As powerline communication (PLC) technology does not require dedicated cabling and network setup, it can be used to easily connect multitude of IoT devices deployed in enterprise environments for sensing and control related applications. IEEE has standardized the PLC protocol in IEEE 1901, also known as HomePlug AV (HPAV) which has been widely adopted in mainstream PLC devices. A key weakness of HPAV protocol is that it does not support spectrum sharing. Currently, each link in an HPAV PLC network operates over the whole available spectrum, and only one link can operate at any time within a single collision domain. In this work, through an extensive measurement study of HPAV PLCs in a real enterprise environment using commodity off-the-shelf (COTS) HPAV PLC devices, we discover that spectrum sharing can significantly benefit enterprise level PLC networks. To this end, we propose a distributed spectrum sharing technique for enterprise HPAV PLC networks, and show that fine-grained distributed spectrum sharing on top of current HPAV MAC protocols can significantly boost the aggregated and per-link throughput, by allowing multiple PLC links to communicate concurrently, while requiring only a few modifications to the existing HPAV devices and protocols.

In recent years, it clearly emerged an increasing importance of experimental validation of innovative wireless solutions and applications, due to the increasing complexity of the wireless ecosystem. The WiNTECH workshop explicitly deals with methodological and technical issues that have to be faced for defining, running, controlling and benchmarking experiments on wireless solutions. The workshop, that this year reach the 13th edition, has a consolidated tradition for bringing together an important number of researchers and industry players working in different aspects of experimental wireless communications. The workshop will serve as a forum for sharing experiences and results with real testbeds, experimental evaluation, prototyping and empirical characterization of wireless technologies.

As powerline communication (PLC) technology does not require dedicated cabling and network setup, it can be used to easily connect multitude of IoT devices deployed in enterprise environments for sensing and control related applications. IEEE has standardized the PLC protocol in IEEE 1901, also known as HomePlug AV (HPAV), which is widely adopted PLC standard. A key weakness of HPAV protocol is that it does not support any spectrum sharing strategies. Currently, each link in an HPAV PLC network operates over the whole available spectrum, and only one link can operate at any time within a single collision domain. We conducted a large scale measurement study using commodity HPAV PLC devices and analyzed channel characteristics of PLC networks in a real enterprise environment across space, time, and spectral dimensions. Based on our findings, we propose a distributed spectrum sharing technique for enterprise PLC networks, and show that fine-grained distributed spectrum sharing on top of current HPAV MAC protocols can boost the aggregated and per-link throughput by up to 60% and 250% respectively, by allowing multiple PLC links to communicate concurrently, while requiring a few modifications to the existing HPAV PLC devices and protocols.

Millimeter-wave (mmWave) technology is emerging as the most promising solution to meet the multi-fold demand increase for mobile data. Very short wavelength, high directionality, together with sensitivity to rampant blockages and mobility, however, render state-of-the-art mmWave technologies unsuitable for ubiquitous wireless coverage. In this work, we design and implement UbiG - a mmWave wireless access network - that can deliver ubiquitous gigabits per second wireless access consistently to the commercial-off-the-shelf IEEE 802.11ad devices. UbiG has two key design components: (1) a fast probing based beam alignment algorithm that can identify the best beam consistently with guaranteed latency in a mmWave link, and the algorithm scales well even with a very large number of beams; and (2) an infrastructure-side predictive ranking based fast access point switching algorithm to ensure seamless gigabits per second connectivity under mobility and blockage in a dense mmWave deployment. Our IEEE 802.11ad testbed experiments show that UbiG performs close to an "Oracle" solution that instantaneously knows the best beam and access point for gigabits per second data transmission to users.

RF-based (mainly Wi-Fi and BLE) localization systems have been gaining the significant amount of attention from industry, since they do not require the deployment of any special and costly infrastructure such as cameras and depth sensors but leverage existing wireless Access Points (APs) and mobile clients. However, extensive field trials have shown that such solutions can be inaccurate (e.g., meter-level localization error) and/or impractical (e.g., requiring centrally-controlled, dense AP deployments). In this paper, we focus on millimeter-wave wireless networks, which are becoming increasingly popular for both indoors and outdoors connectivity, to overcome the aforementioned limitations and to provide accurate 3D localization. Specifically, we propose mWaveLoc, a system that exploits the small wavelength (millimeter) and directional communication of millimeter-wave (e.g., in 60 GHz) networks for accurate and practical 3D localization. mWaveLoc relies only on a single AP to track a device's position and works with existing off-the-shelf 802.11ad 60 GHz devices. Our implementation and experimental results on commodity 802.11ad testbeds show that mWaveLoc can achieve centimeter-level AP-client distance estimation accuracy and decimeter-level 3D localization accuracy in Line-Of-Sight settings.

