Optimal resource allocation in wireless ad hoc networks: a price-based approach

The shared-medium multihop nature of wireless ad hoc networks poses fundamental challenges to the design of effective resource allocation algorithms that are optimal with respect to resource utilization and fair across different network flows. None of the existing resource allocation algorithms in wireless ad hoc networks have realistically considered end-to-end flows spanning multiple hops. Moreover, strategies proposed in wireline networks are not applicable in the context of wireless ad hoc networks, due to their unique characteristics of location-dependent contention. In this paper, we propose a new price-based resource allocation framework in wireless ad hoc networks to achieve optimal resource utilization and fairness among competing end-to-end flows. We build our pricing framework on the notion of maximal cliques in wireless ad hoc networks, as compared to individual links in traditional wide-area wireline networks. Based on such a price-based theoretical framework, we present a two-tier iterative algorithm. Distributed across wireless nodes, the algorithm converges to a global network optimum with respect to resource allocations. We further improve the algorithm toward asynchronous network settings and prove its convergence. Extensive simulations under a variety of network environments have been conducted to validate our theoretical claims.     

Simultaneous routing and resource allocation via dual decomposition

In wireless data networks, the optimal routing of data depends on the link capacities which, in turn, are determined by the allocation of communications resources (such as transmit powers and bandwidths) to the links. The optimal performance of the network can only be achieved by simultaneous optimization of routing and resource allocation. In this paper, we formulate the simultaneous routing and resource allocation (SRRA) problem, and exploit problem structure to derive efficient solution methods. We use a capacitated multicommodity flow model to describe the data flows in the network. We assume that the capacity of a wireless link is a concave and increasing function of the communications resources allocated to the link, and the communications resources for groups of links are limited. These assumptions allow us to formulate the SRRA problem as a convex optimization problem over the network flow variables and the communications variables. These two sets of variables are coupled only through the link capacity constraints. We exploit this separable structure by dual decomposition. The resulting solution method attains the optimal coordination of data routing in the network layer and resource allocation in the radio control layer via pricing on the link capacities.     

A Bidding Algorithm for Optimized Utility-Based Resource Allocation in Ad Hoc Networks

This article proposes a scheme for bandwidth allocation in wireless ad hoc networks. The quality of service (QoS) levels for each end-to-end flow are expressed using resource-utility functions, and our algorithms aim to maximize aggregated utility. The shared channel is modeled as bandwidth resources defined by maximal cliques of mutual interfering links. We propose an entirely novel resource allocation algorithm that employs auction mechanisms where flows are bidding for resources. The bids depend both on the flow's utility function and the intrinsically derived shadow prices. Then we combine it with a utility-aware on-demand shortest path routing algorithm where shadow prices are used as a natural distance metric. We also show that the problem can be formulated as a linear programming problem. Thus we can compare the performance of our scheme to the centralized optimal LP solution, registering results very close to the optimum. We isolate the performance of the price-based routing and show its advantages in hotspot scenarios, and also propose an asynchronous version that is more feasible for ad hoc environments. Experimental results of a comparison with the state-of-the-art approach based on Kelly's utility maximization framework show that our approach exhibits superior performance for networks with both increased mobility or increased allocation period.     

Optimal Routing and Data Aggregation for Maximizing Lifetime of Wireless Sensor Networks

An optimal routing and data aggregation scheme for wireless sensor networks is proposed in this paper. The objective is to maximize the network lifetime by jointly optimizing data aggregation and routing. We adopt a model to integrate data aggregation with the underlying routing scheme and present a smoothing approximation function for the optimization problem. The necessary and sufficient conditions for achieving the optimality are derived and a distributed gradient algorithm is designed accordingly. We show that the proposed scheme can significantly reduce the data traffic and improve the network lifetime. The distributed algorithm can converge to the optimal value efficiently under all network configurations.      

A Cross-Layer Optimization Framework for Multihop Multicast in Wireless Mesh Networks

The optimal and distributed provisioning of high throughput in mesh networks is known as a fundamental but hard problem. The situation is exacerbated in a wireless setting due to the interference among local wireless transmissions. In this paper, we propose a cross-layer optimization framework for throughput maximization in wireless mesh networks, in which the data routing problem and the wireless medium contention problem are jointly optimized for multihop multicast. We show that the throughput maximization problem can be decomposed into two subproblems: a data routing subproblem at the network layer, and a power control subproblem at the physical layer with a set of Lagrangian dual variables coordinating interlayer coupling. Various effective solutions are discussed for each subproblem. We emphasize the network coding technique for multicast routing and a game theoretic method for interference management, for which efficient and distributed solutions are derived and illustrated. Finally, we show that the proposed framework can be extended to take into account physical-layer wireless multicast in mesh networks     

