Fine-tuning large language models (LLMs) to align with user preferences is challenging due to the high cost of quality human annotations in Reinforcement Learning from Human Feedback (RLHF) and the generalizability limitations of AI Feedback. To address these challenges, we propose RLTHF, a human-AI hybrid framework that combines LLM-based initial alignment with selective human annotations to achieve full-human annotation alignment with minimal effort. RLTHF identifies hard-to-annotate samples mislabeled by LLMs using a reward model's reward distribution and iteratively enhances alignment by integrating strategic human corrections while leveraging LLM's correctly labeled samples. Evaluations on HH-RLHF and TL;DR datasets show that RLTHF reaches full-human annotation-level alignment with only 6-7% of the human annotation effort. Furthermore, models trained on RLTHF's curated datasets for downstream tasks outperform those trained on fully human-annotated datasets, underscoring the effectiveness of RLTHF.

SARLink is a passive satellite backscatter communication system that uses existing spaceborne synthetic aperture radar (SAR) imaging satellites to provide connectivity in remote regions. It achieves orders of magnitude more range than traditional backscatter systems, enabling communication between a passive ground node and a satellite in low earth orbit. The system is composed of a cooperative ground target, a SAR satellite, and a data processing algorithm. A mechanically modulating reflector was designed to apply amplitude modulation to ambient SAR backscatter signals by changing its radar cross section. These communication bits are extracted from the raw SAR data using an algorithm that leverages subaperture processing to detect multiple bits from a target in a single image dataset. A theoretical analysis of this communication system using on-off keying is presented, including the expected signal model, throughput, and bit error rate. The results suggest a 5.5 ft by 5.5 ft modulating corner reflector could send 60 bits every satellite pass, enough to support low bandwidth sensor data and messages. Using Sentinel-1A, a SAR satellite at an altitude of 693 km, we deployed static and modulating reflectors to evaluate the system. The results, successfully detecting the changing state of a modulating ground target, demonstrate our algorithm's effectiveness for extracting bits, paving the way for ultra-long-range, low-power satellite backscatter communication.

Modular data centers (MDCs) that can be placed right at the energy farms and powered mostly by renewable energy, is a flexible and effective approach to lowering the carbon footprint of data centers. However, the main challenge of using renewable energy is the high variability of power produced, which implies large volatility in powering computing resources at MDCs, and degraded application performance due to the task evictions and migrations. This causes challenges for platform operators to decide the MDC deployment.

Soil carbon is a critical factor in maintaining soil health and combating climate change. Understanding and managing soil carbon levels is essential for sustainable agriculture and environmental protection. However, current methods for measuring soil carbon are time-consuming and costly, hindering efforts to monitor soil health and increase carbon sequestration. In this paper, we propose Scarf, a novel soil carbon sensing approach that combines widely accessible radio frequency (RF) and optical signals to detect soil carbon contents without dedicated hardware. Our key insight is that soil carbon content closely correlates with two indicators: the effective permittivity derived from RF signals and soil lightness determined from soil surface images. We mathematically model the correlations and leverage the non-linear correlation between the two signal modalities to compute soil carbon content. We employ machine learning to model relationships that cannot be captured by traditional mathematical equations. Our experimental results indicate that Scarf can achieve high soil carbon prediction accuracy that is comparable to the state-of-the-art soil carbon sensing techniques which cost US$1000s.

Soil carbon is a key soil property for soil health management and a crucial part of the global carbon cycle. Existing methods for soil carbon determination are too expensive and time-consuming. This paper introduces Scarf, a novel soil carbon sensing technique that leverages Wi-Fi and images and eliminates the need for specialized hardware. Our analysis reveals a strong correlation between soil carbon content and both the soil permittivity obtained from Wi-Fi signals and the soil lightness captured in soil surface images. We develop mathematical models to quantify the relationships between soil carbon content and the two signal modalities, and use the models to estimate soil carbon. We apply machine learning to help handle relationships that mathematical models do not capture. Our experiments demonstrate that Scarf delivers highly accurate soil carbon estimations.

Pico-satellite (picosat) constellations aim to become the de facto connectivity solution for Internet of Things (IoT) devices. These constellations rely on a large number of small picosats and offer global plug-and-play connectivity at low data rates, without the need for Earth-based gateways. As picosat constellations scale, they run into new bottlenecks due to their traditional medium access designs optimized for single (or few) satellite operations. We present CosMAC - a new constellation-scale medium access and scheduling system for picosat networks. CosMAC includes a new overlap-aware medium access approach for uplink from IoT to picosats and a new network layer that schedules downlink traffic from satellites. We empirically evaluate CosMAC using measurements from three picosats and large-scale trace-driven simulations for a 173 picosat network supporting 100k devices. Our results demonstrate that CosMAC can improve the overall network throughput by up to 6.5X over prior state-of-the-art satellite medium access schemes.

Decreasing costs of deploying space vehicles to low-Earth orbit have fostered an emergence of large constellations of satellites. However, high satellite velocities, large image data quantities, and brief ground station contacts create a data downlink challenge. Orbital edge computing (OEC), which filters data at the space edge, addresses this downlink bottleneck but shifts the challenge to the inelastic computational capabilities onboard satellites. In this work, we present Kodan: an OEC system that maximizes the utility of saturated satellite downlinks while mitigating the computational bottleneck. Kodan consists of two phases. A one-time transformation step uses a reference implementation of a satellite data analysis application, along with a representative dataset, to produce specialized ML models targeted for deployment to the space edge. After deployment to a target satellite, a runtime system dynamically selects the best specialized models for each data sample to maximize valuable data downlinked within the constraints of the computational bottleneck. By intelligently filtering low-value data and prioritizing high-value data for transmit via the saturated downlink, Kodan increases the data value density between 89 and 97 percent.

We discuss the potential improvement large language models (LLM) can provide in incident management and how they can overhaul the ways operators conduct incident management today. We propose a holistic framework for building an AI helper for incident management and discuss the several avenues of future research needed to achieve it.

We leverage a novel telemetry source available in public clouds today: periodic summaries of every flow that enters or leaves any VM. A key aspect is that such telemetry can be collected transparently to customers and with minimal impact on their workloads. By consuming this telemetry, we show how one may realize complete and dynamic graphs of the communication inside cloud subscriptions. We describe novel analyses over these communication graphs with implications on network security and management.

Analyzing network topologies and communication graphs is essential in modern network management. However, the lack of a cohesive approach results in a steep learning curve, increased errors, and inefficiencies. In this paper, we present a novel approach that enables natural-language-based network management experiences, leveraging large language models (LLMs) to generate task-specific code from natural language queries. This method addresses the challenges of explainability, scalability, and privacy by allowing network operators to inspect the generated code, removing the need to share network data with LLMs, and focusing on application-specific requests combined with program synthesis techniques. We develop and evaluate a prototype system using benchmark applications, demonstrating high accuracy, cost-effectiveness, and potential for further improvements using complementary program synthesis techniques.

