AoI-Sensitive Data Collection in Multi-UAV-Assisted Wireless Sensor Networks

The freshness of information characterized by the age-of-information (AoI) is a key performance indicator (KPI) in Internet of Things (IoT) networks. In this paper, we consider a wireless sensor network (WSN) with an unmanned aerial vehicle (UAV) as a relay. A sensing-then-forwarding scheme is proposed, where the UAV first collects data packets from all sensor nodes and then forwards to a destination node. The average peak AoI is minimized by jointly optimizing the UAV trajectory, communication power, and service time. The feasibility of the formulated problem can be checked by solving a second order cone programming (SOCP), and a successive convex approximation (SCA) based algorithm with provable convergence is developed to obtain an at least suboptimal solution. Through extensive simulations, the proposed algorithm is shown to provide better convergence and much smaller peak AoI than the alternating optimization (AO) based algorithm.

We consider a multi-source status update system, where each source generates status update packets according to a Poisson process which are then served according to a generally distributed service time. For this multi-source M/G/1/1 queueing model, we consider a self-preemptive packet management policy and derive the moment generating functions (MGFs) of the age of information (AoI) and peak AoI of each source. According to the policy, an arriving fresh packet preempts the possible packet of the same source in the system. Furthermore, we derive the MGFs of the AoI and peak AoI for the globally preemptive and non-preemptive policies, for which only the average AoI and peak AoI have been derived earlier. Finally, we use the MGFs to derive the average AoI and peak AoI in a two-source M/G/1/1 queueing model under each policy. Numerical results show the effect of the service time distribution parameters on the average AoI. The results also highlight the importance of higher moments of the AoI.

Unnamed aerial vehicle (UAV) can be employed to collect sensed data from sensor nodes (SNs) in wireless sensor networks, thanks to its high mobility and great maneuverability. However, it is very challenging for one single UAV with limited energy storage to complete the data collection task while guaranteeing the information freshness of the SNs. In this work, we investigate the AoI-optimal data collection problem in multi-UAV-aided wireless sensor networks, where the SNs may be densely located and multiple UAVs with limited energy supply perform data collection collaboratively. In a dense network, the communication ranges of the SNs might overlap with each other, and there is no need for the UAVs to fly over the SNs one by one and collect their sensing data sequentially. Instead, some points can be selected as data collection points (CPs) at which the UAVs collect data from multiple SNs simultaneously. Accordingly, the multi-UAV-aided data collection problem can be decomposed into three parts. Firstly, the positions of the CPs are determined and the SN-CP association is established based on graph theory. Secondly, the CPs are clustered based on the kernel K-means method with each cluster of CPs being visited by one UAV. Thirdly, the optimal flight trajectory of each UAV is found to minimize the the SNs' maximal age of information (AoI) subject to the limited energy capacity. Simulation results validate the effectiveness of our proposed data collection scheme.

This paper investigates the energy transfer and data collection problem of multiple unmanned aerial vehicle (UAV)-assisted wireless-powered Internet of Things (IoT) networks. To ensure information freshness for IoT devices and reduce UAVs’ energy consumption, we minimize the average Age of Information (AoI) of IoT devices by jointly optimizing the energy harvesting (EH) and data collection time for IoT devices, the selection of data collection points (DCPs), DCP-IoT associations, and task assignment, flight speed, and trajectories of UAVs, subject to the limited endurance of UAVs. As this problem is nonconvex, we propose a novel DCP association and trajectory-planning scheme that seeks age-optimal solutions through an iterative three-step process. First, we calculate the EH and data collection time for IoT devices using Karush–Kuhn–Tucker (KKT) conditions. Then, we introduce an optimal hovering time allocation-based affinity propagation (OHTAP) clustering algorithm to determine optimal DCP locations and establish DCP-IoT associations. Finally, we develop two algorithms to optimize UAVs’ trajectories: an improved partheno-genetic algorithm with enhancement mechanisms (EIPGA) and a hybrid algorithm that combines improved MinMax k-means clustering with EIPGA. Numerical results confirm that our scheme consistently outperforms benchmark schemes in AoI performance and solution stability across diverse scenarios.

