Multi-server federated learning (FL) has emerged as a promising paradigm to alleviate the communication bottlenecks of single-server FL by exploiting edge-level aggregation. In realistic dense deployments, the coverage regions of neighboring edge servers (ESs) often overlap, enabling some clients to communicate with multiple ESs. However, existing multi-server FL schemes fail to fully leverage such overlapping clients for efficient inter-server collaboration, leading to excessive reliance on cloud aggregation and slow convergence under heterogeneous data distributions. To address this challenge, we propose FedOC, a novel multi-server FL framework that explicitly exploits overlapping clients to accelerate training. In FedOC, overlapping clients can serve as relay overlapping clients (ROCs) to enable real-time edge-to-edge model relaying, or as normal overlapping clients (NOCs) that dynamically select edge models for local training based on delivery latency, thereby facilitating indirect data fusion across ESs. By integrating decentralized inter-ES aggregation with latency-aware client training, FedOC significantly reduces the need for frequent cloud aggregation while improving training efficiency. Extensive experiments on MNIST, Fashion-MNIST and CIFAR-10 demonstrate that FedOC achieves substantially faster convergence and higher final accuracy than other baselines. In particular, under strong data heterogeneity and cloud-free aggregation, FedOC improves the final test accuracy by approximately 3%–25% on these datasets compared with other baselines, while significantly reducing the latency required to reach a target accuracy.

Multi-server Federated Learning (FL) has emerged as a promising approach to alleviate the communication bottle-necks of traditional single-server FL. In practical deployments, the coverage areas of different edge servers (ESs) may overlap, allowing clients in overlapping regions to access models from multiple ESs. Leveraging this observation, we enable overlapping clients (OCs) to relay edge models between neighboring ESs and dynamically select suitable models for local training, facilitating multi-hop model propagation across ESs without relying on frequent cloud aggregation. This design significantly reduces communication latency while improving training efficiency. We derive a convergence upper bound for the above OCs-based FL framework, which explicitly quantifies the impact of inter-server propagation on convergence error. Guided by this theoretical result, we formulate an optimization problem that aims to maximize dissemination range of each ES model among all ESs by OCs within a limited latency. To solve this problem, we develop a conflict-graph-based local search algorithm optimizing the routing strategy and scheduling the transmission times of individual ESs to its neighboring ESs. By integrating decentralized inter-ES aggregation with latency-aware client training, our proposed algorithm significantly reduces the need for frequent cloud aggregation while improving training efficiency. Extensive experimental results show remarkable performance gains of our scheme compared to existing state-of-the-art methods.

The Internet of Things (IoT) ecosystem generates vast amounts of multimodal data from heterogeneous sources such as sensors, cameras, and microphones. As edge intelligence continues to evolve, IoT devices have progressed from simple data collection units to nodes capable of executing complex computational tasks. This evolution necessitates the adoption of distributed learning strategies to effectively handle multimodal data in an IoT environment. Furthermore, the real-time nature of data collection and limited local storage on edge devices in IoT call for an online learning paradigm. To address these challenges, we introduce the concept of Multimodal Online Federated Learning (MMO-FL), a novel framework designed for dynamic and decentralized multimodal learning in IoT environments. Building on this framework, we further account for the inherent instability of edge devices, which frequently results in missing modalities during the learning process. We conduct a comprehensive theoretical analysis under both complete and missing modality scenarios, providing insights into the performance degradation caused by missing modalities. To mitigate the impact of modality missing, we propose the Prototypical Modality Mitigation (PMM) algorithm, which leverages prototype learning to effectively compensate for missing modalities. Experimental results on two multimodal datasets further demonstrate the superior performance of PMM compared to benchmarks.

