In this paper, we analyze a large dataset of passive wireless measurements and obtain insights about wireless performance. We monitor 167 homes continuously for 4 months from the vantage point of the gateway, which allows us to capture all the activity on the home wireless network. We report on the makeup of the home wireless network, traffic activity, and performance characteristics. We find that in most homes, a small number of devices account for most of the observed traffic volume and the bulk of this traffic activity occurs in the evenings. Studying link performance, we find that overall, the vast majority of transmissions are carried out at high data rates and the wireless networks have good coverage. We find a small number of episodes where performance is poor; a few homes have a disproportionate number of poor performance reports. Investigating further, we observe that most of these are not caused by poor coverage (pointing to network interference). Our results significantly add to the understanding of home wireless networks and will help ISPs to understand their subscriber networks.

Accurate prediction of wireless network performance is important when performing link adaptation or resource allocation. However, the complexity of interference interactions at MAC and PHY layers, as well as the vast variety of possible wireless configurations make it notoriously hard to design explicit performance models. In this paper, we advocate an approach of “learning by observation” that can remove the need for designing explicit and complex performance models. We use machine learning techniques to learn implicit performance models, from a limited number of real-world measurements. These models do not require to know the internal mechanics of interfering Wi-Fi links. Yet, our results show that they improve accuracy by at least 49% compared to measurement-seeded models based on SINR. To demonstrate that learned models can be useful in practice, we build a new algorithm that uses such a model as an oracle to jointly allocate spectrum and transmit power. Our algorithm is utility-optimal, distributed, and it produces efficient allocations that significantly improve performance and fairness.

It is clear today that mobile video is a major traffic source and that online advertising is a steadily growing business. These trends are leading towards mobile video advertising becoming ubiquitous. We make two contributions towards better understanding mobile video ads and how their impact on mobile device resources can be minimized. We perform the first characterization of a well-defined set of mobile video ads on YouTube, the largest online video service. We then use our findings to design a video ad caching system for smartphones, aiming at minimizing the number of ad downloads to relieve mobile devices from the extra overhead induced by the ever increasing amount of ads. Our trace-driven simulations show that our caching system can save up to 50% data transfer.

Modern mobile devices are equipped with multiple network interfaces, with WiFi to be the preferred choice when it can be associated with an Access Point. Our study using real traces and experiments with 802.11 devices uncovers long idle times, while WiFi is scanning for networks and is listening for incoming traffic. During idle, WiFi consumes a significant amount of energy, which is exacerbated with the 802.11n/ac high speed features (MIMO, channel-bonding). State of the art power saving solutions, including the IEEE 802.11 PSM and SMPS features are user-agnostic, infrastructure-driven and oblivious to the power hungry 802.11n/ac features. As a result they may not save energy in practical settings. To this end, this work presents WiFi idle energy save (WiFi-ies). WiFi-ies leverages user context and behavior information to identify idle times where energy can be saved. We verify WiFi-ies energy savings using trace-driven simulations and prototype implementations on Android smartphones.

Contemporary wireless multihop networks operate much below their capacity due to the poor coordination among transmitting nodes. In this paper, we present XPRESS, a cross-layer backpressure architecture designed to reach the capacity of wireless multihop networks. Instead of a collection of poorly coordinated wireless routers, XPRESS turns a mesh network into a wireless switch. Transmissions over the network are scheduled using a throughput-optimal backpressure algorithm. Realizing this theoretical concept entails several challenges, which we identify and address with a cross-layer design and implementation on top of our wireless hardware platform. In contrast to previous work, we implement and evaluate backpressure scheduling over a TDMA MAC protocol, as it was originally proposed in theory. Our experiments in an indoor testbed show that XPRESS can yield up to 128% throughput gains over 802.11.

We perform an experimental characterization of an indoor MIMO system with directional antennas (our prototype multi-sector antennas). The study reveals that, even without antenna directivity gain, the directionality of signals changes the MIMO channel structure and provides a way to improve MIMO throughput performance. It also shows that it is sufficient for the improvement to consider only a small subset out of all possible antenna direction combinations. Finally, our study shows that it is possible to achieve both throughput gains and interference reduction, thus increasing network spatial reuse.

The IEEE 802.11n standard supports very high physical layer data rates using Multiple Input Multiple Output (MIMO) antenna technologies. Despite such high rates, High Definition (HD) video streaming is still challenging in WLAN deployments. In this paper, we show that the wireless channel probing overhead of existing 802.11n data rate adaptation mechanisms can be detrimental to HD video performance. We propose VARA, a Video-Aware Rate Adaptation protocol that addresses this problem by adapting the frequency and timing of wireless probing to both video encoding rate variations and wireless channel variations. In addition, VARA employs novel strategies that multiplex several Variable Bit Rate (VBR) HD video streams by minimizing their aggregate peak rate requirement. Our experimental evaluations for static and mobile scenarios in a MIMO 802.11n wireless testbed demonstrate the practical benefits of VARA over state-of-the-art 802.11n rate adaptation protocols.

