Network calculus is a powerful methodology of characterizing queueing processes and has wide applications, but few works on applying it to 802.11 by far. In this paper, we take one of the first steps to analyze the backlog bounds of an 802.11 wireless LAN using stochastic network calculus. In particular, we want to address its effectiveness on bounding backlogs. We model a wireless node as a single server with impairment service based on two best-known models in stochastic network calculus: Jiang's and Ciucu's. Interestingly, we find that the two models can derive equivalent stochastic service curves and backlog bounds in our studied case. We prove that the network-calculus bounds imply stable backlogs as long as the average rate of traffic arrival is less than that of service, indicating the theoretical effectiveness of stochastic network calculus in bounding backlogs. From A. Kumar's 802.11 model, we derive the concrete stochastic service curve of an 802.11 node and its backlog bounds. We compare the derived bounds with ns-2 simulations and find that the former are very loose and we discuss the reasons. And we show that the martingale and independent case analysis techniques can improve the bounds significantly. Our work offers a good reference to applying stochastic network calculus to practical scenarios.
Existing works on the performance of 802.11 focus primarily on the throughput and capacity. Bianchi proposed a Markov chain model of 802.11 @cite_20 . A. proposed a probability model of 802.11 @cite_23 which simplifies Bianchi's model and it is shown to be quite accurate even in the multi-hop case @cite_26 . In our paper, we adopt A. Kumar's model to derive the stochastic service curves of an 802.11 node. There are some works on 802.11 queueing analysis based on traditional queueing theory. assumed Poisson traffic arrival and proposed an M G 1 queueing model of 802.11 @cite_19 . Tickoo proposed a G G 1 queueing model of 802.11 @cite_21 @cite_6 . Bredel and Fidler modeled the 802.11 DCF as a fluid GPS scheduler yielding a fair average service rate @cite_25 . And Ciucu analyzed the non-asymptotic throughput and delay distribution in multi-hop wireless networks by network calculus approach considering Aloha systems @cite_24 .
@cite_26 @cite_21 @cite_6 @cite_24 @cite_19 @cite_23 @cite_25 @cite_20
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Analytical models of IEEE 802.11-based WLANs are invariably based on approximations, such as the well-known mean-field approximations proposed by Bianchi for saturated nodes. In this paper, we provide a new approach for modeling the situation when the nodes are not saturated. We study a State Dependent Attempt Rate (SDAR) approximation to model M queues (one queue per node) served by the CSMA CA protocol as standardized in the IEEE 802.11 DCF. The approximation is that, when n of the M queues are non-empty, the attempt probability of the n non-empty nodes is given by the long-term attempt probability of n saturated nodes as provided by Bianchi's model. This yields a coupled queue system. When packets arrive to the M queues according to independent Poisson processes, we provide an exact model for the coupled queue system with SDAR service. The main contribution of this paper is to provide an analysis of the coupled queue process by studying a lower dimensional process and by introducing a certain conditional independence approximation. We show that the numerical results obtained from our finite buffer analysis are in excellent agreement with the corresponding results obtained from ns-2 simulations. We replace the CSMA CA protocol as implemented in the ns-2 simulator with the SDAR service model to show that the SDAR approximation provides an accurate model for the CSMA CA protocol. We also report the simulation speed-ups thus obtained by our model-based simulation. Analysis of the 802.11 CSMA CA mechanism has received considerable attention recently. Bianchi presented an analytic model under a saturated traffic assumption. Bianchi's model is accurate, but typical network conditions are nonsaturated and heterogeneous. We present an extension of his model to a nonsaturated environment. The model's predictions, validated against simulation, accurately capture many interesting features of nonsaturated operation. For example, the model predicts that peak throughput occurs prior to saturation. Our model allows stations to have different traffic arrival rates, enabling us to address the question of fairness between competing flows. Although we use a specific arrival process, it encompasses a wide range of interesting traffic types including, in particular, VoIP. This paper proposes the analytical expressions for the IEEE 802.11 string-topology multi-hop networks using Markov-chain model. For achieving that, the proposed analysis procedure includes two proposals, which are: (i) Bianchi's Markov-chain model is modified for considering a relationship between the backoff timer and frame length, and (ii) the interferences, such as hidden node collision and carrier sensing, among network nodes are expressed by merging the proposed Markov-chain model and airtime expression. The analytical expressions are verified by comparisons with simulation results. We consider the