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.

Online cloud gaming platforms stream game media to multiple end-points (e.g., a television display and a controller-connected headset) via possibly different networks with considerably different latencies. This leads to the media being played out of sync with one another, and severely degrades user experience. Typical approaches that rely on network and software timing measurements fail to reach synchronization goals. In this work, we propose Ekho, a robust and efficient end-to-end approach for synchronizing streams transmitted to two devices. Ekho adds faint, human-inaudible pseudo-noise (PN) markers to the game audio, and listens for these markers in the chat audio captured by the player's microphone to measure inter-stream delay (ISD). The game server then compensates for the ISD to synchronize the streams. We evaluate Ekho in depth, with a corpus of audio samples from popular online games, and demonstrate that it calculates ISD with sub-millisecond accuracy, has low computational overhead, and is resilient to background chatter, compression and microphone quality. In end-to-end tests over WiFi and cellular links with frequent packet loss and playback disruption, Ekho maintains human-imperceptible ISD (< 10 ms) 86.8% of the time. Without Ekho, the ISD exceeds 50 ms at all times.

With its ability to communicate long distances, LoRa promises city-scale IoT deployments for smart city applications. This long-range, however, also increases contention as many thousands of devices are connected. Recently, CSMA has been proposed as a viable MAC for resolving contention in LoRa networks. In this paper, supported by measurements, we demonstrate that CSMA is ineffective in urban deployments. While gateways stationed at rooftops enjoy a long communication range, 70% of the devices placed at street level fail to sense each others' transmissions and remain hidden, owing to obstructions by tall structures. We present Busy Signal Multiple Access (BSMA), where the LoRa gateway transmits a downlink busy signal while receiving an uplink transmission. The IoT devices defer uplink transmissions while a busy signal is present. Practically viable BSMA requires a full-duplex LoRa gateway - i.e., a gateway that can simultaneously receive and transmit in the same channel. We develop the first full Duplex LoRa gateway in the 915 MHz ISM band, overcoming challenges that arise from a 9× greater delay spread and the need for 1000× greater self-interference cancellation. Our implementation works with COTS LoRa devices and improves network capacity by 100% compared to CSMA in bursty loads while being fair to all IoT devices near and far.

Continuously monitoring a wide variety of performance and fault metrics has become a crucial part of operating large-scale datacenter networks. In this work, we ask whether we can reduce the costs to monitor - in terms of collection, storage and analysis - by judiciously controlling how much and which measurements we collect. By positing that we can treat almost all measured signals as sampled time-series, we show that we can use signal processing techniques such as the Nyquist-Shannon theorem to avoid wasteful data collection. We show that large savings appear possible by analyzing tens of popular measurement systems from a production datacenter network. We also discuss some challenges that must be solved when applying these techniques in practice.

LoRa has seen widespread adoption as a long range IoT technology. As the number of LoRa deployments grow, packet collisions undermine its overall network throughput. In this paper, we propose a novel interference cancellation technique -- Concurrent Interference Cancellation (CIC), that enables concurrent decoding of multiple collided LoRa packets. CIC fundamentally differs from existing approaches as it demodulates symbols by canceling out all other interfering symbols. It achieves this cancellation by carefully selecting a set of sub-symbols -- pieces of the original symbol such that no interfering symbol is common across all sub-symbols in this set. Thus, after demodulating each sub-symbol, an intersection across their spectra cancels out all the interfering symbols. Through LoRa deployments using COTS devices, we demonstrate that CIC can increase the network capacity of standard LoRa by up to 10x and up to 4x over the state-of-the-art research. While beneficial across all scenarios, CIC has even more significant benefits under low SNR conditions that are common to LoRa deployments, in which prior approaches appear to perform quite poorly.

