Ground Internet Of Things Research Articles

Hierarchical aerial offload computing algorithm based on the Stackelberg-evolutionary game model

Aerial access networks have been envisioned as a promising 6 G technology to enhance the service experience in underserved areas where terrestrial base stations do not exist. In such scenarios, a hierarchical model of high-altitude platforms (HAPs) and unmanned aerial vehicles (UAVs) is considered to provide aerial computing services for ground Internet of Things (IoT) devices. In this study, we investigate a hierarchical aerial computing system to optimally orchestrate the limited computation resources in both HAPs and UAVs. For offloading services, we formulate a joint resource allocation problem to maximize service satisfaction for terrestrial IoT devices. To solve this problem, we employ the ideas of game theory with centralized decision and decentralized execution. Through the Stackelberg-evolutionary game model, the HAP works as a leader, and selects its price strategy based on the evolutionary learning process. As followers, individual UAVs make decisions to partially offload their computing tasks by considering different objectives. According to the interactive control paradigm, our proposed method can get reciprocal advantages for HAPs, UAVs, and ground IoT devices while adaptively handling dynamic aerial network conditions. Finally, extensive simulation results verify the efficiency of our proposed algorithm to increase the usability of edge servers’ computational resources. Compared with other existing state-of-the-art aerial network offloading protocols, we can improve the profits of system throughput, resource usability and UAV fairness up to 10 %, 10 %, and 15 %, respectively.

Satellite-Based Localization of IoT Devices Using Joint Doppler and Angle-of-Arrival Estimation

While global navigation satellite system (GNSS) technologies have always been the go-to solution for localization problems, they may not be the best choice for some Internet-of-Things (IoT) applications due to the incurred power consumption and cost. In this paper, we present an alternative satellite-based localization method exploiting the signature of Doppler shifts and angle-of-arrival measurements as seen by a low-Earth-orbit (LEO) satellite. We first derive the joint likelihood function of the measurements, which is represented as a combination of three Gaussian distributions. Then, we show that the maximum likelihood problem reduces to a more-efficient mean squared error minimization in the Gaussian case as inferred from real measurements we collected from low-Earth-orbit satellite using a tracking ground station. Thus, we propose utilizing a stochastic optimizer to search for the global minimum of the mean squared error, which represents the location of the ground IoT device as estimated by the satellite platform. The emulated results show that the IoT device localization, in such a realistic model, can be performed with sufficient accuracy for IoT applications.

On Supporting Multiservices in UAV-Enabled Aerial Communication for Internet of Things

Multi-services are of fundamental importance in Unmanned Aerial Vehicle (UAV)-enabled aerial communications for the Internet of Things (IoT). However, the multi-services are challenging in terms of requirements and use of shared resources such that the traditional solutions for a single service are unsuitable for the multi-services. In this paper, we consider a UAV-enabled aerial access network for ground IoT devices, each of which requires two types of services, namely ultra Reliable Low Latency Communication (uRLLC) and enhanced Mobile Broadband (eMBB), measured by transmission delay and effective rate, respectively. We first consider a communication model that accounts for most of the propagation phenomena experienced by wireless signals. Then, we derive the expressions of the effective rate and the transmission delay, and formulate each service type as an optimization problem with the constraints of resource allocation and UAV deployment to enable multi-service support for the IoT. These two optimization problems are nonlinear and nonconvex and are generally difficult to be solved. To this end, we transform them into linear optimization problems, and propose two iterative algorithms to solve them. Based on them, we further propose a linear program algorithm to jointly optimize the two service types, which achieves a trade-off of the effective rate and the transmission delay. Extensive performance evaluations have been conducted to demonstrate the effectiveness of the proposed approach in reaching a trade-off optimization that enhances the two services.

