Jamming antenna selection (JAS) serves as a low-complexity approach to enhance covert communication in Internet of Things (IoT) networks subject to stringent energy and computational limitations. Unlike conventional JAS strategies that assume a fully cooperative jammer selecting a single antenna for artificial noise transmission, this work investigates the optimal number of selected jamming antennas to maximize covert performance with a limited-cooperation jammer. In the considered scenario, the jammer does not provide the necessary cooperation, which prevents the legitimate receiver from acquiring the jammer–receiver channel state information (CSI). According to the degree of CSI availability at the jammer, two practical JAS schemes, random JAS (RJAS) and minimum JAS (MJAS), are investigated to effectively impair the warden’s detection performance. Closed-form expressions are derived for the warden’s expected minimum detection error probability and the legitimate receiver’s transmission outage probability, with the latter evaluated under interference-limited conditions. Leveraging these results, an optimization framework is developed to jointly determine the transmit power, transmission rate, and number of selected jamming antennas to maximize the effective covert rate. Numerical results illustrate that the effective covert rate under the RJAS strategy increases monotonically with the number of selected antennas, whereas the effective covert rate under the MJAS strategy is maximized when selecting two antennas. Both theoretical analysis and simulation results validate the effectiveness of the proposed schemes and highlight the significant covert performance gains achieved by employing multiple jamming antennas in IoT networks with limited cooperation.

The emerging federated learning enables distributed autonomous vehicles to train equipped deep learning models collaboratively without exposing their raw data, providing great potential for utilizing explosively growing autonomous driving data. However, considering the complicated traffic environments and driving scenarios, deploying federated learning for autonomous vehicles is inevitably challenged by non-independent and identically distributed (Non-IID) data of vehicles, which may lead to failed convergence and low training accuracy. In this paper, we propose a novel hierarchically Federated Region-learning framework of Autonomous Vehicles (FedRAV) that adaptively divides a large area containing vehicles into sub-regions based on the defined region-wise distance, and achieves personalized vehicular models and regional models. Specifically, the architecture employs a designated hypernetwork to learn personalized mask vectors per vehicle used in the linear combination of models shared by vehicles in the same region. This approach ensures that the updated vehicular model adopts the beneficial models while discarding the unprofitable ones. We validate our FedRAV framework against existing federated learning algorithms on four real-world autonomous driving datasets in various heterogeneous settings. Extensive experiment results demonstrate that FedRAV framework achieves superior performance than the state-of-the-art algorithms, and improves the accuracy by 9.36%. The source code of FedRAV is available at:https://github.com/yjzhai-cs/FedRAV.

As a novel promising computational paradigm, wireless powered mobile edge computing (WPMEC) has been proposed to offer real-time energy and computing services for Internet of Things (IoT) devices. However, time-varying limited resources such as communication quality and residual energy pose great challenges in devising suitable real-time task offloading and resource allocation strategies to meet users' requirements for low latency and energy consumption. Existing studies either employ raw data offloading methods with significant communication overhead, or utilize ordinary data compression methods that result in poor compression effects. To cope with the challenges, this paper considers introducing the state-of-the-art lossless data compression technology into WPMEC and study the online joint optimization problem of task offloading decision, charging time allocation, and compression proportion allocation with the goal of optimizing task completion time. To tackle this problem, we propose an Intelligent Compression Offloading and adaptive resource Allocation algorithm called ICOA. We first put forward a well-devised framework based on deep reinforcement learning to generate a offloading decision vector set in real-time. Then we standardize the resource allocation problem as a linear programming problem and solve it using the simplex method. The experimental results on a real dataset show that, compared with the benchmark algorithms, the proposed algorithm can effectively reduce the task accomplishing time and energy consumption, and achieve the best approximate ratio. Moreover, ICOA requires low runtime, and can satisfy the real-time and effectiveness requirements very well.

The emerging federated learning enables distributed autonomous vehicles to train equipped deep learning models collaboratively without exposing their raw data, providing great potential for utilizing explosively growing autonomous driving data. However, considering the complicated traffic environments and driving scenarios, deploying federated learning for autonomous vehicles is inevitably challenged by non-independent and identically distributed (Non-IID) data of vehicles, which may lead to failed convergence and low training accuracy. In this paper, we propose a novel hierarchically Federated Region-learning framework of Autonomous Vehicles (FedRAV), a two-stage framework, which adaptively divides a large area containing vehicles into sub-regions based on the defined region-wise distance, and achieves personalized vehicular models and regional models. This approach ensures that the personalized vehicular model adopts the beneficial models while discarding the unprofitable ones. We validate our FedRAV framework against existing federated learning algorithms on three real-world autonomous driving datasets in various heterogeneous settings. The experiment results demonstrate that our framework outperforms those known algorithms, and improves the accuracy by at least 3.69%. The source code of FedRAV is available at: https://github.com/yjzhai-cs/FedRAV.

