Wireless federated analytics face two critical challenges: data privacy and communication efficiency, since the local data may contain sensitive information and the users may be equipped with limited communication capability. Existing methods often adopt a direct combination of privacy-preservation schemes and compression mechanisms but overlook the privacy amplification effect from errors introduced in compression and wireless communication. With such consideration, a Differentially Private Quadrature Amplitude Modulation (DP-QAM) scheme, which leverages privacy amplification from both compression and noisy wireless channels, is proposed. The privacy guarantee is established in terms of the emergingf-DP, and the trade-off between privacy, communication cost, and accuracy in terms of mean square error (MSE) is characterized in the fundamental use cases of distributed mean estimation and frequency estimation, which outperforms the state-of-the-art methods. Moreover, the advantage of the proposed method over the classic Gaussian mechanism is further demonstrated from a rate-distortion perspective. Finally, extensive simulation results validate the effectiveness of the proposed mechanism.

While the recently advocated coded edge computing paradigm is promising for mitigating the unfavorable latency caused by the straggling edge nodes (ENs) in distributed edge networks, it also introduces new challenges in communications. To this end, existing works often treat the transmission of coded computing information as a traditional multi-user communication problem. However, a fundamental difference between multi-user communication and coded computing is that the transmitted messages are often assumed independent in the former while arecorrelatedin the latter, due to task encoding. With this consideration, a novel distributed source coding (DSC)-assisted downlink transmission scheme is proposed in this work for coded edge computing. Particularly, by exploiting such correlation with DSC, the proposed scheme can achieve efficient data compression and flexible load reallocation to better match the channel conditions of the ENs for efficient transmission. In addition, to efficiently fulfill the proposed scheme, a syndrome puncturing based DSC method well-suited to coded edge computing is also developed. To the best of our knowledge, this work is among the first to explore the interesting analogy between the transmission in coded edge computing and the classic DSC problem. The effectiveness of the proposed scheme is manifested through its exemplary integration with non-orthogonal multiple access and corroborated by simulation results.

The advantage assumed by conventional distributed edge computing in handling large-scale tasks is often overshadowed by straggling edge nodes (ENs). This in turn catalyzes the recent emergence of coded edge computing that can effectively mitigate straggling via subtle task encoding. Nonetheless, coded edge computing presents new challenges in communications. In particular, existing offloading schemes are mainly designed for conventional distributed edge computing, where the data offloaded to different ENs are often non-overlapping. This makes them not well-suited to coded edge computing, where substantial redundancy exists among the data offloaded to different ENs due to task encoding. To the best of our knowledge, a tailor-designed efficient offloading scheme for coded edge computing still remains underexplored. With this consideration, a novel coding-aware rate splitting scheme is proposed in this work, which splits the data offloaded to different ENs in a coding-aware manner to avoid transmission redundancy and enables multiple concurrent multi-casts to the ENs. In addition, based on the concave-convex procedure and the sequential parametric optimization framework, two optimization algorithms are developed to minimize the overall latency and the energy consumption under the proposed scheme, respectively. Simulations are conducted to corroborate the effectiveness of the proposed scheme.

Virtual reality (VR) demands extensive computation while imposing strict requirements for ultra-low latency, placing a significant burden on wireless communication systems. In recent years, there has been a growing interest in leveraging unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) as a promising technology to provide flexible computing resources at the edge of wireless networks. To meet the computational demands of VR, we propose a collaborative three-layer edge computing framework assisted by multiple UAVs. This framework enables VR rendering tasks to be executed locally on user devices or offloaded to UAVs and base station (BS) for execution. By jointly optimizing the flight trajectories of UAVs and the rendering modes of users, we aim to maximize the average rendering completion rate, defined as the ratio of successfully completed VR rendering tasks within the specified delay constraints, while minimizing the average energy consumption of UAVs. To enhance adaptability, we adopt a multi-agent twin delayed deep deterministic policy gradient (MATD3) approach that provides an efficient strategy for multi-UAV-assisted VR rendering, even in partially observable scenarios. Simulation results validate our proposed approach and demonstrate that the MATD3 algorithm surpasses the classical multi-agent deep deterministic policy gradient (MADDPG) algorithm in terms of convergence speed and the average rendering completion rate.

The ever-increasing scale and more stringent latency requirements of mobile computing tasks have driven the recent development of distributed edge computing. In distributed edge computing, a large-scale computing task is partitioned into multiple small subtasks and executed in parallel on multiple edge nodes (ENs) to reduce computation delay. In early works of this area, the computation results of the subtasks are often transmitted back in a non-cooperative manner, which may lead to suboptimal downlink communication delay. Replicated edge computing can alleviate this issue by replicating the computing task over multiple ENs to enable cooperative transmission in the downlink. However, this will inevitably entail multi-fold increase of computation costs. To bridge the gap between the conventional distributed edge computing and the replicated edge computing, a novel partial replication based distributed edge computing scheme is proposed in this work. In particular, by judiciously determining the portion of task to be replicated at the ENs, the proposed scheme can harvest cooperative transmission gains while avoiding excessive computational replication costs. Accordingly, a partial replication based delay minimization problem is formulated. By leveraging the generic alternating optimization framework, this problem can be divided into two subproblems of power allocation and task partitioning. Through analysis, a semi-closed form solution is derived for the former non-convex subproblem, while the latter subproblem turns out to be linear. Simulation results are presented to corroborate the effectiveness of the proposed scheme.

Plagued by slow or failing workers (also known as stragglers), the speedup gain assumed by distributed learning often falls short. Although substantial efforts have been devoted to mitigating this straggling effect with coding-theoretic techniques, existing pioneering works often suffer from two issues: dense combination and inflexibility. In particular, a code that involves dense combination of sub-tasks may destroy sparsity and lead to heavy workload. In contrast, an inflexible code that conservatively designs its computation procedure according to the presumed maximum number of stragglers may entail unnecessary redundancy when the actual number of stragglers is small. To this end, a generic framework based on matrix splitting is proposed in this work to construct sparse and flexible codes. Specifically, by splitting an original sparse coding matrix into two sparser sub-matrices, a two-layer coded computation that maintains sparsity can be created accordingly. In the meantime, when the actual number of stragglers is small, the computation may be flexibly terminated at layer-one without executing layer-two, thereby avoiding unnecessary computation. Based on this framework, a novel flexible Bernoulli code is proposed. In addition, by deriving a lower bound in closed-form through the lens of a bipartite graph, its decoding probability is shown to be high and asymptotically one. Moreover, extensive simulations including an application in distributed learning of LeNet are conducted to validate the effectiveness of the proposed scheme.

