To facilitate dynamic spectrum sharing, the FCC has designated certified SAS administrators to implement their own spectrum access systems (SASs) that manage the shared spectrum usage in the novel CBRS band. As a premise, different SAS servers must conduct periodic inter-SAS coordination to synchronize service states and avoid allocation conflicts. However, SAS servers may inevitably stop service for regular upgrades, crash down, or even perform maliciously that deviate from the normal routines, posing a fundamental operation security problem --- the system shall be robust against these faults to guarantee secure and efficient spectrum sharing service. Unfortunately, the incumbent inter-SAS coordination mechanism, CPAS, is prone to SAS failures and does not support real-time allocation. Recent proposals that rely on blockchain smart contracts or state machine replication mechanisms to realize fault-tolerant inter-SAS coordination require all SASs to follow a unified allocation algorithm. They however face performance bottlenecks and cannot accommodate the current fact that different SASs hold their own proprietary allocation algorithms.

No abstract available.

Recent advances in Blockchain-based Federated Learning (FL) aim to address the inherent limitations of traditional FL, such as single node failure and the lack of an appropriate incentive mechanism. This approach replaces the central parameter server in FL with a blockchain that stores and disseminates updated models. However, its decentralized nature introduces significant communication and storage over-head, which considerably constrains its practical application. Additionally, as it allows participants to contribute to the shared model by training locally using private data, it is especially prone to privacy leaks and poisoning attacks. This study introduces a novel framework, BlockFed, designed to substantially reduce overhead and mitigate vulnerabilities in blockchain-based FL systems.

Federated Learning (FL) has been adopted in practical network applications and plays a critical role. As FL allows participants to contribute to the global model by training locally with private data, it is known particularly vulnerable to neural backdoor attacks. This paper proposes a new defense, ScanFed, against neural backdoor attacks to FL systems. It leverages the synchronous nature of FL to effectively single out malicious neuron candidates and further validate if they indeed hijack the model's behaviors. Compared to existing neural backdoor defenses, ScanFed has the following distinct properties. First, it is extremely computation-friendly that is six orders of magnitude faster than state-of-the-art behavior-based backdoor defenses, rendering it highly suitable for large-scale FL systems. Second, it inherits the precise nature of behavior-based backdoor detection, making it significantly more effective than similarity-based defenses against advanced attacks. Third, it is robust to biased models uploaded by clients with non-IID (Independent and Identically Distributed) data, which is very common in practical FL systems. In addition, it is a plug-n-play scheme that can be seamlessly integrated into existing FL systems. To the best of our knowledge, this is the first behavior-based defense that enables scalable, efficient and accurate neural backdoor detection of FL systems in non-IID scenarios. This work delivers a ScanFed prototype and fully tests it in various settings of datasets, neural architectures, and backdoor attacks. The experiments demonstrate ScanFed achieves competitive accuracy and minimal detection time.

Blockchain and distributed ledger technology started as a decentralized infrastructure to enable and manage digital currency like Bitcoin without relying on a central authority. One of the attractive features provided by blockchain technology is its append-only “immutability” feature, which means the stored data cannot be modified or manipulated by any means once it is validated in the blockchain ledger. Such immutability helps traceability, auditing, and non-repudiation, which builds decentralized trust among un-trusted parties. Despite that, immutability if misused could lead to the permanent existence of sensitive information and misinformation in the blockchain. Incidents like broadcasting illegal content have already taken their place in blockchain systems. Such incidents call for prompt solutions for mitigation. One emerging research theme, “redactable distributed ledgers” such as redactable blockchain provides approaches for modifying ledgers with certain controllability. This article aims to survey the current research landscape about redactable distributed ledgers. We will first describe the motivations behind redactable distributed ledgers. Compared to other relevant surveys, we comprehensively summarized and briefly explained the underlying technologies for supporting redactable distributed ledgers. We mainly focused on chameleon hash-based redactable blockchain structure and classifications, with detailed comparisons and illustrations. Further, we tackled new distributed ledger structures, including the new state-of-the-art block matrix structure. Furthermore, new applications that can be enabled by redactable distributed ledgers and future research directions are discussed in detail. This article emphasizes the motivation of utilizing the redactable distributed ledgers in several critical applications to mitigate misuse of immutability features threatening the original known design of distributed ledgers.

We report a new neural backdoor attack, named Hibernated Backdoor, which is stealthy, aggressive and devastating. The backdoor is planted in a hibernated mode to avoid being detected. Once deployed and fine-tuned on end-devices, the hibernated backdoor turns into the active state that can be exploited by the attacker. To the best of our knowledge, this is the first hibernated neural backdoor attack. It is achieved by maximizing the mutual information (MI) between the gradients of regular and malicious data on the model. We introduce a practical algorithm to achieve MI maximization to effectively plant the hibernated backdoor. To evade adaptive defenses, we further develop a targeted hibernated backdoor, which can only be activated by specific data samples and thus achieves a higher degree of stealthiness. We show the hibernated backdoor is robust and cannot be removed by existing backdoor removal schemes. It has been fully tested on four datasets with two neural network architectures, compared to five existing backdoor attacks, and evaluated using seven backdoor detection schemes. The experiments demonstrate the effectiveness of the hibernated backdoor attack under various settings.

Federated learning (FL) is a collaborative machine learning framework to enable different clients such as Internet of Things (IoT) devices to participate in a machine learning model training process, while preserving data privacy. Client selection is critical to determine the performance of FL. Most of the existing client selection methods aim to maximize the number of selected clients, who can upload their local models before the deadline, in each global iteration, thus potentially accelerating the model convergence rate. However, these methods ignore the fact that most of the IoT devices are powered by on-board batteries and harvested green energy from the environment to prolong battery life. Hence, clients selected by these methods may not have sufficient energy to upload their local models in a global iteration or are unable to participate in the training process in the near future due to battery drainage. In this paper, we propose a novel client selection, entitled “EnerGy-AwaRe CliEnt SElection for Green FeDerated Learning (GREED)”, to optimize the trade-off between maximizing the number of selected clients and minimizing the energy drawn from batteries for the selected clients, while ensuring that all the selected clients have sufficient energy to upload their local models before the deadline. The performance of GREED is validated via extensive simulations.

The full potential of Internet of Things (IoT) data is often unrealized because data are restricted to single-purpose use cases or accessible to a few, siloed users. The opportunities are significantly greater when data can be shared using a marketplace concept. This provides a framework where many suppliers of data can interact with consumers of data, such as artificial intelligence (AI) developers and IoT service providers. Their marketplace interactions provide a means to identify the demand for specific types of data and to incentivize suppliers to offer high quality and dependable data. Standardization is a prerequisite for an effective marketplace in terms of setting the rules for interactions and in establishing a technical baseline for data supply and consumption. This article introduces an IoT data marketplace framework and illustrates its application through a set of smart city and intelligent transport system deployments. It highlights the use of oneM2M, which is an open standard for the middleware layer in the IoT technology stack. oneM2M’s middleware capabilities reside between IoT devices and communication networks, and the AI/ML and IoT applications that consume IoT data. The proposed IoT data marketplace also includes licensing, usage tracking, and secured data sharing features. These provide marketplace users with the tools to control data sharing and monetization. Through reference to multiple deployments of the IoT data marketplace, this article highlights the benefits of standardization for replicable solutions in multiple IoT segments, interoperability, and business model innovation involving multistakeholder ecosystems.

In a free space optics (FSO)-based drone-assisted mobile network, a drone-mounted base station (DBS) can be rapidly deployed over a place of interest to relay traffic between Internet of Things (IoT) devices and a macro base station (MBS), and FSO is applied as the fronthaul solution between the DBS and the MBS to provide a high link capacity at a long distance. However, due to inevitable optical beam misalignment, having a smaller divergence angle of the optical beam may increase the outage probability of the FSO-based fronthaul link. On the other hand, having a larger divergence angle may reduce the capacity of the FSO-based fronthaul link. So, it is critical but challenging to determine the divergence angle in order to optimize the tradeoff between minimizing the outage probability and maximizing the capacity for the FSO-based fronthaul link in the context of the FSO-based drone-assisted mobile network. In this article, we formulate an optimization problem to determine the optimal divergence angle that can minimize the link outage probability, while guaranteeing the capacity of the FSO-based fronthaul link no less than the threshold. The link outage and capacity aware divergence angle (LEARN) algorithm is designed to efficiently solve the problem. The performance of LEARN is demonstrated via extensive simulations.

Federated learning (FL) is a distributed machine learning technique to address the data privacy issue. Participant selection is critical to determine the latency of the training process in a heterogeneous FL architecture, where users with different hardware setups and wireless channel conditions communicate with their base station to participate in the FL training process. Many solutions have been designed to consider computational and uploading latency of different users to select suitable participants such that the straggler problem can be avoided. However, none of these solutions consider the waiting time of a participant, which refers to the latency of a participant waiting for the wireless channel to be available, and the waiting time could significantly affect the latency of the training process, especially when a huge number of participants are involved in the training process and share the wireless channel in the time-division duplexing manner to upload their local FL models. In this paper, we consider not only the computational and uploading latency but also the waiting time (which is estimated based on an M/G/1 queueing model) of a participant to select suitable participants. We formulate an optimization problem to maximize the number of selected participants, who can upload their local models before the deadline in a global iteration. The Latency awarE pARticipant selectioN (LEARN) algorithm is proposed to solve the problem and the performance of LEARN is validated via simulations.

The data poisoning attack has raised serious security concerns on the safety of deep neural networks, since it can lead to neural backdoor that misclassifies certain inputs crafted by an attacker. In particular, the sample-targeted backdoor attack is a new challenge. It targets at one or a few specific samples, called target samples, to misclassify them to a target class. Without a trigger planted in the backdoor model, the existing backdoor detection schemes fail to detect the sample-targeted backdoor as they depend on reverse-engineering the trigger or strong features of the trigger. In this paper, we propose a novel scheme to detect and mitigate sample-targeted backdoor attacks. We discover and demonstrate a unique property of the sample-targeted backdoor, which forces a boundary change such that small "pockets" are formed around the target sample. Based on this observation, we propose a novel defense mechanism to pinpoint a malicious pocket by "wrapping" them into a tight convex hull in the feature space. We design an effective algorithm to search for such a convex hull and remove the backdoor by fine-tuning the model using the identified malicious samples with the corrected label according to the convex hull. The experiments show that the proposed approach is highly efficient for detecting and mitigating a wide range of sample-targeted backdoor attacks.

The spectrum access system (SAS) is being deployed as a key component of the emerging spectrum sharing paradigm to address the spectrum crunch facing the U.S. wireless industry. Ensuring security and privacy of this system against potential attacks is a task of paramount importance. In this article, we first introduce the SAS system, describing its three-tier access model, its functional architecture, and the spectrum management protocol. We then provide a comprehensive analysis of a variety of security and privacy attacks that an SAS is vulnerable to, and discuss their countermeasures. We identify key challenges, formalize threat models, and organize the discussion of SAS security into four categories: 1) SAS server security and privacy; 2) citizens broadband radio service device security; 3) security of environment sensing capability; and 4) communication protocol security. Finally, we suggest future research directions for spectrum management security.

The papers in this special section focus on hybrid human-artificial intelligene (AI) for multimedia computing. Multimedia computing has experienced a tremendous growth in the last decades, with applications ranging from multimedia information retrieval and analysis to multimedia compression and communication. However, the increasing volume and complexity of multimedia data driven by the large-scale spread of various new devices and sensors is posing a serious challenge to traditional multimedia computing algorithms. Artificial intelligence (AI), in particular deep learning techniques, has improved the performance of multimedia computing algorithms for many tasks, including computer vision and natural language processing. But unlike humans, AI is poor at solving tasks across multiple domains or in dealing with an uncontrolled dynamic environment. Hybrid Human-Artificial Intelligence (HH-AI) is an emerging field that aims at combining the benefits of human intelligence, such as semantic association, inference, and generalization with the computing power of AI.

Industrial Internet is applicable across a broad industrial spectrum including manufacturing, aviation, road and rail transport, power, oil and gas, healthcare, smart cities and buildings. Some of the major impacts of the Industrial Internet include the development of new and innovative services and products, which in turn also has economic benefits. The purpose of this special issue is to bring together research studies proposing novel techniques, algorithms, models, and solutions to address challenges such as interoperability, security, and privacy associated with Industrial Internet, blockchain and Cyber-physical systems. We accepted seven articles after two review rounds consisting of three reviews from experts in the areas. The special issue contains seven articles organized in the following categories. 1) Secure searching for edge-cloud assisted industrial Internet of Things (IoT) devices. 2) Privacy protection framework for mobile crowdsensing in Industrial Internet of Things (IIoT). 3) Content privacy for autonomous vehicles in cyberphysical system (CPS). 4) Delegated Proof of Stake (DPoS) consensus mechanism in blockchain. 5) Balancing privacy and accountability for industrial mortgage management. 6) Performance and security in wireless blockchain networks. 7) False data injection attacks in networked control systems.

