As networks expand and evolve, their increasing complexity introduces significant security challenges, necessitating robust Intrusion Detection Systems (IDS). Traditional IDS often struggle to detect sophisticated cyberattacks due to their reliance on raw network data and primitive feature extraction techniques. To address these limitations, we propose an Image-enhanced Encoder-based Deep Learning scheme for Intrusion Detection Systems (IEDL-IDS), which combines image-based transformation and encoder-based feature extraction to detect complex intrusion patterns in network traffic. Technically, IEDL-IDS consists of three sequential modules. The preprocessing module transforms raw network traffic into RGB images to reveal temporal and spatial patterns. Thereafter, the encoder module processes the RGB images to extract latent features. Finally, the classifier module utilizes the latent features for high-accuracy intrusion detection. Notably, IEDL-IDS is highly flexible, as its built-in classifier can be easily replaced with any neural network-based model. This feature highlights the adaptability of IEDL-IDS in balancing detection performance with resource constraints, thereby meeting the diverse needs of network security applications. Our experimental results demonstrate that IEDL-IDS outperforms the state-of-the-art IDS schemes. On the CICIoT dataset, IEDL-IDS achieves a classification accuracy of 99.91% for binary classification and 95.66% for multi-class classification. Similarly, it attains 99.61% and 98.25% accuracy on the NSL-KDD dataset, and 99.27% and 96.42% on the ToN_IoT dataset, for binary and multi-class tasks, respectively. Notably, despite its high detection performance, IEDL-IDS maintains a competitive computational footprint, making it a practical and scalable solution for real-world intrusion detection deployments.

In Industrial Internet of Things (IIoT) applications, data from a variety of sensors is collected continuously to monitor and manage industrial processes. However, missing data caused by network disruptions, sensor malfunctions, or hardware failures seriously affects the performance of datadriven models, leading to unreliable predictions and increased maintenance costs. To address this challenge, we propose Decayaware Transformer-enhanced GAIN (DT-GAIN), a novel imputation framework for multivariate time-series data in IIoT applications. DT-GAIN extends and enhances the Generative Adversarial Imputation Nets (GAIN) framework by integrating the Transformer architecture, which captures long-range dependencies essential for accurate imputation of multivariate timeseries industrial data. In addition, DT-GAIN incorporates a timedecay mechanism that accounts for temporal irregularities by weighing observations based on their recency, thereby improving the ability of the proposed method to handle varying intervals between observations and missing values. In our research, we thoroughly compare DT-GAIN with state-of-the-art imputation methods, including LSTM-based GAIN (L-GAIN), Transformerbased GAIN (T-GAIN), the original GAIN, and SAITS. Our experimental results indicate that DT-GAIN outperforms the methods under investigation in terms of Root Mean Squared Error (RMSE) across various missing rates, particularly excelling in high-missing-data scenarios.

Intrusion Detection Systems (IDSs) play a critical role in cybersecurity by identifying and mitigating network attacks. However, adversarial attacks can exploit vulnerabilities in IDSs based on machine learning (ML) techniques, causing misclassification of malicious traffic. Traditional adversarial approaches focus on IDS evasion but neglect the functional integrity of malicious traffic, limiting practical applicability. In this paper, we propose a realism-preserving adversarial traffic generation scheme, AWGAN, which combines Wasserstein GAN (WGAN) with an autoencoder-based refinement process. The proposed scheme ensures that adversarial traffic retains its malicious characteristics while effectively evading IDS detection. In our research, we evaluate AWGAN using the CICIDS 2017 dataset and compare its performance against WGAN in two scenarios: modifying all features and modifying only non-functional features. Our experimental results demonstrate that AWGAN achieves an excellent balance between IDS evasion and traffic fidelity.

The proliferation of computation-intensive applications, such as autonomous driving, has urged mobile devices to alleviate their local computation pressure using external computing resources. As a promising solution, Multi-access Edge Computing tackles this problem by offloading computational tasks from mobile devices to edge servers. However, existing offloading schemes suffer from two fundamental limitations. First, they lack built-in measures to prevent deadline misses. For safety-critical applications, including autonomous driving, a deadline miss could result in catastrophic consequences. Second, existing schemes typically update offloading policies periodically. Namely, a policy based on the current system state is generated for a time window consisting of multiple time slots. Since system states could change from one time slot to the next one, the generated policy might not work well during the entire window. In this article, we propose a novel offloading scheme for safety-critical applications, Constrained Reinforcement Learning-based Offloading (CRLO). With CRLO, a safety layer is added to the learning-based policy generator, which effectively eliminates deadline misses. Furthermore, a long-sequence forecasting model, Informer, is utilized to predict temporally dependent system states, which helps to generate appropriate offloading policies. Our experimental results indicate that CRLO outperforms existing schemes in terms of deadline satisfaction and task completion time.

In our digitally interconnected world, the demand for robust security measures has become increasingly apparent, given the escalating threat of cyberattacks on the Internet. Intrusion Detection Systems (IDS) have emerged as vital safeguards for Internet network infrastructure. Despite the significant advancements in IDS over the past decades, there remains much room for improvement, especially with recent advances in machine learning and deep learning. In this paper, we propose a Misclassified sample based Ensemble transfer learning framework for IDS (ME-IDS) in order to effectively detect malicious intrusions. Technically, ME-IDS employs frequency encoding to handle categorical features and utilizes a feature selection method to mitigate the curse of dimensionality. In addition, it leverages three hyper-parameter-tuned variants of a transfer learning model in its ensemble learning stage, ultimately resulting in high detection accuracy. Our experimental results based on a publicly available IDS dataset, UNSW-NB15, indicate that ME-IDS leads to an impressive accuracy of 99.72%, significantly outperforming the state-of-the-art detection systems.

Cell-Free Massive MIMO (CF-mMIMO) is regarded as one of the most promising wireless technologies for future applications. It is expected to tackle the inter-cell interference problem with traditional cellular networks in order to achieve high quality of service (QoS). The coexistence of Human-Type Communication (HTC) and Machine-Type Communication (MTC) poses a challenge to today’s and future communication technologies, including CF-mMIMO. However, there have been few studies on the support of CF-mMIMO for HTC/MTC coexistence. In this paper, we present a comprehensive resource allocation mechanism for CF-mMIMO with HTC/MTC coexistence, which consists of two components. First, to handle the device activity detection issue caused by the absence of orthogonal pilot sequence, we propose a pilot assignment scheme based on the continuous method and a device activity detection scheme based on the spatial separation characteristics of CF-mMIMO, aiming at reducing the activity detection error probability. Second, we formulate a multi-objective optimization problem (MOOP) and devise a power control method based on MOOP to deal with the conflict caused by the different service requirements of HTC and MTC. Numerical results show that the proposed pilot assignment scheme outperforms the comparable pilot assignment schemes in terms of activity detection error probability. In addition, combined with the proposed pilot assignment scheme, the devised MOOP power control method leads to higher HTC normalized sum rate and MTC energy efficiency than the comparable power control methods.

