TheComputing Power Network(CPN) is a distributed system that integrates computing resources to optimize utilization, but ensuringQuality of Service(QoS) is challenging due to high demand and complex heterogeneous connections. Accurate workload prediction is essential for maintaining QoS, yet the diverse and complex user requirements in CPN make prediction difficult. To address this challenge, we propose an ensemble workload prediction model with fluctuation division control for workload prediction in CPN, comprising three key components. First, we use theThree-Way Decision(3WD) approach to partition workload fluctuations, controlling granularity thickness and applying clustering to capture dynamic workload characteristics. Second, we develop tailored prediction methods for each of the three partitioned regions and ensembles them to enhance overall prediction performance. Third, the ensemble prediction method is applied to each region to obtain the final predicted values. The proposed method introduces an innovative fluctuation division control strategy for characteristic mining to capture dynamic workload fluctuation patterns and designs the effective ensemble workload prediction model deal with the problem of non-stationary workload prediction in CPN. Experimental results on trace datasets from Alibaba and Dinda demonstrate that the proposed model improves the higher average prediction accuracy by up to 26.06%$\sim$66.4% than the comparison methods.

The computing power network links distributed and heterogeneous computing resources via the network, to enable efficient configuration and utilization of computing power. However, scheduling computing resources within this network presents several challenges, such as resource heterogeneity, vast search spaces, uncertainty, high constraints, and real-time requirements. To simulate the real-world computing power network scheduling problem, this paper integrates cloud servers, fog servers, and edge servers into a unified computing power network, considering their respective GPU, CPU, and bandwidth resources. We introduce a Dual-Tree Genetic Programming (DTGP) approach that simultaneously optimizes two critical decisions—routing and sequencing—to automatically evolve computing power network scheduling heuristics for real-time decision-making. Additionally, to improve the performance of DTGP, we propose new terminal sets tailored to fit within these two GP trees. Experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art methods in six test scenarios, achieving up to 40% reduction in completion time.

Cloud-edge collaborative computing (CECC) facilitates the sharing of computing resources by collaboratively scheduling tasks among servers, thereby maximizing task execution efficiency. Task scheduling and resource allocation (TS-RA) are two interrelated issues that significantly affect the efficient utilization of computing resources. In this paper, we decouple the joint optimization problem of TS-RA and propose a novel model based on multi-agent reinforcement learning (TRMARL), which is applicable to distributed task scheduling and resource allocation in a heterogeneous CECC system. TRMARL consists of two modules: 1) the task scheduling module, where we introduce a value factorization algorithm to maximize joint rewards of distributed scheduling actions; 2) the resource allocation module, where we present a proximal policy optimization (PPO) algorithm based mechanism to optimize resource allocation. TRMARL efficiently captures the state difference among heterogeneous servers through a graph attention network-based recurrent deep Q-network (GAT-based recurrent-DQN) architecture and learns different strategies for heterogeneous services through a multi-expert schema. The experimental results demonstrate that TRMARL effectively improves the task completion rate, reduces average system latency, and enhances convergence stability in a heterogeneous CECC system.

Collaborative perception, fusing information from multiple agents, can extend perception range so as to improve perception performance. However, temporal asynchrony in real-world environments, caused by communication delays, clock misalignment, or sampling configuration differences, can lead to information mismatches. If this is not well handled, then the collaborative performance is patchy, and what's worse safety accidents may occur. To tackle this challenge, we propose CoDynTrust, an uncertainty-encoded asynchronous fusion perception framework that is robust to the information mismatches caused by temporal asynchrony. CoDynTrust generates dynamic feature trust modulus (DFTM) for each region of interest by modeling aleatoric and epistemic uncertainty as well as selectively suppressing or retaining single-vehicle features, thereby mitigating information mismatches. We then design a multi-scale fusion module to handle multi-scale feature maps processed by DFTM. Compared to existing works that also consider asynchronous collaborative perception, CoDynTrust combats various low-quality information in temporally asynchronous scenarios and allows uncertainty to be propagated to downstream tasks such as planning and control. Experimental results demonstrate that CoDynTrust significantly reduces performance degradation caused by temporal asynchrony across multiple datasets, achieving state-of-the-art detection performance even with temporal asynchrony. The code is available at https://github.com/CrazyShout/CoDynTrust.

As end-to-end autonomous driving advances toward real-world deployment, ensuring the safety of autonomous vehicles (AVs) has become a critical requirement for their commercial viability. While rule-based AVs have traditionally undergone rigorous testing in both real-world and simulated environments before deployment, data-driven autonomous models are typically trained on real-world datasets, limiting their generalization to simulation environments. This poses a significant challenge for the development and testing of end-to-end autonomous driving. To address this issue, we propose Retrieval-Augmented Learning for Autonomous Driving (RALAD), a novel framework designed to bridge the real-to-sim gap in a cost-effective manner. RALAD consists of three key components: (1) domain adaptation via an enhanced Optimal Transport (OT) method, which retrieves the most similar scenarios between real and simulated environments; (2) feature fusion across similar scenarios, enabling the construction of a feature mapping between real-world and simulated domains; and (3) feature extraction freezing with fine-tuning on the fused features, allowing the model to learn simulation-specific characteristics through feature mapping. We evaluate RALAD on three monocular 3D object detection models, and the results demonstrate that our approach significantly improves model accuracy in simulation. Additionally, we use real autonomous vehicle for testing in real-world scenarios, and have established simulated scenes similar to reality for further testing, which illustrate the effectiveness of our method.