The ability to assess the quality of fruit and vegetables at scale can revolutionize the agriculture sector and significantly reduce food waste. In this paper, we present AgriTera, a novel solution for accurate non-invasive, and contract-free fruit ripeness sensing via sub-terahertz wireless signals. The key idea is that sugar and water concentrations in fruit (that are associated with fruit ripening) leave unique non-uniform footprints in the wide band spectrum of the reflected signal off of the fruits. AgriTera utilizes the sub-THz bands for its wide bandwidth, sensitivity to water, mm-scale penetration depth, and non-ionizing features that offer high-resolution inferences from the peel as well as the pulp underneath the peel. We develop a chemometric model that translates the reflection spectra to well-known ripeness metrics, namely Dry Matter and Brix. We conduct extensive over-the-air experiments with commercially available sub-THz transceivers. We compare our results with ground truth values captured by a specialized quality sensor and a vision-based scheme that infers ripeness based on changes in the appearance of the fruit. We demonstrate that AgriTera can accurately estimate Brix and Dry Matter in three different types of fruit with an average Normalized RMSE value of 0.55%, an error that yields a negligible impact on taste and is imperceivable by the consumer.

We consider the problem of hosting financial exchanges in the cloud. Exchanges necessitate strong fairness guarantees for competing participants, particularly for use cases such as "high frequency trading". Today, exchanges achieve such guarantees by providing equal latency across all market participants in their on-premise deployments. However, ensuring equal latency for fairness is notably challenging in current multi-tenant cloud deployments, mainly due to factors such as network congestion and non-equidistant network paths.

This article presents a neural network-based solution to recover pixels occluded by clouds in satellite images. We leverage radio frequency (RF) signals in the ultrahigh-/superhigh-frequency band that penetrates clouds to help reconstruct the occluded regions in multispectral images. We introduce the first multimodal multitemporal method for cloud removal. Our model uses publicly available satellite observations and produces daily cloud-free images. Experimental results show that our system outperforms several baselines on multiple metrics. We also demonstrate use cases of our system in digital agriculture, flood monitoring, and wildfire detection.

In this paper, we consider the problem of supporting modern financial exchange services on the cloud premises. Important exchange services rely on predictable, equal latency from the servers to the participants for fair competition. Existing cloud networks, however, are unable to offer such property, as they were not originally designed for this purpose. We attempt to tackle the problem of unfairness that stems from the lack of determinism in cloud networks. We argue that predictable or bounded latency is not necessary to achieve fairness. Inspired by the use of logical clocks in distributed systems, we propose a new approach that instead corrects for differences in latency to the participants for fairness. We evaluate our approach in simulation and show that it is feasible to achieve fairness under highly variable network latency. Our approach is deployable in contemporary cloud environments; it avoids limitations of state-of-the-art and outperforms it.

Satellite data fusion involves images with different spatial, temporal, and spectral resolution. These images are taken under different illumination conditions, with different sensors and atmospheric noise. We use classic super-resolution algorithms to synthesize commercial satellite images (Pléiades) from a public satellite source (Sentinel-2). Each super-resolution method is then further improved by adaptive sharpening to the location by use of matrix completion (regression with missing pixels). Finally, we consider ensemble systems and a residual channel attention dual network with stochastic dropout. The resulting systems are visibly less blurry with higher fidelity and yield improved performance.

This paper asks: “Can we detect whether a fragile product, made of porcelain or glass is damaged as it travels along the supply chain, without opening its packaging?” We ask this question in the context of the multi-billion dollar global supply chain industry of fragile products that experience large overheads due to product returns. This paper presents MiLTOn, a novel acoustic and mm-wave based solution for through-box non-invasive product integrity sensing that is sensitive to even minute sub-mm cracks in the object. MiLTOn is inspired by acoustic vibrometry used for instance to monitor cracks in railroads. Unlike traditional vibrometry, MiL-TOn is unique in its ability to sense products non-invasively using an external transducer and microphone, neither of which are in direct physical contact of the object within the box. MiLTOn pro-cesses measurements from the microphone to design a robust and environment-independent product signature that can be used to sense presence of product defects. Our extensive evaluation on a large number of fragile products of diverse materials demonstrates 97% accuracy in identifying product damage.

Renewable energy is becoming an important power source for data centers, especially with the zero-carbon waste pledges made by big cloud providers. However, one of the main challenges of renewable energy sources is the high variability of power produced. Traditional approaches such as batteries or transmitting to the grid fall short on scale, overhead, or "green-ness". We propose Virtual Battery: instead of adapting the availability of power to match the computation demand we shift computational demand to meet the availability of power. Virtual batteries shift demand by requiring applications to either be flexible and delay-tolerant or proactively migrating to where power is (going to be) available. We show that using multiple virtual battery sites in combination can meet the needs of modern applications. Moreover, we show how an intelligent network and power aware co-scheduler can not only provide availability despite variability but also help mitigate migration related network overhead by over 30% in total and 4.2x at peak.

This paper presents a deep learning approach for a versatile Micro-climate prediction framework (DeepMC). Micro climate predictions are of critical importance across various applications, such as Agriculture, Forestry, Energy, Search & Rescue, etc. To the best of our knowledge, there is no other single framework which can accurately predict various micro-climate entities using Internet of Things (IoT)data. We present a generic framework (DeepMC) which predicts various climatic parameters such as soil moisture, humidity, windspeed, radiation, temperature based on the requirement over a period of 12 hours - 120 hours with a varying resolution of 1 hour - 6hours, respectively. This framework proposes the following new ideas: 1) Localization of weather forecast to IoT sensors by fusing weather station forecasts with the decomposition of IoT data at multiple scales and 2) A multi-scale encoder and two levels of attention mechanisms which learns a latent representation of the interaction between various resolutions of the IoT sensor data and weather station forecasts. We present multiple real-world agricultural and energy scenarios, and report results with uncertainty estimates from the live deployment of DeepMC, which demonstrate that DeepMC outperforms various baseline methods and reports 90%+ accuracy with tight error bounds.

Analytics with three-dimensional imagery from drones are driving the next generation of remote monitoring applications. Today, there is an unmet need in providing such analytics in an interactive manner, especially over weak Internet connections, to quickly diagnose and solve problems in the commercial industry space of monitoring assets using drones in remote parts of the world. Existing mechanisms either compromise on the quality of insights by not building 3D images and analyze individual 2D images in isolation, or spend tens of minutes building a 3D image before obtaining and uploading insights. We present Visage, a system that accelerates 3D image analytics by identifying smaller parts of the data that can actually benefit from 3D analytics and prioritizing building, and uploading the localized 3D images for those parts. To achieve this, Visage uses a graph to represent raw 2D images and their relative content overlap, and then identifies the various subgraphs using application knowledge that are good candidates for localized 3D image based insights. We evaluate Visage using data from multiple real deployments and show that it can reduce analytics-latency by up to four orders of magnitude.

Large constellations of Low Earth Orbit satellites promise to provide near real-time high-resolution Earth imagery. Yet, getting this large amount of data back to Earth is challenging because of their low orbits and fast motion through space. Centralized architectures with few multi-million dollar ground stations incur large hour-level data download latency and are hard to scale. We propose a geographically distributed ground station design, L2D2, that uses low-cost commodity hardware to offer low latency robust downlink. L2D2 is the first system to use a hybrid ground station model, where only a subset of ground stations are uplink-capable. We design new algorithms for scheduling and rate adaptation that enable low latency and high robustness despite the limitations of the receive-only ground stations. We evaluate L2D2 through a combination of trace-driven simulations and real-world satellite-ground station measurements. Our results demonstrate that L2D2's geographically distributed design can reduce data downlink latency from 90 minutes to 21 minutes.

Low Earth Orbit satellites for Earth observation have become very popular in recent years due to their ability to take high-resolution images of the Earth at high revisit rates. These satellites collect hundreds of GigaBytes of imagery during their orbit. This data needs to be downloaded using ground stations on Earth. However, due to the low altitudes, the satellites move fast with respect to a ground station, and consequently, have a few minutes time window to download the data to a single station. We propose a geographically distributed ground station design, DGS, that improves robustness and reduces downlink latency. DGS is the first system to use a hybrid ground station model, where only a subset of ground stations are uplink-capable. This paper evaluates the feasibility of this design using simulations and empirical measurements.