Communication overhead has become one of the major bottlenecks in the distributed training of modern deep neural networks. With such consideration, various quantization-based stochastic gradient descent (SGD) solvers have been proposed and widely adopted, among which SignSGD with majority vote shows a promising direction because of its communication efficiency and robustness against Byzantine attackers. However, SignSGD fails to converge in the presence of data heterogeneity, which is commonly observed in the emerging federated learning (FL) paradigm. In this article, a sufficient condition for the convergence of the sign-based gradient descent method is derived, based on which a novel magnitude-driven stochastic-sign-based gradient compressor is proposed to address the non-convergence issue of SignSGD. The convergence of the proposed method is established in the presence of arbitrary data heterogeneity. The Byzantine resilience of sign-based gradient descent methods is quantified, and the error-feedback mechanism is further incorporated to boost the learning performance. Experimental results on the MNIST dataset, the CIFAR-10 dataset, and the Tiny-ImageNet dataset corroborate the effectiveness of the proposed methods.

Timely gathering of sensing data is critical in wireless sensor networks (WSNs). However, in delay-sensitive applications, maintaining the freshness of collected data poses a significant challenge. To tackle this issue, an age of information (AoI)-aware data collection method leveraging unmanned aerial vehicle (UAV) and intelligent reflective surface (IRS) is proposed in this work. Particularly, a UAV is employed to traverse over ground sensor nodes (SNs) and reliably collect their sensing data where the received signal strength is enhanced through IRS. The UAV’s flight trajectory and its association with SNs, as well as the IRS phase control strategy are jointly optimized to minimize the weighted sum of the average AoI of the SNs and energy consumption of the UAV. However, this optimization is complicated by potential inaccuracies in IRS channel state estimation. To tackle this challenge, we propose an enhanced deep reinforcement learning (DRL) framework that incorporates a dual-network agent with two nested neural networks (NNs): UAV-NN, which jointly optimizes the UAV trajectory and SN association, and IRS-NN, which dynamically adjusts IRS phase shifts based on sampled channel states, UAV position, and associated SN. By integrating this architecture into proximal policy optimization (PPO) and deep Q-network (DQN), we develop two novel algorithms: PPO-RAC and DQN-RAC, tailored for IRS-assisted UAV data collection. Extensive simulations validate their effectiveness across diverse scenarios, demonstrating significant AoI reduction compared to baseline methods.

As the advantages promised by distributed edge computing in large-scale computation applications are often offset by a few straggling edge nodes (ENs), coded edge computing has emerged recently as an effective countermeasure by creating redundant computation using coding theory. However, existing pioneering works on coded edge computing often treat the corresponding information transmission as a traditional multi-user communication problem, in which the messages from the users are often assumed independent. In contrast, in coded edge computing, due to task encoding, the computation results of the ENs are correlated. With this consideration, a novel distributed source coding (DSC) assisted downlink transmission scheme is proposed in this work, to demonstrate that, if properly exploited, such correlation can lead to more efficient transmission of the coded computation results. To the best of our knowledge, this work is among the first to explore the interesting analogy between transmission in coded edge computing and the classic DSC problem. To further support the proposed scheme, an efficient and practical DSC method is also developed, by exploiting the unique features of coded computing. The effectiveness of the proposed scheme is corroborated by simulation results.

Low Earth Orbit (LEO) satellite networks provide global connectivity but are vulnerable to security threats such as link flooding attacks. To defend against such attacks, stateof-the-art approaches employ SDN to acquire a global view of the network, enabling the detection and mitigation of malicious traffic. However, in LEO constellation networks, the distributed nature of satellites across a large spatial scale introduces significant latency in both satellite-to-ground and inter-satellite links, with latency reaching up to tens of milliseconds, while attack traffic dynamically adapts within sub-milliseconds. As a result, existing defense systems face challenges in countering these attacks effectively due to the increased reaction time caused by link latency. In this paper, we leverage programmable switches to build a real-time defense system against link flooding attacks (LFA) in LEO constellation networks. To achieve this, we analyze the practical constraints encountered in the deployment of LFA attacks against state-of-the-art LEO satellite systems. We observe that despite the ability of bots to initiate attack traffic from any location worldwide, an anomalous distribution of flow rate on the affected links can still be detected. We propose SatShield, an in-network defense system that filters out suspicious traffic (heavy flows) in the network and mitigates these threats by leveraging programmable packet scheduling. By using SatShield, we are able to achieve real-time identification and rate-limiting of attacks at line rate on a per-packet basis. We implement SatShield with P4 in a commercial programmable switch and evaluate it with real-world traffic traces. Our evaluation shows that SatShield autonomously identifies LFA attack flows and rapidly mitigates LFA attacks.

Vehicular fog computing (VFC) that supports inter-vehicular task offloading emerges as a promising complement to handle the explosive growth of computation-intensive tasks in Intelligent Transportation Systems (ITS). Nonetheless, as the fog access points (F-APs) in crowed areas are often overloaded, the conventional single F-AP VFC may become incompetent and energy-inefficient. To tackle the issue, a novel scheme of non-orthogonal multiple access (NOMA)-enabled multi-F-AP VFC with partial offloading is proposed in this work. However, the corresponding energy minimization turns out to be a highly non-trivial non-linear mixed-integer programming problem. To this end, the optimal power allocation is derived by exploiting monotonicity while good task splitting ratio and user association are found through successive convex approximation (SCA)-based interior-point method and game theoretic approach, respectively. Extensive simulations based on MATLAB show that, in the considered scenarios, the proposed scheme can fulfill a more balanced offloading and better exploit the available computing resources, thereby leading to an approximately 30% energy consumption reduction compared to the baselines.