When dealing with a large number of devices, the existing indexing solutions for the discovery of Internet of Things (IoT) sources often fall short to provide an adequate scalability. This is due to the high computational complexity and communication overhead that is required to create and maintain the indices of the IoT sources particularly when their attributes are dynamic. This paper presents a novel approach for indexing distributed IoT sources and paves the way to design a data discovery service to search and gain access to their data. The proposed method creates concise references to IoT sources by using Gaussian mixture models. Furthermore, a summary update mechanism is introduced to tackle the change of sources availability and mitigate the overhead of updating the indices frequently. The proposed approach is benchmarked against a standard centralized indexing and discovery solution. The results show that the proposed solution reduces the communication overhead required for indexing by three orders of magnitude while depending on IoT network architecture it may slightly increase the discovery time.

The Single Instruction Multiple Data (SIMD) architecture of Graphic Processing Units (GPUs) makes them perfect for parallel processing of big data. In this paper, we present the design, implementation and evaluation of G-Storm, a GPU-enabled parallel system based on Storm, which harnesses the massively parallel computing power of GPUs for high-throughput online stream data processing. G-Storm has the following desirable features: 1) G-Storm is designed to be a general data processing platform as Storm, which can handle various applications and data types. 2) G-Storm exposes GPUs to Storm applications while preserving its easy-to-use programming model. 3) G-Storm achieves high-throughput and low-overhead data processing with GPUs. 4) G-Storm accelerates data processing further by enabling Direct Data Transfer (DDT), between two executors that process data at a common GPU. We implemented G-Storm based on Storm 0.9.2 and tested it using three different applications, including continuous query, matrix multiplication and image resizing. Extensive experimental results show that 1) Compared to Storm, G-Storm achieves over 7χ improvement on throughput for continuous query, while maintaining reasonable average tuple processing time. It also leads to 2.3χ and 1.3χ throughput improvements on the other two applications, respectively. 2) DDT significantly reduces data processing time.

The resource reservation is one of the key techniques to ensure the quality of service (QoS) of a multimedia application. In mobile cloud computing (MCC), the resource reservation and allocation (RRA) in advance can significantly reduce the total provisioning cost of cloud service providers. However, the uncertain features of mobile users' demands for resources make RRA challengeable. In MCC, the QoS of a mobile application, such as voice IP or video, is determined by both of the radio resource (RR) and the cloud virtual machine resource (VMR) allocated to the mobile application, so we should jointly allocate these two types of resources. In this paper, RRA with uncertain demands of mobile users is formulated as a robust optimization model. Logarithmic utility functions are defined to capture the mobile users' satisfaction, which show how to match the allocations between RRs and VMRs according to the resource demands of the mobile applications. Then, a robust joint resource reservation and allocation algorithm in MCC (JRRA-MCC) is proposed to realize the optimal provisioning of RRs and VMRs. Simulation results show that the proposed JRRA-MCC can minimize the total resource provisioning cost of cloud service providers and enhance the resource utilization efficiently.

Device-to-device (D2D) enabled cloud radio access networks (C-RANs) are potential solutions for further improving spectral efficiency (SE) and decreasing latency by allowing direct communication between two user equipments. Due to the acquirement of global channel state information (CSI) and the execution of centralized algorithms in the uplink D2D enabled C-RANs, heavy burdens are put on fronthaul and the baseband unit pool. To tackle this challenge, a game-theoretic approach to mode selection and resource allocation for potential D2D pairs is proposed with a distributed manner, in which pairs are endowed with decision-making capabilities. The proposal is categorized into three stages: communication mode and subchannel selection, remote radio head (RRH) association, and reinforcement learning based strategy update. The core idea is that D2D pairs autonomously optimize the mode selection and resource allocation without global CSIs under several practical constraints. Simulation results show that enabling D2D can significantly improve SE for C-RANs. Furthermore, the performance gain is mainly determined by the fronthaul capacity and the distance between D2D transmitters and RRHs.

With the advances in personal computing devices and the prevalence of broadband network and wireless mobile network accesses, end-users are no longer pure content consumers, but contributors, too. In today's crowdsourced streaming systems, numerous broadcasters lively stream their video content, e.g., live events or online game scenes, to fellow viewers. Compared to professional video producers and broadcasters, these new generation broadcasters are geo-distributed globally and highly heterogeneous in terms of the generated video quality and the network/system configurations. The scalability and heterogeneity challenges therefore lie on both broadcasters and the viewers, which call for massive transcoding, and two critical issues: 1) choosing video representation set that maximizes viewer satisfaction and 2) allocating computational resources that minimize operational costs, must be systematically optimized in the global scale. In this paper, we present a generic framework utilizing the powerful and elastic cloud computing services for crowdsourced live streaming with heterogeneous broadcasters and viewers. We jointly consider the viewer satisfaction and the service availability/pricing of geo-distributed cloud resources for transcoding. We develop an optimal scheduler for allocating cloud instances with no regional constraints. We then extend the solution to accommodate regional constraints, and discuss a series of practical enhancements, including popularity forecasting, initialization latency, and viewer feedbacks. Our solutions have been evaluated under diverse networks and cloud system configurations as well as parameter settings. The trace-driven simulation confirms the superiority of our design, while our Planetlab-based experiment offers further practical hints toward real-world migration.

To preserve the privacy of multimedia big data and achieve the efficient data aggregation in wireless multimedia sensor networks (WMSNs), a distributed compressed sensing--based privacy-preserving data aggregation (DCSPDA) approach is proposed in this article. First, in this approach, the original multimedia sensor data are compressed and measured by distributed compressed sensing (DCS) and the compressed data measurements are uploaded to the sink, by which the inherent characteristics between sensor data can be obtained. Second, the original multimedia data are jointly recovered and the common and innovation sparse components are obtained through solving the optimization problem and linear equations at the sink. Third, through least squares support vector machine (LSSVM) learning of the sparse components, the sparse position configuration can be determined and disseminated for each node to conduct the privacy-preserving data configuration. After receiving the configuration message, original multimedia sensor data are accordingly customized, compressed, and measured by the common measurement matrix, aggregated at the cluster heads, and transmitted to the sink. Finally, the aggregated multimedia sensor data are recovered by the sink according to the data configuration to achieve the privacy-preserving data aggregation and transmission. Our comparative simulation results validate the efficiency and scalability of DCSPDA and demonstrate that the proposed approach can effectively reduce the communication overheads and provide reliable privacy-preserving with low computational complexity for WMSNs.

Intermittently connected wireless networks (ICWNs) have been studied in recent years to solve the disruption problem in mobile ad hoc networks and improve the utilization of temporary links raised by node movements. In ICWNs, the packet storing-carrying-forwarding principle is adopted through the cooperation between multiple nodes. Therefore, it is critical to include the connection status of nodes in designing efficient packet forwarding mechanism. In this paper, a node service ability aware packet forwarding mechanism is proposed based on the connection status. First, the connection model is established to analyze the transition of connection status; moreover, the service ability can be evaluated according to the connection strength and connection availability. Second, packet forwarding levels are determined based on their transmitting status to exploit the limited buffer resources. Consequently, the efficient packet forwarding mechanism can guarantee the flexibility of packet transmission in both complex and dynamic network scenarios. Numerical results show that about 20% delivery ratio increase can be achieved by the proposed mechanism, while the overheads and latency are reduced.

Coordinated Multi-Point (CoMP) is an effective way to improve user performance in next-generation wireless cellular networks, such as 3GPP LTE-Advanced(LTE-A). The base station cooperation can reduce interference, and increase the signal to interference and noise ratio (SINR) of cell-edge users and improve the system capacity. However, the base station cooperation also adds additional power consumption for signal processing and sharing information through back-haul links between cooperative base stations. As such, CoMP may potentially consume more energy. This paper studies such energy consumption issue in CoMP, presents a semi-dynamic CoMP cluster division algorithm based on energy efficiency (SCCD-EE) that can effectively adapt to users' real-time interference, and employs the idea of Maximal Independent Set (MIS) to solve the problem of cluster overlapping. To verify the feasibility of the proposed algorithm, this paper performs comprehensive evaluations in terms of energy efficiency and system capacity. The simulation results show that the proposed semi-dynamic cluster division algorithm can not only improve the system capacity and the quality of service (QoS) of cell-edge users, but also achieve higher network energy efficiency compared with static cluster methods and Non-CoMP approaches. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract In this paper, we develop a delay‐centric parallel multi‐path routing protocol for multi‐hop cognitive radio ad hoc networks. First, we analyze the end‐to‐end delay of multi‐path routing based on queueing theory and present a new dynamic traffic assignment scheme for multi‐path routing with the objective of minimizing end‐to‐end delay, considering both spectrum availability and link data rate. The problem is formulated as a convex problem and solved by a gradient‐based search method to obtain optimal traffic assignments. Furthermore, a heuristic decentralized traffic assignment scheme for multi‐path routing is presented. Then, based on the delay analysis and the 3D conflict graph that captures spectrum opportunity and interference among paths, we present a route discovery and selection scheme. Via extensive NS2‐based simulation, we show that the proposed protocol outperforms the benchmark protocols significantly and achieves the shortest end‐to‐end delay. Copyright © 2015 John Wiley & Sons, Ltd.

This paper proposes a Contrarian Probabilistic Model (CPM) to evaluate the effectiveness of contrarians' investment in preferred stocks using big data from Tradeline. CPM accommodates the unique features of investment data which are often correlated, nested, heterogeneous, non-normal with missing values. The clustering and statistical inference are integrated in CPM, which enables joint investment behavior trajectory pattern recognition and risk analyses based on the entire variance-covariance structure between and within clusters. The empirical study using CPM provides a finer and comprehensive evaluation of contrarian investment in preferred stocks. Two distinctive investment behavior trajectory clusters were identified, showing a few high-risk-seeking contrarians achieved high returns over five year long-term investment, while the majority of contrarians did not outperform glamour stockholders in preferred stock investment. Although CPM was developed using historical data, it could be developed into an analytical tool for online near real time big investment data analyses.

Based on empirical studies, the feature of random initialization in Particle Swarm Optimization (PSO) based Fuzzy c-means (FCM) methods affects the computational performance especially in big data. As the data points in high-density areas are more likely near the cluster centroids, we design a new algorithm to guide the initialization according to the data density patterns. Our algorithm is initialized by fusing the data characteristics near the cluster centers. Our evaluation results from real data show that our approach can significantly improve the computational performance of PSO-based Fuzzy clustering methods, while preserving comparable clustering performance.

Aggregation Point (AP) plays a vital role in smart grid, which forwards data stream between the client terminals and the control center in smart grid communication system. In this paper, we investigate two representative AP planning models: wired and wireless, and develop efficient algorithms to address the formulated AP planning problems in a cost-efficient way. For the wired case, a 5-approximation algorithm is proposed to minimize the total capital expenditure with the consideration of the installation cost of each AP in the neighborhood area networks (NANs) and the connecting cost between the AP and the home area network (HANs) served by it. For the wireless media-based networking scenario, an O(log W)-approximation algorithm is presented to minimize the total deployment cost of the opening APs under their coverage constraints, where W is the maximum capacity among these APs. Numerical results show that our proposed approximation algorithms have great advantages compared to other heuristic methods.

To realize an efficient utilization of harvested energy and improve throughput with the help of a battery, a harvest-use-store power splitting (PS) relaying strategy with distributed beamforming is proposed for the wireless-powered multi-relay scenario in this paper. To this end, harvested energy via PS can be accumulated and stored for future usage, which affects throughput at subsequent time slots due to the battery operations including the charging and discharging. As a result, PS and battery operations are coupled with distributed beamforming, such that the throughput optimization problem is intractable looking. To address the intractability of the optimization, a layered optimization method with an ideal non-causal channel state information (CSI) assumption is proposed. As a result, the optimal joint PS and battery operation design in the proposed strategy is derived in a decoupled manner. Simulation results confirm the accuracy of the proposed method, and revealed that the proposed strategy has significant performance gains over the conventional designs.

Despite the increasing capabilities, mobile devices still cannot satisfy the computation requirement of many applications. Intuitively, this can be solved by outsourcing tasks to external resources such as a remote server, cloud, or closely deployed cloudlet. However, all of them require extra infrastructures. In this paper, we consider a proximate-mobile-device based communication system in which all tasks and resources are under the control of a central scheduler. We propose a friendship-based task scheduling algorithm to address the contentions when resources are not sufficient. We also present two attack models including the denial-of-service (DoS) attack and the collusion attack. We evaluate the performance of the proposed algorithm along with another contribution-based task scheduling algorithm through extensive experiments.

