Milan Erdelj

Help from the Sky: Leveraging UAVs for Disaster Management

This article presents a vision for future unmanned aerial vehicles (UAV)-assisted disaster management, considering the holistic functions of disaster prediction, assessment, and response. Here, UAVs not only survey the affected area but also assist in establishing vital wireless communication links between the survivors and nearest available cellular infrastructure. A perspective of different classes of geophysical, climate-induced, and meteorological disasters based on the extent of interaction between the UAV and terrestrially deployed wireless sensors is presented in this work, with suitable network architectures designed for each of these cases. The authors outline unique research challenges and possible solutions for maintaining connected aerial meshes for handoff between UAVs and for systems-specific, security- and energy-related issues. This article is part of a special issue on drones.

UAV-assisted disaster management: Applications and open issues

The fast-paced development of Unmanned Aerial Vehicles (UAVs) and their use in different domains, opens a new paradigm on their use in natural disaster management. In UAV-assisted disaster management applications, UAVs not only survey the affected area but also assist in establishing the communication network between the disaster survivors, rescue teams and nearest available cellular infrastructure. This paper identifies main disaster management applications of UAV networks and discusses open research issues related to UAV-assisted disaster management.

Wireless Sensor Networks and Multi-UAV Systems for Natural Disaster Management

Abstract This work identifies the role of Wireless Sensor Networks (WSN) and Unmanned Aerial Vehicles (UAV) in the context of natural disaster management. Main applications of systems involving WSN and UAV are classified according to the disaster management phase, and a review of relevant research activities is provided along with the research and development challenges that still remain unsolved. The main objectives of this work are to present technical results useful to improve the wellbeing of people, and push the state of the art one step forward in the definition of a complete disaster management system.

Multiple point of interest discovery and coverage with mobile wireless sensors

Environmental monitoring has become a typical application of wireless sensor networks. The concept of monitoring certain Points of Interest (PoIs) instead of the whole sensor field helps in reducing the costs of the deployment and improving the performance in terms of coverage. However, the problems of multiple PoI coverage, environment exploration and data report are still solved separately and there are no works that combine the aforementioned problems into a single deployment scheme. In this work, we present a novel approach for mobile sensor deployment, where we combine multiple PoI coverage with network connectivity preservation and environment exploration in order to capture the dynamics of the monitored area. We examine the performance of our scheme through extensive simulation campaigns.

Design and implementation of architecture for multi-robot cooperation in the context of WSN

The concept of autonomous mobile agents gets a lot of attention in the domain of wireless sensor networks (WSN) or wireless sensor and actuator networks (WSAN). Multiple robots that coordinate or cooperate with other sensors, robots or human operator, allow the WSN/WSAN to perform tasks that are far beyond the scope of single robot unit. In this work, we describe the robot middleware architecture that allows networked multi-robot control and data acquisition in the context of wireless sensor networks. Furthermore, we present three examples of robot network deployment and illustrate the proposed architecture usability: the robotic network deployment with the goal of covering the Point of Interest, adaptable multi-hop video transmission scenario, and the case of obtaining the energy consumption during the deployment.

Recharging versus replacing sensor nodes using mobile robots for network maintenance

Wireless sensor networks (WSNs) have been of very high interest for the research community for years, but the quest for deploying a self-sustained network and effectively prolonging its lifetime has not found a satisfactory answer yet. Two main approaches can be identified that target this objective: either “recharging” or “replacing” the sensor nodes that are running out of energy. Of particular interest are solutions where mobile robots are used to execute the above mentioned tasks to automatically and autonomously maintain the WSN, thus reducing human intervention. Recently, the progress in wireless power transfer techniques has boosted research activities in the direction of battery recharging, with high expectations for its application to WSNs. Similarly, also sensor replacement techniques have been widely studied as a means to provide service continuity in the network. The objective of this paper is to investigate the limitations and the advantages of these two research directions. Key decision points must be identified for effectively supporting WSN self-maintenance: (i) which sensor nodes have to be recharged/replaced; (ii) in which order the mobile robot is serving (i.e., recharging/replacing) the nodes and by following which path; (iii) how much energy is delivered to a sensor when recharged. The influence that a set of parameters, relative to both the sensors and the mobile robot, has on the decisions will be considered. Centralized and distributed solutions are compared in terms of effectiveness in prolonging the network lifetime and in allowing network self-sustainability. The performance evaluation in a variety of scenarios and network settings offers the opportunity to draw conclusions and to discuss the boundaries for one technique being preferable to the other.

Points of interest coverage with connectivity constraints using wireless mobile sensors

The coverage of Points of Interest (PoI) is a classical requirement in mobile wireless sensor applications. Optimizing the sensors selfdeployment over a PoI while maintaining the connectivity between the sensors and the sink is thus a fundamental issue. This article addresses the problem of autonomous deployment of mobile sensors that need to cover a predefined PoI with a connectivity constraints and provides the solution to it using Relative Neighborhood Graphs (RNG). Our deployment scheme minimizes the number of sensors used for connectivity thus increasing the number of monitoring sensors. Analytical results, simulation results and real implementation are provided to show the efficiency of our algorithm.

Drones, Smartphones and Sensors to Face Natural Disasters

Many efforts are being done in order to recognize and forecast the occurrence of a natural disaster, in order to react in an efficient manner to the disaster in course of happening, and to quickly and efficiently assess the damage, fix and restore normal state [2–6]. Large-scale natural disasters test the most fundamental human instinct of survival by inflicting massive, and often unpredictable loss to life and property. Various types of natural disasters have been classified in [1] according to the technology that can be used to respond to them: geophysical (earthquake, tsunami, volcano, landslide, avalanche), hydrological (flash-floods, debris flow, floods), climatological (extreme temperature, drought, wildfire) andmeteorological (tropical storm, hurricane, sandstorm, heavy rainfall), among others, have caused losses of many lives in addition to increase in material losses in the order of 100% – 150% over the period of last 30 years [7]. Acknowledging the need for bolstering disaster resilience, this paper contributes a vision of leveraging the latest advances in wireless sensor network (WSN) technology and unmanned aerial vehicles (UAVs) to enhance the ability of network-assisted disaster prediction, assessment and response. Around 47% of the overall losses and 45% of the insured losses derived from inland flooding that occurred in Europe, Canada, Asia and Australia. Altogether, at around US

Mobile Robot Deployment in the Context of WSN

45bn, losses from natural catastrophes were below the average amount for the past ten years (US

UAVs that fly forever: Uninterrupted structural inspection through automatic UAV replacement

85bn). Insured losses totaled approximately US