In recent years,Coded Edge Computing(CEC) has been greatly studied as a promising technology to effectively mitigate the impact of stragglers and provide confidentiality in edge collaborative computing. It is crucial to verify the correctness of both intermediate results and the final result especially in untrustable and unreliable edge computing scenarios. However, the existing works on verification in CEC always verify and directly discard the whole incorrect intermediate results. In this paper, we propose thePartial Decode and Compare(PDC) verification scheme, which can fully utilize the correct part in the incorrect intermediate results to reduce the complexity and tolerate more abnormal edge devices. The PDC verification scheme consists of two parts:Final Result Verification(FRV) andAbnormal Edge Device Identification(AEDI). By deeply analyzing the decoding impact of the intermediate results on the final result, the PDC verification scheme divides the intermediate results and final results intosubresult vectors. It decodes, compares, and verifies the final result in units of subresult vectors. In this way, the obtained parts which verified to be correct do not need to participate in the following verification. Therefore, it can significantly reduce the verification overhead including both the number of required decoding rounds and the complexity of each decoding round. Based on the correct final result verified by the PDC verification scheme, we also propose anAbnormal Edge Devices Identificationscheme to identify all abnormal edge devices that return incorrect intermediate results. We then present extensive theoretical analyses and simulation experiments of the PDC verification scheme, which demonstrates that the PDC verification scheme can tolerate a higher ratio of incorrect intermediate results and achieve lower verification overhead than the state-of-the-art verification works. Therefore, the proposed PDC verification scheme enables CEC to provide reliable services in unstable and unreliable edge computing scenarios.

Competitive influence maximization (CIM) has been studied for years due to its wide application in many domains. Most current studies primarily focus on the microlevel optimization by designing policies for one competitor to defeat its opponents. Furthermore, current studies ignore the fact that many influential nodes have their own starting prices, which may lead to inefficient budget allocation. In this article, we propose a novel competitive bidding influence maximization (CBIM) problem, where the competitors allocate budgets to bid for the seeds attributed to the platform during multiple bidding rounds. To solve the CBIM problem, we propose a fairness-aware multiagent CBIM (FMCBIM) framework. In this framework, we present a multiagent bidding particle environment (MBE) to model the competitors’ interactions and design a starting price adjustment mechanism to model the dynamic bidding environment. Moreover, we put forward a novel multiagent CBIM (MCBIM) algorithm to optimize competitors’ bidding policies. Extensive experiments on five datasets show that our work has good efficiency and effectiveness.

Recently, edge computing has demonstrated increasing potential to provide low-latency computing services. Coded edge computing can not only make full use of the resources of heterogeneous edge computing servers, but also significantly reduce the negative effects of slow computing devices on computing time. Nevertheless, since edge servers may be unreliable or untrustworthy, the user will decode and get incorrect computation results even if it uses one incorrect sub-computation result returned by faulty edge servers. In this paper, for the existing coded edge computing schemes, we focus on the distributed matrix-matrix multiplication and design a general and efficientDecode-and-Compare Verification(DCV) scheme to verify the correctness of computation results and identify faulty edge servers by utilizing the properties of coded computing itself. The DCV scheme contains two components: (1) computation result verification,i.e., obtain the computation result and verify its correctness, and (2) faulty edge server identification,i.e., identify the faulty edge servers by verifying the correctness of returned sub-computation results. For both the independent and collusion faulty edge server models, we conduct solid theoretical analyses on the required decoding rounds, the coding redundancy and the successful verification probability to demonstrate that the correct computation result can be efficiently verified. We also conduct a lot of experiments on the DCV scheme from different aspects and the results show that it achieves much less computation time to get the correct computation result compared with other potential schemes, including homomorphic encryption and local computation.

In order to better assist the rehabilitation treatment of patients with musculoskeletal injury, standard rehabilitation actions are needed to guide the musculoskeletal rehabilitation process. With more and more urgent demands, the musculoskeletal rehabilitation evaluation systems have attracted a high degree of attention. Experts have proposed a series of systems based on laser, ultrasound, and image, which can give reasonable recognition and judgment. However, these systems either require specialized and expensive equipment or can be affected by ionizing radiation. How to construct a musculoskeletal rehabilitation evaluation system with low cost, good effect, and little injury is still a great challenge. In this article, we propose MSEva, a musculoskeletal rehabilitation evaluation system based on EMG signals. Specifically, the system uses EMG sensors to collect a large amount of data for five rehabilitation actions. Secondly, MSEva uses Wavelet Transform (WT) to extract the signal features and then puts the processed data into the Long Short-Term Memory (LSTM) network for model training. Finally, the system uses the LSTM model to evaluate the normality of the EMG response of rehabilitation actions. The results show that the average accuracy of MSEva reaches 94.37%, which has important evaluation value in guiding the rehabilitation of musculoskeletal patients.

