Wireless sensor network localization opens the door to many location based applications. In
this thesis, some solutions obtained from localization algorithms are investigated. There are
two categories of problem on localization. Range-based methods are applied to the situation
in which information on the distances between each pair of nodes is available. Algorithms are
developed to estimate the location of each sensor in the network. Usually, the distance
between each pair of nodes is estimated by the signal strength received between them, and
this information is very noisy. Range-free methods, which are also called connectivity-based
methods, assume that the distances between any two nodes are unknown but the connectivity
information between them is known. If the distance between any two nodes in the network is
within a communication range, connectivity between these two nodes is said to be established.
In a range-based scenario, with the information of inter-sensor distance measurements as
well as the absolute locations of the anchors, the objective is to obtain the location of all the
unknown nodes. Two new localization methods based on gradient descent are shown in the
thesis. The gradient descent methods would minimize the difference between the measured
distances and the distances obtained from the estimated locations. From a comparison with
other well-known localization methods, the two newly developed gradient descent algorithms
can reach better accuracy at the expense of computational complexity. This is not surprising
as the proposed algorithms are iterative in nature.
For range-free scenario, a new model utilizing all the information derived from
connectivity-based sensor network localization is introduced. Unlike other algorithms which
only utilize the information on connections, this model makes use of both information on
connections and disconnections between any pair of nodes. The connectivity information
between any pair of nodes is modeled as convex and non-convex constraints. The localization
problem is solved by an optimization algorithm to obtain a solution that would satisfy all the
constraints established in the problem. The simulation has shown that better accuracy is
obtained when compared with algorithms developed by other researchers.
Another solution for the range-free scenario is obtained with the use of a two-objective
evolutionary algorithm called Pareto Archived Evolution Strategy (PAES). In an evolutionary
algorithm, the aim is to search for a solution that would satisfy all the convex and non-convex
constraints of the problem. The number of wrong connections and the summation of
corresponding distances are set as the two objectives. A starting point on the location of the
unknown nodes is obtained using a solution from the result of all convex constraints. The
final solution can reach the most suitable configuration of the unknown nodes as all the
information on the constraints (convex and non-convex) related to connectivity have been
used. From the simulation results, a relationship between the communication range and
accuracy is obtained.
In this thesis, another evolutionary algorithm has been examined to obtain a solution for
our problem. The solution is based on a modified differential evolution algorithm with
heuristic procedures peculiar to our domain of application. The characteristics of the sensor
network localization are thoroughly investigated and utilized to produce corresponding
treatment to search for the reasonable node locations. The modified differential evolution
algorithm uses a new crossover step that is based on the characteristics of the problem. With
the combination of some heuristics, the solution search can move the node to jump out of
local minimums more easily, and give better accuracy than current algorithms.
In the last part of the thesis, a novel two-level range connectivity-based sensor network
localization problem is proposed, which would enrich the connectivity information. In this
new problem, the information of the connectivity between any pair of nodes is either strong,
weak or zero. Again, a two-objective evolutionary algorithm is used to search for a solution
that would satisfy all the convex and non-convex constraints of the problem. Based on
simulations on a range of situations, a suitable range value for the second range is found. / published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy
| Identifer | oai:union.ndltd.org:HKU/oai:hub.hku.hk:10722/174511 |
| Date | January 2012 |
| Creators | Qiao, Dapeng., 乔大鹏. |
| Contributors | Pang, GKH |
| Publisher | The University of Hong Kong (Pokfulam, Hong Kong) |
| Source Sets | Hong Kong University Theses |
| Language | English |
| Detected Language | English |
| Type | PG_Thesis |
| Source | http://hub.hku.hk/bib/B47849538 |
| Rights | The author retains all proprietary rights, (such as patent rights) and the right to use in future works., Creative Commons: Attribution 3.0 Hong Kong License |
| Relation | HKU Theses Online (HKUTO) |
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