Machine Learning (ML) has been widely applied to successfully address a variety of different problems across diverse domains, such as robotics, healthcare, and finance. However, high-complexity ML algorithms often require overlong computation time, which significantly impacts their feasibility. Distributed Machine Learning (DML) has been used to tackle the slow computation problem with high-complexity ML algorithms. Nevertheless, with DML, the computation results from all participating computing devices need to be collected in order to complete an ML task. When part of the participating devices, known as the stragglers, cannot return their results in time, the overall computation time will be extended. Distributed Coded Machine Learning (DCML) is a promising solution to mitigate the negative impact of the stragglers. With DCML, redundancy is injected into an ML task so that only a subset of the results from participating devices are required to finish the ML task. In DCML, how to select proper participating devices, referred to as workers, and how to allocate appropriate workloads to the selected workers are two challenging problems. In this paper, we consider a DCML scenario where numerous computing devices are available for an ML task. These devices are willing to offer their computation capacity in exchange for compensation. To encourage the computing devices to participate in the distributed computation, a reverse auction-based incentive mechanism is employed. With the objective of minimizing both the completion time of the ML task and the compensation for participating devices, we propose a Deep reinforcement learning based workload Allocation and worker Selection scheme for DCML, DAS. To our knowledge, this is the first attempt to simultaneously tackle both the workload allocation and worker selection issues in DCML. Our experimental results indicate that DAS outperforms the state-of-the-art schemes in terms of completion time and compensation.

Industrial Internet of Things (IIoT), a branch of the Internet of Things (IoT) for the industrial sector, plays a vital role in integrating industrial equipment, monitoring equipment health, and improving the overall efficiency of industrial production process. Accurately predicting the remaining useful life (RUL) of IIoT equipment is a crucial task in prognostic health management (PHM), which analyzes the degradation trend of industrial equipment to schedule maintenance activi-ties in a timely manner. Artificial Intelligence (AI) techniques, such as Recurrent Neural Networks (RNNs) and Long Short-Term Memory Networks (LSTMs), have been widely used in RUL prediction. However, these techniques face challenges in incorporating long-sequence information to capture degradation trends and predicting long-term RUL values. In this paper, we propose an Informer-based method, Co-Informer, for long-term RUL prediction. Co-Informer utilizes a series of sensor data to provide the predicted RUL values during an upcoming time window. In our research, extensive experiments are carried out with C-MAPSS, a widely used turbofan engine degradation dataset provided by NASA. Our experimental results indicate that Co-Informer outperforms the state-of-the-art schemes for RUL prediction in terms of Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE).

Due to the escalating utilization of communication networks and the prevailing occurrence of cyber attacks, intrusion detection systems (IDSs) have emerged as imperative components in network security. Machine learning (ML) and deep learning (DL) based IDSs have gained popularity due to their detection capability and adaptability. However, this type of schemes are susceptible to adversarial attacks, which involve minor perturbations to attack features causing misclassification. Autoencoders (AEs) have proven effective in mitigating adversarial attacks in computer vision, but their capacity for enhancing IDSs remains relatively unexplored. In this paper, we focus on the use of AEs to detect adversarial network flows. Specifically, we propose an AE-enhanced IDS (AE-IDS) that leverages the power of AEs to improve the robustness of IDSs against adversarial attacks. Our experimental results indicate that AE-IDS outperforms the baseline schemes under investigation in terms of accuracy and detection rate. We believe that AE-IDS showcases the potential of using AEs to enhance the robustness of IDSs, providing improved security against sophisticated and evolving cyber threats.

Over the past decade, the Electric Vehicle (EV) market has witnessed remarkable expansion. Nevertheless, apprehensions regarding prolonged charging durations frequently contribute to range anxiety. Battery Swap Station (BSS) is a promising solution to the range anxiety problem. Typically, when an EV arrives at a BSS, the depleted battery in the EV is replaced with a fully charged one, then the depleted battery is charged at the maximum speed. In this paper, we propose a novel battery swapping/charging scheme for BSS, Reinforcement Learning based Charging (RLC), to serve as many EVs as possible and minimize the total electricity cost. Specifically, with RLC, for the EVs that do not need full batteries, partially charged ones will be provided. To reduce the electricity cost whenever possible, RLC tries to shift the charging time of a battery to a low-electricity-price period. Technically, RLC uses Deep Deterministic Policy Gradient (DDPG), a Deep Reinforcement Learning (DRL) algorithm, to optimize the charging strategy for the batteries in BSS. Our experimental results indicate that RLC outperforms the existing charging/swapping schemes in terms of battery service rate and total electricity cost.

Intrusion detection system (IDS) has become an essential component of modern communication networks. The major responsibility of an IDS is to monitor communication networks for malicious attacks or policy violations. Over the past years, machine learning (ML) and deep learning (DL) have been employed to construct effective IDS. However, recent studies have shown that the reliability of ML/DL-based IDS is questionable under adversarial attacks. In this paper, we propose a framework based on Wasserstein generative adversarial networks (WGANs) to generate adversarial traffic to evade ML/DL-based IDS. Compared with the existing adversarial attack generation schemes, the proposed framework only involves highly restricted modification operations and the output of the framework is carefully regulated, ultimately preserving the type of the intended malicious traffic. In our research, we validated the effectiveness of the proposed framework by launching adversarial attacks of varied types against multiple ML/DL-based IDS. Our experimental results in terms of detection rate and evasion increase rate indicate that the proposed framework can completely deceive the IDS based on Naive Bayes (NB), Logistic Regression (LR), Random Forest (RF), and Recurrent Neural Network (RNN). In addition, the framework can partially evade the IDS based on Decision Tree (DT), Gradient Boosting (GB), and Multilayer Perceptrons (MLP).

