The deployment of Internet of remote things (IoRT) devices in remote areas with insufficient communication infrastructure can employ the unmanned aerial vehicles (UAVs) for data collection. The previous works only considered the IoRT devices information coverage probability, but ignored the IoRT devices energy coverage probability in the UAV networks. This letter analyzes the joint information-energy coverage probability performance in UAV networks with hybrid energy harvesting (EH). Firstly, the closed-form expressions of the information coverage probability and the energy coverage probability are derived by utilizing the Laplace transform and the Campbell theorem, respectively. On this basis, the closed-form expression of the joint information-energy coverage probability is derived by the law of large numbers (LLN). Finally, the numerical results confirm the validity of the joint information-energy coverage probability performance.

The integration of mobile edge computing (MEC) and air-ground integrated network is viewed as a crucial technology for Internet of Remote Things (IoRT) devices. It provides widespread service coverage and allows the tasks of IoRT devices to be executed by the uncrewed aerial vehicles (UAVs) and the high altitude platforms (HAPs). In this article, we investigate a joint partial task offloading, resource allocation, and UAV trajectory design problem to minimize the total task offloading delay of all IoRT devices in the air-ground integrated MEC network. Given that the problem is nonconvex and hard to solve by the traditional methods, we convert it into a Markov decision process (MDP) and leverage the deep reinforcement learning method to address it. Considering the complexity of the MDP grows with the number of the IoRT devices and the UAVs increasing, the primal problem is decomposed into two subproblems: 1) the UAV trajectory design and IoRT device power control subproblem, and 2) the partial task offloading and resource allocation subproblem. To address these two subproblems, we apply the basic concepts of the multiagent deep deterministic policy gradient (MADDPG) and the independent proximal policy optimization (IPPO) methods, respectively. Additionally, we introduce the enhanced prioritized experience replay and noise value to improve both the convergence performance and rate. This leads to the development of the MADDPG-improved prioritized experience replay (MADDPG-IPER) algorithm and noise value-IPPO (NV-IPPO) algorithm. Based on the solution of these two subproblems, a joint partial task offloading, resource allocation, and UAV trajectory design (JPTORAUTD) algorithm is proposed. Simulation results present that the proposed JPTORAUTD algorithm outperforms other benchmark algorithms in terms of reducing the total task offloading delay.

Semi-Asynchronous Federated Learning (SAFL) leverages the benefits of synchronous and asynchronous updates, effectively addressing the straggling effect caused by heterogeneity among clients. However, most research focuses on synchronous FL, overlooking the incentive mechanisms crucial for active participation in SAFL. Moreover, client-specific private information including data quality, computational resources, and privacy preferences is multi-dimensional and inaccessible to the server, yet essential for effective decision-making. To address these challenges, we propose a novel SAFL framework incorporating a learning-based contract with the consideration of multi-dimensional private information. Specifically, we integrate model staleness and data quality into the aggregated weight design in the proposed SAFL. Then, we formulate a server utility maximization problem to optimize local iterations and reward allocation for different client types, ensuring theoretical guarantees of convergence, individual rationality (IR), and incentive compatibility (IC). Extensive simulations on real-world datasets demonstrate that our approach significantly enhances global accuracy and convergence speed compared to existing works on aggregation and contract design methods.

The integration of vehicle edge computing (VEC) and air-ground integrated network is considered as a key technology to achieve autonomous driving. It exploits the ubiquitous service coverage and enables tasks to be offloaded to various components, such as high-altitude platform (HAP), unmanned aerial vehicle (UAV), and roadside unit (RSU). In this article, we address the challenge of minimizing the overall task offloading delay in the air-ground integrated VEC network through a joint multicomputation equipment selection and multidimensional resource allocation (JCESRA) problem. Considering the nonconvexity inherent in the problem, we employ the fundamental idea of the block coordinate descent (BCD) method to tackle it. Initially, we exclude the HAP and decompose the primal problem into three subproblems: 1) low-altitude computation equipment selection; 2) joint bandwidth and computation resource allocation; and 3) UAV trajectory design. The first subproblem, which involves integer programming, is solved by using the many-to-one matching method. Meanwhile, we utilize the CVX and successive convex approximation (SCA) method to solve the last two subproblems, respectively. Considering the matching externality, we utilize the coalition game method to deal with it. Based on the solutions of the three subproblems, the JCESRA algorithm without considering the HAP has been proposed. Subsequently, we consider the HAP into the problem. Because the task offloading decision and computation resource allocation of the HAP problem can be viewed as a knapsack problem, we utilize the dynamic programming method to solve it. Because some tasks are offloaded to the HAP, there are some redundant computation resources in UAVs and RSU. We reallocate the computation resources of UAVs and RSU to further reduce the task offloading delay. At last, we present the complete JCESRA algorithm. The simulation results unequivocally indicate that the proposed JCESRA algorithm outperforms other algorithms by significantly reducing the task offloading delay.