A dilemma in cloud-radio access networks (C-RANs) is to balance the cluster scale and the cooperative gains. In this paper, cluster formation for downlink transmissions in C-RANs is studied. In particular, with a fixed intro-cluster cooperation strategy, an explicit expression for the successful access probability is derived by applying stochastic geometry. By using the obtained theoretical result as a utility function, the problem of grouping remote radio heads is formulated as a coalitional formation game, and then two distributed algorithms based on the merge and split approach are obtained as efficient solutions for the cases with and without cluster size constraints, respectively. Compared with grand cluster formation, which is a centralized method, simulation results show that the proposed approaches can achieve better performance with smaller cluster settings.

As the evolution of cloud radio access networks (CRANs), heterogeneous cloud radio access networks (H- CRANs) are now recognized as promising paradigm to achieve high spectral and energy efficiency through taking advantages of both heterogeneous networks and C- RANs. In H-CRANs, the heterogeneous processing node (HPN) guarantees the basic quality of service (QoS) requirement for the user equipment, while remote radio heads (RRHs) are deployed to provide enhanced QoS performances. Inter-tier interference between HPNs and RRHs should be coordinated for achieving high throughput gains in H-CRANs. In this paper, the transmit power for both RRHs and HPNs are researched to mitigate this inter-tier interference. The throughput maximizing problem with and without interference coordination under the power and interference constraints are developed. Since this kind of optimization problem is not convex, these two non- convex optimization problems are transformed into the form of matrix. Through the Perron-Frobenius theory, the optimal power allocation solution is derived. Simulation results show that the proposed solution is converged, and it can achieve significant performance gains.

To support the unrelenting demand of high spectral efficiency and gigabit data rates driven by fast developing smart applications and internet of things, heterogeneous cellular networks (HCNs) with the multiple-antenna configuration have been presented as promising paradigms. In this paper, the downlink transmission performances of K-tier HCNs with multiple-antenna configurations are analyzed, where base stations (BSs) in each tier may differ in terms of the spatial density, transmit power, cell-bias factor, and the number of transmit antennas. Particularly, the service success probability is analytically developed with the stochastic geometry, which can evaluate the transmission reliability and congestion. The impacts of the cell association and user scheduling on the service success probability are derived with closed-form expressions, and the spatial multiplexing gains from the multiple-antenna configuration are exploited. The Monte Carlo simulation results demonstrate that the derived expressions for the service success probability are matched well, and indicate that the proper number of antennas should be chosen, which is strictly related to the densities of BSs and users in each tier.

Cloud radio access network (CRAN) has been seen as an effective means to address the challenges faced by cellular radio networks, such as high capital expenditure and operating expense, high energy consumption and low spectral efficiency. Especially, CRAN has the potential to equip a cellular network with the load-balancing capability to cope with the non-uniformly distributed traffic in the service area. In this paper, we develop an optimal load balancing scheme for CRAN-based cellular systems by employing an infinite optimization technique. A fairness index is defined to measure the load balancing level of the cellular system and monitored by periodically inspecting the load distribution among all cells. When the fairness index is below a warning threshold, we divide the service zone into compact and connected subregions based on an infinite optimization formulation. Each subregion served by a cell has almost equal area and almost equal throughput requirement. To avoid yielding ill-shaped subregion that is difficult to be covered by a practical cell, a penalty term is introduced to the objective function. Then we update the cell association of each user so that the fairness index return to an acceptable level. Numerical experiments show that our proposal can provide performance-guaranteed load balancing for the cellular network with almost no additional operating expense.

The dynamic optimization problem for radio resource allocation in two-tier heterogeneous networks (HetNets) under the hybrid access mode is researched in this paper. To make the average utility of user throughput arbitrarily close to the optimum and maintain system queue stability under instantaneous and average power constraints, radio resource allocation optimization problem is formulated, and the corresponding solution that takes admission control, cell association, congestion control, subchannel and power allocation into account altogether is proposed, which is based on the Lyapunov optimization framework. The solution decomposes the optimization problem into three subproblems, in which the first two are linear and the third is mixed-integer non-linear. Both analysis and simulation results have verified that the proposal can achieve a significant utility performance gain under maintaining the queue stability with an [O(V), O(1/V)] tradeoff between throughput-utility optimality and traffic queue congestion.

This study considers spectrum sharing problems in the heterogeneous wireless networks where different device‐to‐device (D2D) users coexist with the cellular users. The authors propose a novel scheme, called ‘spectrum partition‐based D2D transmission’ (SPDT), to improve spectrum efficiency of the D2D and cellular networks. In SPDT, the D2D users assist the cellular transmissions to gain some spectrum released from the cellular system. Then, the obtained spectrum is divided into several frequency bands and each band is assigned to a different D2D pair for its data transmission. Under the quality‐of‐service (QoS) constraints of both the D2D and cellular users, the authors exploit the tradeoff in the power allocation of the D2D transmitters and show that the number of the allowed accessing D2D pairs can be maximised by optimising the D2D transmitters power for SPDT. For comparison the power optimisation problem is also investigated with the objective of maximising the number of allowed users accessing D2D pairs in the power control scheme, called ‘underlay D2D transmission’ (UDT). This is where the D2D users access the spectrum being used by the cellular users with power control while ensuring that the QoS of the cellular transmissions is satisfied. Finally, the simulation results show evident performance gains of the proposed SPDT scheme over the UDT scheme.

In this paper, we investigate the energy consumption issue in cognitive radio (CR) networks. We aim to maximize the energy efficiency of the CR network while considering practical restrictions, including the power budget of the system, the interference thresholds of the primary users (PUs), the rate requirements of the secondary users, and the fairness among them. Particularly, due to the lack of explicit support from the PU system, perfect channel state information may not be acquired. Thus, the interference constraint is posed as chance-constrained form and tackled by Bernstein approximation. Then, we convert the optimization task into a quasi-convex problem via relaxing the integer variables, followed by a simple rounding technique to yield feasible subchannels assignment. We derive a fast algorithm to distribute power among subchannels by exploiting the structure of the power-allocation problem. Moreover, we give an efficient heuristic algorithm for subchannels assignment, which reduces the computation load dramatically. Simulation results show that both our proposed resource allocation schemes perform well in practical scenarios. The energy efficiency obtained by the integer subchannels assignment and the fast power distribution achieves more than 98% of the upper bound. On the other hand, the proposed heuristic subchannels assignment with optimal power allocation achieves a good tradeoff between computation complexity and energy efficiency.

Efficient sensor network design requires a full understanding of the geometric environment in which sensor nodes are deployed. In practice, a large-scale sensor network often has a complex and irregular topology, possibly containing obstacles/holes. Convex network partitioning, also known as convex segmentation, is a technique to divide a network into convex regions in which traditional algorithms designed for a simple network geometry can be applied. Existing segmentation algorithms heavily depend on concave node detection, or sink extraction from the median axis/skeleton, resulting in sensitivity of performance to network boundary noise. Furthermore, since they rely on the network's 2-D geometric properties, they do not work for 3-D cases. This paper presents a novel segmentation approach based on Morse function, bringing together the notions of convex components and the Reeb graph of a network. The segmentation is realized by a distributed and scalable algorithm, named CONSEL, for CONnectivity-based SEgmentation in Large-scale 2-D/3-D sensor networks. In CONSEL, several boundary nodes first flood the network to construct the Reeb graph. The ordinary nodes then compute mutex pairs locally, generating a coarse segmentation. Next, neighboring regions that are not mutex pairs are merged together. Finally, by ignoring mutex pairs that lead to small concavity, we provide an approximate convex decomposition. CONSEL has a number of advantages over previous solutions: 1) it works for both 2-D and 3-D sensor networks; 2) it uses merely network connectivity information; 3) it guarantees a bound for the generated regions' deviation from convexity. We further propose to integrate network segmentation with existing applications that are oriented to simple network geometry. Extensive simulations show the efficacy of CONSEL in segmenting networks and in improving the performance of two applications: geographic routing and connectivity-based localization.

Increasing population density, closer social contact and interactions make epidemic control difficult. Traditional offline epidemic control methods (e.g., using medical survey or medical records) or model-based approach are not effective due to its inability to gather health data and social contact information simultaneously or impractical statistical assumption about the dynamics of social contact networks, respectively. In addition, it is challenging to find optimal sets of people to be quarantined to contain the spread of epidemics for large populations due to high computational complexity. Unlike these approaches, in this paper, a novel cluster-based epidemic control scheme is proposed based on Smartphone-based body area networks. The proposed scheme divides the populations into multiple clusters based on their physical location and social contact information. The proposed control schemes are applied within the cluster or between clusters. Further, we develop a computational efficient approach called UGP to enable an effective cluster-based quarantine strategy using graph theory for large scale networks (i.e., populations). The effectiveness of the proposed methods is demonstrated through both simulations and experiments on real social contact networks.

One fundamental question for wireless power transfer technology is the energy provisioning problem, i.e., how to provide sufficient energy to mobile rechargeable nodes for their continuous operation. Most existing works overlooked the impacts of node speed and battery capacity. However, we find that if the constraints of node speed and battery capacity are considered, the continuous operation of nodes may never be guaranteed, which invalidates the traditional energy provisioning concept. In this paper, we propose a novel metric-Quality of Energy Provisioning (QoEP)-to characterize the expected portion of time that a node sustains normal operation by taking into account node speed and battery capacity. To avoid confining the analysis to a specific mobility model, we study spatial distribution instead. As there exist more than one mobility models corresponding to the same spatial distribution, and different mobility models typically lead to different QoEPs, we investigate upper and lower bounds of QoEP in 1D and 2D cases. We derive tight upper and lower bounds of QoEP for 1D case with a single source, and tight lower bounds and loose upper bounds for general 1D and 2D cases with multiple sources. Finally, we perform extensive simulations to verify our theoretical findings.

With the exploding popularity of wireless communication, the radio spectrum has become a scarce commodity. To further improve the network capacity, various solutions have been proposed to increase spectrum efficiency and network throughput. Small cell network is one of these new trends for next generation mobile network design. One model is using relay stations (RS) as small cell providers to achieve extended coverage, lower cost, and higher network capacity. Considering multiple related physical constraints such as channel capacity, signal to noise ratio (SNR) requirement of subscribers, relay power and network topology, this paper studies a joint signal-aware RS placement and power allocation problem with multiple base stations in wireless relay networks. We presented approximation schemes which first find a minimum number of RS, using maximum transmission power, to cover all the subscribers meeting each SNR requirement, and then ensure communications between any subscriber to a base station by adjusting the transmission power of each RS. Numerical results are presented to confirm the theoretical analysis of our schemes, and to show strong performances of our solutions.

Accurate localization in wireless sensor networks is the foundation for many applications, such as geographic routing and position-aware data processing. In this paper, we develop a new localization protocol based on approximate convex decomposition (ACDL), with reliance on network connectivity information only. ACDL can calculate the node virtual locations for a large-scale sensor network with a complex shape. We first examine one representative localization algorithm and study the influential factors on the localization accuracy, including the sharpness of the angle at the concave point and the depth of the concave valley. We show that after decomposition, the depth of the concave valley becomes irrelevant. We thus define the concavity according to the angle at a concave point, which reflects the localization error. We then propose ACDL protocol for network localization. It consists of four main steps. First, convex and concave nodes are recognized and network boundaries are segmented. As the sensor network is discrete, we show that it is acceptable to approximately identify the concave nodes to control the localization error. Second, an approximate convex decomposition is conducted. Our convex decomposition requires only local information and we show that it has low message overhead. Third, for each convex section of the network, an improved MDS algorithm is proposed to compute a relative location map. Fourth, a fast and low complexity merging algorithm is developed to construct the global location map. Besides, by slight modification on the third step, we propose a variant of ACDL, denoted by ACDL-Tri, which is fully distributed and scalable while the localization accuracy is still comparable. We finally show the efficiency of ACDL by extensive simulations.

Geometric routing or geo-routing has been shown as a promising approach to scalable routing in sensor networks. Despite its success in 2-D networks, very few designs are available for 3-D networks that can ensure short routes using only small per-node state, without incurring high load imbalance on the nodes. In this paper, we propose a novel addressing and routing scheme, i.e., OnionMap, for 3-D sensor networks that achieve the above goals, using solely connectivity information and at a linear message cost. The key idea is to decompose a 3-D network into a set of connected layers, which are then mapped to a set of concentric sphere structures (similar to an onion). On each sphere, a discrete Ricci flow method is used to assign each node a set of coordinates that permits purely greedy routing within that sphere; across the different spheres, a layer alignment algorithm helps rotate and scale the spheres, to form a coherent global coordinate system that guides global routing. Theoretical analysis and simulation show OnionMap's advantages over state-of-the-art solutions in path stretch, per-node storage, and load balance.

