Blazing fast RDMA technology revolutionizes modern distributed systems and propels them to offload performance-critical data paths onto this network fabric. Designing an RDMA-optimized data path needs to clear a main hurdle—non-scalable distributed locks. Through a performance dissection of existing lock schemes, we find that software-based lock request ordering and polling-based lock ownership transfer scale poorly, leading to high NIC contention and heavy network congestion. To resolve these bottlenecks, this paper proposes StreamLock, a scalable lock primitive that co-designs the distributed lock protocol with fast RDMA networks. The core of StreamLock is a novel shared stream abstraction with two mechanisms: (i) scalable request ordering by repurposing the line-speed packet receiving provided by modern NICs; (ii) peer-to-peer notification to achieve one-round-trip-time lock ownership transfer. We implement StreamLock with off-the-shelf RDMA NICs and compare it with state-of-the-art distributed locks. Comprehensive experimental results showcase that StreamLock outperforms them significantly.

Graph Neural Networks (GNNs) have powerful representation capabilities for graph data, achieving excellent performance across various fields. Considering the scarcity of labels in real-world scenarios, graph self-supervised learning (GSSL) has gained increasing attention due to its ability to train without relying on labels. However, recent studies have revealed that GNNs are vulnerable to stealthy backdoor attacks in GSSL scenarios, enabling the encoder to learn backdoor features simply by injecting triggers. Existing graph backdoor defense methods mainly focus on supervised settings and cannot be directly transferred to self-supervised scenarios due to the lack of label guidance. To bridge this gap, we proposeGDetox, the first backdoor defense approach against backdoored encoders in GSSL.GDetoxaims to eliminate backdoor logic in encoders while maintaining the encoder's original performance. Specifically,GDetoxcan purify the graph backdoor encoder based on the self-supervised distillation approach without relying on label information. Further, we introduce an adversarial contrastive learning that augments node representations without relying on labels to enhance teacher model performance, thereby improving distilled encoder performance. We evaluate the defense performance ofGDetoxon four node classifications and four graph classification datasets by comparing with four state-of-the-art (SOTA) defense methods against seven latest backdoor attack methods on GSSL. Extensive experiments demonstrate thatGDetoxfar outperforms the SOTA defense methods, reducing the attack success rate to 4% with negligible degradation in encoder performance (within 2%) in both node-level and graph-level tasks.

Federated Learning (FL) enables machine learning tasks to be performed on distributed data in a privacy-preserving manner, but faces challenges related to the heterogeneity of device systems. This necessitates the customization of resource requirements to accommodate the diverse capacities of participating clients. However, existing approaches struggle to generate resource-customized, ready-to-use inference models while still incurring substantial resource consumption throughout the system workflow. In response, this paper introduces a novel framework, FedRPN, which incorporates Resource-customized Prototypical Networks. RPN leverages resource-customized pre-trained models as the initial point and employs unbiased proto-typical classification, enabling rapid convergence, resource efficiency, and robustness to non-IID data. Additionally, we propose a globally-aware training strategy that produces deployable inference models of varying capacities. Building on RPN, we propose a two-stage process comprising prototype construction followed by model fine-tuning, which further enhances performance. Experimental results demonstrate that FedRPN reduces computational and communication resource consumption by 48% and 43%, respectively, while delivering improved performance.

Advanced Persistent Threats (APTs) pose serious challenges to host-level security analysis due to their stealthy, multi-stage nature, and the sparsity of forensic traces. Existing detection systems often rely on node-level or graph-level anomaly scoring, making it difficult to capture fine-grained abnormal interactions. This paper proposes EdgeTrace, a context-aware framework for APT detection and traceback based on anomalous edge modeling. The method constructs provenance graphs from audit logs and employs a masked graph autoencoder to learn contextual semantics without requiring attack labels. Suspicious interactions are identified through reconstruction loss and further processed via semantic clustering and causal path inference to reconstruct attack chains. EdgeTrace consists of four key modules: provenance graph modeling, contextual representation learning, semantic deviation inference, and causal path reconstruction. Experiments on open-source datasets including StreamSpot and DARPA Engagement 3 demonstrate that the proposed method outperforms existing approaches in key metrics such as F1score, AUC, and attack path coverage, showing strong potential for host-level threat detection and interpretable analysis.

With the rapid development of autonomous driving, how to enable unmanned vehicles (UVs) to efficiently avoid multiple dynamically moving obstacles, especially obstacle vehicles (OVs), has become a vital issue in the context of the Internet of Vehicles (IoV). This requires not only high-level adaptability to dynamic and complex traffic environments, but also extraordinarily agility in reacting to possible collision hazards with safer and proactive collision avoidance. Conventional methods, e.g., artificial potential field (APF), may overreact to distant targets which have no risk in collision, generating a false evasion direction when facing multiple OVs. To this end, we propose a novel improved APF-based algorithm along with the trajectory prediction. Specifically, to measure the safety distance for vehicle maneuvering, a trajectory prediction method integrated with unscented kalman filter (UKF) is developed. Then, an obstacle filtering method utilizing sensor information and trajectory prediction results is applied for wiping off collision-free targets. Afterwards, by employing APF method combining with avoidance strategies based on virtual forces and window-based collision detection, the potential pushing effect caused by multiple OVs is mitigated. Experimental results show that, given the scenario of collision avoidance with multiple OVs, the proposed solution can achieve an obstacle avoidance success rate of around 90%, which is about 20% higher than the best benchmark algorithms, simultaneously demonstrating advantages in efficiency and safety.

Graph Neural Networks (GNNs) are highly susceptible to numerous adversarial attacks, among which the backdoor attack is one of the toughest to deal with due to the fact that it can lead to misclassification of the model. Similar to Deep Neural Networks (DNNs), backdoor attacks in GNNs work by an attacker changing a portion of the graph data with a hidden trigger and modifying their labels to target labels, which induces the model to learn the trigger feature during its training phase. Although recent defense techniques have emerged, approaches based on explainability and data isolation often fail to detect malicious samples with covert triggers, while discrepancy learning methods tend to degrade performance by removing useful features. To overcome these limitations, we propose a novel backdoor defense method, namedGraphCleanse, on GNNs that can effectively eliminate the possible backdoor features during the training process. Specifically,GraphCleansecan easily break the strong correlation between backdoor features and target labels based on graph contrastive training. To further improve the model accuracy, we present a mutual information maximization method to learn the important feature information in the labeled credible samples and unlabeled suspicious samples by clustering the features obtained from the graph contrastive encoder. Compared with the potential solutions, such as randomized smoothing,GraphCleanseeffectively avoids the negative influence of backdoored samples while maintaining a high model performance. Extensive experimental evaluations on four benchmark datasets demonstrate thatGraphCleansecan reduce the attack success rate to 10% with less performance degradation (within 7%).

Recent years have witnessed increasing privacy concerns towards machine learning. To protect privacy in machine learning, federated learning has been proposed as a decentralized privacy-preserving framework where clients upload the parameters rather than private data. However, training a fair federated learning model in heterogeneous environments is still challenging. First, heterogeneous data distributions lead the global model fail to show high accuracy on all distributions. Second, the federated learning training process exposes and exacerbates potential biases in heterogeneous training data. Third, the local bias of each client can be propagated through parameter sharing, biasing the global model. In this work, we propose a two-stage fairness-aware federated learning framework (HeteroFair) to achieve fairness under heterogeneous data distributions. Initially, we introduce the fairness constraint to the loss function and propose a local adaptive weighting algorithm to adjust the proportion of the fairness constraint, achieving fair training in heterogeneous environments. Then, we present a fairness-aware aggregation reweighting algorithm that reduces the mismatch between local and global fairness to achieve fair federated learning. Extensive evaluation results demonstrate the effectiveness of our proposed framework in achieving fairness and high accuracy under het-eroaeneous data distributions.