With the advancement of computational capabilities in edge devices such as intelligent sensors in the Industrial Internet of Things (IIoT), these sensors evolving beyond simple data collection to support complex computational tasks. This advancement provides new opportunities for adopting distributed learning approaches in IIoT. In this study, we focus on enhancing learning performance in an industrial assembly line scenario where multiple distributed sensors sequentially collect real-time data with distinct feature spaces. However, existing research lacks an online distributed learning framework tailored for such IIoT settings. To address this gap, we propose the Denoising and Adaptive Online Vertical Federated Learning (DAO-VFL) algorithm, a novel algorithm that leverages the computing potential of edge sensors while addressing key challenges such as communication overhead and data privacy. DAO-VFL effectively manages continuous data streams and adapts to shifting learning objectives. Furthermore, it can address critical challenges prevalent in industrial environment, such as communication noise and heterogeneity of sensor capabilities. To support the proposed algorithm, we provide a comprehensive theoretical analysis, highlighting the effects of noise reduction and adaptive local iteration decisions on the regret bound. Experimental results on two real-world datasets further demonstrate the superior performance of DAO-VFL compared to benchmarks.

With the increasing number and enhanced capabilities of IoT devices in smart buildings, these devices are evolving beyond basic data collection and control to actively participate in deep learning tasks. Federated Learning (FL), as a decentralized learning paradigm, is well-suited for such scenarios. However, the limited computational and communication resources of IoT devices present significant challenges. While existing research has extensively explored efficiency improvements in Horizontal FL, these techniques cannot be directly applied to Vertical FL due to fundamental differences in data partitioning and model structure. To address this gap, we propose a Lightweight Vertical Federated Learning (LVFL) framework that jointly optimizes computational and communication efficiency. Our approach introduces two distinct lightweighting strategies: one for reducing the complexity of the feature model to improve local computation, and another for compressing feature embeddings to reduce communication overhead. Furthermore, we derive a convergence bound for the proposed LVFL algorithm that explicitly incorporates both computation and communication lightweighting ratios. Experimental results on an image classification task demonstrate that LVFL effectively mitigates resource demands while maintaining competitive learning performance.

Mobile devices within the Mobile Edge Computing (MEC) system can offload tasks from the near server to reduce the retrieval latency. Combining collaborative learning for MEC will make progress in both speeding up the convergence of the task-offloading algorithms applied at the end devices side and relieving resources lacking pressure on servers at edge server or cloud server sides. However, stochastic bandit algorithms are incompetent in the presence of complex environments and rarely consider device-to-device communication among users, lacking flexibility under a centralized system. Thus, in this paper, we propose an Upper Confidence Bound (UCB) basedAdaptiveProbabilityUpdating algorithm (APU) and apply it at users side guiding them offloading tasks efficiently. APU could let users manage their own probability-based distributed task offloading strategy and based on it select the most adaptive server. Drawing on a long line of research in network communication and traditional MAB frameworks, we build a multi-agent variant of APU and its corresponding MEC system. Furthermore, we take the regret analysis of the proposed algorithms by rigorous mathematical proof of the sub-linearity convergence, and at the end of this paper, we do some simulation experiments to demonstrate the effectiveness.

Cloud storage makes it easier for users to access and share data remotely, but it often requires integration with cryptographic technologies to address consumer-oriented applications, such as fine-grained data access, secure data sharing and retrieval. This paper focuses on the fine-grained access problem of media applications based on time sequence, that is, certain critical media applications based on time sequences should ideally be accessible only to authorized clients. The traditional keyword-based searchable encryption (SE) allows effective search and access over encrypted data while preserving data privacy, but most existing solutions do not support temporal access control (i.e., a mechanism that grants access permissions to users within a specified time range). In this paper, we propose PAC-MC, an efficient password-based access control framework for media cloud relying on content control with the time sequence attribute. PAC-MC not only supports multi-keyword search using any monotonic boolean formulas but also allows media owners to control content-encryption keys for different time periods with an updatable password. Furthermore, it supports the self-retrieval of content-encryption keys. In addition, PAC-MC is provably secure under the standard model. Finally, the detailed performance evaluation results and experimental comparisons indicate that PAC-MC is very efficient and outperforms the previous solutions in terms of computation, communication, and storage costs.

