Introducing Chirpy, a hardware module designed for swarm robots that enables them to locate each other and communicate through audio. With the help of its deep learning module (AudioLocNet), Chirpy is capable of performing localization in challenging environments, such as those with non-line-of-sight and reverb. To support concurrent transmission, Chirpy uses orthogonal audio chirps and has an audio message frame design that balances localization accuracy and communication speed. As a result, a swarm of robots equipped with Chirpies can on-the-fly construct a path (or a potential field) to a location of interest without the need for a map, making them ideal for tasks such as search and rescue missions. Our experiments show that Chirpy can decode messages from four concurrent transmissions with a Bit Error Rate (BER) of at a distance of 250 cm, and it can communicate at Signal-to-Noise Ratios (SNRs) as low as -32 dB while maintaining ≈ 0 BER. Furthermore, AudioLocNet demonstrates high accuracy in classifying the location of a transmitter, even in adverse conditions such as non-line-of-sight and reverberant environments.

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The Internet of Things (IoT), a recent technology that has enabled many innovative applications, has dominated the world by creating smart systems and applications. It is predicted that the number of connected gadgets in IoT will double by 2030 compared to 2020. Thus, it is essential to address the key challenges, such as scalability, ubiquitous global coverage, and real-time connectivity, that arise due to this immense growth of IoT devices. However, extending the existing terrestrial networks such as mobile towers to under-served regions of the world including remote areas, oceans, and mountains, to achieve scalability and global coverage is not a cost-viable solution. Space, on the other hand, can be a suitable platform to solve the majority of these existing/upcoming problems in the IoT domain. Space is the next frontier for innovations in IoT. The main idea is to employ space technologies for IoT applications. Space Internet of Things (Space-IoT), as we call, is a concept that involves a satellite, or a network of them, to address the main challenges in terrestrial IoT deployments – global coverage, scalability, and connectivity. Space-IoT is opening up a world of new possibilities for several applications. Small satellites are the building blocks of Space-IoT. They represent a formidable mobile computing platform enabling large-scale space applications at a fraction of the cost of larger satellites. Space-IoT calls for hundreds or thousands of small satellites that can communicate directly with various IoT devices on Earth. However, access to space has been expensive due to the high satellite development and launch costs. Miniaturizing a satellite can reduce launch costs but presents a range of interdisciplinary challenges that must be tackled. Resources are severely constrained in terms of size, mass, and available power. Addressing these challenges requires different communities to push the envelope in the design and realization of miniaturized subsystems of a small satellite. In this dissertation, we chart out a vision for Space-IoT and innovations in embedded and wireless systems for Space-IoT applications. We enlist several important challenges that need to be addressed immediately to bring the vision of Space-IoT to reality. This thesis targets one of the most significant tradeoffs – miniaturization leading to constrained energy while not compromising the reliability of operations of subsystems. We consider three subsystems of a satellite: communication, attitude determination, and health monitoring, to demonstrate the inter-dependencies and novel ways to tackle them. Further, we explain with examples what we envision for the next decade to facilitate Space-IoT. In Space-IoT, the IoT nodes on Earth are expected to communicate with (small) satellites directly over hundreds of kilometres. Both these terrestrial nodes and the satellites in space are energy-constrained. Hence, the communications must not only be energy-efficient but also support long range. Moreover, the received signal strength and the Signal to Noise Ratio (SNR) on the receiver decrease as the communication distance increases. Further, Doppler shift is inevitable in Low Earth Orbit bound satellite communication. Boosting the transmission power and adopting high-gain large antennas are obvious solutions for reliable communication, however, not feasible with miniaturization and energy minimization as our objectives. One of the solutions to support low-power, long-range communication is to improve the demodulation technique to decode signals