Both capacity and energy efficiency are crucial for next-generation wireless networks. This paper investigates energy efficiency in cooperative cellular networks. Based on the double auction theory, we model the optimal relay assignment problem, which aims at improving the performance of cell-edge users (CEUs) with energy efficiency optimization. In the proposed auction-based model, the selfish nature of users is taken into consideration, which means users in the idle state are unwilling to relay the information for active CEUs unless they are paid enough. Therefore, we use mark-up to determine the bid and ask. Furthermore, the energy efficiency (EE) is defined and the model for optimizing the EE is built. An energy-efficient maximum weighted matching algorithm (EE-MWM) is proposed to solve the EE optimization problem. Finally, the performance of EE-MWM is evaluated in terms of EE, capacity and social welfare, which shows that EE-MWM can greatly improve the performance of cooperative cellular networks.

Abstract Cooperative communications can obtain spatial diversity, high channel capacity, and reliable transmission without multiple antennas, and thus, it has become a hot topic in recent years. Different from existing research, this paper pays attention on cooperative media access control (MAC) mechanism, which considers both physical gain and MAC overhead caused by cooperation. To this end, a dynamic cooperative MAC mechanism for wireless networks, called DCMAC, is proposed. DCMAC can obtain the useful channel state information through broadcasting characteristic of wireless channel, choose the suitable helpers to relay data with our proposed helpers selection algorithm, and reserve wireless channel efficiently and dynamically. Numerical results show the effectiveness of DCMAC to improve the system performance.

In this paper, we present a novel cross-job framework for MapReduce scheduling, which aims to minimize the total processing time of a sequence of related jobs by combining reduce and map phases of two consecutive jobs and streaming data between them. The proposed framework has the following desirable properties: (1) It can accelerate the execution of a sequence of related MapReduce jobs by achieving a good tradeoff between data locality and parallelism. (2) It can support all the existing MapReduce applications with no changes to their source code. (3) It is a general framework, which can work with different scheduling algorithms. We built a new MapReduce runtime system called cross-job Hadoop by integrating the proposed cross-job framework into Hadoop. We conducted extensive experiments to evaluate its performance using PageRank and an Apache Pig application. Our experimental results show that the cross-job Hadoop can significantly reduce both the total processing time of a job sequence and the size of data transferred over the network.

Coordinated multi-point(CoMP) is regarded as a key technology in LTE-Advanced to eliminate the inter-cell interference and improve the user throughput, especially in cell edge. Traditional single-threshold coordinated user division, irrespective of whether it is based on distance or other metrics, has a potential problem whereby channel state variations lead to frequent user mode changes between coordination and non-coordination states, placing undue constrains on the system backhaul consumption. On account of such weakness, a novel user division method based on double-threshold is proposed in this paper to reduce the user handover with static clustering. As a result, a user with coordination has a relatively stable status between the two thresholds. The judging threshold adjusts in line with the user's last transmitting mode over the time. Furthermore, Zero-forcing precoding and water-filling power allocation are employed. Numerical results show that the proposed algorithm provides user throughput improvement and achieves a good balance between system throughput and backhaul overhead, providing even more benefits especially in scenarios where the channel state changes rapidly.

The recent forecast of billions of devices, all connected to the Internet and generating low-rate monitoring, measurement, or automation data that many end-users/applications frequently request, signifies the need for applying in-network caching techniques to Internet-of-Things (IoT) traffic. Although time delay is not critically important for small-sized IoT content, the expected total traffic load on the Internet from a large number of devices is significant. However, the main challenge as opposed to the typically cached content at content routers, e.g. multimedia files, is that IoT data are transient and therefore require different caching policies. This paper studies in-network caching of IoT data at content routers in the Internet. An IoT data item is uniquely defined not only by its time and location tags, but also a time-range value set by end-users/applications. We provide a model for the trade-off between multihop communication costs and the freshness of a transient data item. Results show that the model can successfully capture the effect of data transiency and can accurately represent the expected gains of a caching system: considerable savings in terms of reduction of network load, especially for highly requested data items.

Saving power on base stations (BS) becomes a critical issue in wireless cellular networks. Many existing work has proposed to schedule BS into sleep to save energy. However, in reality, it is very difficult to shut down and reboot BSs frequently due to numerous technical issues and performance requirements. In this work, we propose a much more practical solution and offer a new perspective on implementing Green Wireless Networking by embracing the hot-trended small cell network idea. Instead of putting BSs into sleep, we tactically reduce the coverage (and the power usage) of each BS, and strategically place microcells (relay stations) to offload the traffic transmitted to/from BSs in order to save total power consumption. We propose approximation algorithms for various network design scenarios, with different wireless network setups and different power saving optimization objectives. Extensive numerical results are presented to confirm our theoretical analysis.

Low-power access points, such as pico base stations (BSs), femto BSs, and relays are introduced to the next generation cellular systems to enhance coverage and improve system capacity. Deploying low-power access points to offload the conventional macro BSs is deemed as a spectrum- and cost-efficient way to meet the sharp increase of traffic requirements of cellular networks. However, it also leads to heterogeneous network framework and raises new challenges for cell planning. In this paper, we study the minimum cost cell planning problem in such a heterogeneous network. Our optimization task is to select a subset of candidate sites to lay BSs, including macro BSs, pico BSs and relays, to minimize the total deployment cost while satisfying the rate requirements of the demand nodes (DNs) served by the cellular network. We prove that the general case of the formulated problem is APX-hard, where a DN is constrained to be associated with only one BS. However, if each DN can be served by multiple BSs, which is a reasonable case for practical cellular systems, we show it is not APX-hard and develop an approximation algorithm to work out promising solutions. Our proposed algorithm guarantees an approximation ratio of O(logR) to the global optimum, where R is the maximum achievable capacity of the BSs. Numerical results indicate that our proposal can significantly reduce the deployment cost of the cellular network with given rate requirements of DNs compared to other cell planning schemes.

Discusses the inaugural issue of IEEE Internet of Things Journal and reports on its scope and content.

Sensor networks are invariably coupled tightly with the geometric environment in which the sensor nodes are deployed. Network boundary is one of the key features that characterize such environments. While significant advances have been made for 2D cases, so far boundary extraction for 3D sensor networks has not been thoroughly studied. We present CABET, a novel Connectivity-Based Boundary Extraction scheme for large-scale 3D sensor networks. To the best of our knowledge, CABET is the first 3D-capable and pure connectivity-based solution for detecting sensor network boundaries. It is fully distributed, and is highly scalable, requiring overall message cost linear with the network size. A highlight of CABET is its non-uniform critical node sampling , called r'-sampling , that selects landmarks to form boundary surfaces with bias toward nodes embodying salient topological features. Simulations show that CABET is able to extract a well-connected boundary in the presence of holes and shape variation, with performance superior to that of some state-of-the-art alternatives. In addition, we show how CABET benefits a range of sensor network applications including 3D skeleton extraction, 3D segmentation, and 3D localization.

Using Device-to-device (D2D) communications in a cellular network is an economical and effective approach to increase the transmission data rate and extend the coverage. Nevertheless, the D2D underlaid cellular network is challenging due to the presence of inter-tier and intra-tier interferences. With necessarily lower antenna heights in D2D communication links, the fading channels are likely to contain strong line-of-sight components, which are different from the Rayleigh fading distribution in conventional two-tier heterogeneous networks. In this paper, we derive the success probability, spatial average rate, and area spectral efficiency performances for both cellular users and D2D users by taking into account the different channel propagations that they experience. Specifically, we employ stochastic geometry as an analysis framework to derive closed-form expressions for above performance metrics. Furthermore, to reduce cross-tier interferences and improve system performances, we propose a centralized opportunistic access control scheme as well as a mode selection mechanism. According to the analysis and simulations, we obtain interesting tradeoffs that depend on the effect of the channel propagation parameter, user node density, and the spectrum occupation ratio on the different performance metrics. This work highlights the importance of incorporating the suitable channel propagation model into the system design and analysis to obtain the realistic results and conclusions.

As a sensor network grows large, it may become increasingly complex in topology due to its close ties to the surrounding environment. Previous work has shown that proper geometric processing of the network (e.g., boundary detection and localization) can provide very helpful information for applications to optimize their performance. To that end, numerous algorithms have been developed, providing a variety of inspiring solutions, yet exhibiting an ad hoc style in principle and implementation. In this paper we show that the crux of solving many of the problems caused by complex topology is to identify the concave nodes, nodes that are located at concave network corners, where the boundary has an inner angle greater than π. The knowledge of such nodes makes several important tasks, namely geometric embedding, full localization, convex segmentation, and boundary detection, relatively easier or perform significantly better, as confirmed by simulations. These findings suggest that concave nodes can serve as a basic supporting structure for general geometric processing tasks and geometry-related applications in sensor networks.

Although user cooperation improves sensing accuracy, a large number of secondary users (SUs) reporting decisions may cause significant overhead. In this paper, we propose a distributed scheme, called adaptive cooperative sensing (ACS), to reduce the sensing overhead while satisfying sensing accuracy requirements. In ACS, an anchor SU requires cooperative sensing only when it does not detect the presence of primary user (PU) by itself. When performing cooperative sensing, both selective reporting and sequential detection are employed. We derive the generalized-form expressions of false alarm and detection probabilities over Rayleigh fading channels with considering reporting errors for ACS. The sensing overheads are also analyzed. Then, we study overhead minimization problems and show that the sensing time allocation can be optimized to minimize the miss detection probability and sensing overhead, respectively. By simulations, the effectiveness and efficiency of ACS are validated.

Small cell network is the new trend for next generation mobile network design. One feasible model is using Relay stations (RS) as small cell providers to achieve extended coverage, lower cost, and higher network capacity. This paper studies Signal-aware relay station placement and power allocation problem in wireless relay networks with multiple base stations in the field. This problem consists of both subscriber coverage problem and relay power optimization problem, which have not been extensively studied together in previous works. This work takes into account physical constraints such as channel capacity, signal to noise ratio (SNR) requirement of subscribers, relay power cost and network topology. We set up a two-step goal that is firstly to find minimum number of RS in order to cover all the subscribers meeting each SNR requirement, and then to ensure communications built between any subscriber to a base station. In order to ensure each subscriber's SNR, transmission power of each RS should be adjustable. Thus, minimizing power cost of RSs is our goal in the second step. We divide the problem into two sub-problems, Lower-tier Coverage Relay Allocation (LCRA) problem and Upper-tier Connectivity Relay Allocation (UCRA) problem. For the LCRA problem, we present two approximation solutions based on minimum hitting set and maximum independent set. For the UCRA problem, an approximation algorithm and an optimal algorithm are proposed. At the end, an approximation solution for our original problem, which combines the approaches of the two sub-problems, is provided. Numerical results are presented to confirm the theoretical analysis of our schemes, and to show strong performances of our solutions.

With wireless resource virtualization, multiple Mobile Virtual Network Operators (MVNOs) can be supported over a shared physical wireless network and traffic loads in a Base Station (BS) can be easily migrated to more power-efficient BSs in its neighborhood such that idle BSs can be turned off or put into sleep to save power. In this paper, we propose to leverage load migration and BS consolidation for green communications and consider a power-efficient network planning problem in virtualized Cognitive Radio Networks (CRNs) with the objective of minimizing total power consumption while meeting traffic load demand of each MVNO. First, we present a Mixed Integer Linear Programming (MILP) to provide optimal solutions. Then we present a general optimization framework to guide algorithm design, which solves two subproblems, channel assignment and load allocation, in sequence. For channel assignment, we present a (Δ1)-approximation algorithm (where Δ is the maximum number of BSs a BS can potentially interfere with). For load allocation, we present a polynomial-time optimal algorithm for a special case where BSs are power-proportional as well as two effective heuristic algorithms for the general case. In addition, we present an effective heuristic algorithm that jointly solves the two subproblems. It has been shown by extensive simulation results that the proposed algorithms produce close-to-optimal solutions, and moreover, achieve over 45% power savings compared to a baseline algorithm that does not migrate loads or consolidate BSs.