Resource-Efficient Coverage Path Planning for UAV-Based Aerial IoT Gateway

Unmanned Aerial Vehicles (UAVs) have a lot of potential for developing new applications in a variety of fields, such as traffic monitoring, security, and military applications. In the vast nature of the Internet of Things (IoT) network, UAVs could work as Aerial Gateway (AG) for communications among low-powered and distributed ground IoT Devices (IDs). This research concentrates on the path planning and deployment system that may facilitate decisionmaking and guaranteed resource-efficient UAV mission assignment in serving ground IDs. Due to limited resources, it is essential to take into account several factors when designing such a system, including the AG flight time, the coverage radius, and the ground-to-air system's achievable data rate. As a result, the Energy Efficient Coverage Path Planning (EECPP) algorithm has been proposed. The EECPP is composed of two algorithms: the Stop Point Prediction Algorithm using K-Means, and Path Planning Algorithm using Particle Swarm Optimization. The outcome demonstrates that, in terms of total flight distance, EECPP outperforms Close Enough Traveling Salesman Problem (CETSP) by 19.99%. EECPP reduced energy usage by an average of 56.15% as opposed to Energy-Efficient Path Planning (E2PP). Due to its mobility nature with the addition of effective path planning, the AG is able to hover at each stop point, making it ideal for usage in crowded regions with high demand, emergency circumstances, and distant locations with no access to fixed base stations.

Self-Organized and Distributed Green Resource Allocation for Space–Air–Ground IoT Networks

To deal with the explosion connections and data volume for emergency communication or hot spot capacity enhancement with massive Internet of Things (IoT) devices, deploying aerial base stations (AeBS) on unmanned aerial vehicles (UAV) to generate heterogeneous space-air-ground networks is considered to be a quite effective method. However, the flying AeBSs and back-hauling to existing heterogeneous networks (HetNets) lead to network energy consumption a key point. To ensure the energy-efficient operation of space-air-ground networks for smart IoT applications, we put forward the cluster-based HetNets energy-efficient resource allocation mechanism (CHERA). The scheme first divides the entire network into multiple independent BS clusters with the K-means++ algorithm for distributed Energy efficiency (EE) optimization. Then we propose a greedy BS sleeping strategy and a Lagrangian-dual-based optimal power allocation algorithm for the maximization of EE in each BS cluster. The EE optimization of space-air-ground IoT networks is implemented under the SON framework to make sure of the efficient and reliable operation of the network. Simulation results indicate that energy consumption is effectively decreased with the mechanism. It boosts the EE of space-air-ground networks by 23.8% compared with a baseline algorithm in which BSs are all in active mode with no power optimization. The result is expected to be useful for achieving future green space-air-ground networks IoT applications.

Fair Energy-Efficient Resource Optimization for Multi-UAV Enabled Internet of Things

Unmanned aerial vehicle (UAV) enabled Internet of Things (IoT) can keep network connectivity when the ground infrastructures are paralyzed. However, its transmission perform will be restricted due to the limited energy of the UAV. In this paper, a multi-UAV enabled IoT is proposed, where the UAVs as base stations send information to the ground IoT nodes via downlink within the flight time. And a fair energy-efficient resource optimization scheme for the IoT is studied to ensure fair energy consumption of multiple UAVs. The optimization problem seeks to maximize the minimum energy efficiency of each UAV by jointly optimizing communication scheduling, power allocations and trajectories of the UAVs. We decompose the non-convex optimization problem into three sub-optimization problems and solve them by Dlinkelbach method and successive convex approximation (SCA). Then a joint optimization algorithm is put forward to obtain the global optimal solutions by iteratively optimizing the three sub-optimization problems. The simulations results show that the multi-UAV enabled IoT can achieve significant performance improvement, and the energy efficiency between UAVs can achieve relative fairness by the fair resource optimization.