Large food-service companies typically support a wide range of operations (catering, vending machines, repairs), each with different operational characteristics (manpower, vehicles, tools, timing constraints, etc.). While the advances in Internet-based technologies facilitate the adoption of automated scheduling systems, the complexity and heterogeneity of the different operations hinders the design of comprehensive optimization solutions. Indeed, our collaboration with Compass Group, one of the largest food-service companies in the world, reveals that many of its workforce assignments are done manually due to the lack of scheduling solutions that can accommodate the complexity of operational constraints. Further, the diversity in the nature of operations prevents collaboration and sharing of resources among various services such as catering and beverage distribution, leading to an inflated fleet size.

Longer range, in rural/urban IoT networks, allow a large geographical coverage with only a few base-stations, making their deployment and operation economical. In this paper we explore the 150-174 MHz spectrum for long range IoT networks comprising unlicensed MURS and licensed VHF narrowbands. Range in these bands is boosted by the lower RF frequencies as well as higher transmit powers allowed by the FCC. Through a 400 sq km wide area deployment study, we show that, these spectrum bands can provide > 20× the geographical coverage than that in the 900 MHz ISM band LoRa. Increased range translates to greater uplink IoT device traffic. The key contribution of this paper is a novel technique - Blind Distributed MU-MIMO, that allows capacity to scale with the number of antennas (base- stations) while not requiring any coordinated channel measurements between the devices and IoT base- stations. This requirement is crucial since in IoT networks power constrained IoT devices typically sleep and wake up to transmit short messages in response to unpredictable events without any coordination with the base-stations. We demonstrate the efficacy of Blind Distributed MU-MIMO through a real wide area deployment.

Interference in WiFi deployments is a growing problem due to the increasing popularity of WiFi. Therefore it is important that APs find the right channel to operate upon. Through a large scale measurement study involving over 10,000 WiFi APs we show that channel measurements and selection are most effective when performed frequently (every few minutes). This is because of the highly dynamic nature of WiFi traffic congestion. Our key contribution in this paper is a novel approach to distributed channel selection -- Ineffective time Quantum (IQ) Hopping, that is simple enough to be described in three lines and has provable optimality guarantees. IQ-Hopping does not require any explicit channel measurements and can react within a matter of several seconds to bad channel conditions, including microwave ovens, hidden interferers, or dynamically varying congestion. Through implementation and experiments on off-the-shelf WiFi routers (OpenWRT, MadWiFi), we demonstrate the effectiveness of IQ-Hopping.

No abstract available.

We consider the problem of tracking physical browsing by users in indoor spaces such as retail stores. Analogous to online browsing, where users choose to go to certain webpages, dwell on a subset of pages of interest to them, and click on links of interest while ignoring others, we can draw parallels in the physical setting, where a user might walk purposefully to a section of interest, dwell there for a while, gaze at specific items, and reach out for the ones that they wish to examine more closely.

Near Field Communication (NFC) enables physically proximate devices to communicate over very short ranges in a peer-to-peer manner without incurring complex network configuration overheads. However, adoption of NFC-enabled applications has been stymied by the low levels of penetration of NFC hardware. In this paper, we address the challenge of enabling NFC-like capability on the existing base of mobile phones. To this end, we develop Dhwani, a novel, acoustics-based NFC system that uses the microphone and speakers on mobile phones, thus eliminating the need for any specialized NFC hardware. A key feature of Dhwani is the JamSecure technique, which uses self-jamming coupled with self-interference cancellation at the receiver, to provide an information-theoretically secure communication channel between the devices. Our current implementation of Dhwani achieves data rates of up to 2.4 Kbps, which is sufficient for most existing NFC applications.

