Micro aerial vehicles (MAVs), an emerging class of aerial drones, are fast turning into high value mobile sensing assets. While MAVs have a large sensory gamut at their disposal; vision continues to dominate the external sensing scene, with limited usability in scenarios that offer acoustic clues. Therefore, we endeavor to provision a MAV auditory system (i.e., ears); and as part of this goal, our preliminary aim is to develop a robust acoustic localization system for detecting sound sources in the physical space-of-interest. However, devising this capability is extremely challenging due to strong ego-noise from the MAV propeller units, which is both wideband and non-stationary. It is well known that beamformers with large sensor arrays can overcome high noise levels; but in an attempt to cater to the platform (i.e., space, payload and computation) constraints of a MAV, we propose DroneEARS: a binaural sensing system for geo-locating sound sources. It combines the benefits of sparse (two elements) sensor array design (for meeting the platform constraints), and our proposed mobility-aided beamforming (for overcoming the severe ego-noise and its other complex characteristics) to significantly enhance the received signal-to-noise ratio (SNR). We demonstrate the efficacy of DroneEARS by empirical evaluations, and show that it provides a SNR improvement of 15-18 dB compared to many conventional and widely used techniques. This SNR gain translates to a source localization accuracy of approximately 40 cm within a scan region of 6m × 3m , that is, one order of magnitude better than competing methodologies.

Cross-correlation is a popular signal processing technique used in numerous location tracking systems for obtaining reliablerangeinformation. However, its efficient design and practical implementation has not yet been achieved on mote platforms that are typical in wireless sensor network due to resource constrains. In this paper, we proposeStructS-XCorr: cross-correlation via structured sparse representation, a new computing framework for ranging based on$\ell _1$-norm minimization[1]and structured sparsity. The key idea is to compress the ranging signal samples on the mote by efficient random projections and transfer them to a central device; where a convex optimization process estimates the range by exploiting the sparse signal structure in the proposedcorrelationdictionary. Through theoretical validation, extensive empirical studies and experiments on anend-to-endacoustic ranging system implemented on resource limited off-the-shelf sensor nodes, we show that the proposed framework can achieve up totwo orders of magnitudebetter performance compared to other approaches such as working on DCT domain and downsampling. Compared to the standard cross-correlation, it is able to obtain range estimates with a bias of 2-6 cm with 30 percent and approximately 100 cm with 5 percent compressed measurements. Its structured sparsity model is able to improve the ranging accuracy by 40 percent under challenging recovery conditions (such as high compression factor and low signal-to-noise ratio) by overcoming limitations due to dictionary coherence.

Aerial drones (or flying robots) based inspection monitoring is fast turning into an high value asset for applications across sectors. Our focus in this work is to develop the "Ear" or the auditory sensing system of the drone so as to advance it sensing abilities beyond vision (the default external sensor) for it to better serve a wide range of inspection scenarios. However, devising this capability is extremely challenging due to severe acoustic self-interference from its rotor blades. In this work, we investigate the related challenges and overcome some of the initial hurdles by proposing a subspace based denoising algorithm, followed by its application in electricity power distribution systems.

The ubiquitous location sensing trend has increased the demand for low-cost GPS receivers; but their energy needs are still too high. For delay-tolerant applications, investigations show that significant energy saving can be obtained by offloading a few milliseconds of raw signal samples and leveraging the greater processing power of the cloud for obtaining a position fix. In an attempt to reduce the energy cost of this data offloading operation, we propose SparseGPS+. Based on the sparse decomposition model, it overcomes many limitations of SparseGPS [1] to yield better signal-to-noise ratio and detection accuracy; which translates to 30% more energy savings compared to the state-of-the-art.

A leading cause of mortality of hospitalized patients are hospital acquired infections (HAI). Unclean hands of healthcare personnel (HCP) are the most common factor contributing to HAI, but their strict compliance to hand hygiene protocols is difficult to supervise. In this work, we propose CleanHands: a simple, low-cost and scalable monitoring and alerting system to ensure adequate thoroughness of disinfection. CleanHands uses a combination of low-cost Bluetooth low energy (BLE) beacon tags and mobile phones for HCP tracking. It integrates infection control models and state-following algorithms for alarming in the event of noncompliance to hand hygiene. Our preliminary experiments in a mockup, small scale intensive care unit (ICU) facility shows promising results with less than 5% false positives.

The primal problem with the Internet of Things is the lack of interoperability at various levels, and more predominately at the device level. While there exists multitude of platforms from multiple manufacturers, the existing ecosystem still remains highly closed. In this work, we propose SNaaS or Sensor/Network as a Service: a service layer that enables the creation of the plug-n-play infrastructure, across platforms from multiple vendors, necessary for interoperability and successful deployment of large-scale systems. We present the design and implementation of SNaaS, along with preliminary microbenchmarks.