Over the past years, Distributed Machine Learning (DML) has been employed to tackle the high complexity problem with many Machine Learning (ML) algorithms. With DML, the original computation task involved in an ML algorithm is first split into multiple subtasks, which are forwarded to a group of computing devices in a distributed environment. Thereafter, the subtask results are collected in order to arrive at the final result for the original task. Despite the advantages of DML, the overall computation time can be seriously increased if some subtask results cannot be collected in a timely manner due to device malfunctions or network glitches. Recently, Distributed Coded Machine Learning (DCML) has been proposed to mitigate the problem with DML. Specifically, DCML employs coding techniques to inject redundancy into the original computation task. With the injected redundancy, DCML does not need to collect all subtask results to construct the result for the original task. So far, how to split the original task and thereafter assign an appropriate workload to each computing device in DCML has been a challenging problem. In this paper, we propose a novel work allocation scheme for DCML, DWA, to tackle the challenging problem. Our experimental results indicate that DWA outperforms the existing DCML schemes.

Over the past years, augmented reality (AR) based on mobile phones has gained great attention. When multiple phones are used in AR applications, collaborative simultaneous localization and mapping (SLAM) is considered one of the enabling technologies, i.e., multiple mobile phones complete the localization and mapping through collaboration. However, the state-of-the-art collaborative SLAM systems not only suffer from the delays introduced by a high-complexity graph optimization problem, but also may exhibit varying levels of accuracy across dissimilar environments or different types of mobile devices. In this paper, we propose a scalable and robust collaborative SLAM system, point-line-based Collaborative SLAM (ColSLAM). Technically, ColSLAM includes two innovative features that help achieve satisfactory scalability and robustness. First, a mapping cacher (MC) is designed for each agent on the server, which uses global keyframes to detect loop closures, updates the cached local map, and quickly responds to the agent's pose drifts. With MC, each agent's local pose is corrected using global knowledge in real-time. Secondly, to improve the robustness performance, ColSLAM employs point-line-fusion-based Visual Inertial Odometry (VIO), point-line-fusion-based NetVLAD loop detection, and an enhanced geometric verification and relative pose calculation method called PNPL. Empirical evaluations based on the EuRoc dataset and real degenerate environments demonstrate that ColSLAM outperforms the existing collaborative SLAM systems in terms of accuracy, robustness, and scalability.

Wireless data aggregation (WDA) is a pivotal enabling technique for Internet of Things (IoT). Recently, an emerging WDA technique, over-the-air computation (AirComp), has been proposed to perform fast data aggregation, with enhanced spectrum utilization and shortened transmission delay. AirComp leverages the superposition property of a wireless multiple access channel to accomplish functional computation of data, which can support the model aggregation in distributed machine learning and fusion of sensing data. In this paper, we consider a multi-cell AirComp system with spectrum sharing, in which every user in a multi-cell network shares a common part of the spectrum. The mean squared error (MSE) is used as the performance metric to quantify the computation accuracy at each access point (AP). With the aim of coordinating the MSEs among multiple APs, two different objective functions, weighted sum MSE and proportional fairness, are adopted. Accordingly, to minimize the weighted sum MSE, the block coordinate descent (BCD) method is employed. The analytical solutions are also provided. To minimize the proportional fairness, a tractable solution is developed, which is based on the successive convex approximation (SCA) technique. The effectiveness of the proposed schemes is validated by our numerical results.

Mobile Edge Computing (MEC) is an emerging computation paradigm that enables mobile devices to offload computation-intensive tasks to edge servers in order to speed up task processing. However, there are still a few challenging problems with MEC before it can be widely adopted. For instance, due to storage and computation constraints, only a limited set of services can be deployed on an edge server. If a mobile device requires a specific service, it can only offload its task to the edge servers that provide the corresponding service. In addition, a task might be composed of several dependent subtasks. The subtask that depends on other subtasks cannot be offloaded until the prerequisite subtasks are completed. Finally, due to the heterogeneity of edge servers, offloading a task to different edge servers could lead to varied energy consumption performance. Few studies consider both of these scenarios and to fulfill the gap, we investigate the task offloading problem in MEC under the dependency and service caching constraints. Specifically, we propose a heuristic algorithm named Table Based Task Offloading Algorithm (TBTOA), which is capable of predicting the impact of offloading decisions. Our experimental results indicate that TBTOA outperforms the existing offloading schemes for MEC in terms of makespan and energy consumption.

User-Centric Cell-Free Massive MIMO (UC CF-mMIMO) is a promising technology to improve the performance of mobile networks. With UC CF-mMIMO, each access point (AP) only provides services for a limited number of users. However, which AP should be selected for a user is still a challenging problem. In this paper, we propose an AP selection strategy for UC CF -mMIMO, which utilizes congestion games to balance user data rate and required AP fronthaul capacity. Our numerical results indicate that the proposed AP selection strategy outperforms the existing approaches in terms of required AP fronthaul capacity while guarantees user data rate.

In this article, we consider a parking lot that manages the charging processes of its parked electric vehicles (EVs). Upon arrival, each EV requests a certain amount of energy. This request should be fulfilled before the EV’s departure. It is of critical importance to coordinate the EVs’ charging rates to smooth out the load profile of the parking lot because inappropriate charging rates can lead to sharp spikes and fluctuations on the load profile, imposing negative effects on the power grid. Meanwhile, empirical studies show that many parking lots exhibit statistical patterns on EV dynamics. For example, the bulk of EVs arrives during rush hours. Therefore, in this article, we incorporate such patterns into charging rate coordination. Although the statistical patterns can be summarized from historical data, they are difficult to be analytically modeled. As a result, we adopt a model-free deep reinforcement learning approach. We also take the latest continuous charging rate control technology into consideration. The decision variables are thus continuous and a policy gradient algorithm is needed to perform reinforcement learning. Technically, we first formulate the problem as a Markov decision process (MDP) with unknown state transition probabilities. To further derive a deep policy gradient algorithm, the challenge lies in the inconsistent and state-dependent action space of the MDP model, due to the constraint to satisfy EVs’ energy demands before their scheduled departure. To tackle the challenge, we design a customized model for neural network training by extending the action space to be consistent and state independent, and revise the reward function to penalize the neural network output if it is beyond the action space of the original MDP model. With this customized model, we then develop a deep policy gradient algorithm based on the proximal policy gradient framework. Numerical results show that our algorithm outperforms the benchmarks.