Mobile crowdsensing (MCS) has been applied for signal map construction in smart city. MCS leverages the mobility of users and the sensors embedded in mobile phones to collect and transfer sensing data. However, it is still costly for MCS to cover large-scale regions. Accordingly, data recovery algorithms are proposed, which allow participants to collect only few signal data and infer the rest by leveraging spatial-temporal correlation of signals. However, existing work only considered the temporal and 2-D spatial correlation in the plane, while the altitude dimension is not exploited. In this paper, we give an attempt to exploit the 3-D spatial-temporal correlation of signals to infer missing data and reconstruct ground signal map, where UAVs can be used to collect signals from the air. Two UAV-assisted ground signal map construction schemes are proposed based on propagation loss (PL) and convolution neural network (CNN). To further reduce the number of aerial samples required and reduce the cost incurred by UAV, a random-active sampling strategy is proposed to select more valuable signals in our work. Extensive simulations are performed which show that the proposed framework and schemes perform well under extremely high missing rate situations and outperform pure ground-based data recovery schemes. In addition, experiments for both indoor and outdoor are conducted to further verify the effectiveness of the proposed schemes.

Container–based microservice provisioning, with its elasticity in terms of the layered structure, enables the sharing of common layers among different edge computing tasks, both within and across edge servers (ES). However, due to the potential hardware breakdowns, each ES may prone to failures, affecting its lifetime (i.e., the time-length that an ES works continuously without interruptions), and in turn leading to the collapse of their hosted/provided microservices or the other ESs’ microservices requesting common layers from it. To address such an issue, in this paper, we study the microservice deployment optimization with layer sharing for maximizing the system-wide reliability while satisfying all tasks’ delay requirements. Considering dynamic task generations and the asynchronization of various decision variables with different triggers, we design an online optimization algorithm by leveraging an improved Lyapunov technique integrating randomized rounding, Lagrangian method and convex optimization, which iteratively solves the problem over different timescales. Theoretical analyses and simulations evaluate the performance of the proposed solution, showing that it can achieve an increase of 12.4% in reliability and a reduction of 28.57% in total delay, compared to the counterparts.

As a privacy-preserving distributed learning paradigm, federated learning (FL) has been proven to be vulnerable to various attacks, among which backdoor attack is one of the toughest. In this attack, malicious users attempt to embed backdoor triggers into local models, resulting in the crafted inputs being misclassified as the targeted labels. To address such attack, several defense mechanisms are proposed, but may lose the effectiveness due to the following drawbacks. First, current methods heavily rely on massive labeled clean data, which is an impractical setting in FL. Moreover, an in-avoidable performance degradation usually occurs in the defensive procedure. To alleviate such concerns, we proposeBadCleaner, a lossless and efficient backdoor defense scheme via attention-based federated multi-teacher distillation. Firstly,BadCleanercan effectively tune the backdoored joint model without performance degradation, by distilling the in-depth knowledge from multiple teachers with only a small part of unlabeled clean data. Secondly, to fully eliminate the hidden backdoor patterns, we present an attention transfer method to alleviate the attention of models to the trigger regions. The extensive evaluation demonstrates thatBadCleanercan reduce the success rates of state-of-the-art backdoor attacks without compromising the model performance.

Recent studies have demonstrated that backdoor attacks can cause a significant security threat to federated learning. Existing defense methods mainly focus on detecting or eliminating the backdoor patterns after the model is backdoored. However, these methods either cause model performance degradation or heavily rely on impractical assumptions, such as labeled clean data, which exhibit limited effectiveness in federated learning. To this end, we proposeFLPurifier, a novel backdoor defense method in federated learning that can effectively purify the possible backdoor attributes before federated aggregation. Specifically,FLPurifiersplits a complete model into a feature extractor and classifier, in which the extractor is trained in a decoupled contrastive manner to break the strong correlation between trigger features and the target label. Compared with existing backdoor mitigation methods,FLPurifierdoesn’t rely on impractical assumptions since it can effectively purify the backdoor effects in the training process rather than an already trained model. Moreover, to decrease the negative impact of backdoored classifiers and improve global model accuracy, we further design an adaptive classifier aggregation strategy to dynamically adjust the weight coefficients. Extensive experimental evaluations on six benchmark datasets demonstrate thatFLPurifieris effective against known backdoor attacks in federated learning with negligible performance degradation and outperforms the state-of-the-art defense methods.

In this paper, a novel hierarchical game framework for physical layer security (PLS) aware wireless communications with dynamic trilateral coalitions is studied. In the considered system, legitimate users (LUs) aim to transmit secret data to associated base stations (BSs) via uplink communications under the threat of eavesdroppers (EVs), while there also exists jammers (JAs) which may choose to form coalitions with either LUs for increasing their secrecy transmission rates or EVs for increasing their eavesdropping rates in exchange for potential rewards. Different from the existing work, we explore such complicated while dynamic coalition relationships under uncertainties of wireless systems (e.g., time-varying channel conditions), and formulate a hierarchical game integrated with a dynamic trilateral coalition formation game to model strategic interactions among LUs, JAs and EVs. Particularly, we first analyze stability conditions of the trilateral coalitions and propose a hedonic coalition selection and formation algorithm for reaching the stable coalition partition in each time slot. On top of this, we propose a deep reinforcement learning (DRL) based solution, which can achieve the equilibrium with long-term performance guarantees for the hierarchical game running over multiple time slots with dynamic evolutions. Simulations evaluate the proposed solution and show its superiority over counterparts.

In this paper, a novel two-timescale resource management framework for mobile edge computing (MEC) is constructed. Unlike existing studies, for providing seamless and cost-efficient MEC services, this work aims to strike the balance between service migration and task rerouting for mobile devices (MDs) whenever handovers occur (i.e., switching access from one edge server to another). Considering the network dynamics (e.g., randomness of MDs’ task generations and time-varying channel conditions) and the asynchronization of different management decisions with different triggers, we formulate an online optimization problem for jointly determining: 1) large-timescale decisions, including which edge server should be selected to access, and whether service migration or task rerouting should be chosen for each MD in each large time frame; and 2) small-time scale decisions, including how computing and communication resources should be allocated among MDs with task offloading requests in each small time slot. Then, we propose an online algorithm based on the improved Lyapunov method, together with an iterative algorithm integrating randomized rounding and Lagrange dual techniques, which solves the problem to asymptotic optimum in terms of the long-term average service delay. Theoretical analyses and simulations evaluate the performance of the proposed solution and show its superiority over counterparts.

In this paper, a novel two-timescale resource management framework for mobile edge computing (MEC) is constructed. For providing seamless and cost-efficient MEC services, this work aims to strike the balance between service migration and task rerouting for mobile devices (MDs) whenever handovers occur (i.e., switching access from one edge server to another). Considering the network dynamics (e.g., randomness of MDs' task generations and time-varying channel conditions) and the asynchronization of different management decisions with different triggers, we formulate an online optimization problem for jointly determining$i$) large-timescale decisions, including access selection and service migration or task rerouting selection for each MD, and ii) small-time scale decisions, including computing and communication resource allocations. Then, we propose a two-timescale low-complexity algorithm based on the improved Lyapunov method, which solves the problem to asymptotic optimum in terms of the long-term system-wide average service delay. Theoretical analyses and simulations evaluate the performance of the proposed solution, and show its superiority over counterparts.