As mobile devices proliferate and mobile applications diversify, Mobile Edge Computing (MEC) has become widely adopted to efficiently allocate computing resources at the network edge and alleviate network congestion. In the MEC initial phase, the absence of vital information presents challenges in devising task-offloading policies, and identifying malicious devices responsible for providing inaccurate feedback is complex. To fill in such gaps, we introduce a consortium blockchain-enabledCommitteeVoting basedTaskOffloadingModel (CVTOM) to collaboratively formulate resource allocation policies and establish deterrence against malicious servers producing erroneous results intentionally. Different voting principle mechanisms of each committee member are first designed in a Blockchain-enabled system which helps to represent the system's resource status. Additionally, we propose a Multi-armed Bandits relatedThompsonSampling basedAdaptivePreferenceOptimization (TSAPO) algorithm for task-offloading policy, enhancing the timely identification of potent edge servers to improve computing resource utilization which first considers dynamic edge server space and parallel computing scenarios. The solid proof process greatly contributes to the theoretical analysis of the TSAPO. The simulation experiments demonstrate the delay and budget can be reduced by around 25% and 10% respectively, showcasing the superior performance of our approach.

With the development of the fifth generation (5G) technology, Mobile Edge Computing (MEC) is becoming a useful architecture, which is envisioned as a cloud extension version. Users within the MEC system could save more time on data transmission, in which the tasks can be executed on the edge side. Multi-armed bandits (MAB) are powerful tools that help users offload tasks to their best servers in the MEC system. However, as the system scale grows, the traditional MAB algorithms are weak and the channel condition deteriorates rapidly, which will lead to an increasing time cost of edge tasks offloading and computing. Therefore, in this paper, we propose aBatch-basedMulti-userServerElimination (BMSE) to solve such a problem. BMSE contains two sub-algorithms, BMSE inUserLevel (BMSE-UL) and BMSE inSystemLevel (BMSE-SL). BMSE-UL is applied among users for the initial stage which can help them discard servers with obvious bad performance. BMSE-SL is applied in the whole MEC system which can guide users offloading tasks collectively. Furthermore, we establish the optimality of the proposed algorithms by proving the sub-linearity convergence of their regrets and demonstrate the effectiveness through extensive experiments.

The widespread deployment of low-cost, vulnerable IoT devices allows attackers to exploit them to generate botnets and launch distributed denial-of-service (DDoS) attacks, which has become a serious security challenge for ensuring quality of service (QoS). For cost-effective defense against DDoS, we propose a novel hybrid defense method that includes proactive moving target defense (MTD) and passive security control to resist DDoS threats at different stages in IoT networks in this paper. We construct a multi-stage Markov game model to portray the game as a competition between the attacker and the defender for the control duration of the attack surface, and design an optimal defense strategy algorithm. In particular, we introduce a new parameter of action execution interval expectation in the game and add node importance evaluation in the reward quantification so that the optimal action execution interval of each defense technique can be output. We also consider the possibility that advanced attackers may launch DDoS on the SDN controller in the game. The experimental results demonstrate that our proposed method can defend against DDoS cost-effectively and ensure the QoS in IoT networks with acceptable overhead.

To address the challenges posed by the heterogeneity inherent in federated learning (FL) and to attract high-quality clients, various incentive mechanisms have been employed. However, existing incentive mechanisms are typically utilized in conventional synchronous aggregation, resulting in significant straggler issues. In this study, we propose a novel asynchronous FL framework that integrates an incentive mechanism based on contract theory. Within the incentive mechanism, we strive to maximize the utility of the task publisher by adaptively adjusting clients' local model training epochs, taking into account time delay and test accuracy. In the asynchronous scheme, considering client quality, we devise aggregation weights and an access control algorithm to facilitate asynchronous aggregation. Through experiments conducted on the MNIST dataset, our framework achieved a test accuracy that is 3.12% and 5.84% higher than the accuracy achieved by FedAvg and FedProx without any attacks, respectively. Under attacks, the framework exhibits a 1.35% accuracy improvement over the ideal Local SGD. Furthermore, aiming for the same target accuracy, our framework demands notably less computation time than both FedAvg and FedProx.

