Marine environments are delicate ecosystems which directly influence local climates, flora, fauna, and human activities. Their monitorization plays a key role in their preservation, which is most commonly done through the use of environmental sensing buoy networks. These devices transmit data by means of satellite communications or close-range base stations, which present several limitations and elevated infrastructure costs. Unmanned Aerial Vehicles (UAV) are another alternative for remote environmental monitoring which provide new types of data and ease of use. These aircraft are mainly used in video capture related applications, in its various light spectrums, and do not provide the same data as sensing buoys, nor can they be used for such extended periods of time. The aim of this research is to provide a flexible, easy to deploy and cost-effective Wireless Sensor Network (WSN) for monitoring marine environments. This proposal uses a UAV as a mobile data collector, low-power long-range communications and sensing buoys as part of a single WSN. A complete description of the design, development, and implementation of the various parts of this system is presented, as well as its validation in a real-world scenario.

Unmanned Aerial Vehicle Based Wireless Sensor Network for Marine-Coastal Environment Monitoring.

Game theory has emerged as a fruitful paradigm for the design of networked multiagent systems. A fundamental component of this approach is the design of agents’ utility functions so that their self-interested maximization results in a desirable collective behavior. In this article, we focus on a well-studied class of distributed resource allocation problems, where each agent is requested to select a subset of resources with the goal of optimizing a given system-level objective. Our core contribution is the development of a novel framework to tightly characterize the worst-case performance of any resulting Nash equilibrium (price of anarchy) as a function of the chosen agents’ utility functions. Leveraging this result, we identify how to design such utilities so as to optimize the price of anarchy through a tractable linear program. This provides us with a priori performance certificates applicable to any existing learning algorithm capable of driving the system to an equilibrium. Part II of this article specializes these results to submodular and supermodular objectives, discusses the complexity of computing Nash equilibria, and provides multiple illustrations of the theoretical findings.

Utility Design for Distributed Resource Allocation—Part I: Characterizing and Optimizing the Exact Price of Anarchy

Unmanned aerial vehicles (UAVs) have been applied for both civilian and military applications; scientific research involving UAVs has encompassed a wide range of scientific study. However, communication with unmanned vehicles are subject to attack and compromise. Such attacks have been reported as early as 2009, when a Predator UAV’s video stream was compromised. Since UAVs extensively utilize autonomous behavior, it is important to develop an autopilot system that is robust to potential cyber-attack. In this paper, we present a biometric system to encrypt communication between a UAV and a computerized base station. This is accomplished by generating a key derived from a user’s EEG Beta component. We first extract coefficients from Beta data using Legendre’s polynomials. We perform encoding of the coefficients using Bose-Chaudhuri-Hocquenghem encoding and then generate a key from a hash function. The key is used to encrypt the communication between XBees. Also we have introduced scenarios where the communication is attacked. When communication with a UAV is attacked, a safety mechanism directs the UAV to a safe home location. This system has been validated on a commercial UAV under malicious attack conditions.

Reliable Security Algorithm for Drones Using Individual Characteristics From an EEG Signal

Many countries suffer from various natural disasters, including heavy rains, that are associated with further flood and landslide disasters. Based on our experiences of different disasters response, we develop a joint international operation framework for a disaster site management with distributed heterogeneous robotic teams that consist of unmanned aerial, ground, surface, and underwater vehicles. The artificial intelligence-based information collection system, which is targeting to become a worldwide standard, contains interaction protocols, thematic mapping approaches, and map fusion processes. The project provides a new working framework and control strategies for heterogeneous robotic teams’ cooperative behavior in sensing, monitoring, and mapping of flood and landslide disaster areas. In this paper, we present an overview of the system and a first stage toward robot interaction protocols development and the system modeling within robot operating system’s Gazebo environment.

Artificial Intelligence Based Framework for Robotic Search and Rescue Operations Conducted Jointly by International Teams

Cooperative networks of low-cost unmanned aerial vehicles (UAVs) are attracting researchers because of their potential to enhance UAV performance. Cooperative networks can be used in many a...

Cooperative localization of unmanned aerial vehicles using GNSS, MEMS inertial, and UWB sensors

Developed societies have a high level of preparedness for natural or man-made disasters. But such incidents cannot be completely prevented, and when an incident like an earthquake or an accident in a chemical or nuclear plant hits a populated area, rescue teams need to be employed. In such situations it is a necessity for rescue teams to get a quick overview of the situation in order to identify possible locations of victims that need to be rescued and dangerous locations that need to be secured. Rescue forces must operate quickly in order to save lives, and they often need to operate in dangerous environments. Hence, robot-supported systems are increasingly used to support and accelerate search operations. The objective of the SENEKA concept is to network the various robots and sensor systems used by first responders in order to make the search for victims and survivors more quick and efficient. SENEKA targets the integration of the robot-sensor network into the operation procedures of the rescue teams. The aim of this paper is to inform on the goals and first research results of the ongoing joint research project SENEKA.

Andreas Wenzel

Papers

SENEKA - sensor network with mobile robots for disaster management