The recent rapid growth in mobile data traffic entails a pressing demand for improving the throughput of the underlying wireless communication networks. Network node deployment has been considered as an effective approach for throughput enhancement which, however, often leads to highly nontrivial nonconvex optimizations. Although convex-approximation-based solutions are considered in the literature, their approximation to the actual throughput may be loose and sometimes lead to unsatisfactory performance. With this consideration, in this article, we propose a novel graph neural network (GNN) method for the network node deployment problem. Specifically, we fit a GNN to the network throughput and use the gradients of this GNN to iteratively update the locations of the network nodes. Besides, we show that an expressive GNN has the capacity to approximate both the function value and the gradients of a multivariate permutation-invariant function, as a theoretic support to the proposed method. To further improve the throughput, we also study a hybrid node deployment method based on this approach. To train the desired GNN, we adopt a policy gradient algorithm to create datasets containing good training samples. Numerical experiments show that the proposed methods produce competitive results compared with the baselines.

Coded edge computing is envisioned as a promising solution to cope with the ever-increasing large-scale and computation-intensive mobile applications. Besides alleviating the computation straggling issue, task encoding in coded edge computing is also beneficial to the transmission of computation results. Nonetheless, existing pioneering works in this direction mainly take an information-theoretical perspective and assume the ideal scenarios of high signal-to-noise ratio. To the best of our knowledge, the issue of power control still remains largely unexplored for coded edge computing. In this work, two novel power control schemes are developed for coded edge computing in dynamic wireless environments, which apply to the repetition encoded task computing and the general linearly encoded task computing, respectively. However, the corresponding optimization problems turn out to be non-convex and highly non-trivial. To this end, by exploiting the underlying structural property, a novel partition-based iterative optimization method is developed to obtain the closed-form expression of the optimal dynamic power control strategy for repetition encoded task computing. For the case of more general linearly encoded task computing, the corresponding problem is transformed into a sum-of-ratio problem and then solved iteratively. Simulations are conducted to corroborate the effectiveness of the proposed schemes.

Distributed edge computing has been advocated as a key enabling technology to tackle large-scale intelligence applications, which is however hampered by the straggling effect. To overcome straggling, coded edge computing emerges as a promising solution by creating judiciously designed redundant computations using coding theory. Nonetheless, existing transmission schemes for coded edge computing that make edge nodes (ENs) transmit independently are often sub-optimal, as the computation results are correlated due to coding redundancy. This entails a pressing need for more effective transmission for coded edge computing. With this consideration, a noveltask-decoding assisted cooperative transmissionscheme is proposed in this work to facilitate cooperative transmission in general coded edge computing settings. Specifically, by exploiting the structural relation among the encoded sub-tasks, a task-decoding mechanism is developed to enable ENs to reconstruct computation results ofallother ENs, so that they can cooperatively transmit withanyother EN by forming a virtual multi-antenna system. To characterize the delay performance of the proposed scheme, an analytic bound with closed-form expression is derived first, followed by a more accurate algorithmic bound for scenarios with a relatively small recovery threshold. Simulations are conducted to validate the effectiveness of the proposed scheme.

Recent years have witnessed a renewed interest in reinforcement learning (RL) due to the rapid growth of the Internet of Things (IoT) and their associated intelligent information processing and decision-making demands. As the slow learning speed is one of the major stumbling blocks of the classic RL algorithms, substantial efforts have been devoted to developing faster RL algorithms. Among them, post-decision state (PDS) learning is a prominent one, which can often improve the learning speed by orders of magnitude by exploiting the structural property of the underlying Markov decision processes (MDPs). However, conventional PDS learning requires prior information about the PDS transition probability, which may not be always available in practice. To lift this limitation, a novel blind PDS (b-PDS) learning algorithm is proposed in this work by leveraging the generic two-timescale stochastic approximation framework. By introducing an extra estimating procedure about the PDS transition probability, b-PDS learning can achieve a similar improvement of learning speed as conventional PDS learning while excluding the need for prior information. In addition, by analyzing the globally asymptotically stable equilibrium of the corresponding ordinary differential equation (o.d.e.), the convergence and optimality of b-PDS learning are established. Moreover, extensive simulation results are provided to validate the effectiveness of the proposed algorithm. Over the considered random MDPs, it has been observed that, to reach 90% of the best possible time average reward, the proposed b-PDS learning can reduce the learning time by 70% compared to$Q$-learning and 30% compared to Dyna.

Driven by the ever-increasing scale and intensity of the computing tasks arising from various mobile applications, distributed edge computing has fostered wide research interests. It can effectively reduce the task processing delay by partitioning the original large-scale task into several small subtasks and offloading them to multiple edge nodes (ENs) for parallel computing. In edge computing, as the mobile user usually tends to offload computing tasks to closer ENs to save transmit power, the attacker may stealthily infer user location by exploiting this feature. Although there have been some pioneering works on offloading related location privacy, they mainly focused on the scenario where each task can only be offloaded to a single EN, and may not be directly applicable to distributed edge computing. Besides, the privacy issues considered in existing works are mainly based on good heuristics, and there is still a lack of concrete examples of location privacy attacks in edge computing. To the best of our knowledge, the location privacy issue in distributed edge computing still remains largely unexplored in existing literature. With this consideration, a location inference attack based on matrix sequential probability ratio test (MSPRT) is identified in this work. Besides, a countermeasure based on dynamic multi-EN selection is proposed, together with a location privacy-aware and energy-efficient distributed offloading scheme based on the generic Lyapunov optimization framework. Both theoretic analysis and simulations based on real-world channel measurements are employed to validate the feasibility of the identified MPSRT attack and the effectiveness of the proposed defense scheme.