Agricultural soil moisture measurement is usually done with extensive in situ sensor deployments. These sensor networks are often difficult to install and maintain. Ground penetrating radars have also been used to do fine-grained moisture measurements in farm fields, but usually require the radar to be in or near contact with the soil. In this paper we propose a hybrid approach that combines an in situ backscatter reflector with an ultra-wideband radar that is small enough to be handheld or mounted to a drone. The underground backscatter reflector allows for accurate time-of-flight measurements. The time-of-flight is determined by the permittivity of the soil, which is influenced primarily by the soil water content for non-saline soils. We performed both laboratory and in situ measurements, achieving an average accuracy within 0.013 cm3/ cm3of the ground truth with a 90th percentile of 0.034 cm3/cm3. This demonstrates the feasibility of our approach.

We present a sensing system that determines soil moisture via RF using backscatter tags paired with a commodity ultra-wideband RF transceiver. Despite decades of research confirming the benefits, soil moisture sensors are still not widely adopted on working farms for three key reasons: the high cost of sensors, the difficulty of deploying and maintaining these sensors, and the lack of reliable internet access in rural areas. We seek to address some of these obstacles by designing a low-cost soil moisture sensing system that uses a hybrid approach of pairing completely wireless backscatter tags with a mobile reader.(p)(/p)We designed and built a backscatter tag prototype and tested our system both in laboratory and in situ at an organic farm field. Our backscatter tag has a projected battery lifetime of up to 15 years on 4xAA batteries, and can operate at a depth of at least 30cm and up to 75cm. It achieves an average accuracy within 0.01-0.03cm3/cm3of the ground truth with a 90th percentile of 0.034cm3/cm3, which is comparable to state-of-the-art commercial soil sensors, at an order of magnitude lower cost.

Recent work in networked systems has shown that using aerial imagery for farm monitoring can enable precision agriculture by lowering the cost and reducing the overhead of large scale sensor deployment. However, acquiring aerial imagery requires a drone, which has high capital and operational costs, often beyond the reach of farmers in the developing world. In this paper, we present TYE (Tethered eYE), an inexpensive platform for aerial imagery. It consists of a tethered helium balloon with a custom mount that can hold a smartphone (or a camera) with a battery pack. The balloon can be carried using a tether by a person or a vehicle. We incorporate various techniques to increase the operational time of the system, and to provide actionable insights even with unstable imagery. We develop path-planning algorithms and use that to develop an interactive mobile phone application that provides the user instant feedback to guide users to efficiently traverse large areas of land. We use computer vision algorithms to stitch orthomosaics by effectively countering wind-induced motion of the camera. We have used TYE for aerial imaging of agricultural land for over a year, and envision it as a low-cost aerial imaging platform for similar applications.

A farm's soil moisture and soil electrical conductivity (EC)readings are extremely valuable for a farmer. They can help her reduce water use and improve productivity. However, the high cost of commercial soil moisture sensors and the inaccuracy of sub-1000 dollar EC sensors have limited their adoption. In this paper, we present the design and implementation of a system, called Strobe, that senses soil moisture and soil EC using RF propagation in existing Wi-Fi bands. Strobe overcomes the key challenge of limited bandwidth availability in the 2.4 GHz unlicensed spectrum using a novel multi-antenna technique. It maps the propagation time and amplitude of Wi-Fi signals received by different antennas to the soil permittivity and EC, which in turn depend on soil moisture and salinity. Our experiments with USRP, WARP, and commodity Wi-Fi cards show that Strobe can accurately estimate soil moisture and EC using Wi-Fi, thereby showing the potential of a future in which a farmer can sense soil in their farm without investing 1000s of dollars in soil sensing equipments.

A farm's soil moisture and soil electrical conductivity (EC)readings are extremely valuable for a farmer. They can help her reduce water use and improve productivity. However, the high cost of commercial soil moisture sensors and the inaccuracy of sub-1000 dollar EC sensors have limited their adoption. In this paper, we present the design and implementation of a system, called Strobe, that senses soil moisture and soil EC using RF propagation in existing Wi-Fi bands. Strobe overcomes the key challenge of limited bandwidth availability in the 2.4 GHz unlicensed spectrum using a novel multi-antenna technique. It maps the propagation time and amplitude of Wi-Fi signals received by different antennas to the soil permittivity and EC, which in turn depend on soil moisture and salinity. Our experiments with USRP, WARP, and commodity Wi-Fi cards show that Strobe can accurately estimate soil moisture and EC using Wi-Fi, thereby showing the potential of a future where a farmer can sense soil in their farm without investing 1000s of dollars in sensing tools.

As the scale and density of data centers continue to grow, cost-effective data center management (DCM) is becoming a significant challenge for enterprises hosting large-scale online and cloud services. Machines need to be monitored, and the scale of operations mandates an automated management with high reliability and real-time performance. The limitations of today’s typical DCM network are many-fold. Primarily, it is a fixed wired network, and hence scaling it for a large number of servers increases its cost. In addition, with server densities increasing over recent years, this network also has to be cabled correctly and the management of this network parallels the complexity of managing a data network, since it needs to be networked with multiple switches and routers. In this article, we propose a wireless sensor network as a cost-effective networking solution for DCM while satisfying the reliability and latency performance requirements of DCM. We have developed CapNet, a real-time wireless sensor network for power capping, a time-critical DCM function for power management in a cluster of servers. CapNet employs an efficient event-driven protocol that triggers data collection only on the detection of a potential power capping event. We deploy and evaluate CapNet in a data center. Using server power traces, our experimental results on a cluster of 480 servers inside the data center show that CapNet can meet the real-time requirements of power capping. CapNet demonstrates the feasibility and efficacy of wireless sensor networks for time-critical DCM applications.

Image alignment tasks require accurate pixel correspondences, which are usually recovered by matching local feature descriptors. Such descriptors are often derived using supervised learning on existing datasets with ground truth correspondences. However, the cost of creating such datasets is usually prohibitive. In this paper, we propose a new approach to align two images related by an unknown 2D homography where the local descriptor is learned from scratch from the images and the homography is estimated simultaneously. Our key insight is that a siamese convolutional neural network can be trained jointly while iteratively updating the homography parameters by optimizing a single loss function. Our method is currently weakly supervised because the input images need to be roughly aligned. We have used this method to align images of different modalities such as RGB and near-infra-red (NIR) without using any prior labeled data. Images automatically aligned by our method were then used to train descriptors that generalize to new images. We also evaluated our method on RGB images. On the HPatches benchmark, our method achieves comparable accuracy to deep local descriptors that were trained offline in a supervised setting.

The proliferation of Internet of Things (IoT) devices has led to the deployment of various types of sensors in the homes, offices, buildings, lawns, cities, and even in agricultural farms. Since IoT applications rely on the fidelity of data reported by the sensors, it is important to detect a faulty sensor and isolate the cause of the fault. Existing fault detection techniques demand sensor domain knowledge along with the contextual information and historical data from similar near-by sensors. However, detecting a sensor fault by analyzing just the sensor data is non-trivial since a faulty sensor reading could mimic non-faulty sensor data. This paper presents a novel primitive, which we call the Fall-curve - a sensor's voltage response when the power is turned off - that can be used to characterize sensor faults. The Fall-curve constitutes a unique signature independent of the phenomenon being monitored which can be used to identify the sensor and determine whether the sensor is correctly operating.

