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Image restoration is a long-standing problem in signal processing and low-level computer vision. Previous studies have shown that imposing a low-rank Tucker decomposition (TKD) constraint could produce impressive performances. However, the TKD-based schemes may lead to the overfitting/underfitting problem because of incorrectly predefined ranks. To address this issue, we prove that the n -rank is upper bounded by the rank of each Tucker factor matrix. Using this relationship, we propose a formulation by imposing the nuclear norm regularization on the latent factors of TKD, which can avoid the burden of rank selection and reduce the computational cost when dealing with large-scale tensors. In this formulation, we adopt the Minimax Concave Penalty to remove the impulsive noise instead of the l

Per-flow connection state monitoring is crucial for detecting malicious traffic or anomalies in networks. The monitoring is extremely challenging in high-speed networks, and would involve high computation and memory costs. We propose a novel stateful Bloom filter (stateBF) to enable a highly compact, low-overhead, and accurate flow-state storage service for the monitoring of the per-flow connection states. Unlike the standard Bloom filter and its various extensions, we design a special cell-based data structure for stateBF instead of bit array to track both the state value and the number of times the same state value is inserted to stateBF. We further design four stateBF operations for advanced flow-state management. To enable efficient stateBF operations, they are designed to be bitwise for the simple implementation. We have done extensive simulations with data traces from public MAWI and from a university campus. Our performance results demonstrate that stateBF can support per-flow state storage services in high speed networks with low storage space, and high querying speed and accuracy.

Monitoring the performance of a large network would involve a high measurement cost. To reduce the overhead, sparse network monitoring techniques may be applied to select paths or time intervals to take the measurements, while the remaining monitoring data can be inferred leveraging the spatial-temporal correlations among data. The quality of missing data recovery, however, highly relies on the specific inference technique adopted. Tensor completion is a promising technique for more accurate missing data inference by exploiting the multi-dimensional data structure. However, data processing for higher dimensional tensors involves a large amount of computation, which prevents conventional tensor completion algorithms from practical application in the presence of large amount of data. This work takes the initiative to investigate the potential and methodologies of performing parallel processing for high-speed and high accuracy tensor completion over Graphics Processing Units (GPUs). We propose a GPU-accelerated parallel Tensor Completion scheme (GPU-TC) for accurate and fast recovery of missing data. To improve the data recovery accuracy and speed, we propose three novel techniques to well exploit the tensor factorization structure and the GPU features: grid-based tensor partition , independent task assignment based on Fisher-Yates shuffle , sphere facilitated and memory-correlated scheduling . We have conducted extensive experiments using network traffic trace data to compare the proposed GPU-TC with the state of art tensor completion algorithms and matrix-based algorithms. The experimental results demonstrate that GPU-TC can achieve significantly better performance in terms of two relative error ratio metrics and computation time.

The attacks, faults, and severe communication/system conditions in Mobile Crowd Sensing (MCS) make false data detection a critical problem. Observing the intrinsic low dimensionality of general monitoring data and the sparsity of false data, false data detection can be performed based on the separation of normal data and anomalies. Although the existing separation algorithm based on Direct Robust Matrix Factorization (DRMF) is proven to be effective, requiring iteratively performing Singular Value Decomposition (SVD) for low-rank matrix approximation would result in a prohibitively high accumulated computation cost when the data matrix is large. In this work, we observe the quick false data location feature from our empirical study of DRMF, based on which we propose an intelligent Light weight Low Rank and False Matrix Separation algorithm (LightLRFMS) that can reuse the previous result of the matrix decomposition to deduce the one for the current iteration step. Depending on the type of data corruption, random or successive/mass, we design two versions of LightLRFMS. From a theoretical perspective, we validate that LightLRFMS only requires one round of SVD computation and thus has very low computation cost. We have done extensive experiments using a PM 2.5 air condition trace and a road traffic trace. Our results demonstrate that LightLRFMS can achieve very good false data detection performance with the same highest detection accuracy as DRMF but with up to 20 times faster speed thanks to its lower computation cost.

