Max–min fair survivable networks

In this paper, we discuss applications of max–min fairness (MMF) in survivable networks. We focus on two specific applications intended to face failure situations and provide several computational results for each of them. The first application, called simple robust routing, generalizes the multipath routing in order to achieve acceptable levels of traffic demand satisfaction in case of single link failures while avoiding classical rerouting procedures. Such a method can be seen as a special case of dedicated resource recovery schemes. The second application is concerned with two shared resource restoration strategies and the corresponding problems of computing the MMF minimum traffic demand satisfaction ratio vectors associated with the set of single link failures. We consider the local rerouting and end-to-end rerouting without stub-release strategies. Computational results for realistic network instances provide a comparison of different routing and rerouting strategies in terms of traffic satisfaction rate. The question of estimating the bandwidth overhead, which can be required by the “simple robust routing scheme” in comparison with the classical restoration schemes, is also studied and answers based on computational results are provided. This work is in continuation of our earlier works on MMF (Nace et al., IEEE Trans Netw 14:1272–1281, 2006; Nace et al., Comput Oper Res 35:557–573, 2008).     

Achieving Max–Min lifetime and fairness with rate allocation for data aggregation in sensor networks

We consider the rate allocation problem for data aggregation in wireless sensor networks with two objectives: (1) maximizing the minimum (Max–Min) lifetime of an aggregation cluster and (2) achieving fairness among all data sources. The two objectives are generally correlated with each other and usually, they cannot be maximized simultaneously. We adopt a lexicographic method to solve this multi-objective programming problem. First, we recursively induce the Max–Min lifetime for the aggregation cluster. Under the given Max–Min lifetime, we then formulate the problem of maximizing fairness as a convex optimization problem, and derive the optimal rate allocation strategy by iterations. We also present low-complexity algorithms that an aggregation cluster can use to determine the Max–Min network lifetime and the fair rate allocation. Our simulation results validate our analytical results and illustrate the effectiveness of the approach.     

Radio resource management for LTE-A relay-enhanced cells with spatial reuse and max–min fairness

One of the major issues in LTE-Advanced (LTE-A) systems is the poor capacity at the cell edge. This is mainly due to the interference experienced by the users as a result of the aggressive frequency reuse usually implemented. Relaying offers an attractive solution for this problem by offering better links than those with the eNodeB (eNB) for the terminals suffering from high path loss or high interference. However, adding relays complicates the resource allocation problem at the eNB and therefore the need for more efficient schemes arises. This is also aggravated by the reuse of resource blocks (RBs) by the relays to fully exploit the scarce spectrum, which, in turn, leads to intra-cell interference. In this paper, we study the joint power and resource allocation problem in LTE-A relay-enhanced cells that exploit spatial reuse. To guarantee fairness among users, a max–min fair optimization objective is used. This complex problem is solved using coordinate ascent and the difference of two convex functions (DC) programming techniques and the proposed scheme indeed converges to a local-optimum quickly. This is shown to be a satisfactory solution according to the simulation results that indicate an almost sevenfold increase in the 10th percentile capacity when compared to previously proposed solutions.     

Maximizing network utilization with max–min fairness in wireless sensor networks

The state-of-the-art for optimal data-gathering in wireless sensor networks is to use additive increase algorithms to achieve fair rate allocation while implicity trying to maximize network utilization. For the quantification of the problem we present a receiver capacity model to capture the interference existing in a wireless network. We also provide empirical evidence to motivate the applicability of this model to a real CSMA based wireless network. Using this model, we explicitly formulate the problem of maximizing the network utilization subject to a max–min fair rate allocation constraint in the form of two coupled linear programs. We first show how the max–min rate can be computed efficiently for a given network. We then adopt a dual-based approach to maximize the network utilization. The analysis of the dual shows the sub-optimality of previously proposed additive increase algorithms with respect to bandwidth efficiency. Although in theory a dual-based sub-gradient search algorithm can take a long time to converge, we find empirically that setting all shadow prices to an equal and small constant value, results in near-optimal solutions within one iteration (within 2% of the optimum in 99.65% of the cases). This results in a fast heuristic distributed algorithm that has a nice intuitive explanation—rates are allocated sequentially after rank ordering flows based on the number of downstream receivers whose bandwidth they consume. We also investigate the near optimal performance of this heuristic by comparing the rank ordering of the source rates obtained from the heuristic to the solutions obtained by solving the linear program.

