In the energy internet era, distributed energy storage systems will be widely used in various industrial, commercial, and residential scenarios. However, the modeling of energy flow and the digital flow of information operate on different temporal and spatial scales in traditional energy storage systems, lacking deep integration between energy flow and information flow. This prevents it from completely addressing the complex battery cell balancing and low safety issues in current distributed energy storage systems. The proposal of software-defined energy storage provides a new approach to solve the inherent problems in the battery industry by introducing the idea of internet shielding terminal differences into energy storage system. We proposed a software-defined reconfiguration battery system (SRBS) in this paper, which can manage different types of energy storage media to adapt to varying loads and operating conditions. Energy-type batteries, power-type batteries, and supercapacitors are integrated together to form an efficient and safe energy storage and power supply system. The hardware architecture and software control method of SRBS are described in detail. Finally, a real-world prototype is charged and discharged to show the successful coexistence and operation of different energy storage media within a single system.

The fifth-generation (5G) communication technology development makes it possible for distributed resources to participate in power system frequency regulation. This paper models the whole-chain communication time delay of distributed frequency regulation resources participating in automatic generation control (AGC) via a 5G network. The whole-chain communication time delay consists of time delay in the radio access network, backhaul network, and transmission and core networks. We integrate the time delay model into the power system frequency regulation model to reveal its impact on frequency regulation performance. In addition, we analyze the feasibility of the distributed variable renewable energy (VRE) participating in AGC and calculate the capabilities needed for different VRE penetrations. Finally, we provide a case study on the HRP-38 system. The results show that the communication time delay will substantially impact the frequency regulation performance when the VRE penetration is above 40%. In addition, distributed VRE may be a promising AGC resource. Distributed VRE that accounts for approximately 5% of the total installed VRE capacity is needed to participate in AGC via a 5G network, which could maintain the frequency performance of the power system with above 65% VRE penetrated at a normal level.

Mobile social networks allow users to acquire multimedia contents to their mobile devices via wireless communications. To alleviate the network traffic and latency for transmitting contents, user preferences can be predicted and popular contents can be cached at the edge of network. However, for edge caching over the indoor mobile social networks raises challenging issues. The user preference for mobile data is location-dependent in different areas of indoor environment, such that various edge nodes need to maintain its distinctive prediction model instead of using the universal one. The limited computing power for edge nodes over indoor mobile social networks also hinders effective model training on the edge. In this paper, we propose a collaborative edge caching framework that enables personalized modeling for content popularity prediction. Specifically, a non-additive measure based feature selection scheme is proposed to realize efficient yet accurate modeling on resource-constrained edge nodes. A collaborative learning algorithm is designed to reduce the computational overheads over mobile social networks by extracting global knowledge on simplifying model training through feature selection. Extensive simulation validates the effectiveness and efficiency of our proposed framework.

Malware traffic classification (MTC) is a key technology for anomaly and intrusion detection in secure Industrial Internet of Things (IIoT). Traditional MTC methods based on port, payload, and statistic depend on the manual-designed features, which have low accuracy. Recently, deep-learning methods have attracted a significant attention due to their high accuracy in terms of classification. However, in practical application scenarios, deep-learning methods require a large amount of labeled samples for training, while the available labeled samples for training are very rare. Furthermore, the preparation of a large amount of labeled samples requires a lot of labor costs. To solve these problems, this article proposes three methods based on semisupervised learning (SSL), transfer learning (TL), and domain adaptive (DA), respectively. Our proposed methods use a large amount of unlabeled data collected in the Internet traffic, which can greatly improve the classification accuracy with few labeled samples. Then, we use the DA method to solve the mismatch problem between the source domain and the target domain in the TL process. The proposed method is not only applicable to the shallow network but also to the deep neural network structure, and can achieve better classification results. Experimental results show that our proposed methods can satisfy the requirement of MTC in the case of few labeled samples in IIoT. The source code for all the experiments is available at GitHub.The code of this article can be downloaded from GitHub link:https://github.com/yzjh/Keras-MTC-DA-Ladder.

Malicious websites attempt to install malware on user’s devices without permission, which can disrupt device operation, steal personal information, and even acquire access to the device for future attacks. Accurate detection of malicious website behaviors is crucial for network security but still faces challenges. Firstly, various types and semantics of website features are required to identity the wide range of malicious characteristics, leading to massive training data and computational overhead. Secondly, to reduce model dimensionality, a proper selection of website features is essential but difficult due to the complex relations among features that can affect each other’s contribution to detection outcomes. In this paper, we propose a lightweight feature-based detection scheme against malicious websites considering the interaction measures among features and the overhead-accuracy tradeoff. Specifically, we systematically characterize the interactions among website features in a non-additive manner to indicate the aggregated impacts of feature subsets. Then we propose a quantification method to measure the feature interactions based on multivariate regression. With this method, important features are selected to substantially reduce the model dimension and computational complexity while maintaining desirable accuracy. Meanwhile, the proposed scheme provides an interpretable model that preserves the physical meanings of original features. It allows users to balance the overhead-accuracy tradeoff for detection model training through feature subset selection to fit the requirements and constraints of real applications.

With the upcoming next generation wireless network, vehicles are expected to be empowered by artificial intelligence (AI). By connecting vehicles and cloud server via wireless communication, federated learning (FL) allows vehicles to collaboratively train deep learning models to support intelligent services, such as autonomous driving. However, the large number of vehicles and increasing size of model parameters bring challenges to FL-empowered connected vehicles. Since communication bandwidth is insufficient to upload full-precision local models from numerous vehicles, model compression is usually conducted to reduce transmitted data size. Nevertheless, conventional model compression methods may not be practical for resource-constrained vehicles due to the increasing computational overhead for FL training. The overhead for downloading global model can also be omitted by existing methods since they are originally designed for centralized learning instead of FL. In this paper, we propose a ternary quantization based model compression method on communication-efficient FL for resource-constrained connected vehicles. Specifically, we firstly propose a ternary quantization based local model training algorithm that optimizes quantization factors and parameters simultaneously. Then, we design a communication-efficient FL approach that reduces overhead for both upstream and downstream communications. Finally, simulation results validate that the proposed method demands the lowest communication and computational overheads for FL training, while maintaining desired model accuracy compared to existing model compression methods.

Malware traffic classification (MTC) is a key technology for solving anomaly detection and intrusion detection problems. And hence it plays an important role in the field of network security. Traditional MTC methods based on port, payload and statistic depend on the manual-designed features, which have low accuracy. Recently, deep learning methods have attracted significant attention due to their high accuracy in terms of classification. However, in practical application scenarios, deep learning methods require a large amount of labeled samples for training, while the available labeled samples for training are very rare. Furthermore, the preparation of a large amount of labeled samples requires a lot of labor costs. To solve these problems, this paper proposes two methods based on semi-supervised learning (SSL) and transfer learning (TL), respectively. Our proposed methods use a large amount of unlabeled data collected in the Internet traffic, which can greatly improve the accuracy classification with few labeled samples. Through experiments, we obtained the best method to improve the accuracy of few labeled samples in different situations. Experiment results show that our proposed methods can satisfy the requirement of MTC in the case of few labeled samples.

Due to the vulnerability of the Internet of Things (IoT), it is indispensable to provide adequate security services. These services are generally implemented through security protocols or algorithms and running on energy-sensitive IoT devices. In order to design energy-efficient algorithms to prolong the lifetime of IoT devices, the energy characteristics of those algorithms should be analyzed primarily. In this paper, we conduct an integrated static analysis method with dynamic tracing to provide a quantitative energy profile for popular security algorithms such as AES128, RSA, and SHA256. Specifically, binary instructions executed in the invoked function are measured and counted through remotely debugging each program on the Arm development board. Then, the fine-grained energy consumption inside a program is revealed by combining the instruction statistics and instruction-level energy model. The experimental results show that the energy consumption of a program is mainly consumed by a few primary functions and CPU-memory interaction instructions, and hence the functions can be implemented in different ways to reduce energy consumption. This meaningful energy consumption evaluation method for security algorithms is able to guide to optimizing existing algorithms for embedded security.

Blockchain (BC) and artificial intelligence (AI) are often utilized separately in energy trading systems (ETSs). However, these technologies can complement each other and reinforce their capabilities when integrated. This article provides a comprehensive review of consensus algorithms (CAs) of BC and deep reinforcement learning (DRL) in ETS. While the distributed consensus underpins the immutability of transaction records of prosumers, the deluge of data generated paves the way to use AI algorithms for forecasting and address other data analytic-related issues. Hence, the motivation to combine BC with AI to realize secure and intelligent ETS. This study explores the principles, potentials, models, active research efforts and unresolved challenges in the CA and DRL. The review shows that despite the current interest in each of these technologies, little effort has been made at jointly exploiting them in ETS due to some open issues. Therefore, new insights are actively required to harness the full potentials of CA and DRL in ETS. We propose a framework and offer some perspectives on effective BC-AI integration in ETS.

The emerging technology of cybertwin is expected to bring revolutionary benefits to the sixth-generation (6G) network in respect of communication, resources allocation, and digital asset management. Empowered by ubiquitous artificial intelligence (AI), cybertwin is capable of adjusting the requests for computing resources to support network services by analyzing user’s demands for quality of experience and resource scarcity in the market. For resource-constrained applications, such as connected vehicles in the 6G network, cybertwin can intelligently determine the time-varying requests of computing resources for various vehicles at different times. However, the current service architecture executes AI algorithms with universal configurations for all vehicles. This causes the difficulty of customizing the complexity of AI algorithms to maintain adaptive to cybertwin’s decisions on dynamic resources allocation. In this article, we propose an adaptive AI framework based on efficient feature selection to cooperate with cybertwin’s resource allocation. This proposed framework can adaptively customizing AI model complexity with available computing resources. Specifically, we systematically characterize the aggregated impacts of all feature combinations on the modeling outcomes of AI algorithms. By utilizing nonadditive measures, the interactions among features can be quantified to indicate their contributions to the modeling process. Then, we propose an efficient algorithm to obtain accurate interaction measures for adaptive feature selection to balance the tradeoff between modeling accuracy and computational overhead. Finally, extensive simulations are conducted to validate that our proposed framework substantially reduces the overhead of AI algorithms while guaranteeing desired modeling accuracy for cybertwin-driven connected vehicles in 6G.

Panoramic virtual reality video (PVRV) is becoming increasingly popular since it offers a true immersive experience. However, the ultra-high resolution of PVRV requires significant bandwidth and ultra-low latency for PVRV streaming, something that makes challenging the extension of this application to mobile networks. Besides bandwidth, the frequent perspective viewport rendering induces a heavy computational load on battery-constrained mobile devices. To attack these problems jointly, this paper proposes a PVRV streaming system that is designed for modern multiconnectivity-based millimeter wave (mmWave) cellular networks in conjunction with mobile edge computing (MEC). First, mmWave is deployed to support the high bandwidth needs of PVRV streaming. Next, the multiple mmWave links that tend to suffer from outages are coupled with a sub-6 GHz link to ensure disruption-free wireless communication. With the help of an MEC server, the tradeoff among link adaptation, transcoding-based chunk quality adaptation, and viewport rendering offloading is sought to improve the wireless bandwidth utilization and mobile device's energy efficiency. Simulation results show that the proposed scheme can improve the streaming performance in both energy efficiency and the quality of received viewport over the state-of-the-art schemes.

Demand response (DR) programs are designed to affect the energy consumption behavior of end-users in smart grid. However, most existing pricing designs for DR programs ignore the influence of end-users’s diversity and personal preference. Thus, in this paper, we investigate an incentive pricing design based on the utility maximization rule with consideration of end-users’ preference and appliances’ operational patterns. In particular, the utility company determines the pricing policy by trading off the budget revenue and social obligation, while each end-user aims to maximize their own utility profits with high satisfaction level by scheduling multiclass appliances. We formulate the conflict and cooperative relationship between the utility company and end-users as a Stackelberg game, and the equilibrium points are obtained by the backward induction method, which exists and is unique. At the equilibrium, the utility company adopts real-time pricing (RTP) scheme to coordinate end-users to fulfill the benefit of themselves, i.e., under such price, end-users automatically maximize overall utility profits of the overall system. We propose a distributed algorithm and an adaptive pricing scheme for the utility company and end-users to jointly achieve the best performance of the entire system. Finally, extensive simulation results based on real operation data show the effectiveness of the proposed scheme.

Demand side management will be a key component of future smart grid that can help reduce peak load and adapt elastic demand to fluctuating generations. In this paper, we consider customers that operate different appliances and propose a demand response approach based on utility maximization. Each appliance provides a certain benefit depending on the pattern or volume of power it consumes. Each customer wishes to optimally schedule its power consumption so as to maximize its individual net benefit subject to various consumption and power flow constraints. We show that there exist time-varying prices that can align individual optimality with social optimality, i.e., under such prices, when the customers selfishly optimize their own benefits, they automatically also maximize the social welfare. The utility company can thus use dynamic pricing to coordinate demand responses to the benefit of the overall system. We propose a distributed algorithm for the utility company and the customers to jointly compute this optimal prices and demand schedules. Finally, we present simulation results that illustrate several interesting properties of the proposed scheme.

The paradigms of Fog and Edge Computing along with Internet of Things (IoT) promise to make everything, especially smart devices, as part of the Internet environment, where highly-centralized computing infrastructures are gradually decentralized into micro data centers deployed at the edges of each network. As a result, access latency has been reduced dramatically because of the decreases in geographical distance. So far, a great deal of research effort has been focused on how to satisfy the ever-increasing demand for Fog and Edge Computing resources. Correspondingly, the traditional centralized power supply system need to be decentralized among other computing and communications resources. However, how to design, operate and manage a fully distributed power supply system to support the same service agreement level as in cloud computing under fog and edge computing largely remains unknown. In this article, we propose and develop a new power supply system for Fog and Edge computing based on our previous work on digital energy systems. Experimental results based on a real-world case study of the proposed system are presented to validate the effectiveness and efficiency of the proposed power supply system.

In the chain of stereoscopic video processing, stereoscopic video coding and viewing are usually two independent stages. Conventional stereoscopic video coding puts an emphasis on improving the coding efficiency by seeking the optimal tradeoff between the coding bit rate and the signal-based distortion, while neglecting the perceptual behaviors of binocular combination when stereoscopic video is viewed by human beings. In this paper, we propose to utilize binocular combination to optimize the stereoscopic video coding from the perspective of perceptual quality measurement. Specifically, we propose a novel binocular-combination-oriented measurement for visual distortion and then derive the Lagrange multiplier for the binocular-combination-oriented rate-distortion optimization (RDO). Via extensive subjective tests, the results show that the proposed perceptual RDO can save more than 5% BD rate over the traditional RDO in multiview extension of High Efficiency Video Coding for stereoscopic video coding.