Maximizing the Lifetime of Wireless Sensor Networks through Optimal Single-Session Flow Routing

Wireless sensor networks are becoming increasingly important in recent years due to their ability to detect and convey real-time, in-situ information for many civilian and military applications. A fundamental challenge for such networks lies in energy constraint, which poses a performance limit on the achievable network lifetime. We consider a two-tier wireless sensor network and address the network lifetime problem for upper-tier aggregation and forwarding nodes (AFNs). Existing flow routing solutions proposed for maximizing network lifetime require AFNs to split flows to different paths during transmission, which we call multisession flow routing solutions. If an AFN is equipped with a single transmitter/receiver pair, a multisession flow routing solution requires a packet-level power control at the AFN so as to conserve energy, which calls for considerable overhead in synchronization among the AFNs. In this paper, we show that it is possible to achieve the same optimal network lifetime by power control on a much larger timescale with the so-called single-session flow routing solutions, under which the packet-level power control and, thus, strict requirement on synchronization are not necessary. We also show how to perform optimal single-session flow routing when the bit-rate of composite flows generated by AFNs is time-varying, as long as the average bit-rate can be estimated.     

An Efficient Cross-Layer Optimization Algorithm for Data Transmission in Wireless Sensor Networks

In this paper, we propose a cross layer congestion optimization scheme for allocating the resources of wireless sensor networks to achieve maximization of network performance. The congestion control, routing selection, link capacity allocation, and power consumption are all taken account to yield an optimal scheme based on the Lagrangian optimization. The Lagrangian multiplier is adopted to adjust power consumption, congestion rate, routing selection and link capacity allocation, so that the network performance can be satisfied between the trade-off of efficiency and fairness of resource allocation. The proposed algorithm can significantly achieve the maximization of network performance in relieving the network congestion with less power consumption. Excellent simulation results are obtained to demonstrate our innovative idea, and show the efficiency of our proposed algorithm.     

Load balancing techniques for lifetime maximizing in wireless sensor networks

Energy consumption has been the focus of many studies on Wireless Sensor Networks (WSN). It is well recognized that energy is a strictly limited resource in WSNs. This limitation constrains the operation of the sensor nodes and somehow compromises the long term network performance as well as network activities. Indeed, the purpose of all application scenarios is to have sensor nodes deployed, unattended, for several months or years.This paper presents the lifetime maximization problem in “many-to-one” and “mostly-off” wireless sensor networks. In such network pattern, all sensor nodes generate and send packets to a single sink via multi-hop transmissions. We noticed, in our previous experimental studies, that since the entire sensor data has to be forwarded to a base station via multi-hop routing, the traffic pattern is highly non-uniform, putting a high burden on the sensor nodes close to the base station.In this paper, we propose some strategies that balance the energy consumption of these nodes and ensure maximum network lifetime by balancing the traffic load as equally as possible. First, we formalize the network lifetime maximization problem then we derive an optimal load balancing solution. Subsequently, we propose a heuristic to approximate the optimal solution and we compare both optimal and heuristic solutions with most common strategies such as shortest-path and equiproportional routing. We conclude that through the results of this work, combining load balancing with transmission power control outperforms the traditional routing schemes in terms of network lifetime maximization.     

无线多跳网络中一种基于网络编码的机会路由

近年来机会路由和网络编码是两种利用无线信道广播特性提高网络性能的新兴技术。相比传统的静态路由决策,机会路由利用动态和机会路由选择减轻无线有损链路带来的影响。网络编码可以提高网络的资源利用率。但编码机会依赖于多个并发流所选路径的相对结构。为了创造更多的网络编码机会和提高网络吞吐量,本文提出了一种基于流间网络编码的机会路由(ORNC)算法。在ORNC中,每个分组转发的机会路径选择是基于网络编码感知的方式进行的。当没有编码机会时,采用背压策略选择下一跳转发路径以平衡网络负载。仿真结果表明本文提出的ORNC算法能够提高无线多跳网络的吞吐量。     

Adaptive Optimization-based Routing in Wireless Mesh Networks

Routing is a critical component in wireless mesh networks. The inherent shared-medium nature of the wireless mesh networks, however, poses fundamental challenges to the design of effective routing policies that are optimal with respect to the resource utilization. Node churns and traffic fluctuations exacerbate such a problem. In this paper, we propose a novel adaptive routing algorithm for multiple subscribers in wireless mesh networks. We view a mesh network with multiple nodes as an entity that optimizes some global utility function constrained by the underlying MAC layer interference. By solving the optimization problem, the network is driven to an efficient operating point with a certain routing policies for each node. We then use this operating point information to adaptively find better paths, which is able to gear the network towards optimal routing. Further, we take the fluctuations of the network into consideration and thus render our algorithm more robust for a variety of network situations. Simulations demonstrate the efficiency and efficacy of our algorithm.     