Mobile edge computing (MEC) is a promising cloud computing convergence paradigm that improves the quality of services and reduces the traffic load on the core network. By deploying base stations (BSs) endowed with computing resources on the edge of network, MEC system can response to the user requests more efficiently and faster than the traditional cloud center which is far away from the end users. However, limited by the capacity of the computing resource and radio resource on a BS, only a few services can be placed on each BS, and the number of users that each BS can serve in each time slot is also limited. Moreover, in a densely deployed network, service placement decisions of adjacent BSs are influenced by each other, because their communication ranges are overlapped. Thus, service provider (SP) who deploys the BSs on the MEC system has to coordinate service placement among BSs so as to maximize its profit. Moreover, there are different kinds of users on the MEC system, some of them would like to pay more for higher priority to acquire computing resource. Therefore, SPs need to design a pricing method to distinguish users' priorities to get higher profit. In this paper, we will design a novel method to coordinate service placement among BSs and propose a pricing method that considering the difference of users. Our service placement method can theoretically achieve optimal result in single BS. And the simulation results also indicate that our service placement method achieve better performance in the cluster than the existing methods.

With the proliferation of wireless networks, such as WiFi and LTE/5G, Mobile Edge Computing (MEC), is expected to be a promising solution to the resource constraint problem in mobile devices. Technically, MEC is composed of two types of devices: resource-hungry end devices and resource-rich base stations equipped with edge servers. Despite the popularity of MEC, efficient task offloading and resource allocation have been two challenging problems to be tackled. In this paper, we propose an innovative scheme, HTR, that jointly solves the task offloading and resource allocation problem in MEC. Specifically, the problem of task offloading and resource allocation is formulated as a Mixed Integer Non-Linear Programming (MINLP) problem. To reduce the computation complexity of the solution to the MINLP problem, HTR decouples the MINLP problem into two sub-problems: one of them solves the resource allocation problem while the other tackles the task offloading issue. With this carefully-designed approach, both the task offloading and resource allocation problem could be solved with light computation complexity. Our experiment results indicate the HTR outperforms the existing task offloading/resource allocation schemes.

With the development of wireless network technologies, such as LTE/5G, Mobile Cloud Computing (MCC) has been proposed as a solution for mobile devices that need to carry out high-complexity computation with limited resources. Technically, with MCC, high-complexity computation tasks are offloaded from mobile devices to cloud servers. However, MCC does not work well for time-sensitive mobile applications due to the relatively long latency between mobile devices and cloud servers. Mobile Edge Computing (MEC), is expected to solve the problem with MCC. With MEC, edge servers, instead of cloud servers, are deployed at the edge of the network to provide offloading services to mobile devices. Since edge servers are much closer to mobile devices, the resulting latency is significantly lower. Despite the advantages of MEC over MCC, edge servers are not as resource-abundant as cloud servers. Consequently, when many offloaded tasks arrive at an edge server, admission control needs to be in place to arrive at the best performance. In this paper, we propose a Deep Reinforcement Learning (DRL) based admission control scheme, DAC, to maximize the system throughput of an edge server. Our experimental results indicate that DAC outperforms the existing admission control schemes for MEC in terms of system throughput.

In this paper, we propose an innovative framework to improve the physical layer security of cognitive radio nonorthogonal multiple access (CR-NOMA) networks. Specifically, we consider a cooperative spectrum-sharing mechanism, where a cognitive transmitter serves as a relay and assists primary/cognitive transmissions using the NOMA principle in the presence of a passive eavesdropper. Technically, we propose a new cooperative jamming scheme, where the primary/cognitive receivers and the primary transmitter are recruited as jammers to transmit artificial noise in order to intentionally confuse eavesdropper. Under a practical assumption that only the statistical channel state information of eavesdropper is available, the secrecy outage probability (SOP) is used as the performance metric. An optimization problem of maximizing the minimum confidential information rate among the primary and cognitive receivers is formulated. We devise a successive convex approximation based power allocation algorithm to efficiently solve the non-convex optimization problem. Our simulation results indicate that the proposed scheme outperforms the orthogonal multiple access based schemes in terms of minimum confidential information rate.

Energy consumption is one of the most important issues for mobile devices such as smartphones and laptops. For mobile devices that execute multiple computation-intensive or delay-sensitive applications simultaneously, Mobile Edge Computing (MEC) based offloading provides a promising solution to the energy problem. However, blindly offloading all tasks to MEC servers is not the best choice because transferring a simple task to a MEC server via wireless networks might consume more energy than processing the task locally. In addition, Dynamic Voltage and Frequency Scaling (DVFS) could be utilized to reduce the energy consumption associated with locally processed tasks by appropriately lowering CPU frequency. In this paper, we propose a realtime reinforcement learning based offloading scheme, RRLO, which is based on both MEC-based offloading and DVFS-based energy consumption reduction. Technically, RRLO jointly learns the optimal offloading policy and DVFS-based scheduling method. Depending on the workload and network condition, RRLO not only determines whether a task should be offloaded to a MEC server, but also selects the best DVFS method used to schedule local tasks. Our simulation results indicate that RRLO outperforms the existing MEC-based offloading schemes.

In this paper, we investigate a NOMA assisted multi-antenna two-way relay system, where users apply NOMA to support bidirectional superposition transmission with the aid of multiple relays. Specifically, we propose a multiple-access broadcast NOMA strategy together with a joint antenna-and-relay selection scheme to enhance its transmission reliability. Analytical closed-form expressions for the outage probability and diversity order are derived to evaluate the system performance achieved by the proposed strategy with the joint antenna-and-relay selection scheme. Based on the analytical result, we further optimize the power allocation to reduce the outage probability. Our numerical results show that the proposed mechanism significantly outperforms existing benchmark strategies in terms of the outage probability.

In this paper, we investigate a non-orthogonal multiple access (NOMA) assisted multi-antenna two-way relay system, where multi-antenna users apply NOMA to support bidirectional superposition transmission via multiple multi-antenna relays. Specifically, we propose two cooperative strategies, namely multiple-access broadcast NOMA and time division broadcast NOMA. For each of the two cooperative strategies, we devise a joint antenna-and-relay selection scheme to enhance the transmission reliability. Analytical expressions for the outage probability and diversity order are derived to evaluate the system performance achieved by the proposed cooperative strategies with the corresponding joint antenna-and-relay selection schemes. To further reduce the outage probability, we use the derived analytical results as objective functions to optimize the transmit power allocation under both cooperative strategies. Finally, extensive simulations are carried out to validate the accuracy of the derived analytical results. Our simulation results indicate that the proposed strategies significantly outperform existing benchmark strategies in terms of outage probability and diversity order.

