LoRaWANs, a widely accepted IoT connectivity solution, adopt a simple (ALOHA-like) MAC layer, enabling low-power communication at the cost of scalability due to packet collisions. Hence, current studies on LoRaWAN conclude that the network does not support dense deployments. Several alternative MACs are proposed but they stumble upon well-known limitations: time division eliminates the asynchrony of LoRa nodes but requires feedback from the gateways; carrier-sensing-based protocols are heavily constrained by the reduced sensing ranges of the devices, thus creating a large number of hidden terminals, leading to collisions. To enhance LoRaWAN to cater to both low- and high-density deployments, in this paper, we propose Spreading Factor MAC (SFMAC), a novel, practical, distributed, and energy-efficient MAC protocol. SFMAC, a channel-sensing-based MAC, takes an unconventional approach to eliminate hidden terminals - by operating with pairs of SFs, wherein the higher SF is used for channel sensing and the lower for data transmission. Bleeps are transmitted in the higher SF as they can be sensed at longer ranges. SFMAC does not require any change in hardware or the LoRaWAN protocol. We demonstrate that the fundamental tradeoff made by SFMAC - utilizing two SFs per data transmission instead of using all for data - works extremely well due to the elimination of hidden terminals. Through real-world experiments on 30 SX1261 devices and data-driven ns-3 simulations, we showcase that SFMAC increases goodput and channel utilization by manifolds over state-of-the-art protocols such as p-CARMA, np-CECADA, and LMAC.

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.

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.

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.

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.

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.

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.

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.

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.

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.