Energy-Efficient UAV-Based IoT Communications With WiFi Suppression in 5 GHz ISM Bands

Unmanned Aerial Vehicle (UAV) based communication is a promising technology for offering wireless communication services. UAVs can typically be adopted as aerial base stations to collect data from ground Internet of Things (IoT) devices. However, the Ground-to-Air link of UAV-based IoT communications is prone to be interfered by surrounding WiFi devices in the Industrial, Scientific, Medical (ISM) band. This paper aims to enable reliable uplink communications for IoT devices in 5 GHz ISM bands, thereby reducing the hover time and energy consumption of the UAV. To this end, we first study and design suppression frames suitable for WiFi devices. Then a suppression method is proposed, which is shown to be effective for all the tested devices, based on emulation performance verification of the frames for multiple commercial off-the-shelf (COTS) WiFi devices. Moreover, by taking the energy consumption of the UAV and multiple transmission parameters of the suppression method into account, we formulate an optimization problem for the desired uplink, which is designed to minimize the energy consumption of the UAV, under the constraints of satisfying the link channel capacity. Given that the optimization problem is non-convex mixed-integer nonlinear programming, this paper proposes an efficient algorithm that takes advantage of the suppression performance of the utilized frames. Numerical simulations demonstrate the performance of the proposed algorithm in terms of the WiFi device density, UAV weight, channel capacity requirement and parameters of the suppression method. The communication quality of the desired Ground-to-Air link is shown to be significantly improved, while the consumed energy is demonstrated to be obviously reduced.

Joint Multi-UAV Path Planning and LoS Communication for Mobile-Edge Computing in IoT Networks With RISs

This article addresses a joint path planning and communication scheduling problem for a team of unmanned aerial vehicles (UAVs) equipped with central processing units and performing computing tasks for Internet of Things (IoT) devices located on uneven terrain. On such terrains, the Line of Sight (LoS) between a UAV and a ground IoT device may be blocked by buildings or mountains. In this article, reconfigurable intelligent surfaces (RISs) are deployed on the ground to improve wireless communication between UAVs and IoT devices. Computing tasks and their results can be sent to and from UAVs either directly or via RIS-reflected paths. The objective is to develop a joint multi-UAV path planning/transmission scheduling algorithm that maximizes the number of computing tasks successfully and timely performed by the UAVs and transmitted back to the IoT ground devices and minimizes the total energy consumption of the UAVs. An effective path planning algorithm for this optimization problem is proposed. A mathematically rigorous proof of its asymptotic optimality is given. The developed algorithm is computationally effective and easily implementable in real time. Computer simulations and comparisons with other effective methods prove the effectiveness of the developed approach.

UAV Trajectory Planning for AoI-Minimal Data Collection in UAV-Aided IoT Networks by Transformer

Maintaining freshness of data collection in Internet-of-Things (IoT) networks has attracted increasing attention. By taking into account age-of-information (AoI), we investigate the trajectory planning problem of an unmanned aerial vehicle (UAV) that is used to aid a cluster-based IoT network. An optimization problem is formulated to minimize the total AoI of the collected data by the UAV from the ground IoT network. Since the total AoI of the IoT network depends on the flight time of the UAV and the data collection time at hovering points, we jointly optimize the selection of hovering points and the visiting order to these points. We exploit the state-of-the-art transformer and the weighted A*, which is a path search algorithm, to design a machine learning algorithm to solve the formulated problem. The whole UAV-IoT system is fed into the encoder network of the proposed algorithm, and the algorithm’s decoder network outputs the visiting order to ground clusters. Then, the weighted A* is used to find the hovering point for each cluster in the ground IoT network. Simulation results show that the trained model by the proposed algorithm has a good generalization ability to generate solutions for IoT networks with different numbers of ground clusters, without the need to retrain the model. Furthermore, results show that our proposed algorithm can find better UAV trajectories with the minimum total AoI when compared to other algorithms.