Accurate segmentation of the prostate is important to image-guided prostate biopsy and brachytherapy treatment planning. However, the incompleteness of prostate boundary increases the challenges in the automatic ultrasound prostate segmentation task. In this work, an automatic coarse-to-fine framework for prostate segmentation was developed and tested. Our framework has four metrics: first, it combines the ability of deep learning model to automatically locate the prostate and integrates the characteristics of principal curve that can automatically fit the data center for refinement. Second, to well balance the accuracy and efficiency of our method, we proposed an intelligent determination of the data radius algorithm-based modified polygon tracking method. Third, we modified the traditional quantum evolution network by adding the numerous-operator scheme and global optimum search scheme for ensuring population diversity and achieving the optimal model parameters. Fourth, we found a suitable mathematical function expressed by the parameters of the machine learning model to smooth the contour of the prostate. Results on the multiple datasets demonstrate that our method has good segmentation performance.

Recently, federated learning (FL) becomes a emerging research area, and the combination of edge computing and FL is one of the important research contents. However, there are many kinds of edge devices in heterogeneous federated learning, such as personal computers, embedded devices, and the resource-limited devices will reduce the efficiency of FL. In this paper, we propose an efficient linear coded federated learning under multiple stragglers (LCFLMS) to (1) accelerate the training speed and improve the efficiency of heterogeneous FL under multiple stragglers and (2) provide the certain level of privacy protection. We design a client-based multiple stragglers task outsourcing (C-MSTO) algorithm and a server-based multiple stragglers task outsourcing (S-MSTO) algorithm to meet the model calculation acceleration in general environment under multiple stragglers. In the process of outsourcing, the raw data are protected by using linear coding computing (LCC) scheme. Finally, the experimental results demonstrate that LCFLMS reduces the training time by 90.22% when the performance difference between clients in FL system is large.

To improve safety in public transportation, a major issue is how to avoid traffic accidents. To this end, a recent report has demonstrated that more than 90% of accidents in the United States were due to drivers’ abnormal behaviors. Relevant to this observation, many recent studies have proposed to use different sensors to monitor drivers’ behaviors and apply learning algorithms to detect abnormal behaviors. Nevertheless, most existing systems are expensive and inconvenient to be deployed or significantly affected by the environment. In this article, we propose and develop a novel and effective solution, namely, SafeDriving, that collects signals from electromyography (EMG) sensors and then utilizes an effective deep-learning model to detect abnormal behaviors in real time. Specifically, we first utilize a wearable EMG sensor that can be attached to a driver’s forearm to collect a large amount of sensing data from human drivers, for which we define five typical abnormal driving behaviors (i.e., fetching forward, picking up, turning the steering wheel sharply, turning back, and touching sunroof) and label each sample accordingly. Next, using the labeled data, we design and train multiple state-of-the-art classifiers to improve the performance of SafeDriving, e.g., convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU). The extensive experiments demonstrate that GRU can lead to the best performance with an average accuracy of 93.94%. Based on this observation, we further investigate other important factors, such as the binding area of the sensor, the tightness of binding, the duration of the sample, etc. The proposed SafeDriving system provides an effective approach to reliably assess drivers’ driving behaviors with affordable commodity sensors and be further used in public safety.

In recent years, edge computing has attracted significant attention because it can effectively support many delay-sensitive applications. Despite such a salient feature, edge computing also faces many challenges, especially for efficiency and security, because edge devices are usually heterogeneous and may be untrustworthy. To address these challenges, we propose a unified framework to provide efficiency and confidentiality by coded distributed computing. Within the proposed framework, we use matrix multiplication, a fundamental building block of many distributed machine learning algorithms, as the representative computation task. To minimize resource consumption while achieving information-theoretic security, we investigate two highly-coupled problems, (1) task allocation that assigns data blocks in a computing task to edge devices and (2) linear code design that generates data blocks by encoding the original data with random information. Specifically, we first theoretically analyze the necessary conditions for the optimal solution. Based on the theoretical analysis, we develop an efficienttask allocationalgorithm to obtain a set of selected edge devices and the number of coded vectors allocated to them. Using the task allocation results, we then designsecure coded computingschemes, for two cases, (1) with redundant computation and (2) without redundant computation, all of which satisfy the availability and security conditions. Moreover, we also theoretically analyze the optimization of the proposed scheme. Finally, we conduct extensive simulation experiments to demonstrate the effectiveness of the proposed schemes.

Recently, edge computing (EC) has attracted wide attention as a novel and promising computing mode with high real-time and low-latency characteristics. However, users' privacy and the limited resources have become major concerns in the implementation of EC because edge devices are usually heterogeneous and untrustworthy. Although many related works have protected the user's privacy, they did not take the storage resource limitation of heterogeneous edge devices into consideration and their schemes may cause high communication load. In this paper, we propose PCHEC, a Private Coded computation scheme for Heterogeneous Edge Computing, to protect the user's privacy and minimize the communication load. Specifically, PCHEC first gives a storage allocation scheme to minimize the communication load in EC where the heterogeneous edge devices have different storage limits. Secondly, PCHEC utilizes linear coding to mix the target data with other information for the protection of the user's privacy. To evaluate the efficiency of PCHEC, we make theoretically analysis and conduct extensive simulations. The experiments show PCHEC effectively reduces the communication load by up to 70% compared with other schemes.