Due to the features of on-demand deployment and flexible observation, unmanned aerial vehicles (UAVs) are promising for serving as the next-generation aerial sensors by using their onboard sensing devices. Compared to a single UAV with limited sensing coverage and communication capability, multi-UAV cooperation is able to realize more effective sensing and transmission (S&T) services, and delivers the sensory data to the control center more efficiently for further analysis. Nevertheless, most existing works on multi-UAV sensing mainly focus on mutually exclusive task allocation and independent data transmission, which did not fully exploit the benefit of multi-UAV sensing and communication. Motivated by this, we propose a novel multi-UAV cooperative S&T scheme with overlapped sensing task allocation. Although overlapped task allocation may sound counter-intuitive, it can actually foster cooperative transmission among multiple UAVs through a virtual multi-antenna system and thus reduce the overall sensing mission completion time. To obtain the optimal task allocation and transmit power of the proposed scheme, a mission completion time minimization problem is formulated. To solve this problem, a condition that specifies whether it is necessary for the UAVs to perform overlapped sensing is derived. For the cases of overlapped sensing, this time minimization problem is transformed into a monotonic optimization and is solved by the generic Polyblock algorithm. To efficiently evaluate the mission completion time in each iteration of the Polyblock algorithm, new auxiliary variables are introduced to decouple the otherwise sophisticated joint optimization of transmission time and power. While for the degenerated case of non-overlapped sensing, the closed-form expression of the optimal transmission time is derived, which provides insights into the optimal solution and facilitates the design of an efficient double-loop binary search algorithm to optimally solve the degenerated problem. Finally, simulation results demonstrate that the proposed scheme significantly reduces the mission completion time over benchmark schemes.

The dramatic growth in computing capability and the inherent mobility of the unmanned aerial vehicles (UAVs) foster the recent surge of interests in incorporating UAVs into edge computing systems to facilitate on-demand deployment and extended coverage. Nonetheless, due to the limited communication capability of the UAVs, single-UAV edge computing systems may still be incompetent when serving remote users. Although the traditional multi-UAV relay network can be a viable solution, it fails to exploit the computing capability of the UAVs. With this consideration, a multi-hop task offloading with on-the-fly computation scheme is proposed in this work to enable a more powerful multi-UAV remote edge computing network. To solve the corresponding joint resource allocation and deployment problem, two efficient algorithms are proposed. One of them can find the global optimal strategy in a special case, while the other can obtain a good local optimal strategy in the general cases. Both algorithms have a complexity only linear in the number of UAVs and admit distributed implementation. In addition to analysis, numerical results are provided to corroborate the effectiveness of the proposed scheme.

The rapid growth in scale and complexity of mobile applications fosters the development of the coded edge computing paradigm. By exploiting the redundancy in the encoded subtasks, coded edge computing enables collaborative transmission of multiple edge nodes and is promising for distributed computing in wireless fading environments. Nonetheless, to the best of our knowledge, due to challenges arising from the selection of the coding parameters, offloading strategy design for coded edge computing in general fading environments still remains open. With this consideration, the coded offloading problem is studied in this work and a delay-optimal coded offloading scheme is proposed. In particular, when the offloaded tasks are encoded by$(k,r)$linear codes, transmission diversity gains can be obtained by performing edge node selection to mitigate fading. However, the corresponding optimization problem turns out to be a highly non-trivial non-linear mixed-integer programming. To this end, through in-depth analysis based on order statistics, it is found that the average processing delay of the offloaded tasks admits a favorable$V$-structure with respect to the coding parameter$r$, under arbitrary fading distribution. This key theoretic result allows us to efficiently solve the original problem using monotonic optimization. Simulations are conducted to validate our analysis and corroborate the effectiveness of the proposed scheme.

In federated learning (FL), reducing the communication overhead is one of the most critical challenges since the parameter server and the mobile devices share the training parameters over wireless links. With such consideration, we adopt the idea of SignSGD in which only the signs of the gradients are exchanged. Moreover, most of the existing works assume Channel State Information (CSI) available at both the mobile devices and the parameter server, and thus the mobile devices can adopt fixed transmission rates dictated by the channel capacity. In this work, only the parameter server side CSI is assumed, and channel capacity with outage is considered. In this case, an essential problem for the mobile devices is to select appropriate local processing and communication parameters (including the transmission rates) to achieve a desired balance between the overall learning performance and their energy consumption. Two optimization problems are formulated and solved, which optimize the learning performance given the energy consumption requirement, and vice versa. Furthermore, considering that the data may be distributed across the mobile devices in a highly uneven fashion in FL, a stochastic sign-based algorithm is proposed. Extensive simulations are performed to demonstrate the effectiveness of the proposed methods.

We investigate a transmission mechanism aiming to improve the data rate between a base station (BS) and a user equipment (UE) through deploying multiple relaying UAVs. We consider the effect of interference incurred by another established communication network, which makes our problem challenging and different from the state of the art. We aim to design the 3D trajectories and power allocation for the UAVs to maximize the data flow of the network while keeping the interference on the existing communication network below a threshold. We utilize the mobility feature of the UAVs to evade the (un)-intended interference caused by (un)-intentional interferers. To this end, we propose an alternating-maximization approach to jointly obtain the 3D trajectories and the UAVs transmission powers. We handle the 3D trajectory design by resorting to spectral graph theory and subsequently address the power allocation through convex optimization techniques. We also approach the problem from the intentional interferer’s perspective where smart jammers chase the UAVs to effectively degrade the data flow of the network. We also extend our work to the case for multiple UEs. Finally, we demonstrate the efficacy of our proposed method through extensive simulations.

Driven by the burgeoning development of unmanned aerial vehicle (UAV) technology, the recently advocated multi-UAV edge computing paradigm is anticipated to greatly enhance the coverage and on-demand deployment capability of the edge networks. One of the prominent advantage of this paradigm is to allow the UAVs to participate in the edge computing process by executing some computing tasks at their onboard processors. To this end, a key prerequisite is that the corresponding computing services must be placed onboard beforehand. Nonetheless, unlike its counterpart for conventional ground edge systems, the service placement issue in multi-UAV edge computing systems remains much less explored. To the best of our knowledge, this work is among the first to consider the joint service placement and resource allocation problem for multi-UAV edge computing. Due to the mutual influence between service placement and resource allocation, this problem turns out to be a computationally intractable mixed-integer nonlinear programming. Fortunately, through our analysis, it is found that this problem can be divided into two subproblems that are submodular and convex, respectively. Based on this observation and the general alternative optimization framework, an efficient joint service placement and resource allocation scheme that can find a reasonably good solution with only a linear complexity is proposed. In addition to the analysis, simulations are conducted to validate the effectiveness of the proposed scheme.