Utilizing the federated learning (FL) technique, data owners can collaboratively train artificial intelligence models, retaining all training data on their premises to minimize the potential for personal data breaches. However, self-interested users (e.g., free riders) bring new challenges that hinder the development of FL techniques. To this end, we propose a two-stage client selection scheme comprising a multiarmed bandit (MAB)-based candidate client selection method and an auction-based training client selection method. Specifically, our client selection scheme initially formulates the FL system into an MAB system, where clients are the arms and the server is the player. Then, we quantify the similarity between a local model and the server side, which is the designed metric for model aggregation and reward computation updating based on the fuzzy mathematical strategy. Next, based on the Thompson Sampling strategy, the server can intelligently determine the reward of each client, and clients with more significant rewards have the chance for local model training. With an auction method, the server can determine the training clients to reduce the training cost while maximizing each client’s revenue. Extensive experiments on real-world data sets demonstrate that the proposed scheme outperforms representative FL schemes (i.e., FedAvg, FedProx, FedMax, and MFL) regarding the model’s convergence rate and cost in FL systems with free riders.

Federated Learning empowers data owners to collectively train an artificial intelligence model without exposing data. However, the heterogeneous resources and the self-interested users bring new challenges hindering the development of federated learning. To this end, we propose a Multi-attribute Auction-based Grouped Federated Learning scheme, called MAGFL, comprising a grouped federated learning framework and a multi-attribute auction-based group selection strategy. Initially, our grouped federated learning framework clusters clients into groups according to local characteristics. Then, we propose a quality assessment method to assess the quality of each group based on a fuzzy approach. Furthermore, the FL server distributes economic rewards to training clients to motivate more clients to join the FL system, which is likened to a multi-attribute auction market where each group agent bids for training opportunities. Moreover, we design a novel global model update method with added Adam (i.e., Adaptive Moment Estimation) operations into the global update stage, which can fully utilize the local and global update direction to accelerate the convergence rate of scheme MGAFL. Extensive experiments on real-world datasets demonstrate that the proposed scheme outperforms representative federated learning schemes (i.e., FedAvg, FedProx, and FedAvg-Adam) regarding the model's convergence rate and capacity to deal with heterogeneous systems.

Over-the-air Computation (AirComp) has been demonstrated as an effective transmission scheme to boost the efficiency of federated edge learning (FEEL). However, existing FEEL systems with AirComp scheme often employ traditional synchronous aggregation mechanisms for local model aggregation in each global round, which suffer from the stragglers issues. In this paper, we propose a semi-asynchronous aggregation FEEL mechanism with AirComp scheme (PAOTA) to improve the training efficiency of the FEEL system in the case of significant heterogeneity in data and devices. Taking the staleness and divergence of model updates from edge devices into consideration, we minimize the convergence upper bound of the FEEL global model by adjusting the uplink transmit power of edge devices at each aggregation period. The simulation results demonstrate that our proposed algorithm achieves convergence performance close to that of the ideal Local SGD. Furthermore, with the same target accuracy, the training time required for PAOTA is less than that of the ideal Local SGD and the synchronous FEEL algorithm via AirComp.

Distributed learning has been widely adopted to train a global model from local data. However, its performance can be severely affected by stragglers. Recently, some research has been dedicated to resolving the straggler problem by adopting gradient coding, the essence of gradient coding is to solve the straggler problem by adding data redundancy. However, the large amount of data redundancy as well as computation and communication overhead that it brings is still hard to be resolved. Besides, the complexity of the encoding and decoding will increase linearly with the number of the local workers. To this end, in this paper, we design a lightweight coding method in the computing phase and seek to ensure fair transmission in the communication phase. Specifically, to tolerate stragglers in computing phase, we propose a two-stage dynamic coding scheme, part of the workers start computing the partial gradients from the data partitions assigned in the first stage, and the remaining workers for computation in the second stage is decided based on which workers have finished in the first stage. To further tolerate stragglers in the communication phase, a perturbed Lyapunov function is designed to maximize admission data balancing fairness as well as the throughput. The experimental result verifies the derived properties and demonstrates that our proposed solution can achieve a better performance for practical network parameters and benchmark data in terms of accuracy and resource utilization in the distributed learning system.