The proliferation of Internet of Things (IoT) devices has led to the deployment of various types of sensors in the homes, offices, buildings, lawns, cities, and even in agricultural farms. Due to the diverse nature of IoT deployments and the likelihood of sensor failures in-the-wild, a key challenge in the design of IoT systems is ensuring the integrity, accuracy, and fidelity of sensor data.

Knowledge about active radio transmitters is critical for multiple applications: spectrum regulators can use this information to assign spectrum, licensees can identify spectrum usage patterns and provision their future needs, and dynamic spectrum access applications can efficiently pick operating frequency. To achieve these goals, we need a system that continuously senses and characterizes the radio spectrum. Current measurement systems, however, do not scale over time, frequency and space and cannot perform transmitter detection. We address these challenges with theSpectrum Observatory, an end-to-end system for spectrum measurement and characterization. This paper details the design and integration of the Spectrum Observatory, and describes and evaluates the first unsupervised method for detailed characterization of arbitrary transmitters calledTxMiner. We evaluate TxMiner on real-world spectrum measurements collected by the Spectrum Observatory between 30 MHz and 6 GHz and show that it identifies transmitters robustly. Furthermore, we demonstrate the Spectrum Observatory’s capabilities to map the number of active transmitters and their frequency and temporal characteristics, to detect rogue transmitters, and identify opportunities for dynamic spectrum access.

More than half of the world's population face barriers in accessing the Internet. A recent ITU study estimates that 2.6 billion people cannot afford connectivity and that 3.8 billion do not have access. Recent proposals for providing low-cost connectivity include fielding of drones and long-lasting balloons in the stratosphere. We propose a more economical alternative, which we refer to as Wi-Fly, that leverages existing commercial planes to provide Internet connectivity to remote regions. In Wi-Fly we enable communication between a lightweight Wi-Fi device on commercial planes and ground stations, resulting in connectivity in regions that do not otherwise have low-cost Internet connectivity. Wi-Fly leverages existing ADS-B signals from planes as a control channel to ensure that there is a strong link from the plane to the ground, and that the stations intelligently wake up and associate to the appropriate AP. For our experimentation, we have customized two airplanes to conduct measurements. Through empirical experiments with test flights and simulations, we show that Wi-Fly and its extensions have the potential to provide connectivity to the most remote regions of the world at a significantly lower cost than existing alternatives.

Low-Power Wide-Area Network (LPWAN) heralds a promising class of technology to overcome the range limits and scalability challenges in traditional wireless sensor networks. Recently proposed Sensor Network over White Spaces (SNOW) technology is particularly attractive due to the availability and advantages of TV spectrum in long-range communication. This paper proposes a new design of SNOW that is asynchronous, reliable, and robust. It represents the first highly scalable LPWAN over TV white spaces to support reliable, asynchronous, bi-directional, and concurrent communication between numerous sensors and a base station. This is achieved through a set of novel techniques. This new design of SNOW has an OFDM based physical layer that adopts robust modulation scheme and allows the base station using a single antenna-radio (1) to send different data to different nodes concurrently and (2) to receive concurrent transmissions made by the sensor nodes asynchronously. It has a lightweight MAC protocol that (1) efficiently implements per-transmission acknowledgments of the asynchronous transmissions by exploiting the adopted OFDM design; (2) combines CSMA/CA and location-aware spectrum allocation for mitigating hidden terminal effects, thus enhancing the flexibility of the nodes in transmitting asynchronously. Hardware experiments through deployments in three radio environments - in a large metropolitan city, in a rural area, and in an indoor environment - as well as large-scale simulations demonstrated that the new SNOW design drastically outperforms other LPWAN technologies in terms of scalability, energy, and latency.

It is a promising vision to exploit white spaces , that is, vacant VHF and UHF TV channels, to meet the rapidly growing demand for wireless data services in both outdoor and indoor scenarios. While most prior works have focused on outdoor white space, the indoor story is largely open for investigation. Motivated by this observation and discovering that 70% of the spectrum demand comes from indoor environment, we carry out a comprehensive study to explore indoor white spaces. We first conduct a large-scale measurement study and compare outdoor and indoor TV spectrum occupancy at 30+ diverse locations in a typical metropolis—Hong Kong. Our results show that abundant white spaces are available in different areas in Hong Kong, which account for more than 50% and 70% of the entire TV spectrum in outdoor and indoor scenarios, respectively. Although there are substantially more white spaces indoors than outdoors, there have been very few solutions for identifying indoor white space. To fill in this gap, we develop the first data-driven, low-cost indoor white space identification system for White-space Indoor Spectrum EnhanceR (WISER), to allow secondary users to identify white spaces for communication without sensing the spectrum themselves. We design the architecture and algorithms to address the inherent challenges. We build a WISER prototype and carry out real-world experiments to evaluate its performance. Our results show that WISER can identify 30%--40% more indoor white spaces with negligible false alarms, as compared to alternative baseline approaches.

Wireless sensor networks (WSNs) face significant scalability challenges due to the proliferation of wide-area wireless monitoring and control systems that require thousands of sensors to be connected over long distances. Due to their short communication range, existing WSN technologies such as those based on IEEE 802.15.4 form many-hop mesh networks complicating the protocol design and network deployment. To address this limitation, we propose a scalable sensor network architecture - called Sensor Network Over White Spaces (SNOW) - by exploiting the TV white spaces. Many WSN applications need low data rate, low power operation, and scalability in terms of geographic areas and the number of nodes. The long communication range of white space radios significantly increases the chances of packet collision at the base station. We achieve scalability and energy efficiency by splitting channels into narrowband orthogonal subcarriers and enabling packet receptions on the subcarriers in parallel with a single radio. The physical layer of SNOW is designed through a distributed implementation of OFDM that enables distinct orthogonal signals from distributed nodes. Its MAC protocol handles subcarrier allocation among the nodes and transmission scheduling. We implement SNOW in GNU radio using USRP devices. Experiments demonstrate that it can correctly decode in less than 0.1ms multiple packets received in parallel at different subcarriers, thus drastically enhancing the scalability of WSN.

Pervasive Wi-Fi connectivity is attractive for users in places not covered by cellular services (e.g., when traveling abroad). However, the power drain of frequent Wi-Fi scans undermines the device's battery life, preventing users from staying always connected and fetching synced emails and instant message notifications (e.g., WhatsApp). We study the energy overhead of scan and roaming in detail and refer to it as the scan tax problem. Our findings show that the main processor is the primary culprit of the energy overhead. We propose a simple and effective architectural change of offloading scans to the Wi-Fi radio. We design and build WiScan to fully exploit the gain of scan offloading. Our experiments demonstrate that WiScan achieves 90%+ of the maximal connectivity, while saving 50-62% energy for seeking connectivity.

Latency-sensitive applications (e.g., wireless gaming and TV remote play) are increasingly popular in home WiFi networks. Such millisecond-level latency requirements call for new fine-grained approaches at the link layer. In this paper, we show that current solutions work well for throughput but not for latency due to the long tail of the packet delay distribution. We thus propose LLRA, a new latency-aware rate adaptation scheme that reduces the tail latency for delay-sensitive applications. LLRA takes concerted design in rate control, frame aggregation scheduling and software/hardware retransmission dispatching. Our implementation and evaluation confirm the viability of LLRA in 802.11n home networks.