In this paper, a novel hierarchical game framework for physical layer security (PLS) with dynamic trilateral coalitions is studied. In the considered system, legitimate users (LUs) aim to transmit secret data to associated base stations (BSs) via uplink communications under the threat of eavesdroppers (EVs), while there also exists jammers (JAs) which may choose to form coalitions with either LUs for increasing their secrecy transmission rates or EVs for increasing their eavesdropping rates in exchange for potential rewards. Different from the existing work, we explore such complicated while dynamic coalition relationships under the uncertainties of wireless systems (e.g., time-varying channel conditions), and formulate a hierarchical game integrated with a dynamic trilateral coalition formation game to model the strategic interactions among all three parties, i.e., LUs, JAs and EVs, in PLS. Particularly, we first analyze stability conditions of the trilateral coalitions. On top of this, we further propose a deep reinforcement learning (DRL) based approach for reaching the equilibrium with long-term performance guarantees for the hierarchical game. Simulations evaluate the proposed solution and show its superiority over counterparts.

In the era of cloud computing, many applications are migrated to public servers not fully controlled by users who may fear their critical operations or data from being compromised by attackers. Previous studies have shown that Intel SGX enclaves can improve applications’ security in many market products. Yet they mainly rely on developers to reprogram and recompile the application into an SGX-aware version. To address this problem, we propose SAPPX, an SGX-based program retrofitting method that can automatically partition COTS application binaries into two parts without breaking the original program semantics. The first part of the application runs in user space, while the second part is executed in an SGX enclave to protect the user’s sensitive information. We have implemented a prototype of SAPPX on x86/Linux platforms and evaluated its performance using real-world applications and SPECCPU 2017 benchmarks. The experimental results show that the average overhead of the proposed approach is up to 19%.

Existing smart contract vulnerability detection efforts heavily rely on fixed rules defined by experts, which are inefficient and inflexible. To overcome the limitations of existing vulnerability detection approaches, we propose a GNN based approach. First, we construct a graph representation for a smart contract function with syntactic and semantic features by combining abstract syntax tree (AST), control flow graph (CFG), and program dependency graph (PDG). To further strengthen the presentation ability of our approach, we perform program slicing to normalize the graph and eliminate the redundant information unrelated to vulnerabilities. Then, we use a Bidirectional Gated Graph Neural-Network model with hybrid attention pooling to identify potential vulnerabilities in smart contract functions. Experiment results show that our approach can achieve 89.2% precision and 92.9% recall in smart contract vulnerability detection on our dataset and reveal the effectiveness and efficiency of our approach.

In this paper, a long-term workload management problem for multi-server edge computing with server collaboration is studied. In the considered model, mobile users’ computation-intensive tasks are generated dynamically over the time and offloaded to associated edge servers according to pre-determined subscription agreements. Upon receiving the subscribed workload, each edge server can then decide to whether participate in server collaboration for enabling workload re-allocation (i.e., workload exchange) with other heterogeneously configured edge servers. Unlike most of the existing work, this paper takes into account both competitions and collaborations among strategic edge servers in sharing their computing capacities. To achieve the equilibrium for each edge server in minimizing its expected cost (including energy consumption, delay, transmission, configuration and pricing costs), a joint optimization is formulated for determining i) its amount of workload to undertake, ii) compensation price charged from peers, and iii) computing speed to adopt. To efficiently solve this problem, we propose a novel cooperative queueing game approach, which integrates a convex optimization, a core cost sharing scheme and a mapping rule. Theoretical analyses and extensive simulations are conducted to evaluate the performance of the proposed solution, and demonstrate its superiority over counterparts.

Federated learning is proposed to solve data islands and protect privacy. Especially in the big data environment, participating users can build a model together without sharing private sensitive data. However, as the number of end devices becomes larger, and the model becomes more complex, high concurrent access to the cloud server often brings communication delay, and it is also a great challenge to the computing power of end devices. To address this problem, we introduce Unmanned Aerial Vehicle (UAV) swarms as mobile edge nodes for end devices. UAV swarms can provide caching and computing resources for end devices. Therefore, we can implement edge aggregation of parameters on UAV swarms to reduce direct access to the cloud server. Meanwhile, the distributed end-edge-cloud federated learning architecture based on UAV swarms is an open environment, which may have potential malicious end devices or external channel eavesdropping. Malicious end devices or external eavesdroppers may maliciously poison training data sets or model parameters to reduce the classification accuracy of the model. In order to resist malicious poisoning, on UAV swarms we can calculate the cosine similarities between local parameters and their edge aggregation parameters to exclude malicious parameters, which do not conform to the trend of collaborative convergence. Then, the reliable parameters can be aggregated again, and uploaded to the cloud server with Schnorr signature to ensure the authenticity of the data. We analyze the security of the proposed scheme, and verify through experiments that it can resist malicious poisoning effectively and improve the accuracy of the model.

In this paper, an energy-efficient resource management framework for industrial Internet of Things (IIoT) with closed-loop control on end devices, edge servers (ESs) and cloud center (CC) is studied. In the considered model, each ES aggregates the data collected by industrial sensors (i.e., end devices) and forms computation tasks for corresponding data analysis. In order to minimize the system-wide energy consumption, while maintaining a guaranteed service delay and a satisfied data processing accuracy for each IIoT application, a joint optimization of i) sensors’ sampling rate adaption, ii) ESs’ preprocessing mode selection and iii) edge-cloud communication and computing resource allocation, is formulated. Further taking into account the time-varying channel conditions and randomness of data arrivals, we propose a low-complexity online algorithm, which solves the problem in a dynamic manner. Performance analyses and simulation results show that the proposed algorithm is superior compared to counterparts in terms of energy efficiency and delay performance under service satisfaction constraints.

Summary With the development of the Internet of Things (IoT) technology, various attacks and threats have emerged. The advanced persistent threat (APT) refers to a class of advanced multiple‐steps attacks among diverse attack activities, which brings severe threats to the IoT systems ascribe to its pertinence, concealment, and permeability. However, the existing technologies and methods fail to timely recognize the APT attack activities (especially the zero‐day exploits) in a comprehensive scope. To address this problem, we propose a novel method of cyber situation perception for IoT systems, which based on zero‐day attack activity recognition within APT (CSPAPTM). Moreover, we also design an edge computing framework for applying CSPAPTM to the typical IoT systems. Specifically, we first provide a cyber situation perception ontology construction module for describing the APT attack activities. Then, a malicious C&C DNS mining method (MCCDRM) is proposed to control the APT malicious activity correlation analysis trigger, which can effectively decrease the computing overhead. Finally, we propose a zero‐day attack activity recognition method within APT (ZDAARA), which acts on system call instances to recognize the malicious activities, which cannot be detected by IDS. A relatively mature access control mechanism PO‐SAAC is also applied to our method. Through the coalescent of these methods, CSPAPTM can accomplish the cyber situation perception effectively by the zero‐day attack activities recognition in the IoT systems. The exhaustive experimental results demonstrate that the two kernel modules, that is, MCCDRM and ZDAARA in our CSPAPTM, can achieve both higher F1 score and acceptable false positive rate.

While cloud computing services such as cloud storage are fairly mature, it remains challenging to design efficient and secure cryptographic schemes to facilitate fine-grained access control and achieve other features. For example, existing ciphertext-policy attribute-based encryption schemes do not generally have in-place limits on the number of access or access duration, which can consequently be exploited to perform economic denial of sustainability and other attacks. In addition, improving system efficiency can be challenging in large-scale operations. Thus, in this paper, we present a blockchain-based access control with k $k$ -times tamper resistance. Our proposed approach allows one to set and enforce quota/limits on the number of accesses allowed for each user, whose integrity is ensured using blockchain. In addition, our proposed approach also allows multiple attribute authorities to coexist and work together with a central authority to facilitate secret key distribution and improve system efficiency. We then evaluate the security and the efficiency of our proposed approach to demonstrate its utility.

Narrowband Internet of Things (NB-IoT) standardized by the 3GPP has attracted significant attention since its appearance. It provides extended coverage, high capacity, reduced device processing complexity, and low-power consumption to meet the requirements of a wide range of IoT applications. In particular, NB-IoT is expected to bring IoT devices prolonged lifetime up to ten years. Radio access (RA) plays a key role in the total power consumption of NB-IoT devices. Specifically, the enhanced coverage levels (ECLs) configure the user equipment (UE) with different random-access resources and power consumption during packet transmissions. In this article, we examine the ECL selection strategies for reducing the power consumption of NB-IoT. We develop two testbeds to conduct extensive field measurements related to ECL selection. Based on the measurement results, we analyze the key issues in the ECL selection process. Then, we propose an adaptive RA approach for UE, which includes two novel strategies for predictive ECL selection and opportunistic packet transmission. Evaluations show that, with the configuration of ECL selected by our adaptive approach, the UE can reduce the radio power consumption up to 36% while maintaining the same block error rate (BLER) during uploading under real-world settings.

Edge–cloud collaboration is critical in the Industrial Internet of Things (IIoT) for serving computation-intensive tasks (e.g., bearing fault monitoring) that require low-response delay, low energy consumption, and high processing accuracy. In this article, an energy-efficient resource management framework for IIoT with closed-loop control on end devices, edge servers, and cloud center is studied. In the considered model, each edge server aggregates the data collected by industrial sensors (i.e., end devices) and forms computation tasks for corresponding data analysis. In order to minimize the system-wide energy consumption, while maintaining a guaranteed service delay and a satisfied data processing accuracy for each IIoT application, a joint optimization of: 1) sensors’ sampling rate adaption; 2) edge servers’ preprocessing mode selection; and 3) edge–cloud communication and computing resource allocation is formulated. Further taking into account the time-varying channel conditions and randomness of data arrivals, we propose a low-complexity online algorithm, which solves the problem in a dynamic manner. Particularly, the Lyapunov optimization method is first utilized to decompose the long-term problem into a series of instant ones [mixed-integer nonlinear programming (MINLP) problems], and then a Markov approximation algorithm is applied to solve such instant problems to near optimum with the consideration of future impacts. Performance analyses and simulation results show that the proposed algorithm is feasible under long-term service satisfaction constraints, and its energy consumption and service delay are approximately 20% and 28% lower than those of the benchmark schemes, respectively.

Industrial Internet of Things (IIoT) systems are key enabling infrastructures that sustain the functioning of production and manufacturing. To satisfy the intelligence demands, federated learning has been envisioned as a promising technique for IIoT applications with privacy training requirements. However, research works have shown that, by training the local model on crafted poisoning samples malicious participants can jeopardize the functionalities of the global model. In this article, we propose a robust federated learning method, named RobustFL, in IIoT systems to defend against poisoning attacks. The main idea is that we conduct an adversarial training framework, in which an extra logits-based predictive model is built at the server-side to predict which participant a given logit belongs to. Meanwhile, the federated model is adversarially trained to prevent this predictive behavior, thus mitigating the poisoning attack influences. We evaluate the poisoning attack and our defense method on three benchmark datasets. Experimental results demonstrate the superiority of our proposed method in terms of high accuracy and efficiency in defending against poisoning attacks.

Narrowband-Internet of Things (NB-IoT) is an emerging cellular communication technology designed for low-power wide-area applications. Cell selection determines the channel of user device and hence is an important issue in cellular networks. In this article, we make the first attempt to examine and optimize the cell selection in NB-IoT networks by field measurement. We conduct measurements at 30 different locations which involve five typical application scenarios of NB-IoT. Two kinds of NB-IoT modules and two network operators are also involved in the measurements. We find four potential issues on the cell selection of the User Equipment (UE) through the measurements. We propose an adaptive cell selection approach to optimize the cell selection of UE. The simulation test based on real-world measurement data shows that the cell selected by the adaptive approach can improve the coverage level and reduce the power consumption for UE.

Federated learning has attracted attention in recent years due to its native privacy-preserving features. However, it is still vulnerable to various membership inference attacks, such as backdoor, poisoning, and adversarial attacks. Membership Inference attack aims to discover the data used to train the model, which leads to privacy leaking ramifications on participants who use their local data to train the shared model. Recent research on countermeasure methods mainly focuses on protecting the parameters and has limitations in guaranteeing privacy while restraining the loss of the model. This paper proposes Fedefend, which applies adversarial examples to defend against membership inference attacks in federated learning. The proposed approach adds well-designed noise to the attack features of the target model of each iteration becomes an adversarial example. In addition, we also consider the utility loss of the model and use an adversarial method to generate noise to constrain the loss to a certain extent, which efficiently achieves a trade-off between privacy security and loss of the federated learning model. We evaluate the proposed Fedefend on two benchmark datasets, and the experimental results demonstrate that Fedefend has a good performance.

Fog computing is recently a novel distributed computing paradigm that performs a significant achievement in the latency-sensitive smart Internet of Things (IoT) applications. However, the security and privacy issues, such as data leakage, still challenge the wide deployment of fog computing infrastructure. To guarantee data confidentiality and meanwhile achieving fine-grained access control, Ciphertext-Policy Attribute-Based Encryption (CP-ABE) promises to provide a flexible access policy for securely sharing data among users, fog nodes, and cloud center. However, due to the complicated cryptographic operations, CP-ABE has met a significant drawback that requires heavy computation resources on the user-side. In this paper, we propose an outsourced access control scheme with hidden access structures, named OAC-HAS, in fog-enhanced IoT systems. The contributions of our OAC-HAS scheme are three-folds. Firstly, we introduce a fog-cloud computing (FCC) environment which has the outsourcing capability. Then, we design an outsource verification mechanism to guarantee the correctness of executing cryptographic operations on the fog nodes. Finally, we also provide a privacy guarantee that prevents information leakage from the access structures. Security analysis and experimental results show that the proposed OAC-HAS scheme achieves flexible access policy, privacy-preserving, and high efficiency in fog-enhanced IoT systems.

Abstract Low‐rate Distributed Denial of Service (LDDoS) attacks, in which the attackers send packets to a victim at a sufficiently low rate to avoid being detected, are considered to be a subtype of DDoS attacks and a potential threat to Internet security. However, an overwhelming attack paradigm on the Internet has rarely been reported due to the harsh requirements for launching LDDoS attacks; therefore, most existing LDDoS attacks are constructed and evaluated through theoretical deduction and/or simulation tests. In this backdrop, the authors aim to figure out what the conditions for launching a successful LDDoS attack are, and how harmful an attack could be. They first analyse the characteristics of LDDoS attacks, and derive the conditions and parameters for initiating LDDoS attacks using a queuing model. Based on the analysis results, an LDDoS algorithm is presented. Then, an LDDoS validation prototype is built on a Network Function Virtualization network to validate the derived parameters and conditions. Finally, a series of experiments are conducted on the testbed, and the results show that a successful LDDoS attack could be achieved based on the derived algorithm; however, its attack effect only lasts for a short time compared with its DDoS counterparts.

Edge computing is a key-enabling technology that meets continuously increasing requirements for the intelligent Internet-of-Things (IoT) applications. To cope with the increasing privacy leakages of machine learning while benefiting from unbalanced data distributions, federated learning has been wildly adopted as a novel intelligent edge computing framework with a localized training mechanism. However, recent studies found that the federated learning framework exhibits inherent vulnerabilities on active attacks, and poisoning attack is one of the most powerful and secluded attacks where the functionalities of the global model could be damaged through attacker's well-crafted local updates. In this article, we give a comprehensive exploration of the poisoning attack mechanisms in the context of federated learning. We first present a poison data generation method, named Data_Gen, based on the generative adversarial networks (GANs). This method mainly relies upon the iteratively updated global model parameters to regenerate samples of interested victims. Second, we further propose a novel generative poisoning attack model, named PoisonGAN, against the federated learning framework. This model utilizes the designed Data_Gen method to efficiently reduce the attack assumptions and make attacks feasible in practice. We finally evaluate our data generation and attack models by implementing two types of typical poisoning attack strategies, label flipping and backdoor, on a federated learning prototype. The experimental results demonstrate that these two attack models are effective in federated learning.

Recent research advancement of wireless sensing technology has made device-free interaction in the WiFi-enabled IoT environment possible. Although gesture-based interaction with such a smart environment greatly improves usability, it also introduces many security problems, such as shoulder surfing attacks. By spoofing the gestures of legitimate users, the attacker could easily access private information or services and cause even worse consequences. A secure interaction mechanism for this environment is required to prevent attackers without compromising the usability, while the limited recognition ability and low robustness of WiFi sensing make this target extremely challenging. To this end, we propose a secure interaction mechanism called secure interaction via WiFi Signal (SiWi), which provides the ability to resist shoulder surfing attacks without compromising the usability by using just WiFi signals. SiWi innovates in a concurrent interaction/authentication framework with only three elemental gestures (push, swing, and wave) and four types of identity-related imperceptible/hidden features (time distribution, direction, angle, and distance). HMM and Fresnel model-based algorithms are used to recognize the gestures and extract hidden features robustly and efficiently. Extensive experiments in a real implemented system were conducted to investigate the effectiveness of the proposed secure interaction system. The results show that our system can achieve an average accuracy of 93% to identify legitimate users and 97% to resist the spoofer.

Driven by the advancements in 5G-enabled Internet of Things (IoT) technologies, the IoT devices have shown an explosive growth trend with massive data generated at the edge of the network. However, IoT systems exhibit inherent vulnerability for diverse attacks, and Advanced Persistent Threat (APT) is one of the most powerful attack models that could lead to a significant privacy leakage of systems. Moreover, recent detection technologies can hardly meet the demands of effective security defense against APTs. To address the above problems, we propose an APT Prediction Method based on Differentially Private Federated Learning (APTPMFL) to predict the probability of subsequent APT attacks occurring in IoT systems. It is the first time to apply a federated learning mechanism for aggregating suspicious activities in the IoT systems, where the APT prediction phase does not need any correlation rules. Moreover, to achieve privacy-preserving property, we further adopt a differentially private data perturbation mechanism to add the Laplacian random noises to the IoT device training data features, so as to achieve the maximum protection of privacy data. We also present a 5G-enabled edge computing-based framework to train and deploy the model, which can alleviate the computing and communication overhead of the typical IoT systems. Our evaluation results show that APTPMFL can efficiently predict subsequent APT behaviors in the IoT system accurately and efficiently.

Advanced Persistent Threats (APTs) are the most sophisticated attacks for modern information systems. Currently, more and more researchers begin to focus on graph-based anomaly detection methods that leverage graph data to model normal behaviors and detect outliers for defending against APTs. However, previous studies of provenance graphs mainly concentrate on system calls, leading to difficulties in modeling network behaviors. Coarse-grained correlation graphs depend on handcrafted graph construction rules and, thus, cannot adequately explore log node attributes. Besides, the traditional Graph Neural Networks (GNNs) fail to consider meaningful edge features and are difficult to perform heterogeneous graphs embedding. To overcome the limitations of the existing approaches, we present a hierarchical approach for APT detection with novel attention-based GNNs. We propose a metapath aggregated GNN for provenance graph embedding and an edge enhanced GNN for host interactive graph embedding; thus, APT behaviors can be captured at both the system and network levels. A novel enhancement mechanism is also introduced to dynamically update the detection model in the hierarchical detection framework. Evaluations show that the proposed method outperforms the state-of-the-art baselines in APT detection.

Cognitive computing over big data brings more development opportunities for enterprises and organizations in industrial informatics, and can make better decisions for them when they face data security challenges. To satisfy the requirement of real-time data storage in industrial Internet of Things (IoT), the remote unconstrained storage cloud is usually used to store the generated big data. However, the characteristic of semitrust of the cloud service provider determines that the data owners will worry about whether the data stored in cloud computing has been corrupted. In this article, a secure storage auditing is proposed, which supports efficient key updates and can be well used in cognitive industrial IoT environment. Moreover, the proposed basic auditing can be extended to support batch auditing that is suitable for multiple end devices to audit their data blocks simultaneously in practice. In addition, a hybrid data dynamics method is proposed, which employs a hash table to store the data blocks and uses a linked list to locate the operated data block. Compared with previous methods, the data block location time in the proposed data dynamics can be reduced by 40%. The security analysis results demonstrate that the proposed scheme can be proved to be correct, and is secure under computational differ-hellman (CDH) and discrete logarithm (DL) assumptions.

Traffic flow prediction has received rising research interest recently since it is a key step to prevent and relieve traffic congestion in urban areas. Existing methods mostly focus on road-level or region-level traffic prediction, and fail to deeply capture the high-order spatial-temporal correlations among the road links to perform a road network-level prediction. In this paper, we propose a network-scale deep traffic prediction model called TrafficGAN, in which Generative Adversarial Nets (GAN) is utilized to predict traffic flows under an adversarial learning framework. To capture the spatial-temporal correlations among the road links of a road network, both Convolutional Neural Nets (CNN) and Long-Short Term Memory (LSTM) models are embedded into TrafficGAN. In addition, we also design a deformable convolution kernel for CNN to make it better handle the input road network data. We extensively evaluate our proposal over two large GPS probe datasets in the arterial road network of downtown Chicago and Bay Area of California. The results show that TrafficGAN significantly outperforms both traditional statistical models and state-of-the-art deep learning models in network-scale short-term traffic flow prediction.

Along with the exponentially increasing quantity of intelligent terminals connected to the Internet, the spectrum competition among users becomes more and more severe in wireless networks. The network have not the ability to satisfy all communication requirements due to the significantly increasing users and demanded rates. Energy-aware admission control has been proved to be an efficient way to tackle the infeasibility caused by the severe spectrum competition among users. However, the traditional admission control is limited by gradually removing chosen users, and pays less attention to the fairness. In this article, we elaborate the concept of the fairness in a max-min optimization problem with respect to the transmission rates, by leveraging the model of bit error rates with Q-function for general fading communications. Then, we make use of the max-min rate fairness to smartly determine the subset of users to be admitted in wireless networks. Meanwhile, the overall energy consumption is minimized and the network fairness is guaranteed. In particular, the algorithms can tackle more than one user at each iteration. Numerical evaluations show the effectiveness of the algorithms.

Consensus mechanism plays an important role in blockchain. At present, mainstream consensus mechanisms include proof of work (PoW), proof of stake (PoS), and delegated proof of stake (DPoS). PoW, as is widely used in virtual currency, results in significant energy consumption; PoS and DPoS are proposed to reduce energy waste caused by PoW, but their disadvantage is that they tend to create Matthew Effect (ME): “the rich get richer.” In order to balance the discourse power of new nodes and elder ones, this paper proposes a flexible consensus mechanism called proof of engagement (PoE), based on the activity and contribution of network nodes. We analyze the incentive compatibility of PoE from the perspective of mechanism design. In our simulation experiments, we tested the profit changes under PoW, PoS, and PoE. The results illustrate it is easier for new nodes to accumulate their profits under PoE than under PoW or PoS, so as to reduce the negative impacts of ME.

Mobile crowdsensing (MCS) has been applied for signal map construction in smart city. However, it is still costly for MCS to cover large-scale regions. Accordingly some data recovery algorithms are proposed, which allow participants to collect only few signal data and infer the rest of missing data by leveraging spatial-temporal correlation of signals. However, existing work only considered the temporal and 2-D spatial correlation in the plane, while the altitude dimension is not exploited. In this paper, we give a first attempt to exploit the 3-D spatial-temporal correlation of signals to infer missing data in the ground and reconstruct ground signal map. An UAV-assisted ground signal map construction scheme is proposed based on matrix completion (MC). Specifically, UAVs can be used to collect signals in the air and aerial-ground signal mappings are performed to assist the ground signal inference. Extensive simulations are performed which show that the proposed scheme performs well under extremely high missing rate situations and outperform pure ground-based data recovery schemes.

With the rise of privacy concerns in traditional centralized machine learning services, the federated learning, which incorporates multiple participants to train a global model across their localized training data, has lately received signifi-cant attention in both industry and academia. However, recent researches reveal the inherent vulnerabilities of the federated learning for the membership inference attacks that the adversary could infer whether a given data record belongs to the model’s training set. Although the state-of-the-art techniques could successfully deduce the membership information from the centralized machine learning models, it is still challenging to infer the membership to a more confined level, user-level. In this paper, We propose a novel user-level inference attack mechanism in federated learning. Specifically, we first give a comprehensive analysis of active and targeted membership inference attacks in the context of the federated learning. Then, by considering a more complicated scenario that the adversary can only passively observe the updating models from different iterations, we incorporate the generative adversarial networks into our method, which can enrich the training set for the final membership inference model. The extensive experimental results demonstrate the effectiveness of our proposed attacking approach in the case of single-label and multi-label.

NVMe (Non-Volatile Memory Express) is a protocol designed specifically for SSD (Solid State Drive), which has significantly improved the performance of SSD storage devices. However, the traditional kernel-space IO path hinders the performance of NVMe SSD devices. In this paper, a user-space raw file system (URFS) based on NVMe SSD is proposed. Through the design of the user-space multi-process shared cache, multiple applications can share access to SSD to reduce the amount of SSD access; NVMe-oriented log-free data layout and Multi-granularity IO queue elastic separation technology are used to improve system performance and throughput. Experiments show that, compared to traditional file systems, URFS performance is improved by more than 23% in CDN (Content Delivery Network) scenarios, and URFS performance is improved more in small file scenarios and read-intensive scenarios.

Open wireless channels make a wireless ad hoc network vulnerable to various security attacks, so it is crucial to design a routing protocol that can defend against the attacks of malicious nodes. In this paper, we first measure the trust value calculated by the node behavior in a period to judge whether the node is trusted, and then combine other QoS requirements as the routing metrics to design a secure routing approach. Moreover, we propose a deep learning-based model to learn the routing environment repeatedly from the data sets of packet flow and corresponding optimal paths. Then, when a new packet flow is input, the model can output a link set that satisfies the node's QoS and trust requirements directly, and therefore the optimal path of the packet flow can be obtained. The extensive simulation results show that compared with the traditional optimization-based method, our proposed deep learning-based approach cannot only guarantee more than 90% accuracy, but also significantly improves the computation time.

Edge computing is envisioned to be a powerful platform that provides efficient data storage and computation services in the smart Internet-of-Things (IoT) systems. In this data-intensive architecture, protecting user-side data privacy is one of the most critical concerns to prevent privacy leakage from any other untrusted entities. Aiming to resist this concern, many privacy-preserving data aggregation (PPDA) schemes have been proposed for various cloud-enabled IoT applications. However, due to the resource-constrained nature of the smart IoT devices, the conventional PPDA solutions, in terms of both privacy and performance requirements, are unsuitable in edge computing. To address this challenge, we propose a lightweight and verifiable PPDA scheme, named LVPDA, for the edge-computing-enabled IoT system, where the Paillier homomorphic encryption method and an online/offline signature technique are combined to ensure the privacy preserving and integrity verification during the data aggregation process. A detailed security analysis indicates that LVPDA is existentially unforgeable under the chosen message attack (EU-CMA) and the data integrity can be guaranteed with formal proof under q -strong Diffie-Hellman (q -SDH) assumptions. Compared with other PPDA methods, our scheme can achieve lightweight PPDA in terms of less computational complexity and communication overhead.

Cloud capability is considered to be extended to the edge of the Internet for improving the security of data transmission. Compressive sensing (CS) has been widely studied as a built-in privacy-preserving layer to provide some cryptographic features while sampling and compressing, including data confidentiality guarantees and data integrity guarantees. Unfortunately, most existing CS-based ciphers are too lightweight or highly complex to meet the requirements of both high security of transmitting the captured data over the Internet and low energy consumption of sensing devices in the Internet of Things (IoT). In this article, a secure transmission framework for CS data by combining CS-based cipher and edge computing is proposed. From the perspective of security, the double-layer encryption mechanism and double-layer authentication mechanism are rooted in it by performing some privacy-preserving operations, including CS-based encryption, CS-based hash, information splitting, strong encryption, and feature extraction. Most significantly, the proposed framework is very useful for resource-limited IoT applications.

The auction mechanism is deemed to be an effective method to address the problem of spectrum scarcity. Numerous spectrum auction mechanisms can alleviate spectrum shortage under the consideration of truthfulness, social welfare maximization and spectrum reusability, while the privacy preservation and preferences of primary/secondary users have not been fully discussed. In this paper, we propose a matching based double auction mechanism for spectrum trading with differential privacy (MastDP) to protect the privacy of buyers/sellers from the untrustworthy auctioneer, other buyers/sellers and other potential parties. Each participant adds distributed differential private noise following Geom(α) distribution to his bid value and encrypts the noisy bid value. The auctioneer can decrypt only the sum of all uploaded noisy bid values and determines the clearing price by using its private key. Based on the clearing price, the matching theory is adopted to maximize the winning participants' revenue while fully considering their preferences and spectrum reuse. Simulation results show that MastDP achieves satisfactory performance in terms of economic properties' privacy preservation and spectrum trading efficiency.

Federated learning has emerged as a promising solution for big data analytics, which jointly trains a global model across multiple mobile devices. However, participants' sensitive data information may be leaked to an untrusted server through uploaded gradient vectors. To address this problem, we propose a privacy-enhanced federated learning (PEFL) scheme to protect the gradients over an untrusted server. This is mainly enabled by encrypting participants' local gradients with Paillier homomorphic cryptosystem. In order to reduce the computation costs of the cryptosystem, we utilize the distributed selective stochastic gradient descent (DSSGD) method in the local training phase to achieve the distributed encryption. Moreover, the encrypted gradients can be further used for secure sum aggregation at the server side. In this way, the untrusted server can only learn the aggregated statistics for all the participants' updates, while each individual's private information will be well-protected. For the security analysis, we theoretically prove that our scheme is secure under several cryptographic hard problems. Exhaustive experimental results demonstrate that PEFL has low computation costs while reaching high accuracy in the settings of federated learning.

