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Energy efficient reader scheduling algorithms in RFID networks

Description
Creator: 

Campioni, Fabio

Start Date: 
2016
End Date: 
2016
Date Range: 
6 September 2016
Language: 
English
Physical Description: 

842 KB of textual records. - 1 PDF.

0.5 cm of textual records. - 1 thesis.

Notes: 

Audience: Undergraduate. -- Dissertation: Thesis (BCS). -- Algoma University, 2017. -- Submitted in partial fulfillment of course requirements for COSC 4235. -- Includes figures. -- Contents: Thesis.

Abstract: The goal of this thesis is to find a localized algorithm to efficiently schedule battery powered readers in RFID networks. RFID (Radio Frequency Identication) networks consist of readers and tags and are used to track physical items using radio communication. Tags are placed on objects to be tracked, and are read by readers placed throughout the environment. By scheduling readers based on energy between active and low power states, readers last longer, therefore improving overall network lifetime. The development of a software simulator allows immediate testing of algorithms. In addition, a new centralized algorithm was developed based on the set cover problem that produces disjoint sets of readers. After testing these algorithms, results are graphed and calculations are performed to compare their performance. The results show that the localized algorithm achieves very similar performance to a centralized algorithm, while being easier to implement and more flexible to changing networks. In this report, a brief introduction to the technical aspects, history, and applications of RFID will be presented. Then, development of the algorithm will be explained, as well as the process of implementing and testing these algorithms. Then, the results of several experiments will be presented, as well as a description of these results. Several algorithms' performance will be compared and discussed.

Physical Location
rec_shelfloc: 
2017-016-001
Repository: 
Algoma University Archive
Container Number: 
001
Language: 
Description Level: