, namely the cost for battery replacement and impacts on the environment. In EH-WSNs, each node harvests ambient energy, such as light, heat, vibration, and uses it for sensing, computations, and wireless communications, where the amount of harvested energy of each node varies depending on their surrounding environments. MAC protocols for EH-WSNs need to be designed to achieve high throughput and fairness, however, the conventional MAC protocols proposed for EH-WSNs do not adapt to the harvesting rate of each node, resulting in poor fairness. In this paper, we propose a fair MAC protocol based on polling scheme for EH-WSNs. The proposed scheme adjusts contention probability of each node according to the individual harvesting rate, thereby increasing throughput achieved by nodes with low harvesting rate. We evaluate throughput and fairness of the proposed fair polling scheme by computer simulations, and show that the proposed scheme can improve fairness with little degradation of the overall network throughput. Traditional wireless sensor networks (WSNs) face the problem of a limited-energy source, typically batteries, resulting in the need for careful and effective utilization of the energy source. However, inevitable energy depletion will eventually disturb the operation of a WSN. Energy harvesting (EH) technology is acquiring particular interest, because it has the potential to provide a continuous energy supply in battery-powered WSNs. Solar energy is the most effective environmental energy for EH-WSNs because of its high energy intensity, which comes from a non-controllable source. Therefore, the prediction of future energy availability is a critical issue, as the amount of the harvestable energy may vary over time. In this paper, a novel solar energy prediction algorithm with Q-learning, called Q-learning-based solar energy prediction (QL-SEP), is proposed. Q-learning is an effective way of predicting future actions based on past observations. The distinctive feature of QL-SEP is that not only past days’ observations but also the current weather conditions are considered for prediction. The performance of QL-SEP is simulated in this paper using real-world measurements obtained from a solar panel in comparison with the state-of-art approaches. This paper investigates the optimal resource allocation in wireless powered communication network with user cooperation, where two single-antenna users first harvest energy from the signals transmitted by a multi-antenna hybrid access point (H-AP) and then cooperatively send information to the H-AP using their harvested energy. To explore the system information transmission performance limit, an optimization problem is formulated to maximize the weighted sum-rate (WSR) by jointly optimizing energy beamforming vector, time assignment, and power allocation. Besides, another optimization problem is also formulated to minimize the total transmission time for given amount of data required to be transmitted at the two sources. Because both problems are non-convex, we first transform them to be convex by using proper variable substitutions and then apply semi-definite relaxation to solve them. We theoretically prove that our proposed methods guarantee the global optimum of both problems. Simulation results show that system WSR and transmission time can be significantly enhanced by using energy beamforming and user cooperation. It is observed that when the total amount of information of two users is fixed, with the increase of the information amount of the user relatively farther away from the H-AP, the transmission time of the user cooperation scheme decreases while that of the direct transmission increases. Besides, the effects of user position on the system performances are also discussed, which provides some useful insights.