THE DESIGN OF A POWER SYSTEM FOR A WIRELESS SENSOR NETWORK FOR LONG-TERM OPERATION

Wireless sensor networks (WSNs) have shown wide applicability to many fields including monitoring of environmental, civil, and industrial settings. WSNs however are resource constrained by many competing factors that span their hardware, software, and networking. One of the central resource constrains is the charge consumption of WSN nodes. With finite energy supplies, low charge consumption is needed to ensure long lifetimes and success of WSNs. This thesis details the design of a power system to support long-term operation of WSNs. The power system’s development occurs in parallel with a custom WSN from the Queen’s MEMS Lab (QML-WSN), with the goal of supporting a 1+ year lifetime without sacrificing functionality. The final power system design utilizes a TPS62740 DC-DC converter with AA alkaline batteries to efficiently supply the nodes while providing battery monitoring functionality and an expansion slot for future development. Testing tools for measuring current draw and charge consumption were created along with analysis and processing software. Through their use charge consumption of the power system was drastically lowered and issues in QML-WSN were identified and resolved including the proper shutdown of accelerometers, and incorrect microcontroller unit (MCU) power pin connection. Controlled current profiling revealed unexpected behaviour of nodes and detailed current-voltage relationships. These relationships were utilized with a lifetime projection model to estimate a lifetime between 521-551 days, depending on the mode of operation. The power system and QML-WSN were tested over a long term trial lasting 272+ days in an industrial testbed to monitor an air compressor pump. Environmental factors were found to influence the behaviour of nodes leading to increased charge consumption, while a node in an office setting was still operating at the conclusion of the trail. This agrees with the lifetime projection and gives a strong indication that a 1+ year lifetime is achievable. Additionally, a light-weight charge consumption model was developed which allows charge consumption information of nodes in a distributed WSN to be monitored. This model was tested in a laboratory setting demonstrating +95% accuracy for high packet reception rate WSNs across varying data rates, battery supply capacities, and runtimes up to full battery depletion.

Optimizing The Nasal Allergen Challenge Model For The Study Of Allergic Rhinitis

Background The Allergic Rhinitis Clinical Investigator Collaborative (AR-CIC) uses a Nasal Allergen Challenge (NAC) model to study the pathophysiology of AR and provides proof of concept for novel therapeutics. The NAC model needs to ensure optimal participant qualification, allergen challenge, clinical symptoms capture and biological samples collection. Repeatability of the protocol is key to ensuring unbiased efficacy analysis of novel therapeutics. The effect of allergen challenge on IL-33 gene expression and its relation to IL1RL1 receptor and cytokine secretion was investigated. Methods Several iterations of the NAC protocol was tested, comparing variations of qualifying criteria based on the Total Nasal Symptom Score (TNSS) and Peak Nasal Inspiratory Flow (PNIF). The lowest allergen concentration was delivered and TNSS and PNIF recorded 15 minutes later. Participants qualified if the particular criteria for the protocol were met, otherwise the next higher allergen concentration (4-fold increase), was administered until the targets were reached. Participants returned for a NAC visit and received varying allergen challenge concentrations depending on the protocol, TNSS/PNIF were recorded at 15 minutes, 30 minutes, 1 hour, and hourly up to 12 hours, a 24 hour time point was added in later iterations. Repeatability was evaluated using a 3-4week interval between screening, NAC1, and NAC2 visits. Various biomarker samples were collected. Results A combined TNSS and PNIF criterion was more successful in qualifying participants. The cumulative allergen challenge (CAC) protocol proved more reliable in producing a robust clinical and biomarker response. Repeatability of the CAC protocol was achieved with a 3-week interval between visits, on a clinical and biological basis. IL-33 cytokine is an important biomarker in initiating the inflammatory response in AR in humans. IL-33 and IL1RL1 expression might employ a negative feedback mechanism in human nasal epithelial cells. Comparing the clinical and biological response to ragweed vs cat allergen challenge, proved the CAC protocol’s suitability for use employing different allergens. Conclusion The AR-CIC’s CAC protocol is an effective method of studying AR, capable of generating measurable and repeatable clinical and biomarker responses, enabling better understanding of AR pathophysiology and ensuring that any change would be purely due to medication under investigation in a clinical trial setting.