We present HUM-High-frequency UAV Messaging: an acoustic side channel communication system we design for localized drone-to-drone communications. We generate Pulse Width Modulated (PWM) signals from drone motors to carry information and improve communication reliability by mitigating propeller noise interference through modifications to the propeller's physical design. These modifications reduce propeller noise in the designated acoustic spectrum by up to 7 dB. We deploy a custom ultrasonic microphone shield specifically designed for decoding in the receiver. HUM's improved signal-to-noise ratio enables up to 80x higher data rates compared to the existing design from the literature while providing better scalability. HUM supports simultaneous decoding across 16 drones within 8 m, range as seen in real flight tests. The cost of this performance is minimal; we experimentally demonstrate that HUM has a marginal impact on flight dynamics and battery life.

Surveillance and monitoring are highly critical in many application scenarios like wildlife conservation, restricted areas such as nuclear spillover, and border security. Moreover, in these scenarios, intrusions do not happen frequently thus, conventional surveillance is overkill and expensive that also requires extensive human involvement which can be arduous, expensive, and inefficient. To address these issues we propose an end-to-end smart acoustic surveillance solution for intrusion detection using a simple low-cost system called Balls for Walls (B4 W). The objective is to create a network of sensors that could also be remotely launched. The nodes responsible for surveillance employ audio sensors which are packaged within hard balls thus allowing the launch of these sensors from a distance of over 500 m. We use microphones for detecting human activity inferred through sensing the sound of footsteps against background noise. We evaluate the systems across five different terrain types. We propose a novel, low complexity detection algorithm called SEED which leverages signal energy and shape to distinguish humans from ambient noise. B4 W offers a maximum detection rate of 98.3% on dry leaves and a low false alarm rate of 0.9%. The system is energy efficient to last a maximum of 170 days and it is orientation agnostic. The proposed system has been extensively tested across varying terrains and ambient signal scenarios to demonstrate its efficacy.

UAVs are becoming versatile and valuable platforms for various applications. However, the main limitation is their flying time. We present BEAVIS, a novel aerial robotic platform striking an unparalleled trade-off between the maneuverability of drones and the long-lasting capacity of blimps. BEAVIS scores highly in applications where drones enjoy unconstrained mobility yet suffer from limited lifetime. A nonlinear flight controller exploiting novel, unexplored, aerodynamic phenomena to regulate the ambient pressure and enable all translational and yaw degrees of freedom is proposed without direct actuation in the vertical direction. BEAVIS has built-in rotor fault detection and tolerance. We explain the design and the necessary background in detail. We verify the dynamics of BEAVIS and demonstrate its distinct advantages, such as agility, over existing platforms including the degrees of freedom akin to a drone with 11.36× increased lifetime. We exemplify the potential of BEAVIS to become an invaluable platform for many applications.

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.