Energy harvesting (EH)–enabled relaying has attracted considerable attention as an effective way to prolong the operation time of energy-constrained networks and extend coverage beside desired survivability and rate of transmission. In related literature, the Harvest-Store-Use (HSU) model is usually utilized to describe the energy flow behavior of the EH system. However, the half-duplex (HD) constraint of HSU that harvested energy can only be used after being temporally stored in energy buffer may reduce effective transmission time. Thus, we first construct the full-duplex (FD) energy flow behavior model of the EH system where the harvested energy can be tuned to power load and being stored simultaneously. The FD model is then proved to be equivalent with the HSU model when time interval is small enough. Considering some key physical variabilities, for example, the wireless channel and the amount of harvested energy, the transmission adaptation problem for multiple relays embedded with FD EH systems is formulated with the objective to improve the utilization of the harvested energy. We tackle the problem by using a centralized optimization algorithm by jointly tuning the factors, including power control for source and relay nodes, relay selection and dynamic switching among four relay transmission mode, namely HD amplify-and-forward (AF), HD decode-and-forward (DF), FD AF, and FD DF. The centralized optimization algorithm is proposed on the basis of dual decomposition and serves as a benchmark. To enable relays to individually make their own decisions, a distributed algorithm with relatively higher complexity is given by using consensus optimization in conjunction with the alternating direction method of multipliers, and a sub-optimal algorithm with low complexity is provided. The proposed algorithms are shown to have good performance via simulations for a range of different EH rates and prediction errors.

IP networks have become the dominant platform for video delivery. However, bandwidth-hungry video is pushing networks to their limits: costs are rising for the operators and the viewing experience is not always satisfactory for the users. When considering 3D video delivery, the previous problems are exacerbated because of the higher volume of data that must be communicated, and the difficulty in characterizing the viewing experience of the end user. Consequently, network operators may be reluctant to deliver 3D video due to costs and unclear quality improvements to their users. In this setting, the true immersive experience of 3D video remains elusive. In this paper, we focus on the efficient delivery of 3D video in terms of quality and energy cost over centrally controlled networks. As a representative example of a centrally controlled network, a software-defined network (SDN) is assumed. Our approach is based on a comprehensive network-dependent 3D quality of experience (3DQoE) model and an energy cost model for 3D video streaming. By using the developed models, we formulate the problem of energy-efficient and 3DQoE-optimized 3D video flow path routing. The particular characteristic of video/depth rate allocation presented in 3D video is embedded seamlessly into the selection of the optimal routing paths for multiple 3D video streams. The formulated problem is NP-hard and is solved with a heuristic algorithm based on the branch-and-bound method after significant reduction of the solution search space. Extensive 3D video streaming experiments are conducted over an OpenFlow-based SDN with subjective and objective evaluations and they highlight the significant benefits of the proposed approach.

Information Centric Networking (ICN) leverages built-in caching capacity, termed as in-network caching, to support fast and efficient content distribution. The profits of ICN brought by its widespread in-network caching infrastructures has been well explored, but how to incentivize network players to deploy the socially optimal number of caches was largely ignored. Network players might not want to deploy or contribute their cache resources, if they do not have clear economic incentives, as cache deployment implies an extra capital expenditure. In this paper, we focus on the economic incentive interactions in caching deployment and usage between several types of network players in ICN. In particular, a Stackerlberg game model is used to capture the interactions between content provider (CP) and Internet service providers (ISPs). By using backward induction, it is proven that an equilibrium of the proposed model exists, under which both ISPs and CP can achieve their maximum utility. Numerical results show the effectiveness of the proposed model and the findings can provide important insights for network players to design efficient content sharing and caching schemes.

Compared with the traditional power grid, Energy Internet is motivated by the concept of intelligent energy sharing and coordination, achieving higher penetration of renewable energy and economic saving. In this paper, we propose a new framework for the time-slotted Peer-to-Peer (P2P) energy sharing and coordination in Energy Internet, which aims to achieve flexible and efficient distributed energy management and control. In this framework, users are equipped with distributed generators (DGs), distributed energy storage systems (DESs) and smart meters; the P2P energy sharing fashion is supported, where users can buy/sell electric from/to utility company and their neighboring users. The energy sharing and coordination problem is formulated as a convex optimization problem with the objective to minimize the economic cost of users. Then, a distributed algorithm is proposed, in combination with alternating direction method of multipliers (ADMM). On the basis of a real-world dataset of renewable energy and real-time electricity price, both analytical and numerical results show the effectiveness of the proposed framework and algorithm in terms of not only fast convergence in a time slot but also economic saving prominently for a long time application.

Energy harvesting (EH) enabled relaying has attracted lots of interests recently, as the network energy consumption can be reduced and the coverage range can be extended simultaneously. In most existing literatures, the Harvest-Store-Use (HSU) model is utilized to describe the energy flow behavior of the EH system. However, the half-duplex (HD) constraint of HSU that harvested energy can only be used for powering load after being temporally stored in energy storage unit may reduce the effective transmission time. Thus, we first model the full-duplex (FD) energy flow behavior of the EH system where harvested energy can be tuned to power load and being stored simultaneously, and then prove the FD model is equivalent to the HSU model when time interval is small enough. With consideration of some key physical variabilities, e.g., the wireless channel and the amount of harvested energy, and the energy consumption difference between FD and HD relaying protocols, we further model the transmission optimization problem to improve the utilization of harvested energy by optimizing the short-term throughput. Finally, to numerically obtain the optimized short-term throughput, we propose the joint power adaption, relay selection and transmission protocol switching algorithm. Results show that the performance of the proposed algorithm outperforms that of fixed relaying algorithms, e.g., the short-term throughput of the proposed algorithm is improved by about 40% comparing with fixed HD relaying algorithm, with 20 user equipments, and loop interference power and EH rate equal to 23 dB and 120 J/s, respectively.

Video services are proliferating over today's mobile Internet and efforts have been made to improve their performance. The cross-layer video streaming that jointly optimizes the parameters at different protocol layers is a feasible solution but without bandwidth aggregation of multiple wireless networks. The cross-network video streaming can realize the bandwidth aggregation by using multiple overlapping wireless networks simultaneously. However, parameters at different protocol layers are optimized independently in the cross-network optimization. In this paper, we propose a joint cross-network and cross-layer optimized video streaming scheme that utilizes the bandwidth aggregation of cross-network video streaming and further improve the performance by jointly optimizing the parameters of different protocol layers in each network with a cross-layer manner. In the proposed scheme, the LTE and WCDMA networks are adopted. The bit-rate of the video at application layer, the rate allocation among networks and the parameters of physical layers in each network are jointly optimized. Experimental results show that the proposed scheme gains higher quality of experience in terms of PSNR than the state-of-the-art schemes.

Today, how to accurately predict the quality of experience (QoE) of the networking service is a very important issue for the network operator to optimize the service. However, due to the complex multi-dimensional characteristics of QoE, QoE estimation is extremely challenging. With utilizing the advantages of quality of service (QoS) in evaluating the networking performance, we exploit QoS/QoE correlation to predict QoE by building a QoSto-QoE mapping relationship. To fully consider the inter-dependency among QoS parameters towards forming the QoE, a Choquet integral based fuzzy measurement method is used to map QoS to QoE. Via extensive experiments in mobile VoD applications, the advancement and effectiveness of the proposed method are verified.

Abstract In wireless sensor networks, one of the primary requirements is that sensor data acquired from the physical world can be interchanged with all interested collaborative entities in a secure, reliable manner. Because of highly unpredictable nature of the environments caused by malicious attacks or potential threats, minimizing transmission cost between source and sink nodes with jointly considering the security of the whole network is a critical issue. This paper considers two optimization problems of deriving the minimum cost paths from multiple source nodes to the sink node under the guaranteed level of the vulnerability. The link or node vulnerability is defined as a metric, which characterizes the degree of link or node sharing among paths. With the defined link vulnerability, the link vulnerability‐constrained minimum cost paths problem is first formulated, and two polynomial‐time algorithms are developed for deriving the optimal paths. For the node‐vulnerability‐constrained minimum cost paths problem, we adopt the network conversion and then achieve the optimal solution with previous proposed algorithms. The necessary condition for solution existence, the optimality of the proposed algorithms, and the related properties of tree network are further theoretically analyzed. Extensive simulations show the significant performance improvements achieved by our proposed algorithms.Copyright © 2014 John Wiley & Sons, Ltd.

ABSTRACT In flat wireless sensor networks, one fundamental issue is region coverage, which usually addresses whether the given region is sufficiently covered by sensing disks of sensor nodes or not. Although numerous research works have been carried out on region coverage, it still lacks in‐depth understanding on the relations between region coverage and sensing topology defined with the intersections of sensing areas of sensor nodes. In this paper, we consider the region coverage problem by using the sensing topology proposed in our previous work. Based on the notion of sensing topology, we prove that the given region can be partitioned into a number of the smallest cells, each of which is defined by sensing links among sensor nodes. Then, we investigate the sufficient and necessary conditions for the existence of coverage holes for the specific polygon graph residing in the partitioned cells. Further, two polynomial time algorithms are presented for dividing the given region covered by the whole network and detecting the coverage holes existing in the interior area of the partitioned cells, respectively. The experiment results show that our proposed algorithms are effective for detecting the coverage holes. Copyright © 2014 John Wiley & Sons, Ltd.

Abstract Interactive multimedia applications such as peer‐to‐peer (P2P) video services over the Internet have gained increasing popularity during the past few years. However, the adopted Internet‐based P2P overlay network architecture hides the underlying network topology, assuming that channel quality is always in perfect condition. Because of the time‐varying nature of wireless channels, this hardly meets the user‐perceived video quality requirement when used in wireless environments. Considering the tightly coupled relationship between P2P overlay networks and the underlying networks, we propose a distributed utility‐based scheduling algorithm on the basis of a quality‐driven cross‐layer design framework to jointly optimize the parameters of different network layers to achieve highly improved video quality for P2P video streaming services in wireless networks. In this paper, the quality‐driven P2P scheduling algorithm is formulated into a distributed utility‐based distortion‐delay optimization problem, where the expected video distortion is minimized under the constraint of a given packet playback deadline to select the optimal combination of system parameters residing in different network layers. Specifically, encoding behaviors, network congestion, Automatic Repeat Request/Query (ARQ), and modulation and coding are jointly considered. Then, we provide the algorithmic solution to the formulated problem. The distributed optimization running on each peer node adopted in the proposed scheduling algorithm greatly reduces the computational intensity. Extensive experimental results also demonstrate 4–14 dB quality enhancement in terms of peak signal‐to‐noise ratio by using the proposed scheduling algorithm. Copyright © 2015 John Wiley & Sons, Ltd.

Abstract Recently, benefiting from rapid development of energy harvesting technologies, the research trend of wireless sensor networks has shifted from the battery‐powered network to the one that can harvest energy from ambient environments. In such networks, a proper use of harvested energy poses plenty of challenges caused by numerous influence factors and complex application environments. Although numerous works have been based on the energy status of sensor nodes, no work refers to the issue of minimizing the overall data transmission cost by adjusting transmission power of nodes in energy‐harvesting wireless sensor networks. In this paper, we consider the optimization problem of deriving the energy‐neutral minimum cost paths between the source nodes and the sink node. By introducing the concept of energy‐neutral operation, we first propose a polynomial‐time optimal algorithm for finding the optimal path from a single source to the sink by adjusting the transmission powers. Based on the work earlier, another polynomial‐time algorithm is further proposed for finding the approximated optimal paths from multiple sources to the sink node. Also, we analyze the network capacity and present a near‐optimal algorithm based on the Ford–Fulkerson algorithm for approaching the maximum flow in the given network. We have validated our algorithms by various numerical results in terms of path capacity, least energy of nodes, energy ratio, and path cost. Simulation results show that the proposed algorithms achieve significant performance enhancements over existing schemes. Copyright © 2016 John Wiley & Sons, Ltd.

Distributed caching in the radio access network (RAN) has been a common approach for improving QoE per- ceived by mobile users and reducing backhaul traffic load. In this paper, we focus on the problem of cooperative and online searching and routing for content objects cached according to their local popularity in small- cell networks (SCNs), considering practical issues of implementation complexity and content prefer- ence diversity, which is different from prior arts mainly focusing on global content caching optimization with pre-knowledge of content requests and/or mobility pattern of users. We propose an in-RAN caches orchestration strategy (named as iCacheOS), where cooperative content searching and sharing is achieved through content joint wireless and backhaul routing algorithm. Specifically, the in-RAN caches orchestration framework is designed under the software defined networking inspired system architecture consisting of the central orchestrator and in-RAN caches with the local routing function; the content joint wireless and backhaul routing problem is constructed with the objective to minimizing the SCN content transmission delay, which is proved to be a quadratic assignment problem (QAP); to assure high computation efficiency, a joint dimension reduction, Lagrangian relaxation and accelerated branch and bound algorithm is pro- posed. Numerical results reveal that iCacheOS can significantly reduce the content transmission delay and outgoing backhaul traffic load, and is more robust to content preference diversities, in comparison with other strategies.

Heterogenous cellular networks (HCN) consist of macrocells and small cells that are overlaid in the same geographical area. Hence, is critical that the high power macrocell shuts off its transmissions for a fraction of the time to allow the low power small cells to transmit without interference. This is the time-domain resource partitioning (TDRP) mechanism. In this paper we investigate video communication in HCNs when TDRP is employed. More specifically we consider the problem of maximizing the average video quality of all users, by jointly optimizing the rate allocated to each specific video stream and the quality that it is streamed. The resulting mixed integer linear program (MILP) formulation is solved numerically. Simulation results indicate clearly that as the small cells and the users are increased the proposed system can improve significantly the video quality.

In this paper, we propose TCP-FNC (TCP with fast network coding), which is designed to reduce the decoding delay for TCP with network coding. TCP-FNC retains the framework of TCP/NC and includes two schemes to meet the demand of online network coding. First, we develop a feedback based scheme named FCWL, which can efficiently reduce the waiting delay and work well with both RTT-based and loss-based TCP variants. Second, we propose an optimized progressive decoding algorithm named EFU for Gaussian-Jordan elimination, which can further reduce the computation delay from O(n2) to O(n). Evaluation results indicate that our TCP-FNC can achieve shorter decoding delay than TCP/NC and VON without trading off its goodput performance. We verify that TCP-FNC can reduce the decoding delay by 33% on average.

