Optimization Based Self-localization for IoT Wireless Sensor Networks

In this paper we propose an embedded optimization framework for the simultaneous self-localization of all sensors in wireless sensor networks making use of range measurements from ultra-wideband (UWB) signals. Low-power UWB radios, which provide time-of-arrival measurements with decimeter accuracy over large distances, have been increasingly envisioned for realtime localization of IoT devices in GPS-denied environments and large sensor networks. In this work, we therefore explore different non-linear least-squares optimization problems to formulate the localization task based on UWB range measurements. We solve the resulting optimization problems directly using non-linear-programming algorithms that guarantee convergence to locally optimal solutions. This optimization framework allows the consistent comparison of different optimization methods for sensor localization. We propose and demonstrate the best optimization approach for the self-localization of sensors equipped with off-the-shelf microcontrollers using state-of-the-art code generation techniques for the plug-and-play deployment of the optimal localization algorithm. Numerical results indicate that the proposed approach improves localization accuracy and decreases computation times relative to existing iterative methods

Enabling optimization-based localization for IoT devices

In this paper, we propose an embedded optimization approach for the localization of Internet of Things (IoT) devices making use of range measurements from ultra-wideband (UWB) signals. Low-cost, low-power UWB radios provide time-of-arrival measurements with decimeter accuracy over large distances. UWB-based localization methods have been envisioned to enable feedback control in IoT applications, particularly, in GPS-denied environments, and large wireless sensor networks. In this paper, we formulate the localization task as a nonlinear least-squares optimization problem based on two-way time-of-arrival measurements between the IoT device and several UWB radios installed in a 3-D environment. For the practical implementation of large-scale IoT deployments we further assume only approximate knowledge of the UWB radio locations. We solve the resulting optimization problem directly on IoT devices equipped with off-the-shelf microcontrollers using state-of-the-art code generation techniques for plug-and-play deployment of the nonlinear-programming algorithms. This paper further provides practical implementation details to improve the localization accuracy for feedback control in experimental IoT applications. The experimental results finally show that subdecimeter localization accuracy can be achieved using the proposed optimization-based approach, even when the majority of the UWB radio locations are unknown

Hardware And Software For Reproducible Research In Audio Array Signal Processing

In our demo, we present two hardware platforms for prototyping audio array signal processing. Pyramic is a 48-channel microphone array fitted on an FPGA and Compact Six is a portable microphone array with six microphones, closer to the technical constraints of consumer electronics. A browser based interface was developed that allows the user to interact with the audio stream from the arrays in real time. The software component of this demo is a Python module with implementations of basic audio signal processing blocks and popular techniques like STFT, beamforming, and DoA. Both the hardware design files and the software are open source and freely shared. As part of a collaboration with IBM Research, their beamforming and imaging technologies will also be portrayed. The hardware will be demonstrated through an installation processing the microphone signals into light patterns on a circular LED array. The demo will be interactive and let visitors play with different algorithms for DoA (SRP, FRIDA [1], Bluebild) and beamforming (MVDR, Flexibeam [2]). The availability of an open platform with reference implementations encourages reproducible research and minimizes setup-time when testing and benchmarking new audio array signal processing algorithms. It can also serve as a useful educational tool, providing a means to work with real-life signals

Alfalfa Seed Decontamination in Salmonella Outbreak

Based on in vitro data, the U.S. Food and Drug Administration recommends chemical disinfection of raw sprout seeds to reduce enteric pathogens contaminating the seed coats. However, little is known about the effectiveness of decontamination at preventing human disease. In 1999, an outbreak of Salmonella enterica serotype Mbandaka occurred in Oregon, Washington, Idaho, and California. Based on epidemiologic and pulsed-field gel electrophoresis evidence from 87 confirmed cases, the outbreak was linked to contaminated alfalfa seeds grown in California’s Imperial Valley. Trace-back and trace-forward investigations identified a single lot of seeds used by five sprout growers during the outbreak period. Cases of salmonellosis were linked with two sprout growers who had not employed chemical disinfection; no cases were linked to three sprout growers who used disinfection. This natural experiment provides empiric evidence that chemical disinfection can reduce the human risk for disease posed by contaminated seed sprouts

N-rotor vehicles: modelling, control, and estimation

Quad-rotors are becoming more and more commonplace as technological advancements increase capabilities and reduce costs. Quad-rotor vehicles are encountered both for domestic entertainment and industrial applications, some examples are: toys for people of all ages, professional cinematography, or, inspection of industrial scale structures and processes. One reason why quad-rotors have become so pervasive is their mechanical simplicity, which lends itself to the high operational reliability. Moreover, the control and estimation techniques required to stabilise a quad-rotor around hover require only the control theory taught to under-graduates, while acrobatic feats and fleet manoeuvres inspire many directions in current research. The learning objective of this course are two-fold: (i) the students will gain knowledge about and understanding of the modelling and control theory for a quad-rotor application, and (ii) the students will make the connection to and apply theory taught in the under-graduate control system classes. By implementing the control and estimation algorithms on a real quad-rotor, the students will gain experience with how decisions in the modelling and design stage affect real-world performance

Approximate Dynamic Programming: theoretical guarantees and practical algorithms for a continuous space setting

Many problems in science and engineering can be cast as discrete time, continuous space, infinite horizon stochastic optimal control problems. The so-called value function and Q-function both characterise the solution of such problems, but are both intractable to compute in all but a few special cases. This thesis focuses on methods that approximate the value function and Q-function. We consider the linear programming approach to approximate dynamic programming, which computes approximate value functions and Q-functions that are point-wise under-estimators of the optimal by using the so-called Bellman inequality. For this approximation method we provide theoretical guarantees on the value function and Q-function approximation error, and also for the sub-optimality of a policy generated using such lower-bounding approximations. In particular, the online performance guarantee is obtained by analysing an iterated version of the greedy policy, and the fitting error guarantee by analysing an iterated version of the Bellman inequality. These guarantees complement the existing bounds that appear in the literature. Given a collection of lower-bounding approximate value functions, an improved approximation can be constructed by taking the point-wise maximum over the collection, however, the challenge is how to compute the collection itself. To address this challenge, we introduce a novel formulation, referred to as the point-wise maximum approach to approximate dynamic programming, and use this to propose algorithms that iteratively construct a collection of lower-bounding value functions with the objective of maximising the point-wise maximum of the collection. We empirically demonstrate the advantages of the proposed algorithm through a range numerical examples that indicate classes of problems where the proposed algorithms improves upon state-of-the-art methods. A key result from the numerical studies is that the proposed algorithm can provide practically useful sub-optimality bounds for the online performance of any policy, even when the collection of approximate value functions is itself impractical to use for a policy