Considering the problem of rapid collection and reliable transmission of emergency information, as well as the emergency response missions and natural environment characteristics of emergency sites, this article proposes a novel unmanned aerial vehicle (UAV) path planning framework that considers waypoint and motion. First, the waypoint planning improves the reliability of the uplink and the downlink data transmission to the greatest extent by jointly optimizing UAVs 3-D position and transmit power of UAV and users’ equipment (UE). Second, the motion planning allows the UAVs to traverse all waypoints in the shortest amount of time, which allows more time for data collection by maximizing the acceleration of each UAV. Our results show that the proposed path planning algorithm can significantly improve the normalized Quality-of-Experience (QoE) index and effectively shorten the flight time of each UAV for different assignments. Therefore, the method proposed in this article can effectively solve the key problems in the technical system of emergency response and provide a scientific decision-making basis for the post-disaster emergency response.

Multiobjective UAV Path Planning for Emergency Information Collection and Transmission

In post-disaster scenarios, it is challenging to provide reliable and flexible emergency communications, especially when the mobile infrastructure is seriously damaged. This article investigates the unmanned aerial vehicle (UAV)-based emergency communication networks, in which UAV is used as a mobile aerial base station for collecting information from ground users in affected areas. Due to the breakdown of ground power system after disasters, the available energy of affected user equipment (UE) is limited. Meanwhile, with the complex geographical conditions after disasters, there are obstacles affecting the flight of UAV. Aiming at maximizing the uplink throughput of UAV networks during the flying time, we formulate the UAV trajectory optimization problem considering UE energy limitation and location of obstacles. Since the constraint on UE energy is dynamic and long-term cumulative, it is hard to be solved directly. We transform the problem into a constrained Markov decision-making process (CMDP) with UAV as agent. To tackle the CMDP, we propose a safe-deep-Q-network (safe-DQN)-based UAV trajectory design algorithm, where the UAV learns to selects the optimal action in reasonable policy sets. Simulation results reveal that: 1) the uplink throughput of the proposed algorithm converges within multiple iterations and 2) compared with the benchmark algorithms, the proposed algorithm performs better in terms of uplink throughput and UE energy efficiency, achieving a good trade-off between UE energy consumption and uplink throughput.

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Trajectory Optimization for UAV Emergency Communication With Limited User Equipment Energy: A Safe-DQN Approach

Efficient and reliable systems are required to detect and monitor disasters such as wildfires as well as to notify the people in the disaster-affected areas. Internet of Things (IoT) is the key paradigm that can address the multitude problems related to disaster management. In addition, an unmanned aerial vehicles (UAVs)-enabled IoT platform connected via cellular network can further enhance the robustness of the disaster management system. The UAV-enabled IoT platform is based on three main research areas: (i) ground IoT network; (ii) communication technologies for ground and aerial connectivity; and (iii) data analytics. In this paper, we provide a holistic view of a UAVs-enabled IoT platform which can provide ubiquitous connectivity to both aerial and ground users in challenging environments such as wildfire management. We then highlight key challenges for the design of an efficient and reliable IoT platform. We detail a case study targeting the design of an efficient ground IoT network that can detect and monitor fire and send notifications to people using named data networking (NDN) architecture. The use of NDN architecture in a sensor network for IoT integrates pull-based communication to enable reliable and efficient message dissemination in the network and to notify the users as soon as possible in case of disastrous situations. The results of the case study show the enormous impact on the performance of IoT platform for wildfire management. Lastly, we draw the conclusion and outline future research directions in this field.

https://www.mdpi.com/1996-1073/12/14/2706/pdf

Unmanned Aerial Vehicles enabled IoT Platform for Disaster Management

To meet the rapid growth of diverse traffic demand in mobile networks, unmanned aerial vehicles (UAVs) are proposed to support terrestrial networks by carrying base stations towards demand when needed. However, how to deploy the UAVs effectively and efficiently is a challenging task. In this paper, we investigate this problem from the load balancing perspective. Our goal is to make the traffic loads of UAVs almost equal so that the networks can be stable and robust to unexpected events. In the pre-processing stage, we introduce a clustering method to divide users into several categories so as to initialize the positions of UAVs in the maximal local density areas. Then, we propose a user association strategy to make the UAVs serve almost equal traffic demand, where our optimization task is to minimize the maximum traffic demand of subregions with constraints on the capacity and the shape of subregions. Third, we propose a local search procedure to relocate UAVs based on backtracking line search to refine the load balance among the system. By alternately invoking user association and location algorithms, we can obtain (near) optimum UAV positions. Finally, we adjust the altitude of each UAV to decrease power consumption of the system. Numerical results indicate that our proposal can serve more users compared with the SINR-based strategy. Moreover, the traffic capacity among UAVs and the areas of subregions of UAVs are more balanced.

Location Optimization and User Association For Unmanned Aerial Vehicles Assisted Mobile Networks

Next generation mobile networks need to expand towards uncharted territories in order to enable the digital transformation of society. In this context, aerial devices such as unmanned aerial vehicles (UAVs) are expected to address this gap in hard-to-reach locations. However, limited battery-life is an obstacle for the successful spread of such solutions. Reconfigurable intelligent surfaces (RISs) represent a promising solution addressing this challenge since on-board passive and lightweight controllable devices can efficiently reflect the signal propagation from the ground BSs towards specific target areas. In this paper, we focus on air-to-ground networks where UAVs equipped with RIS can fly over selected areas to provide connectivity. In particular, we study how to optimally compensate flight effects and propose RiFe as well as its practical implementation Fair-RiFe that automatically configure RIS parameters accounting for undesired UAV oscillations due to adverse atmospheric conditions. Our results show that both algorithms provide robustness and reliability while outperforming state-of-the-art solutions in the multiple conditions studied.

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RISe of Flight: RIS-Empowered UAV Communications for Robust and Reliable Air-to-Ground Networks

In this letter, we investigate how a rotary-wing unmanned-aerial vehicle (UAV) acts as a wireless base station to provide emergency communication service for a post-disaster area with unknown user distribution. The formulated optimization task is to find out the optimal path starting and ending at the same point to serve as many as possible users under limited battery capacity. We show that this problem can be transformed to an extended multi-armed bandit (MAB) problem, for which we propose two efficient path planning algorithms. Simulation results show that, in terms of the total number of served users, our proposed algorithms outperform the straightforward helical path that scans the entire area by circles with increasing radius.

UAV-Assisted Emergency Communications: An Extended Multi-Armed Bandit Perspective

Recently, unmaned aerial vehicle (UAV) assisted wireless network has been recognized as a promising technology for post-disaster communications, in which multiple UAVs are dynamically deployed in the air as cellular base stations to provide public wireless connectivities when the ground infrastructure collapses. However, due to the non- uniformity of post-disaster traffic distribution and the large scale of post-disaster area, trajectory planning is considered as a major challenge for UAV- assisted post-disaster communications in many aspects including coverage, energy efficiency and computational complexity. In this paper, we consider a general trajectory planning problem for multi-UAV assisted wireless networks in a post-disaster scenario. We show that the problem can be formulated as a multi-depot vehicle routing problem. Then, we propose two heuristic algorithms that can efficiently utilize the battery of UAVs to improve the coverage performance. Simulation results show that, compared to the intuitive greedy algorithm, the coverage ratio can be improved by 8% and 28%, respectively, by using the proposed algorithms.

Yu Lin

Papers

UAV-Assisted Emergency Communications: An Extended Multi-Armed Bandit Perspective

Trajectory Planning for Multi-UAV Assisted Wireless Networks in Post-Disaster Scenario