In this paper, we study the trade-off between the aggregate MAC flow throughput and the fairness in IEEE 802.11 based wireless mesh networks (WMNs) that utilize scheduling on top of the CSMA/CA access scheme. We use a toy WMN topology to allow us to understand easily this trade-off and propose an analytical model to study the interaction between contending links in this topology. Based on this model, we formulate the bandwidth scheduling problem as an aggregate MAC flow throughput maximization problem subject to the fairness requirements dictated by the scheduler. This study is dictated by the need to understand the limits of such scheduling algorithms which have proliferated in recent years to balance the throughput and fairness of WMNs without modifying the CSMA/CA protocol or the binary exponential backoff due to the non programmability of the MAC and backoff procedure in modern commercial Wi-Fi chipset. As an example, we evaluate our previously proposed bandwidth scheduling mechanism - the so-called distributed fair MAC scheduler (DFMS) to validate our model on one hand and demonstrate the efficiency of our scheduler on the other.

In this paper, we study jointly the problems of rate control and contention resolution in multi-cell WLANs based on the IEEE 802.11 MAC protocol in the presence of hidden terminals. We adopt the network utility maximization (NUM) framework to formulate the problem as a nonlinear optimization problem. Unlike previous approaches that require maximal weight scheduling or target simple MAC protocols without hidden terminals, our problem formulation considers a realistic IEEE 802.11-based MAC layer model. The resulting formulation being non-convex, we propose a simple scheme to transform this non-convex problem into a convex one. We derive a simple distributed algorithm to solve the problem via Lagrangian dual decomposition. Our algorithm does not require exchanges of topology or spatial information about the mobile clients between APs. Practical issues like random backoff, carrier sensing, frame retransmission and optimal contention window (CW) setting are considered in our design. The effectiveness, accuracy and convergence speed of our analytically derived algorithm are verified via numerical experiments and simulations.

In this paper, we study the problem of determining the optimal association in multi-cell WLANs in the presence of hidden terminals and inter-AP collisions. Unlike most work in this area which deal with networks without inter-AP interference, we reveal that association control alone is not sufficient to achieve fair throughput allocation and load balancing across APs. Instead, we advocate the joint association control, rate control and contention control to improve network performance. Based on this, we formulate a cross-layer optimization problem whose objective is to allocate downlink throughput according to the proportional fairness principle. As the problem turns out to be a non-convex mixed integer programming problem, which is known to be NP-hard, we relax it into a continuous convex problem and propose a distributed algorithm to solve it. We then design a simple yet effective distributed approximation algorithm to construct an solution that fulfills the discrete integral association constraint. The output of the algorithm provides the optimal association, the maximum achievable rate for each downlink flow and each AP's optimal average backoff time. Numerical experiments and simulation results show that our algorithm converges rapidly and works effectively.

There have been a number of TCP variants proposed in the literature to combat random packet losses in wireless networks. In this paper we first perform a simulation study on the performance of these TCP variants in the context of IEEE 802.11 WLAN with various channel conditions, generated using different wireless error models and show that none of the existing TCP variants has superior performance over the conventional TCP in such WLANs. We also observe that in IEEE 802.11 WLAN efficiency problem with TCP protocols only exists when the number of active wieless nodes is small and the channel condition is extremely bad. We further demonstrate that employing the delayed ACK mechanism and SACK option at the TCP receiver can generally improve the performance. With the option to modify the TCP implementation at the receivers we further propose TCP-WOW algorithm, which estimates the flow queue size on the forward path and further uses such estimates as quantitative indication of the congestion level to dynamically guide the congestion response. TCP-WOW is robust to reverse traffic interactions. TCP-WOW also shows a better RTT fairness and consistently good performance in our IEEE 802.11 WLAN simulations.

In this paper, we study the bandwidth allocation problem on a bi-directional end-to-end path in a last-mile wireless mesh network. We first propose an analytical model to study the interaction between contending links. Based on this model, we formulate the bandwidth allocation problem as a constrained maximization problem that captures real world complexities such as hidden node collisions, multi-hop flows and maximum packet transmission retry limit. The objective of the optimization problem is to maximize the downlink end-to-end throughput under a predefined uplink end-to-end throughput requirement. The optimal setting of each mesh router's contention windows can be obtained from the solution of the optimization problem. Our approach does not require any hardware modification and application layer rate control. Simulation results show that the proposed methodology works very well, the target uplink bandwidth can be reached and the downlink throughput is increased significantly when compared to the IEEE 802.11 standard.