Gaining a full knowledge of end-to-end network performance is important for some advanced network management and services. Although it becomes increasingly critical, end-to-end network monitoring usually needs active probing of the path and the overhead will increase quadratically with the number of network nodes. To reduce the measurement overhead, matrix completion is proposed recently to predict the end-to-end network performance among all node pairs by only measuring a small set of paths. Despite its potential, applying matrix completion to recover the missing data suffers from low recovery accuracy and long recovery time. To address the issues, we propose MC-GPU to exploit Graphics Processing Units (GPUs) to enable parallel matrix factorization for high-speed and highly accurate Matrix Completion. To well exploit the special architecture features of GPUs for both task independent and data-independent parallel task execution, we propose several novel techniques: similar OD (origin and destination) pairs reordering taking advantage of the locality-sensitive hash (LSH) functions, balanced matrix partition, and parallel matrix completion. We implement the proposed MC-GPU on the GPU platform and evaluate the performance using real trace data. We compare the proposed MC-GPU with the state of the art matrix completion algorithms, and our results demonstrate that MC-GPU can achieve significantly faster speed with high data recovery accuracy.

Due to the popularity of mobile computing and mobile sensing, users’ traces can now be readily collected to enhance applications’ performance. However, users’ location privacy may be disclosed to the untrusted data aggregator that collects users’ traces. Cloaking users’ traces with synthetic traces is a prevalent technique to protect location privacy. But the existing work that synthesizes traces suffers from the social relationship based de-anonymization attacks. To this end, we propose $W^{3}{-}tess$ that synthesizes privacy-preserving traces via enhancing the plausibility of synthetic traces with social networks. The main idea of $W^{3}{-}tess$ is to credibly imitate the temporal, spatial, and social behavior of users’ mobility, sample the traces that exhibit similar three-dimension mobility behavior, and synthesize traces using the sampled locations. By doing so, $W^{3}{-}tess$ can provide “ differential privacy ” on location privacy preservation. In addition, compared to the existing work, $W^{3}{-}tess$ offers several salient features. First, both location privacy preservation and data utility guarantees are theoretically provable. Second, it is applicable to most geo-data analysis tasks performed by the data aggregator. Experiments on two real-world datasets, loc-Gwalla and loc-Brightkite, have demonstrated the effectiveness and efficiency of $W^{3}{-}tess$ .

The inference of the network traffic data from partial measurements data becomes increasingly critical for various network engineering tasks. By exploiting the multi-dimensional data structure, tensor completion is a promising technique for more accurate missing data inference. However, existing tensor completion algorithms generally have the strong assumption that the tensor data have a global low-rank structure, and try to find a single and global model to fit the data of the whole tensor. In a practical network system, a subset of data may have stronger correlation. In this work, we propose a novel localized tensor completion model (LTC) to increase the data recovery accuracy by taking advantage of the stronger local correlation of data to form and recover sub-tensors each with a lower rank. Despite that it is promising to use local tensors, the finding of correlated entries faces two challenges, the data with adjacent indexes are not ones with higher correlation and it is difficult to find the similarity of data with missing tensor entries. To conquer the challenges, we propose several novel techniques: efficiently calculating the candidate anchor points based on locality-sensitive hash (LSH), building sub-tensors around properly selected anchor points, encoding factor matrices to facilitate the finding of similarity with missing entries, and similarity-aware local tensor completion and data fusion. We have done extensive experiments using real traffic traces. Our results demonstrate that LTC is very effective in increasing the tensor recovery accuracy without depending on specific tensor completion algorithms.

Currently, remote monitoring systems have evolvedto respond to the peculiar needs in the healthcare industry, suchas Cardiovascular diseases, which is an essential foundation in themodern concept of the smart city. We propose a system to monitorpatient current health conditions as a healthcare system basedon the widely spread available low-cost technologies, namely,GSM and Arduino platform. Statistics show that hypertensiveheart disease and blood pressure are risk factors for the highdeath rate. To decrease it, preventive measures should be appliedproviding a health monitoring system to save the patient life atan acceptable time. The objective of this paper is to developa prototype of a low-cost system, that is capable of performingremote monitoring of a patient’s heart rate and body temperaturein real-time and stores the data on the memory card using low-cost hardware such as Arduino, and also, using the servicesprovided by the GSM network, to enable communication witha health specialists cell phone upon request or if there areanomalies in the values measured by the sensors. The studyfocuses on heartbeat rate using the Difference Operation Method(DOM), described by Yeh & Wang, and body temperature, thus,in case of emergency, a short call is done, and then an SMS issent to the health specialist’s mobile containing measured values.Using this algorithm and after some tests using data from theMIT BIH Database distribution, we manage to find the bestsample frequency and eliminate the distortion of the signal. sample frequency and eliminate the distortion of the signal.