Resource Management in UAV Enabled MEC Networks

Mobile edge cloud networks can be used to offload computationally intensive tasks from Internet of Things (IoT) devices to nearby mobile edge servers, thereby lowering energy consumption and response time for ground mobile users or IoT devices. Integration of Unmanned Aerial Vehicles (UAVs) and the mobile edge computing (MEC) server will significantly benefit small, battery-powered, and energy-constrained devices in 5G and future wireless networks. We address the problem of maximising computation efficiency in U-MEC networks by optimising the user association and offloading indicator (OI), the computational capacity (CC), the power consumption, the time duration, and the optimal location planning simultaneously. It is possible to assign some heavy tasks to the UAV for faster processing and small ones to the mobile users (MUs) locally. This paper utilizes the k-means clustering algorithm, the interior point method, and the conjugate gradient method to iteratively solve the non-convex multi-objective resource allocation problem. According to simulation results, both local and offloading schemes give optimal solution.

Completion Time Minimization for Data Collection in a UAV-enabled IoT Network: A Deep Reinforcement Learning Approach

In this paper, we study the completion time minimization in an unmanned aerial vehicle (UAV)-enabled Internet of Things (IoT) network, where the UAV tries to collect all the data generated by the ground IoT devices for further processing. To simplify the analysis, the continuous time horizon is discretized into several time slots. The duration of each time slot is set to be less than the pre-defined threshold such that the UAV's location can be considered as unchanged during each time slot. In our work, we aim to minimize the completion time of the UAV by optimizing the association scheme of the IoT devices, the location (i.e., the trajectory) and velocity of the UAV at each time slot. However, the formulated problem is challenging to solve by traditional optimization methods considering the unknown number of time slots (which leads to the unknown number of decision variables) and non-convex functions. We thus reformulate it as a Markov decision process (MDP) and propose a deep deterministic policy gradient (DDPG)-based method to efficiently solve it. The DDPG-based algorithm uses deep function approximators instead of finding the action that maximizes the state-action value, and is therefore well suited to solve high-dimensional, continuous control problems. Extensive numerical results are presented to validate the effectiveness of our proposed algorithm.

Joint Communication, Computing, and Caching Resource Allocation in LEO Satellite MEC Networks

Driven by the urgent requirement of ubiquitous and reliable coverage for global users, the low earth orbit (LEO) satellite network has attracted numerous attentions from the academic and industry circles. By deploying multi-access edge computing (MEC) servers in LEO satellites, computation offloading and content caching services can be provided for remote Internet-of-Things (IoT) devices without proximal servers. In this paper, the joint optimization of computation offloading, radio resource allocation and caching placement in LEO satellite MEC networks are investigated. The problem is formulated to minimize the total delay of all ground IoT devices while ensuring the energy, computing and caching constraints. To solve this mixed-integer and non-convex problem, a Lagrange dual decomposition (LDD)-based algorithm is proposed to obtain the closed-form optimal solution. Then, a heuristic algorithm is proposed to further reduce the computation complexity. Numerical results validate that both algorithms are effective compared to the optimal exhaustive search, the full local computing and the full MEC methods. Besides, the offloading ratio and the average delay of all IoT devices with different numbers and computing capacities of devices and satellites are also demonstrated.

Joint Wireless Charging and Data Collection for UAV-Enabled Internet of Things Network

Internet of Things (IoT) devices usually lack perpetual power supply since they are generally deployed at remote areas with limited battery capacities. Moreover, the computing power of the IoT devices is usually limited and the generated data need to be offloaded to a more powerful computing server for further processing. In this article, we study a novel unmanned aerial vehicle (UAV)-enabled IoT network, where the UAV delivers energy to the ground IoT devices by employing wireless power transfer (WPT) in the downlink and collects data in the uplink. In our work, we try to minimize the total energy consumption of the UAV by determining the trajectory and charging power of the UAV, the resource allocation, and the transmission scheme subject to task collection and resource budget requirements. To make the formulated problem more tractable, we decompose the primal problem into three subproblems (i.e., the transmission association problem, the trajectory design problem, and the resource allocation problem) and utilize the block coordinate descent (BCD) method to solve them alternately. Since the trajectory design problem is still highly nonconvex, we further transform it into a convex one by leveraging the successive convex approximation (SCA) technique. In the simulation, we provide extensive numerical results to corroborate the effectiveness of our proposed algorithm.

