Power Management and Power Consumption Optimization Techniques in Wireless Sensor Networks

A Wireless Sensor Network (WSN) is a distributed collection of resource constrained tiny nodes capable of operating with minimal user attendance. Due to their flexibility and low cost, WSNs have recently become widely applied in traffic regulation, fire alarm in buildings, wild fire monitoring, agriculture, health monitoring, building energy management, and ecological monitoring. However, deployment of the WSNs in difficult-to-access areas makes it difficult to replace the batteries - the main power supply of a sensor node. It means that the power limitation of the sensor nodes appreciably constraints their functionality and potential applications. The use of harvesting components such as solar cells alone and energy storage elements such as super capacitors and rechargeable batteries is insufficient for the long-term sensor node operation. With this thesis we are going to show that long-term operation could be achieved by adopting a combination of hardware and software techniques along with energy efficient WSN design. To demonstrate the hardware power management, an energy scavenging module was designed, implemented and tested. This module is able to handle both alternating current (AC) based and direct current (DC) based ambient sources. The harvested energy is stored in two energy buffers of different kind, and is delivered to the sensor node in accordance with an efficient energy supply switching algorithm. The software part of the thesis presents an analytical criterion to establish the value of the synchronization period minimizing the average power dissipated by a WSN node. Since the radio chip is usually the most power hungry component on a board, this approach can help one to decrease the amount of power consumption and prolong the lifetime of the entire WSN. The following part of the thesis demonstrates a methodology for power consumption evaluation of WSN. The methodology supports the Platform Based Design (PBD) paradigm, providing power analysis for various sensor platforms by defining separate abstraction layers for application, services, hardware and power supply modules. Finally, we present three applications where we use the designed hardware module and apply various power management strategies. In the first application we apply the WSN paradigm to the entertainment area, and in particular to the domain of Paintball. The second one refers to a wireless sensor platform for monitoring of dangerous gases and early fire detection. The platform operation is based on the pyrolysis product detection which makes it possible to prevent fire before inflammation. The third application is connected with medical research. This work describes the powering of wireless brain-machine interfaces.