This paper designs a novel opportunistic adaptive non-orthogonal multiple access (OA-NOMA) strategy, where a base station (BS) employs NOMA to serve a near user (NU)-far user (FU) pair opportunistically scheduled from M NUs and K FUs. In particular, the NOMA transmission to the scheduled NU-FU pair adaptively operates in one of two modes: Direct NOMA mode, in which the BS directly serves the scheduled NU-FU pair with using NOMA; Cooperative NOMA mode, in which the scheduled NU receives the messages intended by both scheduled users from the BS, and then forwards the message intended by the scheduled FU. For the OA-NOMA strategy, a scheduling candidate acquisition method and a probabilistic user pair scheduling scheme are proposed to guarantee the transmission reliability and improve the scheduling fairness, respectively. To evaluate the scheduling fairness, we develop a max-min fairness criterion and show that the OA-NOMA strategy approximately achieves max-min fairness. The reliability of the OA-NOMA strategy is also evaluated in terms of outage probability and diversity order. For the outage probability, we derive an approximate expression and numerically verify its tightness. For the diversity order, we show that the proposed OA-NOMA strategy achieves a diversity order of M.

Location-Based Service (LBS) has been widely deployed. One of the key components of LBS is the positioning algorithm. For outdoor environments, the Global Positioning System (GPS) has been used as the default positioning scheme. However, GPS requires the line of sight to the satellites. When the line of sight is blocked, GPS simply stops working. To tackle the problem with GPS, varied WiFi-based positioning schemes have been proposed. However, the positioning precision of the existing methods is not satisfactory. In this paper, we present a high-precision positioning scheme named Deep Learning based Positioning (DLP). Technically, DLP utilizes both Received Signal Strength Indicator (RSSI) and Channel State Information (CSI) to improve the positioning precision. In detail, a deep neural network is used to model the received RSSI and CSI measurements, which leads to satisfactory positioning accuracy. Our experimental results acquired from a large-scale testbed indicate that DLP outperforms the existing positioning schemes in terms of positioning precision.

Mobile Edge Computing (MEC) is a burgeoning paradigm that pushes data and services away from remote clouds to distributed Base Stations (BSs) equipped with MEC servers, which are deployed by Service Providers (SPs) at the edge of cellular networks. Normally, a SP prefers to use its own BSs, instead of those deployed by other SPs, to provide data and storage services. This can not only improve the quality of user experience but also increase its own revenue. In a densely deployed MEC network where a User Equipment (UE) tends to be covered by multiple BSs from varied SPs, how to allocate the resources in the BSs to provide the best service is a challenging problem. In this paper, we propose a novel resource allocation scheme, Decentralized Multi-SP Resource Allocation (DMRA), for densely-deployed MEC networks in order to maximize the total profit of all SPs and provide high-quality services. Our experimental results indicate that the proposed scheme outperforms the existing resource allocation algorithms for MEC.

The papers in this special issue focus on trustworthiness in multimedia communications. Recently, social multimedia content is being delivered to users with a high quality of experience (QoE) with the advance of multimedia technologies and social networks. However, as a huge amount of social users have various demands to exchange and share multimedia content with each other, it becomes a new challenge for the current social multimedia analytics and delivery to deal with the various attacks perpetrated by malicious users or through spam contents. Therefore, the trust and risk management for social multimedia content based on the social tie of users become of prime importance to face the unpredicted threats and subsequent damage. This Special Section aims to provide a premier forum for researchers working on the trust-based social multimedia analytics and delivery. It also provides the opportunity for both academic and industrial researchers to discuss recent results and provide solutions to the above-mentioned challenges.

Multicasting is an important task in Wireless Sensor Networks (WSNs). The minimum energy multicasting problem has been studied intensively. In duty-cycled WSNs, switching between the active and sleep state makes this problem more complicated. In this paper, the problem of minimum energy multicasting with adjustable transmission power in duty-cycled WSNs is studied. Specifically, we propose a novel algorithm named ATPM, with which an extended graph is first constructed, then a multicast tree and the transmission schedule are generated. Our experimental results indicate that the proposed algorithm outperforms the state-of-art algorithms in terms of energy cost.

Sweep coverage is an important problem in Wireless Sensor Networks (WSNs). Technically, sweep coverage makes use of mobile sensor nodes that move around to collect sensing data from Points of Interest (POIs) at a low cost. Since POIs can often be sensed remotely, mobile sensor nodes do not have to arrive at the location of POIs to gather sensing data. Sweep radius, the maximum distance between a mobile sensor node and a POI that enables sensing, is an important factor in sweep coverage planning. In addition, because mobile sensor nodes are typically powered by batteries, they tend to have a limited lifetime. To continue the coverage, mobile sensor nodes have to periodically return to the base station to replenish their energy. In this paper, sweep coverage based on sweep radius and energy limitation is formulated as the (t, T, R)-SCBR problem. To tackle the (t, T, R)-SCBR problem, a centralized algorithm (i.e. CPS) and a distributed algorithm (i.e. DPP) are proposed. Through extensive simulations, we found that the proposed algorithms significantly outperform the existing schemes.

Wireless Sensor Networks(WSNs) are expected to be used in a variety of different applications. One of the most important problems in WSNs is sweep coverage. Sweep coverage utilizes mobile sensor nodes to monitor Points Of Interests (POIs). Thanks to the mobility, sweep coverage can cover more POIs using fewer sensor nodes. In practice, mobile sensor nodes can often collect the data from POIs at a distance via wireless communication. Namely, mobile sensor nodes do not have to reach the physical location of each POI in order to collect the sensing data. Consequently, the route required to provide a sweep coverage can be significantly shortened if the communication range of POIs can be fully utilized. In this paper, we first define the novel problem of Sweep Coverage Based on POI Communication Range. Then we present a centralized and a distributed algorithm, RS and DRS, to solve the novel sweep coverage problem. The performance of the proposed algorithms is evaluated via extensive simulations.