We consider the problem of locating devices such as laptops, desktops, smartphones etc. within an office environment, without requiring any special hardware or infrastructure. We consider two widely-studied approaches to indoor localization: (a) those based on Radio Frequency (RF) measurements made by devices with WiFi or cellular interfaces, and (b) those based on Acoustic Ranging (AR) measurements made by devices equipped with a speaker and a microphone. A typical office environment today comprises devices that are amenable to either one or both these approaches to localization. In this paper we ask the question, "How can we combine RF and AR based approaches in synergy to locate a wide range of devices, leveraging the benefits of both approaches?" The key contribution of this paper is Centaur, a system that fuses RF and AR based localization techniques into a single systematic framework that is based on Bayesian inference. Centaur is agnostic to the specific RF or AR technique used, giving users the flexibility of choosing their preferred RF or AR schemes. We also make two additional contributions: making AR more robust in non-line-of-sight settings (EchoBeep) and adapting AR to localize speaker-only devices (DeafBeep). We evaluate the performance of our AR enhancements and that of the Centaur framework through microbenchmarks and deployment in an office environment.

Radio Frequency (RF) fingerprinting, based onWiFi or cellular signals, has been a popular approach to indoor localization. However, its adoption in the real world has been stymied by the need for sitespecific calibration, i.e., the creation of a training data set comprising WiFi measurements at known locations in the space of interest. While efforts have been made to reduce this calibration effort using modeling, the need for measurements from known locations still remains a bottleneck. In this paper, we present Zee -- a system that makes the calibration zero-effort, by enabling training data to be crowdsourced without any explicit effort on the part of users. Zee leverages the inertial sensors (e.g., accelerometer, compass, gyroscope) present in the mobile devices such as smartphones carried by users, to track them as they traverse an indoor environment, while simultaneously performing WiFi scans. Zee is designed to run in the background on a device without requiring any explicit user participation. The only site-specific input that Zee depends on is a map showing the pathways (e.g., hallways) and barriers (e.g., walls). A significant challenge that Zee surmounts is to track users without any a priori, user-specific knowledge such as the user's initial location, stride-length, or phone placement. Zee employs a suite of novel techniques to infer location over time: (a) placement-independent step counting and orientation estimation, (b) augmented particle filtering to simultaneously estimate location and user-specific walk characteristics such as the stride length,(c) back propagation to go back and improve the accuracy of ocalization in the past, and (d) WiFi-based particle initialization to enable faster convergence. We present an evaluation of Zee in a large office building.

We investigate the following fundamental question—how fast can information be collected from a wireless sensor network organized as tree? To address this, we explore and evaluate a number of different techniques using realistic simulation models under the many-to-one communication paradigm known as convergecast. We first consider time scheduling on a single frequency channel with the aim of minimizing the number of time slots required (schedule length) to complete a convergecast. Next, we combine scheduling with transmission power control to mitigate the effects of interference, and show that while power control helps in reducing the schedule length under a single frequency, scheduling transmissions using multiple frequencies is more efficient. We give lower bounds on the schedule length when interference is completely eliminated, and propose algorithms that achieve these bounds. We also evaluate the performance of various channel assignment methods and find empirically that for moderate size networks of about 100 nodes, the use of multifrequency scheduling can suffice to eliminate most of the interference. Then, the data collection rate no longer remains limited by interference but by the topology of the routing tree. To this end, we construct degree-constrained spanning trees and capacitated minimal spanning trees, and show significant improvement in scheduling performance over different deployment densities. Lastly, we evaluate the impact of different interference and channel models on the schedule length.

The increase in WiFi physical layer transmission speeds from 1~Mbps to 1 Gbps has reduced transmission times for a 1500 byte packet from 12 ms to 12 us. However, WiFi MAC overheads such as channel access and acks have not seen similar reductions and cumulatively contribute about 150 us on average per packet. Thus, the efficiency of WiFi has deteriorated from over 80% at 1 Mbps to under 10% at 1 Gbps.

Traffic patterns in manufacturing machines exhibit strong temporal correlations due to the underlying repetitive nature of their operations. A MAC protocol can potentially learn these patterns and leverage them to efficiently schedule nodes' transmissions. Recently, with the advent of low power MEM based sensors, wireless sensing in these machines has gained prominence. Communication in control loops must cater to extremely low hard real-time latencies while embracing low-power design principles. In this paper, we present a novel MAC, i-MAC, a wireless MAC protocol that learns to expect and plan for traffic bursts and consequently coordinate node transmissions efficiently.