Cross-layer optimization of wireless networks using nonlinear column generation

We consider the problem of finding the jointly optimal end-to-end communication rates, routing, power allocation and transmission scheduling for wireless networks. In particular, we focus on finding the resource allocation that achieves fair end-to-end communication rates. Using realistic models of several rate and power adaption schemes, we show how this cross-layer optimization problem can be formulated as a nonlinear mathematical program. We develop a specialized solution method, based on a nonlinear column generation technique, and prove that it converges to the globally optimal solution. We present computational results from a large set of networks and discuss the insight that can be gained about the influence of power control, spatial reuse, routing strategies and variable transmission rates on network performance.     

Hop Constrained Energy-Efficient Broadcasting: Insights from Massively Dense Ad Hoc Networks

We consider source-initiated broadcast session traffic in an ad hoc wireless network operating under a hard constraint on the end-to-end delay between the source and any node in the network. We measure the delay to a given node in the number of hops data travels from the source to that node, and our objective in this paper is to construct an energy-efficient broadcast tree that has a maximum depth Delta, where Delta; represents the end-to-end hop constraint in the network. We characterize the optimal solution to a closely related problem in massively dense networks using a dynamic programming formulation. We prove that the optimal solution can be obtained by an algorithm of polynomial time complexity O(Delta²). The solution to the dynamic program indicates that there is a single optimal policy applicable to all massively dense networks. Elaborating on the insights provided by the structure of the problem in massively dense networks, we design an algorithm for finding a solution to the hop constrained minimum power broadcasting problem in general networks. By extensive simulations, we demonstrate that our proposed optimization-based algorithm generates broadcast trees within 20% of optimality for general dense networks.     

Node-Based Optimal Power Control, Routing, and Congestion Control in Wireless Networks

In wireless networks, important network functionalities such as power control, rate allocation, routing, and congestion control must be optimized in a coherent and integrated manner. In this work, an interference-limited wireless network is considered, whereby power control and routing variables are chosen to minimize the sum of link costs which depend on both link capacities and link flow rates. The necessary conditions for optimality are established. These conditions are sufficient for optimality if link cost functions are jointly convex, and imply Pareto optimality if link costs are strictly quasi-convex. Network algorithms based on the scaled gradient projection method, where power control and routing are performed on a node-by-node basis, are presented. For these algorithms, explicit scaling matrices and stepsizes are found which lead to more distributed implementation, and which guarantee fast convergence to a network configuration satisfying the optimality conditions, starting from any initial configuration with finite cost. Refinements of the algorithms for more accurate link capacity models are presented, and the results are extended to wireless networks where the physical-layer rate region is given by an arbitrary convex set. Finally, it is shown that the power control and routing algorithms can naturally be extended to incorporate congestion control.      

基于流的无线网络编码

针对无线链路的不可靠性和物理层的广播特性,将网络编码与无线网络相结合,提出一种基于流的无线网络编码算法——FNC。该算法在先验参考路径的基础上,充分利用每个节点处的路由表项,探测编码机会,在有编码机会的节点对满足编码条件的数据流进行网络编码,达到提高网络吞吐量的目的。仿真结果表明：FNC有效提高了整个网络的吞吐量,改善了网络性能。     

Game-theoretic models for reliable path-length and energy-constrained routing with data aggregation in wireless sensor networks

Path length, path reliability, and sensor energy-consumption are three major constraints affecting routing in resource constrained, unreliable wireless sensor networks. By considering the implicit collaborative imperative for sensors to achieve overall network objectives subject to individual resource consumption, we develop a game-theoretic model of reliable, length and energy-constrained, sensor-centric information routing in sensor networks. We define two distinct payoff (benefit) functions and show that computing optimally reliable energy-constrained paths is NP-Hard under both models for arbitrary sensor networks. We then show that optimal length-constrained paths can be computed in polynomial time in a distributed manner (using O(E) messages) for popular sensor network implementations using geographic routing. We also develop sensor-centric metrics called path weakness to measure the qualitative performance of different routing schemes and provide theoretical limits on the inapproximability of computing paths with bounded weakness. Heuristics for computing optimal paths in arbitrary sensor networks are described along with simulation results comparing performance with other routing algorithms.     

Variable Bit Rate Flow Routing in Wireless Sensor Networks

Since energy constraint is a fundamental issue for wireless sensor networks, network lifetime performance has become a key performance metric for such networks. In this paper, we consider a two-tier wireless sensor network and focus on the flow routing problem for the upper tier aggregation and forwarding nodes (AFNs). Specifically, we are interested in how to perform flow routing among the nodes when the bit rate from each source node is time-varying. We present an algorithm that can be used to construct a flow routing solution with the following properties: (1) If the average rate from each source node is known a priori, then flow routing solution obtained via such algorithm is optimal and offers provably maximum network lifetime performance; (2) If the average rate of each source node is unknown but is within a fraction (epsiv) of an estimated rate value, then network lifetime by the proposed flow routing solution is within 2epsiv/1-epsiv from the optimum. These results fill in an important gap in theoretical foundation for flow routing in energy-constrained sensor networks.     

