Abstract-The use of drone base stations to provide wireless connectivity for ground terminals is becoming a promising part of future technologies. The design of such aerial networks is however different compared to cellular 2D networks, as antennas from the drones are looking down, and the channel model becomes height-dependent. In this paper, we study the effect of antenna patterns and height-dependent shadowing. We consider a random network topology to capture the effect of dynamic changes of the flying base stations. First we characterize the aggregate interference imposed by the cochannel neighboring drones. Then we derive the link coverage probability between a ground user and its associated drone base station. The result is used to obtain the optimum system parameters in terms of drones antenna beamwidth, density and altitude. We also derive the average LoS probability of the associated drone and show that it is a good approximation and simplification of the coverage probability in low altitudes up to 500 m according to the required signal-to-interference-plusnoise ratio (SINR).

Bluetooth Low Energy (BLE) represents the low-power, low-cost extension of the Bluetooth communication technology envisioned for the Internet of Things. Mesh protocols on top of BLE are currently emerging and the standard is currently being released. This paper first proposes a detailed measurement based comparison of two mesh approaches that fit within BLE operation: flooding and connection oriented networking. Using metrics such as packet delivery ratio (PDR), end-to-end delay and power consumption we conclude that the optimal mesh approach depends on the application. It is shown that for a comparable performance in terms of PDR and overhead, flooding can trade a lower end-to-end delay for a higher power consumption when compared to the connected mesh. We then propose an architecture, called Bluetooth Now, that is able to automatically switch the network between the two based on message priority. Our measurement results confirm the reliable delivery of important and urgent data sent using the Bluetooth Now paradigm, while saving battery life when transmitting non-time critical messages.

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Bluetooth Low Energy (BLE) is envisioned as one of the most prominent technologies for the Internet of Things (IoT). The recent release of BLE mesh has attracted interest from both the industrial and academic communities, with new proposals for optimizing its performance being published on a regular basis. However, the majority of these publications rely on analysis and simulations, potentially neglecting the effect of real life environments and hardware limitations. Moreover, no framework is available to experimentally compare the performance of different mesh protocols on top of BLE. This paper presents the design and implementation of a low-cost, modular and fully automated testbed capable of running multiple mesh protocols. It is also remotely accessible for any third party interested in performing network experiments with it. Apart from discussing the testbed architecture, we provide results obtained from a measurement driven comparison of two protocols that fit within BLE operation: flooding and connection based. These results show that there is a trade off between the two approaches and that the optimal mesh protocol to use is application dependent, which should be taken into consideration when heading towards the next release of BLE mesh.

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This work presents a study on the feasibility of energy harvesting from modulated signals that are present normally in daily life environments, i.e. Bluetooth Low Energy. First, the design of the RF-to-DC conversion circuit is presented, considering the input excitation being a signal modulated in frequency. Next, a measurement campaign has been conducted considering as input signal both a frequency-modulated excitation given by a signal generator, and the communication packets sent by means of a commercial Bluetooth board. The experimental results show that it is possible to harvest about 1.3 nJ for every BLE packet sent with a transmitter-receiver distance up to 20 cm with 4 dBm of transmitted power.

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A versatile and intuitive way of interacting with smart devices consists of a 3D-touchscreen in the air above the device, combined with gesture recognition. In this paper, the limitations of hand tracking and gesture recognition using existing hardware in smartphones will be investigated. To accomplish hand tracking and gesture recognition, the two loudspeakers of the device send ultrasonic tones in combination with a Maximum Length Sequence (MLS), after which the microphones pick up the potentially altered reflected signal again. The Doppler-response on the tones yields information about the speed of the hand above the device and the acoustic channel estimation obtained from the MLS estimates the distance at which the hand is present. The position estimation module estimates the 3D-position of the hand in a predefined grid of eight regions and hereby achieves an accuracy above 77 % in all cases. For the gesture recognition, features are extracted from both the Doppler-response as well as the acoustic channel estimation. A simple decision tree then classifies the performed gesture. Experimental results show that the accuracy of the gesture recognition module achieves an accuracy between 87 and 100 %.