The recent advances in wireless sensor networks and sensing techniques enable various time-sensitive applications that require timely exchange of updates between a Base Station (BS) and ground terminals. In practice, the ground terminals may not be able to communicate with the BS directly due to constraints in transmit power and communication capability, and mobile agents are commonly employed to help collect and deliver the updates. In particular, the emerging mobile crowd sensing (MCS) provides an appealing cost-effective paradigm for such employment. However, in this case, the mobile agents are required to share their locations with the ground terminals and the BS, which incurs location privacy concerns and may deter them from participating in the information delivery process. With this consideration, a location privacy-aware payment mechanism, which can stimulate the mobile agents to report their locations with differential privacy levels desired by the BS, is proposed. Furthermore, considering that the BS usually has a limited budget, it is essential to properly select the set of mobile agents to perform the information collection tasks. Therefore, a cost-efficient mobile agent selection algorithm is proposed. Finally, simulation results are presented to demonstrate the effectiveness of the proposed method.

Driven by the dramatic growth in computing capability and the inherent mobility of the unmanned aerial vehicles (UAVs), the recently advocated UAV edge computing paradigm is expected to enhance the coverage and the on-demand deployment capability of existing terrestrial edge computing systems. Nonetheless, due to the limited onboard resource of the UAV, single- UAV edge computing systems may still be incompetent when serving remote users. Although using multiple UAVs to form a traditional relay network is a viable solution to remote edge computing, it fails to exploit the computing capability of the UAVs. This entails a pressing need to develop multi-UAV remote edge computing mechanisms that allow the UAVs to handle part of the computation tasks using their local processors while conducting multi-hop computation task offloading. To achieve the best performance in such cases, the UAVs have to properly split their power budget for communication and computation and also move to suitable service locations. Nonetheless, finding the optimal UAV power allocation and deployment turns out to be an intractable high-dimensional monotonic optimization problem, even for a mild number of UAVs. To overcome this challenge, a more efficient algorithm that has a complexity only linear in the number of UAVs is developed by exploiting the special structure of this problem. In addition to analysis, numerical results are provided to validate the effectiveness of the proposed scheme.

The wireless offloading feature of the recently advocated mobile-edge computing (MEC) imposes a risk of disclosing private user data to eavesdroppers. Physical-layer security approaches that are built on information theoretic methods can be applied to defend eavesdropping in MEC. Nonetheless, directly incorporating existing physical-layer security technique may introduce extra energy and delay costs to the resource-limited mobile device and thus substantially disrupt the users' offloading decisions. To fulfill effective secure offloading in MEC, there is a compelling need to properly optimize existing physical-layer security techniques and develop new offloading schemes accordingly. With this consideration, a novel physical-layer assisted secure offloading scheme is proposed in this work, in which the edge server proactively broadcasts jamming signals to impede eavesdropping and leverages full-duplex communication technique to effectively suppress the self-interference. Finding the optimal jamming signal and the corresponding optimal offloading ratio turns out to be a challenging bilevel optimization problem. The special structure of the secure offloading problem is exploited to develop efficient offloading algorithms. Numerical results are presented to validate the effectiveness of the proposed scheme.

The limited information processing capability and battery life of mobile devices is becoming a bottleneck in delivering more advanced and high-quality services to the customers. To address this problem, the recently advocated mobile-edge computing (MEC) architecture is promising, where the essential idea is to bring the computation resource to the network edge and allow users to wirelessly offload resource demanding computation tasks to the nearby MEC servers for potentially faster execution and lower battery consumption. Nonetheless, the existing understanding of the privacy aspect of MEC is still far from complete. In this work, a user presence inference attack that invades user privacy by exploiting the feature tasks offloaded from users is identified for MEC. Existing privacy-preserving techniques developed for other applications cannot be applied to defeat this attack in MEC, as they may disrupt the optimal task offloading scheduling and cause severe degradation in user experience. With this consideration, a novel privacy-preserving and cost-efficient (PEACE) task offloading scheme that can preserve user privacy while still ensure the best possible user experience is developed in this work based on the generic Lyapunov optimization framework. The effectiveness of the proposed scheme is validated through both analysis and simulations.

The deployment of the unmanned aerial vehicle (UAV) has been foreseen as a promising technology for the next generation communication networks. The distance limitation imposed by the line of sight RF connection can be removed by using RF coverage from existing commercial cellular service. In this work, we consider a transmission mechanism that aims to improve the data rate between a terrestrial base station (BS) and user equipment (UE) through deploying multiple UAVs relaying the desired data flow. Considering the coexistence of this network with other established communication networks, we take into account the effect of interference, which is incurred by the existing nodes. Our primary goal is to optimize the three-dimensional (3D) trajectories and power allocation for the relaying UAVs to maximize the data flow while keeping the interference to existing nodes below a predefined threshold. An alternating-maximization strategy is proposed to solve the joint 3D trajectory design and power allocation for the relaying UAVs. To this end, we handle the information exchange within the network by resorting to spectral graph theory and subsequently address the power allocation through convex optimization techniques. Simulation results show that our approach can considerably improve the information flow while the interference threshold constraint is met.

As compared to the conventional cloud computing, the wireless offloading feature of the recently advocated mobile-edge computing (MEC) imposes a new risk of disclosing possibly private and sensitive user data to eavesdroppers. Physical-layer security approaches built on information theoretic methods are believed to provide a stronger notion of privacy than cryptography, and therefore, may be more suitable for defending eavesdropping in MEC. Nonetheless, incorporating a physical-layer security technique may fundamentally change the mobile users' offloading decisions. This suggests a compelling need for new judiciously designed offloading schemes that can jointly reap the benefits of both physical-layer security and MEC. With this consideration, a novel physical-layer assisted privacy-preserving offloading scheme is proposed in this work, in which the edge server proactively broadcasts jamming signals to impede eavesdropping and leverages full-duplex communication technique to effectively suppress the self-interference. Finding the optimal jamming power of the edge server and the corresponding optimal offloading ratio of the mobile user turns out to be a challenging bilevel optimization problem. By exploiting the structure of the considered problem, two efficient algorithms are developed for delay optimal and energy optimal privacy-preserving offloading, respectively. Numerical results are presented to validate the effectiveness of the proposed schemes.