Offloading computation intensive applications to mobile cloud is promising for overcoming the problems of limited computational resources and energy of mobile devices. However, without considering the competition relationship of mobile users and cloudlets in the mobile cloud computing system, existing studies lack an incentive mechanism for the system to achieve efficient application offloading and cloud resource provisioning. In this paper, we design MPTMG, a Multi-dimensional Pricing mechanism based on Two-sided Market Game. We propose three types of prices: a multi-dimensional price corresponding to multi-dimensional resource allocation, a penalty price to encourage fair and high quality cloud services, and a benefit discount factor to motivate more even provisioning of resources on different dimensions in the cloud. Based on these prices, we propose a distributed price-adjustment algorithm for efficient resource allocation and QoS-aware offloading scheduling. We prove that the algorithm can converge in a finite number of iterations to the equilibrium core allocation at which the mobile cloud system achieves the Pareto efficiency by maximizing the total system benefit. To the best of our knowledge, this is the first paper that applies economic theories and pricing mechanisms to manage application offloading in mobile cloud systems. The simulation results demonstrate that our proposed pricing mechanism can significantly improve the system performance.

A node can provide a file to other nodes after downloading the file or data from the Internet. When more than one node have obtained the same file, this is considered a multisource transmission, in which all these nodes can act as candidate providers (sources) and transmit the file to a new request node (destination) together. In cases where there is negligible or no interference, multisource transmission can improve the download throughput because of parallel transmissions through multiple paths. However, this improvement is not guaranteed due to wireless interference among different paths. Wireless interference can be alleviated by the multiradio and multichannel technique. Because the source and multipath routing selections interact with channel assignment, the multisource transmission problem with multiradio and multichannel presents a significant challenge. In this paper, we propose a distributed joint source, routing, and channel selection scheme. The source selection issue can be concurrently solved via multipath finding. There are three sub-algorithms in our scheme, namely, interference-aware routing algorithm, channel assignment algorithm, and local routing adjustment algorithm. The interference-aware routing algorithm is used to find paths sequentially and is jointly executed with the channel assignment algorithm. After finding a new path, the local routing adjustment algorithm may be executed to locally adjust the selected paths so as to further reduce wireless interference. Extensive simulations have been conducted to demonstrate that our algorithms can effectively improve the network aggregate throughput, as well as reduce delay and packet loss probability.

The prediction of short-term volatile traffic becomes increasingly critical for efficient traffic engineering in intelligent transportation systems. Accurate forecast results can assist in traffic management and pedestrian route selection, which will help alleviate the huge congestion problem in the system. This paper presents a novel hybrid DTMGP model to accurately forecast the volume of passenger flows multi-step ahead with the comprehensive consideration of factors from temporal, origin-destination spatial, and frequency and self-similarity perspectives. We first apply discrete wavelet transform to decompose the traffic volume series into an appropriation component and several detailed components. Then we propose a more efficient tracking model to forecast the appropriation component and a novel Gaussian process model to forecast the detailed components. The forecasting performance is evaluated with real-time passenger flow data in Chongqing, China. Simulation results demonstrate that our hybrid model can achieve on average 20%–50% accuracy improvement, especially during rush hours.

Segment routing (SR) is a new network paradigm to optimize network performance. Through leveraging source routing, SR is able to achieve more fine-grained control of data flow in the SR domain. However, it is difficult to introduce large-scale full SR network into existing traditional network due to the economic constraints and immature operation technology. Thus, incrementally deploying SR nodes into an existing network is preferred, which forms the hybrid IP/SR scenario. In the hybrid network, with SR enabled devices, the routing path of flow can be dynamically adjusted. Network utility is an important factor to reflect the user's satisfaction with the allocated bandwidth. In this paper, we propose a bandwidth allocation algorithm to maximize network utility in hybrid IP/SR network and thus improve customer's satisfaction. The simulation results show that the bandwidth allocation algorithm proposed is able to improve the utility of network significantly, and the utility of network will increase with the number of SR nodes deployed in the network.