In this paper, we put forward a novel scalable and distributed routing algorithm, called SINUS, for sensor networks deployed on the surface of complex-connected 3D settings such as tunnels, whose topologies are often theoretically modeled as high genus 3D surfaces. SINUS is carried out by first slicing the genus-n surface along a maximum cut set based on Morse theory and Reeb graph, in order to form a genus-0 surface with 2n boundaries. Then, it groups these 2n boundaries into two groups each of which is next connected together. By doing so, a genus-0 surface with exactly two boundaries emerges, which can be flattened into a strip, using the Ricci flow algorithm and next mapped to a planar annulus by Möbius Transform. By assigning nodes virtual coordinates on the planar annulus, SINUS finally realizes a variation of greedy routing to enable individual nodes to make local muting decisions. Our simulation results show that SINUS can achieve low-stretch routing with guaranteed delivery, as well as balanced traffic load.

In this paper, we analyze the noise distribution and correlation characteristic on each subcarrier in OQAM-OFDM systems, and present a method based on the maximum likelihood (ML) criterion to improve the channel estimation. Then, the performance results are obtained by considering an IEEE 802.22 channel model and the proposed method is compared to the conventional frequency domain averaging (FDA) method. Simulation results show that the proposed method is an efficient method to improve the channel estimation.

Relay nodes are introduced to the next generation cellular networks to enhance coverage and improve system capacity, leading to a new radio network planning paradigm. In this paper, we study two planning problems for cellular networks with relay nodes: Minimum cost cell planning and budgeted cell planning. The former is to minimize the total installation cost for opening base stations (BSs), including macro BSs and relay nodes, while satisfying all users' traffic demands. The latter is to maximize the number of users with predefined traffic demands under a given budget. Both of the problems are NP-hard. We present approximation algorithms to work out promising solutions to these problems. For the minimum cost cell planning, we develop an O(logW)-approximation algorithm, where W is the maximum capacity of macro BSs. For the budgeted cell planning, we prove that the problem is NP-hard to approximate and give an e−1 over 3e−1-approximation algorithm for a special case of the problem, which is general enough to meet practical requirements.

Virtual MIMO (Multiple Input Multiple Output) groups multiple single-antenna mobile devices to form an antenna array, offering higher degrees of freedom and improved spatial diversity gain as a real MIMO does, yet with much lower costs. In this paper, we focus on the user grouping problem in uplink transmission from multiple single-antenna users to one multiple-antenna base station. State-of-the-art solutions mostly target two single-antenna users, solving a pairing problem. Having more than two uplink users in a grouping has yet to be addressed. Intuitively, a higher number of users in a VMIMO group offers better spectrum efficiency, and thus more throughput gains could be expected; the group dynamics however becomes higher too, making fairness harder to be achieved with reasonable computation overhead. To address these challenges, we present a novel solution that decomposes the VMIMO user grouping into two steps. We lighten the computations in user grouping by using instantaneous signal to noise ratio (SNR) as selection criteria, and combining it with proportional fairness for larger groups of users. Lightweight computation in using instantaneous SNR in our solution allows faster grouping and feasible scheduling for a large number of users, as well as fast decision on the efficiency of the number of users in each group. We have evaluated our solution under different network configurations, and the results demonstrate that it achieves much higher data throughput as compared to existing solutions and also well preserves fairness.

Cognitive Radio (CR) is an attractive technology to deal with current spectrum scarcity problem, while cooperative relay can make distributed receivers benefit from spatial diversity and combat severe fading in wireless environment. CR with cooperative relay is potentially a promising paradigm for developing spectrum-efficient wireless systems. In this paper, we study the resource allocation in Orthogonal Frequency Division Multiplexing (OFDM)-based CR networks with cooperative relays. Since the formulated optimization task defines a mixed integer programming problem that is generally hard to solve, we propose a two-stage method to produce near optimal solutions. Particularly, by jointly considering the Signal-to-Noise Ratios (SNRs) of OFDM subchannels and the interferences introduced to primary users, we propose an efficient subchannel assignment scheme for the CR system, as well as transmission mode selection strategy. Furthermore, we develop a fast algorithm to distribute power among subchannels, which can always work out the optimal power allocation with a reasonable complexity by exploiting the structure of the problem. Numerical results show that our proposal can significantly increase the throughput of the CR system compared with other schemes, and the proposed algorithm converges quickly and stably.

Peer-to-peer file sharing systems, most notably BitTorrent (BT), have achieved tremendous success among Internet users. Recent studies suggest that long-term relationships among BT peers could be explored for peer cooperation, so as to achieve better sharing efficiency. However, whether such long-term relationships exist remain unknown. From an 80-day trace of 100,000 real world swarms, we find that less than 5% peers can meet each other again throughout the whole period, which largely invalidates the fundamental assumption of these peer cooperation protocols. Yet the recent emergence of online social network applications sheds new light on this problem. In particular, a number of BT swarms are now triggered by Twitter, reflecting a new trend for initializing sharing among communities. In this paper, we for the first time examine the challenges and potentials of accelerating peer-to-peer file sharing with Twitter social networks. We show that the peers in such swarms have stronger temporal locality, thus offering great opportunity for improving their degree of sharing. Based on the Hadamard Transform of peers' online behaviors, we develop a social index to quickly locate peers of common patterns. We further demonstrate a practical cooperation protocol that identifies and utilizes the social relations with the index. Our PlanetLab experiments indicate that the incorporation of social relations remarkably accelerates the downloading time. The improvement remains noticeable even in a hybrid system with a small set of socially active peers only.

In this paper we study the Resource Allocation (RA) in Orthogonal Frequency Division Multiplexing (OFDM)-based Cognitive Radio (CR) networks, under the consideration of many practical limitations such as imperfect spectrum sensing, limited transmission power, different traffic demands of secondary users, etc. The general RA optimization framework leads to a complex mixed integer programming task which is computationally intractable. We propose to address this hard task in two steps. For the first step, we perform subchannel allocation to satisfy heterogeneous users' rate requirements roughly and remove the intractable integer constraints of the optimization problem. For the second step, we perform power distribution among the OFDM subchannels. By exploiting the problem structure to speedup the Newton step, we propose a barrier-based method which is able to achieve the optimal power distribution with an almost linear complexity, significantly better than the complexity of standard techniques. Moreover, we propose a method which is able to approximate the optimal solution with a constant complexity. Numerical results validate that our proposal exploits the overall capacity of CR systems well subjected to different traffic demands of users and interference constraints with given power budget.

A frequency quadrupling scheme for optical mm-wave signal generation is proposed and demonstrated based on dual-electrode dual-parallel integrated Mach-Zehnder modulator (MZM) consisting of three dual-electrodes MZMs. The electrical filters or optical filters are not required in this scheme. The theoretical analysis is presented to configurate the phases and DC biases of the integrated MZM. The simulation and experiment verification are both conducted to evaluate the performance of the proposed scheme. With 9GHz RF drive signal, the optical sideband suppression ratio (OSSR) higher than 35dB is demonstrated experimentally. The 36GHz mm-wave is generated with the RF spurious suppression ratio (RFSSR) over 30dB.

ABSTRACT Mobile ad‐hoc networks (MANETs) have attracted significant research attention recently because of the fast growth of laptops, personal digital assistant, and 802.11/Wi‐Fi wireless networking. However, the flexible deployment nature and the lack of fixed infrastructure make MANETs suffer from a variety of security attacks. In this paper, we show how an adversary can utilize a colluding injected attack (CIA) in MANET by injecting malicious nodes in the network, while hiding their identities from other legitimate nodes. These injected nodeswill work together(colluding) to create a collision at an arbitrary node, thus preventing it from receiving or relaying any packet. Because of this collision, a legitimate node could be reported as malicious nodes by monitoring nodes in the neighborhood. In this work, we propose a monitoring verification scheme to mitigate the effect of the CIA attack. Our proposed scheme is able to accurately detect malicious nodes in the network compared with previous detection schemes. Through simulations, we show that our proposed scheme outperforms previous detection schemes in terms of true/false detection of any malicious behavior in the network caused by the CIA attack. Copyright © 2013 John Wiley & Sons, Ltd.

ABSTRACT The traditional malware detection schemes based on specific signature give an unsatisfactory performance as disposing the previously unknown malware, so the general features of binary files should be explored to solve this problem. Recently, classification algorithms were employed successfully to choose the features in unknown malicious code, and most of the works use byte or operation code sequencen‐gram representation of the executables. However, thesen‐gram representations are heavily dependent on the training data. In this paper, we present a graph‐based method to detect unknown malware. The function call graph of an executable, which includes the functions and the call relations between them, is selected as the representation of the executable in this method. The features are defined according to both the statistical information and the topology of the function call graph. They are extracted and processed through machine learning to classify unknown Portable Executable files. For the sake of fixed sum of the features, the graph‐based method can avoid so many features found in other methods. In our experiments, three types of malware datasets were tested, and as high as 96.8% accuracy can be achieved. Furthermore, it can achieve 92.1% accuracy when only 5% of the dataset is served as training set. Copyright © 2012 John Wiley & Sons, Ltd.

3D video applications such as 3D-TV and 3D games have become more and more popular in recent years. These applications raised significant challenges in the media security, processing and transmissions. Especially, when 3D videos are delivered over wireless networks, the video streaming suffers the potential malicious attacks. One of the most important security challenging issues is how to guarantee the integrity of media content over error-prone wireless networks. To address this challenge, in the paper, we for the first time propose an authentication approach for 3D video transmission over wireless networks, which can improve the reconstructed media quality under error-prone wireless environment with lower authentication overheads and energy consumption. The proposed method is based on color-depth 3D video coding approach, which can save bandwidth, be tolerable to packet losses and thus satisfy the users' Quality of Experience (QoE) requirements. Our major contribution in this paper includes: (1) designing a joint source-channel-authentication coding framework for color-depth-based 3D video transmission; (2) proposing a media quality prediction model for color-depth-based 3D video transmission; (3) developing optimization for graph-based authentication on 3D video transmission to improve reconstructed media quality, reduce authentication overheads and energy consumption. Experimental results demonstrated the effectiveness of our proposed solutions.

We study the problem of skeleton extraction for large-scale sensor networks with reliance purely on connectivity information. Existing efforts in this line highly depend on the boundary detection algorithms, which are used to extract accurate boundary nodes. One challenge is that in practical this could limit the applicability of the boundary detection algorithms. For instance, in low node density networks where boundary detection algorithms do not work well, the extracted boundary nodes are often incomplete. This paper brings a new view to skeleton extraction from a distance transform perspective, bridging the distance transform of the network and the incomplete boundaries. As such, we propose a distributed and scalable algorithm for skeleton extraction, called DIST, based on DIStance Transform, while incurring low communication overhead. The proposed algorithm does not require that the boundaries are complete or accurate, which makes the proposed algorithm more practical in applications. First, we compute the distance transform of the network. Specifically, the distance (hop count) of each node to the boundaries of a sensor network is estimated. The node map consisting of the distance values is considered as the distance transform (the distance map). The distance map is then used to identify skeleton nodes. Next, skeleton arcs are generated by controlled flooding within the identified skeleton nodes, thereby connecting these skeleton arcs, to extract a coarse skeleton. Finally, we refine the coarse skeleton by building shortest path trees followed by a prune phase. The obtained skeleton is robust to boundary noise or shape variations. Besides, we present two specific applications that benefit from the extracted skeleton: identifying complete boundaries and shape segmentation. First, with the extracted skeleton using DIST, we propose to identify more boundary nodes to form a meaningful boundary curve. Second, the utilization of the derived skeleton to segment the network into approximately convex pieces has been shown to be effective.

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

Opportunistic networks are the emerging networks featured by partitions, long disconnections, and topology instability, where the message propagation depends on the cooperation of nodes to fulfill a “store-carry-and-forward” fashion. But due to constrained energy and buffer, some nodes may behave selfishly, which will involve damage to the existing routing approaches and seriously degrade the performance of opportunistic networks. Aiming at the above problem, this paper proposes a novel bargaining based incentive protocol (BIP) for opportunistic networks, which exploits two-person bargaining model and allows a node to pay and charge according to its state and the attributes of messages. In addition, the proposed BIP protocol can tackle the issue of blind cooperation when the resources are very scarce. Extensive simulation results demonstrate the effectiveness and the practicality of the proposed BIP protocol in terms of high delivery ratio, low energy consumption and small average delay.

Cognitive radio (CR) network with cooperative relay is potentially a promising technique to solve current spectrum inefficiency and spectrum scarcity problem. In this paper, we study the power allocation in cognitive radio (CR) networks with cooperative relay. The interference to the licensed primary system and the power budget of the CR network are jointly considered. The resulting model is general and the formulated optimization task generally hard to solve. To deal with the intractable coupling variables in the concerned problem, we propose an alternating optimization method, which can work out the optimal solution in an iteration manner and converges rapidly. Experiment results show that our proposed method can significantly increase system capacity, comparing with other representative ones. Furthermore, the proposed method gives a general power optimization framework for CR networks with cooperative relay and can be extended to other scenarios easily.

