We know you hate the Internet of Things, but it’s saving megafauna from poachers

For much of this decade, organizations seeking to protect wildlife have attempted to use emerging technology as a conservation tool, allowing small numbers of people to monitor and manage data from animals over a wide area. Nowhere is that effort more focused—and more desperate—than in the regions of Africa where illegal animal trade is threatening to wipe out endangered animals such as rhinos, elephants, pangolins, and lions.

Here, several organizations are applying Internet of Things (IoT) technology to protect animals, providing rangers with data that helps them intercept poachers before they can get to their quarry.

Many conservation efforts elsewhere use IoT to try to track the location of animals, such as Vodafone’s IoT tagging of Scottish harbor seals and tracking of endangered dugongs in Philippines. But in Africa, the task of protecting rhinos is slightly different—it’s about tracking people, specifically the poachers who hunt down the rhinos for their tusks.

Rhinos, of course, aren’t unique in needing such intervention. Based on data from the Great Elephant Census (GEC), a continent-wide survey conducted by Microsoft cofounder Paul Allen’s Vulcan Inc., Africa’s savanna elephant population declined by 30 percent between 2007 and 2014 for instance. That’s a loss of 144,000 elephants. Current data shows the rate of decline of the elephant population is now eight percent per year, and ivory poachers are the main reason for that decline.

But things are even more bleak for rhinos. While the black rhino has bounced back from near extinction in 1995 (rebounding from a population of less than 2,500 to approximately 5,000 today), the Western Black Rhino was declared extinct in 2011. And the last male Northern White Rhino died this March in Sudan. Overall, rhinos in South Africa are being killed at a rate of more than three per day. If that continues, rhinos will be extinct in South Africa by 2025.

But networking and sensor technology, in combination with analytics, are now offering ways to better manage populations of these animals and intercept pending threats. By connecting sensors to the cloud (public or private) over low-power networks, these tech conservationists can offer essential intelligence on human activities near protected animals and help intercept poachers before they can do harm—both to the animals and the rangers guarding them.

Eyes and ears and clouds

For a few years now, The Lindbergh Foundation’s Air Shepherd program has used a combination of drones and data analytics in a South African trial to protect rhinos. That program has been expanded to elephant protection in Malawi and Zimbabwe with crowdfunding.

As Ars reported in 2015, Air Shepherd uses a combination of local intelligence and intelligent image processing from drone sensor data to build a model for what’s going on around and within the parks its crews protect. Based on animal movement patterns and the proximity of human threats, the drones are deployed to give rangers advance warning of poachers’ movements. But Air Shepard’s drones require trained pilots, and drones—while often an effective deterrent—can’t always be on station to detect poaching attempts.

So another effort at South Africa’s Welgevonden Game Reserve has tried a different sort of IoT approach, tracking the behavior of other herd animals (such as zebras, impalas and gazelles). These monitors act as “sentinels,” essentially watching for variations in animal movement in response to different potential threats. The Welgevonden experiment, based on a collaboration with Wageningen University in the Netherlands and IBM, tracks the movements of these collared herbivores by utilizing collars transmitting data via a 3G wireless network, and the effort inputs that data into an IBM Watson system in the cloud. The analytics of the Watson IoT platform are being trained to detect the difference in behavior in herd animals based on whether they are coming in contact with natural predators, tourists, or potential poachers.

While the IBM Watson solution has been effective so far, it remains dependent on a connection to the cloud. And for much of Africa, Internet connectivity is not exactly guaranteed—especially in wildlife areas, where it may be dependent on high-latency satellite connections or sparsely deployed cellular networks. Private wireless networks can provide a local backbone for communications, but there still needs to be some sort of back-end connection for a cloud-driven IoT solution to work.

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