More than 4 billion people are not connected to the Internet. This is either because there is no infrastructure or because Internet access is not affordable. This digital divide is extreme in Africa. At Microsoft, we have been investigating various technologies to bridge this divide. In this paper we describe our research around the TV White Spaces, and how we have leveraged it, and worked with our partners to connect communities in Kenya, Tanzania, Ghana, Botswana, Namibia and South Africa.

No abstract available.

WiFi Display, also called Miracast, is an emerging technology that allows a mobile device (source) to duplicate its screen content to an external display (sink) via a peer-to-peer WiFi link. Despite its diverse application scenarios and growing popularity, Miracast consumes substantial power due to a combination of video encoding/decoding and transmission. In this paper, we first conduct a measurement study to quantify and model key parameters that scale Miracast's power consumption. We then propose a set of optimization mechanisms to bypass redundant codec operations, reduce video tail traffic, and relocate the Miracast channel dynamically to maximize transmission efficiency. We have implemented this energy-efficient Miracast framework on an Android smartphone. Experimental results show that the legacy Miracast system costs 1.3 to 2.4 Watts. Our framework reduces the power consumption by 29% to 61%, depending on the Miracast application's video traffic patterns. Our optimization mechanisms do not affect the video quality, and can even reduce the latency of certain Miracast applications.

Different battery chemistries perform better on different axes, such as energy density, cost, peak power, recharge time, longevity, and efficiency. Mobile system designers are constrained by existing technology, and are forced to select a single chemistry that best meets their diverse needs, thereby compromising other desirable features. In this paper, we present a new hardware-software system, called Software Defined Battery (SDB), which allows system designers to integrate batteries of different chemistries. SDB exposes APIs to the operating system which control the amount of charge flowing in and out of each battery, enabling it to dynamically trade one battery property for another depending on Application And/Or User Needs. Using microbenchmarks from our prototype SDB implementation, and through detailed simulations, we demonstrate that it is possible to combine batteries which individually excel along different axes to deliver an enhanced collective performance when compared to traditional battery packs.

Scalable and comprehensive testing of mobile apps is extremely challenging. Every test input needs to be run with a variety of contexts, such as: device heterogeneity, wireless network speeds, locations, and unpredictable sensor inputs. The range of values for each context, e.g. location, can be very large. In this paper we present Caiipa, a cloud service for testing apps over an expanded mobile context space in a scalable way. It incorporates key techniques to make app testing more tractable, including a context test space prioritizer to quickly discover failure scenarios for each app. We have implemented Caiipa on a cluster of VMs and real devices that can each emulate various combinations of contexts for tablet and phone apps. We evaluate Caiipa by testing 265 commercially available mobile apps based on a comprehensive library of real-world conditions. Our results show that Caiipa leads to improvements of 11.1x and 8.4x in the number of crashes and performance bugs discovered compared to conventional UI-based automation (i.e., monkey-testing).

Data center management (DCM) is increasingly becoming a significant challenge for enterprises hosting large scale online and cloud services. Machines need to be monitored, and the scale of operations mandates an automated management with high reliability and real-time performance. Existing wired networking solutions for DCM come with high cost. In this paper, we propose a wireless sensor network as a cost-effective networking solution for DCM while satisfying the reliability and latency performance requirements of DCM. We have developed Cap Net, a real-time wireless sensor network for power capping, a time-critical DCM function for power management in a cluster of servers. Cap Net employs an efficient event-driven protocol that triggers data collection only upon the detection of a potential power capping event. We deploy and evaluate Cap Net in a data center. Using server power traces, our experimental results on a cluster of 480 servers inside the data center show that Cap Net can meet the real-time requirements of power capping. Cap Net demonstrates the feasibility and efficacy of wireless sensor networks for time-critical DCM applications.

It is a promising vision to utilize white spaces, i.e., vacant VHF and UHF TV channels, to satisfy skyrocketing wireless data demand in both outdoor and indoor scenarios. While most prior works have focused on exploring outdoor white spaces, the indoor story is largely open for investigation. Motivated by this observation and that 70% of the spectrum demand comes from indoor environments, we carry out a comprehensive study of exploring indoor white spaces. We first present a large-scale measurement of outdoor and indoor TV spectrum occupancy in 30+ diverse locations in a typical metropolis Hong Kong. Our measurement results confirm abundant white spaces available for exploration in a wide range of areas in metropolises. In particular, more than 50% and 70% of the TV spectrum are white spaces in outdoor and indoor scenarios, respectively. While there are substantially more white spaces in indoor scenarios than in outdoor scenarios, there is no effective solution for identifying indoor white spaces. To fill in this gap, we propose the first system WISER (for White-space Indoor Spectrum EnhanceR), to identify and track indoor white spaces in a building, without requiring user devices to sense the spectrum. We discuss the design space of such system and justify our design choices using intensive real-world measurements. We design the architecture and algorithms to address the inherent challenges. We build a WISER prototype and carry out real-world experiments to evaluate its performance. Our results show that WISER can identify 30%-50% more indoor white spaces with negligible false alarms, as compared to alternative baseline approaches.

Email is a key application used on smartphones. Even when the phone is in stand-by mode, users expect the phone to continue syncing with an email server to receive new mes-sages. Each such sync operation wakes up the smartphone for data reception and processing. In this paper, we show that this "cost of email sync" in stand-by mode constitutes a significant source of energy consumption, and thus reduces battery life. We quantify the power performance of different existing email clients on two smartphone platforms, An-droid and Windows Phone, and study the impact of system parameters such as email size, inbox size, and pull vs. push. Our results show that existing email clients do not handle email sync in an energy efficient way. This is because the underlying protocols and architectures are not designed for the specific needs of operating in stand-by mode. Based on our findings, we derive general design principles for energy-efficient event handling on smartphones, and apply these principles to the case of email sync and implement our techniques on commercial smartphones. Experimental results show that our techniques are able to significantly reduce energy cost of email sync by 49.9% on average with our experiment settings.

One of the key distinctive requirements of white-space networks is the power asymmetry. Static nodes are allowed to transmit with 15dB-20dB higher power than mobile nodes. This poses significant coexistence problems, as high-power nodes can easily starve low-power nodes. In this paper, we propose Weeble, a novel distributed and state-less MAC protocol that solves the coexistence problem. One of the key building blocks is an adaptive preamble support, an add-on to the PHY layer that allows high-power nodes to detect a low-power transmission even when the difference in transmit power is as high as 20dB. The other key building block is a MAC protocol that exploits the adaptive preambles functionality. It implements a virtual carrier-sensing and automatically adapts the preamble size to optimize network performance. We extensively evaluate our system in a test-bed and in simulations. We show that we can prevent starvation of low-power nodes in almost all existing scenarios and improve the data rates of low-power links several-fold over existing MACs, and as a trade-off we decrease the throughput of the rest of the system by 20%-40%.

Battery life is a critical performance and user experience metric on mobile devices. However, it is difficult for app developers to measure the energy used by their apps, and to explore how energy use might change with conditions that vary outside of the developer's control such as network congestion, choice of mobile operator, and user settings for screen brightness. We present an energy emulation tool that allows developers to estimate the energy use for their mobile apps on their development workstation itself. The proposed techniques scale the emulated resources including the processing speed and network characteristics to match the app behavior to that on a real mobile device. We also enable exploring multiple operating conditions that the developers cannot easily reproduce in their lab. The estimation of energy relies on power models for various components, and we also add new power models for components not modeled in prior works such as AMOLED displays. We also present a prototype implementation of this tool and evaluate it through comparisons with real device energy measurements.

