Abstract With the advantages of flexibility and mobility, unmanned aerial vehicles (UAVs) have been widely used in the wireless rechargeable sensor networks (WRSNs) to collect data and supply energy for ground sensor nodes. Due to the limited battery capacity of UAVs and the continuity requirement of WRSN, mobile unmanned vehicles (MUVs) are introduced as mobile charging stations to ensure the energy supply for UAVs and mitigate energy wastage. This paper investigates the problem of Joint Optimization Mission Allocation and Cooperative Trajectory Planning for data collection in WRSNs. The goal is to maximize the minimum energy efficiency by optimizing mission allocation including UAV trajectory and MUV travel. This problem is proved to be NP-hard and solved by two proposed algorithms. The first algorithm incorporates the clustering utilizing the K-Means algorithm and genetic algorithm. The second algorithm is a self-attention architecture based on the reinforcement learning framework and formulate an actor-critic algorithm for training. The simulation results show the feasibility and efficiency of the proposed algorithms, which achieve better performance. The first algorithm has more advantages when the distribution of sensor nodes is relatively concentrated; and the second algorithm may be more suitable when more comprehensive global path planning optimization is required.

In Mobile Crowdsensing (MCS), incentive mechanisms are crucial for encouraging mobile users to join tasks while users selfishly pursue personal benefit maximization. While most existing studies focus on the interaction between the requester and users, the internal value of socially aware user relationships remains underexplored. Users naturally form social connections, assisting or collaborating on tasks, but current mechanisms often neglect asymmetric social effects, which can lead to unequal willingness to cooperate and eventual breakdowns in collaboration (e.g., less profitable users refusing to cooperate). To end this, we propose an integrated incentive mechanism that models the interaction between the requester and users as a two-stage Stackelberg Game (SG) while accounting for pairwise asymmetric social effects. Pairwise cooperation is governed by the Iterated Prisoner’s Dilemma (IPD), with users employing Zero-Determinant (ZD) strategies to ensure cooperation despite unequal payoffs. Additionally, a plug-and-play sub-algorithm is introduced to filter low-quality or malicious users simultaneously and evaluate task redundancy, enhancing system robustness. We rigorously prove the existence of the Nash equilibrium, design an efficient iterative algorithm for our proposed mechanism, and validate its effectiveness through extensive experiments on real-world social datasets, which demonstrate that our method significantly improves system utility and cooperation stability while ensuring quality of service requirements.

This paper investigates the framework of wireless rechargeable sensor network (WRSN) assisted by multiple mobile unmanned vehicles (MUVs) and laser-charged unmanned aerial vehicles (UAVs). On the basis of framework, we cooperatively investigate the trajectory optimization of multi-UAVs and multi-MUVs for charging WRSN (TOUM) problem, whose goal aims at designing the optimal travel plan of UAVs and MUVs cooperatively to charge WRSN such that the remaining energy of each sensor in WRSN is greater than or equal to the threshold and the time consumption of UAV that takes the most time of all UAVs is minimized. The TOUM problem is proved NP-hard. To solve the TOUM problem, we first investigate the multiple UAVs-based TSP (MUTSP) problem to balance the charging tasks assigned to every UAV. Then, based on the MUTSP problem, we propose the TOUM algorithm (TOUMA) to design the detailed travel plan of UAVs and MUVs. We also present an algorithm named TOUM-DQN to make intelligent decisions about the travel plan of UAVs and MUVs by extracting valuable information from the network. The effectiveness of proposed algorithms is verified through extensive simulation experiments. The results demonstrate that the TOUMA algorithm outperforms the solar charging method, the base station charging method, and the TOUM-DQN algorithm in terms of time efficiency. Simultaneously, the experimental results show that the execution time of TOUM-DQN algorithm is significantly lower than TOUMA algorithm.