This paper proposes a novel approach, named DIST, to skeleton extraction from incomplete boundaries using the idea of {\em distance transform}, a concept in the computer graphics area. The main contribution is a distributed and low-cost algorithm that produces accurate network skeletons without requiring that the boundaries be complete or tight. The algorithm first establishes the network's distance transform -- the hop distance of each node to the network's boundaries. Based on this, some {\em critical skeleton nodes} are identified. Next, a set of {\em skeleton arcs} are generated by controlled flooding, connecting these skeleton arcs then gives us a coarse skeleton. The algorithm finally refines the coarse skeleton by building shortest path trees, followed by a prune phase. The obtained skeletons are robust to boundary noise and shape variations.

In sensor networks, skeleton (also known as medial axis) extraction is recognized as an appealing approach to support many applications such as load-balanced routing and location free segmentation. Existing solutions in the literature rely heavily on the identified boundaries, which puts limitations on the applicability of the skeleton extraction algorithm. In this paper, we conduct the first work of a connectivity-based and boundary free skeleton extraction scheme, in sensor networks. In detail, we propose a simple, distributed and scalable algorithm that correctly identifies a few skeleton nodes and connects them into a meaningful representation of the network, without reliance on any constraint on communication radio model or boundary information. The key idea of our algorithm is to exploit the necessary (but not sufficient) condition of skeleton points: the intersection area of the disk centered at a skeleton point x should be the largest one as compared to other points on the chord generated by x, where the chord is referred to as the line segment connecting x and the tangent point in the boundary. To that end, we present the concept of \epsilon-centrality of a point, quantitatively measuring how "central" a point is. Accordingly, a skeleton point should have the largest value of \epsilon-centrality as compared to other points on the chord generated by this point. Our simulation results show that the proposed algorithm works well even for networks with low node density or skewed nodal distribution, etc. In addition, we obtain two by-products, the boundaries and the segmentation result of the network.

Reliability and energy efficiency are critical issues in wireless sensor networks. In this work, we study Delay-bounded Energy-constrained Adaptive Routing (DEAR) problem with reliability, differential delay, and transmission energy consumption constraints in wireless sensor networks. We aim to route the connections in a manner such that link failure does not shut down the entire stream but allows a continuing flow for a significant portion of the traffic along multiple paths. This flexibility enabled by a multi-path routing scheme has the tradeoff of differential delay among the different paths. This requires increased memory in the base station to buffer the traffic until the data arrives on all the paths. Therefore, differential delay between the multiple paths should be bounded in a range to reduce additional hardware cost in the base station. Moreover, the energy consumption constraint should also be satisfied when transmitting packets among multiple paths. We present a pseudo-polynomial time solution to solve a special case of DEAR, representing edge delays as integers. Next, an (1+α)-approximation algorithm is proposed to solve the optimization version of the DEAR problem. An efficient heuristic is provided for the DEAR problem. We present numerical results confirming the advantage of our schemes as the first solution for the DEAR problem.

A cardinal prerequisite for the system design of a sensor network, is to understand the geometric environment where sensor nodes are deployed. The global topology of a large-scale sensor network is often complex and irregular, possibly containing obstacles/holes. A convex network partition, so-called segmentation, is to divide a network into convex regions, such that traditional algorithms designed for a simple geometric region can be applied. Existing segmentation algorithms highly depend on concave node detection on the boundary or sink extraction on the medial axis, thus leading to quite sensitive performance to the boundary noise. More severely, since they exploit the network's 2D geometric properties, either explicitly or implicitly, so far there has been no general 3D segmentation solution. In this paper, we bring a new view to segmentation from a Morse function perspective, bridging the convex regions and the Reeb graph of a network. Accordingly, we propose a novel distributed and scalable algorithm, named CONSEL, for CONnectivity-based SEgmentation in Large-scale 2D/3D sensor networks. Specifically, several boundary nodes first perform flooding to construct the Reeb graph. The ordinary nodes then compute mutex pairs locally, thereby generating the coarse segmentation. Next the neighbor regions which are not mutex pair are merged together. Finally, by ignoring mutex pairs which leads to small concavity, we provide the constraints for approximately convex decomposition. CONSEL is more desirable compared with previous studies: (1) it works for both 2D and 3D sensor networks; (2) it only relies on network connectivity information; (3) it guarantees a bound for the regions' deviation from convexity. Extensive simulations show that CONSEL works well in the presence of holes and shape variation, always yielding appropriate segmentation results.

Accurate localization in wireless sensor networks is the foundation for many applications, such as geographic routing and position-aware data processing. An important research direction for localization is to develop schemes using connectivity information only. These schemes primary apply hop counts to distance estimation. Not surprisingly, they work well only when the network topology has a convex shape. In this paper, we develop a new Localization protocol based on Approximate Convex Decomposition (ACDL). It can calculate the node virtual locations for a large-scale sensor network with arbitrary shapes. The basic idea is to decompose the network into convex subregions. It is not straight-forward, however. We first examine the influential factors on the localization accuracy when the network is concave such as the sharpness of concave angle and the depth of the concave valley. We show that after decomposition, the depth of the concave valley becomes irrelevant. We thus define concavity according to the angle at a concave point, which can reflect the localization error. We then propose ACDL protocol for network localization. It consists of four main steps. First, convex and concave nodes are recognized and network boundaries are segmented. As the sensor network is discrete, we show that it is acceptable to approximately identify the concave nodes to control the localization error. Second, an approximate convex decomposition is conducted. Our convex decomposition requires only local information and we show that it has low message overhead. Third, for each convex subsection of the network, an improved Multi-Dimensional Scaling (MDS) algorithm is proposed to compute a relative location map. Fourth, a fast and low complexity merging algorithm is developed to construct the global location map. Our simulation on several representative networks demonstrated that ACDL has localization error that is 60%-90% smaller as compared with the typical MDS-MAP algorithm and 20%-30% smaller as compared to a recent state-of-the-art localization algorithm CATL.

In this paper we study the resource allocation in OFDM-based cognitive radio (CR) networks, under the consideration of many practical limitations such as imperfect spectrum sensing, limited transmission power, different traffic demands of secondary users, etc. We formulated this general problem as a mixed integer programming task. Considering that this optimization task is computationally intractable, we propose to address it in two steps. For the first step, we perform subchannel allocation to satisfy heterogeneous users' rate requirement roughly and remove the integer constraints of the optimization problem. For the second step, we perform power allocation among the subchannels. By exploiting the problem structure to speedup the Newton step, we propose a Barrier-based method which is able to achieve the optimal power distribution with a complexity of O(N), where N is the number of active OFDM subchannels, significantly better than the complexity of O(N3) of standard techniques. Moreover, we proposed a method which is able to approximate the optimal solution with a constant complexity. Numerical results validate that our proposal exploits the overall capacity of CR systems well subjected to different traffic demands of users.

In cognitive radio networks, the idle spectrum bands that cognitive users sensed are usually discontinuous. Only one idle spectrum band may not be able to fulfill cognitive users' bandwidth requirements. In order to let cognitive users access allocated spectrum bands successfully and further improve the efficiency of spectrum utilization, we propose spectrum aggregation-based graph coloring algorithm (SAGCA), a spectrum allocation algorithm in cognitive radio networks. SAGCA considers bandwidth requirements of cognitive users and limitation of spectrum range that equipment can utilize due to hardware constraint. Numerical results show that the proposed algorithm can achieve greater performance in total bandwidth and percentage of cognitive users that networks can support compared to the original algorithm.

In this paper, we propose a novel framework of cognitive radio assisted cooperation (CRAC) for downlink transmissions in orthogonal frequency-division multiple access (OFDMA) - based cellular networks. In the proposed CRAC framework, relay stations are deployed in each cell and have spectrum sensing capability. In turn, they can access unoccupied white space to opportunistically obtain additional sub-channels to assist relaying information for cellular users. One of promising novelties is that the proposed CRAC considers joint resource allocation which includes transmission mode selection, relay station allocation, and transmit power/sub-channel allocation, to cost-effectively provide services and applications. Specifically, we first formulate the joint resource allocation as a sum utility maximization problem with power constraints on the base station and relay stations, which is a mixed integer programming problem. Then, we leverage dual decomposition method and derive a centralized optimal solution. Extensive simulation results are presented and demonstrate that the proposed CRAC can achieve a significant performance improvement in terms of the downlink network throughput while maintaining comparable fairness among cellular users in contrast to the traditional relay-based cooperation approach.

The recent advent of high speed trains introduces new mobility patterns in wireless environments. The LTE-A (Long Term Evolution of 3GPP - Advanced) networks have largely tackled the Doppler effect problem in the physical layer and are able to keep wireless service with 100Mpbs throughput within a cell in speeds up to 350 km/h. Yet the much more frequent handovers across cells greatly increases the possibility of service interruptions, and the problem is prominent for multimedia communications that demand both high-throughput and continuous connections. In this paper, we present a novel LTE-based solution to support high throughput and continuous multimedia services for high speed train passengers. Our solution is based on a Cell Array that smartly organizes the cells along a railway, together with a femto cell service that aggregates traffic demands within individual train cabins. Given that the movement direction and speed of a high-speed train are generally known, our Cell Array effectively predicts the upcoming LTE cells in service, and enables a seamless handover that will not interrupt multimedia streams. To accommodate the extreme channel variations, we further propose a scheduling and resource allocation mechanism to maximize the service rate based on periodical signal quality changes. Our simulation under diverse network and railway/train configurations demonstrates that the proposed solution achieves much lower handover latency and higher data throughput, as compared to existing solutions. It also well resists to network and traffic dynamics, thus enabling uninterrupted quality multimedia services for passengers in high speed trains.

ABSTRACT The p‐cycle design problem has been extensively studied because it can provide both ring‐like fast self‐protection speed and spare capacity efficiency of path protection scheme. However, p‐cycle provisioning for dynamic traffic has not been fully addressed. Most related works have not considered link capacity in the construction of p‐cycles, which may cause problems in practice because the protection paths may not have enough backup bandwidth. In this paper, with the consideration of link capacity, we present a sufficient and necessary condition that guarantees p‐cycles for providing enough protection bandwidth. Based on this condition, we propose an effective solution to provide connections for dynamic requests with the property that each link used for a connection is protected by a p‐cycle. Simulation results show that our dynamic p‐cycle provisioning solution outperforms the traditional path protection scheme. Copyright © 2011 John Wiley & Sons, Ltd.

The recently emerged user-generated contents (UGC) services, social networking services (SNS), as well as the pervasive wireless mobile network services have formed social media which has drastically changed the content distribution landscape. Today such UGC applications as YouTube allow any user to be a content provider, generating enormous amount of video contents that are quickly and extensively propagated on the Internet through such SNSes as Facebook and Twitter. Unfortunately, the existing UGC sites are facing critical server bottlenecks and the surges created by the social networking users would make the situation even worse. To better understand the challenges and opportunities therein, we investigate users' social behavior and personal preference of online video sharing from both real-trace measurement study on a popular social networking website and a user questionnaire survey. Our data analysis reveals an interesting coexistence of live streaming and storage sharing, and that the users are generally more interested in watching their friend's videos. It further suggests that even though the traffic is significant, most users are willing to share their resources to assist others, implying user collaboration is a rational choice in this context. In this paper, we present Coordinated Live Streaming and Storage Sharing (COOLS), a system for efficient peer-to-peer posting of user-generated videos. Through a novel ID code design that embeds nodes' locations in an overlay, COOLS leverages stable storage users and yet inherently prioritizes living streaming flows. We also present the improvement of the basic overlay design. The evaluation results show that, as compared to other state-of-the-art solutions, COOLS successfully takes advantage of the coexistence of live streaming and storage sharing, providing better scalability, robustness, and streaming quality.

Top-k query has long been a crucial problem in multiple fields of computer science, such as data processing and information retrieval. In emerging cyber-physical systems, where there can be a large number of users searching information directly into the physical world, many new challenges arise for top-k query processing. From the client's perspective, users may request different sets of information, with different priorities and at different times. Thus, top-k search should not only be multidimensional, but also be across time domain. From the system's perspective, data collection is usually carried out by small sensing devices. Unlike the data centers used for searching in the cyber-space, these devices are often extremely resource constrained and system efficiency is of paramount importance. In this paper, we develop a framework that can effectively satisfy demands from the two aspects. The sensor network maintains an efficient dominant graph data structure for data readings. A simple top-k extraction algorithm is used for user query processing and two schemes are proposed to further reduce communication cost. Our methods can be used for top-k query with any linear convex query function. The framework is adaptive enough to incorporate some advanced features; for example, we show how approximate queries and data aging can be applied. To the best of our knowledge, this is the first work for continuous multidimensional top-k query processing in sensor networks. Simulation results show that our schemes can reduce the total communication cost by up to 90 percent, compared with a centralized scheme or a straightforward extension from previous top-k algorithm on 1D sensor data.

