In this paper, we present a generic plug-and-play controller that ensures fair and efficient operation of IEEE 802.11 infrastructure wireless local area networks with multiple co-channel access points, without any change to hardware firmware of the network devices. Our controller addresses performance issues of TCP transfers in multi-AP WLANs, by overlaying a coarse time-slicing scheduler on top of a cascaded fair queuing scheduler. The time slices and queue weights, used in our controller, are obtained from the solution of a constrained utility optimization formulation. A study of the impact of coarse time-slicing on TCP is also presented in this paper. We present an improved algorithm for adaptation of the service rate of the fair queuing scheduler and provide experimental results to illustrate its efficacy. We also present the changes that need to be incorporated to the proposed approach, to handle short-lived and interactive TCP flows. Finally, we report the results of experiments performed on a real testbed, demonstrating the efficacy of our controller.
SMARTA @cite_1 is a centralized controller that sets and dynamically adjusts the operating parameters of enterprise WLAN Access Points (e.g., channel frequency and power allocation) to optimize a predefined objective. In @cite_1 , the authors describe several simple tests to estimate the interference environment and obtain a . Based on this conflict-graph, the controller executes channel assignment and power control algorithms that aim to minimize the number of conflicting transmissions. We note that SMARTA focuses on system configuration, and that there are no mechanisms for controlling the rate of traffic on the links. Also, SMARTA does not incorporate the capacity of the wide-area-network (WAN)-WLAN links into its design. Therefore, SMARTA may not perform efficiently in networks with WAN-WLAN traffic. Further, we would like to note that SMARTA is evaluated via simulations and not on a real testbed.
@cite_1
2057556122 2111854969 2129263279 2168941875
Optimally choosing operating parameters for access points in an enterprise wireless LAN environment is a difficult and well-studied problem. Unlike past work, the SMARTA self-managing wireless LAN architecture dynamically adjusts both access point channel assignments and power levels in response to measured changes in the wireless environment to optimize arbitrary objective functions, while taking into account the irregular nature of RF propagation, and working with unmodified legacy clients. We evaluate the SMARTA architecture through simulation and show that our solution is not only feasible, but also provides significant improvements over existing approaches. For example, in a realistic scenario, SMARTA can provide 50 more throughput and 40 lower mean per-packet delay than a hand-optimized configuration. Moreover, SMARTA can automatically reconfigure channels and power levels in response to both small and large changes in the RF environment due to client movement. The MAC layer of the 802.11 standard, based on the CSMA CA mechanism, specifies a set of parameters to control the aggressiveness of stations when trying to access the channel. However, these parameters are statically set independently of the conditions of the WLAN (e.g. the number of contending stations), leading to poor performance for most scenarios. To overcome this limitation previous work proposes to adapt the value of one of those parameters, namely the CW, based on an estimation of the conditions of the WLAN. However, these approaches suffer from two major drawbacks: i) they require extending the capabilities of standard devices or ii) are based on heuristics. In this paper we propose a control theoretic approach to adapt the CW to the conditions of the WLAN, based on an analytical model of its operation, that is fully compliant with the 802.11e standard. We use a Proportional Integrator controller in order to drive the WLAN to its optimal point of operation and perform a theoretic analysis to determine its configuration. We show by means of an exhaustive performance evaluation that our algorithm maximizes the total throughput of the WLAN and substantially outperforms previous standard-compliant proposals. We propose an efficient client-based approach for channel management (channel assignment and load balancing) in 802.11-based WLANs that lead to better usage of the wireless spectrum. This approach is based on a “conflict set coloring” formulation that jointly performs load balancing along with channel assignment. Such a formulation has a number of advantages. First, it explicitly captures interference effects at clients. Next, it intrinsically exposes opportunities for better channel re-use. Finally, algorithms based on this formulation do not depend on specific physical RF models and hence can be applied efficiently to a wide-range of in-building as well as outdoor scenarios. We have performed extensive packet-level simulations and measurements on a deployed wireless testbed of 70 APs to validate the performance of our proposed algorithms. We show that in addition to single network scenarios, the conflict set coloring formulation is well suited for channel assignment where multiple wireless networks share and contend for spectrum in the same physical space. Our results over a wide range of both simulated topologies and in-building testbed experiments indicate that our approach improves application level performance at the clients by upto three times (and atleast 50 ) in comparison to current best-known techniques. In this paper, we design and test the performance of a distributed and adaptive resource management controller, which allows the optimal exploitation of cognitive radio and soft-input soft-output data fusion in vehicular access networks. The goal is to allow energy and computing-limited car smartphones to utilize the available vehicle-to-infrastructure (V2I) WiFi connections for performing traffic offloading toward local or remote clouds by opportunistically acceding to a spectral-limited wireless backbone built up by multiple roadside units (i.e., cloudlets). We cast the resource management problem into a suitable constrained stochastic network utility maximization problem and derive the optimal cognitive resource manager that dynamically allocates the access time windows at the serving roadside units (i.e., the access points), together with the access rates and traffic flows at the served vehicular clients (i.e., the secondary users of the wireless backbone). The developed controller provides hard reliability guarantees to the cloud service provider (i.e., the primary user of the wireless backbone) on a per-slot basis. Furthermore, it is able to acquire context information about the currently available bandwidth–energy resources to quickly adapt to the mobility-induced abrupt changes in the state of the vehicular network.