Unmanned aerial vehicle (UAV) has been widely deployed in efficient data collection for Internet of Things (IoT). UAV can not only act as a relay, but also as an energy source to provide information and energy transmission for ground sensor nodes (SNs). This paper studies the efficient multi-UAV-assisted data collection problem in wireless powered IoT. Specifically multiple UAVs wirelessly charge SNs using radio frequency (RF) energy transfer, and the SNs then use the harvested energy to upload the updates of the sensed information to the UAVs, thus improving the freshness of collected data and extending the service time of the SNs. The problem is modeled as a partially observed Markov decision process (POMDP) with a large observation and action space, where each UAV acts as an intelligent agent to learn the environment and make decisions independently. The value-decomposition network (VDN) algorithm is employed to find the optimal strategy in the multi-agent deep reinforcement learning framework. Simulation results validate the effectiveness of the proposed data collection approach compared to two baseline policies.

Unmanned aerial vehicle (UAV)-enabled communication has emerged as an appealing technology for efficient data collection in wireless sensor networks (WSNs). This paper considers a scenario where multiple UAVs collect data from a group of sensor nodes (SNs) on the ground. We study the fundamental tradeoff between the aerial cost , which is defined by the propulsion energy consumption and operation costs of all UAVs, and the ground cost , which is defined as the energy consumption of all SNs. To characterize such a tradeoff, an optimization problem is formulated to minimize the weighted sum of the above two costs, by optimizing the UAV trajectory jointly with wake-up time allocation, as well as the transmit power of all SNs. As the formulated problem is non-convex, it is difficult to be optimally solved in general. To tackle this issue, we decouple it into two sub-problems: UAV trajectory and wake-up time allocation optimization, as well as SN transmit power optimization. We propose an iterative algorithm to solve the two sub-problems by leveraging successive convex approximation and alternating optimization techniques. In addition, a new approach is proposed to design the UAV initial trajectory with multiple travelling salesman problem (MTSP) technique. Simulations are conducted to corroborate our study and show the flexible tradeoff achieved by the proposed design for cost balance between UAVs and SNs.

Introduction Autonomous unmanned aerial vehicles (UAVs) are considered with increasing interest in a diverse set of civil and commercial applications, such as environmental and natural disaster monitoring, border surveillance, emergency assistance, search and rescue missions, delivery of goods, and construction [208, 132, 362, 265, 434, 110, 280, 163]. Use of single or multiple UAVs as communication relays or aerial base stations for network provisioning in emergency situations and for public safety communications has been of particular interest due to UAVs fast deployment and large coverage capabilities [331, 193, 206, 429]. The deployed aerial platform size and the number of necessary platforms are key factors in determining the on-board communication system in addition to the coverage and capacity requirements [73]. Small-scale multi-rotors are of particular interest in practice due to their ease of deployment, low acquisition and maintenance costs, high maneuverability, and ability to hover. Research and development of small-scale UAVs focused initially on control issues, such as flight stability, maneuverability, and robustness, followed by designing autonomous vehicles capable of waypoint flights with minimal user intervention. Considering the limited flight time and payload capacity of these vehicles and the area sizes the aforementioned applications might span, the interest is shifting more and more toward collaborative multiple UAV systems for efficient and successful missions. The number of UAVs might be from tens to hundreds traveling over tens of meters to kilometers for different applications (see Figure 3.1 for an illustration). Wireless communication is an essential factor not only for network provisioning with UAVs but also for successful deployment of systems consisting of multiple small-scale UAVs. It is highly likely that high-performance links and connectivity in three-dimensional space will be required for several applications (e.g., monitoring certain areas) with data delivery meeting a certain quality of service demands [35].The question arises as to which wireless technology should be employed for aerial networks that consist of air-to-ground and air-to-air links, and data needs to be delivered regardless of significant height and orientation differences. Taking into account the quality of service to be satisfied over diverse links and the high node mobility, it is not yet clear as to whether networking protocols developed for ground networks are readily deployable on UAV networks.

