Optimal UAV Deployment for Data Collection in Deadline-based IoT Applications

The deployment of UAVs is one of the key challenges in UAV-based communications while using UAVs for IoT applications. In this article, a new scheme for energy efficient data collection with a deadline time for the Internet of things (IoT) using the Unmanned Aerial Vehicles (UAV) is presented. We provided a new data collection method, which was set to collect IoT node data by providing an efficient deployment and mobility of multiple UAV, used to collect data from ground internet of things devices in a given deadline time. In the proposed method, data collection was done with minimum energy consumption of IoTs as well as UAVs. In order to find an optimal solution to this problem, we will first provide a mixed integer linear programming model (MILP) and then we used a heuristic to solve the time complexity problem. The results obtained in the simulation results indicate the optimal performance of the proposed scheme in terms of energy consumption and the number of used UAVs.

This paper studies an unmanned aerial vehicle (UAV)-assisted Internet of Things (IoT) data collection system, where a UAV is employed as a data collection platform for a group of ground IoT devices. Our objective is to minimize the energy consumption of this system by optimizing the UAV's deployment, including the number and locations of stop points of the UAV. When using evolutionary algorithms to solve this UAV's deployment problem, each individual usually represents an entire deployment. Since the number of stop points is unknown a priori , the length of each individual in the population should be varied during the optimization process. Under this condition, the UAV's deployment is a variable-length optimization problem and the traditional fixed-length mutation and crossover operators should be modified. In this paper, we propose a differential evolution algorithm with a variable population size, called DEVIPS, for optimizing the UAV's deployment. In DEVIPS, the location of each stop point is encoded into an individual, and thus the whole population represents an entire deployment. Over the course of evolution, differential evolution is employed to produce offspring. Afterward, we design a strategy to adjust the population size according to the performance improvement. By this strategy, the number of stop points can be increased, reduced, or kept unchanged adaptively. In DEVIPS, since each individual has a fixed length, the UAV's deployment becomes a fixed-length optimization problem and the traditional fixed-length mutation and crossover operators can be used directly. The performance of DEVIPS is compared with that of five algorithms on a set of instances. The experimental studies demonstrate its effectiveness.

In this paper, the problem of unmanned aerial vehicles (UAV) deployment is investigated for visible light communication (VLC)-enabled UAV networks. Here, UAVs can simul-taneously provide communications and illumination services to ground users. In this model, ambient illumination distribution of the service area must be considered since it can cause interference over the VLC link and affects the illumination requirements of users. This problem is formulated as an optimization problem, which jointly considers UAV deployment, user association, power efficiency, and predictions of the illumination distribution. To solve this problem, we first need to predict illumination distribution to proactively determine the UAV deployment and user association so as to minimize total transmission power of UAVs. To predict the illumination distribution of the entire service area, a federated learning framework based on the machine learning algorithm of convolutional auto-encoder (CAE) is proposed. Compared to the centralized machine learning algorithms that requires complete illumination data for centralized training, the proposed algorithm enables the UAVs to train their local CAE with partial illumination data and cooperatively build a global CAE model that can predict the entire illumination distribution. Using these predictions, the optimal UAV deployment and user association policy that minimizes the total transmission power of UAVs is determined. Simulation results demonstrate that the proposed approach reduces the transmission power of UAVs up to 14.8% and 25.1%, respectively, compared to the local CAE prediction models and the conventional optimal algorithm without illumination distribution predictions.

In this paper, the problem of optimizing the deployment of unmanned aerial vehicles (UAVs) equipped with visible light communication (VLC) capabilities is studied. In the studied model, the UAVs can simultaneously provide communications and illumination to service ground users. Ambient illumination increases the interference over VLC links while reducing the illumination threshold of the UAVs. Therefore, it is necessary to consider the illumination distribution of the target area for UAV deployment optimization. This problem is formulated as an optimization problem whose goal is to minimize the total transmit power while meeting the illumination and communication requirements of users. To solve this problem, an algorithm based on the machine learning framework of gated recurrent units (GRUs) is proposed. Using GRUs, the UAVs can model the long-term historical illumination distribution and predict the future illumination distribution. In order to reduce the complexity of the prediction algorithm while accurately predicting the illumination distribution, a Gaussian mixture model (GMM) is used to fit the illumination distribution of the target area at each time slot. Based on the predicted illumination distribution, the optimization problem is proved to be a convex optimization problem that can be solved by using duality. Simulations using real data from the Earth observations group (EOG) at NOAA/NCEI show that the proposed approach can achieve up to 22.1% reduction in transmit power compared to a conventional optimal UAV deployment that does not consider the illumination distribution. The results also show that UAVs must hover at areas having strong illumination, thus providing useful guidelines on the deployment of VLC-enabled UAVs.

