Condition monitoring can reduce machine breakdown losses, increase productivity and operation safety, and therefore deliver significant benefits to many industries. The emergence of wireless sensor networks (WSNs) with smart processing ability play an ever-growing role in online condition monitoring of machines. WSNs are cost-effective networking systems for machine condition monitoring. It avoids cable usage and eases system deployment in industry, which leads to significant savings. Powering the nodes is one of the major challenges for a true WSN system, especially when positioned at inaccessible or dangerous locations and in harsh environments. Promising energy harvesting technologies have attracted the attention of engineers because they convert microwatt or milliwatt level power from the environment to implement maintenance-free machine condition monitoring systems with WSNs. The motivation of this review is to investigate the energy sources, stimulate the application of energy harvesting based WSNs, and evaluate the improvement of energy harvesting systems for mechanical condition monitoring. This paper overviews the principles of a number of energy harvesting technologies applicable to industrial machines by investigating the power consumption of WSNs and the potential energy sources in mechanical systems. Many models or prototypes with different features are reviewed, especially in the mechanical field. Energy harvesting technologies are evaluated for further development according to the comparison of their advantages and disadvantages. Finally, a discussion of the challenges and potential future research of energy harvesting systems powering WSNs for machine condition monitoring is made.

Energy Harvesting Technologies for Achieving Self-Powered Wireless Sensor Networks in Machine Condition Monitoring: A Review.

Energy harvesting in wireless sensor networks: A taxonomic survey

The Wireless Sensor Networks (WSN) are the basic building blocks of today’s modern internet of Things (IoT) infrastructure in smart buildings, smart parking, and smart cities The WSN nodes suffer from a major design constraint in that their battery energy is limited and can only work for a few days depending upon the duty cycle of operation The main contribution of this research article is to propose an efficient solar energy harvesting solution to the limited battery energy problem of WSN nodes by utilizing ambient solar photovoltaic energy Ideally, the Optimized Solar Energy Harvesting Wireless Sensor Network (SEH-WSN) nodes should operate for an infinite network lifetime (in years) In this paper, we propose a novel and efficient solar energy harvesting system with pulse width modulation (PWM) and maximum power point tracking (MPPT) for WSN nodes The research focus is to increase the overall harvesting system efficiency, which further depends upon solar panel efficiency, PWM efficiency, and MPPT efficiency Several models for solar energy harvester system have been designed and iterative simulations were performed in MATLAB/SIMULINK for solar powered DC-DC converters with PWM and MPPT to achieve optimum results From the simulation results, it is shown that our designed solar energy harvesting system has 87% efficiency using PWM control and 96% efficiency ( η s y s ) by using the MPPT control technique Finally, an experiment for PWM controlled SEH-WSN is performed using Scientech 2311 WSN trainer kit and a Generic LM2575 DC-DC buck converter based solar energy harvesting module for validation of simulation results

https://www.mdpi.com/2224-2708/7/3/40/pdf

Modeling and Optimisation of a Solar Energy Harvesting System for Wireless Sensor Network Nodes

The next generation’s sensor nodes will be more intelligent, energy conservative, and perpetual lifetime in the set-up of wireless sensor networks (WSNs). These sensors nodes are facing the overwhelming challenge of energy consumption which gradually decreases the lifetime of overall network. The wireless power transfer (WPT) is one of the most emerging technologies of energy harvesting that deploys at the heart of sensor nodes for efficient lifetime solution. A wireless portable charging device (WPCD) is drifting inside the WSN to recharge all the nodes which are questing for the eternal life. In this paper, we aspire to optimize a multi-objective function for charging trail of WPCD, and self-learning algorithm for data routing jointly. We formulated that the objective functions can optimize the fair energy consumption as well as maximize the routing efficiency of WPCD. The fundamental challenge of the problem is, to integrate the novel path for WPCD by applying the Nodal A* algorithm. We proposed a novel method of sensor node’s training for intellectual data transmission by using of clustering and reinforcement learning (SARSA) defined as clustering SARSA (C-SARSA) along with an optimal solution of objective functions. The whole mechanism outperforms in terms of trade-off between energy consumption and stability (fair energy consumption among all nodes) of the WSN; moreover, it prolongs the lifetime of the WSN. The simulated results demonstrate that our proposed method did better than compared literature in terms of energy consumption, stability, and lifetime of the WSN.

Optimal Wireless Charging Inclusive of Intellectual Routing Based on SARSA Learning in Renewable Wireless Sensor Networks

The Internet of Things (IoT) can manage a large number of smart wireless devices and form a networking infrastructure connected to the Internet. Traditional batteries in IoT produce environmental concerns and have limited operational life. Harvesting and converting ambient environmental energy is an effective and important approach for sustainable green power used in wireless and portable devices in IoT. This contribution reviews the state-of-the-art development of different energy harvesting sources including mechanical, light/solar, wind, sound, RF, biomechanical and pyroelectric energy. Power density generated from ambient source ranges widely from 0.001 μW/cm2 (RF WiFi) to 100 mW/cm2 (outdoor solar). Depends on application areas and working principles, typical power consumptions of IoT sensor nodes are in the order of mW (1---750 mW) in active mode and μW (0---60 μW) in sleep mode (Mathna et al. in Talanta 75:613---623, 2008; Magno et al. in IEEE Trans Ind Electron 61:1871---1881, 2014; Baranov et al. in Sens Actuators A 233:279---289, 2015; Somov et al. in Procedia Eng 87:520---523, 2014; Spirjakin et al. in Sens Actuators A 247:247---253, 2016; Samson et al. in Sens Actuators A 172:240---244, 2011). Therefore, efficient energy storage and management strategies are important for IoT development. These parts are discussed in order to provide the sustainable power. MEMS based energy harvesting devices may be widely employed in various areas, such as military monitoring, remote weather station, bluetooth headsets, and environment detection. This review focuses on the low power and self powered IoT applications: sensors, wearables, and RF-MEMS. With the advance of nanofabrication techniques, IoT devices will become smaller and enter into the era of Internet of Nano-Things.

Siti Khadijah binti Salahuddin

Papers

Solar Energy Harvester for Industrial Wireless Sensor Nodes