Matrix completion has emerged very recently and provides a new venue for low cost data gathering in Wireless Sensor Networks (WSNs). Existing schemes often assume that the data matrix has a known and fixed low-rank, which is unlikely to hold in a practical system for environment monitoring. Environmental data vary in temporal and spatial domains. By analyzing a large set of weather data collected from 196 sensors in ZhuZhou, China, we reveal that weather data have the features of low-rank, temporal stability, and relative rank stability. Taking advantage of these features, we propose an on-line data gathering scheme based on matrix completion theory, named MC-Weather, to adaptively sample different locations according to environmental and weather conditions. To better schedule sampling process while satisfying the required reconstruction accuracy, we propose several novel techniques, including three sample learning principles, an adaptive sampling algorithm based on matrix completion, and a uniform time slot and cross sample model. With these techniques, our MC-Weather scheme can collect the sensory data at required accuracy while largely reducing the cost for sensing, communication, and computation. We perform extensive simulations based on the data traces from weather monitoring and the simulation results validate the efficiency and efficacy of the proposed scheme.

Traffic anomaly detection is critical for advanced Internet management. Existing detection algorithms generally convert the high-dimensional data to a long vector, which compromises the detection accuracy due to the loss of spatial information of data. Moreover, they are generally designed based on the separation of normal and anomalous data in a time period, which not only introduces high storage and computation cost but also prevents timely detection of anomalies. Online and accurate traffic anomaly detection is critical but difficult to support. To address the challenge, this paper directly models the monitoring data in each time slot as a 2-D matrix, and detects anomalies in the new time slot based on bilateral principal component analysis (B-PCA). We propose several novel techniques in OnlineBPCA to support quick and accurate anomaly detection in real time, including a novel B-PCA-based anomaly detection principle that jointly considers the variation of both row and column principal directions for more accurate anomaly detection, an approximate algorithm to avoid using iteration procedure to calculate the principal directions in a close-form, and a sequential anomaly algorithm to quickly update principal directions with low computation and storage cost when receiving a new data matrix at a time slot. To the best of our knowledge, this is the first work that exploits 2-D PCA for anomaly detection. We have conducted extensive simulations to compare our OnlineBPCA with the state-of-art anomaly detection algorithms using real traffic traces Abilene and GEANT. Our simulation results demonstrate that, compared with other algorithms, our OnlineBPCA can achieve significantly better detection performance with low false positive rate, high true positive rate, and low computation cost.

The inference of the network traffic matrix from partial measurement data becomes increasingly critical for various network engineering tasks, such as capacity planning, load balancing, path setup, network provisioning, anomaly detection, and failure recovery. The recent study shows it is promising to more accurately interpolate the missing data with a 3-D tensor as compared with the interpolation methods based on a 2-D matrix. Despite the potential, it is difficult to form a tensor with measurements taken at varying rate in a practical network. To address the issues, we propose a Reshape-Align scheme to form the regular tensor with data from variable rate measurements, and introduce user-domain and temporal-domain factor matrices which take full advantage of features from both domains to translate the matrix completion problem to the tensor completion problem based on CANDECOMP/PARAFAC decomposition for more accurate missing data recovery. Our performance results demonstrate that our Reshape-Align scheme can achieve significantly better performance in terms of several metrics: error ratio, mean absolute error, and root mean square error.