Traffic prediction is practically important to facilitate many real applications in urban areas such as relieving traffic congestion. Traditional traffic prediction models are mostly statistic based methods, and they cannot effectively capture the nonlinear, stochastic and time-varying characteristics of the urban transportation systems. Another limitation of these methods is that they usually focus on analyzing one or several roads or road segments, but are not capable to predict the traffic conditions of all the road segments in a large transportation network of a city as a whole. Therefore, in recent years, deep neural network based methods for forecasting the road network-scale traffic have been emphasized greatly. However, most existing deep neural network methods model the traffic data of a road network as "images" rather than graphs, and thus they suffer from the blurry prediction issue and do not perform well on the task of multi-step traffic prediction. In this paper, We propose a network-scale deep traffic prediction model called GCGAN by combining adversarial training and graph CNN. Specifically, we propose a Generative Adversarial Net based prediction framework to address the blurry prediction issue by introducing the adversarial training loss. To predict the traffic conditions in multiple future time intervals simultaneously, we design a sequence to sequence (Seq2Seq) based encoder-decoder model as the generator of GCGAN. To fully capture the spatial correlations among the road segments of a transportation network, we propose to apply a graph convolution network (GCN) in both generator and discriminator of GCGAN for feature learning. We evaluate our proposal over a large real traffic dataset in the arterial road network of downtown Chicago. The results show that GCGAN significantly outperforms both traditional statistic based methods and recent state-of-the-art deep learning methods.

The drone swarm that performs missions in an autonomous and intelligent overall collaborative manner is an important operational factor in the complex flight space. However, the complex and changing battlefield environment greatly affects the mission execution and survivability of drone swarm. In this paper, we formulated the problem of formation reconfiguration to avoid collision with obstacles with minimum cost in a complex flight space as a two-stage stochastic programming with considering uncertain moving obstacles, where we first calculate the optimal formation parameters of the drone swarm that can avoid certain fixed obstacles with minimum reconfiguration cost in the first-stage. For the uncertain moving obstacles, we calculate the extra cost for avoiding in the second-stage after the distribution of moving obstacles is known, and add this part of the cost to the first-stage to ensure the overall reconfiguration cost is minimized. Moreover, the sample average approximation (SAA) method is exploited to solve this stochastic programming problem. Extensive experimental results from the OMNeT ++ simulation environment indicate both the feasibility and effectiveness of our proposed two-stage stochastic programming approach and the better performs, compared with the existing approach.

With the increasing complexity of tasks that are crowdsourced, requesters need to form teams of professional workers that can satisfy complex task skill requirements. Team crowdsourcing in social networks (SNs) provides a promising solution for complex task crowdsourcing, where the requester hires a team of professional workers that are also socially connected can work together collaboratively. Previous social team formation approaches have mainly focused on the algorithmic aspect for social welfare maximization; however, within the traditional objective of maximizing social welfare alone, selfish workers can manipulate the crowdsourcing market by behaving untruthfully. This dishonest behavior discourages other workers from participating and is unprofitable for the requester. To address this strategic social team crowdsourcing problem, truthful mechanisms are developed to guarantee that a worker's utility is optimized when he behaves honestly. This problem is proved to NP-hard, and two efficient mechanisms are proposed to optimize social welfare while reducing time complexity for different scale applications. For small-scale applications where the task requires a small number of skills, a binary tree network is first extracted from the social network, and a dynamic programming-based optimal team is formed in the binary tree. For large-scale applications where the task requires a large number of skills, a team is formed greedily based on the workers' social structure, skill, and working cost. For both mechanisms, the threshold payment rule, which pays each worker his marginal value for task completion, is proposed to elicit truthfulness. Finally, the experimental results of a real-world dataset show that compared to the benchmark exponential VCG truthful mechanism, the proposed small-scale-oriented mechanism can reduce computation time while producing nearly the same social welfare results. Furthermore, compared to other state-of-the-art polynomial heuristics, the proposed large-scale-oriented mechanism can achieve truthfulness while generating better social welfare outcomes.

In this paper, we investigate the profit maximization problem for the mobile virtual network operator in cognitive radio networks considering the uncertain property of users' spectrum demand. In order to achieve more revenues while simultaneously satisfying the needs of users, the cognitive mobile virtual network operator chooses to dynamically sense the idle spectrum in the licensed band which is more economic, and at the same time leases the spectrum from the spectrum owner which guarantees more stable spectrum resources. However, the fluctuant spectrum demand of users imposes unprecedented challenges on the decision making process. To deal with the uncertain features of the users' demand, a flexible distribution uncertainty model is developed. Particularly, a reference distribution is introduced based on historical data and then a uncertainty set is defined to confine the spectrum demand. The uncertainty model developed allows the actual users' spectrum requirement to fluctuate around the reference distribution. Chance constraint approximations and robust optimization approaches are developed to transform and then solve the optimization problem. Simulation results based on the real-world traces evaluate the performance of the proposed scheme and investigate the parameter impacts on the system utilities. Our research may also help shed some insights on the investment policy making for the mobile virtual network operator.

Adapting the data rate is an important performance in mobile cloud networking, especially for the fast growth of intelligent terminals. We study a max-min fairness problem for the mobile cloud networking to guarantee the minimal transmit data rate, by leveraging the bit error rate (BER) with Q-function for modeling achievable data rates. We propose a distributed power control algorithm to obtain the optimal solution. Then, we address a total power minimization problem with the given rate requirement constraints. When there are plenty of users and excessive interferences, its feasibility issue is solved by making use of the max-min fairness of the networks. We propose a dynamic algorithm that adapts the rate requirements to minimize the total energy consumption and to simultaneously provide fairness guarantees. Numerical simulations show the efficient performance of the proposed algorithms.

In some large-scale RFID systems where tags carry values, the extrema are critical statistics. We consider estimating the extrema, i.e., estimating the maximum and minimum values simultaneously. A straightforward approach is to perform binary search on the possible values, each time requesting tags with values in a certain range to respond. We show that this approach is suboptimal due to the interframe overhead between two frames in practical RFID systems. We propose a class of protocols to find the minimum value or maximum value separately, and show how to select the best protocol according to the hardware parameters of RFID systems. We then revise the protocol to estimate the minimum and maximum values simultaneously, and give the optimal parameters. Extensive simulations show that our protocol gives the smallest estimation error within any allocated time.

Edge computing has emerged as the key enabling technology that empowers the IoT with intelligence and efficiency. In this data enriched infrastructure, privacy-preserving data aggregation (PPDA) is one of the most critical services. However, the security and privacy-preserving requirements and online computational cost still present practical concerns in edge computing for resource-constraint edge terminals. To cope with this challenge, we present a lightweight privacy-preserving data aggregation scheme named LPDA-EC for edge computing system by employing the online/offline signature technique, Paillier homomorphic cryptosystem, and double trapdoor Chameleon hash function in this paper. The proposed LPDA-EC scheme can achieve data confidentiality and privacy-preserving, ensuring that the edge server and control center are agnostic of the user's private information during the whole aggregation process. Through detailed analysis, we demonstrate that our scheme is existentially unforgeable under chosen message attack (EU-CMA) and ensures data integrity with formal proofs under q-Strong Diffie-Hellman (q-SDH) assumptions. Numerical results indicate that the LPDA-EC scheme has less computational and communication overheads.

Compressed sensing for wireless sensor networks has attracted a lot of research attention in the last decade for its advantages in energy saving, robustness, and so on. Nevertheless, existing solutions mostly focus on the data compression performance while neglecting the energy efficiency. In this paper, we first present the joint resource allocation problem formulation based on compressed sensing. Then a distributed algorithm to compute the sampling rate and routes utilizing local network status is proposed. We conduct extensive experiments based on meteorological wireless sensor networks to verify the merit of our mechanism; it is shown that the proposed mechanism is able to achieve very high efficiency in terms of network lifetime and sensing quality compared with existing approaches.