Clock synchronization is highly desirable in distributed systems, including many applications in the Internet of Things and Humans (IoTH). For IoTH, Bluetooth Low Energy (BLE) - a subset of the recent Bluetooth v4:0 stack - provides a low-power and loosely coupled mechanism for sensor data collection with ubiquitous units (e.g., smartphones and tablets) carried by humans. This fundamental design paradigm of BLE is enabled by a range of broadcast advertising modes. While its operational benefits are numerous, the lack of a common time reference in the broadcast mode of BLE has been a fundamental limitation. This work presents and describes CheepSync: a time synchronization service architecture for BLE advertisers. We implement CheepSync on custom designed nRF24Cheep beacon platforms (as broadcasters) and commercial off-the-shelf Android ported smartphones (as passive listeners); and show that the average (single hop) time synchronization accuracy is in the 10 μs range.

In this work, an algorithm is proposed for self-localization of a target node using power measurements from beacon nodes transmitting from known locations. The geographical area is overlaid with a virtual grid, and the problem is treated as one of testing overlapping subsets of grid cells for the presence of the target node. The proposed algorithm is validated both by Monte Carlo simulations as well as using experimental data collected from commercially-off-the-shelf bluetooth low energy (BLE) beacon nodes.

The past couple of years have seen a heightened interest in the Internet of Things (IoT), transcending industry, academia and government. As with new ideas that hold immense potential, the optimism of IoT has also exaggerated the underlying technologies well before they can mature into a sustainable ecosystem. While 6LoWPAN has emerged as a disruptive technology that brings IP capability to networks of resource constrained devices, a suitable radio technology for this device class is still debatable. In the recent past, Bluetooth Low Energy (LE) — a subset of the Bluetooth v4.0 stack — has surfaced as an appealing alternative that provides a low-power and loosely coupled mechanism for sensor data collection with ubiquitous units (e.g., smartphones and tablets). When Bluetooth 4.0 was first released, it was not targeted for IP-connected devices but for communication between two neighboring peers. However, the latest release of Bluetooth 4.2 offers features that makes Bluetooth LE a competitive candidate among the available low-power communication technologies in the IoT space. In this paper, we discuss the novel features of Bluetooth LE and its applicability in 6LoWPAN networks. We also highlight important research questions and pointers for potential improvement for its greater impact.

The global positioning system (GPS) system is a dominant wireless technology that enables reliable location sensing for a diverse range of outdoor mobile applications. Following rising demands for location sensing, low-cost GPS receivers are becoming widely available; but their energy demands are still too high to be useful for many of these applications. For energy efficient GPS sensing, the possibility of offloading a few milliseconds of raw signal samples and leveraging the greater processing power of the cloud for obtaining a position fix is being actively investigated. In an attempt to reduce the energy cost of this data offloading operation, we propose SparseGPS: a lightweight GPS acquisition mechanism based on sparse approximation.

Location sensing provides endless opportunities for a wide range of applications in GPS-obstructed environments, where, typically, there is a need for a higher degree of accuracy. In this article, we focus on robust range estimation , an important prerequisite for fine-grained localization. Motivated by the promise of acoustic in delivering high ranging accuracy, we present the design, implementation, and evaluation of acoustic (both ultrasound and audible) ranging systems. We distill the limitations of acoustic ranging and present efficient signal designs and detection algorithms to overcome the challenges of coverage, range, accuracy/resolution, tolerance to Doppler's effect, and audible intensity. We evaluate our proposed techniques experimentally on TWEET, a low-power platform purpose-built for acoustic ranging applications. Our experiments demonstrate an operational range of 20m (outdoor) and an average accuracy ≈2cm in the ultrasound domain. Finally, we present the design of an audible-range acoustic tracking service that encompasses the benefits of a near-inaudible acoustic broadband chirp and approximately two times increase in Doppler tolerance to achieve better performance.

Cross-correlation is a popular signal processing technique used in numerous localization and tracking systems for obtaining reliable range information. However, a practical efficient implementation has not yet been achieved on resource constrained wireless sensor network platforms. We propose cross-correlation via sparse representation: a new framework for ranging based on l1-minimization. The key idea is to compress the signal samples on the mote platform by efficient random projections and transfer them to a central device, where a convex optimization process estimates the range by exploiting its sparsity in our proposed correlation domain. Through sparse representation theory validation, extensive empirical studies and experiments on an end-to-end acoustic ranging system implemented on resource limited off-the-shelf sensor nodes, we show that the proposed framework, together with the proposed correlation domain achieved up to two order of magnitude better performance compared to naive approaches such as working on DCT domain and downsampling. Furthermore, compared to cross-correlation results, 30-40% measurements are sufficient to obtain precise range estimates with an additional bias of only 2-6cm for high accuracy application requirements, while 5% measurements are adequate to achieve approximately 100cm precision for lower accuracy applications.