Retransmissions reduce the efficiency of data communication in wireless networks because of: (i) per-retransmission packet headers, (ii) contention overhead on every retransmission, and (iii) redundant bits in every retransmission. In fact, every retransmission nearly doubles the time to successfully deliver the packet. To improve spectrum efficiency in a lossy environment, we propose a new in-frame retransmission scheme using uACKs. Instead of waiting for the entire transmission to end before sending the ACK, the receiver sends smaller uACKs for every few symbols, on a separate narrow feedback channel. Based on these uACKs, the sender only retransmits the lost symbols after the last data symbol in the frame, thereby adaptively changing the frame size to ensure it is successfully delivered. We have implemented uACK on the Sora platform. Experiments with our prototype validate the feasibility of symbol-level uACK . By significantly reducing the retransmistion overhead, the sender is able to aggressively use higher data rate for a lossy link. Both improve the overall network efficiency. Our experimental results from a controlled environment and an 9-node software radio testbed show that uACK can have up to 140% throughput gain over 802.11g and up to 60% gain over the best known retransmission scheme.

The 2010 FCC ruling on white spaces proposes relying on a database of incumbents as the primary means of determining white space availability at any white space device (WSD). While the ruling provides broad guidelines for the database, the specifics of its design, features, implementation, and use are yet to be determined. Furthermore, architecting a network where all WSDs rely on the database raises several systems and networking challenges that have remained unexplored. Also, the ruling treats the database only as a storehouse for incumbents. We believe that the mandated use of the database has an additional opportunity: a means to dynamically manage the RF spectrum. Motivated by this opportunity, in this paper, we present SenseLess, a database-driven white spaces network. As suggested by its very name, in SenseLess, WSDs rely on a database service to determine white spaces availability as opposed to spectrum sensing. The service, using a combination of an up-to-date database of incumbents, sophisticated signal propagation modeling, and an efficient content dissemination mechanism to ensure efficient, scalable, and safe white space network operation. We build, deploy, and evaluate SenseLess and compare our results to ground truth spectrum measurements. We present the unique system design considerations that arise due to operating over the white spaces. We also evaluate its efficiency and scalability. To the best of our knowledge, this is the first paper that identifies and examines the systems and networking challenges that arise from operating a white space network, which is solely dependent on a channel occupancy database.

This paper introduces models and a system for designing 802.11 wireless LANs (WLANs) using flexible channelization— the choice of an appropriate channel width and center frequency for each transmission. In contrast to current 802.11 systems that use fixed width channels, the proposed system, FLUID, configures all access points and their clients using flexible channels. We show that a key challenge in designing such a system stems from managing the effects of interference due to multiple transmitters employing variable channel widths, in a network-wide setting. We implemented FLUID in an enterprise-like setup using a 50 node testbed (with off-the shelf wireless cards) and we show that FLUID improves the average throughput by 59 percent across all PHY rates, compared to existing fixed-width approaches.

TV white spaces offer an exciting opportunity for increasing spectrum availability, but white space devices (WSDs) cannot interfere with primary users, including TV channels and wireless microphones (mics). Mics are particularly challenging because their use is dynamic and it is hard to avoid interference since mic receivers are receive-only devices. For this reason the FCC and other regulatory agencies have made very conservatives rules that require WSDs to vacate any TV channel that is used by a mic. However, our measurements show that mics typically require only 5% of a channel, wasting as much as 95% of the spectrum.

This paper introduces models and a system for designing 802.11 wireless LANs (WLANs) using flexible channelization -- the choice of an appropriate channel width and center frequency for each transmission. In contrast to current 802.11 systems that use fixed width channels, the proposed system, FLUID, configures all access points and their clients using flexible channels. We show that a key challenge in designing such a system stems from managing the effects of interference due to multiple transmitters employing variable channel widths, in a network-wide setting. We implemented FLUID in an enterprise-like setup using a 50 node testbed (with off-the shelf wireless cards) and we show that FLUID improves the average throughput by 59% across all PHY rates, compared to existing fixed-width approaches.

This paper presents MAUI, a system that enables fine-grained energy-aware offload of mobile code to the infrastructure. Previous approaches to these problems either relied heavily on programmer support to partition an application, or they were coarse-grained requiring full process (or full VM) migration. MAUI uses the benefits of a managed code environment to offer the best of both worlds: it supports fine-grained code offload to maximize energy savings with minimal burden on the programmer. MAUI decides at run-time which methods should be remotely executed, driven by an optimization engine that achieves the best energy savings possible under the mobile device's current connectivity constrains. In our evaluation, we show that MAUI enables: 1) a resource-intensive face recognition application that consumes an order of magnitude less energy, 2) a latency-sensitive arcade game application that doubles its refresh rate, and 3) a voice-based language translation application that bypasses the limitations of the smartphone environment by executing unsupported components remotely.

Fault localization in enterprise networks is extremely challenging. A recent approach called Sherlock makes some headway into this problem by using an inference algorithm over a multi-tier probabilistic dependency graph that relates fault symptoms with possible root causes (e.g., routers, servers). A key limitation of Sherlock is its scalability because of the use of complicated inference algorithms based on Bayesian networks. We present a fault localization system called Spotlight that essentially uses two basic ideas. First, it compresses a multi-tier dependency graph into a bipartite graph with direct probabilistic edges between root causes and symptoms. Second, it runs a novel weighted greedy minimum set cover algorithm to provide fast inference. Through extensive simulations with real service dependency graphs and enterprise network topologies reported previously in literature, we show that Spotlight is about 100× faster than Sherlock in typical settings, with comparable accuracy in diagnosis.

IP multicast applications such as live lecture broadcasts are being increasingly used in enterprise and campus networks. In many cases, end hosts access these multicast streams using Wi-Fi networks. However, multicast over Wi-Fi suffers from several well-known problems such as low data rate, high losses and unfairness vis-a-vis other contending unicast transmissions. In this paper we present DirCast, a system to solve many of these problems. DirCast requires no changes to the 802.11 MAC protocol or the wireless access points. Software changes are required on clients only if they wish to participate in multicast sessions. The aim of DirCast system is to minimize the airtime consumed by the multicast traffic, while simultaneously improving client experience. To meet these goals, the DirCast converts multicast packets to unicast packets targeted to certain selected clients; other clients receive these packets by listening in promiscuous mode. The target clients are carefully selected to minimize loss rate experienced by the non-targeted clients. If necessary, clients are forced to change the AP they are associated with. In addition, DirCast uses proactive adaptive FEC to further reduce the loss rate and implements a novel virtual multicast interface in order to be compatible with the security needs of the enterprise. We demonstrate the effectiveness of DirCast using extensive experiments in a Wi-Fi prototype implementation and through large-scale simulations.

The proposed demonstration is based on commodity 802.11 wireless cards and a low cost 2.4GHz sniffing device and shows how current WLAN based networks can benefit from spectrum awareness and dynamic access to the assigned band. The demonstrator presents a solution to problems in current wireless networks, like inefficient radio spectrum usage and limited ability to withstand interferences. The presented spectrum-aware radio management framework flexibly negotiates transmission parameters based on spectrum usage and application requirements and opportunistically utilizes available bandwidth. A key feature of this demonstrator is the fact that it breaks the traditional fixed channel bandwidth limitation and enables dynamic bandwidth allocation according to the needs of applications. It continuously monitors the assigned spectrum, tracks application behaviour, and dynamically adapts to the radio environment, such as interference and competition. Furthermore, a cross-media roaming mechanism is provided in the framework to support seamless handovers within and between radio technologies. Our enhancements are transparent to upper layer applications and will automatically benefit any software using network resources. The presented demonstrator uses wireless video streaming in a typical household scenario to illustrate the benefits without changes to the multimedia applications.