Age‐optimal design for UAV‐assisted grant‐free non‐orthogonal massive access: Mean‐field game approach

Due to the capability of providing flexible network coverage, unmanned aerial vehicles (UAVs) are a promising solution to support the Internet-of-Things (IoT) applications, especially for the area without terrestrial network coverage. In this paper, a UAV-assisted IoT network is considered, where a UAV serves as an aerial base station to serve ground IoT devices. To support the massive access, grant-free non-orthogonal communications are also adopted, where packets generated by IoT devices can skip the handshake process and share a channel with other packets for transmission. With the aim to maintain the information freshness, the age of information (AoI) for the network is first derived, and an AoI minimization problem is formulated as a Stackelberg game where the UAV is the leader and ground IoT devices are followers. However, solving such a problem is challenging as the number of involved followers is large. To simplify the problem, a mean-field game based approach is proposed, where the average behaviours of followers are considered instead of individual strategies. Simulation results show that the computational complexity of the proposed scheme is almost unchanged with the number of IoT devices while the performance is close to the optimal one.

A buffer-aware dynamic UAV trajectory design for data collection in resource-constrained IoT frameworks

The emergence of unmanned aerial vehicle (UAV)-enabled technology in the Internet of Things (IoT) era leads to a significant reduction in data collection delays when accumulating sensory data from ground IoT nodes (INs). As a flying data collector, the UAV hovers at a limited number of Access Points (APs) to collect data, outperforming ground data collectors in terms of transmission energy consumption, data delivery reliability, and timeliness. However, the INs have a finite amount of buffer capacity to store the data that must be collected before they overflow. As a result, the data gathering route for UAVs should be adaptable to INs’ buffer deadline in order to minimize data loss. In this paper, a buffer-aware dynamic UAV trajectory design protocol is proposed for data collection from resource-constrained INs. A distributed AP nomination strategy is proposed in order to reduce UAV hovering latency. Furthermore, using machine learning approaches, a modified ant colony optimization algorithm is constructed to minimize the data loss penalty due to buffer overflow. Finally, the performance of the proposed scheme is evaluated against several state-of-the-art protocols with regards to parameters such as data loss penalty, packet delivery ratio, and network lifetime.

Covert Communication for Cellular and X2U-Enabled UAV Networks with Active and Passive Wardens

Cellular and X2U-enabled UAV networks are a promising network paradigm to support constantly growing Internet of Things (IoT) applications in 5G and beyond wireless networks, wherein X2U includes the UAV-to-UAV (U2U) and ground IoT device-to-UAV (G2U) communications. However, such networks pose a significant challenge to secure wireless communications due to the open and broadcasting features of wireless channels. Covert communication is an attractive technique to hinder adversaries (i.e., wardens) from detecting the existence of data transmission for guaranteeing secure IoT communications. This article investigates the covert communication issue for a promising network scenario consisting of a BS, UAV swarm, multiple ground IoT devices and wardens. Especially, each UAV/ground IoT device can select cellular or X2U communication mode according to a flexible mode selection scheme which can cover the cellular network and ad hoc network as special cases by setting a bias factor. We design two types of wardens: active wardens who not only detect the legitimate transmission but also jet noise to interfere with legitimate signals, and passive wardens who only detect the legitimate transmission. Cooperative jamming technique is further employed to resist the attacks of wardens. Then, numerical results are provided to explore the effects of the flexible mode selection and the number of wardens on the covert performances like covert capacity and detection error probability. We finally present a vision for future research in the cellular and X2U-enabled UAV networks.