Nowadays, the combination of edge computing and artificial intelligence has become a mainstream trend. Based on edge computing and image classification technologies, we design and implement a secure distributed image classification reasoning system for heterogeneous edge computing. The functions of the system consists of two parts: model distributed deployment and image classification reasoning. Firstly, we have designed three distributed deployment schemes for the model deployment on edge devices: random, static and dynamic deployment schemes. Secondly, we have designed three secure distributed image classification reasoning schemes: uncoded, 2-replication and MDS coding reasoning schemes. These reasoning schemes can protect the security of image data in the process of image reasoning and meet the weak security standard. Our system uses edge devices as computing devices, so it has the advantages of low computing cost and saving bandwidth. The experimental results show that our system can protect the security of image data, also has favorable stability and efficiency under the environment of heterogeneous edge computing.

In recent years, edge computing (EC), as an emerging technology, has been widely used in various industries. It can meet the needs of industries in real-time business, application intelligence, security and privacy protection. However, edge devices may not always be trustworthy in the edge computing environment. Moreover, traditional edge computing systems have ignored the fact that the computation capability of user device can also be used. In this paper, we propose the Minimum Computation Latency Secure Edge Computing (MCLSEC) scheme to minimize computation latency and provide the security of computing data by utilizing linear coding and the resources of both edge devices and user device. Specifically, we consider the matrix multiplication as a computation task, which is an important module in many application operations, such as machine learning, big data analysis, etc. We firstly theoretically analyze the total computation latency of edge devices and user device in the coded edge computing. We then give the design of the MCLSEC scheme, which includes of the coding scheme and the task allocation scheme. Moreover, we also give theoretical analysis to show the proposed MCLSEC scheme is secure and optimal. Finally, we conduct extensive simulation experiments to show the effectiveness of the proposed scheme. Compared with the existing schemes, MCLSEC scheme significantly reduces the computation latency of edge computing while ensuring data confidentiality.

Edge computing is a promising technology that can fulfill the requirements of latency-critical and computation-intensive applications. To further enhance the performance, coded edge computing has emerged because it can optimally utilize edge devices to speed up the computation. In this paper, we tackle a major security issue in coded edge computing: how to verify the correctness of results and identify attackers. Specifically, we propose an efficient verification scheme, namely Decode-and-Compare (DC), by leveraging both the coding redundancy of edge devices and the properties of linear coding itself. To design the DC scheme, we conduct a solid theoretical analysis to show the required coding redundancy, the expected number of decoding operations, and the tradeoff between them. To evaluate the performance of DC, we conduct extensive simulation experiments and the results confirm that the DC scheme can outperform existing solutions, such as homomorphic encryption and computing locally at the user device.

In recent years, the computation security of edge computing has been raised as a major concern since the edge devices are often distributed on the edge of the network, less trustworthy than cloud servers and have limited storage/ computation/ communication resources. Recently, coded computing has been proposed to protect the confidentiality of computing data under edge device's independent attack and minimize the total cost (resource consumption) of edge system. In this paper, for the cooperative attack, we design an efficient scheme to ensure the information-theory security (ITS) of user's data and further reduce the total cost of edge system. Specifically, we take matrix multiplication as an example, which is an important module appeared in many application operations. Moreover, we theoretically analyze the necessary and sufficient conditions for the existence of feasible scheme, prove the security and decodeability of the proposed scheme. We also prove the effectiveness of the proposed scheme through considerable simulation experiments. Compared with the existing schemes, the proposed scheme further reduces the total cost of edge system. The experiments also show a trade-off between storage and communication.

In the past decade, unmanned aircraft systems (UASs) have been widely used in various civilian applications, most of which involve only a single unmanned aerial vehicle (UAV). In the near future, more and more UAS applications will be facilitated by the cooperation of multiple UAVs. In such applications, it is desirable to utilize a general control platform for cooperative UAVs. However, existing open-source control platforms cannot fulfill such a demand because (1) they only support the leader-follower mode, which limits the design options for fleet control, (2) existing platforms can support only certain type of UAVs and thus lack compatibility, and (3) these platforms cannot accurately simulate a flight mission, which may cause a big gap between simulation and real-world flight. To address these issues, we propose a general control and monitoring platform for cooperative UAS, namely, CoUAS , which provides a set of core cooperation services of UAVs, including synchronization, connectivity management, path planning, energy simulation, and so on. To verify the applicability of CoUAS, we design and develop a prototype in which an embedded path planning service is provided to complete any task with the minimum flying time while considering the network connectivity and coverage. Experimental results by both simulation and field test demonstrate that the proposed system is viable.

In recent years, edge computing has attracted increasing attention for its capability of facilitating delay-sensitive applications. In the implementation of edge computing, however, data confidentiality has been raised as a major concern because edge devices may be untrustable. In this paper, we propose a design of secure and efficient edge computing by linear coding. In general, linear coding can achieve data confidentiality by adding random information to the original data before they are distributed to edge devices. To this end, it is important to carefully design code such that the user can successfully decode the final result while achieving security requirements. Meanwhile, task allocation, which selects a set of edge devices to participate in a computation task, affects not only the total resource consumption, including computation, storage, and communication, but also coding design. In this paper, we study task allocation and coding design, two highly-coupled problems in secure coded edge computing, in a unified framework. In particular, we take matrix multiplication, a fundamental building block of many distributed machine learning algorithms, as the representative computation task, and study optimal task allocation and coding design to minimize resource consumption while achieving information-theoretic security.