We consider an Internet of Things (IoT) monitoring system, in which an IoT device monitors a physical process and transmits randomly generated status updates to its associated access point (AP) as timely as possible. The timeliness of the status updates is characterized by a recently introduced metric, termed the age of information (AoI), which is defined as the time elapsed since the generation of the last successfully received status update. The channel between the IoT device and the AP is considered to be error-prone and thus the status updates suffer from packet loss. Assuming that the AP provides no feedback to the IoT device, we adopt a practical truncated automatic repeat request (TARQ) scheme: the IoT device keeps transmitting the current status update repeatedly until the maximum allowable transmission times is reached or a new status update is generated. We characterize the inherent age-energy tradeoff for the considered IoT monitoring system. Specifically, a larger value of the maximum allowable transmission times reduces the average AoI, at the cost of incurring higher average energy consumption at the IoT device. Based on the evolution of AoI, we derive the closed-form expressions of the average AoI, the average peak AoI, and the average energy consumption. We then minimize the average AoI by optimizing the transmit power of the IoT device and the maximum allowable transmission times under an average transmit power constraint. Simulations validate the theoretical analysis and reveal that under the same average transmit power constraint, the adopted TARQ scheme achieves a lower average AoI than the classical ARQ scheme that allows an infinite number of retransmission times.

Wireless sensor networks (WSNs) are an important means of collecting data in a variety of situations, such as the monitoring of large or hazardous areas. The retrieval of WSN data can yield better results through the use of unmanned aerial vehicles (UAVs), for example, concerning the increase in the amount of data collected and the decrease in the time between the collection and use of the data. In particular, disaster areas may be left without communication resources and with high residual risk to humans, at which point a WSN can be quickly launched by air to collect relevant data until other measures can be established. The set of rules of each problem’s component (e.g., number of UAVs, UAVs dislocation control, sensors, communication) is considered the approaches to solve the problem. In this meaning, some studies present approaches for the use of UAVs for the collection of WSN data, focusing primarily on optimizing the path to be covered by a single UAV and relying on long-range communication that is always available; these studies do not explore the possibility of using several UAVs or the limitations on the range of communication. This work describes DADCA, a distributed scalable approach capable of coordinating groups of UAVs in WSN data collection with restricted communication range and without the use of optimization techniques. The results reveal that the amount of data collected by DADCA is similar or superior to path optimization approaches by up to 1%. In our proposed approach, the delay in receiving sensor messages is up to 46% shorter than in other approaches, and the required processing onboard UAVs can reach less than 75% of those using optimization-based algorithms. The results indicate that the DADCA can match and even surpass other presented approaches, since the path optimization is not a focus, while also incorporating the advantages of a distributed approach.

With the development of Internet of Things (IoT) technology, wireless sensor networks (WSNs) composed of multiple sensor nodes (SNs) have been able to provide a large number of high-quality services, such as continuous environmental monitoring, automatic control, and transmission of data for intelligent decision-making. SNs in WSNs are typically energy limited and often exist in large numbers, which are widely distributed in hard-to-reach areas. Hence, it is difficult to collect data from SNs without infrastructure support such as base stations (BSs). The low cost and high maneuverability of unmanned aerial vehicles (UAVs) provide an efficient solution for data acquisition in WSNs but with the challenge of control system of the UAV. In this paper, we proposed a deep reinforcement learning based UAV control system to intelligently solve the data harvesting problem in WSNs. Our optimization target is to jointly optimize the trajectory of the UAV and the bandwidth resources of the airborne base station to maximize the data acquisition success rate when the UAV is limited in energy. The objective function and constraints of this problem are highly non-convex. To solve this problem, we first modeled the movement of the UAV and the bandwidth allocation of the airborne base station in each time slot as a Markov decision process (MDP). Then we designed a deep reinforcement learning architecture to solve the above MDP. Compared with the existing data harvesting algorithms, the proposed method has a great improvement in data acquisition success rate.