Massive Internet of Things (IoT) devices have been playing a critical role in both the cyber and physical worlds. Various cyber attacks pose significant risks to IoT. Machine learning-based intrusion detection system (IDS) has earned much research attention. However, the intrusion prevention system (IPS) is rarely explored. Realtime intrusion prevention is quite challenging because the decision has to be made during a flow rather than after it finishes. Restricted by aligning with the shortest flows, existing IPSs generally inspect only the very first packets, leading to information loss for accurate detection. In this article, we first measure the information loss quantitatively. Then we devise Sniper, an IoT IPS scheme consisting of a flow length predictor, a novel feature space, and an enhanced ensemble learning algorithm. The flow length predictor guides a proper prevention time point to preserve as much information as possible. The proposed Markov matrix-based feature encoding method further saves more information than existing ones. The enhanced learning algorithm ensures a low-false positive rate (FPR), which is critical for IPSs. We benchmark Sniper with one closed-world and three open-world data sets. The results show that Sniper achieves a 99.89% prevention rate and 0.03% FPR, which is superior to the five state-of-the-art baseline models.

Federated Learning (FL), allowing data owners to conduct model training without sending their raw data to third-party servers, can enhance data privacy in Mobile Edge Computing (MEC) which brings data processing closer to the data sources. However, the heterogeneity of local data and constrained local resources in MEC bring new challenges hindering the development of FL. To this end, we propose an Auction-based Cluster Federated Learning scheme, called ACFL, comprising a clustered FL framework and an auction-based client selection strategy. Our clustered FL framework first introduces a mean-shift clustering algorithm to FL, which can intelligently cluster clients according to their local data distribution. Then, we select clients from each cluster using an auction mechanism to participate in FL training, which can mitigate the impact of data heterogeneity on model convergence and balance energy consumption. Moreover, we prove the proposed clustered FL framework converges at a sublinear rate. Extensive experiments conducted on real-world datasets demonstrate that the proposed FL scheme outperforms the conventional FL schemes in terms of convergence rate and energy balance.

Federated learning (FL) can train a global model from clients' local data set, which can make full use of the computing resources of clients and performs more extensive and efficient machine learning on clients with protecting user information requirements. Many existing works have focused on optimizing FL accuracy within the resource constrained in each individual round, however there are few works comprehensively consider the optimization for latency, accuracy and energy consumption over all rounds in wireless federated learning. Inspired by this, in this paper, we investigate FL in wireless networks where client selection and bandwidth allocation are two crucial factors which significantly affect the latency, accuracy and energy consumption of clients. We formulate the optimization problem as a mixed-integer problem, which is to minimize the cost of time and accuracy within the long-term energy constrained over all rounds. To address this optimization problem, we propose a per-round energy drift plus cost (PEDPC) algorithm from an online perspective, and the performance of the PEDPC algorithm is verified in simulation results in terms of latency, accuracy and energy consumption in IID and NON-IID data distributions.

We investigate a cooperative federated learning framework among devices for mobile edge computing,named (CFLMEC), where devices co-exist in a shared spectrum with interference. Keeping in view the time-average network throughput of cooperative federated learning framework and spectrum scarcity, we focus on maximize the admission data to the edge server or the near devices, which fills the gap of communication resource allocation for devices with federated learning. In CFLMEC,devices can transmit local models to the corresponding devices or the edge server in a relay race manner, and we use a decomposition approach to solve resource optimization problem by considering maximum data rate on sub-channel, channel reuse and wireless resource allocation in which establishes a primal-dual learning framework and batch gradient decent to learn the dynamic network with outdated information and predict the sub-channel condition. With aim at maximizing throughput of devices, we propose communication resource allocation algorithms with and without sufficient sub-channels for strong reliance on edge servers (SRs) in cellular link, and interference aware communication resource allocation algorithm for less reliance on edge servers (LRs) in D2D link. Extensive simulation results demonstrate the CFLMEC can achieve the highest throughput of local devices comparing with existing works, meanwhile limiting the number of the sub-channels.

