To address the challenges of dynamic jamming and resource optimization in Unmanned Aerial Vehicle (UAV) confrontation communication networks, this paper proposes a hierarchical game framework for joint trajectory design and power allocation. These elements are inherently coupled: jamming degrades channel quality, prompting UAVs to adapt both their trajectories to improve spatial positioning and reduce jamming exposure, and their power allocation to maintain communication effectiveness under interference. The framework captures this interplay through a differential game for trajectory optimization and a Stackelberg game for power allocation, with the Nash equilibrium (NE) rigorously defined and its existence proved. To overcome the complexity of solving the Hamilton-Jacobi-Bellman equation, an adaptive dynamic programming algorithm is employed to approximate the value functions for both trajectory and power optimization. Simulations validate the effectiveness of the proposed framework, demonstrating that it accurately captures the strategic interactions between UAVs and provides robust decision support for resource optimization in dynamic adversarial environments.

As the Internet of Things (IoT) technology and artificial intelligence (AI) technology continue to evolve, many envisaged concepts regarding smart cities are gradually becoming a reality. However, the proliferation of numerous IoT devices in smart cities has led to several challenges. The existing 5G networks are incapable of meeting the requirements of these devices in terms of channel capacity and network coverage. Additionally, traditional cloud-based centralized machine-learning methods fail to ensure the privacy of user data. At this juncture, space-air–ground information network, along with federated learning (FL), are perceived as viable solutions to address these issues. This article focuses on addressing FL challenges in smart cities using the space-air–ground information network. Here, data distribution heterogeneity leads to increased federated training time and higher energy costs. This article begins by analyzing the reasons for the nonindependent and nonidentically distributed (Non-IID) data collected by devices in this scenario. Subsequently, from the perspective of device selection, this article proposes a node selection model based on near-edge strategy optimization, termed “low node selection in FL” (LCNSFL). Finally, the LCNSFL algorithm is compared with federated averaging algorithms based on random selection strategies and the FedProx algorithm. Experimental results demonstrate that the FL model aided by the LCNSFL algorithm achieves the target accuracy with fewer communication rounds, considerably reducing the required training time and energy costs compared to the other two algorithms.

With the development of autonomous driving technology, the accuracy requirements for electronic maps are becoming increasingly high.And high-precision maps, as a key carrier of intelligent IoT System in the field of transportation, has become a necessity for autonomous driving. The drawing of high-precision maps cannot be separated from the data collection of convoys, but there are security issues such as pseudo mobility attacks and low data privacy in this process. Therefore, a hybrid authentication architecture for map collection vehicles and base stations based on alliance blockchain is proposed. This architecture has significant effectiveness in resisting mobile pseudo base station attacks, achieving authentication of access point vehicles by mobile base stations. At the same time, in order to effectively adapt to high-precision map scenarios, the proposed key distribution strategy and high-precision map update method are both lightweight. Last but not least, we analyze and evaluate the performance of ESMU through rigorous theoretical analysis and extensive experimental verification. The results demonstrate its high efficiency and strong robustness.

In this paper, a mobile edge computing (MEC) assisted multi-layer architecture is proposed to support the implementation of federated learning in Internet of Things (IoT) networks. In this architecture, when performing a federated learning based task, data samples can be partially offloaded to MEC servers and cloud server rather than only processing the task at the IoT devices. After collecting local model parameters from devices and MEC servers, cloud server makes an aggregation and broadcasts it back to all devices. An optimization problem is presented to minimize the total federated training latency by jointly optimizing decisions on data offloading ratio, computation resource allocation and bandwidth allocation. To solve the formulated NP hard problem, the optimization problem is converted into quadratically constrained quadratic program (QCQP) and an efficient algorithm is proposed based on semidefinite relaxation (SDR) method. Furthermore, the scenario with the constraint of indivisible tasks in devices is considered and an applicable algorithm is proposed to get effective offloading decisions. Simulation results show that the proposed solutions can get effective resource allocation strategy and the proposed multi-layer federated learning architecture outperforms the conventional federated learning scheme in terms of the learning latency performance.

