16916124
Improving throughput by tuning carrier sensing in 802.11 wireless networks
In this paper, we study the impacts of physical carrier sensing and channel rate on the throughput of 802.11 wireless networks with chain topology. Firstly, we show that by adopting different carrier sensing thresholds for the RTS and CTS transmissions, the blocking problem caused by exposed terminals can be greatly alleviated. In 802.11 wireless networks with this modification, the spatial reuse ratio under certain channel rates can be increased to 13, which is the highest value to our best knowledge. Secondly, in multi-rate networks, we demonstrate that 13 is still the best value of spatial reuse ratio in terms of maximizing the achievable data rate under certain conditions. Thirdly, this paper proposes a new method to address the intra-flow contention by decreasing the carrier sensing threshold of the source node. This method requires less response time than that of the traditional method which adjusts the backoff window size. Finally, extensive simulations are implemented in NS2 and the results show that our scheme significantly improves the throughput of 802.11 wireless networks with chain topology.
45281264
Optimum Power Assignment for Maximum Throughput in CDMA Personal Communication Systems with Integrated Voice/Data Traffic
A CDMA personal communication system with integrated voice/data traffic is considered, in which the link error performance is controlled according to the voice error rate requirement, and the acceptable data traffic error rate is ensured by ARQ. Optimum power assignment (or allocation) between voice and data users is investigated to maximize the total system throughput. A graphical method (the tangent method) is described to obtain this optimum power allocation. The maximum throughput is expressed as a function of other system parameters. The tangent method is further used to measure the impact of transmission quality on the maximum data throughput. Numerical results and a design example are given for a power controlled wideband IS-95 type wireless personal communication system.
4966601
Power controlled network protocols for Multi-Rate ad hoc networks
In this paper, we propose for multi-rate ad hoc networks a cross-layer design using power control, called MRPC. MRPC consists of two parts. First, we propose a multi-rate power controlled MAC protocol, called MRPC-MAC. By carefully controlling the transmission power, it can enable concurrent transmissions, which is otherwise impossible for the IEEE 802.11 standard. Second, we propose a multi-rate power controlled routing protocol, called MRPC-Routing. Different from traditional routing protocols, MRPC-Routing is not intended to find end-to-end paths, rather, it determines the next hop right before transmitting packets at the MAC layer. In this protocol, it uses the effective transport capacity as the routing metric such that short links with high bandwidth are preferred and more concurrent transmissions can be enabled. Having these coupled power controlled MAC protocol and routing protocol, MRPC can greatly improve the spatial reuse and the network throughput. Simulation results also show MRPC-MAC, MRPC-routing, and especially MRPC, can improve the network throughput significantly.
7724198
Admission Control for Providing QoS in Wireless Mesh Networks
An admission control algorithm should be properly designed to guarantee the quality of service (QoS) in wireless mesh networks (WMNs). Based on channel business ratio, an admission control algorithm (ACA) is proposed to provide QoS for realtime and non-realtime traffic. For realtime traffic, all the nodes on a route make the admission control decision based on the estimation of available bandwidth. For non-realtime traffic, a rate adaption algorithm is proposed to adjust the sending rates of the source nodes to prevent a network from entering a saturated status. Finally, we demonstrate the effectiveness by simulations in NS-2.
5315359
Fuzzy interpolative reasoning via scale and move transformations
Interpolative reasoning does not only help reduce the complexity of fuzzy models but also makes inference in sparse rule-based systems possible. This paper presents an interpolative reasoning method by means of scale and move transformations. It can be used to interpolate fuzzy rules involving complex polygon, Gaussian or other bell-shaped fuzzy membership functions. The method works by first constructing a new inference rule via manipulating two given adjacent rules, and then by using scale and move transformations to convert the intermediate inference results into the final derived conclusions. This method has three advantages thanks to the proposed transformations: 1) it can handle interpolation of multiple antecedent variables with simple computation; 2) it guarantees the uniqueness as well as normality and convexity of the resulting interpolated fuzzy sets; and 3) it suggests a variety of definitions for representative values, providing a degree of freedom to meet different requirements. Comparative experimental studies are provided to demonstrate the potential of this method
36670732
Selection of features in transparent fuzzy modelling
The paper discusses a methodology for feature selection in the context of fuzzy modelling that simplifies domains, all the while retaining the expressive power of linguistic fuzzy rules. The approach is described with respect to a few example applications.
25318357
Towards Fuzzy Compositional Modelling
Compositional modelling (CM) has been applied to synthesize automatically plausible scenarios in many problem domains with promising results. However, it is assumed that the generic and reusable model fragments within the knowledge base can all be expressed by precise and crisp information. This paper presents an initial attempt to extend the existing CM work to allow the generation of scenario spaces which are capable of representing, storing and supporting inference about imprecise or ill-defined data, by the use of fuzzy sets. A knowledge representation formalism for both fuzzy parameters and fuzzy constraints is incorporated into the representation of conventional model fragments. The applicability of the proposed method is illustrated by means of a simple worked example for supporting crime investigation.
993211
Approximation-based feature selection and application for algae population estimation
Abstract
This paper presents a data-driven approach for feature selection to address the common problem of dealing with high-dimensional data. This approach is able to handle the real-valued nature of the domain features, unlike many existing approaches. This is accomplished through the use of fuzzy-rough approximations. The paper demonstrates the effectiveness of this research by proposing an estimator of algae populations, a system that approximates, given certain water characteristics, the size of algae populations. This estimator significantly reduces computer time and space requirements, decreases the cost of obtaining measurements and increases runtime efficiency, making itself more viable economically. By retaining only information required for the estimation task, the system offers higher accuracy than conventional estimators. Finally, the system does not alter the domain semantics, making any distilled knowledge human-readable. The paper describes the problem domain, architecture and operation of the system, and provides and discusses detailed experimentation. The results show that algae estimators using a fuzzy-rough feature selection step produce more accurate predictions of algae populations in general.

30104568
Rough set-aided keyword reduction for text categorization
The volume of electronically stored information increases exponentially as the state of the art progresses. Automated information filtering (IF) and information retrieval (IR) systems are therefore acquiring rapidly increasing prominence. However, such systems sacrifice efficiency to boost effectiveness. Such systems typically have to cope with sets of vectors of many tens of thousands of dimensions. Rough set (RS) theory can be applied to reducing the dimensionality of data used in IF/IR tasks, by providing a measure of the information content of datasets with respect to a given classification. This can aid IF/IR systems that rely on the acquisition of large numbers of term weights or other measures of relevance. This article investigates the applicability of RS theory to the IF/IR application domain and compares this applicability with respect to various existing TC techniques. The ability of the approach to generalize, given a minimum of training data is also addressed. The background of RS theory is presented, with an illustrative example to demonstrate the operation of the RS-based dimensionality reduction. A modular system is proposed which allows the integration of this technique with a large variety of different IF/IR approaches. The example application, categorization of E-mail messages, is described. Systematic experiments and their results are reported and analyzed.