In large-scale wireless sensor networks (WSNs), the position information of individual
sensors is very important for many applications. Generally, there are a small number
of position-aware nodes, referred to as the anchors. Every other node can estimate its
distances to the surrounding anchors, and then employ trilateration or triangulation for
self-localization. Such a system is easy to implement, and thus popular for both terrestrial
and underwater applications, but it suffers from some major drawbacks. First, the density
of the anchors is generally very low due to economical considerations, leading to poor
localization accuracy. Secondly, the energy and bandwidth consumptions of such systems
are quite significant. Last but not the least, the scalability of a network based on fixed
anchors is not good. Therefore, whenever the network expands, more anchors should be
deployed to guarantee the required performance. Apart from these general challenges,
both terrestrial and underwater networks have their own specific ones. For example, realtime
channel parameters are generally required for localization in terrestrial WSNs. For
underwater networks, the clock skew between the target sensor and the anchors must
be considered. That is to say, time synchronization should be performed together with
localization, which makes the problem complicated.
An alternative approach is to employ mobile anchors to replace the fixed ones. For
terrestrial networks, commercial drones and unmanned aerial vehicles (UAVs) are very
good choices, while autonomous underwater vehicles (AUVs) can be used for underwater
applications. Mobile anchors can move along a predefined trajectory and broadcast beacon
signals. By listening to the messages, the other nodes in the network can localize themselves
passively. This architecture has three major advantages: first, energy and bandwidth consumptions can be significantly reduced; secondly, the localization accuracy can be much
improved with the increased number of virtual anchors, which can be boosted at negligible
cost; thirdly, the coverage can be easily extended, which makes the solution and the network
highly scalable.
Motivated by this idea, this thesis investigates the mobile node-aided localization and
tracking in large-scale WSNs. For both terrestrial and underwater WSNs, the system
design, modeling, and performance analyses will be presented for various applications,
including: (1) the drone-assisted localization in terrestrial networks; (2) the ToA-based
underwater localization and time synchronization; (3) the Doppler-based underwater localization;
(4) the underwater target detection and tracking based on the convolutional
neural network and the fractional Fourier transform. In these applications, different challenges
will present, and we will see how these challenges can be addressed by replacing
the fixed anchors with mobile ones. Detailed mathematical models will be presented, and
extensive simulation and experimental results will be provided to verify the theoretical
results. Also, we will investigate the channel estimation for the fifth generation (5G) wireless
communications. A pilot decontamination method will be presented for the massive
multiple-input-multiple-output communications, and the data-aided channel tracking will
be discussed for millimeter wave communications. We will see that the localization problem
is highly coupled with the channel estimation in wireless communications