Ambient Sound-Based Collaborative Localization of Indeterministic Devices

Localization is essential in wireless sensor networks. To our knowledge, no prior work has utilized low-cost devices for collaborative localization based on only ambient sound, without the support of local infrastructure. The reason may be the fact that most low-cost devices are indeterministic and suffer from uncertain input latencies. This uncertainty makes accurate localization challenging. Therefore, we present a collaborative localization algorithm (Cooperative Localization on Android with ambient Sound Sources (CLASS)) that simultaneously localizes the position of indeterministic devices and ambient sound sources without local infrastructure. The CLASS algorithm deals with the uncertainty by splitting the devices into subsets so that outliers can be removed from the time difference of arrival values and localization results. Since Android is indeterministic, we select Android devices to evaluate our approach. The algorithm is evaluated with an outdoor experiment and achieves a mean Root Mean Square Error (RMSE) of 2.18 m with a standard deviation of 0.22 m. Estimated directions towards the sound sources have a mean RMSE of 17.5 ° and a standard deviation of 2.3 °. These results show that it is feasible to simultaneously achieve a relative positioning of both devices and sound sources with sufficient accuracy, even when using non-deterministic devices and platforms, such as Android

Hiding in the Deep: Online Animal Activity Recognition using Motion Sensors and Machine Learning

The activity of animals is a rich source of information that not only provides insights into their life and well-being but also their environment. Animal activity recognition (AAR) is a new field of research that supports various goals, including the conservation of endangered species and the well-being of livestock. Over the last decades, the advent of small, lightweight, and low-power electronics has made it possible to attach unobtrusive sensors to animals that can measure a wide range of aspects such as location, temperature, and activity. These aspects are highly informative properties for numerous application domains, including wildlife monitoring, anti-poaching, and livestock management. In this thesis, we focus on AAR that aims to automatically recognize the activity from motion data – on the animal – while the activities are performed (online). Specifically, we use motion data recorded through an inertial measurement unit (IMU) that comprises an accelerometer, gyroscope, and magnetometer to classify up to eleven different activities

Dataset: Horse Movement Data and Analysis of its Potential for Activity Recognition

We describe and analyze a dataset that comprises horse movement. Data was collected during horse riding sessions and when the horses freely roamed the pasture over 7 days. The dataset comprises 1.8 million 2-second data samples from 18 individual horses, of which 93303 samples from 11 subjects were labeled. Sensor devices were attached to a collar around the neck of the horses while the orientation was not fixed. The devices contained a 3-axis accelerometer, gyroscope, and magnetometer that were sampled at 100 Hz. To demonstrate how this dataset can be used, we evaluated a Naive Bayes classifier with leave-one-out validation. Our results show that a performance of 90% accuracy can be achieved using only the 3D acceleration vector as input. Furthermore, we demonstrate the effect of increased complexity, parameter tuning, and class balancing on classification performance and identify open research challenges. The complete dataset is available online with open access at the 4TU.Centre for Research Data

Synchronization between Sensors and Cameras in Movement Data Labeling Frameworks

Obtaining labeled data for activity recognition tasks is a tremendously time consuming, tedious, and labor-intensive task. Often, ground-truth video of the activity is recorded along with sensordata recorded during the activity. The data must be synchronized with the recorded video to be useful. In this paper, we present and compare two labeling frameworks that each has a different approach to synchronization. Approach A uses time-stamped visual indicators positioned on the data loggers. The approach results in accurate synchronization between video and data but adds more overhead and is not practical when using multiple sensors, subjects, and cameras simultaneously. Also, synchronization needs to be redone for each recording session. Approach B uses Real-Time Clocks (RTCs) on the devices for synchronization, which is less accurate but has several advantages: multiple subjects can be recorded on various cameras, it becomes easier to collect more data, and synchronization only needs to be done once across multiple recording sessions. Therefore, it is easier to collect more data which increases the probability of capturing an unusual activity. The best way forward is likely a combination of both approaches

Error Bounds for Localization with Noise Diversity

In the context of acoustic monitoring, the location of a sound source can be passively estimated by exploiting time-of-arrival and time-difference-of-arrival measurements. To evaluate the fundamental hardness of a location estimator, the Cramer-Rao bound (CRB) has been used by many researchers. The CRB is computed by inverting the Fisher Information Matrix (FIM), which measures the amount of information carried by given distance measurements. The measurements are commonly expressed as actual distances plus white noise. However, the measurements do include extra noise types caused by time synchronization, acoustic sensing latency, and signal-tonoise ratio. Such noise can significantly affect the performance and depend highly on the sensing platforms such as Android smartphones. In this paper, we first remodel the acousticbased distance measurements considering such additive errors. Then, we derive a new FIM with the new statistical ranging error models. As a result, we obtain new CRBs for both noncooperative and cooperative localization schemes that provide better insight into the causality of the uncertainties. Theoretical analysis also proves that the proposed CRBs for localization become the old CRBs when the additional errors are ignored, which gives a robust check for the new CRBs. Thus, the new CRBs can serve as a benchmark for localization estimators with both new and old measurement models. The new CRBs also indicate that there is room to improve current localization schemes; however, it is a daunting challenge

Robust Sensor-Orientation-Independent Feature Selection for Animal Activity Recognition on Collar Tags

Fundamental challenges faced by real-time animal activity recognition include variation in motion data due to changing sensor orientations, numerous features, and energy and processing constraints of animal tags. This paper aims at finding small optimal feature sets that are lightweight and robust to the sensor's orientation. Our approach comprises four main steps. First, 3D feature vectors are selected since they are theoretically independent of orientation. Second, the least interesting features are suppressed to speed up computation and increase robustness against overfitting. Third, the features are further selected through an embedded method, which selects features through simultaneous feature selection and classification. Finally, feature sets are optimized through 10-fold cross-validation. We collected real-world data through multiple sensors around the neck of five goats. The results show that activities can be accurately recognized using only accelerometer data and a few lightweight features. Additionally, we show that the performance is robust to sensor orientation and position. A simple Naive Bayes classifier using only a single feature achieved an accuracy of 94 % with our empirical dataset. Moreover, our optimal feature set yielded an average of 94 % accuracy when applied with six other classifiers. This work supports embedded, real-time, energy-efficient, and robust activity recognition for animals

Nondeterministic Sound Source Localization with Smartphones in Crowdsensing

The proliferation of smartphones nowadays has enabled many crowd assisted applications including audio-based sensing. In such applications, detected sound sources are meaningless without location information. However, it is challenging to localize sound sources accurately in a crowd using only microphones integrated in smartphones without existing infrastructures, such as dedicated microphone sensor systems. The main reason is that a smartphone is a nondeterministic platform that produces large and unpredictable variance in data measurements. Most existing localization methods are deterministic algorithms that are ill suited or cannot be applied to sound source localization using only smartphones. In this paper, we propose a distributed localization scheme using nondeterministic algorithms. We use the multiple possible outcomes of nondeterministic algorithms to weed out the effect of outliers in data measurements and improve the accuracy of sound localization. We then proposed to optimize the cost function using least absolute deviations rather than ordinary least squares to lessen the influence of the outliers. To evaluate our proposal, we conduct a testbed experiment with a set of 16 Android devices and 9 sound sources. The experiment results show that our nondeterministic localization algorithm achieves a root mean square error (RMSE) of 1.19 m, which is close to the Cramer-Rao bound (0.8 m). Meanwhile, the best RMSE of compared deterministic algorithms is 2.64 m