TCP Incast refers to the phenomenon of goodput collapse when multiple synchronized servers send data to the same client in parallel. In this paper, we propose a novel application-level scheduling approach named OSDT (Optimal Staggering Data Transfers) for TCP Incast problem. OSDT limits the number of concurrent TCP flows as well as servers' sending rate to optimal values so that the utilization of link capacity can be maximized without any packet losses. To achieve this, we build an optimization model with the usage of network and application information. Based on this model we can get the optimal values for the parameters in OSDT. Simulation results indicate that OSDT can achieve the highest goodput among all existing application-level scheduling approaches in a wide range of network and application parameters, and its performance is stable. So OSDT can be seen as an effective and scalable solution for TCP Incast problem.

The rapid developments of advanced wireless communication technologies and mobile devices are boosting the uplink multimedia applications. In this paper, a transmit power aware cross-layer optimization scheme is proposed to achieve a good trade-off between the transmit power and the perceived video quality for Long Term Evolution uplink video streaming. Specifically, the video coding quantization parameter and encoding mode at the application layer, and the uplink transmit power as well as modulation and coding scheme at the physical layer are jointly adjusted in the cross-layer optimization. To further improve the perceptual video experience for the end user with limited transmission resources, unequal quality control is performed by enhancing the video quality of region of interest. Additionally, the structural similarity is adopted as the video quality measurement metric to make the optimized video properly preserve the structural information during the cross-layer optimization process. Experimental results show that significant performance improvements in terms of the transmit power reduction and the perceptual video quality are achieved for the proposed transmit power aware cross-layer optimization scheme.

To reduce data access latency, network traffic volume and server load, in-network caching was proposed and has become an intrinsic component of the content-centric network (CCN) architecture. The content-oriented characteristics of in-network caching, such as arbitrary topology, volatile content locations and line speed requirements, make routers content-aware and supportive of fast content distribution. Meanwhile, they also raise new challenges in content placement and request routing, namely, how to optimally make content storage decisions and provision individual router's bandwidth to serve user requests, so as to minimize the bandwidth cost under storage and link capacity limit. To address this problem, we build a distributed in-network caching model to formulate the content placement and request routing in CCN, aiming at minimizing the bandwidth cost with strict storage and bandwidth constraints. Based on the proposed model, we design a scalable, adaptive and low-complexity in-network caching scheme for content placement and request routing and analyze the performance gains via simulations on a real ISP network topology and traffic traces. The experimental results show the proposed model and scheme are superior. Compared with the existing works, we also observe significant performance enhancements in terms of hit ratio of requests, reduction of server load, and bandwidth cost.

Content Centric Networking (CCN) is an emerging next-generation network infrastructure around content dissemination and retrieval, shifting from physical locations to named data. The built-in caching capacity of CCN, termed as in-network caching, promises to enable fast and effective content distribution at a global scale. Because of the in-network caching, the caching strategy of content location potentially affects how to make content searching decisions, while content searching in return decides in which routers content are stored. The relationship between content caching location and content searching has not been fully exploited in CCN or further used for the whole network performance improvement. This paper exploits the content caching location and content searching mechanism of CCN, and proposes a Popularity-driven content Caching Location and Searching scheme (P-CLS) in CCN. P-CLS leverages content access popularity to realize diverse content distribution and reduce content caching redundancy, which also overcomes the oscillation and frequent replacement phenomenon in the existing content caching and searching (CLS) scheme. Extensive simulations via hierarchical and arbitrary caching topologies show that the proposed scheme outperforms the existing caching algorithms.

Quality of Experience (QoE) support becomes more demanding due to the increasing popularity of multimedia services. However, QoE-based multimedia provisioning will impose a high pressure on server capacity and network bandwidth, which becomes one of the major concerns for both content providers and network operators. A common approach for efficient and scalable multimedia service provisioning in content distribution networks (CDN) is to replicate certain “hot” service or content from the original server to multiple replica servers, usually closer to end users. However, how to direct distributed user requests to an appropriate replica server to fundamentally improve the user perceived service quality largely remains unknown. In this paper, we present a user-oriented QoE-driven server selection scheme to solve the aforementioned problem. Experiments carried out on a real-world network have been performed to evaluate the proposed scheme, and the results show that the proposed scheme can improve QoE perceived by end users at least 24.6%.

The gap between the stable system resource supply and the traffic load (i.e, the demand) with temporal fluctuations and spatial disparities results in deficiency in the system resource (e.g, power and spectrum resources) utility in cellular networks. To bridge the variant gap, cell breathing schemes are adopted in prior arts, which mainly focus on the temporal variance. Therefore, there is every reason to further explore the improvement room in terms of efficiency in the system resource utilization of cell breathing, jointly considering temporal fluctuations and spatial disparities of the traffic load, especially under Ultra-Dense HetNets (UDHNs), the promising delivery of 5G networks. We propose the High-Resolution Cell Breathing (HiRCB) strategy for improving energy efficiency for UDHNs: 1) inspired by pixels in digital imaging, the UDHN coverage area is logically divided into Traffic Lattices (TLs), capturing spatial disparities of the traffic load; 2) the TL association problem with the objective of maximizing the accumulative energy efficiency utility is formulated; 3) to solve the formulated problem, the Parallel and Integer TL Association Algorithm (PITLAA) is proposed, featuring parallel computations and algorithmic conversions from multiple to single TL association. Numerical results show that HiRCB improves at least 50% energy efficiency.

In a cognitive radio (CR) network, a secondary user (SU) with multiple interfaces is capable of accessing multiple CR channels in an opportunistic fashion. Therefore, the available channel resources may change dramatically, and the reliabilities of the multiple accessed CR channels are also time-varying. Video streaming in such a multi-channel CR network faces great challenges in guaranteeing the quality of the received video. To deal with these challenges, we adopt H.264/SVC encoded video as the source and firstly optimize the video streaming from the perspective of exploiting more channel resources for the SU by developing a flexible sensing-transmission scheme for opportunistic spectrum access (OSA). In this scheme, the primary user activity, channel sensing result, and channel sensing accuracy are all considered in reducing the unnecessary channel sensings and correspondingly extending the transmission duration for the SU. Based on the flexible sensing-transmission scheme, we then propose a utility-based H.264/SVC video transmission scheme to further improve the expected video quality at the receiver. Specifically , the network abstraction layer units (NALUs) in the SVC video are assigned utilities which accurately reflect their contributions to the video quality, and the total effective utility of expected received video is maximized through perfectly dispatching the NALUs over the multiple CR channels. Both analytical studies and experimental results validate the effectiveness and efficiency of the proposed method.

Abstract Coverage is an importance issue in wireless sensor networks. In this work, we first propose a novel notion of information coverage, which refers to the coverage efficiency of field information covered by deployed sensor nodes. On the basis of information coverage, we consider an optimization problem of how to partition the given field into multiple parcels and to deploy sensor nodes in some selected parcels such that the field information covered by the deployed sensor nodes meets the requirement. First, we develop two effective polynomial‐time algorithms to determine the deployed locations of source nodes for information 1‐coverage andq‐coverage of the field, respectively, without consideration of communication, where informationq‐coverage implies that the field information in terms of information point is covered by at leastqsource nodes. Also, we prove the upper bound in the theoretical for the approximate solution derived by our proposed method. Second, another polynomial‐time algorithm is presented for deriving the deployed locations of relay nodes. In the theoretical, this proposed algorithm can achieve the minimized number of relay nodes. Further, the related information 1‐coverage algorithms are applied in our wireless sensor network‐based automatic irrigation project in precision agriculture. Experimental results show the major trade‐offs of impact factors in sensor deployment and significant performance improvements achieved by our proposed method. Copyright © 2013 John Wiley & Sons, Ltd.

Disparity estimation is an important task in many 3D video surveillance applications. How to generate the disparity information at the front end under limited delay budget is very challenging in a real-time surveillance system. In this paper, we tackle this problem through adopting multiresolution strategy in the disparity estimation process. Our contribution is twofold. First, unlike existing coarse-to-fine strategies based on uniform sampling, we present a fast disparity estimation algorithm based on nonuniform sampling at the coarse level. The disparity values of the non-samples are initially interpolated through Delaunay Triangulation, and then are refined through bilateral filtering. The content-aware nonuniform sampling provides better disparity approximation in the triangulated interpolation, and consequently leads to faster convergence in the refinement procedure. Second, we model the computation time in the disparity estimation process for execution assistance in the delay sensitive surveillance system. Both the sampling cell size and the data resolution are considered in this model, in order to accommodate different runtime requirements. Experimental results demonstrate the efficiency of the proposed scheme.

Due to the special topologies and communication pattern, in today's data center networks it is common that a large set of TCP flows and a small set of TCP flows get into different ingress ports of a switch and compete for a same egress port. However, in this case the throughput share of flows in the two sets will not be fair even though all flows have the same RTT. In this paper, we study this problem and find that TCP's fairness in data center networks is related with not only the network capacity but also the number of flows in the two sets. We propose a mathematical model of the average throughput ratio of the large set of flows to the small set of flows. This model can reveal the variation of TCP's fairness along with the change of network parameters (including buffer size, bandwidth, and propagation delay) as well as the number of flows in the two sets. We validate our model by comparing its numerical results with simulation results, finding that they match well.

Numerous research works have been done on the Choquet integral model due to the tremendous usage in many fields. However, the application is still significantly restricted by the curse of dimensionality, involved in determining the non-additive interaction measures, that can properly reflect the interactions among predictive attributes toward the objective. To this end, in this paper we propose a novel determination method for non-additive interaction measures by the way of solving a sequence of least norm problems and iteratively updating the values of interaction measures, namely least norm learning. This method can achieve a significant reduction on the computation time complexity from O(m × 2n) to O(mn) for solving the Choquet integral model, where ra and n are the numbers of observations and attributes, respectively. Also we achieve to reduce the computation space complexity from O(m × 2n) to 0(2n). A case study on cross-layer optimized wireless multimedia communications is adopted to validate the proposed method. Both analytical and experimental results show the effectiveness of the proposed method.

Due to advantages in spectral efficiency and energy efficiency of its air-interface, the coordinated multi-point (CoMP) transmission has been adopted in 4G and beyond mobile communication systems, such as LTE-A, which enables the transmission cooperation among multiple remote radio units (RRUs). As the main component of a RRU, the power amplifier (PA) is always blamed for its low power efficiency, which is difficult to be further improved by today's hardware technology. To eliminate the on-grid power consumption caused by the low PA efficiency and the corresponding CO2emission, we propose a new CoMP transmission framework, in which all RRUs and associated PAs are powered by the solar power. Then, based on the proposed framework, we derive the optimal coordinated transmission scheduling algorithm for maximizing the throughput with considerations of fast fading channel, limited pre-knowledge about channel state information (CSI), random energy arrival and finite energy storage. Theoretical analyses of the proposed algorithm are given, and numerical results show that our algorithm can achieve a close performance to the optimal transmission scheduling algorithm with a priori knowledge about CSI.

With its ability to promote the wireless spectrum utilization, cognitive radio (CR) is a promising technology for various broadband wireless video applications. A secondary user (SU) with multiple wireless interfaces is capable to sense and access multiple CR channels, each of which can only be accessed in an opportunistic fashion. Therefore, the amount of available channel resources may change dramatically, and the reliabilities of the multiple accessed CR channels are time-varying and different from each other. To improve the quality of the received video in such a multi-channel CR network, we first develop a flexible sensing-transmission structure for dynamic spectrum access according to primary user activity and channel sensing accuracy. Based on the proposed sensing-transmission structure, we then propose a hierarchical-matching scheme to adapt the scalable video stream to the multiple time-varying and reliability-different CR channels, where we consider the priorities and validity of the network abstraction layer units (NALUs) in the transmission scheduling. Under this scheme, the more important NALUs at the global Group Of Pictures (GOP) scope will be dispatched over the more reliable channels. Both analytical studies and experimental results show the effectiveness and efficiency of the proposed method.

Many research efforts have been done to guarantee the quality of service for video streaming over LTE. Among them, cross-layer optimized video delivery is one of the most commonly adopted approaches. However, most existing schemes of cross-layer optimized video delivery adopt PSNR as the optimization target, but do not well consider human vision characteristics. In this paper, we adopt a new metric - Structural Similarity (SSIM) in video quality evaluation and develop a SSIM-based cross-layer optimization scheme with the suppression of propagated error for video delivery over LTE downlink. The modulation and coding scheme at the physical layer of LTE downlink is selected by jointly considering the characteristics of video packets and time-varying channel states. Correspondingly, the quantization parameter of video codec at the application layer is adjusted to make the bit rate of the video stream adapt to the varying throughput of the physical link. Moreover, the error-resilient rate-distortion optimization is adopted in the proposed cross-layer optimization to suppress the effect of error propagation on the video quality degradation. Experimental results show that the proposed cross-layer optimization scheme can maintain more structural information than the conventional schemes in the received video, which correspondingly improves the video quality perceived by the end user.

To reduce network access latency, network traffic volume, and server load, caching capacity was proposed as a component of eNodeBs in the radio access network (RAN). These eNodeB caches require less transport energy but bring additional caching energy through providing each eNodeB with caching capacity. It is thus challenging for eNodeBs to make content placement and request routing decisions so as to minimize the total energy consumption, especially when considering different caching hardware technologies, arrival rate of requests and content popularity. To address this problem, we first build an energy model to formulate the problem of minimizing energy consumption at eNodeB caches. Then a Lagrangian relaxation technique is adopted to find a near-optimal solution to the proposed model. Based on the proposed model and the obtained solution, we design a practical scheme for coordinating the content placement and request routing, and thus ensure the minimum-energy eNodeB caches. Compared with the existing works, our proposal significantly reduces the energy consumption by approximately 28% while keeps a good network performance. Our results also indicate that caching hardware technologies and content popularity greatly affect the content placement, therefore are crucial to the energy efficiency of eNodeB caches.

Information-Centric Networking (ICN) is a new emerging concept, in which the principal paradigm shifts from the traditional end-to-end connection to the information-centric communication model. In ICN, information unit, such as content or service, is distributed in different sites or data centers to provide large-scale services. A common supporting approach for scalable service provisioning is deploying multiple replica servers throughout the network. Accordingly, an efficient and flexible scheme is needed to direct distributed requests to an appropriate replica server. In this paper, we first propose an incrementally deployable ICN architecture based on edge/core separation. And then, a practical anycast-based service provisioning scheme is presented with joint considerations of both servers contexts and the underlying network conditions. Extensive experiments have been performed to evaluate the proposed scheme. Experiment results show that efficient context-aware distributed service provisioning can be achieved.

Scalable video has natural advantages in adapting to the multi-channel wireless networks. And some existing works tried to further optimize the scalable video transmission by combining the crude layer-importance mapping with some extrinsic techniques, such as Forward Error Correction (FEC) and Adaptive Modulation and Coding (AMC). However, the intrinsic flexibility of scalable video streaming over the multichannel wireless networks was neglected. In this paper, we try to exploit the intrinsic flexibility by firstly analyzing the priorities of H.264/SVC video data at the network abstraction layer unit (NALU) level, and then designing the priority-validity delivery scheme for the scalable video streaming. With this strategy, the sub-stream extraction is intelligently adjusted according to the delivery history, and the more important data in a group of pictures (GOP) will be delivered through the more reliable channels. Experimental results also validate the strategy's effectiveness in improving the objective quality and perceptual experience of the received video.