The inference of traffic volume of the whole network from partial traffic measurements becomes increasingly critical for various network engineering tasks, such as capacity planning and anomaly detection. Previous studies indicate that the matrix completion is a possible solution for this problem. However, as a 2-D matrix cannot sufficiently capture the spatial-temporal features of traffic data, these approaches fail to work when the data missing ratio is high. To fully exploit hidden spatial-temporal structures of the traffic data, this paper models the traffic data as a 3-way traffic tensor and formulates the traffic data recovery problem as a low-rank tensor completion problem. However, the high computation complexity incurred by the conventional tensor completion algorithms prevents its practical application for the traffic data recovery. To reduce the computation cost, we propose a novel sequential tensor completion algorithm, which can efficiently exploit the tensor decomposition result based on the previous traffic data to derive the tensor decomposition upon arriving of new data. Furthermore, to better capture the changes of data correlation over time, we propose a dynamic sequential tensor completion algorithm. To the best of our knowledge, we are the first to propose sequential tensor completion algorithms to significantly speed up the traffic data recovery process. This facilitates the modeling of Internet traffic with the tensor to well exploit the hidden structures of traffic data for more accurate missing data inference. We have done extensive simulations with the real traffic trace as the input. The simulation results demonstrate that our algorithms can achieve significantly better performance compared with the literature tensor and matrix completion algorithms even when the data missing ratio is high.

Energy efficiency in data center networks (DCNs) is critical to the operations of modern large-scale data centers. One effective way is to make the size of DCNs elastic along with flow demands by centralized routing and scheduling, i.e., turning off idle network components to reduce the power consumption. As such, software-defined networking (SDN) is widely used for achieving such elasticity conveniently. Meanwhile, the scale and structure of modern DCNs get much larger and more complex. Central control and global computing become impractical due to the heavy time and space complexity. Therefore, distributed power control is necessary for large-scale SDN-DCNs, and yet there are few research achievements in this area. In this paper, we present an extensible energy-efficient mechanism, which: 1) leverages distributed flow routing for both intraand inter-domain elephant flows and 2) extendedly considers distributed energy efficiency for control plane. A local power-saving function is operated within each domain of control plane, and a distributed energy-efficient routing algorithm is computed to optimize the effectiveness for the inter-domain flows. The simulation results demonstrate that this distributed mechanism applies to large-scale DCNs and achieves an effective power saving. Published version

Simultaneous wireless information and power transfer (SWIPT) transmits information and powers wireless nodes with the same radio frequency signal. It can prolong the life time of the energy-constrained wireless nodes. Current works of SWIPT focus on one-hop and two-hop wireless network. In order to verify the performance of SWIPT in multi-hop energy-constrained wireless network (MECWN) where the energy harvested by the receiver node can be as an energy compensation for data forwarding, this paper concurrently considers SWIPT and routing selection in MECWN. To reduce the energy consumption, we first formulate the information and energy allocation problem of link in a forwarding path, which is dependent on the next-hop node, and solve it by an iterative allocation algorithm. A novel routing metric evaluates the energy consumption of link transmitted with or without SWIPT. The energy-aware SWIPT routing algorithm allocates the information and energy of link with allocation algorithm during path finding process. To the best of our knowledge, this is the first solution that takes account of SWIPT and routing in MECWN. Our performance studies demonstrate that our proposed algorithms can effectively exploit those node resources whose energy are not enough and significantly decrease the energy consumption.

Detecting anomalous traffic is a critical task for advanced Internet management. Many anomaly detection algorithms have been proposed recently. However, constrained by their matrix-based traffic data model, existing algorithms often suffer from low accuracy in anomaly detection. To fully utilize the multi-dimensional information hidden in the traffic data, this paper takes the initiative to investigate the potential and methodologies of performing tensor factorization for more accurate Internet anomaly detection. More specifically, we model the traffic data as a three-way tensor and formulate the anomaly detection problem as a robust tensor recovery problem with the constraints on the rank of the tensor and the cardinality of the anomaly set. These constraints, however, make the problem extremely hard to solve. Rather than resorting to the convex relaxation at the cost of low detection performance, we propose TensorDet to solve the problem directly and efficiently. To improve the anomaly detection accuracy and tensor factorization speed, TensorDet exploits the factorization structure with two novel techniques, sequential tensor truncation and two-phase anomaly detection. We have conducted extensive experiments using Internet traffic trace data Abilene and GEANT. Compared with the state of art algorithms for tensor recovery and matrix-based anomaly detection, TensorDet can achieve significantly lower false positive rate and higher true positive rate. Particularly, benefiting from our well designed algorithm to reduce the computation cost of tensor factorization, the tensor factorization process in TensorDet is 5 (Abilene) and 13 (GEANT) times faster than that of the traditional Tucker decomposition solution.

