Power management for energy harvesting

The use of wireless sensor networks in aircraft health management grew exponentially over the past few decades. Wireless sensor networks provide technology that reduces the amount of wiring for aircraft, thereby reducing the weight and cost of aircraft. One of the most significant limitations in the use of wireless sensor networks in aircraft health management systems is the availability of power sources. Developing Wireless Sensor Network nodes that can generate and harvest their autonomous power supply continuously is a bottleneck that has been the preoccupation of engineers for many years. The amount of energy a network of Wireless Sensors can harvest fluctuates and is difficult to predict. As a result, existing predictors of energy harvesting are prone to errors. Models-free schemes such as expert systems are thus preferred for energy management strategies. The main aim of this thesis is to propose expert-based systems for energy harvesting in aircraft to enhance wireless sensor nodes life span by improving energy harvesting, energy storage and packet loss probability. In this context, a novel integrated approach based on the Markov chain was proposed for energy harvesting in aircraft. Simulation results and quantitative analysis showed that the integration of Piezoelectric and Thermoelectric harvesters with stochastic scheduling had a better performance in terms of energy storage, energy harvesting and packet loss probability. There was also an increase in energy storage with five Markov states compared to that of two Markov states. The packet loss probability of the integrated approach with five Markov states was better than that of two Markov states. The results also showed that the integrated approach with five Markov states harvested more energy than two Markov states. The novel integration of LTspice and NS-3 simulators was proposed. The LTspice and NS-3 integration was validated by deploying the Fuzzy logic control approach in energy harvesting. Simulation results and quantitative analysis based on Fuzzy control logic expert system indicated that the integration of LTspice and NS-3 was found to be better in energy harvesting compared to non-fuzzy control systems. The downtime ratio and energy utilization efficiency of the wireless sensor nodes were also found to be better than non-fuzzy control. The power management based LEACH routing protocol was also proposed. The simulation results and quantitative analysis showed that the average harvested energy based on the LEACH routing protocol deployed with fuzzy logic and Markov chain was better compared to those with direct communication based on Markov chain and fuzzy logic systems.