Assessing the fidelity of COTS 802.11 sniffers

Proceedings of: 2009 IEEE INFOCOM, 19 – 25 April 2009, Rio de Janeiro, BrazilRecent measurement studies have analyzed WLAN performance by means of wireless sniffers that passively capture transmitted frames. Also, for relatively large (enterprise) WLAN scenarios, previous work has investigated multi-sniffer deployments with devices placed far apart in order to capture all traffic in the network (even frames transmitted simultaneously by different nodes at non-interfering locations). However, for both these single- and multi-sniffer scenarios, little attention has been given to the fidelity of an individual device, i.e., the ability of a given sniffer to capture all frames that could have been captured by a more faithful device. We assess this fidelity (a term we make precise in this paper) by running controlled experiments inside an anechoic chamber and analyzing the similarities and differences between the trace file from the device under study and those of additional "shadow" devices placed in its close proximity. Our results show that fidelity varies significantly across sniffers, both quantitatively and qualitatively, and that performance may also depend on the nature of the experiment under study and on slight changes of the sniffer position.European Community's Seventh Framework ProgramThis work was funded in part by the National Science Foundation under grants, EEC-0313747 001, ANI-0325868, and EIA-0080119, and by the Ministry of Education and Science of Spain, under a José Castillejo grant, and POSEIDON project (TSI2006-12507-C03-01)Publicad

Predicting expected TCP throughput using genetic algorithm

Predicting the expected throughput of TCP is important for several aspects such as e.g. determining handover criteria for future multihomed mobile nodes or determining the expected throughput of a given MPTCP subflow for load-balancing reasons. However, this is challenging due to time varying behavior of the underlying network characteristics. In this paper, we present a genetic-algorithm-based prediction model for estimating TCP throughput values. Our approach tries to find the best matching combination of mathematical functions that approximate a given time series that accounts for the TCP throughput samples using genetic algorithm. Based on collected historical datapoints about measured TCP throughput samples, our algorithm estimates expected throughput over time. We evaluate the quality of the prediction using different selection and diversity strategies for creating new chromosomes. Also, we explore the use of different fitness functions in order to evaluate the goodness of a chromosome. The goal is to show how different tuning on the genetic algorithm may have an impact on the prediction. Using extensive simulations over several TCP throughput traces, we find that the genetic algorithm successfully finds reasonable matching mathematical functions that allow to describe the TCP sampled throughput values with good fidelity. We also explore the effectiveness of predicting time series throughput samples for a given prediction horizon and estimate the prediction error and confidence.Peer ReviewedPostprint (author's final draft

Network Traffic Aware Smartphone Energy Savings

In today\u27s world of ubiquitous Smartphone use, extending the battery life has become an important issue. A significant contributor to battery drain is wireless networking. Common usage patterns expect Smartphones to maintain a constant Internet connection which exacerbates the problem.;Our research entitled A Network Traffic Approach to Smartphone Energy Savings focuses on extending Smartphone battery life by investigating how network traffic impacts power management of wireless devices. We explore 1) Real-time VoIP application energy savings by exploiting silence periods in conversation. WiFi is opportunistically placed into low power mode during Silence periods. 2.) The priority of Smartphone Application network traffic is used to modifiy WiFi radio power management using machine learning assisted prioritization. High priority network traffic is optimized for performance, consuming more energy while low priority network traffic is optimized for energy conservation. 3.) A hybrid multiple PHY, MAC layer approach to saving energy is also utilized. The Bluetooth assisted WiFi approach saves energy by combining high power, high throughput WiFi with low power, lower throughput Bluetooth. The switch between Bluetooth and WiFi is done opportunistically based upon the current data rate and health of the Bluetooth connection.;Our results show that application specific methods for wireless energy savings are very effective. We have demonstrated energy savings exceeding 50% in generic cases. With real-time VoIP applications we have shown upwards of 40% energy savings while maintaining good call quality. The hybrid multiple PHY approach saves more than 25% energy over existing solutions while attaining the capability of quickly adapting to changes in network traffic