Effective file transfer between vehicles is fundamental to many emerging vehicular infotainment applications in the highway Vehicular Ad Hoc Networks (VANETs), such as content distribution and social networking. However, due to fast mobility, the connection between vehicles tends to be short-lived and lossy, which makes intact file transfer extremely challenging. To tackle this problem, we presents a novel Cluster-based File Transfer (CFT) scheme for highway VANETs in this paper. With CFT, when a vehicle requests a file, the transmission capacity between the resource vehicle and the destination vehicle is evaluated. If the requested file can be successfully transferred over the direct Vehicular-to-Vehicular (V2V) connection, the file transfer will be completed by the resource and the destination themselves. Otherwise, a cluster will be formed to help the file transfer. As a fully-distributed scheme that relies on the collaboration of cluster members, CFT does not require any assistance from roadside units or access points. Our experimental results indicate that CFT outperforms the existing file transfer schemes for highway VANETs.

Many distributed applications, such as BitTorrent, need to know the distance between each pair of network hosts in order to optimize their performance. For small-scale systems, explicit measurements can be carried out to collect the distance information. For large-scale applications, this approach does not work due to the tremendous amount of measurements that have to be completed. To tackle the scalability problem, network coordinate system (NCS) was proposed to solve the scalability problem by using partial measurements to predict the unknown distances. However, the existing NCS schemes suffer seriously from either low prediction precision or unsatisfactory convergence speed. In this paper, we present a novel distributed network coordinate system (DISCS) that utilizes a limited set of distance measurements to achieve high-precision distance prediction at a fast convergence speed. Technically, DISCS employs the innovative robust nonnegative matrix completion method to improve the prediction accuracy. Through extensive experiments based on various publicly-available data sets, we found that DISCS outperforms the state-of-the-art NCS schemes in terms of prediction precision and convergence speed, which clearly shows the high usability of DISCS in real-life Internet applications.

Sweep coverage is an important problem in wireless sensor networks. With sweep coverage, more Points Of Interests (POIs) can be monitored with fewer mobile sensor nodes thanks to the mobility of the nodes. Most existing studies on sweep coverage focus on the trajectory of the mobile sensor nodes to guarantee the sweep coverage of the POIs. Considering the fact that, in many applications, the collected data is only useful during a fixed period, we studied the problem of sweep coverage with return time constraint. This problem requires that the POIs should be covered and the collected data should be delivered to the base station within a preset time window. In this paper, we prove that the problem of finding the minimum number of mobile sensor nodes required to guarantee sweep coverage with return time constraint is NP-hard. In addition, we present two novel heuristic algorithms, G-MSCR and MinD- Expand, to provide sweep coverage with return time constraint in practice. Our experimental results indicate that, compared to MinD-Expand, G-MSCR requires more sensor nodes and leads to shorter return time. To our knowledge, G-MSCR and MinD- Expand are the only algorithms that attempt to solve the problem of sweep coverage with return time constraint.

Mobile indoor localization is the foundation for the location-based features of many pervasive computing applications. Due to the popularity of WiFi networks in indoor environments, WiFi-based indoor localization has been considered to be a promising approach. Despite the feasibility of WiFi-based localization, the existing WiFi-based schemes suffer from the serious problem of low precision. In this paper, we propose a high-precision indoor localization scheme for mobile networks, Mobile Indoor Localization (MIL). Technically, MIL adopts a matrix completion approach which efficiently utilizes the collected information to achieve high localization precision at low computation cost. Our experimental results indicate that MIL outperforms the state-of-the-art mobile indoor localization schemes in terms of localization precision.

Over the past decades, many large-scale social network systems, such as Facebook and Twitter, have been deployed in different countries. How to efficiently analyze the topological characteristics of large-scale social networks has been a challenging problem in the research community. One of the critical topological characteristics is the shortest distance between two nodes in a network. The existing shortest distance algorithms, such as Breadth First Search (BFS), work well with small networks. For a network with billions of nodes, calculating the pairwise shortest distances with these algorithms requires an overlong period of time. In this paper, we present a high-precision ShOrtest Distance Approximation (SODA) scheme, which utilizes a small set of pre-calculated distances to estimate the shortest distance between each pair of nodes in large-scale social networks. Compared with the existing shortest distance estimation schemes for social networks, SODA leads to high estimation accuracy since it utilizes a novel optimization method, Robust Discrete Matrix Decomposition (RDMD), to eliminate the impact of significant errors/outliers and generate the coordinates of the nodes in a network simultaneously. In addition, SODA differentiates the asymmetric distances in directed graphs. Consequently, SODA works well with both directed and undirected social networks. Finally, SODA only involves convex optimization. Therefore, SODA is highly competitive in terms of computation complexity. Our experimental results indicate that SODA outperforms the state-of-the-art shortest distance estimation schemes in terms of estimation accuracy and running time.

Localization is one of the key operations in mobile networks. Due to the limitations of GPS, many researchers have devised a variety of different range-free and range-based localization schemes. Range-free schemes utilize the connectivity information to localize mobile nodes. However, the use of the connectivity information allows a high degree of freedom in terms of pinpointing the location of mobile nodes, which leads to low localization precision. Range-based schemes can achieve high localization precision because they require the fine-granularity distance information. Nevertheless, they normally result in high computation complexity and do not work well when part of the distance measurements are missing. In this paper, we propose a distributed range-based localization scheme, DISCO, that uses a series of minimization problems that only involve convex optimization to arrive at high localization precision and low computation complexity. In addition, when some distance measurements are not available, DISCO utilizes the partial distance information to achieve satisfactory localization results. Furthermore, DISCO is a distributed algorithm, which means that it scales well. The performance of DISCO is analyzed through simulation experiments. An in-depth analysis of the time complexity of DISCO is also included in this paper.

Mobile cloud computing is an emerging area in the cloud computing paradigm, comprising several modes of communication that are governed by varying security standards. WBAN (Wireless Body Area Networks), RFID (Radio Frequency IDentification) and VANET (Vehicular Ad-hoc NETworks) are three example applications that could be based on mobile cloud computing. Considering the fact that the security mechanisms in different applications are highly heterogeneous while the cloud server is common to these applications, we devised a context-adaptive security framework that could be deployed at the cloud premises to provide an additional security layer to mobile cloud computing systems. Furthermore, the framework provides varied techniques to improve the quality of service and reliability of mobile cloud computing. Technically, this multicomponent context-adaptive framework accepts the traffic in different communication modes, prevents attacks by randomly choosing pre-defined algorithms, learns from previous attacks using cognitive model, and rearranges the cloud service model as a self-healing system.