Ubiquitous connectivity and smart technologies gradually transform homes into Intranet of Things, where a multitude of connected, intelligent devices allow for novel home automation services. Providing new services for home users (e.g., energy saving automations) and Internet Service Providers (e.g., network management and troubleshooting) requires an in-depth analysis of various kinds of data (connectivity, performance, usage) collected from home networks. In this paper, we explore new Machine-to-Machine data analysis techniques that go beyond binary association rule mining for traditional market basket analysis considered by previous studies, to analyze individual device logs of home gateways. We introduce a multidimensional patterns mining framework, to extract complex device co-usage patterns of 201 residential broadband users of an ISP, subscribed to a triple-play service. Our results show that our analytics engine provides valuable insights for emerging use cases such as monitoring for energy efficiency, and “things” recommendation.

Despite years of innovative research and development, gigabit-speed 60 GHz wireless networks are still not mainstream. The main concern for network operators and vendors is the unfavorable propagation characteristics due to short wavelength and high directionality, which renders the 60 GHz links highly vulnerable to blockage and mobility. However, the advent of multi-band chipsets opens the possibility of leveraging the more robust WiFi technology to assist 60 GHz in order to provide seamless, Gbps connectivity. In this paper, we design and implement MUST, an IEEE 802.11-compliant system that provides seamless, high-speed connectivity over multi-band 60 GHz and WiFi devices. MUST has two key design components: (1) a WiFi-assisted 60 GHz link adaptation algorithm, which can instantaneously predict the best beam and PHY rate setting, with zero probing overhead; and (2) a proactive blockage detection and switching algorithm which can re-direct ongoing user traffic to the robust interface within sub-10 ms latency. Our experiments with off-the-shelf 802.11 hardware show that MUST can achieve 25-60% throughput gain over state-of-the-art solutions, while bringing almost 2 orders of magnitude cross-band switching latency improvement.

Despite years of innovative research and development, multi-Gbps 60 GHz wireless networks are still not mainstream. The unfavorable propagation characteristics due to short wavelength and high directionality, makes the 60 GHz links highly vulnerable to blockage and mobility. However, the advent of multi-band chipsets opens the possibility of leveraging the more robust WiFi technology to assist 60 GHz in order to provide seamless, Gbps connectivity. In this demonstration, we will present MUST, an 802.11-compliant real-time system that provides seamless, high-speed connectivity over multi-band 60 GHz and WiFi devices. MUST has two key design components: (1) a WiFi-assisted 60 GHz link adaptation algorithm, which can instantaneously predict the best beam and PHY rate setting, with zero probing overhead at 60 GHz; and (2) a proactive blockage detection and switching algorithm which can re-direct ongoing user traffic to the robust interface within sub-10 ms latency. We have implemented MUST on off-the-shelf devices where our experiments show high throughput gain and almost 2 orders of magnitude cross-band switching latency improvement over state-of-the-art solutions.

Major Wi-Fi Access Point (AP) vendors worldwide seek to provide gigabit wireless connectivity, by densely deploying MU-MIMO capable APs, which can support multiple, concurrent data streams to a group of clients, connected to them. However, MU-MIMO gains can only be achieved if an AP can identify groups of clients with homogenous configurations and orthogonal wireless channels, where concurrent transmissions will not cause inter-client interference. Hence, MU-MIMO performance is fundamentally depending on how the clients are assigned to APs. Our experiments with 802.11ac commodity testbeds show that state-of-the-art client assignment algorithms are MU-MIMO oblivious and limit the MU-MIMO grouping opportunities in realistic settings. In this paper, we design and implement MAPS, an MU-MIMO-Aware AP Selection algorithm that is 802.11-compliant and can boost network's MU-MIMO throughput gains. We verified MAPS' gains over legacy designs via extensive experiments with 802.11ac commodity testbeds.