Design an optimization model for task management among Mobile Terminal (MT), Macro cell Base Station (MBS), and multiple Small cell Base Stations (SBS) for the large-scale Mobile Edge Computing (MEC) system, is a challenging issue due to the large number of tasks and SBSs. Inspired by this, we propose a Parallel Optimal Task Allocation Mechanism (POTAM) framework for MEC, which includes Device to Device (D2D)-enabled computing, MBS computing and Edge Computation Resource Distribution (ECRD) computing. In POTAM, we exploit a parallel multi-block Alternating Direction Method of Multipliers (ADMM) based method to model both requirements of delay and energy consumptions, which formulates the task allocation under these requirements as a nonlinear 0–1 integer programming problem. To solve this problem, we develop an efficient combination of conjugate gradient, Newton and linear search techniques based algorithm with Logarithmic Smoothing and Cyclic Block coordinate Gradient Projection (CBGP) methods, which can guarantee convergence and reduce computational complexity with a good scalability. In order to allocate task cooperatively, an optimal approach is proposed, ECRD-A, which is used to find the shortest path among each node. Numerical results demonstrate the effectiveness of the POTAM and it can effectively reduce delay and energy consumption for a large-scale MEC system.

Machine learning based network intrusion system (NIDS) is known to be vulnerable to evasions. Attackers conceal intrusion activities to make them undetected. Researching evasion techniques contributes to evaluating and increasing the robustness of NIDS. Previous evasion approaches modify feature values or packets of an offline network trace as a whole. However, in real scenarios, attackers are constrained to manipulate only outbound packets on the fly. To bridge this assumption gap, we present the first evasion solution for live network traffic against learning based NIDSs. The solution consists of three components: a devised Kalman filter based algorithm to predicate the feature values of live flows, a set of formally constructed atomic packet mutation operators, and a proposed Strength Enhanced Deep Q-learning (SE-DQN) to determine effective mutation operators on outbound packets according to the predicted features. A defense scheme based on adaptive decision threshold adjustment is also provided. Experimental evaluation is presented on various NIDS classifiers and cyber attacks. Results show that SE-DQN achieves an evasion rate of at least 64.2% on most classifiers and even more than 90% on certain ones, and it is three times faster than DQN on learning mutation policy. The defense scheme shows an improvement of at least 76.4% on recall measurement.

Machine learning has been increasingly adopted to detect Android malwares. Most existing studies depend on features in code space such as information flows and API calls. Malware variants would engage these models in a never-ending war. Inspired by the observation that some variants share similar or even identical user interfaces (UIs), this paper explores employing visible UI screenshot as the indicator to build a novel Android malware detection method. To achieve this vision, we built the first Android Application Screenshot Dataset (AnASD) consisting of more than twenty thousand UI screenshots produced by both benign applications and malwares. A thorough analysis was conducted to characterize the dataset, especially the UI difference between benign applications and malwares. Then a set of state of the art deep learning classifiers on AnASD were trained and evaluated. The results of both sim-ilarity measurement and classification performance proved the feasibility to detect Android malwares based on user interfaces. To facilitate the research community, the dataset is free available at https://doi.org/10.6084/m9.figshare.14445768.

We consider the problem of the intelligent and efficient resource management framework in mobile-edge computing (MEC), which can reduce delay and energy consumption, and features distributed optimization and efficient congestion avoidance. In this article, we present a cooperative learning framework for resource management in MEC from an alternating direction method of multipliers (ADMMs) perspective, named the CL-ADMM framework. First, computing a task requires both the user personal data and corresponding program that processes it, to efficiently cache program in a group, a novel program popularity estimation scheme is proposed, which is based on a semi-Markov process model. Then, a greedy program cooperative caching mechanism is established, which can effectively reduce delay and energy consumption. Second, to address group congestion, a dynamic task migration scheme based on improved cooperative Q-learning is proposed, which can effectively reduce delay and alleviate congestion. Third, to minimize delay and energy consumption for resource allocation in a group, we formulate it as an optimization problem with a large number of variables, and then exploit a novel ADMM-based scheme to solve this problem, which can reduce the complexity of the problem with a new set of auxiliary variables, these subproblems are all convex problems that can be solved by using a primal-dual approach, which guarantees its convergence. Finally, we prove its convergence by using the Lyapunov theory. The numerical results demonstrate the effectiveness of the CL-ADMM framework in reducing delay and energy consumption in MEC.

