Directional antennas are a promising solution for improving the range of client devices and capacity of wireless networks. Unfortunately, in LP-WAN systems directional antennas tend to be both large and expensive due to operating at subGHz frequencies. However, if a client device is willing to forgo improvements in antenna gain, it is possible to realize compact and low-cost antennas that provide spatial diversity control. In this paper, we show that by increasing spatial diversity in LPWAN clients with limited (or no) client gain, we can dramatically increase overall network capacity and improve client battery life by avoiding re-transmissions. This type of directional control can also be used for hot-spot management by more effectively load balancing clients across gateways.We performed a sensitivity analysis using a combination of real hardware and simulation to explore the impact of various switchable antenna geometries on network capacity under a variety of deployment configurations. We then designed and evaluated three prototype multi-sector array clients: (1) a switchable patch antenna configuration, (2) a digital phase-shift nulling configuration, and (3) a low-cost switched PCB element phase-shift system. Each design explores a different hardware cost vs antenna beam performance operating point. We experimentally see that our real antenna beam patterns, captured in an anechoic chamber, perform in a similar manner to our simulated prediction models in terms of beam pattern and in simulation improve network capacity by up to 28% from interference isolation alone and up to 95% when offloading hot spots between four gateways. We also perform a small measurement study of how often our final design changes its configuration when deployed over multiple days on a campus testbed.

In this paper, we demonstrate Adaptive Millimetro as an extension of Millimetro, an ultra-low power millimeter-wave (mmWave) retro-reflector presented in [1], for high mobility scenarios. Adaptive Millimetro makes use of automotive radars and enables communication with and accurate localization of roadside infrastructure overextended distances (i.e. >100m). Millimetro achieves this by designing ultra-low-power retro-reflective tags that operate in the mmWave frequency band and can be embedded in road signs, pavements, bi-cycles, or even the clothing of pedestrians. Millimetro addresses the severe path loss problem of mmWave signals by combining coding gain and retro-reflective antenna front-end to achieve long-range operation. However, highly mobile scenarios may still experience unreliable performance due to the Doppler effect changing the received signals. In this paper, we demonstrate a simple solution for robust localization in high mobility by implementing a Moving Target Indication (MTI) filter and an adaptive Kalman filter. We also present an augmented reality app, as an in-car AR platform, that uses Adaptive Millimetro’s algorithms to estimate the tag positions and overlay a virtual box at the estimated locations.

This paper presents Millimetro, an ultra-low-power tag that can be localized at high accuracy over extended distances. We develop Millimetro in the context of autonomous driving to efficiently localize roadside infrastructure such as lane markers and road signs, even if obscured from view, where visual sensing fails. While RF-based localization offers a natural solution, current ultra-low-power localization systems struggle to operate accurately at extended ranges under strict latency requirements. Millimetro addresses this challenge by re-using existing automotive radars that operate at mmWave frequency where plentiful bandwidth is available to ensure high accuracy and low latency. We address the crucial free space path loss problem experienced by signals from the tag at mmWave bands by building upon Van Atta Arrays that retro-reflect incident energy back towards the transmitting radar with minimal loss and low power consumption. Our experimental results indoors and outdoors demonstrate a scalable system that operates at a desirable range (over 100 m), accuracy (centimeter-level), and ultra-low-power (< 3 uW).

Conventional wireless communication systems are typically designed assuming a single transmitter-receiver pair for each link. In Low-Power Wide-Area Networks (LP-WANs), this one-to-one design paradigm is often overly pessimistic in terms of link budget because client packets are frequently detected by multiple gateways (i.e. one-to-many). Prior work has shown massive improvement in performance when specialized hardware is used to coherently combine signals at the physical layer.

In this demonstration, we present OpenChirp, an open-source Low-Power Wide-Area Networking (LPWAN) infrastructure. OpenChirp is a management framework that provides data context, storage, visualization, and access control over the web. At the physical layer of the system, we present LPRAN, a low-cost high-performance software-defined radio hardware platform that can receive signals up to -30 dB below the noise floor. Using our LPRAN hardware, it is possible to operate on raw I/Q streams in the cloud to perform collaborative tasks across multiple gateways such as jointly decoding weak signals and localization.

Low-Power Wide-Area Networks (LPWANs) are an emerging wireless platform which can support battery-powered devices lasting 10-years while communicating at low data-rates to gateways several kilometers away. Not all such devices will experience the promised 10 year battery life despite the high density of LPWAN gateways expected in cities. Transmission from devices located deep within buildings or in remote neighborhoods will suffer severe attenuation forcing the use of slow data-rates to reach even the closest gateway, thus resulting in battery drain. This paper presents Charm, a system that enhances both the battery life of client devices and the coverage of LPWANs in large urban deployments. Charm allows multiple LoRaWAN gateways to pool their received signals in the cloud, coherently combining them to detect weak signals that are not decodable at any individual gateway. Through a novel hardware and software design at the gateway, Charm carefully detects which chunks of the received signal need to be sent to the cloud, thereby saving uplink bandwidth. We present a scalable solution to decoding weak transmissions at city-scale by identifying the set of gateways whose signals need to be coherently combined over time. In evaluations over a test network and from simulations using traces from a large LoRaWAN deployment in Pittsburgh, Pennsylvania, Charm demonstrates a gain of up to 3x in range and 4x in client battery-life.

The reduction of the consumption of energy, through its efficient use, is regarded as one of the ways of reducing the impact of mankind on the environment. Buildings consume a significant amount of energy, namely for heating, cooling and illumination. Over the last decades, more energy efficient equipment, new building materials and construction techniques have enabled more energy efficient buildings. However, human behaviour has a large impact on the energy consumption of each building, with similar buildings presenting very distinct energy footprints, due to their occupants' behaviour. The problem of creating more sustainable energy consumption habits has recently received a lot of attention from the research community. Systems capable of reducing energy consumption, by enforcing more correct behaviours, may reduce costs for companies and help improve the environmental outlook. This paper proposes a novel system to address the energy consumption problem and inadequate habits of people in office buildings. It's a highly flexible distributed office management system that can scale from an individual node in an office to the whole building. The goal is to reduce global building energy consumption without significantly affecting the users' comfort level. An approach is used where the building services are adjusted to its occupancy level and users' needs based on their location. Users are driven to better energy usage habits through access to information and feedback. Our proposal is presented in detail and validated in the context of an academic institution, more specifically at the Taguspark campus of Instituto Superior Técnico. The developed system is now operational and being used as a flexible, easily programmable, research platform.