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Title: Reducing the Impact of Crowd Geo-localization on Battery Life by Proposing Novel Information Distribution Algorithm
???metadata.dc.contributor.*???: Prof. Jean-Marc Pierson
Assoc. Prof. Georges Da-Costa
Dr. Dejene Ejigu
Asferaw, Samuel
Keywords: Anchor Nodes;Range-free Localization;Smallest Communication Overlap Polygon;Unknown Nodes;Wireless Nodes;True Intersection Points;Wireless Networks
Issue Date: Mar-2017
Publisher: Addis Ababa University
Abstract: Geo-localization is an inevitable challenge and problem which has been studied for many years when dealing with wireless nodes as they are equipped with limited life batteries. Emergence of ‘crowd of wireless node users’ across the world (especially in big cities and emerging big cities in developing countries) and increasing sensor network applications are exacerbating the problem. Existing wireless node localization systems depend on power hungry systems like GPS. Advances in battery design have been slow and difficult; therefore, it is essential for alternative strategies to be employed to realize the goals of reducing the impact of crowd geo-localization on battery life. Based on the type of information required for localization, localization methods can be divided into two categories: range-based and range-free protocols. Since range-based methods demand battery hungry dedicated ranging components, range-free methods are explored. This thesis focuses on the investigation of novel smart algorithms for crowd geo-localization. We have proposed the following three but related smart novel rang-free localization algorithms for wireless networks:  Centre of the Smallest Communication Overlap Polygon (CSCOP) localization algorithm  Selective Anchor Nodes CSCOP localization algorithm  Immune to Radio Range Difference (IRRD) localization algorithm To evaluate the accuracy and reliability of the proposed algorithms, we have conducted extensive MATLAB simulations and comparisons with other state-of-the-art related work on: iv average, minimum, and maximum location error in a scenario of both sparse and dense crowd. Moreover, to see the probability of error distribution of the proposed algorithms, probability distribution of location error is developed using MATLAB normal fit function. We have also investigated computational complexity analysis of the algorithm using “Big O” notation along with detailed elementary operations used in the algorithms. Results obtained from our experiments show our algorithms significantly out-perform other related work in the domain in case of both sparse and dense crowd geo-localization. When we compare localization accuracy performance of our algorithms in case of sparse and dense crowd scenarios, they perform better in dense crowd scenario. For example, the average location error of our CSCOP algorithm in case of dense crowd with 30 anchor nodes involved is 3.7176 (in % radio range) which means 0.7435m error while its error in sparse crowd with 8 anchor nodes is 8.3552 (in % radio range) i.e., 1.6710m in our simulation scenario, where communication range is 20m. The algorithms, in addition to estimating reliable location, also define the smallest communication overlap polygon (SCOP) which can be applied in search and rescue operations as margins where the node situated. Moreover, unlike other related works which require at least three anchor nodes, the proposed algorithms work starting with two anchor nodes. Furthermore, unlike other related algorithms, our Immune to Radio Range Difference algorithm breaks the traditional assumption that says “anchor nodes have the same radio range”, by working in both homogeneous and heterogeneous radio ranges which is a break through finding in the domain area. Although, this work focuses on the crowd networks, applications of the proposed geo-localization algorithms could range to all wireless networks from cellular to sensor.
Description: Presented in Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Information Technology (IP Networking and Wireless Mobile Internet)
Appears in Collections:Thesis - Information Science

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