Erasure coding is widely used in distributed storage systems (DSSs) to efficiently achieve fault tolerance. However, when the original data need to be updated, erasure coding must update every encoded block, resulting in long update time and high bandwidth consumption. Exiting solutions are mainly focused on coding schemes to minimize the size of transmitted update information, while ignoring more efficient utilization of bandwidth among update racks. In this article, we propose a parallel Cross-rack Pipelining Update scheme (CPU), which divides the update information into small-size units and transmits these units in parallel along with an update pipeline path among multiple racks. The performance ofCPUis mainly determined by slice size and update path. More slices bring finer-grained parallel transmissions over cross-rack links, but also introduces more overheads. An update path that traverses all racks with large-bandwidth links provide short update time. We formulate the proposed pipelining update scheme as an optimization problem, based on a new theoretical pipelining update model. We prove the optimization problem is NP-hard and develop a heuristic algorithm to solve it based on the features of practical DSSs and our implementations, includingBig chunkandSmall overhead. Specifically, we determine the best update path first by solving a max-min problem and then decide the slice size. We further simplify the slice size selection by offline learning a range of interesting (RoI), in which all slice sizes provide similar performance. We implementCPUand conduct experiments on Amazon EC2 under a variety of scenarios. The results show thatCPUcan reduce the average update time by 48.2 percent, compared with the state-of-the-art update schemes.

In this study, the authors investigate subcarrier and power allocation for point‐to‐point data transmission under imperfect channel state information in multi‐user orthogonal frequency‐division multiple‐access (OFDMA) networks. The resource optimisation problem is formulated to handle inter competition (the resource competition among different users) and intra competition (the resource assignment between channel estimation and data transmission for each individual user). The authors employ the non‐cooperative game with pricing approach to solve the optimisation problem. By analysing optimal relationship between channel estimation cost and total occupied resource for each user, the two‐level resource competition is simplified to the problem including only inter competition, which is modelled as a non‐cooperative subcarrier and power allocation game with subcarrier pricing. In this game, the utility function is defined as the number of received data bits per joule of energy, which is associated with the estimate error and cost, the data transmit power and the number of data‐subcarriers. The authors prove the existence, uniqueness and Pareto efficiency of Nash equilibrium (NE) for the proposed resource allocation game. Furthermore, a distributed resource allocation algorithm is designed to achieve the NE.

In this paper, we investigate the multiservice delivery between the source-destination pairs in distributed selfish wireless networks (SeWN), where selfish relay nodes (RN) expose their selfish behaviors, i.e., forwarding or dropping multiservices. Owing to the effect of the RNs' node-selfishness on the multiservices, a distributed framework of the node-selfishness management is constructed to manage the RN's node-selfishness information (NSI) in terms of its available resources, the employed incentive mechanism and the quality-of-service (QoS) requirements, and the other RNs' NSI in terms of their historical behaviors. In this framework, the RNs' NSI includes the degree of node-selfishness (DeNS), the degree of intrinsic selfishness (DeIS) and the degree of extrinsic selfishness (DeES). Under the distributed node-selfishness management, a path selection criterion is designed to select the most reliable and shortest path in terms of RNs' DeISs affected by their available resources, and the optimal incentives are determined by the source to stimulate forwarding multiservices of the RNs in the selected path. Our simulation results demonstrate that this framework effectively manages the RNs' NSI, and the optimal strategies of both the path selection and the incentives are determined.

In this paper, we design an adaptive multi-homing resource allocation algorithm for time-varying heterogeneous wireless networks (HetNet), where the algorithm iteration timescale is the same to the network state acquisition timescale. First, the network utility maximization is characterized by a stochastic optimization model. Second, the multi-homing resource allocation (MHRA) algorithm is developed to accommodate the dynamic wireless network states, i.e., time-varying wireless channels between the access points (AP) and mobile terminals and as well the queuing dynamics at the APs. Then, we investigate the tracking error between the MHRA algorithm output and the target optimal resource allocation solution. Based on these results, an adaptive-compensation multi-homing resource allocation (AMRA) algorithm is proposed to offset the tracking error so as to enhance the network utility. Specifically, we give a sufficient condition that the AMRA algorithm asymptotically tracks the moving equilibrium point with no tracking errors. Finally, we derive a tradeoff between network utility and media transmission delay, where the increase of average delay is approximately linear in V and the increase of network utility is at the speed of 1/V with the control parameter V. Simulation results validate the theoretical analysis of our proposed scheme.

In this paper, we design an energy-efficient cross-layer optimization framework for media streaming over time-varying wireless network. The energy efficiency (EE) is characterized by the stochastic optimization model subject to the network stability, which is also used to depict the average media delivery delay. In harmony with the hierarchical architecture of the wireless network, the problem of stochastic optimization of media streaming is decomposed by the Lyapunov drift theory into two subproblems, associated with the flow control in transport layer and the power allocation in physical (PHY) layer. Specifically, the dynamic cross-layer control algorithm for media streaming is developed for adapting to the time-varying network state information, i.e. time-varying channel state information (CSI) of mobile terminal (MT)-access points (AP) links and dynamic queue state information (QSI) at APs. We derive a tradeoff between EE and media streaming delay, where the increase of average delay is approximately linear in V and the increase of EE is at the speed of 1/V with the control parameter V. Simulation results validate the theoretical analysis of our proposed scheme.