Fairness and Optimal Stochastic Control for Heterogeneous Networks

We consider optimal control for general networks with both wireless and wireline components and time varying channels. A dynamic strategy is developed to support all traffic whenever possible, and to make optimally fair decisions about which data to serve when inputs exceed network capacity. The strategy is decoupled into separate algorithms for flow control, routing, and resource allocation, and allows each user to make decisions independent of the actions of others. The combined strategy is shown to yield data rates that are arbitrarily close to the optimal operating point achieved when all network controllers are coordinated and have perfect knowledge of future events. The cost of approaching this fair operating point is an end-to-end delay increase for data that is served by the network.     

Network coded multicast for multi-hop D2D communication systems

ABSTRACT

In this paper, we consider the wireless communication systems where multi-hop Device-to-Device (D2D) networks can coexist with the conventional cellular networks by sharing the downlink resource of cellular users (CUs). A multicast data flow is distributed over the multi-hop D2D networks where network coding (NC) can be employed at the intermediate nodes. To maximize the utility of the multicast flow, we formulate a joint optimization problem for the systems while guaranteeing the quality-of-service (QoS) for regular CUs. We propose a subgradient algorithm to solve the optimization problem by decomposing it into three sub-problems: multicast rate control, NC subgraph selection, and downlink resource reusing. In particular, we develop a greedy algorithm to deal with the downlink resource reusing sub-problem for it is NP hard. Numerical and simulation results prove the superior performance of the proposed techniques compared with the conventional routing scheme.     

Fuzzy chessboard clustering and artificial bee colony routing method for energy‐efficient heterogeneous wireless sensor networks

Energy is an extremely critical resource for battery‐powered wireless sensor networks (WSNs), thus making energy‐efficient protocol design a key challenging problem. However, uneven energy consumption is an inherent problem in WSNs caused by multi‐hop routing and many‐to‐one traffic pattern among sensors. In this paper, we therefore propose a new clustering method called fuzzy chessboard clustering (FFC), which is capable to overcome the bottleneck problem and addressing the uneven energy consumption problem in heterogeneous WSNs. We also propose an energy‐efficient routing method called artificial bee colony routing method (ABCRM) to find the optimal routing path for the heterogeneous WSNs. ABCRM seeks to investigate the problems of balancing energy consumption and maximization of network lifetime. To demonstrate the effectiveness of FCC‐ABCRM in terms of lessening end‐to‐end delay, balancing energy consumption, and maximization of heterogeneous network lifetime, we compare our method with three approaches namely, chessboard clustering approach, PEGASIS, and LEACH. Simulation results show that the network lifetime achieved by FCC‐ABCRM could be increased by nearly 25%, 45%, and 60% more than that obtained by chessboard clustering, PEGASIS, and LEACH, respectively. Copyright © 2013 John Wiley & Sons, Ltd.     

On the performance analysis of the minimum-blocking and bandwidth-reallocation channel-assignment (MBCA/BRCA) methods for quality-of-service routing support in mobile multimedia ad hoc networks

Quality-of-service (QoS) routing is the key to support multimedia services in wireless multihop networks. The goal of QoS routing is to find satisfactory paths that support the end-to-end QoS requirements of the multimedia flows. Previous work has demonstrated a framework for supporting QoS routing in mobile ad hoc networks, where two novel mechanisms for dynamic channel assignment, called the minimum-blocking and bandwidth-reallocation channel-assignment (MBCA/BRCA) algorithms, were proposed. MBCA/BRCA are on-demand channel assignment methods that reactively provide a differentiated service treatment to multimedia traffic flows at the link level using novel techniques for end-to-end path QoS maximization. Efficient QoS routing is then accomplished by giving the routing mechanism access to QoS information, thus coupling the coarse grain (routing) and fine grain (congestion control) resource allocation. In this paper, the specifics and individual mechanisms of the MBCA/BRCA algorithms are presented, whereas their effectiveness and the manner in which they interact in order to contribute to the overall protocol performance is examined and documented. The system performance is studied through simulations experiments under various QoS traffic flows and network scenarios. The protocol's behavior and the changes introduced by variations on some of the mechanisms that make up the protocol is further investigated. As demonstrated, the MBCA/BRCA methods are able to increase system's aggregate traffic by 2.8 Kb/s, on average, comparing to a non-MBCA/BRCA dynamic channel-allocation scheme.