International audience

Powerline communication (PLC) provides inexpensive, secure and high speed network connectivity, by leveraging the existing power distribution networks inside the buildings. While PLC technology has the potential to improve connectivity and is considered a key enabler for sensing, control, and automation applications in enterprises, it has been mainly deployed for improving connectivity in homes. Deploying PLCs in enterprises is more challenging since the power distribution network is more complex as compared to homes. Moreover, existing PLC technologies such as HomePlug AV have not been designed for and evaluated in enterprise deployments. To this end, we give guidlines for designing PLC networks for providing ubiquitous connectivity in enterprises, based on measurement study of PLC performance in enterprise settings using commodity HomePlug AV PLC devices. Based on our findings, we propose that careful planning of PLC network topology, routing and spectrum sharing can significantly boost performance of enterprise PLC networks.

Multi-User MIMO, the hallmark of IEEE 802.11ac and the upcoming 802.11ax, promises significant throughput gains by supporting multiple concurrent data streams to a group of users. However, identifying the best-throughput MU-MIMO groups in commodity 802.11ac networks poses three major challenges: a) Commodity 802.11ac users do not provide full CSI feedback, which has been widely used for MU-MIMO grouping. b) Heterogeneous channel bandwidth users limit grouping opportunities. c) Limited-resource on APs cannot support computationally and memory expensive operations, required by existing algorithms. Hence, state-of-the-art designs are either not portable in 802.11ac APs, or perform poorly, as shown by our testbed experiments. In this paper, we design and implement MUSE, a lightweight user grouping algorithm, which addresses the above challenges. Our experiments with commodity 802.11ac testbeds show MUSE can achieve high throughput gains over existing designs.

As the ever growing mobile data traffic challenges the economic viability and performance of cellular networks, innovative solutions that harvest idle user-owned network resources are gaining increasing interest. In this work, we propose leasing wireless bandwidth and cache space of residential 802.11 (WiFi) access points (APs) for offloading mobile data. This solution not only reduces cellular network congestion, but, due to caching, improves also the user-perceived network performance without overloading the backhaul links of the APs. To encourage residential users to contribute their bandwidth and cache resources, we design monetary incentive (reimbursement) schemes. The offered reimbursements directly determine the amounts of available bandwidth and cache space in every AP, which in turn affect the caching policy (where to cache each content file) and the routing policy (where to route each mobile data request). In order to reduce operator's total cost for serving mobile data requests and leasing resources, we introduce a framework for the joint optimization of incentive, caching, and routing policies. Using a novel WiFi usage dataset collected from 167 residences, we show that in densely populated areas with relatively costly network capacity upgrades, our proposal can halve operator's total cost, while reimbursing up to 9€ per month each residential user.

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.

In recent years, ISPs in mature residential broadband markets have been moving away from flat-rate pricing schemes to ones that involve usage limits (or "data caps"). These changes have led to a lively debate about the merits of data capping. While previous work has studied the theoretical benefits of data capped pricing models, there is relatively little work that provides insights into exactly how to design data capped tariffs, and their impact on the parties involved, i.e., ISPs and their broadband subscribers. In this paper, we formulate the problem of selecting optimal data capped tariffs based on a simple, extensible model of ISP costs and revenues, and a novel model that captures how users evaluate a set of offered tariffs.

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.

This work experimentally studies the energy consumption of multiple-antenna MIMO 802.11 devices. Our measurements reveal an increase in power consumption and speed with the number of antennas. State of the art proposals have limitations to save energy in MIMO 802.11 networks. First, they focus on either maximizing speed or minimizing power consumption. Second, they only seek to minimize energy for the receiver side of mobile devices. As a result, they present limitations to utilize MIMO speed gains and to save energy in MIMO 802.11 infrastructure. To this end, we design Collaborative MIMO Energy Save (CMES), which seeks to identify the transmitter-receiver most energy efficient antenna setting, at runtime. Our experiments with commodity MIMO 802.11n testbeds confirm that CMES can provide energy savings in real scenarios.