As the Internet evolves, traffic management has become a high-priority problem. Without a network centric traffic management scheme, severe fairness problems can arise. For instance, unfairness occurs with TCP traffic in the presence of careless or subversive misbehaving flows, or even in the presence of other TCP flavors if we consider inter-protocol unfairness. In this paper we propose a new AQM scheme, Stochastic Fair Traffic Management (SiFTM), to help achieve fair bandwidth allocation among flows even in the presence of misbehaving ones. SiFTM relies on multi-level hashing to perform packet classification and on a random sampling mechanism to identify and punish misbehaving flows harshly. SiFTM improves the fairness index among TCP flows even with different TCP flavors. Through simulations we show that SiFTM outperforms other related schemes, especially in the presence of misbehaving flows. Furthermore, we argue that with the fair bandwidth allocation it provides, SiFTM deprives end systems of the incentive to behave unresponsively as a means to enjoying better services. It thus promotes rational behavior and self imposed network-friendly congestion control in the transport protocol.

Due to the limited number of orthogonal channels and the dense deployment of IEEE 802.11 access points (APs), interference between neighboring basic service sets (BSSs) are very common in indoor environment. One consequence of such neighboring interference is the inter-BSSs hidden terminal problem in which some mobile stations may suffer from throughput starvation. In this paper, we advocate and develop a methodology to allocate bandwidth as a mean of combating such starvation problem. We first propose an analytical model to study the interaction between contending links in the network, then based on this model, we demonstrate how the contention window (CW) can be adjusted in order to allocate the upload and download bandwidth to sustain some predefined bandwidth requirement with only the basic access scheme is used. Our approach does not require any hardware modification, and is compatible with most WLAN chipsets. The optimal window setting can be obtained by simply solving a nonlinear system once without any iterative algorithm. Simulation are invoked to support the effectiveness of the proposed methodology in achieving the target bandwidth even under saturation condition.

In this paper, we consider the throughput allocation problem in an extended (or multi-cells) IEEE 802.11 wireless network. We first demonstrate the severe throughput imbalance that can take place between downlink TCP flows even in simple multi-cell WLANs. Then to solve this unfairness problem, we derive an analytical model that describes the interaction between different TCP flows at the MAC layer, and formulate the throughput allocation problem as a nonlinear optimization problem subject to certain fairness requirements. Real world complexity such as hidden terminals, packet transmission retry limit, and the unique characteristics of TCP traffic are considered. Unlike alternative approaches that rely on modifying the TCP sender or implementing active queue management at the network layer, solving our optimization problem yields the optimal MAC layer contention windows settings that can lead each TCP flow to its target end-to-end throughput. Among the practically appealing characteristics of this approach are: i) its locality and the possibility of implementing it at the AP and within the ESS, whereas alternative approaches rely on changing the TCP traffic source; and ii) its simplicity, i.e., it is achieved by controlling a single parameter per-node, whereas alternative approaches based on AQM require the tuning of many parameters and achieve mitigated results. Simulation results show that our approach can achieve a fair throughput allocation and attest to the accuracy of our proposed method.

In this paper, we develop a Markov model to evaluate the performance of WLAN in the presence of hidden terminals. We focus on characterizing and studying the scenario of an access point with a population of clients partitioned into two mutually hidden groups, and derive a model to estimate the network throughput and the individual nodes' throughput. We propose a general methodology to solve the Markov chain by forming a nonlinear system of equations which describes the relationship between the conditional state transition probabilities, the transmission attempts probability and the steady state probability. Unlike alternative approaches, it is not necessary to invoke iterative methods to solve this system. We also demonstrate how our methodology and model can be used to estimate the optimal stable rate for each source in the WLAN as a means for rate control to achieve a given fairness principle between hidden groups of nodes and avoid starvation or congestion collapse for one group. Simulation results attest to the accuracy of our proposed model.

