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.
In recent times, a large body of work is being concentrated on Energy Harvesting Wireless Sensor Nodes (EHWSN). Several energy harvesting sources like solar, thermo, acoustic, wind, RF and wave for driving low power embedded devices are discussed in @cite_16 . Many energy harvesting WSNs have been implemented in past like Trio @cite_32 , AmbiMax @cite_3 , Prometheus @cite_6 . However, literature on Energy Harvesting systems is mostly limited to single node networks or extensive simulation study. Many of the work focus on the system building, efficiency and viability of energy harvesting mechanisms. In @cite_6 authors shows improvement in lifetime where a supercapacitor and battery are charged together from a solar panel by adjusting their duty cycles. Recently Power management and data rate maximization in EHWSN was discussed in @cite_24 . The authors analytically find the required amount of energy to be harvested to ensure energy neutral operation in each slot.
@cite_32 @cite_3 @cite_6 @cite_24 @cite_16
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AmbiMax is an energy harvesting circuit and a supercapacitor based energy storage system for wireless sensor nodes (WSN). Previous WSNs attempt to harvest energy from various sources, and some also use supercapacitors instead of batteries to address the battery aging problem. However, they either waste much available energy due to impedance mismatch, or they require active digital control that incurs overhead, or they work with only one specific type of source. AmbiMax addresses these problems by first performing maximum power point tracking (MPPT) autonomously, and then charges supercapacitors at maximum efficiency. Furthermore, AmbiMax is modular and enables composition of multiple energy harvesting sources including solar, wind, thermal, and vibration, each with a different optimal size. Experimental results on a real WSN platform, Eco, show that AmbiMax successfully manages multiple power sources simultaneously and autonomously at several times the efficiency of the current state-of-the-art for WSNs In this paper, we present a software control method that maximizes the sensing rate of wireless sensor networks (WSN) that are solely powered by ambient RF power. Different from all other energy harvesting WSN systems, RF powered systems present a new challenge for the energy management. A WSN node repeatedly charges and discharges at short intervals depending on the energy intake. A capacitor is used for an energy storage in the energy harvesting system because of its efficient charge and discharge performance and infinite recharge cycles. When this charging time is too short, the node is more likely to experience an energy shortage. On the contrary, if it is too long, more energy is lost because of the leakage in the capacitor. Therefore, we implemented an adaptive duty cycle control scheme that is optimized for RF energy harvesting. This method maximizes the sensing rate considering the leakage problem, a factor that has never previously been studied in this context. Our control scheme improves efficiency by aggregate evaluation of the operation reliability and leakage reduction. Harvesting energy from the environment is feasible in many applications to ameliorate the energy limitations in sensor networks. In this paper, we present an adaptive duty cycling algorithm that allows energy harvesting sensor nodes to autonomously adjust their duty cycle according to the energy availability in the environment. The algorithm has three objectives, namely (a) achieving energy neutral operation, i.e., energy consumption should not be more than the energy provided by the environment, (b) maximizing the system performance based on an application utility model subject to the above energy-neutrality constraint, and (c) adapting to the dynamics of the energy source at run-time. We present a model that enables harvesting sensor nodes to predict future energy opportunities based on historical data. We also derive an upper bound on the maximum achievable performance assuming perfect knowledge about the future behavior of the energy source. Our methods are evaluated using data gathered from a prototype solar energy harvesting platform and we show that our algorithm can utilize up to 58 more environmental energy compared to the case when harvesting-aware power management is not used. In this paper, a reinforcement learning-based throughput on demand (ToD) provisioning dynamic power management method (RLTDPM) is proposed for sustaining perpetual operation and satisfying the ToD requirements for today's energy harvesting wireless sensor node (EHWSN). The RLTDPM monitors the environmental state of the EHWS and adjusts their operational duty cycle under criteria of energy neutrality to meet the demanded throughput. Outcomes of these observation-adjustment interactions are then evaluated by feedback reward that represents how well the ToD requests are met; subsequently, the observation-adjustment-evaluation process, so-called reinforcement learning, continues. After the learning process, the RLTDPM is able to autonomously adjust the duty cycle for satisfying the ToD requirement, and in doing so, sustain the perpetual operation of the EHWSN. Simulations of the proposed RLTDPM on a wireless sensor node powered by a battery and solar cell for image sensing tasks were performed. Experimental results demonstrate that the achieved demanded throughput is improved 10.7 for the most stringent ToD requirement, while the residual battery energy of the RLTDPM is improved 7.4 compared with an existing DPM algorithm for EHWSN with image sensing purpose.