A centroid type-reduction strategy for computing the centroids of type-2 fuzzy sets based on decomposed $\alpha$ -planes was proposed by Liu. However, it cannot be applied to type-2 fuzzy sets with concave secondary membership functions. In this paper, we extend the Liu's method so that the centroids of type-2 fuzzy sets with concave secondary membership functions can be derived. For each decomposed $\alpha$ -plane, we convert it into a group of interval type-2 fuzzy sets. The union of the centroids of its member interval type-2 fuzzy sets constitutes the centroid of the $\alpha$ -plane. Then, the weighted union of the centroids of the decomposed $\alpha$ -planes becomes the centroid type-reduced set of the original type-2 fuzzy set. When dealing with type-2 fuzzy sets with convex secondary membership functions, our proposed method is reduced to the Liu's method.

Affected by hardware and wireless conditions in WSNs, raw sensory data usually have notable data loss and corruption. Existing studies mainly consider the interpolation of random missing data in the absence of the data corruption. There is also no strategy to handle the successive missing data. To address these problems, this paper proposes a novel approach based on matrix completion (MC) to recover the successive missing and corrupted data. By analyzing a large set of weather data collected from 196 sensors in Zhu Zhou, China, we verify that weather data have the features of low-rank, temporal stability, and spatial correlation. Moreover, from simulations on the real weather data, we also discover that successive data corruption not only seriously affects the accuracy of missing and corrupted data recovery but even pollutes the normal data when applying the matrix completion in a traditional way. Motivated by these observations, we propose a novel Principal Component Analysis (PCA)-based scheme to efficiently identify the existence of data corruption. We further propose a two-phase MC-based data recovery scheme, named MC-Two-Phase, which applies the matrix completion technique to fully exploit the inherent features of environmental data to recover the data matrix due to either data missing or corruption. Finally, the extensive simulations with real-world sensory data demonstrate that the proposed MC-Two-Phase approach can achieve very high recovery accuracy in the presence of successively missing and corrupted data.

Energy efficiency is an important criterion for routing algorithms in the wireless sensor network. Cooperative routing can reduce energy consumption effectively stemming from its diversity gain advantage. To solve the energy consumption problem and maximize the network lifetime, this paper proposes a Virtual Multiple Input Multiple Output based Cooperative Routing algorithm (VMIMOCR). VMIMOCR chooses cooperative relay nodes based on Virtual Multiple Input Multiple Output Model, and balances energy consumption by reasonable power allocation among transmitters, and decides the forwarding path finally. The experimental results show that VMIMOCR can improve network lifetime from 37% to 348% in the medium node density, compared with existing routing algorithms.

Due to uncertain network connectivity, efficiently data forwarding in Mobile social networks (MSNs) becomes challenging. To conquer the problem, an Efficient data forwarding scheme based on geography intimacy (GIDF) for MSNs to achieve higher delivery ratio is proposed. In GIDF, we firstly propose an Intimacy based dynamic community detection algorithm (IDCD), which divide the MSNs into several communities. We propose a novel metric geography intimacy which can quantify the node's geographical information and the friendships between nodes. Based on geography intimacy, we further propose a routing algorithm to forward data. Compared with the geography intimacy between nodes, the next hop is found, further find the route of data forwarding by doing the similar operations. Extensive simulations on real data with the ONE simulator show that GIDF is more efficient than the existing algorithms.