In the industrial Internet of Things (IIoT), edge computing is a pivotal power in enhancing system efficiency and responsiveness. However, traditional edge computing faces some challenges like poor flexibility in communication and susceptibility to blockages. Autonomous aerial vehicles (AAVs)-assisted edge computing can address these challenges due to their flexible deployment and strong Line of Sight (LoS) link capabilities. But it also confronts challenges like signal attenuation and resource constraints. To solve these problems, reconfigurable intelligent surface (RIS) emerges as a promising integration strategy to enhance network communication and computing capabilities. Integrating AAVs and RISs in complex dynamic edge computing system poses a notable challenge in achieving real-time and efficient decision-making. Digital twin (DT) technology is an advanced technology that establishes real-time mapping and interaction between the physical world and virtual models, thereby providing real-time status monitoring and precise offloading decisions for the system. Therefore, this article considers a novel DT-driven edge computing system supported by AAVs equipped with RIS in IIoT. In this system, we focus on the intelligent computation offloading problem, whose objective is to minimize the maximum execution time across all user devices (UDs). To tackle this nonconvex mixed-integer nonlinear optimization problem, we decompose it into the scheduling and offloading optimization problem and the allocation optimization problem. Then, we first propose a multitask reinforcement learning algorithm to solve the scheduling and offloading optimization problem by optimizing the AAV trajectories, UD offloading choices, and RIS phase shifts. Afterward, based on the solution of the scheduling and offloading optimization problem, we propose an alternating iterative algorithm to address the allocation optimization problem through optimizing the offloading ratio and resource allocation. Finally, through extensive simulation experiments, we validate the effectiveness and feasibility of our proposed solution.

In visual Internet of Things(VIoT), visualized sensors like surveillance cameras play as a key component in smart cities, generating a large amount of recorded data in real time. Under this scenario, semantic person retrieval aims to locate certain person from real-world surveillance images based on semantic descriptions. Most previous works were based on the assumption that the semantic description can provide enough details to locate a target person, namely “precise person retrieval”. However, this assumption cannot be satisfied in many real-world applications, where we only have fuzzy semantic descriptions and expect to pick out a set of targets. As the “fuzzy person retrieval” task has not been deeply explored by previous works, we propose a novel efficient one-stage method FuzzyPR. In our work, we perform multi-head visual-semantic feature alignment to against the asymmetry between the text and image information. To improve the model’s ability of ’inference and associative’ during the fuzzy retrieval process, we design a multi-granular semantic retrieval proxy task to improve the associative ability of the localization module. Experimental results demonstrate that FuzzyPR achieves the best retrieval accuracy and efficiency on fuzzy semantic retrieval task.

Acoustic eavesdropping against private or confidential spaces is a significant threat in the realm of privacy protection. While the presence of soundproof material would weaken such an attack, current eavesdropping technology may be able to bypass these protections. Fortunately, existing studies either inadequately cover the full spectrum of human speech due to low-frequency responses or rely heavily on the prior knowledge used to train a model. To address these challenges, this paper introduces mmEcho, a new acoustic eavesdropping method that utilizes millimeter-wave signals to sense vibration induced by sound precisely. Through signal processing techniques such as the intra-chirp scheme and phase calibration algorithm, mmEcho achieves micrometer-level vibration extraction without requiring target-related data. To improve the range of eavesdropping attacks while reducing noise, we optimize radar signals by leveraging the widespread availability of multiple antennas on commercial off-the-shelf radars. We comprehensively evaluate the performance of mmEcho in different real-world settings. Experimental results demonstrate that, with the aid of multi-antenna technology, mmEcho can more effectively reconstruct the audio from the target at various distances, directions, sound insulators, reverberating objects, sound levels, and languages. Compared to existing methods, our approach provides better effectiveness without prior knowledge, such as the speech data from the target.

