4 research outputs found

    Hidden Terminal Detection in Wide-Area 802.11 Wireless Networks

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    The hidden terminal problem is an important issue in wireless networks based on the CSMA medium access control scheme. Hidden terminals pose a complex challenge to network operators trying to identify the underlying cause of performance issues. This thesis describes new methods for the detection and measurement of the hidden terminal problem in wireless networks based on commodity hardware and software platforms. These new methods allow network operators to identify areas of a network where hidden terminals are likely to exist; detect instances of the hidden terminal problem occurring; and estimate the total impact hidden terminals are having on the performance of the network. A new framework for measurement of wireless networks is described which provides a new approach to wireless measurement on Linux based wireless routers. The new framework is used to implement the methods and they are deployed across an operational commercial wireless network and are shown to be useful

    In-kernel passive measurement of the performance impact of hidden terminals in 802.11 wireless networks

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    The negative performance impact of the presence of hidden terminals in wireless networks has been well know for decades. Despite much research in the area, many deployed networks continue to suffer a performance penalty because of hidden terminals. Ad hoc wireless networks are particularly susceptible to hidden terminal collisions because there are fewer opportunities to plan the network in a way that avoids or reduces the number of hidden terminals. Measuring the presence of hidden terminals and the impact they are having on performance is difficult, especially in a network of many nodes. Without such measurements, the users and operators of wireless networks can not tell if performance problems are caused by hidden terminals or some other problem. We introduce new methodology that can detect the presence of hidden terminals and estimate the performance impact they are causing. The methodology requires no additional hardware and is suitable for wide scale deployment and long term operation. The approach is based on in-kernel instrumentation of the wireless network stack. The design, implementation, and testing of the approach are covered. Results from in-lab testing and the measurement of a live commercial 802.11 network are also presented, including a case study where performance was significantly improved

    Using the IEEE 802.11 frame check sequence as a pseudo random number for packet sampling in wireless networks

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    Low power devices such as common wireless router platforms are not capable of performing reliable full packet capture due to resource constraints. In order for such devices to be used to perform link-level measurement on IEEE 802.11 networks, a packet sampling technique is required in order to reliably capture a representative sample of frames. The traditional Berkeley packet filter mechanism found in UNIX-like operating systems does not directly support packet sampling as it provides no way of generating pseudo-random numbers and does not allow a filter program to keep state between invocations. This paper explores the use of the IEEE 802.11 frame check sequence as a source of pseudo-random numbers for use when deciding whether to sample a packet. This theory is tested by analysing the distribution of frame check sequences from a large, real world capture. Finally, a BPF program fragment is presented which can be used to efficiently select packets for sampling

    The efficacy of path loss models for fixed rural wireless links

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    In this paper we make use of a large set of measurements from a production wireless network in rural New Zealand to analyze the performance of 28 path loss prediction models, published over the course of 60 years. We propose five metrics to determine the performance of each model. We show that the state of the art, even for the “simple” case of rural environments, is surprisingly ill-equipped to make accurate predictions. After combining the best elements of the best models and hand-tuning their parameters, we are unable to achieve an accuracy of better than 12 dB root mean squared error (RMSE)—four orders of magnitude away from ground truth
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