Edge computing is attracting more and more attention in recent years to fulfill the requirements of latency-critical and computation-intensive applications. By using the coding redundancy, coded edge computing has emerged to optimize the total computation latency. Compared with the servers in cloud computing, edge devices located at the edge of network may not be reliable and trustworthy. In coded edge computing, even one incorrect intermediate result will lead to the incorrect final result. Therefore, considering the low computation capabilities of edge devices and low latency requirements of user, we study the result verification problem for coded edge computing. Specifically, we propose an efficient Orthogonal Mark (OM) verification scheme by the properties of linear space. We also conduct solid theoretical analysis to show the successful verification probabilities under two kinds of attack models, respectively. Finally, we conduct extensive simulations to show the effectiveness of the proposed OM verification scheme when comparing with basic coded edge computing scheme and Decoding Comparison (DC) scheme.

This paper designs an Intelligent Ashcan Management System (IAMS) which is one of the most important part in a smart city. Traditional ashcan management is inefficient and has many disadvantages, because managers cannot obtain the realtime state of ashcans efficiently. As a result, it often happens that ashcans are full but not collected in time. Moreover, in special cases that ashcans fall, catch fire, etc., managers should find these ashcans and handle these emergencies as soon as possible. To manage ashcans efficiently, economically and intelligently, in this paper, we propose an edge computing based IAMS. Specifically, in IAMS, each ashcan has an intelligent user equipment (UE) equipped with sensors to measure distance, temperature, smog and tilt. For data transmission, IAMS uses Narrow Band Internet of Things (NB-IoT), which has advantages of large transmission range and low cost. Combined with edge computing, the collected data can be processed rapidly and managers can obtain the real-time state of each ashcan. Moreover, managers can view global information through web browser and mobile devices. According to the real-time state of ashcans, we also design an IAMS algorithm to get an efficient garbage collection path based on genetic algorithm. Finally, we deployed the proposed IAMS in Soochow University and experimental results show its efficiency and stability.

Influence Maximization (IM) has been extensively applied to many fields, and the viral marketing in today's online social networks (OSNs) is one of the most famous applications, where a group of seed users are selected to activate more users in a distributed cascading fashion. Many prior work explore the IM problem based on the assumption of given budget. However, the budget assumption does not hold in many practical scenarios, since companies might have no sufficient prior knowledge about the market. Moreover, companies prefer a moderately controllable viral marketing that allows them to adjust marketing decision according to the market reaction. In this paper, we propose a new problem, called Controllable social influence maximization (Cosin), to find a set of seed users inside a controllable scope to maximize the benefit given an expected return on investment (ROI). Like the IM problem, the Cosin problem is also NP-hard. We present a distributed multi-hop based framework for the influence estimation, and design a (1/2 + ϵ)-approximate algorithm based on the proposed framework. Moreover, we further present a distributed implementation to accelerate the execution of algorithm for large-scale social networks. Extensive experiments with a billion-scale social network indicate that the proposed algorithms outperform state-of-the-art algorithms in both benefit and running time.

Summary Currently, Mobile edge computing (MEC) is facing a great challenge that is how to make full use of edge resources to provide a seamless support for compute‐intensive latency‐sensitive applications. Prior studies often make a simple assumption that tasks can be executed upon every edge server, but the assumption does not hold in practical scenarios. Because a specific application task often corresponds to a certain service that provides the corresponding running environment, whereas an edge server only has limited resources and cannot offer too many services. How to decide service deployment of so many types of services among multiple edge servers is also a big challenge. To address the challenge, we study dynamic service deployment for latency‐sensitive applications. We first model the long‐term budget‐constrained latency minimization problem as a multi‐slot latency minimization problem based on the Lyapunov framework. By doing this, the hardness of a problem is significantly reduced, since we never require future information to solve the long‐term optimization. Furthermore, we extend our study by joining the task scheduling optimization, where every edge server is fully utilized in an even more efficient collaborative manner. Our extensive experiments show that the proposed algorithms can bring short latency with low cost.

In cognitive radio networks (CRNs), secondary users need to first discover neighbours and form communication links, referred to as the rendezvous process. Rendezvous between any two secondary users can only be achieved on the same channel. However, the nature of the CRN makes this a challenging problem. Specifically in CRN, not only the network is multi- channel, but the channels available at different nodes may be different. While most of the existing works study pair-wise rendezvous and design channel hopping sequence, in this paper, we focus on the performance improvement of the rendezvous process based on the existing channel hopping sequences with multiple users in CRN. We propose a new strategy, called Group Based Strategy (GBS) to achieve the acceleration, which is flexible to incorporate the existing sequence generation algorithms. Our basic idea is to group the encountered users and schedule rendezvous for them. With the purpose to increase rendezvous diversity, other users or groups can join the group if they get the group rendezvous information. Experiments are conducted to evaluate the proposed scheme. Overall, the performance can be improved by more than 50% under symmetric model or asymmetric model using our accelerating strategy.