Recently, energy harvesting technology has been introduced into wireless sensor networks to solve the traditional battery-powered energy bottleneck problem. However, due to battery capacity limitation, the harvested energy would overflow while the nodes are in the energy saturation status. Aiming at this, we consider topology control approach in the EHWSNs that allows each node to adaptively adjust its transmission power level to utilize the harvested energy efficiently. Specifically, we first model nodes' behaviors as an ordinal potential game where the high harvesting power nodes interact with the low harvesting power nodes to collaboratively maintain the whole network's connectivity. And we theoretically prove the existence of Nash equilibrium in this game. Then, a polynomial-time algorithm has been proposed to achieve Nash equilibrium. Simulation results show that our algorithm exploits the available energy resources in an efficient way and outperforms existing energy-aware algorithm in terms of energy conservation and equilibrium distribution.

To reduce the fossil energy consumption and the operational expenses, the hybrid energy powered cellular network (HybENet) with BSs powered by on-grid or renewable energy is studied. To minimize the total expenses of energy consumption (EEC) and guarantee the quality of service of HybENet, a liquid cell management algorithm, which adaptively and cooperatively adjusts the service coverage of BSs according to the actual load, is proposed. Specifically, the problem of minimizing the total EEC under constraints of the fluctuating arrivals of the renewable energy and the QoS is formulated as a combinatorial optimization problem. Then, we prove that the problem can be decomposed into two sub-problems: 1) the mean of per-link energy minimization problem, from which the closed expression of power allocation is derived; 2) the traffic block assignment problem, which is solved by ant colony optimization. Simulation results show that the total EEC can be effectively reduced.

ABSTRACT Energy‐efficient routing becomes one of the most critical technologies for sustaining the overall network lifetime of wireless sensor networks. In this paper, we propose a novel data transmission scheme between a number of specified source nodes and the single sink, which can efficiently restrict the usage frequency of each relay node, measured by the number of source nodes using it for data transmission. On the basis of the importance of source nodes that is closely related to deployed location, they form a descending sequence such that each node finds the minimum energy path earlier than the succeeding one. Then, the energy‐efficient multiple path algorithm with the computational complexity ofO(n3) is developed for deriving the minimum energy paths, wherenis the number of nodes in the network. Also, a polynomial algorithm is presented for deriving the range of the feasible values ofN0serving as the threshold of the usage frequency of relay nodes, in which each can guarantee the existence of the solution. Further, we theoretically investigate the existence of the solution and the tree‐structured solution usingm‐ary tree. Extensive simulation results show that our proposed scheme can achieve significant performance enhancement. Copyright © 2012 John Wiley & Sons, Ltd.

This paper presents a binocular PTU (pan-tilt unit) camera video object tracking scheme using the MeanShift algorithm and the runtime disparity estimation. The proposed method is to accommodate the requirement of 3D content generation and accurate tracking in more advanced video surveillance applications. The disparity estimation process for each stereoscopic pair is formulated as an energy minimization problem. The iterative solution procedure is implemented in a course-to-fine manner. The estimated disparity is used to scale the tracking window by the MeanShift algorithm, i.e. the size of the tracking area is adjustable according to its inner disparity, and thus the moving object can be better located by the camera. The program maintains the semi-real-time performance and acceptable accuracy as evaluated on a set of standard test data. In our experiment, two PointGrey cameras are controlled through a PTU device. The disparity estimation process on the recorded tracking video (640×480) achieves 6fps on an ordinary PC (2.66GHz CPU, 4GB RAM).

The traditional cross-layer model for video streaming can achieve significant improvement to the end-to-end video quality. However, the video quality measurement in terms of sum of squared error (SSE) in the model does not always correlate well with the perception of the human visual system. In this paper, we propose a perceptual quality driven cross-layer optimization scheme based on the structural similarity (SSIM) index for wireless video streaming. The H.264/AVC encoding parameters on the application layer and the modulation and channel coding modes on the physical layer are mainly considered to optimize the end-to-end perceptual video quality. In order to decrease the computation complexity, a low complexity optimization algorithm based on the rate-quantization (R-Q) model is proposed to reduce the range of the candidate optimization parameters. Experimental results show that the proposed SSIM-based cross-layer optimization scheme achieves better perceptual quality than the SSE-based cross-layer optimization scheme, and the proposed low complexity optimization algorithm can achieve about 62% computation decrease compared to the conventional exhaustive searching algorithm with little loss in perceptual quality.

Information-Centric Networking (ICN) enables caching of addressable content chunks in every cache-equipped router. So it is crucial to make the cached content to be visible locally, in order to satisfy the subsequent request. We achieve this goal in this paper with a novel caching scheme, which combines the content placement with dynamic request routing by selectively creating trails along the content delivery path. In addition, in-network caching requires search or replacement of cached chunks at line-speed, which resorts to the low complexity collaborative caching scheme and limits the cache size at each network level. Coordinate with the feasible cache size allocation, the proposed scheme automatically distributes the content to the proper cache location according to its popularity. Therefore, the proposed self assembly caching scheme could reduce caching redundancy and in turn, make more efficient utilization of available cache resources. The simulation results show that the proposed scheme outperforms existing algorithms.

Cognitive radio (CR) promotes the utilization of the wireless spectrum through admitting the secondary users to access the primary channels in an opportunistic manner. Therefore, the real-time video streaming over the CR network is challenging due to the time-varying channels. In this paper, we propose an adaptive scalable video transmission scheduling method for video streaming over time-varying channels in CR network, in which the end-to-end perceptual visual experience is optimized systematically by considering various experience-influencing factors of the scalable video source, channel, and receiving buffer. Moreover, an enhanced content-based adaptive video playout scheme is developed to further optimize the end-to-end perceptual visual experience by decreasing the receiving buffer underflow probability. The efficiency and effectiveness of the proposed method has been validated by both analytical study and extensive experimental results.

In wireless sensor networks (WSNs), sensor nodes are usually powered by batteries of limited capacity, which results in dynamic changes of available paths for data aggregation due to node failures caused by energy depletion. For transmitting certain amount of data generated by source node, the overall transmission cost is affected by two major factors, using sequence of available paths and amount of data imposed on each path, which becomes a major issue significantly influencing the efficient usage of the networks. To address this issue, we consider the optimization problem of how to minimize the overall transmission cost of given data delivered from the source node to the sink node in the energy-constrained WSN. Specifically, we first describe the problem on the basis of the minimum cost flow theory and derive the upper bound for the data amount in terms of the number of packets that can be successfully transmitted from the source node to the sink node. Then, we propose specific algorithms to derive the optimal paths and their optimal data amounts, and then achieve the minimized overall transmission cost for the certain amount of data. Extensive simulations show that significant performance enhancement can be achieved by using our proposed algorithms.

For an ISP (Internet Service Provider) which has deployed P2P caches in more than one AS (autonomous system), cooperative caching which makes their caches cooperate with each other can save more cost of carrying P2P traffic than independent caching for the ISP. However, existing cooperative caching schemes only use objects' popularity as the measurement to decide which objects should be cached, and cost on intra-ISP links that has great impact on the benefits of cooperative caching is not considered. In this paper, we propose a novel cooperative caching scheme named C-LGV (Cooperative, Lowest Global Value). C-LGV uses cost on intra-ISP links as well as objects' popularity to evaluate each object's global value (a new concept defined to estimate the benefits of caching or replacing an object), and objects with the lowest global values will be evicted when each local cache needs to replace its cached contents. Results of both synthetic and real traces driven simulations indicate that C-LGV can reduce the cost of carrying P2P traffic by at least 23% compared to existing cooperative caching schemes.

There is a growing interest around the world in supplying the communication networks with green energy from natural resources, e.g., solar, wind, and hydro, to reduce carbon footprints. However, the green energy sources and the energy buffer have the limitation of unstable availability and capacity. It is challenging to ensure that the fluctuant energy supply meets the demands of dynamic traffic loads. In this paper, we study the problem of how to ensure the sustainability of green energy powered cellular networks (i.e., green cellular networks). We firstly construct a generalized model to describe the energy evolution process of green energy powered cells. Then, energy dynamics metrics, which are energy level transfer time and energy outage probability, are analyzed by adopting diffusion approximation. Based on the results obtained in the analysis, a liquid cell management scheme is proposed to ensure the sustainability of green energy powered cells by adjusting the cell radii. The scheme performs excellently in improving both the lifetime and green energy utilization of green HeNBs.

Content Centric Networking (CCN) is a new emerging network architecture, shifting from an end-to-end connection to a content-centric communication model. A number of content-oriented characteristics, such as name-based routing, arbitrary topology and ubiquitous caching make caching in CCN different from the previous works. In this paper, we focus on the collaborative caching in CCN and propose a dominating-set-based collaborative caching mechanism, which jointly considers the content placement and request routing in a tightly-couple manner. Extensive experiments have been performed to evaluate the proposed scheme. Simulation results show that the proposed CollaCache proposal outperforms the existing caching and routing mechanisms in CCN.

Scalable 3D video P2P streaming systems can supply diverse 3D experiences for heterogeneous clients with high efficiencies. Data characteristics of the scalable 3D video make the P2P streaming efficiency more depends on the data segmentation algorithm. However, traditional data segmentation algorithm is not very appropriate for scalable 3D video P2P streaming systems. In this paper, we propose a Playback Length Changeable 3D video Segmentation (PLC3DS) algorithm. It considers the particular source-data characteristics of scalable 3D video, and provides different error resilience strengths to video and depth as well as layers with different importance levels in the transmission. The simulation results show that the proposed PLC3DS algorithm can increase the success delivery rates of chunks in more important layers, and further improve the 3D experiences of the client. Moreover, it improves the network utilization ratio remarkably.

This paper presents a multi-camera motion capture system aiming to provide caregivers with timely access to the patient's health status through mobile communication devices. The major components include video capture, object detection, video coding and transmission, error concealment, and video analysis. Our contribution is twofold. First, several novel ideas are developed, including fast object detection, and content-aware and adaptive video coding and transmission. Second, all components are seamlessly integrated in a unified optimization framework dedicated for online data transmission. In the scenario, the subject walked on a treadmill with four tripod cameras capturing the video from different viewpoints. After video compression and transmission over a wireless sensor network, the remote receiver recovered the videos and performed multi-view motion capture for gait analysis. Experimental results show that the presented system design achieves better video quality than traditional video coding and transmission scheme, while the requirement for a low-cost, noninvasive and real-time healthcare monitoring system is accommodated.

The SSIM-based rate distortion optimization (R-DO) has been proved to be an effective way to promote the perceptual video coding performance, and the Lagrange multiplier decision is the key to the SSIM-based RD-optimized video coding. Through extensively analyzing the characteristics of SSIM-based and SSE-based video distortions, this paper presents a low-complexity content-adaptive Lagrange multiplier decision method. The proposed method first estimates frame-level SSIM-based Lagrange multiplier by scaling the traditional SSE-based Lagrange multiplier with the ratio of SSE-based distortion to SSIM-based distortion. Via predicting the macroblock's perceptual importance in the whole frame, the macroblock-level Lagrange multiplier is further refined to promote the accuracy of the Lagrange multiplier decision. Experimental results show that the proposed method can obtain almost the same rate-SSIM performance and subjective quality as the state-of-the-art SSIM-based RD-optimized video coding methods with lower computation overheads.

Cooperation among mobile terminals (MTs) in heterogeneous wireless networks is currently widely investigated. Cooperation among MTs has a lot of advantages such as increasing network throughput, decreasing file download time, saving energy and so on. Since different cooperate MTs have different influence on quality of service and user's experience, MT selection becomes a key issue in MTs cooperation. To improve the quality of service and user's experience, we propose a novel mechanism for MT selection which we call NMC (Network MTs Cooperation) mechanism in this paper. In NMC mechanism, MTs and its access network are integrated as a virtual MT. Then we propose a Cooperative Terminal Selection (CTS) algorithm to select the optimal virtual MT. Simulation results show that NMC can help to save energy consumption of MTs as well as ensure the download rates.

For robust video plus depth based 3D video streaming, video, depth and packet-level forward error correction (FEC) can provide many rate combinations with various 3D visual qualities to adapt to the dynamic channel conditions. Video/depth/FEC rate allocation under the channel bandwidth constraint is an important optimization problem for robust 3D video streaming. This paper proposes a joint video/depth/FEC rate allocation method by maximizing the receiver's 3D visual quality. Through predicting the perceptual 3D visual qualities of the different video/depth/FEC rate combinations, the optimal GOP-level video/depth/FEC rate combination can be found. Further, the selected FEC rates are unequally assigned to different levels of 3D saliency regions within each video/depth frame. The effectiveness of the proposed 3D saliency based joint video/depth/FEC rate allocation method for scalable 3D video streaming is validated by extensive experimental results.

In wireless sensor networks, one of the primary requirements is that sensor data derived from the physical world can be interchanged with all interested collaborative entities in a secure and reliable manner. Due to highly unpredictable environments where sensor nodes are usually deployed, minimizing the transmisstion cost with jointly taking into account the security of the whole network poses a challenging task. With this end, this paper considers an optimization problem of deriving the minimum cost paths from multiple source nodes, which are deployed in the area of interest, to the sink node under the constraint that the vulnerability of the whole network is under the given level. The vulnerability is defined as a metric which characterizes the degree of edge and node sharing among different paths. With the defined vulnerability, vulnerability-constrained minimum cost paths problem is formulated and two polynomial-time algorithms are developed for deriving the optimal paths. The necessary condition for the existence of the optimal solution, and the optimality of the proposed algorithms are analyzed in the theoretical. Extensive simulations show the significant performance enhancements achieved by our proposed algorithms.

Besides energy-efficient technologies, increasing attention is paid on powering cellular networks with renewable energy sources, concerning climate change, fossil fuel prices and energy security. In this paper, we not only aim to reduce the absolute energy consumption of cellular networks, but also provide a guideline to utilize the renewable energy efficiently in cellular networks. The renewable energy sources have the limitation of unstable availability and capacity. It is thus challenging to improve renewable energy efficiency while maintaining the energy supply sustainability. The energy supply sustainability problem is modeled as an optimization problem aiming to maximize the network energy residue ratio (ERR), which is NP-hard. To solve the optimization problem in polynomial time, the network ERR maximization algorithm is proposed after analyzing the relation between energy efficiency and energy depleting rate (EDR). The algorithm maximizes link energy efficiency via power control at physical (PHY) layer and maximizes network ERR via access control at media access control (MAC) layer jointly in a cross-layer manner. The network ERR maximization algorithm performs excellently in improving both the lifetime and the number of users served by renewable energy.