We consider the problem of intelligent and efficient task allocation mechanism in large-scale mobile edge computing (MEC), which can reduce delay and energy consumption in a parallel and distributed optimization. In this paper, we study the joint optimization model to consider cooperative task management mechanism among mobile terminals (MT), macro cell base station (MBS), and multiple small cell base station (SBS) for large-scale MEC applications. We propose a parallel multi-block Alternating Direction Method of Multipliers (ADMM) based method to model both requirements of low delay and low energy consumption in the MEC system which formulates the task allocation under those requirements as a nonlinear 0-1 integer programming problem. To solve the optimization problem, we develop an efficient combination of conjugate gradient, Newton and linear search techniques based algorithm with Logarithmic Smoothing (for global variables updating) and the Cyclic Block coordinate Gradient Projection (CBGP, for local variables updating) methods, which can guarantee convergence and reduce computational complexity with a good scalability. Numerical results demonstrate the effectiveness of the proposed mechanism and it can effectively reduce delay and energy consumption for a large-scale MEC system.

The imbalanced problem indicates that the data set is unevenly distributed, resulting in sub-optimal classifiers to recognize the minority class. Traditional solutions try to design new classifiers to solve this problem or balance the skewed data sets, the former is too costly while the latter has an uncertain effect on different combinations of classifiers and measurements. In this paper, we propose a reinforcement learning-based oversampling method, which can directly produce targeted samples according to the downstream classifiers and measurements. During training, our learning procedure introduces the classification information to the generation process. Moreover, as opposed to oversampling approaches, we have no assumption of the downstream classifiers and performance metrics, and the proposed has a wider application. We carry out experiments on 17 UCI and KEEL data sets, experimental results demonstrate the superior performance of our proposed method.

In recent years, a large number of smart devices will be connected in Internet of Things (IoT) using an ad hoc network, which needs more frequency spectra. The cognitive radio (CR) technology can improve spectrum utilization in an opportunistic communication manner for IoT, forming a promising paradigm known as cognitive radio ad hoc networks, CRAHNs. However, dynamic spectrum availability and mobile devices/persons make it difficult to develop an efficient data transmission scheme for CRAHNs under an obstacle environment. Opportunistic routing can leverage the broadcast nature of wireless channels to enhance network performance. Inspired by this, in this paper, we propose an Obstacle aware Opportunistic Data Transmission scheme (OODT) in CRAHNs from a computational geometry perspective, considering energy efficiency and social features. In the proposed scheme, we exploit a new routing metric, which is based on an obstacle avoiding algorithm using a polygon boundary 1-searcher technology, and an auction model for selecting forwarding candidates. In addition, we prove that the candidate selection problem is NP-hard and propose a heuristic algorithm for candidate selection. The simulation results show that the proposed scheme can achieve better performance than existing schemes.

Recently, the increase of different services makes the design of routing protocols more difficult in mobile ad hoc networks (MANETs), e.g., how to guarantee the QoS of different types of traffics flows in MANETs with resource constrained and malicious nodes. opportunistic routing (OR) can make full use of the broadcast characteristics of wireless channels to improve the performance of MANETs. In this paper, we propose a traffic-differentiated secure opportunistic routing from a game theoretic perspective, DSOR. In the proposed scheme, we use a novel method to calculate trust value, considering node's forwarding capability and the status of different types of flows. According to the resource status of the network, we propose a service price and resource price for the auction model, which is used to select optimal candidate forwarding sets. At the same time, the optimal bid price has been proved and a novel flow priority decision for transmission is presented, which is based on waiting time and requested time. The simulation results show that the network lifetime, packet delivery rate and delay of the DSOR are better than existing works.