The Global Positioning System (GPS) has been widely used to determine the location for a variety of different applications. However, it doesn't work well in indoor environments because it requires the line of sight to the satellites and therefore stops working when the line of sight is not available. High-precision indoor localization is critical to many personal and business applications. After Bluetooth Low Energy (BLE), an energy-efficient version of Bluetooth, is widely deployed, Bluetooth-based indoor localization turns out to be a practical method to locate Bluetooth-enabled devices due to its low battery cost. In this paper, we present two novel BLE-based localization schemes, Low-precision Indoor Localization (LIL) and High-precision Indoor Localization (HIL). Different than most of the existing localization methods that attempt to find the specific location of the object under investigation, LIL and HIL utilize the collected RSSI measurements to generate a small region in which the object is guaranteed to be found. Compared with LIL, HIL leads to smaller localization regions. However, HIL requires an extra data-training phase.

We propose an effective outlier detection algorithm for high-dimensional data. We consider manifold models of data as is typically assumed in dimensionality reduction/manifold learning. Namely, we consider a noisy data set sampled from a low-dimensional manifold in a high-dimensional data space. Our algorithm uses local geometric structure to determine inliers, from which the outliers are identified. The algorithm is applicable to both linear and nonlinear models of data. We also discuss various implementation issues and we present several examples to demonstrate the effectiveness of the new approach.

The security of data transmission is of paramount importance to mobile cloud computing. For security purposes, the data transmission in mobile cloud computing typically requires a mutually-authenticated environment for mobile devices and cloud servers. SSH (secure shell) could be used to satisfy the requirement. However, it makes the authentication process computationally expensive for mobile devices because it involves public key cryptosystem and mobile devices are relatively restricted in terms of computation capacity. This places the onus of establishing and maintaining secure communication sessions on the resourceful cloud servers. We propose a novel mutual authentication scheme, “Message Digest and Location based Authentication (MDLA)”, which involves symmetric key operations. In addition to computational simplicity, MDLA achieves integrity through message digest, and ensures the unpredictability of keys using location vector and timestamp.

Network Coordinate System (NCS) has drawn much attention over the past years thanks to the increasing number of large-scale distributed systems that require the distance prediction service for each pair of network hosts. The existing schemes suffer seriously from either low prediction precision or unsatisfactory convergence speed. In this paper, we present a novel distributed network coordinate system based on Robust Principal Component Analysis, RNC, that uses a few local distance measurements to calculate high-precision coordinates without convergence process. To guarantee the non-negativity of predicted distances, we propose Robust Nonnegative Principal Component Analysis (RUN-PACE) which only involves convex optimization, consequently resulting in low computation complexity. Our experimental results indicate that RNC outperforms the state-of-the-art NCS schemes.

Localization in mobile networks is of paramount importance to a variety of pervasive applications. Due to the limitations of GPS, such as high deployment cost, many researchers have devised a variety of different localization schemes based on the measurements of connectivity or distance between neighboring nodes. The existing schemes suffer seriously from either low localization precision or overlong computation time. In this paper, we present a novel localization scheme based on matrix completion, MALL, that utilizes the collected connectivity and distance information to achieve high-precision localization. Since MALL only involves convex optimization and low-complexity non-convex optimization, it can localize mobile nodes at a fast pace. Furthermore, MALL leads to low communication cost. Through intensive simulation and testbed experiments, we found that MALL outperforms the state-of-the-art localization schemes. An in-depth analysis of the time complexity and communication cost of MALL is also included in this paper.

Localization is a fundamental operation in wireless networks. Location determination is normally accomplished using the Global Positioning System (GPS) for outdoor applications. For indoor localization, GPS does not work due to the lack of the line of sight to satellites. High-precision indoor localization is critical to many personal and business applications. WiFi-based indoor localization was proposed to be a practical method to locate WiFi-enabled devices due to the popularity of WiFi networks. However, it suffers from large localization errors. Our experimental results indicate that this scheme consistently leads to an average error around 3 meters. The existence of different locations with similar WiFi signal strength is the reason behind the large errors. To improve the localization precision, a hybrid indoor localization scheme, HILL, is proposed in this paper. Inspired by the fact that a large number of WiFi-enabled mobile devices have been deployed, HILL uses 3 phases to improve the precision of WiFi-based localization. First of all, it measures the distances between each pair of peer devices through acoustic ranging. Secondly, the Classical Metric Multidimensional Scaling (MDS) method is applied to the collected distances, which results in a graph consistent with the distances. Finally, the graph generated by MDS is embedded onto the graph corresponding to WiFi-based localization in order to achieve high localization precision. Our experimental results indicate that the average localization error of HILL is about 1 meter.

Barrier coverage based on Wireless Sensor Networks (WSNs) has been widely used to prevent intruder trespassing in monitoring systems. Traditionally, enabling perfect barrier coverage is considered the most important goal of barrier coverage studies. Imperfect coverage has been deemed to be a failure. In our research, we attempted to use the redundant sensor nodes in WSNs to prolong the optimal network lifetime of barrier coverage by adding imperfect barrier coverage. Specifically, we devised two schemes, CIBC-1 and CIBC-2, to construct imperfect barrier coverage in order to improve the performance of the existing optimal network lifetime scheduling algorithms for barrier coverage. Our simulation results indicate that our schemes can significantly extend the network lifetime resulting from the state-of-the-art network lifetime scheduling algorithms.

With the development of the World Wide Web (WWW) and virtualization technologies, cloud computing has started to play a key role in the new computation era. Cloud computing can be used to serve a wide range of applications, from personal to organizational, by means of various infrastructure, software, and platform services. However, the ease of access from terminal devices to powerful processing units and information-rich databases makes the cloud susceptible to a variety of different attacks. The widespread use of cloud computing brings with it a hoard of security, privacy and integrity issues. In this paper, we propose an innovative authentication scheme for mobile cloud computing, MDA. The proposed scheme only uses existing hardware and platforms to prevent most of the potential attacks during the authentication process between a mobile device and the cloud. Technically, the encrypted hashed message (i.e. message digest) is employed by MDA to achieve secure authentication. The performance of MDA is evaluated via protocol simulation and security analysis.