Throughput-optimal dynamic broadcasting is an essential cornerstone for the efficient operation of Multi-hop Wireless Networks (MWNs). Most existing algorithms for this problem were developed assuming static interference environments and network connectivity. However, wireless interference environments and network connectivity are inherently time-varying in real-world scenarios, primarily due to uncontrollable interference sources and unreliable links. Such time-varying characteristics make these existing algorithms less robust. In this paper, we study the robust throughput-optimal dynamic broadcasting for MWNs with multi-dimensional time-varying characteristics in terms of interference environments, network connectivity, and data arrival. We model the time-varying link existence states using a random process and characterize the time-varying interference environments through a dynamic variant of the classical Signal-to-Interference-plus-Noise-Ratio (SINR) model. In this variant, the SINR model parameters are dynamically adjusted over time by an adversary. Based on this, we first design a Robust Throughput-optimal Dynamic Broadcast (RTDB) algorithm which makes efficient slot-based max-weight link scheduling, power allocation, and data forwarding decisions in each time slot. We then prove its throughput-optimality in time-varying acyclic directed MWNs under the dynamic SINR model. The effectiveness of RTDB is validated via numerous simulations.

Mobile Crowd-Sensing (MCS) has emerged as a significant approach in various domains for collecting and disseminating sensing data. However, it is a challenging issue to select appropriate participants for a sensing task. This paper introduces a Quality Awareness Incentive Mechanism based on Taxation and Data Aggregation (QIM-TDA), which can choose the reliable participants without losing the platform utility and the data quality. Firstly, we define a reputation model to measure the reliability of participant and select more reliable participants based on their reputation in order to maximize platform utility. Secondly, a truth discovery algorithm is proposed to aggregate the sensing data and ensure the data quality of MCS. Finally, a normalized taxation mechanism is discussed in order to prevent the excessive accumulation of reputation for participant and further enhance the data quality. The simulation results prove that QIM-TDA can significantly improve the data quality and task completion rate compared to some typical mechanisms.

For large-scale LoRa-based Internet-of-Things (IoT) systems, using UAVs to collect data is a promising method, which not only leverages the long-range communication advantages of LoRa but also avoids the high costs associated with deploying fixed-location LoRa gateways. In this paper, we take into account the concurrent data reception capability inherent to LoRa, and propose an innovative algorithm that provides a bi-criteria approximation solution for UAV-assisted LoRa data collection, maximizing the sensor coverage under the UAV's energy budget. Our algorithm is twofold: first, we formulate the problem within a constrained submodular maximization framework incorporating sensor allocation and a Traveling Salesman Problem. Second, proving the NP-hardness of this sensor allocation problem, we develop a constant-factor approximation algorithm for energy-minimum sensor allocation. We present a bi-criteria approximation algorithm that employs a greedy strategy to maximize sensor coverage while adhering to the UAV's energy budget. We evaluate our designs through extensive numerical experiments, demonstrating their efficiency and effectiveness.

Unmanned Aerial Vehicle (UAV) can be used as wireless aerial mobile base station for collecting data from sensors in UAV-based Wireless Sensor Networks (WSNs), which is crucial for providing seamless services and improving the performance in the next generation wireless networks. However, since the UAV are powered by batteries with limited energy capacity, the UAV can not complete data collection tasks of all sensors without energy replenishment when a large number of sensors are deployed over large monitoring areas. To overcome this problem, we study the Real-time Data Collection with Laser-charging UAV (RDCL) problem, where the UAV is utilized to collect data from a specified WSN and is recharged using Laser Beam Directors (LBDs). This problem aims to collect all sensory data from the WSN and transport it to the base station by optimizing the flight trajectory of UAV such that real-time data performance is ensured It has been proven that the RDCL problem is NP-hard. To address this, we initially focus on studying two sub-problems, the Trajectory Optimization of UAV for Data Collection (TODC) problem and the Charging Trajectory Optimization of UAV (CTO) problem, whose objectives are to find the optimal flight plans of UAV in the data collection areas and charging areas, respectively. Then we propose an approximation algorithm to solve each of them with the constant factor. Subsequently, we present an approximation algorithm that utilizes the solutions obtained from TODC and CTO problems to address the RDCL problem. Finally, the proposed algorithm is verified by extensive simulations.