In recent years, the Social Internet of Things (SIoT) has become a research hot topic in the field of wireless networks, which are inseparable relationships between human and devices. As a huge numbers of heterogeneous devices will be connected, it needs more frequency spectrum. The Cognitive radio (CR) technology can improve spectrum utilization in an opportunistic communication manner. However, dynamic spectrum availability and heterogeneous devices make it more difficult for routing design in CR-SIoT. Opportunistic routing (OR) can mitigate drawbacks from CR-SIoT, which leverages the broadcast nature of wireless channels. In this work, we propose an energy and trust aware OR in CR-SIoT, which jointly considers energy efferent, trust and social feature for designing secure OR. In the proposed scheme, we exploit a new routing metric for selecting forwarding candidates and use network coding for the data transmission between trust nodes in multiple types of flows SIoT. In addition, we propose a game-theoretic approach to allocate channel for SIoT which is based on interference factor. Extensive simulation results show that the proposed secure opportunistic routing performs better compared with existing routing in SIoT in terms of packet delivery ratio, network lifetime and average delay. To the best of our knowledge, the proposed routing scheme is the first OR in SIoT.

In this paper, we consider the joint opportunistic routing and channel assignment problem in multi-channel multi-radio (MCMR) cognitive radio networks (CRNs) for improving aggregate throughput of the secondary users. We first present the linear programming optimization model for this joint problem, taking into account the feature of CRNs-channel uncertainty. Then considering the queue state of a node, we propose a new scheme to select proper forwarding candidates for opportunistic routing. Furthermore, a new algorithm for calculating the forwarding probability of any packet at a node is proposed, which is used to calculate how many packets a forwarder should send, so that the duplicate transmission can be reduced compared with MAC-independent opportunistic routing & encoding (MORE) [11]. Our numerical results show that the proposed scheme performs significantly better that traditional routing and opportunistic routing in which channel assignment strategy is employed.

Cognitive radio (CR) is a promising technology to improve spectrum utilization. However, spectrum availability is uncertain which mainly depends on primary user's (PU's) behaviors. This makes it more difficult for most existing CR routing protocols to achieve high throughput in multi-channel cognitive radio networks (CRNs). Inter-session network coding and opportunistic routing can leverage the broadcast nature of the wireless channel to improve the performance for CRNs. In this paper we present a coding aware opportunistic routing protocol for multi-channel CRNs, cognitive radio opportunistic routing (CROR) protocol, which jointly considers the probability of successful spectrum utilization, packet loss rate, and coding opportunities. We evaluate and compare the proposed scheme against three other opportunistic routing protocols with multichannel. It is shown that the CROR, by integrating opportunistic routing with network coding, can obtain much better results, with respect to throughput, the probability of PU-SU packet collision and spectrum utilization efficiency.

We address the problem of mapping divide-and-conquer programs to mesh connected multicomputers with wormhole or store-and-forward routing. We propose the binomial tree as an efficient model of parallel divide-and-conquer and present two mappings of the binomial tree to the 2D mesh. Our mappings exploit regularity in the communication structure of the divide-and-conquer computation and are also sensitive to the underlying flow control scheme of the target architecture. We evaluate these mappings using new metrics which are extensions of the classical notions of dilation and contention. We introduce the notion of communication slowdown as a measure of the total communication overhead incurred by a parallel computation. We conclude that significant performance gains can be realized when the mapping is sensitive to the flow control scheme of the target architecture.

It is shown that in arrays derived by the conventional integral linear transformation, there always exists a basic direction nu such that one and only one out of delta consecutive processors is active at any time unit along any line parallel to nu . Therefore, one can merge delta neighboring processors along lines parallel to nu and derive a new array which is fully efficient. The new array has only 1/ delta processors and has the same computation time complexity. The method is constructive: once the user has chosen the timing function and allocation function, the required nu can be generated automatically. After choosing nu , the appropriate quasi-affine allocation function can also be automatically derived, as can the additional registers, wires, and control for the processors. This indicates that it is always possible to synthesize a fully efficient systolic array in a practical system.>