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Water is an important parameter for crop growth, and the information about the moisture content in soil at different depths is very useful for farmers to determine the best time to water the soil and to irrigate farmland so as to maximize their yield. This thesis project aims to develop a prototype of a multi-depth moisture sensor probe that is part of a large sensing system used in agriculture. The sensor probe has three sets and is required to last for 6-12 months of usage and to be reproduced at a low cost. The sensor probe consists of three sensor boards, on each of which has two different capacitive based sensors and one analog temperature sensor. The three boards are placed approximately 20 cm from each other in the probe. During this project, the two capacitive based sensors were developed, one with arc-shaped plates operating at a frequency less than 1 MHz, and one with electrodes in the form of annular rings operating at a high frequency, approximately 100 MHz. The moisture content in the soil is calculated based on the measurement of the frequency, which depends on the dielectric constant of the soil. For the implementation of the sensor probe, three printed circuit boards (PCBs) for the sensor boards were designed using Altium Designer and then ordered; an STM32 Nucleo board with low power microcontroller was used and the software was implemented in STM32CubeIDE. The lifetime of the sensor probe was calculated for different duty-cycles. With a duty-cycle of 15 minutes, where the sensor probe is active for 1 minute and in sleep mode for 14 minutes, the lifetime of the sensor probe would only be 16 days. With a duty-cycle of 120 minutes instead, with the sensor probe being active for 1 minute, the lifetime is increased to 130 days (less than4,5 months). Due to challenges with the high frequency capacitive sensor, the multi-depth sensor probe does not fully work, and thus cannot be tested with a large testbed. Further work needs to be conducted on the high frequency capacitive sensor and the communication with the gateway.

Indiana University-Purdue University Indianapolis (IUPUI) / This study proposes and evaluates an average consensus scheme for wireless sensor networks. For this purpose, two communication error models, the fading signal error model and approximated fading signal error model, are introduced and incorporated into the proposed decentralized average consensus scheme. Also, a mathematical analysis is introduced to derive the approximated fading signal model from the fading signal model. Finally, differnt simulation scenarios are introduced and their results analyzed to evaluate the performance of the proposed scheme and its effectiveness in meeting the needs of wireless sensor networks.

We investigate sensor control and scheduling strategies to most effectively use the limited resources of an ad hoc network or closed-loop sensor network. In this context, we examine the following three problems. Where to focus sensing? Certain types of sensors, such as cameras or radars, are unable to simultaneously collect high fidelity data from all environmental locations, and thus require some sort of sensing strategy. Considering a meteorological radar network, we show that the main benefits of optimizing sensing over expected future states of the environment are when there are multiple small phenomena in the environment. Considering multiple users, we show that the problem of call admission control (i.e., deciding which sensing requests to satisfy) in the context of a virtualized private sensor network can be solved in polynomial time when sensor requests are divisible or fixed in time. When sensor requests are indivisible but may be shifted in time, we show that the call admission control problem is NP-complete. How to make sensing robust to delayed and dropped packets? In a closed-loop sensor network, data collected by the sensors determines each sensor's future data collection strategy. Network delays, however, constrain the quantity of data received by the time a control decision must be made, and consequently affect the quality of the computed sensor control. We investigate the value of separate handling of sensor control and data traffc, during times of congestion, in a closed-loop sensor network. Grounding our analysis in a meteorological radar network, we show that prioritizing sensor control traffc decreases the round-trip control-loop delay, and thus increases the quantity and quality of the collected data and improves application performance. How to make routing robust to network changes? In wireless sensor and mobile ad-hoc networks, variable link characteristics and node mobility give rise to changing network conditions. We propose a routing algorithm that selects a type of routing subgraph (a braid) that is robust to changes in the network topology. We analytically characterize the reliability of a class of braids and their optimality properties, and give counter-examples to other conjectured optimality properties in a well-structured (grid) network. Comparing with dynamic source routing, we show that braid routing can significantly decrease control overhead while only minimally degrading the number of packets delivered, with gains dependent on node density.

Sensors are getting smaller, inexpensive and sophisticated, with an increased availability. Compared to 25 years ago, an object tracking system now can easily achieve twice the accuracy, a much larger coverage and fault tolerance, without any significant changes in the overall cost. This is possible by simply employing more than just one sensor and processing measurements from individual sensors sequentially (or even in a batch form). %This is the centralized scheme of multi-sensor target tracking wherein the sensors send their individual detections to a central facility, where tracking related tasks such as data association, filtering, and track management etc. are performed. This is also perhaps the simplest solution for a multi-sensor approach and also optimal in the sense of minimum mean square error (MMSE) among all other multi-sensor scenario. In sophisticated sensors, the number of detections can reach thousands in a single frame. The communication and computation load for gathering all such detections at the fusion center will hamper the system's performance while also being vulnerable to faults. A better solution is a distributed architecture wherein the individual sensors are equipped with processing capabilities such that they can detect measurements, extract clutter, form tracks and transmit them to the fusion center. The fusion center now fuses tracks instead of measurements, due to which this scheme is commonly termed track-level fusion. In addition to sub-optimality, the track-level fusion suffers from a very coarse problem, which occurs due to correlations between the tracks to be fused. Often, in realistic scenarios, the cross-correlations are unknown, without any means to calculate them. Thus, fusion cannot be performed using traditional methods unless extra information is transmitted from the fusion center. This thesis proposes a novel and generalized method of fusing any two probability density functions (pdf) such that a positive cross-correlation exists between them. In modern tracking systems, the tracks are essentially pdfs and not necessarily Gaussian. We propose harmonic mean density based fusion and prove that it obeys all the necessary requirements of being a viable fusion mechanism. We show that fusion in this case is a classical example of agreement between the fused and participating densities based on average $\chi^2$ divergence. Compared to other such fusion techniques in the literature, the HMD performs exceptionally well. Transmitting covariance matrices in distributed architecture is not always possible in cases for e.g. tactical and automotive systems. Fusion of tracks without the knowledge of uncertainty is another problem discussed in the thesis. We propose a novel technique for local covariance reconstruction at the fusion center with the knowledge of estimates and a vector of times when update has occurred at local sensor node. It has been shown on a realistic scenario that the reconstructed covariance converges to the actual covariance, in the sense of Frobenius norm, making fusion without covariance, possible. / Thesis / Doctor of Philosophy (PhD)

The piezoelectric energy harvester has become a new powering option for some low-power electronic devices such as MEMS (Micro Electrical Mechanical System) sensors. Piezoelectric materials can collect the ambient vibrations energy and convert it to electrical energy. This thesis is intended to demonstrate the behavior of a piezoelectric energy harvester system at elevated temperature from room temperature up to 82°C, and compares the system’s performance using different piezoelectric materials. The systems are structured with a Lead Magnesium Niobate-Lead Titanate (PMN-PT) single crystal patch bonded to an aluminum cantilever beam, Lead Indium Niobate-Lead Magnesium Niobate-Lead Titanate (PIN-PMN-PT) single crystal patch bonded to an aluminum cantilever beam and a bimorph cantilever beam which is made of Lead Zirconate Titanate (PZT). The results of this experimental study show the effects of the temperature on the operation frequency and output power of the piezoelectric energy harvesting system. The harvested electrical energy has been stored in storage circuits including a battery. Then, the stored energy has been used to power up the other part of the system, a wireless resonator force sensor, which uses frequency conversion techniques to convert the sensor’s ultrasonic signal to a microwave signal in order to transmit the signal wirelessly.

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