The efficient use of energy in wireless sensor networks is critical issue as the battery of a sensor node, in most cases, cannot be recharged or replaced after deployment. In order to detect an event, a sensor node spends most of the time in monitoring its environment, during which a significant amount of energy can be saved by placing the radio in the low power sleep mode when no reception and/or transmission of data is involved. In this paper, we discuss the design of a new MAC protocol for wireless sensor networks whose goal is to extend the lifetime of the network by avoiding major energy waste causes, such as collisions, overhearing and idle listening, without compromising other network performance measures such as network throughput. The performance of the protocol is studied by simulation and is compared to that of the well known S-MAC protocol which is designed to save energy and to the IEEE 802.11 protocol which is designed to maximize throughput.

This paper studies the tradeoff between energy consumption and application performance in wireless sensor networks by investigating the interaction between network lifetime maximization and rate allocation problems. To guarantee the individual performance of sensor nodes, we adopt the network utility maximization (NUM) framework to ensure certain fairness on source rates of sensor nodes. We formulate the network lifetime maximization problem and fair rate allocation problem as constrained maximization problems, and combine them by introducing a system parameter, which characterizes the tradeoff between the two problems. Using Lagrange dual decomposition, the original problem is vertically decomposed into three subproblems: a rate control problem at the transport layer, a contention resolution problem at the MAC Layer, and a cross-layer energy conservation problem. The first and second subproblems jointly solve the congestion problem in sensor networks via congestion prices, and fully distributed algorithms are derived. Furthermore, they are coupled with the cross layer energy conservation problem to solve the network lifetime maximization problem via energy prices. For the third subproblem, we first propose a partially distributed algorithm where network lifetime is a global information, and then by exploring the similarity between max-min rate allocation and network lifetime maximization in sensor networks, we approximate the latter by the NUM framework, and hence formulate the tradeoff problem in the unified NUM framework. As a result, a fully distributed algorithm is derived for the energy conservation problem.

This paper considers the problem of routing in sensor networks from the point of view od data collection. That is, given the initial amount of battery energy in each node, the aim is to determine how much data can each source transmit until the network is partitioned (i. e., until the nodes cannot find end-to-end routes to their respective sinks). In addition, to respond to some specific applications' requirements, when determining such nodal data volume distribution, fairness among nodes is taken into account. The problem is formulated as a concave utility maximization and a sub-gradient algorithm is proposed to solve it distributively. Some numerical results are given and the convergence of the algorithm is discussed.

Sensor networks are deployed to gather some useful data from a field and forward it toward a set of base stations or sinks for data analysis and decision making. Each sensor node is endowed with a finite amount of energy, and each byte transmission or reception costs a certain fixed fraction of energy as well as a variable fraction that depends on the distance between sender and receiver. Maximizing the volume of data collected at the sinks until some particular set of nodes exhaust their battery and partition the network is a very desirable trait in sensor networks, as it equates with a high level of energy efficiency. In this paper we formulate the problem of maximum data collection routing for sensor networks as a utility maximization problem subject to energy constraints, and invoke lagrange relaxation, duality and sub-gradient technique to solve the problem. We then focus on the problem of path oscillation, which is well known to happen in routing algorithms where link costs are function of the traffic load and propose heuristic solutions to address this oscillation problem

The network lifetime and application performance are two fundamental but conflicting desig objectives in wireless sensor networks. Hence there is an intrinsic tradeoff between network lifetime maximization and application performance maximization. Often application performance correlates to the application data rate obtained in sensor networks. We can thus study this tradeoff by investigating the interactions between the network lifetime maximization problem and the rate allocation problem. Severe bias on the allocated rates of some sensor nodes may exist if only the total throughput of the sensor network is maximized, hence we enforce fairness on source rates of sensor nodes by invoking the network utility maximization (NUM) framework. First we consider the network lifetime as global information shared by sensor nodes. We formulate the network lifetime maximization and fair rate allocation both as constrained maximization problems. By introducing a system parameter, we combine these two objectives into a single weighted objective, and characterize the tradeoff between them. Then we give the optimality condition, and derive a partially distributed algorithm. Also, we identify the similarity between network lifetime maximization and max-min rate allocation in networks. Since the latter one can be approximated using NUM framework, we adopt the same idea for the former one, and approximate the optimal solution in the unified NUM framework. Based on this, an efficient fully distributed algorithm is derived.