with low SNR. In this dissertation, we revisit the demodulation approach of a widely used modulation technique - Frequency Shift Keying (FSK). We propose a scheme to demodulate bandpass sampled FSK signals that are influenced by Doppler shift and low SNR. Unlike the state-of-the-art techniques, our approach does not compensate for the Doppler shift but lives with it. To suppress the Doppler effect and improve the SNR of the received signal, we employ a matched filter and the Teager Energy Operator, respectively. With extensive evaluations using actual telemetry signals from two satellites, we demonstrate how our proposed technique outsmarts the state-of-the-art FSK demodulation schemes. Besides the communication subsystem, Global Positioning System (GPS) is one of the essential but significantly energy-guzzling subsystems in a satellite. While big satellites typically do not have any constraints on energy consumption for GPS subsystem, such is not the case in miniaturized satellites. Unlike terrestrial GPS systems, several challenges are imposed on obtaining a position fix in space-borne GPS receivers. The high orbital velocities of a satellite (up to 7.8 km/s) result in a significant Doppler shift in the received signals by the receiver when compared to their terrestrial counterparts. Consequently, the receiver has to search for the GPS signals in a larger Doppler frequency range, thus increasing the signal acquisition duration. Further, the visibility of the GPS satellites to the receiver changes frequently due to high orbital speeds and orbital periods of satellites on which the receiver is mounted. As a result, the receiver needs to search for GPS satellites more often to get a position fix. Likewise, the visibility of GPS satellites is affected adversely if the satellite is tumbling. Due to these constraints, energy conservation techniques such as duty-cycling are not efficient; the receiver is ON most of the time, searching for GPS satellites to obtain a position fix. To this end, we design a low-power, space-qualified GPS receiver for small satellite applications. We propose an algorithm to significantly improve the ability of the receiver to acquire GPS signals as quickly as possible, thus reducing the ON time when it is duty-cycled. We perform long-duration simulations and real-time in-orbit tests on our GPS receiver to evaluate its performance. Further, we demonstrate that up to 96% of energy savings can be achieved on our GPS receiver compared to the state-of-the-art receivers. Space-IoT relies on a constellation of hundreds of satellites to accomplish global coverage. Disruption in services can occur if one of the satellites malfunctions or ceases to work. Certain applications may not endure such risks, especially where satellites are typically employed as secondary communication channels. Hence, it is crucial to monitor the health of satellites regularly. Existing satellites are generally equipped with onboard health monitoring units as a part of the subsystems. However, they are tightly coupled in terms of hardware and software. Any fault in the subsystem may affect its onboard health monitoring modules as they are electrically connected. Hence, we propose a system called Chirper, which is an electrically isolated and independent module that monitors the health of critical subsystems. The Chirper is equipped with multiple sensors that can measure several parameters, such as temperature, bus voltage, current, and rotation rate, of a satellite at specified intervals and transmits them to ground stations through an independent communication module. The proposed system is not only energy-efficient but also measures the different health parameters of a satellite reliably. This work mainly addresses the resilience and energy issues of a satellite. In this dissertation, we present the overall design of the Chirper. We also provide a novel approach to measuring the DC voltage at different locations of a satellite in a completely isolated way. Further, we subject Chirper to different tests in state-of-the-art simulators and a helium balloon to evaluate its capabilities. This thesis advocates that Space-IoT is an ideal complement to terrestrial IoT networks and deployments. Small satellites can bring the vision of Space-IoT into existence. However, several technical breakthroughs need to emerge in small satellites to realize Space-IoT. We tackled some of the primary challenges through theory, experimentation and demonstration on satellites in orbit. With the results obtained, we are convinced that revolutionary transformations can be brought in small satellites to enable Space-IoT and will significantly influence the space related-activities, both in research and development. @en

