A framework for cross-layer measurements in wireless networks

This paper formulates a framework for wireless network performance measurements with the scope of being as generic as possible. The methodology utilises a cross-layer approach in order to address the limitations of traditional layered techniques. A lot of work in the research community uses the channel power (Cp) to predict performance metrics in higher layers. There are currently two methods to measure Cp; either by using a spectrum analyser or from WiFi card information (RSSI). The paper discusses the correct configuration of a spectrum analyser (SA), to measure Cp. This paper, also provides a comparison of both SA and RSSI results produced inside an anechoic chamber for three different applications. The behaviour of the RSSI values showed significant discrepancy with both the SA results and what was intuitively expected. The results pinpoint the necessity of a cross-layer approach and the importance of carefully selected and positioned equipment for the accuracy of the measurements

A Framework for Cross-Layer Measurements in Wireless Networks

This paper formulates a framework for wireless network performance measurements with the scope of being as generic as possible. The methodology utilises a cross-layer approach in order to address the limitations of traditional layered techniques. A lot of work in the research community uses the channel power (Cp) to predict performance metrics in higher layers. There are currently two methods to measure Cp; either by using a spectrum analyser or from WiFi card information (RSSI). The paper discusses the correct configuration of a spectrum analyser (SA), to measure Cp. This paper, also provides a comparison of both SA and RSSI results produced inside an anechoic chamber for three different applications. The behaviour of the RSSI values showed significant discrepancy with both the SA results and what was intuitively expected. The results pinpoint the necessity of a cross-layer approach and the importance of carefully selected and positioned equipment for the accuracy of the measurements

An experimental analysis of the effects of noise on Wi-Fi video streaming

Wireless networks such as WiFi suffer communication performance issues in addition to those seen on wired networks due to the characteristics of the radio communication channel used by their Physical Layers (PHY). Understanding these issues is a complex but necessary task given the importance of wireless networks for the transfer of wide ranging packet steams including video as well as traditional data. Simulators are not accurate enough to allow all the intricacies of such communication to be accurately understood, especially when complex interactions between the protocols of different layers occurs. The paper suggests cross layer measurement as a solution to the problem of understanding and analysis of such complex communication issues and proposes a framework in which appropriate performance measurements can be made from a WiFi network supporting a video streaming application. The framework has been used to collect these measurements at the PHY, MAC, Transport and Application layers. Analysis of the collected measurements has allowed the effects of noise interference at the PHY to be related to the perceived performance at the Application Layer for a video streaming application. This has allowed the effect of the SNR on the download time of a video sequence to be studied

IEEE 802.11 wireless LAN traffic analysis: a cross-layer approach

textThe deployment of broadband wireless data networks, e.g., wireless local area networks (WLANs) [29], experienced tremendous growth in the last several years, and this trend is continuously gaining momentum. In fact, WLAN is becoming an indispensable component of the modern telecommunication infrastructure. Despite this optimistic outlook, however, little is known about the impact of the wireless channel on the characteristics of WLAN traffic. This dissertation characterizes the correlation structures of WLAN channel with traffic statistics from a cross-layer point of view, and provides new measurement methodologies and statistical models for WLAN networks. Currently WLAN standards are designed within the paradigm of the layered network architecture. For example, the architecture of IEEE 802.11 vii is almost identical to the Ethernet. However, wireless networks are fundamentally different from their wired peers due to the shift of transmission media from cables to over-the-air radio waves. This transition exposes wireless systems to the influence of radio propagation, and more importantly, to the temporal and spacial fluctuations of the radio channel that can actually be propagated up to upper layers. However, the current WLAN architecture isolates network layers, and largely ignores this impact. Therefore, we believe that a cross-layer based approach is necessary to understand and reflect this underlying impact of the channel to the upper layers of the network, especially in relation to WLAN traffic behavior. Measurement is one of the fundamental tools used to quantify radio propagation. As part of this dissertation, a complete framework for a measurement methodology, including hardware, software, and measurement procedures, is established. Characteristics of the propagation channel are estimated from measurement data, and the channel knowledge is applied to the upper layers for more realistic and accurate modeling. In WLAN environments, knowledge of the traffic characteristics is essential for proper network provisioning, and for improving the performance of the IEEE 802.11 standard and network devices, e.g., to design improved MAC schemes, or to build better buffer scheduling algorithms with channel knowledge, etc. Built upon extensive WLAN traffic traces, this dissertation work presents cross-layer models for WLAN throughput predictions, traffic statistics, and link layer characteristics. viii The main goal of this dissertation work is to experiment with and develop new methods for identifying channel characteristics. Thereby utilizing this knowledge, we show how to predict and improve WLAN performance. Within the framework of the developed cross-layer measurement methodology, we conducted extensive measurements in different physical environments and different settings such as office buildings and stores, and (1) show that the impact of the propagation channel can be quantified by using simple large scale channel metric (throughput over longer period of time), and (2) also present the existence of a Doppler effect within today’s WLAN packet traffic at sub-second time scales. We also show the real-world WLAN usage pattern from our measurement results. From this data, we conclude that the key issues to study WLAN networks include accurate site-specific propagation channel modeling and real-time autonomous traffic control.Electrical and Computer Engineerin