The recent advances in sensor technologies and smart devices enable the collaborative collection of a sheer volume of data from multiple information sources. As a promising tool to efficiently extract useful information from such big data, machine learning has been pushed to the forefront and seen great success in a wide range of relevant areas such as computer vision, health care, and financial market analysis. To accommodate the large volume of data, there is a surge of interest in the design of distributed machine learning, among which stochastic gradient descent (SGD) is one of the mostly adopted methods. Nonetheless, distributed machine learning methods may be vulnerable to Byzantine attack, in which the adversary can deliberately share falsified information to disrupt the intended machine learning procedures. In this work, two asynchronous Byzantine tolerant SGD algorithms are proposed, in which the honest collaborative workers are assumed to store the model parameters derived from their own local data and use them as the ground truth. The proposed algorithms can deal with an arbitrary number of Byzantine attackers and are provably convergent. Simulation results based on a real-world dataset are presented to verify the theoretical results and demonstrate the effectiveness of the proposed algorithms.

Aerial base station (ABS) is a promising solution for public safety as it can be deployed in coexistence with cellular networks to form a temporary communication network. However, the interference from the primary cellular network may severely degrade the performance of an ABS network. With this consideration, an adaptive dynamic interference avoidance scheme is proposed in this work for ABSs coexisting with a primary network. In the proposed scheme, the mobile ABSs can reconfigure their locations to mitigate the interference from the primary network, so as to better relay the data from the designated source(s) to destination(s). To this end, the single/multi-commodity maximum flow problems are formulated and the weighted Cheeger constant is adopted as a criterion to improve the maximum flow of the ABS network. In addition, a distributed algorithm is proposed to compute the optimal ABS moving directions. Moreover, the trade-off between the maximum flow and the shortest path trajectories is investigated and an energy-efficient approach is developed as well. Simulation results show that the proposed approach is effective in improving the maximum network flow and the energy-efficient approach can save up to 39% of the energy for the ABSs with marginal degradation in the maximum network flow.

The rapid uptake of Internet-of-Things (IoT) devices imposes an unprecedented pressure for data communication and processing on the backbone network and the central cloud infrastructure. To overcome this issue, the recently advocated mobile-edge computing (MEC)-enabled IoT is promising. Meanwhile, driven by the growing social awareness of privacy, significant research efforts have been devoted to relevant issues in IoT; however, most of them mainly focus on the conventional cloud-based IoT. In this paper, a new privacy vulnerability caused by the wireless offloading feature of MEC-enabled IoT is identified. To address this vulnerability, an effective privacy-aware offloading scheme is developed based on a newly proposed deep post-decision state (PDS)-learning algorithm. By exploiting extra prior information, the proposed deep PDS-learning algorithm allows the IoT devices to learn a good privacy-aware offloading strategy much faster than the conventional deep Q-network. Theoretic analysis and numerical results are provided to corroborate the correctness and the effectiveness of the proposed algorithm.

To contest the rapidly developing cyber-attacks, numerous collaborative security schemes, in which multiple security entities can exchange their observations and other relevant data to achieve more effective security decisions, are proposed and developed in the literature. However, the security-related information shared among the security entities may contain some sensitive information and such information exchange can raise privacy concerns, especially when these entities belong to different organizations. With such consideration, the interplay between the attacker and the collaborative entities is formulated as Quantitative Information Flow (QIF) games, in which the QIF theory is adapted to measure the collaboration gain and the privacy loss of the entities in the information sharing process. In particular, three games are considered, each corresponding to one possible scenario of interest in practice. Based on the game-theoretic analysis, the expected behaviors of both the attacker and the security entities are obtained. In addition, the simulation results are presented to validate the analysis.

While providing unprecedented convenience to people's daily life, location-based services (LBSs) may cause serious concerns on users' location privacy, when the system is compromised. Although various location privacy protection mechanisms have been developed for LBSs, the ambient physical environment often imposes some fundamental limitations on their performances. As a result, mobile users may experience a spatial diversity in the achievable location privacy when traveling along their routes. However, to the best of our knowledge, an appropriate location privacy metric that can capture the influence of the ambient environment is still missing in the literature. Also, none of the existing location privacy protection methods can properly leverage such spatial diversity. With this consideration, new ambient environment-dependent location privacy metrics are proposed in this paper, together with a stochastic model that can capture their spatial variations along the user's route. Based on this modeling, a new optimal stopping-based LBS access scheme that allows mobile users to fully leverage the spatial diversity and achieve a substantially better performance is developed. The effectiveness of the proposed scheme is corroborated by both numerical results and simulations over real-world road maps.

Recently, mobile-edge computing (MEC) emerges as a promising paradigm to enable computation intensive and delay-sensitive applications at resource limited mobile devices by allowing them to offload their heavy computation tasks to nearby MEC servers through wireless communications. A substantial body of literature is devoted to developing efficient scheduling algorithms that can adapt to the dynamics of both the system and the ambient wireless environments. However, the influence of these task offloading schemes to the mobile users' privacy is largely ignored. In this work, two potential privacy issues induced by the wireless task offloading feature of MEC, location privacy and usage pattern privacy, are identified. To address these two privacy issues, a constrained Markov decision process (CMDP) based privacy-aware task offloading scheduling algorithm is proposed, which allows the mobile device to achieve the best possible delay and energy consumption performance while maintain a pre-specified level of privacy. Numerical results are presented to corroborate the effectiveness of the proposed algorithm.

Deception has been widely considered in literature as an effective means of enhancing security protection when the defender holds some private information about the ongoing rivalry unknown to the attacker. However, most of the existing works on deception assume static environments and thus consider only myopic deception, while practical security games between the defender and the attacker may happen in dynamic scenarios. To better exploit the defender's private information in dynamic environments and improve security performance, a stochastic deception game (SDG) framework is developed in this work to enable the defender to conduct foresighted deception. To solve the proposed SDG, a new iterative algorithm that is provably convergent is developed. A corresponding learning algorithm is developed as well to facilitate the defender in conducting foresighted deception in unknown dynamic environments. Numerical results show that the proposed foresighted deception can offer a substantial performance improvement as compared to the conventional myopic deception.