Age of Information with network coding

To archive diverse kinds of goal, multiple unmanned aerial vehicles (UAVs) need to cooperate each other. Therefore, the importance of a ground station becomes more important. This paper proposes a framework of the ground station that controls multiple and hetero UAVs. The framework contains multiple UAV control modules and UI interfaces and has the flexibility for adding diverse kinds of UAVs and embedded devices such as commercial UAVs, Arduino-based UAVs, cameras and storage devices.

This paper studies the integration of data collection and offloading for maritime Internet of Things (IoT) systems with multiple unmanned aerial vehicles (UAVs). In the considered multi-UAV maritime IoT system, the UAVs act as the aerial base stations to complete the missions of data collection from buoys and data offloading to the offshore base station (OBS). In this case, the UAVs need to adaptively select the mission mode between data collection and data offloading according to the network resources and mission requirements. In this paper, we aimed to minimize the completion time of data collection and offloading missions for all UAVs by jointly optimizing the UAV trajectories, mission mode selection, transmit power of buoys, and association relationships between the UAVs and buoy/OBS. In order to solve the mixed-integer non-convex minimization problem, we first designed a multi-agent deep reinforcement learning algorithm based on a hybrid discrete and continuous action space to preliminarily obtain the UAV trajectories, mission mode selection, and the transmit power of buoys. Then, we propose an algorithm based on the stable marriage problem to determine the buoy–UAV and UAV–OBS association relationships. Finally, the simulation results show that the proposed algorithms can effectively shorten the total mission completion time of data collection and offloading for the multi-UAV-assisted maritime IoT system.

In many emerging Internet of Things (IoT) applications, the freshness of the is an important design criterion. Age of Information (AoI) quantifies the freshness of the received information or status update. This work considers a setup of deployed IoT devices in an IoT network; multiple unmanned aerial vehicles (UAVs) serve as mobile relay nodes between the sensors and the base station. We formulate an optimization problem to jointly plan the UAVs' trajectory, while minimizing the AoI of the received messages and the devices' energy consumption. The solution accounts for the UAVs' battery lifetime and flight time to recharging depots to ensure the UAVs' green operation. The complex optimization problem is efficiently solved using a deep reinforcement learning algorithm. In particular, we propose a deep Q-network, which works as a function approximation to estimate the state-action value function. The proposed scheme is quick to converge and results in a lower ergodic age and ergodic energy consumption when compared with benchmark algorithms such as greedy algorithm (GA), nearest neighbour (NN), and random-walk (RW).

Information acquisition and rescue operation management can be facilitated by the use of unmanned aerial vehicles (UAVs) equipped with cameras and sensors that are capable of aerial photography and acquire important information from disaster-stricken areas. UAV technology is a relatively low-cost, flexible, and safe solution in missions that are difficult for humans or other systems. UAV usage in pre-and post-disaster assessment, response, and management is an active area of research. There are two important technical aspects of UAV applications in disaster situations. The first is the limited communication range. Information acquired and photography produced by UAVs are only transmitted in real-time to the ground control station (GCS) within its communication range. Remote real-time monitoring of distant areas beyond the UAV's communication range is not possible using a single UAV. It is only possible through communication among several UAVs. The second is the relatively short flight time due to limited battery capacity. UAV replacement is required in order to carry out a persistent monitoring mission. In this paper, we propose a new remote real-time monitoring system of a distant disaster area using multiple UAVs. Wireless communications between multiple UAVs constitutes a UAV network in our proposed system. Moreover, the proposed system replaces the UAVs in-flight with standby UAVs according to the remaining energy levels. We compare the performance of the proposed system with an existing UAV system. Our simulation results show that the proposed system can achieve efficient remote real-time monitoring.