While WiFi-based indoor localization is attractive, the need for a significant degree of pre-deployment effort is a key challenge. In this paper, we ask the question: can we perform indoor localization with no pre-deployment effort? Our setting is an indoor space, such as an office building or a mall, with WiFi coverage but where we do not assume knowledge of the physical layout, including the placement of the APs. Users carrying WiFi-enabled devices such as smartphones traverse this space in normal course. The mobile devices record Received Signal Strength (RSS) measurements corresponding to APs in their view at various (unknown) locations and report these to a localization server. Occasionally, a mobile device will also obtain and report a location fix, say by obtaining a GPS lock at the entrance or near a window. The centerpiece of our work is the EZ Localization algorithm, which runs on the localization server. The key intuition is that all of the observations reported to the server, even the many from unknown locations, are constrained by the physics of wireless propagation. EZ models these constraints and then uses a genetic algorithm to solve them. The results from our deployment in two different buildings are promising. Despite the absence of any explicit pre-deployment calibration, EZ yields a median localization error of 2m and 7m, respectively, in a small building and a large building, which is only somewhat worse than the 0.7m and 4m yielded by the best-performing but calibrationintensive Horus scheme [29] from prior work.

Discrete event control loops in modern-day machines often comprise a large number of sensors (50-200) reporting to a controller. Many discrete control applications must cater to hard real-time requirements i.e., sensors must communicate the occurrence of critical events to the controller within a real-time deadline (usually 5-50 ms) specified by the control system's design requirements. Messages reached after this deadline are considered lost. In the event of traffic bursts where several sensors may attempt to communicate with the PLC at the same time, messages from all the sensors must reach within the specified deadline. The metric for performance in such systems is then the probability that a message from all the sensors succeeds in being received at the controller within this deadline. Further, for such solutions to be viable, the sensors must last for several years without requiring change of batteries. In this paper we examine the potential for using 802.15.4 based radios for wireless sensing in low-latency hard real-time discrete event control applications.

Communication among wireless sensor nodes that employ cheap low-power transceivers is often very sensitive to the variations of the wireless channel. Sensor network routing protocols thus strive to continually adapt to temporal variations in wireless links in order to avoid wasteful transmissions over low-quality links. Such adaptive routing protocols must rely on a scheme that can not only accurately estimate the quality of wireless links in terms of a quantitative measure, such as the packet success rate (PSR), but also quickly adapt to temporal dynamics of the links. Traditionally, the PSR is estimated from the fraction of successful transmissions over a window of test- packets. However, we demonstrate that counting based methods do not react to changes in the wireless channel fast enough and that the only way to address this problem is to estimate the PSR based on the receiver's characteristics and on the signal to noise ratio (SNR) at the receiver. We thus propose a scheme that uses a pre-calibrated SNR-PSR relationship and instantaneous SNR estimates to calculate the PSR of the link. In our scheme, each receiver continuously tracks the SNR using a Kalman Filter to minimize the estimation error and uses a locally available SNR- PSR curve to estimate the PSR. Through extensive experiments we demonstrate that our scheme adapts to variations in the channel faster than counting-based PSR estimators and that it also provides better PSR estimates than these counting-based approaches.

With the advent of miniaturized sensing technology, it has become possible to envision smart structures containing millions of sensors embedded in concrete for autonomously detecting and locating incipient damage. Where are we today in our march towards this vision of autonomous structural health monitoring (SHM) using networked embedded sensing? In this paper, we summarize some of the systems we have developed towards this vision. Wisden is a wireless sensor network that allows continuous monitoring of structures and NetSHM is a programmable system that allows civil engineers to implement and deploy SHM techniques without having to understand the intricacies of wireless sensor networking. We highlight our experiences in developing these systems, and discuss the implications of our experiences on the achievability of the overall vision.