In various resource sharing networks, opportunistic resources with dynamic quality are often present for the users to exploit. As many user tasks are delay-tolerant, this favorably allows the network users to wait for and access the opportunistic resource at the time of its best quality. For such delay-tolerant and opportunistic resource sharing networks, the resource accessing strategies developed in the literature suffer from three limitations. First, they mainly focused on single-user scenarios, whereas the competition from other users is ignored. Second, the influence from the resource seller who may take actions to manipulate the resource sharing procedure is not considered. Third, the impact of the actions from both the network users and the resource seller on the resource quality dynamics is not considered either. To overcome these limitations, a leader-follower controlled Markov stopping game (LF-C-MSG) is developed in this paper. The derived Stackelberg equilibrium strategy of the LF-C-MSG can be used to guide the behaviors of both the network users and the resource seller for better performance and resource utilization efficiency. Two exemplary applications of the proposed LF-C-MSG are presented, along with corresponding numerical results to verify the effectiveness of the proposed framework.

As information systems become ubiquitous, Intrusion Detection Systems (IDSs) have assumed increasing importance. As a result, substantial amount of research efforts have been devoted to developing various intrusion detection algorithms. However, there is still no single detection algorithm that can catch all possible attacks. On the other hand, it is infeasible for practical IDSs to run all the detection algorithms simultaneously due to resource limitation, leaving potential opportunities for the adversaries to explore. This resource scarcity problem becomes more severe when the system is in an ill state (e.g., partially compromised). Enabling collaboration among multiple IDSs may be a viable way to mitigate this problem. Particularly, IDSs in the healthy state can share some of their idle computational resources to those in ill states, so as to improve the overall intrusion detection performance. Considering this, the collaborative IDS configuration problem is formulated as a two-layer stochastic game (SG) in this work and a new algorithm is proposed to solve this two-layer SG. Simulation results show that the proposed algorithm can provide an effective collaborative configuration scheme, leading to significant detection performance gain. Some performance analysis has also been given, and the conditions under which there is a guaranteed improvement in expected system performance have been derived.

Opportunistic resources are often present in various resource sharing networks for the users to exploit, but their qualities often change over time. Fortunately, many user tasks are delay-tolerant, which offers the network users a favorable degree of freedom in waiting for and accessing the opportunistic resource at the time of its best quality. For such delay-tolerant and opportunistic resource sharing networks (DT-ORS-Net), the corresponding optimal accessing strategies developed in existing literature mainly focus on the single-user scenarios, while the potential competition from other peer users in practical multi-user DT-ORS-Net is often ignored. Considering this, a multi-player Markov stopping game (M-MSG) is developed in this work, and the derived Nash equilibrium (NE) strategy of this M-MSG can guide network users to properly handle the potential competition from other peers and thus exploit the time diversity of the opportunistic resource more effectively, which in turn further improves the resource utilization efficiency. Applications in the cloud-computing and the mobile crowdsourcing networks are demonstrated to verify the effectiveness of the proposed method, and simulation results show that using the NE strategy of the proposed M-MSG can provide substantial performance gain as compared to using the conventional single-user optimal one.

Zero-determinant (ZD) strategies that allow a player to unilaterally control the linear combinations of its own and other players' expected rewards in iterated games have recently found wide applications. However, existing ZD strategies mainly focus on some specific scenarios with restrictions on the number of players or actions a player can take. Targeting wider applications and better performance, the ZD strategies along with corresponding existence conditions for general multi-player multi-action iterated games are developed in this work, including existing ones as special cases. In addition, an interesting fact that every player can have at most one master player (that can control the expected reward of the given player) is revealed.

Motivated by information-theoretic security, link signature (LS)-based security mechanisms exploit the ample channel characteristics between wireless devices for security establishment. Nevertheless, LS is originated from wireless environments and hence may exhibit potential vulnerabilities that can be exploited by adversary in the vicinity. As to this, it is widely believed in existing literature on LS that, a half-wavelength guard zone is sufficient to decorrelate the adversary channel from the legitimate one and thereby secures the legitimate LS. However, such an assumption may not hold universally - in some environments, high channel correlations have been observed for much larger spatial separations. Considering this, a comprehensive understanding of channel correlation in different wireless environments is needed for more confident deployment of LS-based security mechanisms. To this end, various well-established channel correlation models are investigated in this work. A set of important physical factors that have significant influence on LS security are identified, and with the obtained insights, extensive simulations are conducted to explore suitable guard zone sizes for LS in several typical indoor and outdoor environments. Experimental results based on universal software radio peripheral (USRP) platforms and GNURadio are also presented to further support the analysis.

Intrusion detection systems (IDSs) assume increasingly importance in past decades as information systems become ubiquitous. Despite the abundance of intrusion detection algorithms developed so far, there is still no single detection algorithm or procedure that can catch all possible intrusions; also, simultaneously running all these algorithms may not be feasible for practical IDSs due to resource limitation. For these reasons, effective IDS configuration becomes crucial for real-time intrusion detection. However, the uncertainty in the intruder's type and the (often unknown) dynamics involved with the target system pose challenges to IDS configuration. Considering these challenges, the IDS configuration problem is formulated as an incomplete information stochastic game in this work, and a new algorithm, Bayesian Nash-Q learning, that combines conventional reinforcement learning with a Bayesian type identification procedure is proposed. Numerical results show that the proposed algorithm can identify the intruder's type with high fidelity and provide effective configuration.

To ensure continuous functioning and satisfactory performance, a wireless communication system has to not only learn and adapt to the unknown and ever-changing wireless environment, but also strategically deal with the usually unfamiliar peers. Incomplete information stochastic game (SG) is a promising model for the corresponding analysis and strategy design. In this work, an exemplary multi-channel spectrum access game (SAG) with unknown environment dynamics and limited information of the other player is considered to illustrate the proposed solution for the corresponding incomplete information SG. To find the best communication strategy in the face of uncertainty, a joint reinforcement learning and type identification algorithm is developed, which is provably convergent under certain technical conditions. Numerical results show that using the proposed algorithm, a wireless user can gradually achieve the same performance as that in the corresponding complete information game.