LISP is an important development and implementation effort toward the resolution to the Internet routing table scalable issues. Although the LISP is based on the idea of the Locator/ID separation, it could not support mobility very well until now. This paper proposes a novel dynamic mobility management scheme in LISP architecture. It improves the LISP by introducing three name spaces and two mapping systems. By dynamic configuring the IDs to the mobile nodes, the proposed scheme could provide flexible mobility support with lower overhead, while maintaining the scalability and aggregatability of the global ID/locator mapping system. Furthermore, a fast location update method is presented in the proposed scheme to provide smooth handover. The proposed scheme is evaluated by comparing with the LISP Mobile Node in handover cost and handover latency.

Information-Centric Network (ICN) aims to achieve the shift of communication model from host-centric to information-centric. In ICN, anycast plays an important role and is even regarded as a primary network primitive in some architectures such as DONA and SCAFFOLD. However, network layer anycast based on route-by-name in these architectures may severely worsen the problem of routing scalability as IP anycast does. On the other hand, the widely used application layer anycast based on lookup-by-name in current Internet exists many flaws such as inaccuracy of server selection and weak responsiveness to dynamic network conditions. Accordingly, how to achieve efficient and flexible anycast becomes a research challenge in future internet. In this paper, a novel anycast routing scheme based on ID/LOC separation is proposed. Then, a decentralized server selection algorithm based on Ant Colony Optimization (ACO) is further investigated. Experiments show that efficient and flexible anycast can be achieved to combine the advantages of route-by-name anycast and lookup-by-name anycast.

Wireless sensor networks(WSNs) are always deployed for monitoring and gathering physical information via lots of sensors. For facilitating the internet usage of WSN, Web service is regarded as the most promising technology to wrap the sensor functionality to form WSN service. With the growth of WSN services, making service ranking to help users select the best performance services becomes more and more important. However, traditional service ranking only considering from user's perspective does not suit WSN service any more due to the dynamic WSN environment and slow-reacting characteristic of user ratings. In order to address this problem, in this article, we propose a context-aware WSN service ranking approach by aggregating the user rating and WSN service context. Firstly, the User QoS Assessment(UQA) and Context QoS Assessment(CQA) are proposed respectively. Then through investigating the performance influence of WSN service brought by their context variation, a fuzzy-based mechanism is further developed for the aggregation of UQA and CQA. The case study and experimental evaluation show the validity of the proposed approach.

Service continuity in two-tier Femtocell networks, especially impaired by the handover latency, remains as a problem to be solved. Motivated by this problems, a Layer 3 prediction handover scheme is proposed, which can be integrated into the user equipment (UE) part of industry-preferable mobile-assisted network-controlled handover (MANCH). The main contributions of the proposed scheme include: 1) based on time series analysis theory, the prediction model of Layer 3 (L3) filtered reference signal received power (RSRP) is constructed to activate L3 handover prior to Layer 2 (L2) handover procedure; 2) in order to improve the reliability and robustness of the L3 prediction model, a two-priority handover trigger event evaluation method is designed, where the classical and prediction handover trigger event evaluation parts co-exist, and the former is prior to the latter; 3) the relationship between defined prediction handover gain (PHG) and the probability of false handover prediction trigger is derived. In simulations, to evaluate the robustness of the proposed scheme, a module of UEs' movement pattern is designed, considering characteristics of the UE's movement pattern. Results of the performance evaluation show that handover latency of indoor handover UEs can be effectively reduced with high prediction accuracy.

The PLCS (Playback Length Changeable Segmentation) algorithm was proposed to improve the success delivery rate of chunks in some important layers including the base layer for SVC based P2P streaming systems. In this paper, we make further research and propose a corresponding chunk scheduling algorithm named PLCCS (Playback Length Changeable Chunk Scheduling). In PLCCS, there are two scheduling windows on clients, including General Window and Emergent Window. Different scheduling strategies are adopted in the two windows to ensure the efficiency of data distribution as well as the playback fluency. Moreover, an adaptive strategy is used in General Window to accommodate the change of the network state. We evaluate the performance of PLCCS through simulations. The results show that compared with two representative EPLS based chunk scheduling algorithms, PLCCS can achieve better video quality on clients by at least 40%, and it decreases the useless packet ratio on clients by at least 15 times. Moreover, the adaptive strategy used in PLCCS makes it suitable to be applied in SVC based P2P streaming systems in heterogeneous network environments.

Network virtualization provides a powerful way to run multiple heterogeneous virtual networks (VNs) at the same time on a shared substrate network. A major challenge in network virtualization is the efficient virtual network embedding: mapping virtual nodes and virtual edges onto substrate networks. Previous researches have presented several heuristic algorithms, which fail to consider the topology attributes of substrate and virtual networks. However, the topology information affects the performance of the embedding obviously. In this paper, for the first time, we exploit the topology attributes of substrate and virtual networks, introduce network centrality analysis into the virtual network embedding, and propose virtual network embedding algorithms based on closeness centrality. Due to considering the topology information, our study is more reasonable than the existing work in coordinating node and edge embedding. In addition, with the guidance of topology quantitative evaluation, the proposed network embedding approaches largely improve the network utilization efficiency and decrease the embedding complexity. Experimental results demonstrate the usability and feasibility of the proposed approach.

Rate control and adaptation is an important issue for video communications over cellular networks. In this paper, we propose a quality-aware feedback-free rate control and adaptation scheme for downlink real-time video communications over Long Term Evolution (LTE) networks. In our scheme, the send bit rate is adapted automatically according to the estimated packet loss due to the expiration of packet delay deadline based on queueing analysis by taking into account both packet queueing delay and transmission delay. This differs from existing rate adaptation methods which adapt send bit rate according to either packet loss or delay information explicitly feedback from the user side. The proposed scheme can maximize the system capacity in terms of the number of supportable users while providing satisfying user experience in terms of the received video quality and playback smoothness. Experimental results demonstrate the effectiveness of the proposed scheme.

With the development of wireless multimedia communication, mobile 3D video will be gradually popular as it can make people enjoy the natural 3D experience anywhere and anytime. In the current stage, mobile 3D video is usually distributed over the error-prone and heterogeneous network which consists of wire-line and wireless channels. In such applications, the transcoding plays an important role to provide the adaptive and error-resilient bit-stream to the wireless channel. To guarantee the high quality mobile 3D video streaming, this paper proposes a transcoding framework with error-resilient video/depth rate allocation, which utilizes the multi-pass re-encodings to build a rate allocation table (Rate-QP-PLR (packet loss rate) table). Through looking up the table, the proposed framework can select the optimal transcoding quantization parameters (QPs) of video and depth to obtain the error-resilient 3D video transmission and therefore it can make the encoded stream adapt to the transition from wire-line to wireless network. Experimental results show that the proposed 3D video transcoding framework can provide the superior rate-distortion performance for the mobile 3D video streaming.

How to provide user-satisfied video services must be solved in the future wireless communication systems. However, most of existing works focused on the PHY-MAC cross layer schemes do not consider the end-to-end video quality of service (QoS) and hence can only provide limited user-perceived video quality improvements. In this paper, a novel video transmission scheme with guaranteed end-to-end QoS over Multiple Input Multiple Output and Orthogonal Frequency Division Multiplexing systems is proposed. The end-to-end video quality is measured by recursive optimal pixel estimation (ROPE) algorithm. Our objective is to minimize the ROPE distortion by jointly optimizing the source encoding parameters of H.264/AVC encoder in the application layer, the adaptive modulation and coding parameters and time-frequency resource block selection in the physical layer. The experimental results demonstrate that our proposed scheme can provide good user perceived quality.

Content-Centric Networking (CCN), a typical future Internet architecture focused on content dissemination and retrieval, brings a paradigm shift in network model by addressing named-data instead of host location. Many fields related to CCN become the research hotspots, such as caching and name-based routing. However, little work has addressed on its transmission protocol in literature. In this paper, we proposed an effective Congestion Control Scheme (CCS) in CCN through combining a router-driven congestion forecast with a requester-driven Interest control protocol. Each router detects the network congestion status and then informs the requester by setting Congestion Information Bits into the returned Data packet passed by. Then the requester dynamically adjusts its Interest sending window according to the received feedback congestion information. Furthermore, in our scheme, a Fair Queuing algorithm is used on each router, allowing max-min fair share of the link capacity. A performance evaluation using simulation designed on OMNet++ network simulator showed that the proposed CCS causes fewer packet losses, decreases Data packet delay, treats each flow fairly and maximizes the use of bandwidth.

Due to the characteristics of the DCF model in 802.11 and P2P users' requirements of both downloading and uploading, P2P traffic frequently causes congestion in WLAN. However, traditional P2P cache cannot alleviates the congestion caused by P2P traffic in WLAN effectively. In this paper, we propose a novel solution called bidirectional cache for this problem. A bidirectional cache which contains a reverse cache as well as a forward cache can be deployed at the AC of a WLAN. Being a novel design, the reverse cache can cache release the uplink bandwidth resource at each AP can be and the congestion can be alleviated effectively. Meanwhile, the forward cache can still act as the traditional P2P cache to reduce the cross domain traffic. Simulation results show that, compared with traditional P2P cache, our bidirectional cache can shorten the file transfer time of P2P applications by at least 42% and improve the throughput of the other Internet applications by at least 28%.

With the progresses in 3D imaging technologies, mobile stereoscopic images are gradually popular since it can provide the anytime and anywhere 3D viewing effects. Due to the heterogeneous transmission environments and various screen sizes of mobile devices, the stereoscopic image transcoding and retargeting are two completely independent adaptation techniques for mobile 3D image streaming. These two processing operations both deal with the disparity estimation. In this work, we propose to integrate stereoscopic 3D image transcoding with retargeting for mobile streaming. The stereoscopic image transcoding and retargeting are coupled through a straightforward link that provides the pixel-based disparity map from the retargeting stage to guide the transcoding for saving some computations while keeping efficient bit allocation. Our experimental results clearly demonstrate the advantage of coupling the transcoding with retargeting in terms of faster transcoding and saliency-based bit allocation for low bit-rate mobile streaming.

This paper introduced a camera surveillance system in wireless communications. The system contains three major modules, PTU (pan-tilt unit) camera control for surveillance video capture, cross-layer control for data compression and transmission, and error concealment for video quality enhancement. Our contribution is twofold. First, a system design for data collection and transmission over wireless networks is presented and is evaluated with physical surveillance equipments. The camera is capable of following the moving target according to the control information. The end-to-end distortion estimation in the delay constrained video coding process takes into account the dynamic channel condition and physical layer modulation and coding scheme (MCS) to determine optimal coding and transmission parameters. Second, multiple error concealment strategies, including interleaving, boundary match and video up-sampling, are applied utilizing the special property of the PTU camera motion.

With advance in mobile 3D display, mobile 3D video is already enabled by the wireless multimedia networking, and it will be gradually popular since it can make people enjoy the natural 3D experience anywhere and anytime. In current stage, mobile 3D video is generally delivered over the heterogeneous network combined by wired and wireless channels. How to guarantee the optimal 3D visual quality of experience (QoE) for the mobile 3D video streaming is one of the important topics concerned by the service provider. In this article, we propose a QoE-oriented transcoding approach to enhance the quality of mobile 3D video service. By learning the pre-controlled QoE patterns of 3D contents, the proposed 3D visual QoE inferring model can be utilized to regulate the transcoding configurations in real-time according to the feedbacks of network and user-end device information. In the learning stage, we propose a piecewise linear mean opinion score (MOS) interpolation method to further reduce the cumbersome manual work of preparing QoE patterns. Experimental results show that the proposed transcoding approach can provide the adapted 3D stream to the heterogeneous network, and further provide superior QoE performance to the fixed quantization parameter (QP) transcoding and mean squared error (MSE) optimized transcoding for mobile 3D video streaming.

Optimal power control for relay-assisted cognitive radio networks in which secondary users are assisted by cognitive relays is investigated. A monomial approximation based power control algorithm (MaPC) is developed to 1) maximize the secondary system weighted sum rates; 2) minimize the secondary system weighted sum powers under the individual and/or total power constraints of secondary users as well as the interference temperature limits of the primary users. The presented power control schemes are formulated using geometric programming and are then converted into equivalent convex optimization problems. Numerical results demonstrate the effectiveness of our proposed schemes.

In wireless sensor networks (WSNs), the nodes are usually powered by batteries with limited capacities, which has become one of the main challenges to the wide deployment of WSN-based applications. The overall network lifetime, which is usually determined by the node with the shortest lifetime because of the unbalanced energy consumption, becomes a major issue. Aiming at this, we consider the optimization problem of how to maximize the data amount that can be transmitted from the source node to the sink node, given an energy-limited wireless sensor network. Specifically, we first analyze the problem on the basis of the max-flow min-cut theorem to obtain the maximum relay capacity in terms of the number of successfully transmitted packets from the source node to the sink node, from the theoretical point of view. Then, we propose our algorithms to achieve the optimal solution. In this way, the expected network lifetime can be maximized. Further, we also prove the correctness of the proposed algorithms. Extensive simulations show that significant performance enhancement can be achieved by using our proposed algorithms.

Micro-electronic industry has been boosting the capability of wireless mobile devices on full-scale. However, battery, as the only power source of most mobile devices, is experiencing a relatively slow development. Therefore, how to optimally utilize the limited battery energy on mobile devices under a predefined performance requirement becomes a critical issue. On the other hand, it is still unclear that how the battery capacity consumption is allocated on different working pattern of a specific video codec under various tempo-spatial scales and parameters, which has posed a design challenge on power management on multimedia communication system. Furthermore, an optimization method is needed to be proposed and experimentally tested to achieve the tradeoff between the computational complexity and the distortion of multimedia delivery in order to discover the relationship and interaction between computational parameters of multimedia communication and battery capacity consumption. In this paper, we set up a measurement system to reveal the battery capacity consumption behavior and its footprinting in a video delivery system using H.264 codec. A systematic optimization framework which jointly considers the coding parameters and transmission parameters is proposed to achieve the tradeoff between battery capacity consumption and quality of services (QoS). Experimental results indicate insight of relationship between multimedia communication parameters and battery capacity consumption under a desired distortion requirement which provides a guideline of optimization for applicable multimedia system.

