Recently, Wireless Sensor Networks (WSNs) have attracted lot of attention due to their pervasive nature and their wide deployment in Internet of Things, Cyber Physical Systems, and other emerging areas. The limited energy associated with WSNs is a major bottleneck of WSN technologies. To overcome this major limitation, the design and development of efficient and high performance energy harvesting systems for WSN environments are being explored. We present a comprehensive taxonomy of the various energy harvesting sources that can be used by WSNs. We also discuss various recently proposed energy prediction models that have the potential to maximize the energy harvested in WSNs. Finally, we identify some of the challenges that still need to be addressed to develop cost-effective, efficient, and reliable energy harvesting systems for the WSN environment.

Energy harvesting in wireless sensor networks: A comprehensive review

Presently, supercapacitors have gained an important space in energy storage modules due to their extraordinarily high power density, although they lag behind the energy density of batteries and fuel cells. This review covers recent approaches to not only increase the power density, rate capability, cyclic stability, etc. of supercapacitors, but also to increase their energy density using hybrid architectures. Electrodes are the most important component of a supercapacitor cell, and thus this review primarily deals with the design of hybrid supercapacitor electrodes offering a high specific capacitance, together with the elucidation of the mechanisms involved therein. The electrode performance significantly depends on the available surface area, porosity and conductivity of the component materials, and thus nano-structuring of the electrode is an elegant approach, which is discussed in the subsections for 0-, 1-, 2-, 3-dimensional hybrid materials, including some miscellaneous hybrids. The fabrication of different hybrid materials using metal oxides, metal sulfides, carbon materials, etc. with conducting polymers such as polyaniline and polypyrrole and their characterization are delineated from the literature data. Here, we primarily focus on the mechanism of energy storage by non-faradic electrical double-layer capacitance and faradaic pseudo-capacitance, discussing the contributions of different component mechanisms towards the total capacitance. In the hybrids, the impact of the component concentration operating via different mechanisms for charge storage on their final electrochemical performance is discussed. The specific capacitance, volumetric capacitance, charge–discharge cycles, Ragone plot, etc. of hybrid supercapacitors are described. Besides household and heavy-duty applications, the state-of-the-art future applications of supercapacitors in robotics, renewable and sustainable energy devices, wearable and self-healing supercapacitors, and biotechnology and their challenges in real-world applications with the scope of future work are elucidated.

A review on the recent advances in hybrid supercapacitors

Wireless Sensor Networks (WSNs) are crucial in supporting continuous environmental monitoring, where sensor nodes are deployed and must remain operational to collect and transfer data from the environment to a base-station. However, sensor nodes have limited energy in their primary power storage unit, and this energy may be quickly drained if the sensor node remains operational over long periods of time. Therefore, the idea of harvesting ambient energy from the immediate surroundings of the deployed sensors, to recharge the batteries and to directly power the sensor nodes, has recently been proposed. The deployment of energy harvesting in environmental field systems eliminates the dependency of sensor nodes on battery power, drastically reducing the maintenance costs required to replace batteries. In this article, we review the state-of-the-art in energy-harvesting WSNs for environmental monitoring applications, including Animal Tracking, Air Quality Monitoring, Water Quality Monitoring, and Disaster Monitoring to improve the ecosystem and human life. In addition to presenting the technologies for harvesting energy from ambient sources and the protocols that can take advantage of the harvested energy, we present challenges that must be addressed to further advance energy-harvesting-based WSNs, along with some future work directions to address these challenges.

https://dl.acm.org/doi/pdf/10.1145/3183338

Energy-Harvesting Wireless Sensor Networks (EH-WSNs): A Review

https://hal.archives-ouvertes.fr/hal-01283728/document

Energy management in Wireless Sensor Networks

Energy harvesting is one of the most promising technologies towards the goal of perpetual operation of wireless sensor networks (WSNs). Environmentally-powered systems, however, have to deal with the variable behavior of ambient energy sources, which results in different amounts and rates of energy available over time. To alleviate the problem of the harvested power being neither constant nor continuous, energy prediction methods can be employed. Such models forecast the source availability and estimate the expected energy intake, allowing the system to take critical decisions about the utilization of the available energy. In this work, we present a novel energy prediction model, named Pro-Energy (PROfile energy prediction model), for multi-source energy harvesting WSNs, which is able to leverage past energy observations to provide accurate estimations of future energy availability. To assess the performance of our proposed solution, we use real-life solar and wind traces that we collected by interfacing TelosB nodes with solar cells and wind micro-turbines, as well as public available traces of solar and wind obtained from weather monitoring stations in the US. A comparative performance evaluation between Pro-Energy and energy predictors previously proposed in the literature, such as EWMA and WCMA, has shown that our solution significantly outperforms existing algorithms for both short and medium term prediction horizons, improving the prediction accuracy up to 60%.

