Wilderness communications

Utilizing Drones to Restore and Maintain Radio Communication During Search and Rescue Operations

INTRODUCTION

The ability of rescuers to maintain contact with incident command (IC) and each other is a critical component of search and rescue (SAR) operations. When rescuers lose radio communication with operation leaders, the effectiveness of operations may be substantially affected. This often occurs owing to the limitations of standard communications equipment in difficult terrain or when victims are beyond line-of-sight. This study investigates the viability of using an aerial drone-repeater system configuration to restore and maintain radio communications between IC and deployed rescuers.

METHODS

SAR operators in Southern Utah identified 10 areas where radio communication is compromised during live rescue operations. Trained SAR personnel were deployed to these areas in a mock exercise. After confirmed loss of communication, a repeater-equipped aerial drone was piloted 122 m above IC to restore communication. Once restored, communication was assessed at regular intervals for the duration of the mock deployment.

RESULTS

In all 10 areas tested, communication was successfully restored. In all cases, once communication was restored, no additional loss of radio contact occurred. The time between communication loss and restoration across the 10 scenarios was 6.5±1.1 (4.4-9.3) min (mean±SD with range).

CONCLUSIONS

This method of restoring radio communication among SAR personnel could drastically improve the ability to assist victims and help mitigate the risks faced by rescuers. SAR leaders should be made aware of the useful applications of drones during SAR operations, especially in instances where communication is compromised.

Positioning Methods and the Use of Location and Activity Data in Forests

In this paper, we provide an overview of positioning systems for moving resources in forest and fire management and review the related literature. Emphasis is placed on the accuracy and range of different localization and location-sharing methods, particularly in forested environments and in the absence of conventional cellular or internet connectivity. We then conduct a second review of literature and concepts related to several emerging, broad themes in data science, including the terms location-based services (LBS), geofences, wearable technology, activity recognition, mesh networking, the Internet of Things (IoT), and big data. Our objective in this second review is to inform how these broader concepts, with implications for networking and analytics, may help to advance natural resource management and science in the future. Based on methods, themes, and concepts that arose in our systematic reviews, we then augmented the paper with additional literature from wildlife and fisheries management, as well as concepts from video object detection, relative positioning, and inventory-tracking that are also used as forms of localization. Based on our reviews of positioning technologies and emerging data science themes, we present a hierarchical model for collecting and sharing data in forest and fire management, and more broadly in the field of natural resources. The model reflects tradeoffs in range and bandwidth when recording, processing, and communicating large quantities of data in time and space to support resource management, science, and public safety in remote areas. In the hierarchical approach, wearable devices and other sensors typically transmit data at short distances using Bluetooth, Bluetooth Low Energy (BLE), or ANT wireless, and smartphones and tablets serve as intermediate data collection and processing hubs for information that can be subsequently transmitted using radio networking systems or satellite communication. Data with greater spatial and temporal complexity is typically processed incrementally at lower tiers, then fused and summarized at higher levels of incident command or resource management. Lastly, we outline several priority areas for future research to advance big data analytics in natural resources.