Networking over UHF white spaces is fundamentally different from conventional Wi-Fi along three axes: spatial variation, temporal variation, and fragmentation of the UHF spectrum. Each of these differences gives rise to new challenges for implementing a wireless network in this band. We present the design and implementation of Net7, the first Wi-Fi like system constructed on top of UHF white spaces. Net7 incorporates a new adaptive spectrum assignment algorithm to handle spectrum variation and fragmentation, and proposes a low overhead protocol to handle temporal variation. builds on a simple technique, called SIFT, that reduces the time to detect transmissions in variable channel width systems by analyzing raw signals in the time domain. We provide an extensive evaluation of the system in terms of a prototype implementation and detailed experimental and simulation results.

Currently deployed IEEE 802.11 WLANs (Wi-Fi networks) share access point (AP) bandwidth on a per-packet basis. However, the various stations communicating with the AP often have different signal qualities, resulting in different transmission rates. This induces a phenomenon known as the rate anomaly problem, in which stations with lower signal quality transmit at lower rates and consume a significant majority of airtime, thereby dramatically reducing the throughput of stations transmitting at high rates.We propose a practical, deployable system, called Soft-Repeater, in which stations cooperatively address the rate anomaly problem. Specifically, higher-rate Wi-Fi stations opportunistically transform themselves into repeaters for stations with low data-rates when transmitting to/from the AP. The key challenge is to determine when it is beneficial to enable the repeater functionality. In this paper, we propose an initiation protocol that ensures that repeater functionality is enabled only when appropriate. Also, our system can run directly on top of today's 802.11 infrastructure networks.We evaluate our system using simulation and testbed implementation, and find that SoftRepeater can improve cumulative throughput by up to 200%.

Traditionally, the channelization structure in IEEE 802.11-based wireless LANs has been fixed: Each access point (AP) is assigned one channel and all channels are equally wide. In contrast, it has recently been shown that even on commodity hardware, the channel-width can be adapted dynamically purely in software. Leveraging this capability, we study the use of dynamic-width channels, where every AP adaptively adjusts not only its center-frequency, but also its channel-width to match its traffic load. This gives raise to a novel optimization problem that differs from previously studied channel assignment problems. We propose efficient spectrum-distribution algorithms and evaluate their effectiveness through analysis and simulations using real-world traces. Our results indicate that by allocating more spectrum to highly-loaded APs, the overall spectrum-utilization can be substantially improved and the notorious load-balancing problem in WLANs can be solved naturally.

We study a fundamental yet under-explored facet in wireless communication -- the width of the spectrum over which transmitters spread their signals, or the channel width. Through detailed measurements in controlled and live environments, and using only commodity 802.11 hardware, we first quantify the impact of channel width on throughput, range, and power consumption. Taken together, our findings make a strong case for wireless systems that adapt channel width. Such adaptation brings unique benefits. For instance, when the throughput required is low, moving to a narrower channel increases range and reduces power consumption; in fixed-width systems, these two quantities are always in conflict. We then present a channel width adaptation algorithm, called SampleWidth, for the base case of two communicating nodes. This algorithm is based on a simple search process that builds on top of existing techniques for adapting modulation. Per specified policy, it can maximize throughput or minimize power consumption. Evaluation using a prototype implementation shows that SampleWidth correctly identities the optimal width under a range of scenarios. In our experiments with mobility, it increases throughput by more than 60% compared to the best fixed-width configuration.

Existing traffic analysis tools focus on traffic volume. They identify the heavy-hitters - flows that exchange high volumes of data, yet fail to identify the structure implicit in network traffic - do certain flows happen before, after or along with each other repeatedly over time? Since most traffic is generated by applications (web browsing, email, p2p), network traffic tends to be governed by a set of underlying rules. Malicious traffic such as network-wide scans for vulnerable hosts (mySQLbot) also presents distinct patterns.

Routing protocols have traditionally been based on finding shortest paths under certain cost metrics. A conventional routing metric models the cost of a path as the sum of the costs on the constituting links. This paper introduces the concept of a Markovian metric, which models the cost of a path as the cost of the first hop plus the cost of the second hop conditioned on the first hop, and so on. The notion of the Markovian metric is fairly general. It is potentially applicable to scenarios where the cost of sending a packet (or a stream of packets) over a link may depend on the previous hop of the packet (or the stream). Such scenario arises, for instance, in a wireless mesh network equipped with local mixing, a recent link layer advance. This scenario is examined as a case study for the Markovian metric. The local mixing engine sits between the routing and MAC layers. It maintains information about the packets each neighbor has, and identifies opportunities to mix the outgoing packets via network coding to reduce the transmissions in the air. We use a Markovian metric to model the reduction of channel resource consumption due to local mixing. This leads to routing decisions that can better take advantage of local mixing. We have implemented a system that incorporates local mixing and source routing using a Markovian metric in Qualnet. The experimental results demonstrate significant throughput gain and resource saving.

The Federal Communications Commission (FCC) is currently exploring the use of TV bands for unlicensed communication. This step has sparked significant interest in the research and corporate community as it opens up new possibilities for high speed and long range wireless communication. In this paper, we present the design and implementation of a complete system that can detect the presence of TV signals, and perform high-speed data communication in an available TV band without interfering with neighboring TV bands. To the best of our knowledge, this is the first known system with all the above capabilities.

A number of studies have shown the abundance of unused spectrum in the TV bands. This is in stark contrast to the overcrowding of wireless devices in the ISM bands. A recent trend to alleviate this disparity is the design of Cognitive Radios, which constantly sense the spectrum and opportunistically utilize unused frequencies in the TV bands. A key challenge in the design of such networks is that of Spectrum Allocation, which enables nodes to reserve chunks of the spectrum for certain periods of time. In this paper, we introduce the concept of a time-spectrum block to model spectrum reservation, and use it to present a theoretical formalization of the spectrum allocation problem. We also present a centralized and a distributed protocol for spectrum allocation and show that these protocols are close to optimal in most scenarios. We have implemented the distributed protocol in QualNet and show that our analysis closely matches the simulation results.

IP based telephony is rapidly gaining acceptance over traditional means of voice communication. Wireless LANs are also becoming ubiquitous due to their inherent ease of deployment and decreasing costs. In enterpriseWi-Fi environments, VoIP is a compelling application for devices such as smart phones with multiple wireless interfaces. However, the high energy consumption of Wi-Fi interfaces, especially when a device is idle,presents a significant barrier to the widespread adoption of VoIP over Wi-Fi.To address this issue, we present Cell2Notify, a practical and deployable energy management architecture that leverages the cellular radio on a smart phone to implement wakeup for the high-energy consumption Wi-Fi radio. We present detailed measurements of energy consumption on smart phone devices, and we show that Cell2Notify, can extend the battery lifetime of VoIPover Wi-Fi enabled smart phones by a factor of 1.7 to 6.4.