http://senseslab.di.uniroma1.it/administrator/components/com_jresearch/files/publications/Pro_Energy_a_novel_energy_pred.pdf

Pro-Energy: A novel energy prediction model for solar and wind energy-harvesting wireless sensor networks

Solar panels are frequently used in wireless sensor nodes because they can theoretically provide quite a bit of harvested energy. However, they are not a reliable, consistent source of energy because of the Sun's cycles and the everchanging weather conditions. Thus, in this paper we present a fast, efficient and reliable solar prediction algorithm, namely, Weather-Conditioned Moving Average (WCMA) that is capable of exploiting the solar energy more efficiently than state-of-the-art energy prediction algorithms (e.g. Exponential Weighted Moving Average EWMA). In particular, WCMA is able to effectively take into account both the current and past-days weather conditions, obtaining a relative mean error of only 10%. When coupled with energy management algorithm, it can achieve gains of more than 90% in energy utilization with respect to EWMA under the real working conditions of the Shimmer node, an active sensing platform for structural health monitoring.

/pdf/prediction-and-management-in-energy-harvested-wireless-25tfw4kisn.pdf

Prediction and management in energy harvested wireless sensor nodes

Several wireless sensor network (WSN) applications leverage energy harvesting technologies such as small size photo-voltaic modules. The advantage of solar energy over other forms of environmental energy is that the available solar power can be predicted with reasonable accuracy allowing the implementation of efficient power management techniques. However accurate predictions of future energy profiles can be expensive in term of memory occupancy and complexity and a trade-off between accuracy and computational effort must be evaluated. In this paper we compare different solar energy prediction algorithms that give estimates future available energy over the time. They are computationally simple and have a small memory footprint to facilitate the implementation in resource limited solar harvesting sensor nodes. Simulation results show that the most effective predictors is possible achieve high accuracy, diverging from real energy profile by less than 10%.

Algorithms for harvested energy prediction in batteryless wireless sensor networks

Comparison of energy intake prediction algorithms for systems powered by photovoltaic harvesters

Energy harvesting sensor nodes (EHSNs) have stringent low-energy consump- tion requirements, but they need to concurrently execute several types of tasks (processing, sensing, actuation, etc.). Furthermore, no accurate models exist to predict the energy harvest- ing income in order to adapt at run-time the executing set of prioritized tasks. In this article, we propose a novel power-aware task scheduler for EHSNs, namely, HOLLOWS: Head-of- Line Low-Overhead Wide-priority Service. HOLLOWS uses an energy-constrained prioritized queue model to describe the residence time of tasks entering the system and dynamically selects the set of tasks to execute, according to system accuracy requirements and expected energy. Moreover, HOLLOWS includes a new energy harvesting prediction algorithm, that is, weather-conditioned moving average (WCMA), which we have developed to estimate the solar panel energy income. We have tested HOLLOWS using the real-life working conditions of Shimmer, a sensor node for structural health monitoring. Our results indicate that HOLLOWS accurately predicts the energy available in Shimmer to guarantee a certain damage monitoring quality for long-term autonomous scenarios. Also, HOLLOWS is able to adjust the use of the incoming energy harvesting to achieve high accuracy for rapid event damage assessment (after earthquakes, fires, etc.).

/pdf/hollows-a-power-aware-task-scheduler-for-energy-harvesting-48k8isgazi.pdf

HOLLOWS: A Power-aware Task Scheduler for Energy Harvesting Sensor Nodes

Small size photovoltaic modules can harvest enough energy to power many personal devices and wireless sensor nodes. The prediction of solar energy intake is possible thanks to the periodical availability of the sunlight and its cyclic behavior. Thus, smart and innovative power management strategies can take advantage from intake prediction algorithms to optimize the energy usage by keeping the system in low power state as long as possible. On the other hand, very accurate predictions need time and energy because of complex calculations, thus an algorithm that can provide the optimal trade-off between computational effort and accuracy is a breakthrough for systems with tight power constraints. In this paper we introduce an innovative, efficient and reliable solar prediction algorithm, the weather conditioned moving average (WCMA). The algorithm has been further enhanced to increase performance using a phase displacement regulator (PDR) which reduces the average error to less than 9.2% at a minimum energy cost. The proposed new algorithm compares favorably with several competing approaches.

Carlo Bergonzini

Papers

Prediction and management in energy harvested wireless sensor nodes

Algorithms for harvested energy prediction in batteryless wireless sensor networks

Comparison of energy intake prediction algorithms for systems powered by photovoltaic harvesters

HOLLOWS: A Power-aware Task Scheduler for Energy Harvesting Sensor Nodes

Comparison of energy intake prediction algorithms for systems powered by photovoltaic harvesters