Power Allocation with Max–Min and Min–Max Fairness for Cognitive Radio Networks with Imperfect CSI

This paper consider the power allocation strategies in the cognitive radio (CR) system in the presence of channel estimation errors. As the user has different channel condition in CR systems, different amount of power resource is required to meets the QoS request. In order to guarantee the fairness of each CR user, ensure the interference from the primary user and other CR users meet the QoS requirement of the CR user and limit the interference that is caused by CR users on primary user within the range into the level that primary user can tolerate, we proposed some new power allocation schemes. The targets are to minimize the maximum power allocated to CR users, to maximize the minimum signal-to-interference-plus-noise ratio (SINR) among all CR users and to minimize the maximum outage probability over all CR users. The first power allocation scheme can be formulated using Geometric Programming (GP). Since GP problem is equivalent to the convex optimization problem, we can obtain the optimal solutions for the first scheme. The latter two power allocation schemes are not GP problems. We propose iterative algorithms to solve them. Simulation results show that proposed schemes can efficiently guarantee the fairness of CR users under the QoS constraint of the primary user and CR users.     

Scalable max–min fairness in wireless ad hoc networks

Our previous work proposes a macro model to perform flow and access control in wireless ad hoc networks. In this paper, we demonstrate specifically how to apply the model to achieve max–min fair rate allocation. Our proposed scheme is simple and scalable when comparing to other techniques in the literature. Moreover, it has the ability to provide stability in mobile environment. Simulation results show that our new method provides a good max–min fair flow assignment, and with that assignment, quality of service guarantees can be achieved for real-time applications.     

A distributed location management strategy for next- generation IP-based wireless networks

1 Introduction Mobility management is a key technical aspect in mobile communication systems. The main purpose of mobility management is to enable mobile terminals or users to communicate with each other continuously while moving—— while minimizing data…     

Auction–Stackelberg game framework for access permission in femtocell networks with multiple network operators

With the explosive growth of indoor data traffic in forthcoming fifth generation cellular networks, it is imperative for mobile network operators to improve network coverage and capacity. Femtocells are widely recognized as a promising technology to address these demands. As femtocells are sold or loaned by a mobile network operator (MNO) to its residential or enterprise customers, MNOs usually employ refunding scheme to compensate the femtocell holders (FHs) providing indoor access to other subscribers by configuring the femtocell to operate in open or hybrid access mode. Due to the selfishness nature, competition between network operators as well as femtocell holders makes it challenging for operators to select appropriate FHs for trading access resources. This inspires us to develop an effective refunding framework, with aim to improve overall network resource utilization, through promoting FHs to make reasonable access permission for well-matched macro users. In this paper, we develop a two-stage auction–Stackelberg game (ASGF) framework for access permission in femtocell networks, where MNO and mobile virtual network operator lease access resources from multiple FHs. We first design an auction mechanism to determine the winner femtocell that fulfils the access request of macro users. We next formulate the access permission problem between the winner femtocell and operators as a Stackelberg game, and theoretically prove the existence of unique equilibrium. As a higher system payoff can be gained by improving individual players’ payoff in the game, each player can choose the best response to others’ action by implementing access permission, while avoiding solving a complicated optimization problem. Numerical results validate the effectiveness of our proposed ASGF based refunding framework and the overall network efficiency can be improved significantly.     