Nowadays, multimedia application and video streaming have gained a great popularity because of the boasting development on mobile hardware, and battery driven devices have emerged with a tremendous speed. Due to the important issue of battery efficiency on mobile devices, many optimization algorithms have being proposed toward various of battery powered platforms and scenarios. Most of the provided solutions choose to aim at the minimization of energy usage under a given task scheduling by adjusting parameters reside in the processes of different schemes. However, the battery discharging characteristics and its instant output pattern are still ignored if the optimization is done only from high level adjustment. In this paper, we propose a battery-aware optimization framework toward the H.264 video coding by applying dynamic frequency scaling on hardware platform. The CPU frequency can be dynamically adjusted according to the instant status of battery in order to maximize the number of the coding frame. Experimental results indicate the efficiency and effectiveness of the proposed optimization framework.

In wireless video streaming, the packet loss often occurs and affects the end-user visual quality. To alleviate the transmission error effects, intra refresh coding is usually used to improve the streaming error resilience ability from the view of source coding. At the physical layer, the adaptive modulation and coding (AMC) is also used to promote the transmission reliability at the transporting level. Both the error control components have their own influences on the received video quality. To achieve the best video transmission performance, it is crucial to make an error control tradeoff between intra refresh coding and AMC. In this paper, we propose an end-to-end video distortion optimized cross-layer error control method which jointly considers the video quantization parameter (QP) and intra refresh rate at the application layer, and AMC at the physical layer for delay-constraint real-time video streaming. The experimental results show that the proposed cross-layer error control streaming method can achieve the superior objective and subjective performances to the layer-independent error control streaming methods with and without cross-layer optimization.

This paper presents a video object detection method under dynamic background with single PTU (pantilt unit) camera. The overall procedure contains two steps. First the moving object is tracked through interactive Mean Shift estimation and PTU control. Camera angular speed is updated online according to the estimated object position, whereby the object can remain in the center area of the image plane. In the second step, the foreground is detected through background subtraction and shape contour. An adaptive correlation thresholding method is applied to mitigate the detection distortion in boundary areas. Variational level set contour is then applied to further remove noises and locate moving areas. Our contribution is to incorporate in this procedure the special property of camera movement on a PTU to realize automatic tracking and efficient background matching for motion detection. As demonstrated in the experiment, the proposed method well outlines the foreground objects.

In this paper, channel adaptive resource allocation and user scheduling problem for cognitive radio multiple-inputmultiple-output orthogonal frequency-division multiple access systems is investigated. Furthermore, stochastic approximation is employed to develop an on-line algorithm which is capable of adaptively learning the underlying channel distribution. The NP-hard combinatorial resource allocation and user scheduling problem is then solved through via the Lagrangian relaxation approach. Numerical results demonstrate the effectiveness of our proposed scheme.

Optimal power control for relay-assisted cognitive radio networks in which secondary users are assisted by cognitive relays is investigated. A monomial approximation based distributed geometric programming algorithm (MaDGP) is developed to maximize the secondary system utility under the individual and total power constraints of secondary users as well as the interference temperature limits of the primary users. The presented power control algorithm is distributively formulated using geometric programming and is then solved using the Lagrangian primal-dual decomposition. Numerical results demonstrate the effectiveness of our proposed algorithm.

Cognitive radio (CR) has been proposed as a promising solution to improve connectivity, self-adaptability, and efficiency of spectrum usage. When used in video applications, user-perceived video quality experienced by secondary users is a very important performance metric to evaluate the effectiveness of CR technologies. However, most of the current research only considers spectrum utilization and effectiveness at medium access control (MAC) and physical layers, ignoring the system performance of the upper layers. Therefore, in this paper, we aim to improve the user experience of secondary users for wireless video services over CR networks. We propose a quality-driven cross-layer optimized system to maximize the expected user-perceived video quality at the receiver end under the constraint of packet delay bound. By formulating network functions such as encoder behavior, cognitive MAC scheduling, transmission, as well as modulation and coding into a distortion-delay optimization framework, important system parameters residing in different network layers are jointly optimized in a systematic way to achieve the best user-perceived video quality for secondary users in CR networks. Furthermore, the proposed problem is formulated into a MIN-MAX problem, and solved by using dynamic programming. The performance enhancement of the proposed system is evaluated through extensive experiments based on H.264/AVC.

The increasing popularity of ubiquitous, interconnected computing devices such as laptops, PDAs, and 3G mobile phones is fostering the emergence of environments where people access their personal information, corporate data and public resources "anytime" and "anywhere". Context, an important concept that can be any information regarding the situation and environment, is often exploited by human beings for communications and actions. Context-aware services have been proposed in the computing field to adapt system behaviors based on the retrieved context data. Although different context-aware systems have been presented in literature, this paper moves one step further by building an adaptive wireless multimedia system with context-awareness using ontology-based models. The proposed system can adapt its behaviors according to the changes of various context data. First, it chooses the appropriate video content based on the retrieved static context data such as user's profile, location, time, and weather forecast. Then, dynamic media adaption is performed to greatly improve the video quality perceived by the end user by adapting to various context data such as varying wireless channel quality, available energy of the end equipment, network congestion and application Quality of Services (QoS). To verify the effectiveness of proposed system, a test bench and its experimental results are also described.

Asymptotic capacity and precoding design are presented in the literature for a special case of wireless correlated multi-hop multi-input multi-output (MIMO) networks, where the channel has only white noise at the destination, no noise at all relay levels, and the number of antennas is very large. This particular channel assumption is not always feasible in compact MIMO design with space limitation and mutual interference. For the sake of generality, by invoking Karush-Kuhn-Tucker (KKT) condition for the optimality of convex programming, in this paper we propose asymptotic capacity expressions and precoding designs for generally correlated wireless multi-hop MIMO channels. The channel at each hop is spatially correlated, the source symbols are mutually correlated, and the additive Gaussian noise at each hop is colored. Simulation results shows that the proposed precoding designs can significantly increase the end-to-end mutual information while it does not require resources of the system such as transmission power or bandwidth.

Internet of Things (IoT) is an emerging network which is designed to combine sensor networks and other things with the Internet. IoT includes numerous things, each of which is allocated an unique identity (UID). UID management plays an very important role in guaranteeing the efficiency of IoT system. Since IoT is a task-oriented network, there must be a coupling relation among UIDs. Based on this consideration, a correlated lookup scheme is proposed to improve the performance of UID management. Meanwhile, the Distributed Hash Table (DHT) is adopted as the underlying structure to construct the basic UID management methodology. To the best of our knowledge, it is the first time that the inherent relation is leveraged to improve the efficiency of UID Management. Extensive simulations are conducted to demonstrate the efficiency of the proposed lookup scheme.

In 3D video communication, how to select the appropriate quantization parameter (QP) for depth map coding is very important for obtaining the optimal view synthesis quality. This paper first analyzes the depth uncertainty induced two kinds of view synthesis errors, namely the original depth error induced view synthesis error and the depth compression induced view synthesis error, and then proposes a quadratic model to characterize the relationship between the view synthesis quality and the depth quantization step size. The proposed model can find the inflexion point in the curve of the view synthesis quality with the increasing depth quantization step size. Experimental results show that, given the rate constraint for depth map, the proposed model can accurately find the optimal QP for depth map coding.

Most existing works on routing for video transmission over multihop wireless networks only focus on how to satisfy the network-oriented quality-of-service (QoS), such as through put, delay, and packet loss rate rather than application-oriented QoS such as the user-perceived video quality. Although there are some research efforts which use application-centric video quality as the routing metric, they either calculate the video quality based on some predefined rate-distortion function or model without considering the impact of video coding and decoding (including error concealment) on routing, or use exhaustive search or heuristic methods to find the optimal path, leading to high computational complexity and/or suboptimal solutions. In this paper, we propose an application-centric routing framework for real-time video transmission in multihop wireless networks, where expected video distortion is adopted as the routing metric. The major contributions of this paper are: 1) the development of an efficient routing algorithm with the routing metric expressed in terms of the expected video distortion and being calculated on-the-fly, and 2) the development of a quality-driven cross-layer optimization framework to enhance the flexibility and robustness of routing by the joint optimization of routing path selection and video coding, thereby maximizing the user-perceived video quality under a given video playback delay constraint. Both theoretical and experimental results demonstrate that the proposed quality-driven application-centric routing approach can achieve a superior performance over existing network-centric routing approaches.

Although current research focused on sensor deployment to balance the energy consumption among the sensor nodes for prolonging the lifetime of wireless sensor networks, sensor nodes with larger communication radius have to consume more energy to overcome wireless channel fading and path loss. Cooperative transmission allows nodes with single-antenna to cooperate on information transmission and/or reception to achieve a lower average error probability than its non-cooperative counterpart under the same transmit energy budge. In this work, we introduce the concept of cooperative transmission into sensor deployment to balance the energy consumption among sensor nodes. The key idea is to deploy an optimal number of cooperative nodes into a given area. In this way, batteries of all sensor nodes are discharged at the same rate. Extensive simulations have been conducted to evaluate the performance of the proposed scheme. Compared with non-cooperative cases, the proposed scheme with cooperative transmission improves the battery energy efficiency by 52.1%, and prolongs the lifetime of wireless sensor network by three times.

As a TCP-Friendly Rate Control protocol on the basis of TCP Reno's throughput equation, TFRC is designed to provide optimal service for unicast multimedia delivery over the wired Internet networks. However, when used in wireless environment, it suffers significant performance degradation. Most of the current research on this issue only focuses on the TFRC protocol itself, ignoring tightly-coupled relation between the transport layer and other network layers. In this paper, we propose a new approach to address this problem, integrating TFRC with application layer and physical layer to form a holistic design for real-time video streaming over wireless multi-hop mesh networks. The goal of the proposed approach is to achieve the best user-perceived video quality by jointly optimizing system parameters residing in different network layers, including the real-time video coding parameters at the application layer, the packet sending rate at the transport layer, and the modulation and coding scheme at the physical layer. The problem is formulated and solved as to find the optimal combination of parameters to minimize the end-to-end expected video distortion constrained by a given video playback delay. Experimental results have validated 2-4 dB PSNR gain achieved by the proposed approach in wireless multi-hop mesh networks.

One of the design challenges of wireless sensor networks is the tradeoff between network operation time and network coverage. Recent studies reveal that the useable battery capacity drops faster at a higher discharge current in a nonlinear fashion. To take advantage of this battery current effect, in this paper we explore a new sensor node deployment scheme to prolong the entire sensor network lifetime as well as each individual sensor node. The key idea of the proposed scheme is to assign a sensor node having higher traffic load to adopt lower transmission power level. In this way, batteries of all nodes in a given area are discharged at the same current, thus they are depleted at the same time. Extensive simulations have conducted to evaluate the performance of the proposed sensor node deployment scheme. Compared with peer work on heterogeneous deployment, the useable battery capacity by using the proposed scheme can be improved by 26.67%, and the operating time per sensor node can be enhanced by 20.95%. Furthermore, the proposed deployment scheme can reduce the number of sensor nodes required to cover the given area, leading to a significant reduction of deployment cost.

Current propagation models used in network simulations neglect obstacles of a propagation environment and employ over-simplified assumptions that the received signal strength is a simple function of distance and the transmission area of a node is circular. These assumptions are in contradiction with the actual tests and the measurement results. To run reasonable simulations for evaluating the performance of a specific outdoor wireless testbed, a novel multipath propagation framework is proposed in this paper, which is based on the ray tracing technique and uses site-specific geographic data of the analyzed area. It provides accurate propagation predictions, and it does not significantly prolong the simulation duration.

Peer-to-Peer (P2P) video streaming services on the Internet have gained increasing popularity during the past few years. However, many problems still need to be addressed before it can be widely deployed in the wireless environment. The existing P2P overlay network architecture hides the underlying network topology, assuming channel quality is always in perfect condition. This works well for the Internet-based services, but hardly meets the user-perceived video quality requirement in wireless environments due to the time-varying nature of wireless channels. Inspired by the tightly-coupled relationship between P2P overlay networks and the underlying networks, we propose a novel scheduling algorithm on the basis of a quality-driven cross-layer design framework to jointly optimize the parameters of different network layers to achieve highly- improved video quality for P2P video streaming applications in multi-hop wireless mesh networks. In this paper, the quality-driven P2P scheduling algorithm is formulated into a distributed distortion-delay optimization problem, where the expected video distortion is minimized under the constraint of a given packet playback deadline to select the optimal combination of system parameters residing in different network layers. Then we provide the algorithmic solution to the formulated problem based on dynamic programming. The distributed optimization running on each partner node adopted in the scheduling algorithm greatly reduces the computational intensity. Extensive experimental results demonstrate 5-15dB quality enhancement in terms of PSNR by using the proposed scheduling algorithm.

Routing for video transmissions over multi-hop wireless networks has gained increasing research interest in recent years. However, most existing works only focus on how to satisfy the network-oriented QoS, such as, throughput, delay, and packet loss rate rather than the user perceived quality. Although there are some research efforts which use application-centric video quality as the routing metric, the calculation of video quality is based on some predefined rate-distortion function or model without exploring the impact of video coding and decoding (including error concealment) on network path selection and the resulting received video quality. Moreover, unlike network- centric routing metrics, such as, hop count, average delay or average success probability of packet transmission, video distortion cannot be calculated either additively or multiplicatively in a hop-by-hop fashion due to the dependency among packets introduced by error concealment. As a result, most existing works use either exhaustive search or heuristic methods to find the optimal path, which leads to high computational complexity or suboptimal solutions to the routing problem of video transmission. In this paper, we propose an application- centric routing framework for real-time video transmission over multi-hop wireless networks, where expected video distortion is used as the routing metric. The major contributions of this work are: 1) the development of an efficient routing algorithm with the routing metric in terms of the expected video distortion being calculated on-the-fly, and 2) the development of a quality-driven cross-layer optimization framework to enhance the flexibility and robustness of routing by the joint optimization of routing path selection and video coding, thereby maximizing the user perceived video quality under a given video playback delay constraint. Both theoretical and experimental results demonstrate that the proposed quality-driven application-centric routing approach can achieve a superior performance over existing network-centric routing approaches.

The 14 papers in this special issue are divided into four categories: theoretical frameworks and schemes; integration of media processing and network protocols; heterogeneity; and quality evaluation.

Real-time video communication over wireless multihop networks has gained significant interest in the last few years. In this paper, we focus our attentions on the problem of source coding and link adaptation for packetized video streaming in wireless multihop networks when network nodes are media-aware. We consider a system where source coding is employed at the video encoder by selecting the encoding mode of each individual macro-block, while error control is exercised through application-layer retransmissions at each media-aware network node. For this system model, the contribution of each communication link on the end-to-end video distortion is considered separately in order to achieve globally optimal source coding and ARQ error control. To reach the globally optimal solution, we formulate the problem of joint source and distributed error control (JSDEC) and devise a low-complexity solution algorithm based on dynamic programming. Extensive experiments have been carried out on the basis of H.264/AVC codec to demonstrate the effectiveness of the proposed algorithm over the existing joint source and channel coding (JSCC) algorithm in terms of PSNR perceived at the decoder under time-varying multihop wireless links.