Wireless Rechargeable Sensor Networks (WRSNs) have been widely utilized and have played an important role in many surveillance application scenarios. The optimization of the charging process is beneficial for guaranteeing continuous coverage and enhancing the charging efficiency of WRSNs. And there are several influence factors of the charging process, like the sensors’ battery consumption mode, the chargers’ charging pattern and the environmental factors, which should be considered into the charging model. Based on the charging model via assigning sensors’ charging priority weights, we introduce the spatio-temporal optimization for charging scheduling (STO-CS) Problem in WRSNs for the goals of meeting the on-demand charging requirements and saving the charging consumption. We prove the NP-hardness of the problem and propose two algorithms to solve it. The first algorithm is based on two-phase dynamic programming and is proved to find the optimal solution when the charging ability is sufficient; the second algorithm adopts the clustering idea with K-Means Algorithm which has better time complexity. A series of simulation experiments are performed to compare the performance of the proposed algorithms in terms of the charging cost and the running time, whose results are analyzed to conclude that they can be applied to the application scenarios with the accuracy requirements and the real-time requirements respectively.

The development of wireless energy transmission technology has significantly propelled the advancement of wireless rechargeable sensor networks (WRSNs). Energy constraint is one of the most critical challenges in application of WRSNs. Integrating unmanned aerial vehicle (UAV) with wireless energy transmission technology has emerged as a promising approach to overcome the energy constraint problem in WRSNs, leveraging the advantages of UAV such as flexibility and maneuverability. In this paper, we consider the system of WRSN assisted by UAV and mobile utility vehicle (MUV), where the UAV serves as a mobile charger for replenishing energy of sensors and the MUV serves as a mobile base station for replacing the battery of UAV with insufficient energy. In the system, we focus on minimizing the death time of sensors and optimizing the energy consumption of UAV. To address this problem, a multi-objective deep Q-network (DQN) algorithm is employed, where the UAV makes online charging scheduling decisions based on real-time network status and utilizes experience replay for optimization. Experimental results demonstrate that the proposed algorithm significantly reduces the sensors’ death time and effectively decreases the energy consumption of UAV. Specially, the performance of proposed algorithm outperforms the three other classical algorithms: genetic algorithm, greedy algorithm, and Q-learning algorithm.

Edge computing is a computational paradigm that brings resources closer to the network edge, such as base stations or gateways, in order to provide quick and efficient computing services for mobile devices while relieving pressure on the core network. However, the current computing power of edge servers are insufficient to handle the high number of tasks generated by access devices. Additionally, some mobile devices may not fully utilize their computing resources. To maximize the use of resources, we propose a novel edge computing system architecture consisting of a resource-constrained edge server and three computing groups. Tasks from each group can be offloaded to either the edge server or the corresponding computing group for execution. We focus on optimizing the computation offloading of devices to minimize the maximum overall task processing latency in the system. This problem is proved to be NP-hard. To solve it, we propose a DQN-based resource utilization task scheduling (DQNRTS) algorithm that has two desirable characteristics: 1) it effectively utilizes the computing resources in the system and 2) it uses deep reinforcement learning to make intelligent scheduling decisions based on system state information. Experimental results demonstrate that the DQNRTS algorithm is capable of reducing the processing latency of the system by converging to optimal solutions.

The Linux kernel is regularly updated to enhance security, improve performance, and introduce new functionalities. Traditional updating methods typically require rebooting, leading to service disruptions and potential data loss. Live-patching technology dynamically updates the kernel modules without rebooting, ensuring continuous service availability. However, this technique has its drawbacks. Since live-patching alters the original structure of data types, it can no longer utilize base offsets to access the members, imposing considerable overheads. This paper proposes LPAH (Live Patching with Alignment Holes), a live patching system that leverages the fragmented space generated by compile-time alignment for data types, to enable effective live patching updates for security vulnerability fixes, feature enhancements, and user-defined patching tasks. LPAH capitalizes on the relationship between these alignment holes and data objects. This approach ensures efficient access to extended data members while preserving the original data's integrity. This approach allows other functions to remain unaffected by updates and replacements through explicit type casts. Extensive experimental results show that LPAH offers valid and robust live patching for multiple real vulnerabilities in the Linux kernel, without degrading performance. Our method provides an efficient way to install security patches in the Linux kernel, and thus reenforces kernel security.