Compared with terrestrial network, the air-ground integrated network consisting of unmanned aerial vehicles (UAVs) and high altitude platforms (HAPs) offers the advantages of large coverage, high capacity, and seamless connection. Therefore, the air-ground integrated network can provide effective communication services for the Internet of remote things (IoRT). In order to reduce the end-to-end (e2e) packet delay and avoid network congestion of the two-hop network, we investigate a joint packet scheduling, resource allocation, and UAV trajectory design problem, with the objective of minimizing the average packet queue delay from HAP to IoRT devices in the air-ground integrated network. This problem is non-convex and difficult to solve by the traditional methods. In order to solve this problem, we reformulate it into a Markov decision process (MDP) firstly. And then, considering there are continuous and discrete hybrid action spaces in the MDP, we separate the primal action spaces into two sub-action spaces, and utilize the basic idea of multi-agent deep deterministic policy gradient (MADDPG) and multi-agent double deep Q network (MADDQN) methods to solve them, respectively. After that, in order to improve the stability, convergence rate and learning efficiency, we introduce the basic idea of adaptive prioritized experience replay, and propose a hybrid MADDPG-adaptive prioritized experience replay (MADDPG-APER) algorithm. Simulation results show that the proposed algorithm can reduce the average packet queue delay compared with other benchmark algorithms.

The next-generation wireless network is perceived to integrate with sensing capability and evolve into the perceptive mobile network (PMN), enabling massive sensing-intensive applications. However, the sensing function will affect the communication performance in cellular networks. To study the sensing and communication performance of PMNs and their interactions, this paper investigates a millimeter-wave PMN with dual-functional base stations (BSs) for simultaneous detection of unauthorized unmanned aerial vehicles (UAVs) and user communication via the unified transmit signal and beamforming. We develop a system-level theoretical framework to investigate the sensing and communication performance of PMNs based on stochastic geometry, which captures the mutual interference and resource contention between the two functions and builds a foundation for the optimization of network configurations. In addition, by leveraging the collaboration of multiple BSs in PMNs, we propose a cooperative sensing strategy combining the monostatic and bistatic sensing processes to enhance the reliability of UAV surveillance. Simulation results verify the effectiveness of the proposed theoretical framework and demonstrate the benefits of cooperative sensing in UAV detection and communication performance, as compared with the standalone sensing by individual BSs.

The rapid development of the agricultural Internet of Things (IoT) is inseparable from the support of wireless sensor networks (WSNs) in recent years. To further facilitate the adaptation of WSNs to agricultural applications, reducing the energy consumption of sensor nodes in agricultural environments has become a crucial problem. Sleep scheduling in randomly deployed WSNs is an effective method to reduce the energy consumption of sensor nodes, which can extend the network lifetime while ensuring network coverage. However, most of the existing sleep scheduling algorithms require frequent information exchange (such as broadcasting to find neighboring nodes and collecting nodes’ residual energy), which will inevitably lead to massive energy consumption. To address this problem, in this article, an unmanned aerial vehicle (UAV)-assisted sleep scheduling algorithm (UAVSS) is proposed, which avoids excessive information exchange among nodes and ensures sufficient network coverage with the least number of sensor nodes. The UAV traverses all the sensor nodes along the shortest path to help information exchange and gather sensing data. Furthermore, the UAV path is planned by using the latest monarch butterfly optimization (MBO) algorithm. Simulation results indicate that the proposed UAVSS can prolong the network lifetime while ensuring the required area coverage.

In the Internet of Things (IoT) for smart healthcare applications, sensors collect a vast amount of healthcare data, while coverage significantly affects the Quality of Service (QoS). In wireless sensor networks (WSNs), the QoS as well as the network lifetime are dramatically degraded with the increment of coverage holes, especially in large-scale hybrid WSNs (LS-HWSNs) where big data are collected by thousands of sensors distributed in a wide monitored area. In a LS-HWSN, two crucial problems, i.e., covering the wide area without coverage holes and designing an energy-efficient manner for dispatching mobile sensors to repair coverage holes, need to be solved. We study the problems from the cutting point of confident information coverage hole repairing (CICHR). To this end, based on the confident information coverage (CIC) model, a CIC hole predicting (CICHP) algorithm, centralized energy-efficient repairing (CEER) algorithm, and distributed energy-efficient repairing (DEER) algorithm are developed. The CICHP algorithm can predict the prior information of CIC holes (CICHs) by using the period-by-period energy consumption information of sensor nodes. Based on the prior information of CICHs, two repairing algorithms: 1) CEER and 2) DEER can schedule mobile sensors to repair CICHs beforehand. Simulation results show that the proposed algorithms can significantly improve the QoS and extend the network lifetime of LS-HWSNs.