The emerging WiMAX technology (IEEE 802.16) is a fourth generation standard for low-cost, high-speed and long range wireless communications for a large variety of civilian and military applications. IEEE 802.16j has introduced the concept of mesh network model and a special type of node called Relay Station (RS) for traffic relay for Subscriber Stations (SSs). A WiMAX mesh network is able to provide larger wireless coverage, higher network capacity and Non-Line-Of-Sight (NLOS) communications. This paper studies a Multi-hop FAir Scheduling for Throughput Optimization (MFASTO) problem in WiMAX mesh networks. The goal here is to maximize the minimum satisfaction ratio among all the SSs. In order to solve the MFASTO problem, an ILP formulation and an efficient heuristic algorithm are proposed in this work. Simulation results are presented to justify the performance and efficiency of our proposed solutions.

Recently, the sensor worm attack has been identified to be one of the serious threats to the wireless sensor networks. However, sensor nodes do not have complicated hardware architectures or operating systems to protect program safety. Sensor worms can exploit the vulnerabilities of sensor nodes, such as the vulnerability of the buffer-overflow. In this paper, we utilize a software diversity approach to defend the sensor worm attacks. A Worm Attack DEfence (WADE) problem, which is to minimize the total number of defective edges with limited software versions, is defined in this paper. To solve the WADE problem, we present a role-base graph coloring scheme. Simulation results illustrate the efficiency and efficacy of our approach.

Top-k query has long been an important topic in many fields of computer science. Efficient implementation of the top-k queries is the key for information searching. With the new frontier such as the cyber-physical systems, where there can be a large number of users searching information directly into the physical world, many new challenges arise for top-k query processing. From the client's perspective, different users may request different set of information, with different priorities and at different times. Thus, the top-k search not only should be multi-dimensional, but also across time domain. From the system's perspective, the data collection is usually carried out by small sensing devices. Unlike the data centers used for searching in the cyber-space, these devices are often extremely resource-constrained and system efficiency is of paramount importance. In this paper, we develop a framework that can effectively satisfy the two ends. The sensor network maintains an efficient dominant graph data structure for data readings. A simple top-k extraction algorithm is used for the user query processing and two schemes are proposed to further reduce communication cost. Our proposed methods can be used for top-k query with any linear convex query function. To the best of our knowledge, this is the first work for continuous multi-dimensional top-k query processing in sensor networks; and our simulation results show that our schemes can reduce the total communication cost by up to 90%, compared with the centralized scheme or a straightforward extension from previous top-k algorithm on one-dimensional sensor data.

Sensor networks are invariably coupled tightly with the geometric environment in which the sensor nodes are deployed. Network boundary is one of the key features that characterize such environments. While significant advances have been made for 2D cases, so far boundary extraction for 3D sensor networks has not been thoroughly studied. We present CABET, a novel Connectivity-bAsed Boundary Extraction scheme for large-scale Three-dimensional sensor networks. To the best of our knowledge, CABET is the first 3D-capable and pure connectivity-based solution for detecting sensor network boundaries. It is fully distributed. A highlight of CABET is its non-uniform critical node sampling, called r r'-sampling, that selects landmarks to form boundary surfaces with bias toward nodes embodying salient topological features. Simulations show that CABET is able to extract a well-connected boundary in the presence of holes and shape variation, with performance superior to that of some state-of-the-art alternatives. In addition, we show how CABET benefits a range of sensor network applications including 3D skeleton extraction and 3D segmentation.

Existing studies have demonstrated that uneven and dynamic usage patterns by the primary users of license-based wireless communication systems can often lead to temporal and spatial spectrum underutilization. This provides an opportunity for the secondary users (SUs) to tap into underutilized frequency bands provided that they are capable of cognitively accessing systems without colliding or impacting the performance of the primary users (PUs). When there are multiple networks with spare spectrum, secondary users can opportunistically choose the best network to access, subject to certain constraints. In cognitive radio systems, this is referred to as the network selection problem for secondary users. This paper develops a Markov queuing model to obtain the maximum allowable arrival rate of secondary users subject to a target collision probability for the primary users. Based on this model, we design a novel Collision-Constrained Network Selection (CCNS) method that maximizes secondary users' throughput subject to a given PU collision probability. Further, we propose two approaches, referred as CCNS-Greedy and CCNS-Energy, which target to reduce collision probability and to decrease energy consumption of secondary users when the system is underloaded. This, however, has one practical drawback in that the proposed CCNS method depends on PU and SU traffic characteristics such as inter-arrival time and service time, which might not be available in real scenario. We next illustrate that a MEAsurement-based Networks Selection (MEANS) scheme can be used to perform network selection for secondary users based on online measurement of PU collision probability of each network. We evaluated the performance based on extensive simulation, which conclusively shows that the proposed schemes achieve the best performance in terms of resulting PU collision probability, SU throughput, and SU energy consumption, when compared to both Random and Greedy strategies.

The emerging WiMAX technology (IEEE 802.16) is the fourth generation standard for low-cost, high-speed and long-range wireless communications for a large variety of civilian and military applications. IEEE 802.16j has introduced the concept of mesh network model and a special type of node called Relay Station (RS) for traffic relay for Subscriber Stations (SSs). A WiMAX mesh network is able to provide larger wireless coverage, higher network capacity and Non-Line-Of-Sight (NLOS) communications. This paper studies a Distance-Aware Relay Placement (DARP) problem in WiMAX mesh networks, which considers a more realistic model that takes into account physical constraints such as channel capacity, signal strength and network topology, which were largely ignored in previous studies. The goal here is to deploy the minimum number of RSs to meet system requirements such as user data rate requests, signal quality and network topology. We divide the DARP problem into two sub-problems, LOwer-tier Relay Coverage (LORC) Problem and Minimum Upper-tier Steiner Tree (MUST) Problem. For LORC problem, we present two approximation algorithms based on independent set and hitting set, respectively. For MUST problem, an efficient approximation algorithm is provided and proved. Then, an approximation solution for DARP is proposed and proved which combines the solutions of the two sub-problems. We also present numerical results confirming the theoretical analysis of our schemes as the first solution for the DARP problem.

Dynamic application placement for clustered web applications heavily influences system performance and quality of user experience. Existing approaches claim that they strive to maximize the throughput, keep resource utilization balanced across servers, and minimize the start/stop cost of application instances. However, they fail to minimize the worst case of server utilization; the load balancing performance is not optimal. What's more, some applications need to communicate with each other, which we called dependent applications; the network cost of them also should be taken into consideration. In this paper, we investigate how to minimize the resource utilization of servers in the worst case, aiming at improving load balancing among clustered servers. Our contribution is two-fold. First we propose and define a new optimization objectives: limiting the worst case of each individual server's utilization, formulated by a min-max problem. A novel framework based on binary search is proposed to detect an optimal load balancing solution. Second, we define system cost as the weighted combination of both placement change and inter-application communication cost. By maximizing the number of instances of dependent applications that reside in the same set of servers, the basic load-shifting and placement-change procedures are enhanced to minimize whole system cost. Extensive experiments have been conducted and effectively demonstrate that: 1) the proposed framework achieves a good allocation for clustered web applications. In other words, requests are evenly allocated among servers, and throughput is still maximized; 2) the total system cost maintains at a low level; 3) our algorithm has the capacity of approximating an optimal solution within polynomial time and is promising for practical implementation in real deployments.

For many applications in wireless sensor networks (WSNs), users may want to continuously extract data from the networks for analysis later. However, accurate data extraction is difficult-it is often too costly to obtain all sensor readings, as well as not necessary in the sense that the readings themselves only represent samples of the true state of the world. Clustering and prediction techniques, which exploit spatial and temporal correlation among the sensor data provide opportunities for reducing the energy consumption of continuous sensor data collection. Integrating clustering and prediction techniques makes it essential to design a new data collection scheme, so as to achieve network energy efficiency and stability. We propose an energy-efficient framework for clustering-based data collection in wireless sensor networks by integrating adaptively enabling/disabling prediction scheme. Our framework is clustering based. A cluster head represents all sensor nodes in the cluster and collects data values from them. To realize prediction techniques efficiently in WSNs, we present adaptive scheme to control prediction used in our framework, analyze the performance tradeoff between reducing communication cost and limiting prediction cost, and design algorithms to exploit the benefit of adaptive scheme to enable/disable prediction operations. Our framework is general enough to incorporate many advanced features and we show how sleep/awake scheduling can be applied, which takes our framework approach to designing a practical algorithm for data aggregation: it avoids the need for rampant node-to-node propagation of aggregates, but rather it uses faster and more efficient cluster-to-cluster propagation. To the best of our knowledge, this is the first work adaptively enabling/disabling prediction scheme for clustering-based continuous data collection in sensor networks. Our proposed models, analysis, and framework are validated via simulation and comparison with competing techniques.

Cognitive radio has been proposed as the means for secondary users to promote the efficient utilization of spectrum. Meanwhile, Cooperative relay is well known as a powerful technology to combat signal fading in a wireless medium. In this paper, we discuss a new relay selection algorithm for cognitive radio ad-hoc networks by utilizing cooperative relay to improve spectrum diversity and transmission rate. We define a cooperation-table which considers several aspects including channel gain, channel available probability and spectrum heterogeneity of secondary nodes. Based on this cooperation-table, a new distributed relay selection algorithm is proposed that choose a relay node to maximize the transmission rate of the source node. Simulation results demonstrate the effectiveness of the relay selection algorithm.

Robustness and reliability are critical issues in network management. To provide resiliency, a popular protection scheme against network failures is the simultaneous routing along multiple disjoint paths. Most previous protection and restoration schemes were designed for all-or-nothing protection and thus, an overkill for data traffic. In this work, we study the Reliable Adaptive Multipath Provisioning (RAMP) problem with reliability and differential delay constraints. We aim to route the connections in a manner such that link failure does not shut down the entire stream but allows a continuing flow for a significant portion of the traffic along multiple (not necessary disjoint) paths, allowing the whole network to carry sufficient traffic even when link/node failure occurs. The flexibility enabled by a multipath scheme has the tradeoff of differential delay among the diversely routed paths. This requires increased memory in the destination node in order to buffer the traffic until the data arrives on all the paths. Increased buffer size will raise the network element cost and could cause buffer overflow and data corruption. Therefore, differential delay between the multiple paths should be bounded by containing the delay of a path in a range. We first prove that RAMP is an NP-hard problem. Then we present a pseudo-polynomial time solution to solve a special case of RAMP, representing edge delays as integers. Next, an (1 + e)-approximation algorithm is proposed to solve the optimization version of the RAMP problem. An efficient heuristic is also provided for the RAMP problem. We also present numerical results confirming the advantage of our schemes as the first solution for the RAMP problem.

Relay Stations (RSs) selection and spectrum allocation is a very important problem in cognitive Multi-hop Cellular Networks (MCN). In this paper, we propose a practical joint RS selection and spectrum allocation scheme to maximize the system capacity while fairness is guaranteed. In the proposed scheme, Mobile Stations (MS) are adaptively and optimally selected to be RS based on spatiality of spectrum sets and multi-hop links. Further, we simplify the optimal scheme and propose the RS non-deterministic heuristic algorithms and then compare with conventional RS predetermined algorithm in which RS are predefined before joint RS selection and resource allocation. Simulation results show that the RS non-deterministic algorithm outperforms the conventional RS predetermined algorithm in terms of spectrum efficiency.

This paper proposes a novel Cross-layer Cooperation Method for IEEE 802.16 mesh networks (CCM). CCM selects cooperation nodes through cross-layer consideration of both channel state of physical layer and control overhead on MAC layer to maximize throughput. CCM uses the enhanced three-way handshaking to allocate the bandwidths between source node and cooperation nodes. Extensive simulations are conducted to demonstrate that CCM can effectively select optimal cooperation nodes according to dynamic network scenarios, and in turn increase channel capacity and improve system performance.

Cooperative communication can obtain spatial diversity without using multiple antennas, and thus achieve more reliable transmission or consume less power. Accordingly, a new cooperative MAC mechanism in wireless networks, the DCMAC, is proposed in this paper. The DCMAC makes full use of the broadcasting characteristics of wireless channel to obtain channel information, chooses the most suitable cooperative nodes, and reserves wireless channel efficiently. Evaluation results show that DCMAC can choose the most suitable cooperative nodes to improve system performance.