There are a lot of recent interests on cooperative communication (CC) in wireless networks. Despite the large capacity gain of CC in small wireless networks with its capability of mitigating fading taking advantage of spatial diversity, cooperative communication can result in severe interference in large networks and even degraded throughput. The aim of this work is to concurrently exploit multi-radio and multi-channel (MRMC) technique and cooperative transmission technique to combat co-channel interference and improve the performance of multi-hop wireless network. Our proposed solution concurrently considers cooperative routing, channel assignment, and relay selection and takes advantage of both MRMC technique and spatial diversity in cooperative wireless networks to improve the throughput. We propose two important metrics, contention-aware channel utilization routing metric (CACU) to capture the interference cost from both direct transmission and cooperative transmission, and traffic aware channel condition metric (TACC) to evaluate the channel load condition. Based on these metrics, we propose three algorithms for interference-aware cooperative routing, local channel adjustment, and local path and relay adaptation respectively to ensure high performance communications in dynamic wireless networks. Our algorithms are designed to be fully distributed and can effectively mitigate co-channel interference and achieve cooperative diversity gain. To our best knowledge, this is the first distributed solution that supports cooperative communications in MRMC networks. Our performance studies demonstrate that our proposed algorithms can efficiently support cooperative communications in multi-radio multi-hop networks to significantly increase the aggregate throughput.

Cooperative communication (CC) for wireless networks has gained a lot of recent interests. It has been shown that CC has the potential to significantly increase the capacity of wireless networks, with its ability of mitigating fading by exploiting spatial diversity. However, most of the works on CC are limited to single radio wireless network. To demonstrate the benefits of CC in multiradio multihop wireless network, this paper studies a joint problem of multiradio cooperative routing and relay assignment to maximize the minimum rate among a set of concurrent communication sessions. We first model this problem as a mixed-integer programming (MIP) problem and prove it to be NP-hard. Then, we propose a centralized algorithm and a distributed algorithm to solve the problem. The centralized algorithm is designed within a branch-and-bound framework by using the relaxation of the formulated MIP, which can find a global $(1+\varepsilon)$ -optimal solution. Our distributed algorithm includes two subalgorithms: a cooperative route selection subalgorithm and a fairness-aware route adjustment subalgorithm. Our simulation results demonstrate the effectiveness of the proposed algorithms and the significant rate gains that can be achieved by incorporating CC in multiradio multihop networks.

Optical codes have long been used to carry small amounts of static data, such as URLs, IDs or other short binary sequences. In this paper, we experiment on the use of sequences of optical codes to form a one-way communication channel. In this context, a sender is made of a surface displaying rapidly changing codes, which are picked up by a receiver's camera and converted back into a binary data stream. After presenting experimental results seeking the combination of frame rate, code size, and error correction level maximizing effective bandwidth, we describe the implementation of a robust communication protocol designed, specifically for lossy, simplex, and low-bandwidth data links. Our findings indicate that such a protocol is sufficient for carrying at least voice-quality audio in real time.

Energy consumed by network constitutes a significant portion of the total power budget in modern data centers. Thus, it is critical to understand the energy consumption and improve the power efficiency of data center networks (DCNs). In doing so, one straightforward and effective way is to make the size of DCNs elastic along with traffic demands, i.e., turning off unnecessary network components to reduce the energy consumption. Today, software defined networking (SDN), as one of the most promising solutions for data center management, provides a paradigm to elastically control the resources of DCNs. However, to the best of our knowledge, the features of SDN have not been fully leveraged to improve the power saving, especially for large-scale multi-controller DCNs. To address this problem, we propose $\text{E}^{3}$ MC, a mechanism to improve DCN’s energy efficiency via the elastic multi-controller SDN. In $\text{E}^{3}$ MC, the energy optimizations for both forwarding and control plane are considered by utilizing SDN’s fine-grained routing and dynamic control mapping. In particular, the flow network theory and the bin-packing heuristic are used to deal with the forwarding plane and control plane, respectively. Our simulation results show that $\text{E}^{3}$ MC can achieve more efficient power management, especially in highly structured topologies such as Fat-Tree and BCube, by saving up to 50% of network energy, at an acceptable level of computation cost.

Traffic matrix is an abstract representation of the traffic volume flowing between sets of source and destination pairs. It is a key input parameter of network operations management, planning, provisioning and traffic engineering. Traffic matrix is also important in the context of OpenFlow-based networks. Because even good measurement systems can suffer from errors and data collection systems can fail, missing values are common. Existing matrix completion methods do not consider traffic exhibit characteristics and only provide a finite precision. To address this problem, this paper proposes a novel approach based on compressive sensing and traffic self-similarity to reconstruct the missing traffic flow data. Firstly, we analyze the real-world traffic matrix, which all exhibit low-rank structure, temporal smoothness feature and spatial self-similarity. Then, we propose Self-Similarity and Temporal Compressive Sensing (SSTCS) algorithm to reconstruct the missing traffic data. The extensive experiments with the real-world traffic matrix show that our proposed SSTCS can significantly reduce data reconstruction errors and achieve satisfactory accuracy comparing with the existing solutions. Typically SSTCS can successfully reconstruct the traffic matrix with less than 32% errors when as much as 98% of the data is missing.