following two views of an IEEE 802.11 based Wireless LAN: (i) as seen by the WLAN medium and (ii) as seen by a user. In the system centric view, we model the WLAN medium as a server that allocates its resources to users in a round Robin manner. This resource sharing model not only provides a simple model for the system, it also enables us to derive the channel service rate and the total delay incurred in transmitting a packet. For Poisson arrivals, the resource sharing model takes the form of an M G 1 PS system with the mean delay being the same as that in an equivalent M M 1 system. We then take a user centric view and model each user's queue as a separate G G 1 queue. We derive the probability distributions for the different delay sources, i.e., random back-off time, random number of collisions and random number of successful transmissions from other users. This user centric model can provide insights into understanding access and queuing delays in 802.11 DCF. Finally, we discuss the utility of these models for functions such as capacity analysis, admission control and QoS enforcement. We present an analytic model for evaluating the queueing delays at nodes in an IEEE 802.11 MAC based wireless network. The model can account for arbitrary arrival patterns, packet size distributions and number of nodes. Our model gives closed form expressions for obtaining the delay and queue length characteristics. We model each node as a discrete time G G 1 queue and derive the service time distribution while accounting for a number of factors including the channel access delay due to the shared medium, impact of packet collisions, the resulting backoffs as well as the packet size distribution. The model is also extended for ongoing proposals under consideration for 802.11e wherein a number of packets may be transmitted in a burst once the channel is accessed. Our analytical results are verified through extensive simulations. The results of our model can also be used for providing probabilistic quality of service guarantees and determining the number of nodes that can be accommodated while satisfying a given delay constraint. This paper presents an analytic model for evaluating the MAC layer queueing delays at wireless nodes using the distributed coordination function of IEEE 802.11 MAC specifications. Our model is valid for finite loads and can account for arbitrary arrival patterns, packet size distributions and number of nodes. Each node is modeled as a discrete time G G 1 queue and we obtain closed form expressions for the delay and queue length characteristics at each node. We derive the service time distribution for the packets at each node while accounting for a number of factors including the channel access delay due to the shared medium, impact of packet collisions, the resulting backoffs as well as the packet size distribution. Our analytical results are verified through extensive simulations and are more accurate than existing models. Accurate analytical expressions of delay and packet reception probabilities, and energy consumption of duty-cycled wireless sensor networks with random medium access control (MAC) are instrumental for the efficient design and optimization of these resource-constrained networks. Given a clustered network topology with unslotted IEEE 802.15.4 and preamble sampling MAC, a novel approach to the modeling of the delay, reliability, and energy consumption is proposed. The challenging part in such a modeling is the random MAC and sleep policy of the receivers, which prevents to establish the exact time of data packet transmission. The analysis gives expressions as function of sleep time, listening time, traffic rate and MAC parameters. The analytical results are then used to optimize the duty cycle of the nodes and MAC protocol parameters. The approach provides a significant reduction of the energy consumption compared to existing solutions in the literature. Monte Carlo simulations by ns2 assess the validity of the analysis. In multi-hop ad hoc networks, stations may pump more traffic into the networks than can be supported, resulting in high packet-loss rate, re-routing instability and unfairness problems. This paper shows that controlling the offered load at the sources can eliminate these problems. To verify the simulation results, we set up a real 6-node multi-hop network. The experimental measurements confirm the existence of the optimal offered load. In addition, we provide an analysis to estimate the optimal offered load that maximizes the throughput of a multi-hop traffic flow. We believe this is a first paper in the literature to provide a quantitative analysis (as opposed to simulation) for the impact of hidden nodes and signal capture on sustainable throughput. The analysis is based on the observation that a large-scale 802.11 network with hidden nodes is a network in which the carrier-sensing capability breaks down partially. Its performance is therefore somewhere between that of a carrier-sensing network and that of an Aloha network. Indeed, our analytical closed-form solution has the appearance of the throughput equation of the Aloha network. Our approach allows one to identify whether the performance of an 802.11 network is hidden-node limited or spatial-reuse limited.