W-NINE: a two-stage emulation platform for mobile and wireless systems

More and more applications and protocols are now running on wireless networks. Testing the implementation of such applications and protocols is a real challenge as the position of the mobile terminals and environmental effects strongly affect the overall performance. Network emulation is often perceived as a good trade-off between experiments on operational wireless networks and discrete-event simulations on Opnet or ns-2. However, ensuring repeatability and realism in network emulation while taking into account mobility in a wireless environment is very difficult. This paper proposes a network emulation platform, called W-NINE, based on off-line computations preceding online pattern-based traffic shaping. The underlying concepts of repeatability, dynamicity, accuracy and realism are defined in the emulation context. Two different simple case studies illustrate the validity of our approach with respect to these concepts

Robust Sensor Networks in Homes via Reactive Channel Hopping

Home area networks (HANs) consisting of wireless sensors have emerged as the enabling technology for important applications such as smart energy and assisted living. A key challenge faced in deploying robust wireless sensor networks (WSNs) for home automation applications is the need to provide long-term, reliable operation in the face of the varied sources of interference found in typical residential settings. To better understand the channel dynamics in these environments, we performed an in-depth empirical study of the performance of HANs in ten real-life apartments. Our empirical study leads to several key insights into designing robust HANs for residential environments. For example, we discover that there is not always a persistently good channel over 24 hours in many apartments; that reliability is strongly correlated across adjacent channels; and that interference does not exhibit cyclic behavior at daily or weekly timescales. Nevertheless, reliability can be maintained through a small number of channel hops. Based on these insights, we propose Adaptive and Robust Channel Hopping (ARCH) protocol, a lightweight receiver-oriented protocol which handles the dynamics of residential environments by reactively channel hopping when channel conditions have degraded. We evaluate our approach through a series of simulations based on real data traces as well as a testbed deployment in real-world apartments. Our results demonstrate that ARCH can reduce the number of packet retransmissions by a median of 42.3% compared to using a single, fixed wireless channel, and can enable up to a 2.2 X improvement in delivery rate on the most unreliable links in our experiment. Due to ARCH\u27s lightweight reactive design, this improvement in reliability is achieved with an average of 6 or fewer channel hops per link per day

From Map to Dist: the Evolution of a Large-Scale Wlan Monitoring System

The edge of the Internet is increasingly becoming wireless. Therefore, monitoring the wireless edge is important to understanding the security and performance aspects of the Internet experience. We have designed and implemented a large-scale WLAN monitoring system, the Distributed Internet Security Testbed (DIST), at Dartmouth College. It is equipped with distributed arrays of “sniffers” that cover 210 diverse campus locations and more than 5,000 users. In this paper, we describe our approach, designs and solutions for addressing the technical challenges that have resulted from efficiency, scalability, security, and management perspectives. We also present extensive evaluation results on a production network, and summarize the lessons learned

Cross-layer wireless bit rate adaptation

This paper presents SoftRate, a wireless bit rate adaptation protocol that is responsive to rapidly varying channel conditions. Unlike previous work that uses either frame receptions or signal-to-noise ratio (SNR) estimates to select bit rates, SoftRate uses confidence information calculated by the physical layer and exported to higher layers via the SoftPHY interface to estimate the prevailing channel bit error rate (BER). Senders use this BER estimate, calculated over each received packet (even when the packet has no bit errors), to pick good bit rates. SoftRate's novel BER computation works across different wireless environments and hardware without requiring any retraining. SoftRate also uses abrupt changes in the BER estimate to identify interference, enabling it to reduce the bit rate only in response to channel errors caused by attenuation or fading. Our experiments conducted using a software radio prototype show that SoftRate achieves 2X higher throughput than popular frame-level protocols such as SampleRate and RRAA. It also achieves 20% more throughput than an SNR-based protocol trained on the operating environment, and up to 4X higher throughput than an untrained SNR-based protocol. The throughput gains using SoftRate stem from its ability to react to channel variations within a single packet-time and its robustness to collision losses.National Science Foundation (U.S.) (Grant CNS-0721702)National Science Foundation (U.S.) (Grant CNS-0520032)Foxconn International Holdings Ltd