Between 1960 and 1990, 95% of the black rhino population in the world was killed. In South Africa, a rhino was killed every 8 h for its horn throughout 2016. Wild animals, rhinos and elephants, in particular, are facing an ever increasing poaching crisis. In this paper, we review poaching detection technologies that aim to save endangered species from extinction. We present requirements for effective poacher detection and identify research challenges through the survey. We describe poaching detection technologies in four domains: perimeter based, ground based, aerial based, and animal tagging based technologies. Moreover, we discuss the different types of sensor technologies that are used in intruder detection systems such as: radar, magnetic, acoustic, optic, infrared and thermal, radio frequency, motion, seismic, chemical, and animal sentinels. The ultimate long-term solution for the poaching crisis is to remove the drivers of demand by educating people in demanding countries and raising awareness of the poaching crisis. Until prevention of poaching takes effect, there will be a continuous urgent need for new (combined) approaches that take up the research challenges and provide better protection against poaching in wildlife areas.

Movement data were collected at a riding stable over seven days. The dataset comprises data from 18 individual horses and ponies with 1.2 million 2-s data samples, of which 93,303 samples have been tagged with labels (labeled data). Data from 11 subjects were labeled. The data from six subjects and six activities were labeled more extensively. Data were collected during horse riding sessions and when the horses freely roamed the pasture over seven days. Sensor devices were attached to a collar that was positioned around the neck of horses. The orientation of the sensor devices was not strictly fixed. The sensors devices contained a three-axis accelerometer, gyroscope, and magnetometer and were sampled at 100 Hz.

ARISE (Authoritative and Rapid Identification System for Essential biodiversity information) is a digital infrastructure with the mission to provide semi-automated identification of all multicellular species in the Netherlands and innovate on biodiversity monitoring solutions. By applying DNA barcoding, artificial intelligence for species recognition based on images, sound and radar, and emerging technology for biodiversity monitoring, we can radically speed up the inventory of life on earth. The infrastructure relies on a species reference database, which brings together new and existing biodiversity information on all multicellular species in the Netherlands. ARISE will be open-access and organize data according to FAIR principles (findable, accessible, interoperable and reusable). We will develop the infrastructure for the Dutch research community with financial support from the Dutch Research Council. ARISE is a partnership between the Naturalis Biodiversity Center, the University of Amsterdam, Westerdijk Institute and the University of Twente. With an Agile development approach, we will build iteratively guided by use cases and real-life challenges. Facilities and services include streamlined and accesible species sampling and (meta)barcoding, machine learning services to analyse digital input and a digital platform where all data and services come together powered by a state of the art data management system based on digital objects. We will be driven by continuous feedback from external scientists and representatives of species monitoring organizations. With ARISE, we will provide the scientific community with the foundation for automated recognition and monitoring of the entire Dutch biodiversity. Furthermore, ARISE will provide a basis for better biodiversity monitoring to provide policy makers with more reliable information on biodiversity and ecosystem change. This presentation will give an overview of work in progress, demonstrating several pilot versions of facilities and services, and what we envision to build in the future.

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.