We present radio-frequency (RF) information harvesting, a chan-nel sensing technique that takes advantage of the energy in the wireless medium to detect channel activity at essentially no en-ergy cost. RF information harvesting is essential for event-driven wireless sensing applications using battery-less devices that har-vest tiny amounts of energy from impromptu events, such as op-erating a switch, and then transmit the event notification to a one-hop gateway. As multiple such devices may concurrently de-tect events, coordinating access to the channel is key. RF infor-mation harvesting allows devices to break the symmetry between concurrently-transmitting devices based on the harvested energy from the ongoing transmissions. To demonstrate the benefits of RF information harvesting, we integrate it in a tailor-made ultra low-power hardware MAC protocol we call Radio Frequency-Distance Packet Queuing (RF-DiPaQ). We build a hardware/software proto-type of RF-DiPaQ and use an established Markov framework to study its performance at scale. Comparing RF-DiPaQ against sta-ple contention-based MAC protocols, we show that it outperforms pure Aloha and 1-CSMA by factors of 3.55 and 1.21 respectively in throughput, while it saturates at more than double the offered load compared to 1-CSMA. As traffic increases, the energy saving of RF-DiPaQ against CSMA protocols increases, consuming 36% less energy than np-CSMA at typical offered loads.@en

Electrocardiogram (ECG) is an important health monitoring signal that is used in various medical diagnosis, especially identifying potential possibility of heart attacks and strokes. Moreover, many patients are in remote places and in many countries the patients to doctors ration is very poor which calls for a miniature hardware that remotely captures ECG and transmits data to the doctors. However, the exact reproduction of ECG requires high bit rate and thus requires transmitting a compressed set of parameters. Further, sending large volumes of annotated raw data to train diagnostic models also compromises the patients privacy. We design and present a system that generates synthetic ECG signals from clinical data in real-time using a highly minimized set of parameters. The system comprises a nonlinear dynamical model whose parameters are trained in real-time to synthesize a signal which matches clinical data with high accuracy. The parameters of the trained system are then transmitted in each cycle of the ECG wave to reconstruct the original signal using the same model at the medical practitioners’ location. The parameter learning problem is highly complicated as one needs to solve a nonlinear, non-convex dynamic optimization problem, which usually only converges to local optima. To address this issue, we propose a novel two-stage algorithm that automatically chooses an initial set of parameters in the vicinity of the global optimum and then performs stochastic gradient descent iterations. We perform experiments to demonstrate the accuracy and real-time performance of the system. We show that on average our system processes clinical data of one second in 0.68s on a microcontroller, with an RMSE error of 0.0038 the average, and 17 parameters per ECG cycle. Our system is also easy to implement, requires minimal storage i.e. only one ECG cycle at any given time, and does not depend on offline training, unlike existing methods.@en

With the advent of Space-IoTs, the rate of launch of satellites has grown significantly. Alongside, the failure rate of satellites has also surged increased tremendously. Satellites are non-repairable systems in orbit, and the financial loss incurred when the satellites fail before their expected mission time is substantial. If the source of a failure is known while the satellite is in orbit, then there is a possibility to revive it by sending appropriate commands from ground stations. In this work, we present a simple, independent satellite health monitoring system called Chirper. The Chirper is equipped with multiple modules such as IMU, isolated voltage and current measurement probes, and an onboard communication channel. We present a new approach to measure low DC voltages in an isolated way, providing a resolution and accuracy of around 1 V. We evaluated the design and performance of the Chirper through simulation, testing it in space systems test facility, and by mounting it on a helium balloon. With extensive experiments we show that 90% of the time the dc voltage measurement error is within 0.8 V, and the maximum error is 0.9 V. We expect to launch the Chirper soon on a space system.

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In this letter, we present Hermes - a novel, low-cost, wireless, batteryless, energy harvesting system for aerial vehicles for sensing wind speed and Angle of Attack (AoA) concurrently. Hermes comprises a set of piezoelectric films which flutter due to incoming wind and the characteristics of this aeroelastic flutter are utilized for determining the wind speed and AoA of the head-wind. Note that in our work we restrict the notion of flutter to high frequency oscillations due to incoming air flow. Hermes consists of five piezoelectric flags that are mounted on rigid clamps specifically placed at different angles. We designed Hermes to maximize the sensing performance and energy harvesting capability simultaneously, without compromising either accuracy or harvesting efficiency. Our current prototype can harvest the power of 440 $\mu$W on average. Over a wide range AoA from $-10^{\circ }$ to $30^{\circ }$, the estimation of the wind speed is within 0.7 km/h error with 90% probability, and AoA error is within $1.2^{\circ }$ with 90% probability. Since Hermes necessitates no wires and batteries and is a low-cost sensor, it is well suited for a range of UAVs, gliders, and aircraft, which require flexible sensor placement and do not require new wiring, which is often complex in aircraft. Hermes is the first of its kind that exploits piezoelectric energy harvesting to simultaneously sense AoA and wind speed. This work is expected to open up new avenues for interdisciplinary research on embedded computing devices for aerospace applications.

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Global Positioning System is a widely adopted localization technique. With the increasing demand for small satellites, the need for a low-power GPS for satellites is also increasing. To enable many state-of-the-art applications, the exact position of the satellites is necessary. However, building low-power GPS receivers which operate in low earth orbit pose significant challenges. This is mainly due to the high speed (∼7.8 km/s) of small satellites. While duty-cycling the receiver is a possible solution, the high relative Doppler shift between the GPS satellites and the small satellite contributes to the increase in Time To First Fix (TTFF), thus increasing the energy consumption. Further, if the GPS receiver is tumbling along with the small satellite on which it is mounted, longer TTFF may lead to no GPS fix due to disorientation of the receiver antenna. In this paper, we elucidate the design of a low-cost, low-power GPS receiver for small satellite applications. We also propose an energy optimization algorithm called F³to improve the TTFF which is the main contributor to the energy consumption during cold start. With simulations and in-orbit evaluation from a launched nanosatellite with our μGPS and high-end GPS simulators, we show that up to 96.16% of energy savings (consuming only ∼ 1/25th energy compared to the state of the art) can be achieved using our algorithm without compromising much (∼10 m) on the navigation accuracy. The TTFF achieved is at most 33 s.