TCP Friendly Rate Control (TFRC) has been designed to provide smoother sending rate than TCP for multimedia applications. However, most existing work on TFRC is restricted within exploring the performance of TFRC itself in wired or wireless networks without considering the interaction between TFRC and other network layers. This paper proposes a quality- driven TFRC framework for real-time video streaming, where real-time video coding at the application layer and the packet sending rate at the transport layer are jointly optimized. The proposed framework is formulated to find the optimal video coding parameters and the sending rate to minimize the end- to-end expected video distortion under a given video playback delay constraint. The proposed framework has been implemented and tested by using both H.264/AVC codec and NS-2 simulator. Experimental results demonstrate that the proposed joint optimization framework can significantly improve the received video quality over the existing schemes, especially when delay bound is tight.

Video transport over multi-hop wireless networks has received significant research interests recently. The majority of the research efforts in this field have been conducted taking the approach of cross-layer optimization. However, video content and user perceived quality have been largely ignored in existing work. In this paper, we integrate video content analysis into video transport over wireless mesh networks (WMN). A content-aware quality-driven cross-layer optimization framework is proposed to achieve the best end-to-end user perceived video quality. In our framework, the extracted video regions of interest (ROI) are discriminatingly coded, transmitted and protected in video encoding, network routing and packet scheduling by different network layers. We aim at the optimization of key parameters of each layer while focusing on their interactions across the holistic network protocol stack. The proposed framework is evaluated by H.264/AVC codec and WMN simulations. Experimental results demonstrate that the proposed framework can effectively provide a good user perceived video quality, especially when the delay requirement is stringent.

In this paper we propose a cross-layer optimized framework which jointly considers the video coding and transmission in wireless mesh networks. In our framework, video coding parameters, network path selection, MAC layer frame size, and modulation and coding schemes at PHY layer are systematically optimized across the entire network protocol stack. To achieve the goal of real-time video communication with high-quality and/or stringent delay requirements, the proposed framework is formulated into a minimum distortion and/or minimum delay problem. The formulated problem is efficiently solved as a Lagrange dual problem, and the solution is tested by H.264/AVC. The system performance in terms of the perceived video quality is compared between the cross-layer optimized system and the current system. Experimental results indicate the effectiveness and efficiency of the proposed optimization framework.

In this paper, we analyze the performance of mobile real time services in a large-scale IEEE 802.11 multi-hop network. We present our field measurements with discussion of performance bottlenecks for VoIP services under mobile scenarios. We utilized our test bed which is located in Nebraska and supported by the University of Nebraska-Lincoln (UNL), US Federal Railroads Administrations (FRA), and major US railroad companies. Our UNL-FRA test bed consists of 8 outdoor access point towers, which are deployed along 3.5 mile of BNSF railroad. Passive measurement approaches are taken to ensure the integrity of collected data and multi-layer stream based packet analyzer has been implemented to provide a global view of the entire performance of the monitored network from the physical layer to the application layer. Based on our analysis of collected data, we conclude that in a typical outdoor 802.11 environment similar to our UNL-FRA test bed, uncertain handoff latencies and lack of Quality of Service guarantee are main performance bottlenecks for real-time applications. Furthermore, we discuss the enhancement strategies to support mobility in high-speed railway networks. We believe our work is one of the initial efforts in wireless mobile network field measurement in terms of scale, methodology, and analysis.

The 802. llg offers backward compatibility to 802.11b operating in 2.4 GHz with the same set of MAC parameters. However, this compatibility causes 802.1 lg to perform differently as 802.11a in terms of radio throughput performance, although both standards employ orthogonal frequency division multiplexing (OFDM) technology to achieve high data rate. In this paper, we compare and present an in- depth analysis of the throughput performance of both 802.11a and 802.1 lg standards. In order to compare the throughput performance of 802.11a and 802.1 lg, a Markov chain model of IEEE 802.11 MAC is adopted to evaluate the saturation throughput. Our analytical and experimental results show an inferior throughput performance for 802.1 lg even with the same OFDM technology as employed for 802.11a.

Cross-layer design and optimization normally involve many system parameters, where each parameter may have a different nonlinear relationship with the system performance metric of interest. Furthermore, interdependency among system parameters may significantly affect the effectiveness and efficiency of cross-layer design. However, it is very difficult to derive the interdependency among system parameters and their different nonlinear relationships to the system performance, especially in a dynamic complex networking system, due to uncertainties and randomness existing in data observation and system modeling. In this paper, we propose a new approach for characterizing the complicated interdependencies existing in cross-layer design. The major contributions made in this paper are: 1) nonlinear and incommensurable observation data are characterized by a data preprocessing procedure, and 2) interdependencies among system parameters under uncertainties are then quantitatively measured by using non-additive measure theory. Simulation results show effectiveness and efficiency of the proposed method. Furthermore, the proposed method can be easily implemented and incorporated in existing cross-layer design and optimization.

Battery-powered embedded networked sensing systems become more and more pervasive with the fast-paced deployment of various remote sensing applications. How to prolong the battery operating time is one of the most challenging areas in the design and development of these sensing systems due to the fact that the battery operation is much more dynamic and complex than considered, which is derived from its application and its internal structure (multiple hardwired series/parallel cells) to produce a specific voltage and capacity. Current research on prolonging the battery operating time is mainly focusing on the low-power hardware and energy-efficient network protocol designs, and simply treats the battery as a passive two-terminal energy source. In this paper, we will propose a novel adaptive, proactive, and reconfigurable multicell battery design for supporting power-aware hardware and energy-efficient network protocols for embedded networked systems, which provides a whole new perspective to look at the energy problems of battery- powered embedded networked sensing systems. A theoretical modeling of the proposed design is provided, and simulation results show that the proposed design can significantly enhance the energy performance, especially for low voltage and low discharge current scenarios.

The many-to-one traffic pattern dominates in sensor networks, where a large number of sensor nodes send data to one sink. A sensor node may only communicate with a small portion of its neighbors. Most existing key management schemes for sensor networks are designed to establish shared keys for all pairs of neighbor sensors, no matter whether they communicate with each other or not, and this causes large overhead. To achieve better security and performance, we adopt a heterogeneous sensor network (HSN) model. In this paper, we propose a novel routing-driven key management scheme, which only establishes shared keys for neighbor sensors that may communicate with each other. Work has demonstrated the feasibility of implementing elliptic curve cryptography on small sensor nodes. We utilize elliptic curve cryptography to design an efficient key management scheme for HSN. The performance evaluation and security analysis show that our key management scheme can provide better security with significant saving on sensor storage space and energy consumption than some existing key management schemes.

Most of the current 802.11b research activities are conducted under stationary environment. As the need for high-speed connectivity in mobile environments increases, it becomes necessary to understand the impact of mobility on performance characteristics of 802.11b. In this paper, we present an investigation of the mobility impact on performance of the 802.11b system with Rician/Rayleigh fading under different client velocities. This includes analysis of the Doppler shift caused by the velocity of transmitter and receiver, the multipath interference due to reflections and diffractions from terrains in the radio service coverage area, and other serious impairing factors. We have also studied the bit error rate performances for various velocities with different data rates. Our analysis includes our analytical and simulation data verified with the results from our 3.5 mile Federal Railroad Administration test bed on the BNSF railroad track in Nebraska. Our results show that the current implementation of 802.11b standard has promising potential to be used in a mobile environment if the line-of-sight path between transmitter and receiver exists.

Interdependency among system parameters may significantly affect the performance metric of interest for cross-layer design and optimization. However, it is very difficult to derive the interdependency among system parameters in a dynamic complex networking system due to uncertainty of data observation and system modeling. Furthermore, current cross-layer design normally includes many system parameters, making the multidimensional cross-layer optimization problem very difficult to solve. In this research, we proposed a new approach for dynamic interdependency measure and significance analysis in cross-layer design and optimization. The major contributions made in this paper are: (1) interdependency among system parameters under uncertainties is measured by using non-additive measure theory; (2) quantitative significance analysis is proposed for dynamically identifying what system parameters have the most significant effect on the performance metrics of interest for system objective function; and (3) we develop a new fast multidimensional optimization method for cross-layer design based on dynamic interdependency measure and significance analysis. We show the effectiveness and feasibility of the proposed approach for cross-layer design and optimization in IEEE 802.11 WLANs.

In this paper, we propose a link adaptation scheme to adapt the packet size as a crucial system parameter for quality of service (QoS) provisioning in multi-rate wireless networks. The link adaptation scheme combines adaptive modulation and coding (AMC) at the physical layer with type-II hybrid automatic repeat request (HARQ) at the data link layer in a cross-layer fashion. We then study the goodput performance of the proposed link adaptation scheme at the medium access control (MAC) layer. We show that there exists an optimal packet size to achieve the maximum goodput in transmitting messages over transport layer sessions. Based on this observation, we provide an efficient and effective algorithm to search the optimal packet size. Finally, we propose a link adaptation architecture, through which the proposed link adaptation scheme can adapt packet sizes to instantaneous channel conditions to fulfill committed QoS in higher layers. Numerical results show that the system adopting the proposed optimal packet sizes has superior goodput performance over the systems using fixed packet sizes.

Video summarization has gained increased popularity in the emerging multimedia communication applications, however, very limited work has been conducted to address the transmission problem of video summary frames. In this paper, we propose a cross-layer optimization framework for delivering video summaries over wireless networks. Within a rate-distortion theoretical framework, the source coding, allowable retransmission, and adaptive modulation and coding have been jointly optimized, which reflects the joint selection of parameters at physical, data link and application layers. The goal is to achieve the best video quality and content coverage of the received summary frames and to meet the delay constraint. The problem is solved using Lagirangian relaxation and dynamic programming. Experimental results indicate the effectiveness and efficiency of the proposed optimization framework, especially when the delay budget imposed by the upper layer applications is small, where more than 10% distortion gain can be achieved.

Several MAC protocols have been proposed for Wireless Sensor Networks (WSNs). These include T-MAC , D- MAC and the more commonly utilized SMAC. In this paper, we propose a new MAC layer approach to support mobility in WSNs. The proposed technique utilizes an adaptive frame size approach to overcome the effect of frame losses caused by the Doppler shifts under mobile scenarios. An Extended Kalman Filter is used to predict the frame size for each transmission, which also directly enhances the energy efficiency of the system. Our results show that based on the adaptive frame size predictor and its comparison with the SMAC protocol, the proposed technique can improve overall system performance and deliver enhanced energy efficiency of 24% under mobility. The current implementation of ns-2 does not take into consideration the packet error rate. As another contribution of our work, we have developed a physical layer model for ns-2, which processes the received frame based not only on the fading characteristics of the signal but also the SNR and relative velocity between the nodes. To characterize a more accurate wireless sensor networks' physical layer, we have modeled the Mica-2 sensors in MATLAB and implemented the model in ns-2 for simulations.

To enhance spectral efficiency and channel utilization in future wireless communication systems, adaptive modulation and coding (AMC) has been advocated at the physical layer. However, since the constellation size and coding rate are chosen based on the corresponding carrier-to-noise ratio (CNR) region, AMC can not guarantee to achieve the maximum spectral efficiency, especially when the number of available AMC modes are limited and the current CNR is low. In this paper, we present a scheme for spectral efficiency enhancement under fading channels. The proposed scheme combines AMC at the physical layer with hybrid automatic repeat request (HARQ) at the data link layer in a cross-layer fashion, where the upper layer HARQ provides an effective means to fine-tune the performance of the lower layer AMC. Both type-I and type-II HARQ are considered in our design. We have analyzed the system PER performance and the spectral efficiency of the proposed cross- layer design scheme. Numerical results show that our scheme can achieve maximum spectral efficiency while satisfying QoS requirements. The cross-layer design with AMC and type-II HARQ provides a large spectral efficiency improvement over the design with AMC and type-I HARQ.

Numerous approaches have been proposed for intrusion detection, especially for anomaly detection, in ad hoc networks. However, little research work has been done in actually implementing such a scheme based on statistical methods. In this paper, we present an efficient anomaly detection algorithm based on a statistical method originated from pattern recognition, which can effectively identify abnormal behavior such as mobility pattern of MANETs. In the proposed algorithm, the mobility pattern of a specific node is characterized by a multi-leaf tree structure, second-level nodes stands for the possible starting points and leaf nodes stand for the destination node of each possible path. Since our algorithm is using statistical method, a normal profile for each node is generated through extensive experiments, where the specific tree generated may have several starting points and ending with several destination points. For each path between any two nodes (parent and children), we can get the distribution of every different mobility pattern. By comparing the mobility patterns with the training data, we can distinguish abnormal nodes from normal behavior nodes in mobile Ad Hoc networks. Simulation results demonstrate that our proposed detection algorithm can achieve good performance in terms of false alarm rate and detection rate for nodes with regular mobility patterns.

With more and more wireless sensor networks being deployed, how to design and manage these large-scale sensor networks, especially for energy efficiency, becomes a very important issue. The current sensor network management tools only offer limited management data such as energy level and location of each sensor. However, these data are isolated over time, not enough to reflect the dynamic nature of the sensor networks. In this paper, we propose a new framework for the network management of a large-scale sensor network, called Energy Map. Based on nonlinear manifold learning algorithms, we will be able to not only visualize the energy level and location of each sensor in a network but also to find the dynamic patterns from a large volume of sensor network data such as which set of sensors has been significantly consuming its energy or how far the cluster members are from the current cluster head. All these information is usually very important to develop a good sensor network protocol stack such as clustering algorithms and routing protocols. Our contribution made in this paper is to introduce nonlinear manifold learning methods to derive the energy distribution of a wireless sensor network. We also show several interesting results and discuss their significance to the energy-efficient designs of wireless sensor networks.

Providing packet fair queueing is difficult in CDMA2000 1xEV-DV networks since there are multiple sessions associated with each user and the packets from each session can be served by multiple code channels. Essentially, this is a multi-session multi-server problem, which so far has received little research. The above problem becomes more complicated in CDMA2000 1xEV-DV networks, where Adaptive Modulation and Coding (AMC) schemes are employed for a higher network resource utilization. This means that the underlying service data rate of each code channel will be dynamically changing with the current perceived channel quality and the current number of available code channels. However, current research on packet fair queueing in wireless networks mostly use fixed service data rates. Therefore, how to design a packet fair queueing scheme with multiple servers and time-varying service data rates is very important for QoS provisioning in CDMA2000 1xEV-DV networks. In this paper, we propose a new packet fair queueing algorithm, where it seamlessly integrates the fair queueing with the CDMA2000 1xEV-DV AMC scheme. Both theoretical analyses and simulation results show that the proposed fair queueing algorithm can provide fairness among different sessions as well as satisfy their QoS requirements.