The continuous demand for content-rich multimedia is pushing for high-speed and secure transmission approaches. In recent years, linear network coding (LNC) has been shown to be a promising technology to improve network throughput, transmission reliability, and information security. In this paper, we study the optimal transmission topology construction and LNC design for a secure multiple-source multicast to deliver the same content with integral link rates, which can be equivalent to the secure multicast problem with a virtual source, i.e., the integer secure virtual-source multicast (ISVM) problem. The objectives of the ISVM problem include the following: 1) satisfy the weakly secure requirements, 2) maximize the secure multicast rate (SMR), and 3) minimize the transmission cost when the SMR is maximized. First, we analyze the necessary and sufficient condition that there exist a transmission topology with integral link rates and a secure LNC that can achieve a given SMR$R$. Then, we model the ISVM problem as an integer linear programming based on the theoretical analysis and design an efficient transmission topology construction algorithm to solve the ISVM problem by utilizing the Lagrangian relaxation and subgradient method. We also analyze the size of finite field required to construct thedeterministic LNCfor a secure virtual-source multicast and the probability that the virtual-source multicast is weakly secure when usingrandom LNCin the ISVM problem. Finally, we design upper and lower bounds for the ISVM problem and conduct extensive simulations to compare the performance of the proposed algorithms with these two bounds.

Summary With the increasing number of users and a huge scale of data, the service providers of Online Social Networks (OSNs) are facing the problem of how to place users' data to multiple servers. Key‐value stores solve the problem based on consistent hashing, and have become a defacto standard. However, random placement manner of hashing cannot preserve social locality, which leads to high intra‐data center traffic and unpredictable response time. Many existing works solve the problem by using graph partitioning algorithms. These works have two drawbacks: First, the social graph is constructed with ordinary pairwise graph that cannot fully reflect multi‐participant interactions often occurring in OSNs. Second, the underlying network topologies of data center have never been considered. This paper investigates the problem of traffic minimization for OSNs data storage. Motivated by maximally preserving both social locality and distance locality, we formulate the problem as two sub‐problems — hypergraph partitioning and partition‐to‐server mapping, and propose a two‐phase data placement (TDP) scheme. Specifically we present two algorithms to solve partition‐to‐server mapping over two widely used network topologies (i.e.,tree and BCube). Evaluations with a large scale Facebook trace show that TDP significantly reduces intra‐data center traffic as well as load balancing across servers. Copyright © 2016 John Wiley & Sons, Ltd.

In recent years, many cross-VM covert channels have been discovered in cloud computing, causing serious security concerns. For such covert channels, some mitigation schemes have been proposed, but usually one mitigation scheme aims at a specific covert channel, which may be inefficient in defending against potential new attacks. In this paper, we propose a generic solution to mitigate the risk of a broad class of timing-based cross-VM covert channels. The design is motivated by our finding that the capacity of most timing-based cross-VM covert channels highly depends on the co-run probability among VMs, where the co-run probability depends not only on how VMs are assigned to servers, but also how VMs are scheduled on a single server, which is related to managing the vCPUs assigned to each VM. We find that the VM co-run probability can be reduced when the number of vCPUs increases, but it also causes extra system overhead in resource utilization. In this paper, we propose a generic VM provisioning and VM scheduling solution to jointly minimize the co-run probability among VMs, meanwhile, maintaining high resource utilization. We experimentally demonstrate that the proposed scheduling algorithm can mitigate the risk of timing-based cross-VM covert channel with lower system overhead. We also conduct simulation of VM provisioning which shows that the proposed solution can achieve the balance between high resource utilization and low risk of information leakage caused by cross-VM covert channels.

For many emerging mobile broadband services and applications, the source and destination are located in the same local region. Consequently, it is very important to design access networks to facilitate efficient local data exchange. In the past few years, most existing studies focus on either the wired or wireless domains. In this paper, we aim to exploit both the wired and wireless domains. Specifically, we consider a Fiber-Wireless access network in which a passive optical network (PON) connects densely deployed base stations. In such a scenario, we propose a novel access scheme, namely, NCDA, where the main idea is to utilize both network coding and device association. To understand the potentials of NCDA, we first formulate a mixed integer nonlinear programming (MINLP) to minimize the weighted number of packet transmissions (WNT), which is related to both the system capacity and energy consumption. We then theoretically analyze the tight upper bounds of the minimal WNT in the PON, which helps us to approximate the original problem by a mixed integer linear programming (MILP). Next, we develop efficient algorithms based on linear programming relaxation to solve the optimal NCDA problem. To validate our design, we conduct extensive simulation experiments, which demonstrate the impact of important network parameters and the promising potentials of the proposed scheme.

The continuous growth of media-rich content calls for more efficient and secure methods for content delivery. In this paper, we will address the optimallinear network coding(LNC) design forsecure unicast streamingagainst passive attacks, under the requirement ofinformation theoretical security. The objectives include 1) satisfying the information theoretical security requirement, 2) maximizing the transmission rate of a unicast stream, 3) minimizing the number of additional random symbols, and 4) minimizing the total bandwidth cost of content delivery. To fulfill the first three objectives, we formulate aninformation theoretically secure unicast streaming(ITSUS) problem, and then solve it by transforming it to a maximum network flow problem with node-capacity constraints. Based on the solution of the ITSUS problem, we develop an efficient algorithm that can find the optimal transmission topology with minimum bandwidth cost in a polynomial amount of time. With the optimal transmission topology, we investigate the design of bothdeterministicLNC and random LNC. For thedeterministicLNC design, we not only prove that it achieves the four objectives but also analyze the size of required finite field. Moreover, for the random LNC design, we analyze the probability that a random LNC scheme satisfies the information theoretical security requirement. Finally, extensive simulation experiments have been conducted, and the results demonstrate the effectiveness of the proposed algorithms.