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High accuracy and device-free indoor localization is still a holy grail to enable smart environments. With the growing privacy concerns and regulations, it is necessary to develop methods and systems that can be low-power, device-free as well as privacy-aware. While IR-based solutions fit the bill, they require many modules to be installed in the area of interest for higher accuracy, or proper planning during installation, or they may not work if the background has multiple heat-emitting objects, etc. In this paper, we propose a custom-built miniature device called LOCI that uses IR sensing. One unit of LOCI can provide three-dimensional localization at best. LOCI uses only a thermopile and a PIR sensor built within a 5x5x2 cm³ module. Since IR-based sensing is used, LOCI consumes around 80 mW. LOCI uses analog waveform from the PIR sensor with the gain of the PIR sensor dynamically controlled through software in real-time to simulate spatial diversity. LOCI proposes low-complexity techniques with sensor fusion to eliminate the noise in the background, which has not been handled in previous works even with sophisticated signal processing techniques. Since LOCI uses raw data from the thermopile, the computations are power-efficient. We present the complete design of LOCI and the proposed methodology to estimate height and location. LOCI achieves accuracies of sub-22 cm with a confidence of 0.5 and sub-35 cm with a confidence of 0.8. The best-case location accuracy is 12.5 cm. The accuracy of height estimation is within 8 cm in majority cases. LOCI can easily be extended to recognize activities.

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Battery operated systems are bulky, expensive, and often add unnecessary burden because of their maintenance. They are also harmful to the environment. However, the design and development of batteryless systems are highly challenging as the energy needs to be harvested from user's activities or the environment. The harvested energy also varies with the activity, environment, and other aspects. In this paper, we present a system employing an energy harvesting switch to power a low-power radio, which transmits data wirelessly in 2.4 GHz ISM band. We provide the details of the design of our system and modules. We evaluate our energy harvesting switch which we built in-house. With evaluations, we show that our system works well, and we demonstrate the transmission of 27 bytes at 200 kbps data rate. Further, by varying the transmission power between-10 dBm and 5 dBm, we transmit data packets of length between 19 and 27 bytes with a single press of the switch.

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These days, the Internet of things (IoT) research is driving large-scale development and deployment of many innovative applications. IoT has indeed brought many smart applications to the doorstep of users. IoT has also made it possible to connect many sensors and control equipment. Here, the authors address an important application for physically challenged. The authors present a brain–computer interface (BCI) system to lock/unlock a wheelchair and control its movements using BCI. The approach presented here uses NeuroSky's MindWave Mobile, a single electrode electroencephalography (EEG) headset that can be connected to any Bluetooth-enabled system. The raw EEG data from the headset is processed on an Android mobile device to extract the electromyography (EMG) patterns that occur due to eye blinks and activity of muscles in the jaw. These patterns are used to control the movement of a wheelchair in all possible directions. A biometric security system is provided to lock and unlock the wheelchair by extracting the information about different brain waves from the raw EEG signal. In this system, only the user knows the password which is generated using brain waves and it can lock/unlock the wheelchair and control it. The proposed system was verified and evaluated using a prototype.

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Two nanosatellites recently launched into space had issues with respect to its stabilization, power and orientation. The signals were intermittent, and amateur radio enthusiasts around the globe were requested to observe the satellites so as to get their health information. As decoding the received signals required proprietary hardware (that could not be sent to everyone), amateur radio receivers recorded the signal using Software Defined Radios (SDRs) and sub-sampled the carrier signals to make it easy to share. The captured signals, modulated using binary Frequency Shift Keying (FSK), included noise and more importantly the frequency shifts due to Doppler, caused by the speed of the satellites (of about 7.8 km/s), thus making decoding a major challenge even for the designated proprietary receivers (failed in some cases). As the existing FSK methods did not work effectively, we were motivated by this challenge to design an effective FSK decoder that works in the presence of Doppler and noise. In this paper, we propose Teager Energy Decoder (TED) based on Teager Energy Operator to decode such Doppler and noise influenced sub-sampled data. TED does not need any Doppler correction mechanisms and can dynamically adapt to the changing frequency shifts. We evaluate TED using simulation as well as from the signals from those two satellites. We show that TED performs better than COTS transceivers and available GNU-radio-based solutions using SDRs. TED is low-complexity algorithm, O(N²), and has been prototyped on a low-power microcontroller. TED can be easily adopted on satellites to decode signals for space Internet of Things applications.

