Please use this identifier to cite or link to this item:
Title: Design and optimization of RFID-enabled wireless sensor network (WSN) monitoring system for biological and pharmaceutical products storage
Authors: Ng, Chun Kit
Advisors: Ip, W. H. (ISE)
Keywords: Biological product -- Storage
Drugs -- Storage
Radio frequency identification systems
Wireless communication systems
Issue Date: 2018
Publisher: The Hong Kong Polytechnic University
Abstract: Biological and Pharmaceutical (B&P) products play a critical role in many different areas in modern life, such as medicine, health-care, pharmacies and biotechnology. These products are highly susceptible to variations of environmental factors, such as temperature, humidity, vibration, tilt, light and atmospheric substance. Therefore, an effective and relatively low-cost product monitoring and tracking system with real-time and continuous monitoring capability is needed for B&P product supply chains. A Wireless Sensor Network (WSN) is a network consisting of a number of sensor nodes with wireless communication capability. Due to the characteristics of a WSN such as wireless communication, small size, easy installation and flexible detection area, this research leverages WSN and Radio Frequency Identification (RFID) to design a B&P product monitoring system, which aims to prevent faults and to enhance the supply chain visibility. The proposed conceptual architecture consists four layers, namely Information Acquisition Layer (IAL), Network Layer (NL), Logic and Processing Layer (LPL) and Service Output Layer (SOL). In IAL, the product state information and the environmental parameters (i.e. temperature, humidity and vibration) are collected by wireless sensor nodes which are widely spread over a large area like a container, warehouse and distribution centre. The collected data are then routed to the data gathering and exchanging module (i.e. the sink of WSN). After that, the data are passed to Data Integration and Analysis Core (DIAC) in LPL through the Internet via Wi-Fi, 3G, Long-Term Evolution (LTE) or other wireless technologies. The NL is used to manage the data routing and aggregation, network construction and configuration of deployed WSNs, and the SOL will deliver service outputs based on the decisions made in the LPL, including visualizing the data, prompting alerts when abnormal conditions occur, triggering the operation of actuators and storing the collected and processed data in the database for querying and further analysis in the future.
To design a WSN, deployment of sensor nodes is an irreplaceable and critical stage. However, designing of a WSN deployment strategy with a high level of performance is a challenging task, since many design factors should be considered, and many of these design factors are interrelated and there are trade-offs between them. In this thesis, two approaches are proposed to deal with the WSN deployment problem. The first approach is a three-stage framework based on statistical methods. The three stages include placing the sensor and relay nodes in the first stage, selecting a sink position in the second stage and placing additional relay nodes in the last stage. This approach uses a heuristic and statistical method to determine the placements of sensor and relay nodes, and the additional relay nodes needed in a target area. In the second approach, two deployment conditions are examined. The first one is the deployment of homogeneous sensor nodes in a 2-D region-of-interest studied. Four objectives were identified: the sensing coverage and network connectivity of the sensor nodes are required to be maximized, simultaneously, the production cost should be minimized while a certain level of fault tolerance should be maintained. To fulfil these objectives, a mathematical model is established by using the weighted sum approach. Then, a meta-heuristics algorithm, named Smart Bat Algorithm (SBA) is proposed to solve the model. SBA is designed based on the Bat Algorithm (BA) and integrated Fuzzy Inference model with decision-theoretical rules in order to determine the direction, velocity and frequency of artificial bats. To evaluate the performance of SBA, several optimization approaches including Greedy Algorithm, Genetic Algorithm (GA), an Ant Colony Optimization (ACO) based metaheuristics optimization method, called the MAX-MIN Ant System (MMAS) and Discrete Bat Algorithm (DBA), are used to solve the model. The simulation result shows that the SBA provides a better WSN deployment plan when considering the four objectives. The second condition studied a more realistic situation of WSN deployment, which is placing sensor nodes and relay nodes in a 3-D environment with obstacles. The proposed solution adopts two-stage deployment approaches. In the first stage, sensor nodes are deployed to satisfy two objectives of cost and coverage. According to the deployment of sensor nodes, a set of relay nodes is deployed in the second stage afterward to satisfy three objectives of cost, network connectivity and fault tolerance. The simulation result shows that SBA provides more robust and high quality results. In summary, the contribution of this thesis includes: Firstly, this thesis presents a relatively objective, quantitative and systematic approach, called semantic similarity analysis for examination of the intellectual structure of IoT. Secondly, a four-layer system architecture is proposed as a flexible and scalable base for a B&P product monitoring system as well as other related information technology systems. Thirdly, a BA-based meta-heuristics algorithm, named Smart Bat Algorithm (SBA), is proposed, whereby SBA delivers high-quality and robust solutions for both homogenous node deployment in 2-D environments and heterogeneous node deployment in 3-D environments of WSN.
Description: xvi, 186 pages : color illustrations
PolyU Library Call No.: [THS] LG51 .H577P ISE 2018 Ng
Rights: All rights reserved.
Appears in Collections:Thesis

Files in This Item:
File Description SizeFormat 
991022168749003411_link.htmFor PolyU Users167 BHTMLView/Open
991022168749003411_pira.pdfFor All Users (Non-printable)4.11 MBAdobe PDFView/Open
Show full item record
PIRA download icon_1.1View/Download Contents

Page view(s)

Citations as of Feb 18, 2019


Citations as of Feb 18, 2019

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.