Ultra-reliable and low-latency communication (uRLLC) plays an important role in the development of 5G-advanced and 6G wireless networks. Combining unmanned aerial vehicles (UAVs) with non-orthogonal multiple access (NOMA) offers a promising solution to achieve improved reliability and lower latency. This is made possible by enabling line-of-sight (LoS) links and concurrent transmissions through the use of UAVs and NOMA, respectively. However, because of the inherent openness of wireless channel, uRLLC faces the security challenges against being eavesdropped. Physical Layer Security (PLS) has been proposed as an efficient method to secure uRLLC, since it uses only the properties of wireless channels (such as fading, interference, and noise). Although the potential benefits of NOMA-UAV provide a better coverage for ground users, it remains a significant challenge since it may provide a LoS link to eavesdroppers. Therefore, in this paper, we investigate the security and reliability performance of UAV and NOMA based uRLLC scenario, under which UAV serves two different types of users with different needs, i.e., secret users and public users. By using stochastic geometry tools, we derive the closed-form expression of the secrecy rate, an important metric in the study of PLS. Additionally, the secure performance is enhanced by maximizing the secrecy rate through optimizing the hovering height and power assignment of UAV. It should be noted that the hovering position is optimized via power allocation when there is only one secret user. Evaluations demonstrate the effectiveness and correctness of our theoretical analysis.

In many edge computing applications, edge devices are required to reach fault-tolerant consensus in order to provide collaborative services in outdoor environments. In this paper, we study a comprehensive$(a,b)$-majority consensus problem based on a novel failure model, which takes$a$distinct opinions as inputs and outputs a$b$-majority opinion as the final agreement. This problem formulation is drastically different from traditional ones, which usually require a majority consensus from the binary opinions of multiple supporters. It is more practical and flexible as it can accommodate more than 2 input opinions and output one that satisfies the application requirement defined by parameter$b$. We also consider physical layer in our failure model while previous models mainly focus on faults occurred in protocol layer and data layer. Based on this more realistic failure model and a more practical consensus problem definition, we present a distributed protocol for$n$edge devices to reach an$(a,b)$-majority consensus within$\Theta (n)$time steps with high probability. Empirical results from our simulation studies validate the fault tolerance property and efficiency of our work in achieving the$(a,b)$-majority consensus.

Internet of Things (IoT) is rapidly spreading and reaching a multitude of different domains, since the fifth generation (5G) wireless technologies are the key enablers of many IoT applications. It is hence apparent that the broadcast nature of IoT devices makes data security unprecedentedly critical. Compared with traditional cryptography algorithms, which cannot cater for the features of IoT devices characterized by the severe limits in terms of energy, computation and storage capabilities, physical layer security (PLS) has been regarded as a promising solution to facilitate secure communications by exploiting the intrinsic randomness of the wireless medium. However, most of previous works assumed that all devices are static, and the impact of mobility on PLS deserves further investigation. In this paper, applying two types of random mobile models, i.e., the models of Random WayPoint (RWP) and Random Direction (RD), we study the impact of mobility on PLS in a scenario with three types of wireless devices (i.e., a destination, multiple interferers and an eavesdropper). Specifically, we establish an analytical framework for secrecy transmission capacity (STC), a fundamental metric in the study of PLS, under RWP and RD models. To the best of our knowledge, this is the first paper to derive STC and present the condition to achieve a positive STC with the consideration of mobility. We conclude that the RWP mobile destination can achieve a higher STC than that achievable in RD mobile and static scenarios, while RWP mobile eavesdropper is a challenging scenario to obtain a positive STC. Therefore, we propose an effective secrecy improvement strategy for the latter. Simulation validates the theoretical analyses.