CrossTrace: Cross-Layer Measurement for IEEE 802.11 Wireless Testbeds

Abstract. In this paper, we introduce and evaluate CrossTrace, a framework for performing cross-layer measurements in IEEE 802.11 based wireless networks. CrossTrace allows tracing of parameters at MAC-, routing and transport layer in a controlled environment and in a repeatable manner. Using CrossTrace, we conduct a comprehensive measurement study in a miniaturized testbed, in which we analyze the behavior of the IEEE 802.11 MAC-layer with respect to signal strength and bit error rate. We derive the delivery probability and bit error rate dependent on signal strength and MAC-layer datarate with and without interfering background traffic. We show that even moderate background traffic can significantly degrade network performance. Such measurements may help to optimize the orchestration between the different protocol layers and may alleviate the development of new cross-layer designs

Data-driven design of intelligent wireless networks: an overview and tutorial

Data science or "data-driven research" is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i) clarifies when, why and how to use data science in wireless network research; (ii) provides a generic framework for applying data science in wireless networks; (iii) gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv) illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v) provides the reader the necessary datasets and scripts to go through the tutorial steps themselves

Cross-layer design of multi-hop wireless networks

MULTI -hop wireless networks are usually defined as a collection of nodes equipped with radio transmitters, which not only have the capability to communicate each other in a multi-hop fashion, but also to route each others’ data packets. The distributed nature of such networks makes them suitable for a variety of applications where there are no assumed reliable central entities, or controllers, and may significantly improve the scalability issues of conventional single-hop wireless networks. This Ph.D. dissertation mainly investigates two aspects of the research issues related to the efficient multi-hop wireless networks design, namely: (a) network protocols and (b) network management, both in cross-layer design paradigms to ensure the notion of service quality, such as quality of service (QoS) in wireless mesh networks (WMNs) for backhaul applications and quality of information (QoI) in wireless sensor networks (WSNs) for sensing tasks. Throughout the presentation of this Ph.D. dissertation, different network settings are used as illustrative examples, however the proposed algorithms, methodologies, protocols, and models are not restricted in the considered networks, but rather have wide applicability. First, this dissertation proposes a cross-layer design framework integrating a distributed proportional-fair scheduler and a QoS routing algorithm, while using WMNs as an illustrative example. The proposed approach has significant performance gain compared with other network protocols. Second, this dissertation proposes a generic admission control methodology for any packet network, wired and wireless, by modeling the network as a black box, and using a generic mathematical 0. Abstract 3 function and Taylor expansion to capture the admission impact. Third, this dissertation further enhances the previous designs by proposing a negotiation process, to bridge the applications’ service quality demands and the resource management, while using WSNs as an illustrative example. This approach allows the negotiation among different service classes and WSN resource allocations to reach the optimal operational status. Finally, the guarantees of the service quality are extended to the environment of multiple, disconnected, mobile subnetworks, where the question of how to maintain communications using dynamically controlled, unmanned data ferries is investigated