End-to-end delay differentiation mechanism for integrated EPON–WiMAX networks

For integration of Ethernet passive optical network and IEEE 802.16 WiMAX, it is imperative to provide end-to-end quality of service due to the heterogeneous MAC nature of EPON and WiMAX. To address this issue, this paper proposes a QoS-enabled transmission mechanism operated on a specially devised hybrid optical network unit-base station architecture for QoS support, especially delay differentiation, between EPON and WiMAX. Simulation results show that the proposed scheme outperforms the existing transmission mechanisms in terms of the end-to-end delay and jitter, due to its QoS continuity between them.     

Energy–delay tradeoff in wireless multihop networks with unreliable links

Energy efficiency and transmission delay are very important parameters for wireless multihop networks. Numerous works that study energy efficiency and delay are based on the assumption of reliable links. However, the unreliability of channels is inevitable in wireless multihop networks. In addition, most of works focus on self-organization protocol design while keeping non-protocol system parameters fixed. While, very few works reveal the relationship between the network performance and these physical parameters, in other words, the best networks performance could be obtained by the physical parameters. This paper investigates the tradeoff between the energy consumption and the latency of communications in a wireless multihop network using a realistic unreliable link model. It provides a closed-form expression of the lower bound of the energy–delay tradeoff and of energy efficiency for different channel models (additive white Gaussian noise, Rayleigh fast fading and Rayleigh block-fading) in a linear network. These analytical results are also verified in 2-dimensional Poisson networks using simulations. The closed-form expression provides a framework to evaluate the energy–delay performance and to optimize the parameters in physical layer, MAC layer and routing layer from the viewpoint of cross-layer design during the planning phase of a network.     

A cross-layer scheme for medium access control with QoS guaranteeing for Ad hoc networks

1 Introduction medium access control (MAC) protocols play a crucial role in determining the performance of Ad hoc networks. However, the design of MAC protocols for Ad hoc networks has traditionally been separated from that of the physical layer. In most …     

Underwater image enhancement with global–local networks and compressed-histogram equalization

Due to the light absorption and scattering, captured underwater images usually contain severe color distortion and contrast reduction. To address the above problems, we combine the merits of deep learning and conventional image enhancement technology to improve the underwater image quality. We first propose a two-branch network to compensate the global distorted color and local reduced contrast, respectively. Adopting this global–local network can greatly ease the learning problem, so that it can be handled by using a lightweight network architecture. To cope with the complex and changeable underwater environment, we then design a compressed-histogram equalization to complement the data-driven deep learning, in which the parameters are fixed after training. The proposed compression strategy is able to generate vivid results without introducing over-enhancement and extra computing burden. Experiments demonstrate that our method significantly outperforms several state-of-the-arts in both qualitative and quantitative qualities.     

Efficient fault-tolerant routing in IoT wireless sensor networks based on path graph flow modeling with Marchenko–Pastur distribution (EFT-PMD)

Wireless Networks - In the internet of things (IoT) based wireless sensor network (WSN), the nodes are scattered to segregate the rapt data in the relevant field of application. In general, sensor...     

Greedy algorithms for actor redeployment in wireless sensor–actor networks

In a wireless sensor–actor network, an actor usually has to provide services as soon as the actor receives the event signals from the sensors. Therefore, the performance of a wireless sensor–actor network depends on the actor deployment. In many circumstances, actors may fail or go out to deal with events, and thus, the sensors covered by the missing actors could be not to be reachable in time. This introduces the necessity of actor redeployment. In this paper, we study the problems of redeploying actors to maximize the number of sensors able to be covered by actors and to maximize the decrease of the residual distances of sensors, respectively. Both problems are shown to be NP-complete. Additionally, we prove that the greedy algorithm for each problem has an approximation ratio of 2. Simulations show that the greedy algorithm for each problem performs well.     

