Prolonging the battery lifetime of sensors has been one of the most important issues in
wireless sensor networks (WSNs). With the development of Wireless Power Transfer
(WPT) technology, sensors can be recharged and possibly have infinite lifetime. One
common approach to achieving this is having a wireless charging vehicle (WCV) move
in the system coverage area and charge sensors nearby when it stops. The duration
that the WCV stays at each charging location, the amount of traffic that each sensor
carries, and the transmission power of individual sensors are closely related, and their
joint optimization affects not only the data transmissions in the WSN but also energy
consumption of the system. This problem is formulated as a mixed integer and nonconvex
optimization problem. Different from existing work that either solves similar
problems using genetic algorithms or considers charging sensors based on clusters,
we consider the optimum charging time for each sensor, and solve the joint
communication and charging problem optimally. Numerical results demonstrate that
our solution can significantly reduce the average power consumption of the system,
compared to the cluster-based charging solution. / Thesis / Master of Applied Science (MASc) / In a wireless sensor network (WSN), sensor nodes monitor the physical environment
and forward the collected data to a data sink for further processing. Sensors are
battery powered and, therefore, prolonging the lifetime of their batteries is critically
important. In a rechargeable WSN (RWSN), prolonging the battery lifetime of
sensors is achieved through reducing communication energy and recharging the batteries
periodically. Reducing the communication energy consumption is done through
choosing the best forwarding sensors (i.e., routing) for data collected by each sensor
and deciding the transmission power of each sensor (i.e., power allocation). Recharging
the batteries is achieved through harvesting energy from external sources. In this thesis,
we consider a RWSN that uses wireless power transfer as the energy harvesting
technology and jointly optimizes charging and communications in order to minimize
the power consumption of the RWSN.
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/27573 |
| Date | January 2022 |
| Creators | Guo, Chunhui |
| Contributors | Zhao, Dongmei, Electrical and Computer Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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