This paper proposes a novel method utilizing chirp signals to substitute the traditional ultra-wideband (UWB) pulse waveform. With the application of the effective iterative method and the development of spectrum detection technology, the Conjugate Gradient (CG) algorithm and Cognitive Radio (CR) technology are employed to realize the adaptive filter and narrow-band interference (NBI) suppression. Utilizing the characteristics of the CG algorithm and its corresponding adaptive filter, we realize the NBI suppression and obtain the low cost and more practical UWB pulse design for a kind of optional proposes. Simulation results show that the CG adaptive filter can suppress the NBI effectively and outperforms the conventional method significantly.

To satisfy the requirement of practical applications, many certificateless encryption schemes (CLE) without pairing have been proposed. Recently, Lai et al . proposed a CLE scheme without pairing and demonstrated that their scheme is provably secure in the random oracle model. The analysis shows that their scheme has better performance than the related schemes. However, Lai et al . ’s scheme is not a standard CLE scheme since the user's public key is used when generating his partial private key. In this study, the authors propose a new CLE scheme. Compared with Lai et al .’s scheme, the authors' scheme is a standard CLE scheme at the cost of increasing the computational cost slightly. Besides, their scheme has better performance than the related schemes except Lai et al .’s scheme. They also show their scheme is provably secure in the random oracle model.

In this work we investigate real-virtual interface pair (RVIP), a new interface system proposed for hybrid network simulation. In the hybrid system, multiple live networks (LNs) and multiple software virtual networks (SVNs) are connected together via standard IP protocols in an arbitrary topology and at real time. RVIP seeks to implement a new Turing-indistinguishable mode so that an LN and its counterpart SVN are indistinguishable in regards to a third-party live node. To realize the new mode, three necessary conditions must be satisfied: (1) All needed changes incurred by introducing an SVN into a live network scenario are put on the simulation's side, RVIP requires that no change is made on any live node; (2) An SVN does not exchange simulation events with LNs, that is, only standard IP protocol interactions between SVN and LN are allowed. (3) Any LN can be dynamically plugged into the hybrid scenario at real time, just like being plugged into an equivalent purely live network.

We study the radio resource management (RRM) in orthogonal frequency division multiple access (OFDMA) involved heterogeneous networks, to maximize the system sum-rate under the proportional user rate constraint. An analytical model which reflects the network access features is presented. Allowing multi-homing access and resource element sharing, the RRM problem can be formulated as a linear programming (LP) problem, and the optimal solution provides upper-bound performance. In order to analyze the network selection criterion, we consider an approximated RRM problem with average resource allocations. Two different multi-homing formulations are used, and both are proven to have the same solution, where the network selection is related to the users' rate ratios between different networks. Then, we propose a low complexity suboptimal RRM algorithm, which is composed of a basic part without multi-homing access and a supplementary part with multi-homing support. The basic part makes single network selection and resource allocations. The network selection algorithm is designed based on the criterion found in the approximated problem. After network selection, an efficient resource allocation algorithm is utilized in the OFDMA network, which can employ the multi-user time and frequency diversity well. If multi-homing is allowed, the supplementary part selects the multi-homing users and reallocates partial resources to further improve the performance. Our simulation results show that the proposed suboptimal algorithm can achieve considerable gains over the previous schemes with minor performance degradation compared with the optimal solution.

Abstract In this paper, a threshold bounded antenna selection scheduler (TBS) and a computational complexity bounded antenna selection scheduler (CCBS) are proposed to reduce computational complexity in multiple input multiple output (MIMO) uplink. In contrast to previous works, a spatially correlated MIMO channel model is considered and a transmitter correlation value (TCV) is newly introduced to assist the antenna selection in addition to the channel gain. For the TBS or CCBS, with predetermined threshold of TCV or ratio of successful antennas (RSAs), full searching (FS) and sub searching (SS) are applied more efficiently to user equipments (UEs) compared with previous schedulers. As a result, the number of candidate antennas in the scheduling set can be reduced, which translates into a lower computational complexity in terms of number of evaluated antenna combinations. Additionally, compared with the TBS, the peak computational complexity can be further reduced by the CCBS. Simulation results show that with proposed schedulers the computational complexity can be reduced by at least 50% with an acceptable compromise of capacity. Copyright © 2010 John Wiley & Sons, Ltd.