Multi-domain residual encoder–decoder networks for generalized compression artifact reduction

A fundamental requirement for designing compression artifact reduction techniques is to restore the artifact free image from its compressed version regardless of the compression level. Most existing algorithms require the prior knowledge of JPEG encoding parameters to operate effectively. Although there are works that attempt to train universal models to deal with different compression levels, some JPEG quality factors (QF) are still missing. To overcome these potential limitations, in this paper, we present a generalized JPEG-compression artifact reduction framework that relies on improved QF estimator and rectified networks to take into account all possible QF values. Our method, called a generalized compression artifact reducer (G-CAR), first predicts QF by analyzing luminance patches with high activity. Then, based on the estimated QF, images are adaptively restored by the cascaded residual encoder–decoder networks learned in multiple domains. Results tested on six benchmark datasets demonstrate the effectiveness of our proposed model.     

Combined user and antenna scheduling scheme for MIMO–OFDM networks

Telecommunication Systems - A combined user and antenna scheduling algorithm implementing spatial multiplexing with limited feedback is proposed for multiple-input multiple-output orthogonal...     

Spectrum pricing for cognitive radio networks with user’s stochastic distribution

Wireless Networks - Amid the dynamic spectrum access in cognitive radio networks, when complex spectrum conditions should be taken into account, how to price the spectrum in order to benefit...     

Energy efficient TDMA-based MAC protocol associated with GAF for wireless sensor networks

The design of media access control (MAC) protocol for wireless sensor networks (WSNs) with the idea of cross layer attracts more and more attention. People can improve the MAC protocol by obtaining certain information regarding the network layer and physical layer. This article synthesizes and optimizes certain cross-layer protocols which have existed. On the basis of the routing, topology information in the network layer, and transmission power information in the physical layer, the time slot assignment algorithm has been improved in the MAC layer. By using geographical adaptive fidelity algorithm (GAF) to divide the grids, controlling of transmission power and scheduling the work/sleep duty cycle for sensor nodes, a new MAC protocol has been proposed to decrease energy consumption and enlarge the lifetime of WSNs. Simulation results show that the MAC protocol functions well.     

Fault-tolerant routing in adversarial mobile ad hoc networks: an efficient route estimation scheme for non-stationary environments

Designing routing schemes that would successfully operate in the presence of adversarial environments in Mobile Ad Hoc Networks (MANETs) is a challenging issue. In this paper, we discuss fault-tolerant routing schemes operating in a network with malfunctioning nodes. Most existing MANET protocols were postulated considering scenarios where all the mobile nodes in the ad hoc network function properly, and in an idealistic manner. However, adversarial environments are common in MANET environments, and misbehaving nodes certainly degrade the performance of these routing protocols. The need for fault tolerant routing protocols was identified to address routing in adversarial environments in the presence of faulty nodes by exploring redundancy-based strategies in networks. It turns out that since the nodes are mobile, the random variables encountered are non-stationary, implying that estimation methods for stationary variables are inadequate. Consequently, in this paper, we present a new fault-tolerant routing scheme that invokes a stochastic learning-based weak estimation procedure to enhance a route estimation phase, which, in turn, is then incorporated in a route selection phase. We are not aware of any reported method that utilizes non-traditional estimates to achieve the ranking of the possible paths. The scheme, which has been rigorously tested by simulation, has been shown to be superior to the existing algorithms.     

Non-coherent distributed space–time coding techniques for two-way wireless relay networks

To overcome the overhead involved with channel estimation, several non-coherent distributed space–time coding (DSTC) strategies for two-way wireless relay networks (TWRNs) using the amplify-and-forward and the decode-and-forward protocol have been recently proposed that do not require channel state information (CSI) at any node to decode the information symbols. In this paper, novel differential DSTC strategies for TWRNs using the two- and three-phase protocol are proposed. In our transmission schemes, the relays do not waste power to transmit information known at the respective destination nodes. This is achieved by combining the symbols from both terminals received at the relays into a single symbol of the unaltered constellation. Furthermore, in our strategies, the direct link between the communicating terminals can be naturally incorporated to further improve the diversity gain. Simulations show a substantially improved performance in terms of bit error rate (BER) of the proposed strategies as compared to the existing strategies.