A new sensor network architecture, active sensor network (ASN), is proposed in this paper. This architecture integrates multiple sensor network-friendly mobile robots into a traditional sensor network. Therefore a closed-loop, dynamic adaptive sensor network is formed, which has many desired merits. In this paper, the distributed sensor node localization and the service set partition among multiple micro mobile robots are studied. For the node localization, a potential-based robot area partition algorithm and a distributed localization algorithm are developed respectively. For service set partition, a load-balanced partitioning algorithm is developed, which adapts to the number of active nodes in the field. The proposed algorithms are verified through simulations

Video summarization has gained increased popularity in the emerging multimedia and communications applications. However, very limited work has been conducted to address the transmission problem of video summary frames. In this paper, we propose a cross-layer optimization framework for delivering video summaries over wireless networks. Within a rate-distortion theoretical framework, the source coding, allowable retransmission, and adaptive modulation and coding have been jointly optimized, which reflects the joint selection of parameters in physical, data link and application layers. The goal here is to achieve the best video quality and content coverage from the received summary frames as well as meet the delay constraint. In this paper, this problem is formulated and solved by using Lagrangian relaxation and dynamic programming. Simulation results indicate the effectiveness and efficiency of the proposed optimization framework, especially when the delay budget imposed by the upper layer applications is small, where more than 12% distortion gain can be achieved

In this paper, we focus on combining hybrid automatic repeat request (HARQ) at the data link layer and adaptive modulation and coding (AMC) at the physical layer in a cross-layer design approach. First, a new AMC adaptation scheme has been proposed with the constraints of system parameters like maximum retransmission number and packet error rate (PER). Then we analyze the system PER performance and the spectral efficiency of the proposed cross-layer AMC adaptation scheme. Numerical results show that our scheme can achieve good spectral efficiency while satisfying the target delay and goodput performance.

In this paper, we present a cross-layer design which combines adaptive modulation and coding (AMC) at the physical layer with type-II hybrid automatic repeat request (HARQ) at the data link layer for QoS provisioning under fading channels. To maximize system spectral efficiency as well as satisfy QoS parameters such as data rate, delay and packet error rate (PER), we have proposed a new cross-layer scheme, where AMC transmits incremental redundancy for combining decoding with type-II HARQ. The proposed cross-layer scheme is designed to meet the target packet error rate within the pre-defined number of retransmissions. Since the number of retransmissions can be determined by the maximum delay bound imposed by upper layer applications, the proposed scheme will provide guaranteed QoS in terms of data rate, delay and packet error rate under fading channels. We have theoretically analyzed the system PER performance, the spectral efficiency and goodput of the proposed cross-layer design scheme. Since the application packet size is one of the most crucial factors to affect the performance of the proposed scheme, we also introduce an adaptive algorithm to derive the optimal information packet size under the current channel condition to achieve the maximum throughput. Numerical results show that the proposed scheme can achieve the maximum spectral efficiency while satisfying the target delay, packet loss rate and goodput imposed by the applications.

CDMA2000 1/spl times/EV-DV has been proposed as one of the global standards of third-generation (3G) networks, which adopts TDM/CDM and adaptive modulation and coding (AMC) techniques to enhance the data rate. The current CDMA2000 1/spl times/EV-DV standard specifies all possible combinations of system parameters, but there is no any further specification in the standard on how to dynamically change the system parameters to support the quality-of-service (QoS) requirements imposed by the upper-layer applications. In the meantime, one of the major deficiencies of previous research work done in this area is that they all are based on the channel models of physical layer such as Rayleigh model, which is unable to capture the link-layer QoS parameters such as queueing delay. Since the dynamic resource allocation usually resides in the data link layer, a wireless channel model at the link layer would be desirable to handle the QoS requirements. In this paper, we develop a dynamic resource allocation scheme using the effective capacity link model to support delay-bounded multimedia services in CDMA2000 1/spl times/EV-DV networks. Extensive simulations have been set up and the simulation results show that the proposed dynamic resource allocation scheme significantly improves the delay and throughput performance for all types of application traffic with various QoS requirements.

Summary form only given. CDMA2000 1xEV-DV has been proposed as one of the global standards of 3G networks, which adopts TDM/CDM and adaptive modulation and coding (AMC) techniques to enhance the data rate up to 3.1 Mbps. Even though the current CDMA 2000 1xEV-DV standard specifies all combinations of the system parameters such as encoder packet size, modulation scheme, the number of time slots per sub-packet, and modulation and coding schemes. However, it is still unknown how to dynamically change physical layer parameters to support the upper layer quality of service (QoS). Given a certain channel quality, to meet the committed quality of services (QoS) in terms of throughput, delay and delay violation probability, we need to select the optimal combination of system parameters. In this paper, we develop a dynamic resource scheduling scheme for supporting delay-bounded multimedia services in CDMA2000 1xEV-DV networks. Simulation results show that the proposed dynamic resource scheduling scheme significantly improves the delay and throughput performance for all types of data traffic with various QoS requirements.

Summary form only given. Link adaptation techniques improve the link quality by adjusting medium access control (MAC) parameters such as frame size, data rate, and sleep time, thereby improving energy efficiency. In this paper, we study a performance comparison of two recently proposed link adaptation techniques based on Kalman filter at MAC layer to enhance energy efficiency of the sensor nodes in mobile wireless sensor networks. These two new approaches use extended Kalman filter (EKF) and unscented Kalman filter (UKF) to predict the optimal frame size for improving energy efficiency and goodput, while minimizing the sensor memory requirement. We designed and verified different network models to evaluate and analyze the proposed link adaptation schemes. The simulation results show that the UKF-based link adaptation algorithm offers a better performance than the EKF-based algorithm due to less errors on estimation and prediction in a nonGaussian nonlinear scenario.

Accurately modeling the IEEE 802.11 MAC is critical to design resource allocation, admission control and scheduling schemes for QoS provisioning in wireless LANs. However, it is difficult to model the saturation throughput performance of the IEEE 802.11 MAC due to the fact that the memory effect existing among different stages in the random backoff procedure. Therefore, Markov chain has been used to characterize the saturation throughput performance of the IEEE802.11 CSMA/CA protocol in many research efforts. However, one of the problems existing in the current research failed to consider all key practical issues altogether such as retry limit, backoff suspensions and the fading channel errors. Therefore, an accurate and realistic model of the IEEE 802.11 MAC protocol with fading channel errors is needed. In this work, we propose a new model based on Markov chain and validate its correctness by theoretical analyses and Ns-2 simulations. We also compared our results with other current major models. Our contributions in this work are: 1) taking the backoff suspension into considerations and derive a more accurate model than other current models; 2) introducing the fading channel errors into the modeling process and derive a close form equation of the saturation throughput performance of the 802.11 DCE simulation results show that the proposed model is same as accurate as other current models, and becomes much more accurate when considering fading channel errors.

cdma2000 1xEV-DV has been proposed as one of the standards for the next generation multimedia wireless generation (MWG) systems, which adopts new techniques such as adaptive modulation and coding (AMC) schemes to enhance wireless data services. Although many possible combinations of system parameters have been specified in the current 1xEV-DV standard, there is no further specification on how to dynamically choose the optimal combination of system parameters, such as the number of Walsh codes, the number of time slots, the modulation scheme, and the channel data rate, according to variations of buffer usage and channel quality. In this paper, we first review the latest cdma2000 1xEV-DV standard focusing on its unique features. Then, we propose a new delay-bounded dynamic resource allocation scheme for the QoS provisioning in cdma2000 1xEV-DV networks. The proposed scheme supports dynamic resource allocation by selecting the optimal combination of system parameters to satisfy the QoS requirements imposed by user applications, based on not only the channel quality but also the queue length. Simulation results show the proposed resource allocation scheme can provide delay-bounded services to multimedia applications such as voice and video.

Self-regulating network utilization is very important to the wireless network service providers to carry out new services such as wireless network gaming, which means that more users with a prepaid level of quality of service can be accommodated in a bandwidth limited network. However, in mobile ad-hoc wireless LANs, it is very difficult to regulate network utilization due to the time-varying nature of many important system parameters such as network load and channel quality. Moreover, the contention-based MAC protocols such as CSMA/CA adopted by IEEE 802.11 standard have been widely deployed in a wireless LAN environment for its flexibility, making self-regulating network utilization even more difficult. In this paper, we have proposed a fully distributed scheme running on each wireless station for self-regulating its network utilization to meet its prepaid service level. Simulation results show that the proposed scheme allows a wireless station to self-regulate the network utilization to its prepaid service level as close as possible by adapting the system parameters of MAC and PHY layers with changes of network environment such as the number of users and channel quality.

Technological advances in low-power digital signal processors, radio frequency (RF) circuits, and micromechanical systems (MEMS) have led to the emergence of wirelessly interconnected sensor nodes. The new technological possibilities emerge when a large number of tiny intelligent wireless sensor nodes are combined. The sensor nodes are typically battery operated and, therefore, energy constrained. Hence, energy conservation is one of the foremost priorities in design of wireless sensor networks (WSNs) protocols. Limited power resources and bursty nature of the wireless channel are the biggest challenges in WSNs. Link adaptation techniques improve the link quality by adjusting medium access control (MAC) parameters such as frame size, data rate, and sleep time, thereby improving energy efficiency. In This work, our study emphasizes optimizing WSNs by building a reliable and adaptive MAC without compromising fairness and performance. Here, we present link adaptation techniques at MAC layer to enhance energy efficiency of the sensor nodes. The proposed MAC uses a variable frame size instead of a fixed frame size for transmitting data. In order to get accurate estimations, as well as reducing the computation complexity, we utilize the extended Kalman filter to predict the optimal frame size for improving energy efficiency and goodput, while minimizing the sensor memory requirement. Next, we designed and verified different network models to evaluate and analyze the proposed link adaptation schemes. The correctness of the proposed theoretical models have been verified by conducting extensive simulations. We also prototype the proposed scheme with the MAC protocol on Berkeley Motes. Both prototype and simulation results show that the proposed algorithms improve the energy efficiency by up to 15%.

In IEEE 802.11 wireless LANs, channel quality, network load, as well as the protocol itself are time-varying, limiting the goodput performance improvement in wireless LANs. Therefore, it becomes critical to dynamically adjust parameters of MAC and PHY layers according to variations of channel quality. In this paper, we propose variable frame size and variable data rate schemes for goodput enhancement. We first propose two optimal frame size predictors: a goodput regulator to maintain the committed goodput for non-greedy applications and an optimal frame size predictor for maximizing the goodput for greedy applications. Then, we propose a data rate drafting scheme and develop a variable size and variable rate (VSVR) scheme for further goodput improvement. Our extensive simulation results show that the proposed VSVR algorithm can double the channel goodput of current implementations. Moreover, the proposed scheme can be easily integrated with the current implementations of the wireless LAN MAC protocol. Copyright © 2004 John Wiley & Sons, Ltd.

In wireless sensor networks, each sensor node is normally powered by batteries without replenish. Radio operations of a sensor node is a big user of the limited energy. This situation is further exaggerated by retransmissions caused by a time-varying channel. Therefore, how to design an energy efficient sensor MAC is one of the most important issues in sensor networks. Unlike other reported work utilizing a fixed frame size, we propose a new approach to enhance the energy efficiency by introducing an optimal frame size predictor into the sensor MAC, combating the unfavorable time-varying channel quality. We also propose a new sensor MAC to balance goodput performance and energy efficiency, as well as keep fairness among different nodes. Simulation results show that the proposed sensor MAC can largely reduce the average number of transmissions. Therefore, the energy efficiency can be improved by at least 10%, still keeping the same good throughput performance. The proposed sensor MAC can be easily implemented in practical.

This paper proposes a reliable and efficient multi-robot coordination algorithm to accomplish an area exploration task given that the communication range of each robot is limited. This algorithm is based on a distributed bidding model to coordinate the movement of multiple robots. Two measures are developed to accommodate the limited-range communications. First, the distances between robots are considered in the bidding algorithm so that the robots tend to stay close to each other. Second, a novel coding mechanism is introduced in the map representation to reduce the exchanged data volume when new communication links are formed. Simulation results show the effectiveness of the use of the nearness measure in the coordination algorithm as well as the new coding mechanism to reduce the amount of map data exchanged. By handling the limited communication range we can make the coordination algorithms more practical in multi-robot applications.

Sensor nodes are usually battery operated and therefore energy constrained. Thus, energy conservation becomes one of the foremost priorities in designing the wireless sensor networks (WSNs). We present the enhancements at the medium access control (MAC) layer in order to improve the energy efficiency and increase the longevity of wireless sensor networks. The proposed MAC uses an optimal frame size instead of using a fixed frame size for every transmission. We present our study of the unscented Kalman filter (UKF) approach to predict the optimal frame size. The proposed variable frame size conserves energy as well as enhance the system performance by minimizing the throughput degradation caused by a time varying wireless channel. We designed and verified a different network model to evaluate and analyze the proposed approach. The simulation results show an improvement in energy efficiency of up to 15 %.

Energy efficiency is critical to wireless network terminal devices, since they are powered by battery in many cases. Our approach to improving energy efficiency is to reduce the idle energy consumption by increasing the amount of information data transmitted by the wireless network interface card for a given amount of energy. We propose a simple TCP quick timeout mechanism (TCP-QT) for the improvement of energy efficiency. Using feedback from the MAC layer, the TCP layer can be made aware of the transmission history of a packet in the MAC layer If a packet has been dropped by the MAC layer, a quick timeout in the TCP layer is triggered, resulting in a fast retransmission of the packet. Thus, the average energy efficiency observed in the application layer is improved, while the current TCP semantics are largely unchanged. Simulation results show that the TCP-QT mechanism can improve the energy efficiency by up to 5 times.

Throughput performance is very important to the QoS provisioning in wireless data networks. We propose a link adaptation algorithm for the throughput performance improvement of the IEEE 802.11 wireless LAN. In this algorithm, an optimal frame size predictor with considerations of collisions and frame errors is proposed. Simulations results show that the throughput performance of the wireless LAN can be enhanced greatly than that achieved by traditional fixed frame size scheme.

We propose a new link adaptation approach for QoS enhancement in wireless networks. We utilize the Kalman filter, which predicts the local optimal frame size based on the channel quality varying based on the derived channel model. We have also presented the simulations and experimental results. The results show that the performance of proposed predictor is much better than other prediction methods like the moving average.

Two simple back-off based fragment adaptive algorithms for improving the throughput of IEEE 802.11 wireless LAN are proposed and studied. The simulation results show major improvement in channel throughput and reduction in system end-to-end delay. The results also indicate that proposed adaptive fragmentation algorithms perform the best when the channel is very noisy.