Intelligent Task Offloading and Energy Allocation in the UAV-Aided Mobile Edge-Cloud Continuum

The arrival of big data and the Internet of Things (IoT) era greatly promotes innovative in-network computing techniques, where the edge-cloud continuum becomes a feasible paradigm in handling multi-dimensional resources such as computing, storage, and communication. In this article, an energy constrained unmanned aerial vehicle (UAV)-aided mobile edge-cloud continuum framework is introduced, where the offloaded tasks from ground IoT devices can be cooperatively executed by UAVs acts as an edge server and cloud server connected to a ground base station (GBS), which can be seen as an access point. Specifically, a UAV is powered by the laser beam transmitted from a GBS, and can further charge IoT devices wirelessly. Here, an interesting task offloading and energy allocation problem is investigated by maximizing the long-term reward subject to executed task size and execution delay, under constraints such as energy causality, task causality, and cache causality. A federated deep reinforcement learning (FDRL) framework is proposed to learn the joint task offloading and energy allocation decision while reducing the training cost and preventing privacy leakage of DRL training. Numerical simulations are conducted to verify the effectiveness of our proposed scheme as compared to three baseline schemes.

Intelligent Spectrum Management and Trajectory Design for UAV‐Assisted Cognitive Ambient Backscatter Networks

In this paper, we consider a novel Internet of Things (IoT) system in smart city called unmanned aerial vehicle‐ (UAV‐) assisted cognitive backscatter network, where a UAV is employed as both a relay and a radio frequency source to help the data transmission between ground IoT backscatter devices (BDs) and a remote data center (DC). However, since the IoT applications are usually not assigned dedicated spectrum resource in smart cities, these data transmissions from BDs to the DC should share the licensed spectrum of cellular users (CUs). Therefore, we aim to maximize the minimum uplink throughput among all BDs while avoiding severe interference to CUs via joint spectrum management and UAV trajectory design. To solve the problem, we propose an iterative method utilizing block coordinated decent to partition the variables into two blocks. For the spectrum management problem, we first prove its convexity with the transmit power and time scheduling and then propose a two‐step method to solve the two variables sequentially. For the UAV trajectory design problem, we resort to the fractional programming method to handle it. Simulation results demonstrate that the proposed algorithm can significantly increase the average max‐min rate of the BDs while guaranteeing the acceptable interference to CUs with a fast convergence speed.

Online Velocity Control and Data Capture of Drones for the Internet of Things: An Onboard Deep Reinforcement Learning Approach

Applications of unmanned aerial vehicles (UAVs) for data collection are a promising means to extend Internet of Things (IoT) networks to remote and hostile areas and to locations where there is no access to power supplies. The adequate design of UAV velocity control and communication decision making is critical to minimize the data packet losses at ground IoT nodes that result from overflowing buffers and transmission failures. However, online velocity control and communication decision making are challenging in UAV-enabled IoT networks, due to a UAV?s lack of up-to-date knowledge about the state of the nodes, e.g., the battery energy, buffer length, and channel conditions.

QoE-Aware Intelligent Satellite Constellation Design in Satellite Internet of Things

In Satellite Internet of Things (SIoT), satellite constellation design is used to satisfy more communication demands with fewer satellites by optimizing satellite orbits in specific areas. However, the diversity of user demands and satellite resource poses a great challenge in evaluating the Quality of Experience (QoE) of the satellite constellation. In this article, a QoE-aware satellite constellation design scheme is proposed to enhance the user satisfaction by constructing QoE factors. First, an SIoT network model is established with low-Earth-orbit (LEO) satellites and ground Internet of Things (IoT) devices, and the problem is formulated to calculate the QoE for different areas after analyzing the dynamic network topology. Then, the QoE factors are defined to assess QoE by taking into account the coverage performance, communication fluency, regional demand capacity, and profitability. Following that, an intelligent optimization algorithm named multilayer tabu search (MLTS) is designed to obtain reasonable satellite orbits with the best QoE, which provides orbit parameters for satellite constellation configuration. The simulation results demonstrate that the designed satellite constellation can effectively improve the users QoE by optimizing the various QoE factors.