Cross-correlation is a popular signal processing technique used for obtaining reliable range information. Recently, a practical and efficient implementation of cross-correlation (via sparse approximation) was demonstrated on resource constrained wireless sensor network platforms, where the key idea was to compress the received signal samples, and transfer them to central device where the range information was retrieved by l1-minimization. Although, this mechanism yields accurate ranging results, its applicability is limited due to its slow execution speed and inaccurate recovery of the correlation peak magnitude, which implicitly provides the useful measure of signal-to-noise ratio. In this work, we propose Fast Gradient Projection (F-GP), a new l1-minimization algorithm, which overcomes the existing limitations, and provides fast and accurate ranging.

Fine-grained location information at long range can benefit many applications of embedded sensor networks and robotics. In this paper, we focus on range estimation - an important prerequisite for fine-grained localization - in the ultrasonic domain for both indoor and outdoor environments, and make three contributions. First, we evaluate the characteristics of broadband signals, and provide useful statistics in their design and engineering to achieve a good trade-off between range and accuracy. Second, to overcome the inaccuracy due to correlation sidelobes, we propose a signal detection technique that estimates the envelope of the correlated pulse using a simple least-square approximation approach, and undertake a simulation study to verify its ranging efficiency on linear chirps. Third, leveraging on the insights obtained from our initial study, we present the design and implementation of TWEET: a mote-based ultrasonic broadband ranging system based on linear chirps using the CSIRO Audio nodes, which comprises of a Fleck-3z mote along with audio codecs and a Blackfin DSP. Our evaluation results indicate that the system is precise enough to support source localization applications: a reliable operational range of 20m (outdoor) and an average accuracy of < 2 cm with a 95% confidence interval of 2 cm.

Location awareness is of benefit to a rich set of applications in indoor environments such as asset tracking, resource discovery, interactive virtual games, location-aware sensor networking, navigation support for humans and robots, etc. There exists a vast array of location sensing systems, but they mostly operate under dense indoor deployment due to the limited range and coverage of the sensing device. In this paper, we focus on improving coverage range - a critical prerequisite for localization. Our experiments using the existing Cricket system reveals many of its limitations. We overcome these hurdles, and present the design, implementation and evaluation of a custom designed omni-directional ultrasonic receiver unit integrated with the existing Cricket motes. The modified Cricket system improves the coverage range by ≈20% in comparison to the original Cricket. The lessons and experiences also provide an analytical and system-level understanding of how various factors affect the ranging characteristics.

The performance of various localisation schemes in sensor networks can be improved by long-range detection. However, its performance deteriorates under noisy environmental conditions. This work presents a system implementation of an acoustic ranging mechanism based on wideband ultrasonic linear chirps and time-reversal technique to counter the negative influence of channel multipath. We investigated the performance of our technique through experimentation with chirps of length 500 milliseconds. The proposed scheme provided distance ranging up to 10 meters, and showed an improvement in peak detection with respect to conventional techniques.

Recent experimental studies in wireless sensor networks (WSNs) have confirmed that asymmetry in the wireless links has a significant effect on the performance of WSN network protocols. Protocols which work in simulation studies often fail when link asymmetry is encountered in real deployments. Characterization of link asymmetry is thus of paramount importance for the design and operation of re-silient WSN protocols in real scenarios. This paper details an empirical study to characterize link asymmetry in WSNs.There are several factors that contribute to link asymmetry in WSNs, the major ones being environmental effects and hardware performance. In this work, we used a systematic approach to measure the effects of hardware performance, i.e. transmitter, receiver and antenna characteristics, on link asymmetry using off-the-shelf WSN devices such as Xbow Mica2 and MicaZ motes. We conducted experiments to study the variations in spectrum utilization in these WSN devices, for both wired and wireless connections, and to ascertain the effects of any frequency and amplitude mismatches on link symmetry between nodes in transmission and reception.

Ultrasound (US) based Cricket indoor location system has limited range when the transmitter and receiver motes are not in the line-of-sight (LOS) positions. It uses narrowband US transducers which are unidirectional and require tilting of the motes in order to improve the signal reception quality. The main focus of this work is to improve the ranging distance of the ultrasound based sensor motes with an objective of successfully deploying in harsh environments where the prime challenge is that of gracefully sustaining the signal characteristics for the purpose of distance estimation. We implement and provide an analysis of two different techniques with respect to the original Cricket: 1) Omni-directional receiver: A dodecahedron arrangement of an array of 3 US transducers. 2) Chirp spread spectrum based broadband transmission scheme and signal correlation technique.

Linear Variable Differential Transformer (LVDT) based sensing systems are effective in structural health monitoring. However, the usable range and accuracy of these sensors are severely affected due to the non-linearity exhibited in their input-output characteristics. This work presents a Field Programmable Gate Array (FPGA) implementation of an Artificial Neural Network (ANN) based non-linearity compensator of a LVDT sensor. Functional Link Artificial Neural Network (FLANN), an improved version of the conventional neural networks has been implemented for simplicity. The result obtained from the FPGA implementation is in good agreement with the simulation result in MATLAB.