Distributed storage systems introduce redundancy to protect data from node failures. After a storage node fails, the lost data should be regenerated at a replacement storage node as soon as possible to maintain the same level of redundancy. Minimizing such a regeneration time is critical to the reliability of distributed storage systems. Existing work commits to reduce the regeneration time by either minimizing the regenerating traffic, or adjusting the regenerating traffic patterns, whereas nodes participating the regeneration are generally assumed to be given beforehand. However, real-world distributed storage systems usually exhibit heterogeneous link capacities, and the regeneration time is highly related to the selection of the participating nodes. In this paper, we consider the minimization of the regeneration time by selecting the participating nodes in heterogeneous networks. We propose optimal node selection algorithms respectively for two cases: 1) the newcomer is not given, 2) both the newcomer and the providers are not given. Analysis shows that the optimal regeneration time can be achieved in each case. We then consider the effect of flexible amount of data blocks from each provider on the regeneration time, and apply this observation to enhance our schemes. Experiment results show that our node selection schemes can significantly reduce the regeneration time, especially in practical networks with heterogeneous link capacities, compared with the scheme based on random node selection.

The increasing demand for media-rich content has driven many efforts to redesign the Internet architecture. As one of the major candidates, information-centric network (ICN) has attracted significant attention, where in-network cache is a key component in different ICN architectures. In this paper, we propose a novel framework for optimal cache management in ICNs which jointly considers caching strategy and content routing. Specifically, we propose a cache management framework for ICNs based on software-defined networking (SDN) where a controller is responsible for determining the optimal caching strategy and content routing via linear network coding (LNC). Under the proposed cache management framework, we formally formulate the problem of minimizing the network bandwidth cost by jointly considering caching strategy and content routing with LNC. We develop an efficient network coding based cache management (NCCM) algorithm to obtain a near-optimal caching and routing solution for ICNs. We further develop a lower bound of the problem and conduct extensive experiments to compare the performance of the NCCM algorithm with the lower bound. Simulation results validate the effectiveness of the NCCM algorithm and framework.

In mobile ad hoc networks MANET, deploying a small number of mobile relays can greatly improve the throughput, delay, and security performance. However, in such a hybrid MANET, it is challenging to determine the optimal locations of mobile relays. In this paper, we study a mobile relay placement problem to maximize the network throughput of hybrid MANET with secure network coding capability. Specifically, we first study the maximal throughput of a hybrid MANET, in which the position of each mobile relay is known. For such a special case, we model the maximal throughput problem as a linear programming problem. On the basis of the understanding of this problem, we then formulate the optimal relay placement problem as an integer linear programming problem. Because integer linear programming is too complex to solve for a large MANET, we propose an efficient near-optimal approximation algorithm based on linear programming-relaxation. Finally, we conduct extensive simulation experiments, which demonstrate the effectiveness of the proposed algorithms. Copyright © 2013 John Wiley & Sons, Ltd.

To protect user privacy in wireless mesh networks (WMNs), it is important to address two major challenges, namely: flow untraceability and movement untraceability, which prevent malicious attackers from deducing the flow paths and the movement tracks of mobile devices. For these two privacy requirements, most existing approaches rely on encrypting the whole packet, appending random padding, and applying random delay for each message at every intermediate node, resulting in significant computational and communication overheads. Recently, linear network coding (LNC) has been introduced as an alternative but the global encoding vectors (GEVs) of coded messages have to be encrypted so as to conceal the relationships between the incoming and outgoing messages. In this paper, we aim to explore the potential of LNC to ensure the flow untraceability and movement untraceability. Specifically, we first determine the necessary and sufficient condition, with which the two privacy requirements can be achieved without encrypting either GEVs or message contents. We then design a deterministic untraceable LNC (ULNC) scheme to provide flow untraceability and movement untraceability when the sufficient and necessary condition is satisfied. Finally, we discuss the effectiveness of the proposed ULNC scheme against traffic analysis attacks in WMNs.

In this paper, we will address the modeling and optimal design of linear network coding (LNC) for secure unicast with multiple streams between the same source and destination pair. The objectives include 1) satisfying the weakly secure requirements, 2) maximizing the transmission data rate, and 3) minimizing the size of the finite field. To fulfill the first two objectives, we formulate a secure unicast routing problem and prove that it is equivalent to a constrained link-disjoint path problem. Based on this fact, we develop an efficient algorithm that can find the optimal unicast topology in a polynomial amount of time. With the given topology, we investigate the design of both weakly secure deterministic LNC and weakly secure random LNC. In the designs of deterministic LNC and random LNC, we prove that the required size of the finite field decreases with the decrease of the number of intermediate nodes in the topology. Therefore, to meet the third objective, we formulate a problem to minimize the number of intermediate nodes. We prove that this problem is NP-Complete and develop an approximation algorithm to solve it. Finally, extensive simulation experiments have been conducted, and the results demonstrate the effectiveness of the proposed algorithms.

In this paper, we study the optimal design of linear network coding (LNC) for secure unicast against passive attacks, under the requirement of information theoretical security (ITS). The objectives of our optimal LNC design include (1) satisfying the ITS requirement, (2) maximizing the transmission rate of a unicast stream, and (3) minimizing the number of additional random symbols. We first formulate the problem that maximizes the secure transmission rate under the requirement of ITS, which is then transformed to a constrained maximum network flow problem.We devise an efficient algorithm that can find the optimal transmission topology. Based on the transmission topology, we then design a deterministic LNC which satisfies the aforementioned objectives and provide a constructive upper bound of the size of the finite field. In addition, we also study the potential of random LNC and derive the low bound of the probability that a random LNC is information theoretically secure.