Cooperative communication for wireless networks has gained a lot of recent interests due to its ability to mitigate fading with exploration of spatial diversity. The objective of this paper is to design an efficient algorithm to minimize the total consumed power of the network while guaranteeing transmission reliability of multiple active transmission pairs through cooperative wireless communications. This problem has not been studied and is much more challenging than relay assignment considered in literature work which simply targets to reduce the transmission power for a single transmission pair. We achieve the objective by jointly considering transmission mode selection, relay assignment and power allocation. This requires us to solve a combinatorial optimization problem, namely Reliable and Energy Efficient Cooperative Communication problem (REECC), which is a hard problem as its complexity increases exponentially with the number of relay nodes. We propose an iterative solution framework by testing different power levels to find the optimal solution. To reduce the computational cost, we design several novel techniques in the solution framework. The simulation results demonstrate that our solution can run very efficiently to obtain the minimum total consumed power while satisfying the reliable transmission requirement.

Secure communication in the multiuser and multieavesdropper (MUME) scenario is considered in this paper. It has be shown that secrecy can be improved when the transmitter simultaneously transmits an information-bearing signal to the intended receivers and artificial noise to confuse the eavesdroppers. Several processing schemes have been proposed to limit the cochannel interference (CCI). In this paper, we propose the increasing security degree of freedom (ISDF) method, which takes an idea from dirty-paper coding (DPC) and ZF beam-forming. By means of known interference precancellation at the transmitter, we design each precoder according to the previously designed precoding matrices, rather than other users' channels, which in return provides extra freedom for the design of precoders. Simulations demonstrate that the proposed method achieves the better performance and relatively low complexity.

Using the Runge-Kutta and Newton-Raphson methods, the electric fileds on the bushing-type insulator surface covered partly by a corona-suppression semiconductor coating with a field-dependent resistivity have been computed for semiconductors having various resistivityfield ield (p-E) characteristics. The field distribution along the insulator surface is strongly dependent on the p-E characteristic and the extension of the semiconductor coating as well as the frequency of the operating voltages. By assigning the highest field along the insulator surface to be the critical field for the onset of corona discharges (or the breakdown strength of air or other medium), the corona inception voltage of the insulators can be evaluated easily. Thus, by the comparison of the field distribution curves, it is easy to determine the type of the p-E characteristic and the extension of the semiconductor coating for optimal corona suppression. For bushing-type insulators, the computed results show that the semiconductors with p = poexp(-BE) or p * pO[2 - exp(aE)], where po, and a are constants, could give optimal corona suppression. The optimal choice depends on the physical parameters of the insulator, the extension of the semiconductor coating and the frequency of the operating voltages, and therefore the constants pO and or a have to be determined for individual cases. In general, for optimal al corona suppression po should decrease with increasing frequency and length of the insulator surface; and or a should increase with increasing length of the insulator surface.

Further theoretical analyses show that it is the physical parameters of the guard gap region which give rise to the anomalous phenomena observed in dielectric loss measurements using a three-electrode system. A Wagner or similar guard balance, which is generally used to make the potentials at the guard and the guarded electrodes identical in magnitude and in phase, may reduce the errors caused by the bridge balance conditions, ons, but cannot alter the physical parameters inherent in the guard gap region. An interfacial dielectric layer such as vaseline between the test sample surfaces and the electrodes enhances the anomalous phenomena under certain conditions. The potential along the guard gap surface varies from point to point although the potentials at the guard and the guarded electrodes are equal in magnitude and in phase. It is the nonuniform field distribution in the guard gap region which results in electrical discharges at the sharp edges of the guard and the guarded electrodes. Experimental results are in good agreement wtih the theoretical analyses. Methods for suppressing these anomalous effects also are discussed.