Collection of environmental data from emergency sites where there is no or minimal cellular infrastructure exists is very critical for efficient response management. Unmanned aerial vehicles (UAV) can provide a tremendous support during that process thanks to their flexibility, agility and lower cost. A mesh network formed among the UAVs can facilitate the data collection process while also keeping the communication among them. However, as the age of the collected information (i.e., from the moment it is generated at the ground sensor node to the moment it is delivered to the emergency response center) defines the success of response tasks, the trajectory of the UAV mesh network should be determined carefully considering the timely delivery of the critical data. In this paper, we study the path planning problem for a UAV mesh network for AoI optimal data collection from the ground IoT devices in such emergency sites with minimal or no infrastructure. We explore different settings that could happen in such scenarios and develop an Integer Linear Programming (ILP) based model for each to optimize the UAV trajectories with the main goal of minimizing the maximum AoI from the collected data. In order to avoid the high complexity of ILP solutions, we also propose relaxed models. Through simulations, we compare the results in different scenarios in terms of the maximum AoI and UAV path lengths and discuss potential drawbacks in each.

Unmanned Aerial Vehicles (UAVs) can help data collection from ground sensors or Internet of Things (IoT) devices deployed even in hard to access areas and deliver them to their destinations as relays. However, the UAV trajectories should be planned carefully due to their limited battery lifetimes. Recently, Age of Information (AoI) has also been considered as a metric to quantify the freshness of the data collected during this process and the path of the UAVs are aimed to be optimized considering AoI. However, existing studies have defined the AoI of the collected data in the context of delivering the collected data to a specific destination only. Moreover, they assume the data is available at each IoT device before the UAV is dispatched. In this paper, we consider a set of base stations distributed in the area that a UAV travels through and define the AoI from the moment the data is generated till it is uploaded to any of the base stations by the cellular-connected UAV. We also consider data generation times at each IoT device requiring the UAV’s arrival to an IoT device after this time. Our goal is to minimize the maximum AoI of any collected data while also minimizing the mission time and the path of the UAV for energy saving. We model and solve the problem using Integer Linear Programming (ILP) and with a heuristic based solution. The results obtained in different scenarios show that heuristic approach can provide close to optimal ILP based results while running much faster.

Cellular-connected Unmanned Aerial Vehicles (UAVs) need consistent cellular network connectivity to effectively accomplish their designated missions. However, when navigating through regions with partial coverage, such as rural areas, the task of planning the flight paths for these UAV missions becomes notably intricate. Algorithms designed to solve this issue require significant computational resources, making them infeasible for active deployment where an algorithm must run in real time using small compute power. Furthermore, these algorithms exponentially scale in run-time with respect to the number of UAVs being considered. To tackle this problem, we model the parameter space as a discrete grid-world, enable collaboration between drones, and gather supervised data from nonlinear programming and unsupervised data from a simulated version of the environment with associated rewards. We then train a Deep Neural Network (DNN) on this data and approximate optimal results by combining imitation and reinforcement learning methods. This DNN can successfully be deployed at fast speeds using relatively small computational power and can generalize to unseen maps where drone collaboration can be used to reduce mission time. By using the results of a network trained on supervised data as a guiding hand during training, our reinforcement learning approach achieves results better than either method in isolation.

Unmanned aerial vehicle (UAV) technology has become increasingly pivotal in various industries including agriculture, emergencies, and transportation. However, there is a growing need for more intuitive and unobtrusive control mechanisms. In response, our team has developed a groundbreaking technique that optimizes drone control and tracking through operator gaze. Through the use of eye-tracking interaction, we have created a more intuitive approach to human operation, which reduces operator workload and improves overall efficiency. After extensive testing on the Parrot ANAFI drone, we have concluded that this implementation has the potential to revolutionize drone control and elevate it to new heights.

International mobile subscriber identity (IMSI) sharing-based aggregated communication aims to connect multiple Internet of Things (IoT) devices to the mobile operator’s core network over the same subscriber line. IoT devices with low data rates and long data sending intervals are first grouped together and assigned the same subscriber identity. They then take turns to perform their data exchanges using the same cellular connection, yielding huge savings in resource (e.g., number of active bearers) usage. Current solutions however do not consider different device traffic characteristics, the flexibility in traffic patterns, and dynamic network environments where new IoT devices join and existing ones leave the network. In this article, we study the problem of the grouping of IoT devices that will share the same subscriber identity based on their traffic patterns which can also be slightly shifted. We also study the efficient regrouping of these devices as the set of devices in the network changes. We first solve the optimal grouping and traffic aggregation problem for the initial and updated network states using integer linear programming (ILP). Then, to avoid the high complexity of ILP solutions, we develop heuristic-based solutions. Through extensive simulations, we show that heuristic-based algorithms can provide close to optimal ILP-based results while running much faster. The results also show that shifting-based grouping provides more resource saving compared to no-shifting-based aggregation and the proposed solution for dynamic environments can maintain the resource saving with a much lower complexity.

Cellular-enabled unmanned aerial vehicles (UAVs) require almost continuous cellular network connectivity to fulfill their missions successfully. However, the area (e.g., rural) they fly over may have partial coverage, making the path planning of such UAV missions a challenging task. Recently a tolerable outage duration is taken into account for such UAVs, and the trajectory optimization under this outage duration is studied. However, these existing studies consider only a single UAV and focus on optimization of each UAV's own path separately even in multi-UAV scenarios. In this paper, we study the trajectory optimization problem for cellular-enabled UAVs by taking into account the collaboration among UAVs. That is, for a given set of UAVs, each with a mission to fly from a starting point to an ending point, we aim to optimize the total mission completion time for all UAVs such that none of them has a connection outage more than a threshold. We let UAVs collaborate and provide connectivity as relays to each other to solve their outage problem and shorten their trajectories. We first model and solve this problem using nonlinear programming after discretization of the problem. Since it takes longer to solve the problem with such an approach, we then provide a graph-based approximate solution that runs fast. Numerical results show that the proposed approximate solution provides close to optimal results and performs better than state-of-the-art solutions that consider each UAV separately without collaboration among UAVs.

Aggregated Internet of Things (IoT) communication aims to use core network resources efficiently by providing cellular access to a group of IoT devices over the same subscriber identity. Leveraging the low data rates and long data sending intervals of IoT devices, several of the IoT devices in the same serving area of the core network are grouped together and take turns to send their data to their servers without causing overlaps in their communication. In this paper, we take this approach further and benefiting from the flexibility in data sending schedules, we aim to increase savings in cellular resources by shifting (delaying or performing earlier) the regular traffic patterns of IoT devices slightly. To this end, we consider two different traffic shifting models, namely, consistent and inconsistent shifting. We first solve the optimal aggregation of IoT devices under each model by using Integer Linear Programming (ILP). In order to avoid the high complexity of ILP solution, we then develop a heuristic based solution that runs in polynomial time. Through simulations, we show that heuristic based solution provides close to optimal results in various scenarios and shifting based aggregated communication offers more resource optimization (i.e., smaller number of bearers needed to connect all IoT devices) than the aggregated communication with no shifting.