The promising wireless powered mobile edge computing (MEC) can offer sustainable energy and fast network service response for nearby wireless terminals (WTs) to satisfy real-time and flexible requirements. Online task offloading and wireless power transfer (WPT) are critical for the wireless powered MEC system to realize powerful function. However, existing researches usually schedule the task offloading of WTs serially to prevent mutual signal interference, and suffer from high task offloading time. Hence, to lower the task offloading time, we adopt the successive interference cancellation (SIC) technology and realize task concurrent offloading of multiple WTs to the edge server (ES). Specifically, we study the SIC-enabled online task concurrent offloading problem with the aim of optimizing the total task completion time. We prove this optimization problem to be NP-hard, and decompose this problem to reduce the problem solving difficulty. With the support of the SIC and deep reinforcement learning (DRL) technology, we present an efficient and intelligent algorithm named SIOA. Our SIOA algorithm provides online offloading decision generating strategies for WTs through the idea of task concurrent offloading and a well-devised DRL structure. Moreover, our SIOA algorithm assigns the ES’s WPT time via a feasible area analysis approach. Our SIOA algorithm can offer demonstrable feasibility assurance, and requires lower task completion time than existing baseline algorithms with low program running time, which is verified by experiments on a real dataset.

Wireless powered Internet of Things (WPIoT) has gained great concern due to its benefits of high deployment flexibility and low maintenance overhead. The minimum delay broadcast scheduling problem is very critical for many applications of WPIoT. However, traditional broadcast scheduling algorithms assume that Internet of Things (IoT) devices always possess sufficient energy to support data transmission or reception, which does not hold in WPIoT with the special feature of using the store-charge-and-forward communication mode. Furthermore, existing solutions rely heavily on the interference-avoiding technology to handle the signal interference problem, and overlook the powerful interference processing capability of the successive interference cancellation (SIC) technology. To efficiently resolve this problem, this article proposes a delay-efficient energy-aware broadcast scheduling algorithm called EABS. EABS algorithm incorporates a novel broadcast link scheduling method by fully considering the special feature of WPIoT and efficiently utilizing the advantage of the SIC technology to significantly improve the broadcast delay. Extensive experiments based on a real-world dataset are conducted to evaluate the performance of our algorithm, and the results demonstrate the better performance of our algorithm than the baseline algorithms.

Traditional traffic classification methods struggle to identify emerging network traffic due to the need for model retraining, which hampers the real-time response of deployed edge devices. Furthermore, emerging network traffic samples are often scarce, traditional methods often treat a session as a single image, thereby overlooking essential structural features. These factors can result in poor generalization ability of the trained model. To overcome these challenges, we propose ConViTML (Convolutional Vision Transformer-based Meta-Learning), a real-time end-to-end network traffic classification framework that employs meta-learning to avoid model retraining. We propose a novel feature extraction network, Convolutional Visual Transformer (ConViT), merging Convolutional Neural Network (CNN) and Visual Transformer (ViT). ConViT can directly extract low-dimensional discriminative features containing basic and structural features of the session, which is vital for improving detection accuracy and accelerating convergence in a data-scarce environment. Furthermore, we employ a Packet-based Relation Network (PRN) to analyze the matching degree of support samples and query samples. Therefore, accurate classification in novel traffic identification tasks can be achieved with just a few labeled samples, eliminating extensive data collection and labeling operations. Finally, we replace various feature extractors and compare our approach with the classic meta-learning framework Relation Network (RelationNet). Extensive experimental results demonstrate that ConViTML outperforms others with various performance indicators.