The Internet of Things (IoT) can be conveniently deployed while empowering various applications, where the IoT nodes can form clusters to finish certain missions collectively. In this paper, we propose to employ unmanned aerial vehicles (UAVs) to assist the clustered IoT data collection with blockchain-based security provisioning. In particular, the UAVs generate candidate blocks based on the collected data, which are then audited through a lightweight proof-of-stake consensus mechanism within the UAV-based blockchain network. To motivate efficient blockchain while reducing the operational cost, a stake pool is constructed at the active UAV while encouraging stake investment from other UAVs with profit sharing. The problem is formulated to maximize the overall profit through the blockchain system in unit time by jointly investigating the IoT transmission, incentives through investment and profit-sharing, and UAV deployment strategies. Then, the problem is solved in a distributed manner while being decoupled into two layers. The inner layer incorporates IoT transmission and incentive design, which are tackled with large-system approximation and one-leader-multi-follower Stackelberg game analysis, respectively. The outer layer for UAV deployment is undertaken with a multi-agent deep deterministic policy gradient approach. Results show the convergence of the proposed learning process and the UAV deployment, and also demonstrated the performance superiority of our proposal as compared with the baselines.

Unmanned aerial vehicles (UAVs), benefit by low cost, fast deployment, and large coverage, are widely applied as aerial relays in providing communication services for the areas with heavy traffic load or disasters. In this paper, the capacity of UAV relaying networks is studied, where the UAVs act as relays for the ground users. Two scenarios of UAVs distributed on an aerial 2-D plane and in a 3-D space are studied. The analysis reveals that in order to control the air-to-ground interference, the altitude of UAVs is at most in the same order to the distance between two adjacent UAVs. Besides, it is discovered that the pathloss exponent value α = 2.5 is a watershed for the capacity of UAV relaying networks, i.e., when α ≤ 2.5, the capacity of UAV relaying networks with the 2-D deployment of UAVs is greater than that with the 3-D deployment of UAVs. Otherwise, the capacity of UAV relaying networks with the 3-D deployment of UAVs is larger than that with the 2-D deployment of UAVs. Furthermore, the mobility of UAVs is studied, and it is verified that the mobility can orderly improve the capacity of UAV relaying networks. The theoretical analysis of this paper provides a guideline for the deployment of UAVs as aerial relays.

Joint differential evolution algorithm in RIS-assisted multi-UAV IoT data collection system

UAV-Aided trustworthy data collection in federated-WSN-enabled IoT applications

There have been increasing interests in employing unmanned aerial vehicles (UAVs) such as drones for telecommunication purpose. In such networks, UAVs act as base stations and provide downloading service to users. Compared with conventional terrestrial base stations, such UAV-BSs can dynamically adjust their locations to improve network performance. However, there exists two important issues in UAV networks, handoff overhead and UAV deployment. The handoff overhead issue is particularly important for UAVs because UAV BSs are connected to cellular BSs via wireless backhaul links, which are costly in terms of spectrum usage and energy consumption. Hence, it is highly desirable to eliminate any unnecessary handoff to minimise the waste of wireless backhaul. The UAV deployment, on the other hand, introduces a new tool for radio resource management, since BS positions are open for network optimisation. In this paper, a smart user association algorithm, named reinforcement learning handoff (RLH), is devised to reduce redundant handoffs in UAV networks and two methods of UAV mobility control are designed to co-operate with the proposed RLH algorithm to optimise the system throughput. In the RLH algorithm, users perform handoffs according to the reward of a reinforcement learning process. In UAV deployment two UAV mobility control methods are proposed respectively base on the SNR estimation and based on the K-Means approach. According to our simulation results, the RLH algorithm can reduce the number of handoffs by 75%.

In this paper, the optimal deployment of multiple unmanned aerial vehicles (UAVs) acting as flying base stations is investigated. Considering the downlink scenario, the goal is to minimize the total required transmit power of UAVs while satisfying the users' rate requirements. To this end, the optimal locations of UAVs as well as the cell boundaries of their coverage areas are determined. To find those optimal parameters, the problem is divided into two sub-problems that are solved iteratively. In the first sub-problem, given the cell boundaries corresponding to each UAV, the optimal locations of the UAVs are derived using the facility location framework. In the second sub-problem, the locations of UAVs are assumed to be fixed, and the optimal cell boundaries are obtained using tools from optimal transport theory. The analytical results show that the total required transmit power is significantly reduced by determining the optimal coverage areas for UAVs. These results also show that, moving the UAVs based on users' distribution, and adjusting their altitudes can lead to a minimum power consumption. Finally, it is shown that the proposed deployment approach, can improve the system's power efficiency by a factor of 20 χ compared to the classical Voronoi cell association technique with fixed UAVs locations.