Rate adaptation is a mechanism unspecified by the IEEE 802.11 standards, yet critical to the system performance by exploiting the multirate capability at the physical layer. In this paper, we conduct a systematic experimental study on rate adaptation over 802.11 wireless networks. Our key contributions are as follows: First, we present a critique on popular design guidelines adopted by many practical algorithms and we uncover their limitations. Our study reveals that these seemingly correct guidelines can be misleading in practice, thus incurring significant performance penalty in certain scenarios. Second, we study the short-term channel dynamics and explore how they guide rate adaptation. To this end, we design and implement a new History-Aware Robust Rate Adaptation Algorithm (HA-RRAA). HA-RRAA uses short-term loss ratio to opportunistically guide its rate change decisions, a cost-effective adaptive RTS filter to prevent collision losses from triggering rate decrease and an adaptive time window to limit transmissions at high loss rates. Our extensive experiments show that HA-RRAA outperforms popular algorithms in all tested scenarios, with goodput gains up to 51.9 percent in field trials.

In this paper, we use real experiments to study multiple-input-multiple-output (MIMO) 802.11n rate adaptation (RA) on a programmable access point (AP) platform. Our case study shows that existing RA solutions offer much lower throughput than even a fixed-rate scheme. It is proven that all such algorithms are MIMO-mode oblivious; they do not differentiate spatial diversity and spatial multiplexing modes. We first design MiRA, a novel MIMO RA scheme that zigzags between intra- and inter-MIMO modes to address MIMO 802.11n dynamics. Second, we examine a window-based RA solution, which runs an independent RA in each MIMO mode in parallel and a signal-to-noise ratio (SNR)-based MIMO RA that differentiates modes using SNR measurements. Our experiments show that MIMO-mode aware designs outperform MIMO-mode oblivious RAs in various settings, with goodput gains up to 73.5% in field trials.

This paper describes a new protocol SMP, which supports multimedia transfer for single-user, multiple-device scenarios. Through its novel naming and control/data plane designs, SMP is able to retain the current client and server protocol operations while placing new functions at the proxy. Our initial evaluation has confirmed its viability.

We study the problem of scalable monitoring of operational 3G wireless networks. Threshold-based performance monitoring in large 3G networks is very challenging for two main factors: large network scale and dynamics in both time and spatial domains. A fine-grained threshold setting (e.g., perlocation hourly) incurs prohibitively high management complexity, while a single static threshold fails to capture the network dynamics, thus resulting in unacceptably poor alarm quality (up to 70% false/miss alarm rates). In this paper, we propose a scalable monitoring solution, called threshold-compression that can characterize the location- and time-specific threshold trend of each individual network element (NE) with minimal threshold setting. The main insight is to identify groups of NEs with similar threshold behaviors across location and time dimensions, forming spatial-temporal clusters to reduce the number of thresholds while maintaining acceptable alarm accuracy in a large-scale 3G network. Our evaluations based on the operational experience on a commercial 3G network have demonstrated the effectiveness of the proposed solution. We are able to reduce the threshold setting up to 90% with less than 10% false/miss alarms.

This paper addresses the problem of frequency domain packet scheduling (FDPS) incorporating spatial division multiplexing (SDM) multiple input multiple output (MIMO) techniques on the 3GPP Long-Term Evolution (LTE) downlink. We impose the LTE MIMO constraint of selecting only one MIMO mode (spatial multiplexing or transmit diversity) per user per transmission time interval (TTI). First, we address the optimal MIMO mode selection (multiplexing or diversity) per user in each TTI in order to maximize the proportional fair (PF) criterion adapted to the additional frequency and spatial domains. We prove that both single-user (SU-) and multi-user (MU-) MIMO FDPS problems under the LTE requirement are NP-hard. We therefore develop two types of approximation algorithms (ones with full channel feedback and the others with partial channel feedback), all of which guarantee provable performance bounds for both SU- and MU-MIMO cases. Based on 3GPP LTE system model simulations, our approximation algorithms that take into account both spatial and frequency diversity gains outperform the exact algorithms that do not exploit the potential spatial diversity gain. Moreover, the approximation algorithms with partial channel feedback achieve comparable performance (with only 1-6 percent performance degradation) to the ones with full channel feedback, while significantly reducing the channel feedback overhead by nearly 50 percent.

The IEEE 802.11n standard has proposed a new Spatial Multiplexing Power Save (SMPS) feature, which allows for a station to retain one active receive chain, to mitigate MIMO circuitry power consumption. But does it work in all cases? Our experiments reveal that SMPS may not always save power compared with multiple active chains at the receiver. Even when it does, it may be proven more energy hungry. In this work, we seek to uncover the “good”, the “bad” and the “ugly” of SMPS using real experiments. We further devise a MIMO Receiver Energy Save (MRES) algorithm, which seeks to identify and set the most energy-efficient receive chain setting, by using a novel, low-overhead sampling scheme. Our prototype experiments show that, MRES outperforms SMPS with energy savings up to 37%.