SUMMARY This paper presents a novel method to study the evolution of Linux kernel components using complex networks to understand how Linux kernel components evolve over time. After analyzing the node degree distribution, clustering coefficient, and average path length of the call graphs corresponding to the kernel components of 130 development versions and 94 stable versions (V1.0 to V2.4.35), we found that the call graphs of the file system, driver, kernel, memory management, and net components are scale‐free, small‐world complex networks. In addition, all of the five components exhibit very strong preferential attachment tendency. With such in‐depth understanding of the features of the Linux kernel components, we propose a generic method that could be used to find major structural changes that occur during the evolution of software systems. Copyright © 2012 John Wiley & Sons, Ltd.

Inspired by the backbone concept in wired networks, virtual backbone is expected to bring substantial benefits to routing in wireless sensor networks (WSNs). Virtual backbone construction based on Connected Dominating Set (CDS) is a competitive approach among the existing methods used to establish virtual backbone in WSNs. Traditionally, CDS size was the only factor considered in the CDS-based approach. The motivation was that smaller CDS leads to simplified network maintenance. However, routing cost in terms of routing path length is also an important factor for virtual backbone construction. In our research, both of these two factors are taken into account. Specifically, we attempt to devise a polynomial-time constant-approximation algorithm that leads to a CDS with bounded CDS size and guaranteed routing cost. We prove that, under general graph model, there is no polynomial-time constant-approximation algorithm unless P = NP. Under Unit Disk Graph (UDG) model, we propose an innovative polynomial-time constant-approximation algorithm, GOC-MCDS-C, that produces a CDS D whose size I D is within a constant factor from that of the minimum CDS. In addition, for each node pair u and v, there exists a routing path with all intermediate nodes in D and path length at most 7 · d(u, v), where d(u, v) is the length of the shortest path between u and v. Our theoretical analysis and simulation results show that the distributed version of the proposed algorithm, GOC-MCDS-D, outperforms the existing approaches.

Data gathering in sensor networks is required to be efficient, adaptable and robust. Recently, compressive sensing (CS) based data gathering shows promise in meeting these requirements. Existing CS-based data gathering solutions require that a transform that best sparsifies the sensor readings should be used in order to reduce the amount of data traffic in the network as much as possible. As a result, it is very likely that different transforms have to be determined for varied sensor networks, which seriously affects the adaptability of CS-based schemes. In addition, the existing schemes result in significant errors when the sampling rate of sensor data is low (equivalent to the case of high packet loss rate) because CS inherently requires that the number of measurements should exceed a certain threshold. This paper presents STCDG, an efficient data gathering scheme based on matrix completion. STCDG takes advantage of the low-rank feature instead of sparsity, thereby avoiding the problem of having to be customized for specific sensor networks. Besides, we exploit the presence of the short-term stability feature in sensor data, which further narrows down the set of feasible readings and reduces the recovery errors significantly. Furthermore, STCDG avoids the optimization problem involving empty columns by first removing the empty columns and only recovering the non-empty columns, then filling the empty columns using an optimization technique based on temporal stability. Our experimental results indicate that STCDG outperforms the state-of-the-art data gathering algorithms in terms of recovery error, power consumption, lifespan, and network capacity.

Wireless sensor networks are expected to be used in many different applications such as disaster relief, environmental control, and intelligent buildings. In this paper, we focus on a sensor network that collects environment data from all sensor nodes periodically. To gather the sensing data quickly and reliably, the scheduling algorithm should be able to coordinate the data transmissions in the network and react to node/link failures effectively. In this paper, we present an innovative scheduling algorithm, Fault-Tolerant Scheduling for data collection (FTS), that leads to short data collection time and high fault tolerance. Our experimental results show that FTS outperforms the DCSB algorithm and exhibits strong fault-tolerant capabilities.

MAP is a connectivity-based routing protocol aimed at improving the load balance performance of traditional geographical routing methods. It attempts to find parallel routing paths by taking advantage of the concept of skeleton in the continuous domain. However, MAP suffers seriously from overloading the sensor nodes that are close to the skeleton. In this paper, we propose a contour-based routing protocol, CAR, that does not require geographical information, produces short routing paths, and achieves outstanding load balancing. Our experimental results show that CAR outperforms MAP in terms of both load balancing and routing path length.

In wireless sensor networks, virtual backbone construction based on connected dominating set is a competitive issue for routing efficiency and topology control. Assume that a sensor networks is defined as a connected unit disk graph (UDG). The problem is to find a minimum connected dominating set of given UDG with minimum routing cost for each node pair. We present a constant approximation scheme which produces a connected dominating set D, whose size |D| is within a factor α from that of the minimum connected dominating set and each node pair exists a routing path with all intermediate nodes in D and with length at most 5 · d(u,v), where d(u,v) is the length of shortest path of this node pair. A distributed algorithm is also provided with analogical performance. Extensive simulation shows that our distributed algorithm achieves significantly than the latest solution in research direction.

There have been many reliable routing algorithms for wired networks. For routing in wireless sensor networks, the reliability aspect has not been paid as much attention. GEM (Graph EMbedding for sensor networks) is an innovative routing algorithm for wireless sensor networks that is based on the idea of graph embedding. However, it cannot survive edge failures well. In this paper, we propose GW-GEM (Greedy-Walk GEM), a GEM-based multi-path routing algorithm that preserves the advantages of GEM and improves its reliability performance significantly. Specifically, in the case where 1% of edges fail in a 900-node simulated network, GEM leads to a path error rate of 9.2% while GW-GEM only results in a path error rate of 1%.

In recent years, many graphs have turned out to be complex networks. This paper presents a novel method to study Linux kernel evolution - using complex networks to understand how Linux kernel modules evolve over time. After studying the node degree distribution and average path length of the call graphs corresponding to the kernel modules of 223 different versions (V1.1.0 to V2.4.35), we found that the call graphs of the file system and drivers module are scale-free small-world complex networks. In addition, both of the file system and drivers module exhibit very strong preferential attachment tendency. Finally, we proposed a generic method that could be used to find major structural changes that occur during the evolution of software systems.

GEM is an ingenious routing algorithm for wireless sensor networks that is based on the idea of graph embedding. However, it cannot survive edge failures well because reliability was not taken into consideration seriously when it was designed. In this paper, we propose UD-GEM, a GEM-based multi-path routing algorithm that improves the reliability performance of GEM significantly. Specifically, in the case that 2% of all edges in the network fail to transfer packets and there are 900 sensor nodes in the experimental network, GEM leads to a path error rate of 12% while UD-GEM only results in a path error rate of 1%.