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Drones developed for interplanetary space missions require full autonomy of operations including safe landing and hovering due to the delay in communication. For operation in low atmospheric densities, coaxial helicopters are best suited and they are capable of handling manoeuvres due to their small footprints and ease of operation. However, the dynamics of the helicopter is coupled in lateral axes which need to be compensated for precise control. The present solutions include vision-based tracking in order to decouple the dynamics, which needs additional hardware. In this paper, a decoupling controller is presented that employs an accelerometer-based force feedback system for measuring the undesired forces in off-axis which does not need any additional hardware. The simulation results indicate that the force feedback methodology is very effective in controlling the off-axis drift of the coaxial helicopter.

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In smartcard-based travel payment systems, passengers have to place the smartcard near the journey registration devices once each for check-in and check-out to authenticate their travel. This is an annoying process when if the journey involves multiple stops. In this paper, we describe a working system of secure energy-efficient automatic ticketing (SEAT) for public transport, which transforms traditional check-in/check-out system into Be-in/Be-out system. In SEAT, a Bluetooth low energy (BLE) enabled smartphone communicates with registration devices to track the journey for pricing without any human intervention. SEAT is vigilant toward the energy consumption of the BLE device under various conditions, and security and privacy threats to the overall ecosystem. We develop models for energy consumption and latency for BLE devices under the influence of mutual interference based on experiments with 32 BLE devices. We utilize these models to develop an energy-efficient protocol for SEAT that is secure and privacy preserving. Our experiments show that a BLE module consumes only 18.3 J daily under the proposed system model, which is less than 0.1% of the total capacity of a typical smartphone battery.@en

Progress in low power miniaturized electronics and wireless technologies have enabled many innovative applications. Of particular interest is the Internet of Things (IoT) that has dominated the world of ICT in the last half a decade enabling many smart systems and applications. At the same time, we also see much enthusiasm with respect to space missions and applications including terrestrial applications, apart from exploring the universe. We believe that innovations in the domain of IoT will significantly influence the space related activities – both research and development. In this article, we chart out the innovations in space and the vision with respect to embedded and wireless systems for space applications. In particular, we bring in the notion of Sensor Wireless Actuator Networks in Space (SWANS) and Space Pixels to explain IoT in space. We explain with examples what we envision for the next decade and also the challenges therein. We briefly put forth our four major targets in the next five years.@en

In this paper we study a general energy minimization problem for mixed-criticality systems on multi-cores, considering different system operation modes, and static & dynamic energy consumption. While making global scheduling decisions, trade-offs in energy consumption between different modes and also between static and dynamic energy consumption are required. Thus, such a problem is challenging. To this end, we first develop an optimal solution analytically for unicore and a corresponding low-complexity heuristic. Leveraging this, we further propose energy-aware mapping techniques and explore energy savings for multi-cores. To the best of our knowledge, we are the first to investigate mixed-criticality energy minimization in such a general setting. The effectiveness of our approaches in energy reduction is demonstrated through both extensive simulations and a realistic industrial application.@en

Pyroelectric Infra-Red (PIR) sensors are used in many applicationsincluding security. PIRs detect the presence ofhumans and animals from the radiation of their body heat.This could be used to trigger events, e.g., opening doors,recording video, etc. PIRs are used widely because of theirlow power consumption.Hitherto, PIR sensors were used for binary event generation– human/animal present or not-present. At the sametime simple binary output hinders the use of PIR sensors ina wide variety of sophisticated applications. In the literature,we find limited characterization of analog output fromPIR sensors that could provide much more information.We built a simple array of PIR sensors and packaged themin a tower. We used two sets of four PIR sensors and tappedtheir analog signals after amplification. Our major contributionis the characterization of analog signals from the PIRsensors. We describe many interesting aspects obtained fromthe analog signals, which have not been explored until now.We also show their correspondence with the range, speedand size of the moving object. Using the characterization ofPIR sensors analog data as well as simple binary decisionsfrom these PIR sensors, we: (i) classify moving object withhigh precision; and (ii) localize the moving object. The majorincentives are low operating power compared to WSNs.We achieve 30 cm accuracy in 80% of the times, whenranging up to 5 m. Over multiple experiments for differentpersons in the range 1-10 m, we show that the error probabilityfor localization is 0.08 at moderate distances (around5-6 m). Our work will help in designing better detectionand application triggers using PIR sensors in the near future.We believe that this work will open up new avenues inthe development of new applications with PIR sensors.@en