Ad-hoc localization systems enable nodes in a sensor network to fix their positions in a global coordinate system using a relatively small number of anchor nodes that know their position through external means (e.g., GPS). Because location information provides context to sensed data, such systems are a critical component of many sensor networks and have therefore received a fair amount of recent attention in the sensor networks literature. The efficacy of these systems is a function of the density of deployment and of anchor nodes, as well as the error in distance estimation (ranging) between nodes. In this paper, we examine how these factors impact the performance of the system. This examination lays the groundwork for the main question we consider in this paper: Can the ability to estimate bearing to neighboring nodes greatly increase the performance of ad-hoc localization systems? We discuss the design of ad-hoc localization systems that use range together with either bearing or imprecise bearing (such as sectoring) information, and evaluate these systems using analysis and simulation.

Structural health monitoring (SHM) is a well-established multi-disciplinary research field. The goal of SHM is to develop technologies and techniques to automatically detect, localize, and classify damages in large structures (ships, bridges, aircraft and buildings). The state of the art in SHM relies on collecting response of these structures to ambient phenomena such as wind, passing vehicles or earthquakes at various points in the structure (either via manual inspections or expensive wired data acquisition systems) to be analyzed centrally. In our demonstration we will show a proof of concept working model of an automated distributed damage detection system using a sensor-actuator network.

Structural monitoring---the collection and analysis of structural response to ambient or forced excitation--is an important application of networked embedded sensing with significant commercial potential. The first generation of sensor networks for structural monitoring are likely to be data acquisition systems that collect data at a single node for centralized processing. In this paper, we discuss the design and evaluation of a wireless sensor network system (called Wisden for structural data acquisition. Wisden incorporates two novel mechanisms, reliable data transport using a hybrid of end-to-end and hop-by-hop recovery, and low-overhead data time-stamping that does not require global clock synchronization. We also study the applicability of wavelet-based compression techniques to overcome the bandwidth limitations imposed by low-power wireless radios. We describe our implementation of these mechanisms on the Mica-2 motes and evaluate the performance of our implementation. We also report experiences from deploying Wisden on a large structure.

Since the introduction of fuzzy clustering by Ruspini (1970), fuzzy logic has provided a family of interesting clustering algorithms which expanded the abilities of 'crisp' techniques. The most popular among these algorithms is the fuzzy c means algorithm (FCM). However, FCM and most of its variants are sensitive to the presence of outliers in the data set. Past attempts to reduce this sensitivity included the addition of a "noise cluster" and the introduction of measures that assess the typicality of a vector to a cluster. In this study we provide additional means for outlier rejection through the introduction of a new variable, the credibility of a vector. Credibility measures the typicality of the vector to the entire data set (not to specific subsets as some previous techniques have done). An outlier is expected to have a low value of credibility compared to a non-outlier. The use of the new variable leads to the credibilistic fuzzy c means algorithm.

We propose a method for optimizing the architecture of a multilayer perceptron (MLP) network. The proposed scheme is a variation of the MLP architecture, in which each neuron's output is modulated by an efficiency factor associated with that node. Nodes with low efficiency factor literally do not participate in the network. We compute the efficiency of a node using a multiplier function with a learnable parameter, which we call the multiplier of that node. Values of the multipliers are learned by a gradient descent along with the weights, aiming to minimize the mean square error. Training starts with all node efficiencies set very low so that there is literally no connection between any of the neurons in the net. As the learning progresses, gradually some of the nodes start acquiring high efficiencies. Training is terminated when performance of the network is satisfactory. At the end of the training nodes with low efficiency are eliminated and a near optimal architectural size for the MLP is obtained. Effectiveness of the proposed scheme is demonstrated on several data-sets.