In this paper, the priority-aware interference-avoidance scheduling for multi-user coexisting wireless networks with heterogeneous traffic demands is addressed. Both admission control and throughput maximization for admitted users are studied. These problems are addressed by a proposed sequential solution framework where at each step a large-scale linear program with a large number of variables is required to be solved. To efficiently solve the large-scale program, an accelerated column generation based method is proposed. In the proposed method, an efficient greedy initialization algorithm is first put forward by exploiting the proposed solution structure. After that, both upper and lower bounds on the optimal objective function of each optimization problem are derived, which are used to significantly alleviate the dependence of the whole solution procedure on deriving optimality of problems. Simulation results show that the proposed algorithm can effectively and efficiently handle the coexistence of multiple users with heterogeneous priorities and traffic demands.

We investigate cache technique from the spatial view and study its impact on the relaying networks. In particular, we consider a dual‐hop relaying network, where decode‐and‐forward (DF) relays can assist the data transmission from the source to the destination. In addition to the traditional dual‐hop relaying, we also consider the cache from the spatial view, where the source can prestore the data among the memories of the nodes around the destination. For the DF relaying networks without and with cache, we study the system performance by deriving the analytical expressions of outage probability and symbol error rate (SER). We also derive the asymptotic outage probability and SER in the high regime of transmit power, from which we find the system diversity order can be rapidly increased by using cache and the system performance can be significantly improved. Simulation and numerical results are demonstrated to verify the proposed studies and find that the system power resources can be efficiently saved by using cache technique.

In order to meet the demand of explosive data traffic, ultradense base station (BS) deployment in heterogeneous networks (HetNets) as a key technique in 5G has been proposed. However, with the increment of BSs, the total energy consumption will also increase. So, the energy efficiency (EE) has become a focal point in ultradense HetNets. In this paper, we take the area spectral efficiency (ASE) into consideration and focus on the tradeoff between the ASE and EE in an ultradense HetNet. The distributions of BSs in the two-tier ultradense HetNet are modeled by two independent Poisson point processes (PPPs) and the expressions of ASE and EE are derived by using the stochastic geometry tool. The tradeoff between the ASE and EE is formulated as a constrained optimization problem in which the EE is maximized under the ASE constraint, through optimizing the BS densities. It is difficult to solve the optimization problem analytically, because the closed-form expressions of ASE and EE are not easily obtained. Therefore, simulations are conducted to find optimal BS densities.

Abstract The problem of target location estimation in a wireless sensor network is considered, where due to the bandwidth and power constraints, each sensor only transmits one‐bit information to its fusion center. To improve the performance of estimation, a position‐based adaptive quantization scheme for target location estimation in wireless sensor networks is proposed to make a good choice of quantizer' thresholds. By the proposed scheme, each sensor node dynamically adjusts its quantization threshold according to a kind of position‐based information sequences and then sends its one‐bit quantized version of the original observation to a fusion center. The signal intensity received at local sensors is modeled as an isotropic signal intensity attenuation model. The position‐based maximum likelihood estimator as well as its corresponding position‐based Cramér–Rao lower bound are derived. Numerical results show that the position‐based maximum likelihood estimator is more accurate than the classical fixed‐quantization maximum likelihood estimator and the position‐based Cramér–Rao lower bound is less than its fixed‐quantization Cramér‐Rao lower bound. Copyright © 2015 John Wiley & Sons, Ltd.

In this paper, a novel system model named as massive multiple-input multiple-output (MIMO) cognitive radio system (CRS) is built and an efficient uplink power control algorithm based on noncooperative game theory with a self-adaptive power threshold scheme is proposed to improve the power efficiency. And then, we give an analytical model for the massive MIMO CRS and the detail of the self-adaptive power threshold scheme. Moreover, we prove the existence of the Nash Equilibrium and the convergence of the proposed algorithm. To evaluate the performance of the proposed algorithm, we do simulations and compare the system performance with two other classical power control algorithms. Simulation results demonstrate that the proposed algorithm can achieve a preferable performance in signal-to-noise-plus interference ratio (SINR) and a higher utility with lower transmission power and faster convergence. It implies that the novel way can achieve higher power efficiency and a better overall system performance.