Measurement studies have shown that uneven and dynamic usage patterns by the primary users of license based wireless communication systems often lead to temporal and spatial spectrum underutilization. This provides an opportunity for secondary users to tap into underutilized frequency bands provided that they are capable of cognitively accessing without colliding or impacting the performance of the primary users. When there are multiple networks with spare spectrum, secondary users can opportunistically choose the best network to access, subject to certain constraints. In cognitive radio systems, this is referred to as the network selection problem. In this paper, multiple network selection strategies namely, random, weighted, and greedy, are comprehensively evaluated. It is found that without adequate admission control, those methods cannot provide sufficient service protection for the primary users. Next, a Markov decision model is applied to obtain the maximum allowable arrival rate for secondary users subject to a target collision probability for the primary users. Based on this model, a Collision-Constrained Network Selection (CCNS) method is proposed that maximizes system throughput subject to a given collision probability. Simulations show that comparing to random, weighted, and greedy strategies CCNS achieves an improved performance in terms of system throughput and collision probability.

Most research works in routing and design of optical networks assume that the optical medium can carry data signals without any bit error. However, physical impairments of the optical signal introduced by optical fibers and components, e.g., power loss, noise, and dispersions, impose fundamental constraints in WDM networks, and must be taken into consideration in the routing and design problems of WDM networks. Only through 3R (optical-electrical-optical) regeneration (reamplification, reshaping, retiming) with OEO conversion can a lightpath be recovered from those impairments. Because 3R regenerators are costly devices and the OEO conversion can affect the efficiency of optical networks we need to use the regenerators efficiently and effectively. In this paper, we study the problem of placing the minimum number of regenerators to accommodate all requests with the consideration of physical impairments. We first propose a novel ILP formulation for an optimal solution and a benchmark for this problem. We then provide an effective heuristic for large-sized WDM networks. Simulation results show that our schemes have good performance in terms of network design and running time.

This paper investigates the performance of EPC-gloabl Generation-2 (Gen-2) ultra high frequency (UHF) radio frequency identification (RFID) protocol. Gen-2 protocol has a critical parameter Q that plays an important role in resolving tag collisions. Gen-2 protocol proposes an adaptive slot-count algorithm to adjust Q dynamically based on the type of replies from tags. In this paper, we define two performance metrics for tag identification: Query Success Rate (QSR) and tag identification speed (TIS). We establish a Discrete-Time Markov Chain (DTMC) model for the Gen-2 and accordingly obtain quantitative results of QSR and TIS. Extensive simulations validate our theoretical analysis and demonstrate that the number of tags has little impact on the performance. In other words, QSR and TIS do not nearly decrease even though the number of tags may be increasing. Our model for Gen-2 protocol is also useful to study the performance of other RFID protocols.

While throughput maximization and service differentiation are two critical issues in wireless local area networks (WLANs), both are separately investigated in most existing work. This paper, from a different angle, addresses how to maximize saturation throughput of a WLAN conditioned that bandwidth differentiation is supported too. A novel model is established for this problem assuming IEEE 802.11e is used. We calculate the optimal values of minimum contention window for stations to maximize the saturation throughput and provide differentiated service as well. The simulation results validate our new model.

While most existing work focuses separately on how to improve WLAN saturation throughout and how to provide differentiated service, few attention is put to study their relationship. In this paper, we investigate the impact of service differentiation on saturation throughput maximization in IEEE 802.11e WLANs and theoretically prove that it is contradictory and impossible to achieve both of them simultaneously. In other words, saturation throughput is maximized without service differentiation or service differentiation reduces the maximal achievable saturation throughput more or less.

Radio frequency identification (RFID) provides a non-line-of-sight and contactless approach for object identification. But if there are multiple tags in the range of an RFID reader, tag collision can take place due to radio signal interference and therefore an anti-collision algorithm is required to

Congestion in wireless sensor networks not only causes packet loss, but also leads to excessive energy consumption. Therefore congestion in WSNs needs to be controlled in order to prolong system lifetime. In addition, this is also necessary to improve fairness and provide better quality of service (QoS), which is required by multimedia applications in wireless multimedia sensor networks. In this paper, we propose a novel upstream congestion control protocol for WSNs, called priority-based congestion control protocol (PCCP). Unlike existing work, PCCP innovatively measures congestion degree as the ratio of packet inter-arrival time along over packet service time. PCCP still introduced node priority index to reflect the importance of each sensor node. Based on the introduced congestion degree and node priority index, PCCP utilizes a cross-layer optimization and imposes a hop-by-hop approach to control congestion. We have demonstrated that PCCP achieves efficient congestion control and flexible weighted fairness for both single-path and multi-path routing, as a result this leads to higher energy efficiency and better QoS in terms of both packet loss rate and delay.

For analysis of the IEEE 802.11 distributed coordination function (DCF), one of the key assumptions commonly accepted is that every node always has at least one frame to transmit. In practice, however, this assumption may or may not be valid, particularly under unsaturated traffic conditions. In this paper, we assess the accuracy in using the traditional M/G/1 queueing model for the DCF as an alternative model at an individual node, and analyze its performance in unsaturated traffic conditions. Analytical and simulation results are compared to demonstrate a reasonable accuracy of the proposed approach for varying traffic loads at an individual node

This paper presents a new model to analyze the channel access delay of 802.11 DCF. Based on this analytical model, the average channel access delay of 802.11 DCF is derived. By means of simulation, the correctness of the analysis is validated, and the channel access delay of 802.11 DCF is further evaluated.

Based on the current standardized IEEE 802.11 distributed coordination function (DCF) protocol, this paper proposes a new efficient collision resolution mechanism, called GDCF. Our main motivation is based on the observation that 802.11 DCF decreases the contention window to the initial value after each success transmission, which essentially assumes that each successful transmission is an indication that the system is under low traffic loading. GDCF takes a more conservative measure by halving the contention window size after c consecutive successful transmissions. This "gentle" decrease can reduce the collision probability, especially when the number of competing nodes is large. We compute the optimal value for c, and the numerical results from both analysis and simulation demonstrate that GDCF significantly improve the performance of 802.11 DCF including throughput, fairness, and energy efficiency. In addition, GDCF is flexible for supporting priority access by selecting different values of c for different traffic types; it is very easy to implement it, as it does not requires any changes in control message structure and access procedures in DCF.

Active queue management (AQM) is an effective method to enhance congestion control, and to achieve tradeoff between link utilization and delay. The de facto standard, random early detection (RED), and most of its variants use queue length as a congestion indicator to trigger packet dropping. The proportional-integral (PI), use both queue length and traffic input rate as congestion indicators; effective stability model and practical design rules built on the TCP control model and abstracted AQM model reveal that such schemes enhance the stability of a system. In this paper, we propose an AQM scheme with fast response time, yet good robustness. The scheme, called loss ratio based RED (LRED), measures the latest packet loss ratio, and uses it as a complement to queue length in order to dynamically adjust packet drop probability. Employing the closed-form relationship between packet loss ratio and the number of TCP flows, this scheme is responsive even if the number of TCP flows varies significantly. We also provide the design rules for this scheme based on the well-known TCP control model. This scheme's performance is examined under various network configurations, and compared to existing AQM schemes, including PI, random exponentially marking (REM), and adaptive virtual queue (AVQ). Our simulation results show that, with comparable complexity', this scheme has short response time, better robustness, and more desirable tradeoff than PI, REM, and AQV, especially under highly dynamic network and heavy traffic load.

This paper proposes a rate-based active queue management algorithm or RAQM. It uses the aggregated traffic input rate to calculate packet drop probability according to an exponential rule. We analyze the stability and investigate practical implementation issues of the RAQM. Simulations are carried out to study RAQM performance and to compare with other AQM algorithms, in particular PI and REM schemes. The results demonstrate that RAQM achieves better stability and faster response as it can quickly regulate the queue length to the expected value with small overshoot. RAQM also obtains better tradeoff between link utilization and queuing delay, and obtains higher goodput with the same buffer size as in PI and REM schemes. Finally RAQM has O(1) complexity, thus is independent of the number of flows.

IP differentiated services (DiffServ) has been standardized by the IETF and is considered as a promising IP QoS solution due to its scalability and ease of implementation. In this paper, we present a novel framework for IP differentiated services (DiffServ) over optical burst switching (OBS), namely, DS-OBS. We present the network architecture, functional model of edge nodes and core nodes, the control packet format, a novel burst assembly scheme at ingress nodes and scheduling algorithm of core nodes. The basic idea is to apply DiffServ capable burst assembly at ingress nodes and perform different per hop behavior (PHB) electronic treatment for control packets of different QoS classes service at core nodes. Simulation results show that the proposed schemes can provide the best differentiated service for expedited forwarding (EF), assured forwarding (AF) and best effort (BE) service in terms of end-to-end delay, throughput and IP packet loss probability.

Hierarchical caching is deployed to scale up the explosive Web growth, and the expiration-based mechanism is adopted as an economic means to support the weak consistency in this context. However, given a hierarchy, the user perceived performance heavily depends on its position. Normally, a user near the hierarchy leaf suffers higher miss rate and longer response time. Such an intrinsic property can discourage users from participating in any hierarchical caching systems. In this paper, we analyze the performance of a proposed approach, i.e., freshness and retrieval threshold based cache prefetching, to mitigate the bias against leaf users. We also use ns-2 to further substantiate our analysis. By adopting this approach with the appropriate parameters, the fairness among users within a caching hierarchy can be considerably improved.

Accurate measurement of network bandwidth is crucial for many flexible Internet applications and protocols. Based on a new multi-packet delay model, this paper presents a new algorithm called ALBP (asymmetric link bandwidth probing) to measure each hop's link bandwidth in both directions in an IP network, and uses the kernel density estimator to produce a final estimate. Compared with previous techniques, ALBP can eliminate (at least partially) the errors introduced by ICMP replies through the use of interarrival time of two probing packets, and avoid the problem of error accumulation. Simulation results show that ALBP is usually more accurate than both one-packet and packet tailgating techniques, and the bandwidth consumption of ALBP is close to that of packet tailgating.

This paper studies the active queue management (AQM) in high-speed routers. One of the original and popularly implemented AQM algorithms is random early detection (RED). But it is hard to configure the parameters involved in RED. Although there are some new TCP-modeling based approaches to overcome this, they really show some instability under such dynamical environments with diverse connections (adaptive or non- adaptive, short or long), link rate, RTT, and buffer size. The major reason is that these approaches are primarily based on TCP model, which has some mismatches under dynamical environment. Since fuzzy-logic control does not need precise control model, it may be suitable for such environment. This paper designs an adaptive fuzzy-based control algorithm (AFRED), which computes the packet drop (or marking) probability according to pre-configured fuzzy logic using the instant queue length as input variable. The important feature of AFRED is that it also designs an adaptive mechanism to dynamically readjust the fuzzy rule so as to make AFRED itself extensively stable for many dynamic environments. Simulation results show that AFRED can effectively and stably control the queue length to the expected value or so. Compared with RED and PI algorithm, AFRED obtains higher goodput and stable queue length than RED and PI, even if introducing UDP flows.

This paper studies the scheduling problem for multiple channels in a wireless local area network environment, where the resource unit assumes to be fixed length slot. The common assumption is: each user can transmits or receive through different channels sequentially, but not simultaneously. In this paper, three scheduling algorithms are proposed. Among them, CSSA schedules each station in contiguous mode, so each station occupies a single channel. The two algorithms, NCSRRA and NCRRA, schedule stations in noncontiguous mode, in that some stations will occupy at least two channels. The CSSA achieves the highest scheduling efficiency, but with relatively high scheduling complexity. The NCSRRA and NCRRA achieve in high efficiency with significantly low complexity.

In this paper, we present an effective scheduling algorithm based on the RPS model, called simple weighted fair queueing (SWFQ). In SWFQ, computation of the system potential function does not require such division or multiplication operations as in MD-SCFQ. Compared with MD-SCFQ, SWFQ has lower complexity and can be easily implemented in chips. We verify the effectivity of proposed SWFQ through strict theoretical analysis.

As an important mechanism to provide QoS guarantee in packet-switched networks, queueing scheduling algorithms have been widely researched. The rate-proportional server (RPS) is a good fluid model that covers the general processor sharing (GPS) models. We can design corresponding packet-level queueing scheduling algorithms through choosing different system potential correction function of RPS. We present an enhanced rate-proportional server (ERPS), which gives the upper and lower bounds of system potential correction function and compute corresponding fairness index. Thus, we can easily devise the needed packet-level queueing scheduling algorithm based on ERPS. We also give a packet-level queueing scheduling algorithm based on ERPS.