Rate adaptation (RA) is a mechanism unspecified by the 802.11 standards, yet critical to the system performance. Although many different design directions have been studied the past years, there are still little insights learned of how short-term channel's past performance can be utilized to limit transmissions at low throughput rates. In this paper, we conduct a systematic experimental study to expose the importance of history aware rate adaptation and explore new techniques to address this space. To this end, we design and implement HA-RRAA, a new robust RA algorithm which uses short-term loss ratio to opportunistically guide its rate selection, a cost-effective, adaptive RTS filter to prevent collision losses from triggering rate decrease and an adaptive probe time window to limit excessive probing at high lossy rates. Our experimental results show gains up to 63% of HA-RRAA over RRAA, RRAA+, SampleRate and ARF, in realistic field trials.

Threshold-based performance monitoring in large 3G networks is very challenging for two main factors: large network scale and dynamics in both time and spatial domains. There exists a fundamental tradeoff between the size of threshold settings and the alarm quality. In this paper, we propose a scalable monitoring solution, called threshold-compression that characterizes the tradeoff via intelligent threshold aggregation. The main insight behind our solution is to identify groups of network elements with similar threshold behaviors across location and time dimensions, thus forming spatial-temporal clusters and generating the associated compressed thresholds within the optimization framework. Our evaluations on a commercial 3G network have demonstrated the effectiveness of our threshold-compression solution, e.g., threshold setting reduction up to 90% within 10% false/miss alarms.

This paper studies MIMO based rate adaptation (RA) in 802.11n wireless networks. Our case study shows that existing RA algorithms offer much lower throughput than even a fixed-rate scheme. The fundamental problem is that, all such algorithms are MIMO oblivious; they do not consider the characteristics of diversity-oriented, single-stream mode and the spatial multiplexing driven, double-stream mode. We propose MiRA, a novel MIMO RA scheme that zigzags between intra- and inter-mode rate options. Our experiments show that MiRA consistently outperforms three representative RA algorithms, SampleRate, RRAA and Atheros MIMO RA, in static, mobility and collision settings.

With the power consumption issue of mobile handset taken into account, single-carrier FDMA (SC-FDMA) has been selected for 3GPP long-term evolution (LTE) uplink multiple access scheme. Like in OFDMA downlink, it enables multiple users to be served simultaneously in uplink as well. However, its single carrier property requires that all the subcarriers allocated to a single user must be contiguous in frequency within each time slot. This contiguous allocation constraint limits the scheduling flexibility, and frequency-domain packet scheduling algorithms in such system need to incorporate this constraint while trying to maximize their own scheduling objectives. In this paper we explore this fundamental problem of LTE SC-FDMA uplink scheduling by adopting the conventional time-domain proportional fair algorithm to maximize its objective (i.e. proportional fair criteria) in the frequency-domain setting. We show the NP-hardness of the frequency-domain scheduling problem under this contiguous allocation constraint and present a set of practical algorithms fine tuned to this problem. We demonstrate that competitive performance can be achieved in terms of system throughput as well as fairness perspective, which is evaluated using 3GPP LTE system model simulations.

This paper describes an unconventional way to apply wireless networking in emerging technologies. It makes the case for using a two-tier hybrid wireless/wired architecture to interconnect hundreds to thousands of cores in chip multiprocessors (CMPs), where current interconnect technologies face severe scaling limitations in excessive latency, long wiring, and complex layout. We propose a recursive wireless interconnect structure called the WCube that features a single transmit antenna and multiple receive antennas at each micro wireless router and offers scalable performance in terms of latency and connectivity. We show the feasibility to build miniature on-chip antennas, and simple transmitters and receivers that operate at 100 − 500 GHz sub-terahertz frequency bands. We also devise new two-tier wormhole based routing algorithms that are deadlock free and ensure a minimum-latency route on a 1000core on-chip interconnect network. Our simulations show that our protocol suite can reduce the observed latency by 20 % to 45%, and consumes power that is comparable to or less than current 2-D wiredmeshdesigns.