Localizing the sources of performance problems in large enterprise networks is extremely challenging. Dependencies are numerous, complex and inherently multi-level, spanning hardware and software components across the network and the computing infrastructure. To exploit these dependencies for fast, accurate problem localization, we introduce an Inference Graph model, which is well-adapted to user-perceptible problems rooted in conditions giving rise to both partial service degradation and hard faults. Further, we introduce the Sherlock system to discover Inference Graphs in the operational enterprise, infer critical attributes, and then leverage the result to automatically detect and localize problems. To illuminate strengths and limitations of the approach, we provide results from a prototype deployment in a large enterprise network, as well as from testbed emulations and simulations. In particular, we find that taking into account multi-level structure leads to a 30% improvement in fault localization, as compared to two-level approaches.

We present a framework for monitoring enterprise wireless networks using desktop infrastructure. The framework is called DAIR, which is short for Dense Array of Inexpensive Radios. We demonstrate that the DAIR framework is useful for detecting rogue wireless devices (e.g., access points) attached to corporate networks, as well as for detecting Denial of Service attacks on Wi-Fi networks.Prior proposals in this area include monitoring the network via a combination of access points (APs), mobile clients, and dedicated sensor nodes. We show that a dense deployment of sensors is necessary to effectively monitor Wi-Fi networks for certain types of threats, and one can not accomplish this using access points alone. An ordinary, single-radio AP can not monitor multiple channels effectively, without adversely impacting the associated clients. Moreover, we show that a typical deployment of access points is not sufficiently dense to detect the presence of rogue wireless devices. Due to power constraints, mobile devices can provide only limited assistance in monitoring wireless networks. Deploying a dense array of dedicated sensor nodes is an expensive proposition.Our solution is based on two simple observations. First, in most enterprise environments, one finds plenty of desktop machines with good wired connectivity, and spare CPU and disk resources. Second, inexpensive USB-based wireless adapters are commonly available. By attaching these adapters to desktop machines, and dedicating the adapters to the task of monitoring the wireless network, we create a low cost management infrastructure.

While 802.11-based wireless hotspots are proliferating, users often have little recourse when the network does not work or performs poorly for them. They are left trying to manually debug the problem, which can be a frustrating and disruptive process. The users' troubles are compounded by the absence of network administrators or an IT department to turn to in many 802.11 hotspot settings (e.g., cafes, airports, conferences).We present WiFiProfiler, a system in which wireless hosts cooperate to diagnose and possibly resolve network problems in an automated manner, without requiring any infrastructural support. The key observation is that even if a host's wireless link to an access point is not working, the host is often within the range of other wireless nodes and is in a position to communicate with them (a little) peer-to-peer. We leverage this ability to create a shared information plane, which enables wireless hosts to exchange a range of information about their network settings and the health of their network connectivity. By aggregating and correlating such information across multiple wireless hosts, we infer the likely cause of the problem. Our implementation on Windows XP shows that WiFiProfiler is effective in diagnosing a range of problems and imposes a low overhead on the participating hosts.

Efficient integration of a multi-hop wireless network with the Internet is an important research problem. In a wireless neighborhood network, a few Internet transit access points (ITAPs), serving as gateways to the Internet, are deployed across the neighborhood; houses are equipped with low-cost antennas, and form a multi-hop wireless network among themselves to cooperatively route traffic to the Internet through the ITAPs. Furthermore, the placement of Internet TAPs is a critical determinant of system performance and resource usage. We explore the placement problem under three wireless link models. For each link model, we develop algorithms to make informed placement decisions based on neighborhood layouts, user demands, and wireless link characteristics. We also extend our algorithms to provide fault tolerance and handle significant workload variation. We evaluate our placement algorithms and show that our algorithms yield close to optimal solutions over a wide range of scenarios we have considered.

There are a number of scenarios where it is desirable to have a wireless device connect to multiple networks simultaneously. Currently, this is possible only by using multiple wireless network cards in the device. Unfortunately, using multiple wireless cards causes excessive energy drain and consequent reduction of lifetime in battery operated devices. We propose a software based approach, called MultiNet, that facilitates simultaneous connections to multiple networks by virtualizing a single wireless card. The wireless card is virtualized by introducing an intermediate layer below IP, which continuously switches the card across multiple networks. The goal of the switching algorithm is to he transparent to the user who sees her machine as being connected to multiple networks. We present the design, implementation, and performance of the MultiNet system. We analyze and evaluate buffering and switching algorithms in terms of delay and energy consumption. Our system is agnostic of the upper layer protocols, and works well over popular IEEE 802.11 wireless LAN cards.

The wide-scale deployment of IEEE 802.11 wireless networks has generated significant challenges for Information Technology (IT) departments in corporations. Users frequently complain about connectivity and performance problems, and network administrators are expected to diagnose these problems while managing corporate security and coverage. Their task is particularly difficult due to the unreliable nature of the wireless medium and a lack of intelligent diagnostic tools for determining the cause of these problems.This paper presents an architecture for detecting and diagnosing faults in IEEE 802.11 infrastructure wireless networks. To the best of our knowledge, ours is the first paper to address fault diagnostic issues for these networks. As part of our architecture, we propose and evaluate a novel technique called Client Conduit, which enables boot-strapping and fault diagnosis of disconnected clients. We describe techniques for analyzing performance problems faced in a wireless LAN deployment. We also present an approach for detecting unauthorized access points. We have built a prototype of our fault diagnostic architecture on the Windows operating system using off-the-shelf IEEE 802.11 cards. The initial results show that our mechanisms are effective; furthermore, they impose low overheads when clients are not experiencing problems.

Capacity improvement is one of the principal challenges in wireless networking. We present a link-layer protocol called Slotted Seeded Channel Hopping, or SSCH, that increases the capacity of an IEEE 802.11 network by utilizing frequency diversity. SSCH can be implemented in software over an IEEE 802.11-compliant wireless card. Each node using SSCH switches across channels in such a manner that nodes desiring to communicate overlap, while disjoint communications mostly do not overlap, and hence do not interfere with each other. To achieve this, SSCH uses a novel scheme for distributed rendezvous and synchronization. Simulation results show that SSCH significantly increases network capacity in several multi-hop and single-hop wireless networking scenarios.

Several routing protocols for mobile ad hoc networks work efficiently only in bidirectional networks. Unidirectional links may exist in a real network due to variations in transmission power of different nodes, noise or other signal propagation phenomena, and heterogeneity in the transmission hardware of nodes in the network. We introduce a sub-layer called sub routing layer, SRL, between the network and the MAC layer to provide a bidirectional abstraction of the unidirectional network to the routing protocols. We present a scalable and efficient way to provide this abstraction by finding and maintaining multi-hop reverse routes to each unidirectional link. We simulate SRL and a modified version of AODV (ad hoc on demand distance vector) that uses SRL to route packets in a unidirectional network. We observed that with SRL, the packet delivery of AODV in unidirectional networks increases substantially. Further our simulations indicate that reverse routes are often only a few hops long and hence the overhead of using SRL is very low.

In recent years, a number of applications of ad-hoc networks have been proposed. Many of them are based on the availability of a robust and reliable multicast protocol. We address the issue of reliability and propose a scalable method to improve packet delivery of multicast routing protocols and decrease the variation in the number of packets received by different nodes. The proposed protocol works in two phases. In the first phase, any suitable protocol is used to multicast a message to the group, while in the second concurrent phase, the gossip protocol tries to recover lost messages. Our proposed gossip protocol is called Anonymous Gossip (AG) since nodes need not know the other group members for gossip to be successful. This is extremely desirable for mobile nodes, that have limited resources, and where the knowledge of group membership is difficult to obtain. As a first step, anonymous gossip is implemented over MAODV without much overhead and its performance is studied. Simulations show that the packet delivery of MAODV is significantly improved and the variation in number of packets delivered is decreased.