Caching popular contents at base stations (BSs) has been regarded as an effective approach to alleviate the backhaul load and to improve the quality of service. To meet the explosive data traffic demand and to save energy consumption, energy efficiency (EE) has become an extremely important performance index for the 5th generation (5G) cellular networks. In general, there are two ways for improving the EE for caching, that is, improving the cache‐hit rate and optimizing the cache size. In this work, we investigate the energy efficient caching problem in backhaul‐aware cellular networks jointly considering these two approaches. Note that most existing works are based on the assumption that the content catalog and popularity are static. However, in practice, content popularity is dynamic. To timely estimate the dynamic content popularity, we propose a method based on shot noise model (SNM). Then we propose a distributed caching policy to improve the cache‐hit rate in such a dynamic environment. Furthermore, we analyze the tradeoff between energy efficiency and cache capacity for which an optimization is formulated. We prove its convexity and derive a closed‐form optimal cache capacity for maximizing the EE. Simulation results validate the proposed scheme and show that EE can be improved with appropriate choice of cache capacity.

In this paper, we propose a new simulator for nonstationary Rice fading channels under nonisotropic scattering scenarios, as well as the improved computation method of simulation parameters. The new simulator can also be applied on generating Rayleigh fading channels by adjusting parameters. The proposed simulator takes into account the smooth transition of fading phases between the adjacent channel states. The time‐variant statistical properties of the proposed simulator, that is, the probability density functions (PDFs) of envelope and phase, autocorrelation function (ACF), and Doppler power spectrum density (DPSD), are also analyzed and derived. Simulation results have demonstrated that our proposed simulator provides good approximation on the statistical properties with the corresponding theoretical ones, which indicates its usefulness for the performance evaluation and validation of the wireless communication systems under nonstationary and nonisotropic scenarios.

In wireless sensor networks, maximizing the lifetime of a data gathering tree is known to be NP-hard, so various (exponential-time) exact algorithms are designed to find the best data gathering tree. The state-of-the-art exact algorithm recursively performs graph decomposition to reduce search space. However, it essentially enumerates all possibilities in adjacent graph decompositions, and cannot handle moderately large sensor networks. In this paper, we propose an exact algorithm using integer linear programming. The challenge is that some constraints are not linear in the optimization problem. To address this challenge, instead of the optimization problem, we formulate the decision problem, and solve each decision problem by integer linear programming. The optimal value is then found by binary search over all possible lifetimes. To reduce the running time, we preprocess the graph by decomposing it into bi- connected subnetworks, and propose various rules to reduce the number of candidate lifetimes. Numerical results on simulated networks show that, within two hours, our algorithm can solve more problem instances than previous algorithms.

Virtualization has been seen as one of the main evolution trends in future cellular networks which enables the decoupling of infrastructure from the services it provides. In this case, the roles of infrastructure providers (InPs) and mobile virtual network operators (MVNOs) can be logically separated and the resources of a base station owned by an InP can be transparently shared by multiple MVNOs, while each MVNO virtually owns the entire BS. Naturally, the issue of resource allocation arises. Specifically, the InP is required to abstract the physical resources into isolated slices for each MVNO who then allocates the resources within the slice to its subscribed users. In this paper, we aim to address this two-level hierarchical resource allocation problem while satisfying the requirements of efficient resource allocation, strict inter-slice isolation, and the ability of intra-slice customization. To this end, we propose a hierarchical combinatorial auction model, based on which a truthful and efficient resource allocation framework is provided. And we show by an illustrative example how the proposed model can be applied for wireless virtualization. Specifically, winner determination problems (WDPs) are formulated for the InP and MVNOs, and computationally tractable algorithms are proposed for solving these WDPs. Also, pricing schemes are proposed for ensuring the incentive compatibility. Note that the proposed model can be generalized for the virtualization of resources with more dimensions (e.g., power, antennas, etc.).

Crowdsourcing has become a popular service computing paradigm for requesters to integrate the ubiquitous human-intelligence services for tasks that are difficult for computers but trivial for humans. This paper focuses on crowdsourcing complex tasks by team formation in social networks (SNs) where a requester connects to a large number of workers. A good indicator of efficient team collaboration is the social connection among workers. Most previous social team formation approaches, however, either assume that the requester can maintain information of all workers and can directly communicate with them to build teams, or assume that the workers are cooperative and be willing to join the specific team built by the requester, both of which are impractical in many real situations. To this end, this paper first models each worker as a selfish entity, where the requester prefers to hire inexpensive workers that require less payment and workers prefer to join the profitable teams where they can gain high revenue. Within the noncooperative SNs, a distributed negotiation-based team formation mechanism is designed for the requester to decide which worker to hire and for the worker to decide which team to join and how much should be paid for his skill service provision. The proposed social team formation approach can always build collaborative teams by allowing team members to form a connected graph such that they can work together efficiently. Finally, we conduct a set of experiments on real dataset of workers to evaluate the effectiveness of our approach. The experimental results show that our approach can: 1) preserve considerable social welfare by comparing the benchmark centralized approaches and 2) form the profitable teams within less negotiation time by comparing the traditional distributed approaches, making our approach a more economic option for real-world applications.

The receiving signal strength (RSS) is crucial for the Internet of Things (IoT), as it is the key foundation for communication resource allocation, localization, interference management, sensing, and so on. Aside from its significance, the measurement process could be tedious, time consuming, inaccurate, and involving human operations. The state-of-the-art works usually applied the fashion of “measure a few, predict many,” which use measurement calibrated models to generate the RSS for the whole networks. However, this kind of methods still cannot provide accurate results in a short duration with low measurement cost. In addition, they also require careful scheduling of the measurement which is vulnerable to measurement conflict. In this paper, we propose a compressive sensing- (CS-) based RSS measurement solution, which is conflict-tolerant, time-efficient, and accuracy-guaranteed without any model-calibrate operation. The CS-based solution takes advantage of compressive sensing theory to enable simultaneous measurement in the same channel, which reduces the time cost to the level of O(log⁡N) (where N is the network size) and works well for sparse networks. Extensive experiments based on real data trace are conducted to show the efficiency of the proposed solutions.

A traditional approach to solving the large delay problem of campus networks is to upgrade the link connecting the gateway to the Internet. Inspired by the emerging content-centric network (CCN) and software defined network (SDN) architecture, we propose an alternative solution where the campus network uses a few CCN routers with caching ability, so that duplicate requests for the same content can be satisfied locally without traffic from the Internet. In our solution, we formulate the problem of deciding the cached content at each router to minimize the total delay of all requests. We prove that the problem is NP-hard, and no polynomial time algorithm can provide a constant approximation ratio, unless P=NP. We then propose an exponential-time exact algorithm and three polynomial-time heuristic algorithms. Numerical results show that our solution can reduce network delay significantly, compared to existing cache decision algorithms.

One ultimate goal of wireless sensor networks is to collect the sensed data from a set of sensors and transmit them to some sink node via a data gathering tree. In this work, we are interested in data aggregation, where the sink node wants to know the value for a certain function of all sensed data, such as minimum, maximum, average, and summation. Given a data aggregation tree, sensors receive messages from children periodically, merge them with its own packet, and send the new packet to its parent. The problem of finding an aggregation tree with the maximum lifetime has been proved to be NP-hard and can be generalized to finding a spanning tree with the minimum maximum vertex load, where the load of a vertex is a nondecreasing function of its degree in the tree. Although there is a rich body of research in those problems, they either fail to meet a theoretical bound or need high running time. In this paper, we develop a novel algorithm with provable performance bounds for the generalized problem. We show that the running time of our algorithm is in the order of O(mnα(m, n)), where m is the number of edges, n is the number of sensors, and α is the inverse Ackerman function. Though our work is motivated by applications in sensor networks, the proposed algorithm is general enough to handle a wide range of degree-oriented spanning tree problems, including bounded degree spanning tree problem and minimum degree spanning tree problem. When applied to these problems, it incurs a lower computational cost in comparison to existing methods. Simulation results validate our theoretical analysis.