In vehicle‐to‐vehicle (V2V) communications, low delay and long propagation distance are very important for multi‐hop safety‐related message broadcasting. Most earlier studies focused on one‐hop broadcasting while little attention has been paid to multi‐hop delay and propagation distance. In this study, a new model for analysing the connectivity probability, average hop count and one‐hop delay of multi‐hop safety‐related message broadcasting in V2V communications is built, taking into account the following factors: propagation distance, one‐hop transmission range, distribution of vehicles, vehicle density, average length of vehicles and minimum safe distance between vehicles. Simulation results demonstrate that the proposed model can provide better performance in terms of multi‐hop delay and there exists an optimal one‐hop transmission range to minimise the multi‐hop delay. After that, A new scheme is proposed to track the optimal one‐hop transmission range by using a Genetic Algorithm. With this scheme, vehicles are allowed to adjust the one‐hop transmission range based on vehicle density to reduce the multi‐hop delay. The proposed scheme is validated by simulations using realistic vehicular traces.

The performance of the vector orthogonal frequency division multiplexing (V‐OFDM) system over fast fading channels is investigated. The channel is time varying within one V‐OFDM data‐block period, which causes the inter‐carrier interference (ICI). With the help of an equivalent V‐OFDM system model and an auxiliary transform matrix, a novel mathematical expression for the received signal with the ICI signal is derived, which clearly expresses how the signal of a target vector block (VB) is interfered by other VBs. The ICI signal is analysed and two theorems about its property are proposed. The first theorem presents the expression of the power of the ICI signal, showing that the ICI signal power increases with the VB number. The other one indicates that ICI signals at different subcarriers are not correlated. With the two theorems, a novel detection method utilising the correlation of the ICI signal is therefore proposed. Numerical results verify the validity of the derived theorems and simulation results demonstrate that the proposed minimum mean square error (MMSE) detection can suppress the ICI effect and it is superior to the conventional MMSE detection in terms of both detection mean square error and bit error rate performance.

We consider the scenario where each sensor in a sensor network observes an instantaneous linear mixture of multiple sources in a sensing field. A portion of sensors transmit their observations to a fusion center simultaneously, through channels with different gains. Due to the bandwidth constraint of links, the sensors' observations are quantized before transmission. A multiple-access interference (MAI) model is therefore adopted to measure the quality of the links under two cases. The fusion center uses a fast independent component analysis (Fast ICA) algorithm with pre-whitening to extract the sources, based on the received data from the transmitting sensors. The performance of source extraction is evaluated by numerical simulations. The results show that the second case is better regarding the impact of MAI on the performance.

In this paper, the problem of source extraction in bandwidth-constrained wireless sensor networks is considered. The sensor observations are assumed to be instantaneous linear mixtures of sources in a sensing field, possibly corrupted by noise. Because of the bandwidth constraint, the observations are quantized before being transmitted. Three kinds of sensor network topologies (cluster-based sensor network, sensor network with a fusion center, and concatenated sensor network) are considered in order to show the impact of topology on the performance and energy efficiency. The mixtures are reconstructed based on the received quantized data, and source extraction is performed by using a fast fixed-point algorithm with prewhitening. This algorithm was proposed by Hyvarinen and Oja, published in Neural Computation, and has the advantage of simplicity and fast convergence. The case of source extraction where the sensed data are undistorted is used as the performance benchmark. The results show that in all these sensor networks, the performance can be close to that of the benchmarking case. The energy efficiency and lifetime of these sensor networks are compared when the same source extraction task is executed. The effects of sensor failure and link failure on the performance are also discussed.

This paper studies a multi-cluster sensor network which is applied for source extraction in a sensing field. Both the performance of source extraction and the total energy consumption in the sensor network are functions of the number of clusters. In this paper, we aim at finding the optimal number of clusters by minimizing the effective energy consumption which is defined as the ratio of the performance of source extraction to the total energy consumption in the sensor network. A particle swarm optimization (PSO) algorithm is employed to form the clusters which enables every cluster to perform source extraction. The existence and the uniqueness of the optimum number of clusters is proven theoretically and shown by numerical simulations. The relationship between the optimum number of clusters and the various system parameters are investigated. A tradeoff between the performance and the total energy consumption is illustrated. The results show that the performance is greatly improved by adopting the multi-cluster structure of the sensor network.

A general noncoherent chaos-shift-keying (CSK) communication system with adjustable weights is proposed in this paper. The performance of the system under additive white Gaussian noise (AWGN) and multipath channel conditions are evaluated. Analytical expressions of the bit error rates are derived. The performance of the system is compared with that of the DCSK system and existing noncoherent CSK systems. The results show that the general CSK system can achieve the same performance as that of the DCSK system. A detailed analysis is presented to show that the DCSK system is the optimal form of such noncoherent systems.