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postgraduate thesis: Solutions for wireless sensor network localization

TitleSolutions for wireless sensor network localization
Authors
Advisors
Advisor(s):Pang, GKH
Issue Date2012
PublisherThe University of Hong Kong (Pokfulam, Hong Kong)
Citation
Qiao, D. [乔大鹏]. (2012). Solutions for wireless sensor network localization. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b4784953
AbstractWireless 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.
DegreeDoctor of Philosophy
SubjectWireless sensor networks.
Location problems (Programming)
Dept/ProgramElectrical and Electronic Engineering

 

DC FieldValueLanguage
dc.contributor.advisorPang, GKH-
dc.contributor.authorQiao, Dapeng.-
dc.contributor.author乔大鹏.-
dc.date.issued2012-
dc.identifier.citationQiao, D. [乔大鹏]. (2012). Solutions for wireless sensor network localization. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b4784953-
dc.description.abstractWireless 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.-
dc.languageeng-
dc.publisherThe University of Hong Kong (Pokfulam, Hong Kong)-
dc.relation.ispartofHKU Theses Online (HKUTO)-
dc.rightsThe author retains all proprietary rights, (such as patent rights) and the right to use in future works.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.source.urihttp://hub.hku.hk/bib/B47849538-
dc.subject.lcshWireless sensor networks.-
dc.subject.lcshLocation problems (Programming)-
dc.titleSolutions for wireless sensor network localization-
dc.typePG_Thesis-
dc.identifier.hkulb4784953-
dc.description.thesisnameDoctor of Philosophy-
dc.description.thesislevelDoctoral-
dc.description.thesisdisciplineElectrical and Electronic Engineering-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.5353/th_b4784953-
dc.date.hkucongregation2012-

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