Deep integration of wireless power transmission and mobile edge computing (MEC) promotes wireless powered MEC to become a new research hotspot in the field of Internet of Things. In this paper, we focus on the joint optimization problem of online offloading decision and charging resource allocation for minimizing task accomplishing time in dynamic time-varying wireless channel scenarios. The optimal solution involves addressing a mixed integer programming problem in real time, which is proved to be NP-hard, and imposes nontrivial challenges to design with conventional optimization methods. To efficiently address this problem, we leverage the deep reinforcement learning (DRL) technology to propose an energy-aware online offloading algorithm called EAOO. EAOO algorithm learns empirically the online offloading decision policies via a well-designed DRL framework, and adopts the feasible solution region analysis method to implement the charging resource allocation. We further propose a novel feasible decision vector generation method, and incorporate the crossover and mutation technology to expand the offloading vector search space with the provable feasibility guarantee. Extensive experimental results show that, our EAOO algorithm outperforms existing baseline algorithms, and achieves near-optimal performance with low CPU execution latency, which well satisfies the practical requirements of real-time and efficiency.

Mobile edge computing (MEC) is a novel computing paradigm that sinks the computing capacity of cloud servers into edge nodes to reduce network latency. By caching the popular content at small base station (SBS) can reduce the heavy backhaul load and the content retransmission in MEC. However, the dynamic and time-varying of the content requests may increase the network cost. In this paper, we study a distributed edge caching optimization problem in MEC scenario with the spatio-temporal requirements. The considered cache control is described as a stochastic differential game (SDG) in which each SBS defines a caching strategy to reduce the cost in terms of the service delay and backhaul link load. To reduce the computational complexity, the original problem can be transformed into a mean field game (MFG). We propose a caching iterative control algorithm that decouples the information interactions between the general SBS and others with the mean field distribution. In addition, we obtain the optimal caching strategy which achieves the existence and uniqueness of the mean field equilibrium (MFE). Simulation results demonstrate that our proposed algorithm can reduce more storage space and total cost compared to the Kim's approach.

Thanks to the promising wireless power transmission (WPT) technology, wireless-powered Internet of Things (WPIoT) can significantly improve the sustainable service ability of Internet of Things (IoT) with low personnel maintenance costs, and thus, shows remarkable and broad prospects in many applications, especially under the abominable and dangerous environment. Minimum delay data aggregation scheduling (MAS) is a problem of cardinal significance in WPIoT with the objective of timely collecting the data of IoT devices. However, due to the residual energy limitation of IoT devices, WPIoT shows the special feature of adopting the store-charge-and-forward communication mode, which brings new research challenges on designing efficient solutions to the MAS problem. We show that the MAS problem under the physical interference model in WPIoT is NP-hard. To tackle this problem, we propose a delay-efficient data aggregation scheduling algorithm called HADA based on an active data aggregation tree construction method and a novel hypergraph-based link scheduling method. Extensive numerical experiments are conducted to evaluate the performance of our proposed algorithm. The results demonstrate that our HADA algorithm can efficiently improve the performance compared with the existing baseline algorithms.

We consider the network-side mobile localization problem in future 5G and beyond wireless networks with distributed multi-antenna base stations (BSs). For this application, we propose a semi-supervised multi-point channel charting (SS-MPCC) framework, which consists of (i) collaborative collection of channel state information (CSI) and other side-information by distributed BSs; (ii) local CSI feature extraction and self-learning of a dissimilarity metric, and (iii) global graph construction and constrained manifold learning. We show that side-information from routine network operations, including timestamps, channel qualities, and a small set of labeled samples, can be exploited to construct a consistent global graph. The graph is then mapped to a 2D channel chart using constrained manifold learning for localization purposes. We evaluate the performance of SS-MPCC in a simulated urban outdoor scenario with realistic user motion. Our results show that SS-MPCC achieves a mean localization error of 5.6 m with only 10% of labeled CSI samples. SS-MPCC does not require accurate synchronization among multiple BSs and is promising for future cellular localization.

Data aggregation scheduling is a critical issue in WSNs. This paper studies the Delay efficient Data Aggregation scheduling problem in multi-Channel asynchronous Duty-cycled WSNs (DDACD problem), which aims to accomplish data aggregation with minimum delay. Existing studies, nevertheless, either focus on non-sleeping scenarios or assume that nodes communicate with one single channel, and thus may have poor performance if directly applied to multi-channel asynchronous duty-cycled scenarios. We first show that the DDACD problem is NP-hard. Then, we propose two new concepts of candidate active conflict graphs (CACGs) and feasible active conflict graphs (FACGs) to depict the relationship of the data aggregation links and present two coloring methods to well separate the links at different time slots or on different channels. Based on these two new concepts and two coloring methods, we propose an efficient data aggregation scheduling algorithm called EDAS, which exploits the fewest-children-first rule to choose the forwarding nodes to benefit the link scheduling. To reduce unused time slots or channels, we further propose a novel algorithm called NDAS by making full use of the characteristics of multi-channel asynchronous duty-cycled WSNs. We prove that our algorithms can achieve provable performance guarantee. The results of extensive simulations confirm the efficiency of our algorithms.