Flow untraceability is one critical requirement for anonymous communication with network coding, which prevents malicious attackers with wiretapping and traffic analysis abilities from relating the senders to the receivers, using linear dependency of the received packets. There have recently been proposals advocating encryptions on the Global Encoding Vectors (GEV) of network coding to thwart such attacks [1], [2]. Nevertheless, there has been no exploration of the capability of networking coding itself, to constitute more efficient and effective algorithms which guarantee anonymity. In this paper, we design a novel, simple, and effective linear network coding mechanism (ALNCode) to achieve flow untraceability in a communication network with multiple unicast flows. With solid theoretical analysis, we first show that linear network coding (LNC) can be applied to thwart traffic analysis attacks without the need of encrypting GEVs. Our key idea is to mix multiple flows at their intersection nodes by generating downstream GEVs from the common basis of upstream GEVs belonging to multiple flows, in order to hide the correlation of upstream and downstream GEVs in each flow. We then design a deterministic LNC scheme to implement our idea, by which the downstream GEVs produced are guaranteed to obfuscate their correlation with the corresponding upstream GEVs. We also give extensive theoretical analysis on the intersection probability of GEV bases and the influential factors to the effectiveness of our scheme, as well as the algorithm complexity to support its efficiency.

In recent years network coding has attracted significant attention in telecommunication. The benefits of network coding to a communication network include the increased throughput as well as secure data transmission. The purpose of this work is to design secure linear network coding against wiretap attack. The problem is to maximize the transmission data rate of multiple unicast streams between a pair of source and destination nodes, under the condition of satisfying the weakly secure requirements. Different from most existing research on network coding that designs the network coding scheme based on a given network topology, we will consider the integrated network topology design and network coding design. Such an integrated approach has not been reported by other researchers. In this paper, we formally introduce the problem, prove the problem is computational intractable, and then develop efficient heuristic algorithms. We first try to find the transmission topology that is suitable for network coding. Based on the topology, we design linear network coding scheme that is weakly secure. We conduct simulations to show that the proposed algorithms can achieve good performance.

Linear network coding is a promising technology that can maximize the throughput capacity of communication network. Despite this salient feature, there are still many challenges to be addressed, and security is clearly one of the most important challenges. In this paper, we will address the design of secure linear network coding. Specifically, we will investigate the network coding design that can both satisfy the weakly secure requirements and maximize the transmission data rate of multiple unicast streams between the same source and destination pair, which has not been addressed in the literature. In our study, we first prove that the secure unicast routing problem is equivalent to a constrained link-disjoint path problem. We then develop efficient algorithm that can find the optimal unicast topology in a polynomial amount of time. Based on the topology, we design deterministic linear network code that is weakly secure and can be constructed at the source node. And finally, we investigate the potential of random linear code for weakly secure unicast and prove the low bound of the probability that a random linear code is weakly secure.

Optical-Wireless (FiWi) access network is a newly emerged access network architecture which integrates passive optical networks (PONs) with wireless mesh networks (WMNs) to provide the ubiquitous, low cost, high bandwidth last mile Internet access. Though the PON subnetwork of FiWi network can provide high bandwidth, the interference in the wireless subnetwork still limits the throughput of FiWi network if all traffic goes online to the Internet. However, when peer-to-peer communication from one wireless client to another wireless client is introduced, the proposed integration of PONs and WMNs can significantly improve the network throughput. In traditional WMNs, peer-to-peer communication from one wireless client to another wireless client is carried in the wireless network, which is subject to interferences in wireless communications. In FiWi network, peer-to-peer communication can be carried through the wireless-optical-wireless mode in which the traffic is sent from the source wireless client to its nearest ONU, which is then sent to the ONU close to the destination wireless client through the PON subnetwork and then delivered to the destination wireless client. Such wireless-optical-wireless communication mode introduced by FiWi networks can sustain the interference in wireless subnetwork, thus improving the network throughput. This paper aims to study the network throughput gain in FiWi network subject to peer-to-peer communications and parameters which can affect the network throughput gain. We first have a fair modeling of FiWi networks and traditional WMNs. We then present an LP based routing algorithm for FiWi networks. Extensive simulations have been carried to study the network throughput gain in FiWi networks subject to peer-to-peer communications compared with traditional WMNs. The work provides insightful observations for fully utilizing advantages brought by the integration of PONs and WMNs in FiWi networks.

In a service overlay network, the services provided by different service providers might span multiple Internet domains. A service provider failure may cause significant performance deterioration. Thus, it is desirable to provide fault tolerant service composition solutions such that the service composition can be switched to the backup service composition solution in case of a service provider failure. To provide 100% protection against a single service provider failure, fault tolerant service composition essentially requires to partition service providers into two disjoint sets, each of them can provide a service composition solution. We study a generalized fault tolerant service composition which aims to find two service composition solutions for each request to minimize the number of shared service providers. Subject to such a primary objective, we also aim to minimize the total service composition cost. We firstly prove that the problem is NP-Complete, and formulate the problem as an integer linear program. We then propose heuristic algorithms to efficiently solve the problem. Simulation results demonstrate the effectiveness of the proposed heuristic algorithms.