Particle Swarm Optimization (PSO) has been widely employed to optimize the deployment of Unmanned Aerial Vehicles (UAVs) in various scenarios, particularly because of its efficiency in handling both single and multi-objective optimization problems. In this paper, a framework for optimizing the deployment of edge-enabled UAVs using Pareto-PSO is proposed for data collection scenarios in which UAVs operate autonomously and execute onboard distributed multi-objective PSO to maximize the total non-overlapping coverage area while minimizing latency and energy consumption. Performance evaluation is conducted using key indicators, including convergence time, throughput, and total non-overlapping coverage area across bandwidth and swarm-size sweeps. Simulation results demonstrate that the Pareto-PSO consistently attains the highest throughput and the largest coverage envelope, while exhibiting moderate and scalable convergence times. These results highlight the advantage of treating the objectives as a vector-valued objective in Pareto-PSO for real-time, scalable, and energy-aware edge-UAV deployment in dynamic Internet of Flying Things environments.

Operating unmanned aerial vehicles (UAVs) for data collection is a promising approach across various practical domains, offering flexibility in challenging environments to facilitate data collection within sensor networks (SNs). However, UAV-assisted data collection in SNs faces several challenges, primarily due to energy constraints at both UAV and SN nodes and the inefficiencies caused by collecting redundant data. Addressing these issues is crucial for improving the efficiency of UAV-assisted data collection. Considering the value of information (VoI) urges the collection of the newly generated data and gives less importance for collecting old data values. Moreover, the collection of all data may lead to collect data representing redundant information which may reduce the network efficiency. This study aims to reduce redundant data collection while deploying the minimum number of UAVs, minimizing energy consumption and maximizing VoI. We first formulate the general problem and solve it as a multi-objective optimization problem. We then decompose the problem into two sub-problems where wepropose real-time approaches including (1) data redundancy avoidance and VoI evaluation, and (2) dynamic UAV deployment and position adaptation. In the first problem, the proposed approach clusters SNs and prioritizes non-redundant data by assigning VoI, while neglecting redundant data. In the second, we consider optimized UAV position adaptation where we generated the problem as a multi-objective optimization problem and solved it as a mixed-integer linear programming problem with constraints related to UAV range, UAV steps, and time constraints. To address these objectives, our proposed approach incorporates deep reinforcement learning (RL-DQN) techniques to optimize UAV deployment, minimizing the number of UAVs while maximizing the number of successfully collected SNs with non-redundant data. The model considers VoI and energy constraints of the SNs, enhancing both efficiency and sustainability. The proposed approach outperforms other algorithms, demonstrating higher efficiency in terms of UAV deployment, served SN, VoI and energy consumption.

Unmanned aerial vehicle (UAV) technology is a promising solution for rapidly providing wireless communication services to ground users, where a UAV has limited service coverage and needs to fly through users at different locations for serving them locally. The existing UAV deployment studies largely assume the users’ demands do not change during UAV deployment. When the users’ demands dynamically change over time, the key challenge is how to adapt the UAV deployment strategy to the partial and even outdated observations on the users’ activities given the UAV's flying speed limit. In this paper, we study dynamic UAV deployment to learn and adapt to the time-varying user activities, where the activity pattern of a user (if out of the UAV service coverage) is hidden from the UAV and follows a time-slotted Markov chain that switches between active and idle states. We formulate the learning-and-adaption based UAV deployment problem as a partially observable Markov decision process (POMDP) to maximize the total discounted hit rate of active users, where the UAV decides for itself whether to chase an active user in a distant location (with delayed reward) or to wait for the idle user in the current location to return to the active state (with smaller service probability) over time. We show there is a fundamental delay-reward tradeoff, and prove that the UAV will optimally follow a threshold-based policy by waiting at an idle user for a time threshold before moving to another user. We also show the UAV is more likely to move if the temporal correlation of each user's idling pattern is stronger or the travel distance between users is shorter. Furthermore, we extend to a more general scenario where the UAV does not even know the parameters of each user's temporal activity distribution, and apply Q-learning to develop another threshold-based deployment policy for a multi-user scenario.

A Design of a Scheduling System for an Unmanned Aerial Vehicle (UAV) Deployment

As the general mobile edge computing (MEC) scheme cannot adequately handle the emergency communication requirements in vehicular networks, unmanned aerial vehicle (UAV)-assisted vehicular edge computing networks (VECNs) are envisioned as the reliable and cost-efficient paradigm for the mobility and flexibility of UAVs. UAVs can perform as the temporary base stations to provide edge services for road vehicles with heavy traffic. However, it takes a long time and huge energy consumption for the UAV to fly from the stay charging station to the mission areas disorderly. In this paper, we design a pre-dispatch UAV-assisted VECNs system to cope with the demand of vehicles in multiple traffic jams. We propose an optimal UAV flight trajectory algorithm based on the traffic situation awareness. The cloud computing center (CCC) server predicts the real-time traffic conditions, and assigns UAVs to different mission areas periodically. Then, a flight trajectory optimization problem is formulated to minimize the cost of UAVs, while both the UAV flying and turning energy costs are mainly considered. In addition, we propose a deep reinforcement learning(DRL)-based energy efficiency autonomous deployment strategy, to obtain the optimal hovering position of UAV at each assigned mission area. Simulation results demonstrate that our proposed method can obtain an optimal flight path and deployment of UAV with lower energy consumption.