The application of digital tracking mechanism introduces a series of leakage problems of users' personal sensitive information related to the trajectory. Therefore, we propose a secure storage scheme for trajectory data. Firstly, four-dimensional spatiotemporal clustering of the trajectory data is performed to reduce the spatiotemporal complexity of data storage. Secondly, the privacy level of the trajectory data in the clusters is measured individually, which ensures the needs for personalized privacy protection are met. Finally, a noise trajectory (NTR) tree based on differential privacy is constructed, and the allocation of privacy budget and noise addition are optimized. Extensive simulations show that our scheme improves in terms of time efficiency, and achieves a flexible and effective balance between data accuracy and privacy.

The increasing availability of autonomous small-size Unmanned Aerial Vehicles (UAVs) has provided a promising way for data gathering from Wireless Sensor Networks (WSNs) with the advantages of high mobility, flexibility, and good speed. However, few works considered the situations that multiple UAVs are collaboratively used and the fine-grained trajectory plans of multiple UAVs are devised for collecting data from network including detailed traveling and hovering plans of them in the continuous space. In this paper, we investigate the problem of the Fine-grained Trajectory Plan for multi-UAVs (FTP), in which m UAVs are used to collect data from a given WSN, where m ≥ 1. The problem entails not only to find the flight paths of multiple UAVs but also to design the detailed hovering and traveling plans on their paths for efficient data gathering from WSN. The objective of the problem is to minimize the maximum flight time of UAVs such that all sensory data of WSN is collected by the UAVs and transported to the base station. We first propose a mathematical model of the FTP problem and prove that the problem is NP-hard. To solve the FTP problem, we first study a special case of the FTP problem when m = 1, called FTP with Single UAV (FTPS) problem. Then we propose a constant-factor approximation algorithm for the FTPS problem. Based on the FTPS problem, an approximation algorithm for the general version of the FTP problem when m > 1 is further proposed, which can guarantee a constant factor of the optimal solution. Afterwards, the proposed algorithms are verified by extensive simulations.

As a promising technology to achieve perpetual operation of battery-powered wireless sensor devices, wireless power transfer has attracted much attention recently. In wireless power transfer, the charger enables the energy to be wirelessly transmitted to the rechargeable sensor devices that are hungry for energy. Previous works mainly focus on maximizing the charging utility or minimizing the charging delay. This paper concerns a more practical issue of placing wireless chargers, which aims at minimizing the deployment cost of chargers while satisfying the overall requirement for charging utility. We investigate the above cost-minimum charger placement problem under two typical scenarios in which omni chargers and directional chargers are used, respectively. To resolve this problem under the two charging models, we first prove its NP-hardness and then propose two approximation algorithms with proven performance guarantees. Finally, we conduct extensive simulation experiments to validate our designs, and the experimental results demonstrate that the proposed algorithms significantly outperform the baselines.

In communication networks such as social networks, wireless networks and biology networks, it is of importance to find all cliques which can help understand the network topology. The clique structures can also be utilized in facilitating message forwarding in wireless networks. For instance, using a set of cliques of maximal and disjoint, one of the nodes in each clique can be elected to forward messages, by which the duplicate message transmissions can be efficiently reduced. Further, it is well known that the maximum clique problem (MCP) is closely related to the maximum independent set and the vertex cover problems. In recent years, the fundamental problem of finding maximal cliques or maximum cliques has attracted lots of attentions. However, few of these works focus on distributed solutions in wireless networks. In this paper, we pay our attention to this missing corner of research. Specifically, we first give a distributed algorithm which can compute all maximal cliques in a wireless network represented by a graph. The algorithm takes O(n) time and uses O(mn) messages, where n is the number of nodes and m the number of edges. Then, with the proposed algorithms MCP (maximum clique problem) and UMCP (unique MCP), we show that a unique maximum clique can be selected from all maximal cliques in O(n) rounds and using O(mn) messages. To the best of our knowledge, our algorithms are the first deterministic distributed solutions for MCP in wireless networks.