Contemporary wireless multi-hop networks operate much below their capacity due to the poor coordination among transmitting nodes. In this paper we present XPRESS, a cross-layer backpressure architecture designed to reach the full capacity of wireless multi-hop networks. Instead of a collection of poorly coordinated wireless routers, XPRESS turns a mesh network into a wireless switch. Transmissions over the network are scheduled using a throughput-optimal backpressure algorithm. Realizing this theoretical concept entails several challenges, which we identify and address with a cross-layer design and implementation on top of our wireless hardware platform. In contrast to previous work, we implement and evaluate backpressure scheduling over a TDMA MAC protocol, as it was originally proposed in theory. Our experiments in an indoor testbed show that XPRESS can yield up to 128% throughput gains over 802.11.

We introduce a measurement-based optimization framework for topology control in dense 802.11 networks using sectorized antennas. We first formulate a topology control optimization problem, where nodes activate their sectorized antenna patterns to minimize network interference and maximize network capacity. In contrast to previous approaches, our formulation is based on a physical interference model that relies on received signal strength (RSS) measurements on multiple antenna patterns of each link. We then introduce a distributed measurement protocol to measure RSS of these antenna patterns and a greedy distributed topology control protocol that uses this information to achieve topologies of minimal interference. The protocols are experimentally evaluated on a dense 802.11 wireless testbed. Extensive measurements show that the protocols operate very close to optimal and yield significant increase in network throughput compared to omni-directional antennas. These results hold for various traffic scenarios in both wireless LAN and mesh network configurations.

Sectorized antennas provide an attractive solution to increase wireless network capacity through higher spatial reuse. Despite their increasing popularity, the real-world performance characteristics of such antennas in dense wireless mesh networks are not well understood. In this demo, we demonstrate our multi-sector antenna prototypes and their performance through video streaming over an indoor wireless network in the presence of interfering nodes. We use our graphical tool to vary the sender, receiver, and interferer antenna configurations and the resulting performance is directly visible in the video quality displayed at the receiver.

Sectorized antennas can increase wireless network capacity through greater spatial reuse. Despite their increasing popularity, their real-world performance characteristics in dense wireless mesh networks are not well understood. This paper conducts a systematic experimental study on a mesh network testbed using commodity 802.11 hardware and multi-sector antennas.

We present the design, implementation, and evaluation of a Time Division Multiplex (TDM) MAC protocol for multi-hop wireless mesh networks using a programmable wireless platform. Extensive research has been devoted to optimal scheduling algorithms for multi-hop wireless networks assuming a perfect TDM MAC protocol. However, the problem of designing and implementing such a protocol has not received due attention. We introduce a design framework that addresses the three main challenges that comprise this problem: (i) How to calibrate and optimize the TDM MAC protocol parameters given a wireless platform, (ii) how to achieve network-wide synchronization with high accuracy, minimal overhead, and most importantly, bounded delay, and (iii) how to integrate the synchronization algorithm with the TDM MAC protocol state machine using minimal hardware resources. We apply our design framework to our platform and evaluate the resulting TDM MAC protocol through controlled experiments in a wireless mesh testbed. The results demonstrate the protocol's ability to provide fairness and graceful performance degradation under packet losses and multi-hop traffic patterns that arise in mesh network deployments.

We present Cross-layer Unicast Transmission Time (X-UTT), a MAC-aware load-independent link cost metric for 802.11-based wireless mesh networks. X-UTT utilizes information acquired from a network-layer unicast probing system and a MAC-layer monitoring system. It is designed to capture the wireless link capacity and be independent of the load induced by self-interference and cross-interference in a mesh network. We present experiments that validate these two properties on our mesh network testbed. These properties can be further exploited in the design of stable path metrics and routing protocols for wireless mesh networks.

Past approaches to routing in mesh networks either (i) do not account for the MAC-layer interactions between the links in a tractable manner, or (ii) are agnostic to load-balancing across gateways. Our answer to these problems is MaLB (MAC-aware and Load Balanced routing algorithm), a greedy, tractable, and distributed mesh routing algorithm. Since the underlying objective function has high combinatorial complexity, MaLB uses a greedy approach. MaLB finds an optimum routing forest (union of trees rooted at the gateways) by taking into account MAC-layer interaction between links, as well as optimum multi-hop association of mesh nodes to gateways. MaLB builds on top of ETP (Expected Through-Put), a recently proposed MAC-aware routing metric. We also propose a low complexity variant of MaLB called LB (Load Balanced routing algorithm) which performs load balancing in a MAC-agnostic manner. MaLB performs especially well in networks with skewed topologies that result from unplanned mesh network deployment, as well as in the presence of gateway failures. Simulations with an enhanced version of ns-2 show that MaLB results in up to 60% higher throughput than a shortest path algorithm with ETX (Expected Transmission Count). Furthermore, MaLB results in up to 30% improvement over the LB algorithm, as well as a shortest path algorithm with ETT (Expected Transmission Time).

We have built an ad hoc protocol evaluation testbed (APE) in order to perform large-scale, reproducible experiments. APE aims at assessing several different routing protocols in a real-world environment instead of by simulation. We present the APE testbed architecture and report on initial experiments with up to 37 physical nodes that show the reproducibility and scalability of our approach. Several scenario scripts have been written that include strict choreographic instructions to the testers who walk around with ORiNOCO equipped laptops. We introduce a mobility metric called virtual mobility that we use to compare different test runs. This metric is based on the measured signal quality instead of the geometric distance between nodes, hence it reflects how a routing protocol actually perceives the network's dynamics.