With the advancement of modern technologies, the security battle between a legitimate system (LS) and an adversary is becoming increasingly sophisticated, involving complex interactions in unknown dynamic environments. Stochastic game (SG), together with multi-agent reinforcement learning (MARL), offers a systematic framework for the study of information warfare in current and emerging cyber-physical systems. In practical security games, each player usually has only incomplete information about the opponent, which induces information asymmetry. This work exploits information asymmetry from a new angle, considering how to exploit local information unknown to the opponent to the player's advantage. Two new MARL algorithms, termed minimax-PDS and WoLF-PDS, are proposed, which enable the LS to learn and adapt faster in dynamic environments by exploiting its private local information. The proposed algorithms are provably convergent and rational, respectively. Also, numerical results are presented to show their effectiveness through two concrete anti-jamming examples.

In this paper, the mobility of network nodes is explored as a new promising approach for jamming defense. To fulfill it, properly designed node motion that can intelligently adapt to the jammer's action is crucial. In our study, anti-jamming mobility control is investigated in the context of the single and multiple commodity flow problems, in the presence of intelligent mobile jammers which can respond to the evasion of legitimate nodes as well. Based on spectral graph theory, two new spectral quantities, single- and multi-weighted Cheeger constants and corresponding eigenvalue variants, are constructed to direct motions of the defender and the attacker in this dynamic adaptive competition. Both analytical and simulation results are presented to justify the effectiveness of the proposed approach. Furthermore, the proposed scheme can also be applied in cognitive radio networks to reconfigure the secondary users in the presence of mobile primary users.

A fundamental assumption of link signature based security mechanisms is that the wireless signals received at two locations separated by more than half a wavelength are essentially uncorrelated. However, it has been observed that in certain circumstances (e.g., with poor scattering and/or a strong line-of-sight (LOS) component), this assumption is invalid. In this paper, a Correlation ATtack (CAT) is proposed to demonstrate the potential vulnerability of the link signature based security mechanisms in such circumstances. Based on statistical inference, CAT explicitly exploits the spatial correlations to reconstruct the legitimate link signature from the observations of multiple adversary receivers deployed in vicinity. Our findings are verified through theoretical analysis, well-known channel correlation models, and experiments on USRP platforms and GNURadio.

This paper presents a novel mechanism, called Ally Friendly Jamming, which aims at providing an intelligent jamming capability that can disable unauthorized (enemy) wireless communication but at the same time still allow authorized wireless devices to communicate, even if all these devices operate at the same frequency. The basic idea is to jam the wireless channel continuously but properly control the jamming signals with secret keys, so that the jamming signals are unpredictable interference to unauthorized devices, but are recoverable by authorized ones equipped with the secret keys. To achieve the ally friendly jamming capability, we develop new techniques to generate ally jamming signals, to identify and synchronize with multiple ally jammers. This paper also reports the analysis, implementation, and experimental evaluation of ally friendly jamming on a software defined radio platform. Both the analytical and experimental results indicate that the proposed techniques can effectively disable enemy wireless communication and at the same time maintain wireless communication between authorized devices.

Collaborative spectrum sensing improves the spectrum state estimation accuracy but is vulnerable to the potential attacks from malicious secondary cognitive radio (CR) users, and thus raises security concerns. One promising malicious user detection method is to identify their abnormal statistical spectrum sensing behaviors. From this angle, two hidden Markov models (HMMs) corresponding to honest and malicious users respectively are adopted in this paper to characterize their different sensing behaviors, and malicious user detection is achieved via detecting the difference in the corresponding HMM parameters. To obtain the HMM estimates, an effective inference algorithm that can simultaneously estimate two HMMs without requiring separated training sequences is also developed. By using these estimates, high malicious user detection accuracy can be achieved at the fusion center, leading to more robust and reliable collaborative spectrum sensing performance (substantially enlarged operational regions) in the presence of malicious users, as compared to the baseline approaches. Different fusion methods are also discussed and compared.

Security concerns are raised for collaborative spectrum sensing due to its vulnerabilities to the potential attacks from malicious secondary users. Most existing malicious user detection methods are reputation-based, which become incapable when the malicious users dominate the network. On the other hand, although Markovian models characterize the spectrum state behavior more precisely, there is a scarcity of malicious user detection methods which fully explore this feature. In this paper, a new malicious user detection method using two proposed conditional frequency check (CFC) statistics is developed under the Markovian model for the spectrum state. With the assistance of one trusted user, the proposed method can achieve high malicious user detection accuracy (≥ 95%) for arbitrary percentage of malicious users that may even be equipped with more advanced sensing devices, and can thus improve the collaborative spectrum sensing performance significantly. Simulation results are provided to verify the theoretical analysis and effectiveness of the proposed method.

Collaborative spectrum sensing is vulnerable to the Byzantine attack. Existing reputation based countermeasures will become incapable when malicious users dominate the network. Also, there is a scarcity of methods that fully explore the Markov property of the spectrum states to restrain sensors' statistical misbehaviors. In this paper, a new malicious user detection method based on two proposed Conditional Frequency Check (CFC) statistics is developed with a Markovian spectrum model. With the assistance of one trusted sensor, the proposed method can achieve high malicious user detection accuracy in the presence of arbitrary percentage of malicious users, and thus significantly improves collaborative spectrum sensing performance.

Abstract In this paper, we first propose a simplified tone reservation (STR) method with low computational complexity which is based on the Fourier series expansion. Then, we analyze how to combine the STR method with the cross antenna rotation and inversion method to reduce the peak‐to‐average power ratio (PAPR) for multi‐input multi‐output orthogonal frequency division multiplexing (MIMO‐OFDM) system. To validate the analytical results, extensive simulations are conducted and the numerical results show the efficiency of the proposed schemes including the PAPR reduction and low computational complexity for MIMO‐OFDM system. Copyright © 2010 John Wiley & Sons, Ltd.