Data aggregation scheduling is a critical issue in wireless sensor networks (WSNs). This paper studies the Delay efficient Data Aggregation scheduling problem in multi-Channel Duty-cycled WSNs (DDACD problem), which aims to accomplish data aggregation with minimum delay. Existing researches, nevertheless, either focus on non-sleeping scenarios, or assume that nodes communicate on one single channel, and thus have poor performance in multi-channel duty-cycled scenarios. In this paper, we first show that DDACD problem is NP-hard. We then propose two new concepts of Candidate Active Conflict Graphs (CACG) and Feasible Active Conflict Graphs (FACG) to depict the relationship of the data aggregation links, and present two coloring methods to well separate the links at different time-slots or on different channels. Based on these two new concepts and two coloring methods, we propose an Efficient Data Aggregation Scheduling algorithm called EDAS, which exploits the fewest-children-first rule to choose the forwarding nodes to benefit the link scheduling. We theoretically prove that our proposed EDAS algorithm can achieve provable performance guarantee. The results of extensive simulations confirm the efficiency of our algorithm.

This paper investigates the problem of maximizing uniform multicast throughput (MUMT) for multi-channel dense wireless sensor networks, where all nodes locate within one-hop transmission range and can communicate with each other on multiple orthogonal channels. This kind of networks show wide application in the real world, and maximizing uniform multicast throughput for these networks is worth deep studying. Previous researches have proved MUMT problem is NP-hard. However, previous researches are either hard to implement, or use too many relay nodes to complete the multicast task, and thus incur high overhead or poor performance. To efficiently solve MUMT problem, we adopt the concept of the maximum independent set with the size constraint, and present one novel Single-Broadcast based Multicast algorithm called SBM based on the concept. We prove that SBM algorithm achieves a constant ratio to the theoretical throughput upper bound. Extensive experimental results demonstrate that, SBM performs better than existing work in terms of both the uniform multicast throughput and the total number of transmissions.

Broadcast is an essential and widely used operation in multihop wireless networks. Minimum latency broadcast scheduling (MLBS) aims to find a collision-free scheduling for broadcast with the minimum latency. Previous work on MLBS mostly assumes that nodes are always active, and, thus, is not suitable for duty-cycled scenarios. In this paper, we investigate the MLBS problem in duty cycled multihop wireless networks (MLBSDC problem). We prove both the one-to-all and the all-to-all MLBSDC problems to be NP hard. We propose a novel approximation algorithm called OTAB for the one-to-all MLBSDC problem, and two approximation algorithms called UTB and UNB for the all-to-all MLBSDC problem under the unit-size and the unbounded-size message models, respectively. The approximation ratios of the OTAB, UTB, and UNB algorithms are at most 17|T|, 17|T| + 20, and (Δ + 22)|T|, respectively, where |T| denotes the number of time slots in a scheduling period, and Δ denotes the maximum node degree of the network. The overhead of our algorithms is at most constant times as large as the minimum overhead in terms of the total number of transmissions. We also devise a method called Prune to further reduce the overhead of our algorithms. Extensive simulations are conducted to evaluate the performance of our algorithms.

Broadcast is an essential and widely-used operation in multi-hop wireless networks. Minimum latency broadcast scheduling (MLBS) aims to provide a collision-free scheduling for broadcast with the minimum latency. Previous work on MLBS mostly assumes that nodes are always active, and thus is not suitable for duty-cycle-aware scenarios. In this paper, we investigate the duty-cycle-aware minimum latency broadcast scheduling (DCA-MLBS) problem in multi-hop wireless networks. We prove both the one-to-all and the all-to-all DCA-MLBS problems to be NP-hard. We propose a novel approximation algorithm called OTAB for the one-to-all DCA-MLBS problem, and two approximation algorithms called UTB and UNB for the all-to-all DCA-MLBS problem under the unit-size and the unbounded-size message models respectively. The OTAB algorithm achieves a constant approximation ratio of 17|T|, where |T| denotes the number of time-slots in a scheduling period. The UTB and UNB algorithms achieve the approximation ratios of 17|T|+20 and (Δ+22)|T| respectively, where Δ denotes the maximum node degree of the network. Extensive simulations are conducted to evaluate the performance of our algorithms.