The Internet of Things (IoT) is scaling rapidly to billions of low power devices, with diverse radio technologies sharing common unlicensed spectrum. Inevitably, this results in rampant cross-technology collisions between the devices that lead to wasteful re-transmissions, draining the battery life of low-power devices significantly. We present CharIoT, the first cross-technology distributed MIMO receiver system that exploits the potential of distributed MIMO to facilitate better co-existence and decoding of a large number of simultaneous low power uplink transmissions from unmodified low-power clients. CharIoT is a recovery-based system that intelligently collects radio samples from teams of light-weight IoT gateways and streams them to the cloud to effectively resolve collisions. At the cloud, CharIoT develops a suite of technology-specific software filters that decouple collisions across diverse technologies, facilitating seamless co-existence across low power radios. An implementation of CharIoT on inexpensive RTL-SDR gateways connected to Raspberry Pis decode collisions of four popular IoT technologies in the 868MHz ISM bands – LoRa, XBee, Z-Wave, and SIGFOX showing gains in throughput of up to 4× and battery life of up to 3.5 years.

Backscatter enables wireless transmissions at dramatically lower power compared to mainstream Internet of Things (IoT) transmitters. This significantly improves battery life or even eliminates the need of batteries for backscatter-based 'tags' operating on energy harvested from the environment. However, trading off complexity for low power consumption exposing backscatter tags to a multitude of security risks. A significant challenge is imposter tags that mimic legitimate tag behaviour, compromising the system data integrity. In this work, we argue that tag simplicity and operation on harvested energy can help identify imposter transmissions, thus safeguarding data integrity. Our experimental study reveals that commonly used low-power tag oscillators demonstrate unique fingerprint patterns when exposed to the dynamics of harvested energy. Based on this observation, we design and propose HarvestPrint, which leverages these fingerprints to differentiate authentic backscatter transmissions from imposter transmissions. Experiments with a tag powered through a small solar cell show the potential of HarvestPrint.

The proliferation of sensors as part of the Internet-of-Things (IoT) paradigm envisions a world of interconnected, interdependent deployments, involving one or multiple radio technologies. Multiradio mesh deployments facilitated using a network of low-cost IoT platforms, such as Raspberry Pis (RPis), ASUS Tinker Board S, Banana Pi M64, etc., seem an ideal fit as wireless backbones for such deployments, with many of these platforms having built-in support for multiple wireless technologies. However, the state-of-the-art routing solutions need to be revisited to suit the capabilities of low-power multiradio solutions, especially while performing best effort transmissions. This is because a majority of the existing routing solutions favor channel diversity while routing, ignoring the possible overheads associated with protocol conversions. We argue that in reality, these overheads can become quite significant in a multiradio network where the radios are quite different in terms of packet formats, sizes, data rates, etc. To prove this, we estimate the impact of these overheads using an experimental study performed across three popular IoT technologies-WiFi, Bluetooth, and ZigBee, using a real-time RPi-based testbed. We stress on how severe this impact could be, by estimating how often these conversions happen in a randomly chosen multihop path, using a probabilistic mathematical framework. Based on this, we develop Chorus, a first-of-its-kind routing algorithm, which estimates the least overhead path, inclusive of conversion overheads. Chorus achieves this by developing a comprehensive layered graph modeling of the given multiradio mesh network. The modeling is capable of handling all conversion overheads, including the factors associated with the possible fragmentation and aggregation. We implement Chorus in an indoor testbed comprising of RPis and demonstrate the performance improvement while routing across the network of RPis. Our results estimate that Chorus can provide an improvement close to 33% in battery usage per RPi with an average reduction of 25% in packet drops.

Several years ago, software radios were seen as the future of commercial wireless infrastructure, given that they could flexibly decode any wireless technology with a simple software update. Yet, despite their numerous advantages, the industry tilted in favor of dedicated chips for wireless infrastructure sacrificing generality for performance. Today, the advent of the Internet of Things (IoT) and the resulting fragmentation of wireless technologies has led to a new billion-dollar industry: multi-technology gateways that support many radio technologies. Predictably, commercial gateways do this through multiple radio chips each decoding dedicated wireless technologies.

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.

Future urban spaces are expected to see Unmanned Aerial Vehicles (UAVs) deployed for wide-ranging applications including product delivery, imaging and search-and-rescue. Yet today's UAVs rely critically on inertial sensors and GPS to remain stable in-flight, meaning they struggle to penetrate urban canyons where GPS is unavailable. This poster presents LoRaTilt, a system that maintains stability of a UAV using a Low-Power Wide-Area Network (LP-WAN) transmitter. We mount a light-weight and ten-year battery-powered LP-WAN transmitter on a UAV and process its signals from base stations up to hundreds of meter away. We demonstrate how our system estimates and corrects for UAV drift in GPS-denied settings with minimal impact on the cost and flight life of a UAV. Our proof-of-concept experiments show a promising median accuracy of 1.2326 mm and 0.0164 radians in estimating the displacement and orientation of a drone with a LoRa LP-WAN radio.

Internet of Things (IoT) in a real world scenario involves collecting data by sensor devices and communicating the sensed data, through gateway devices, to the IoT Cloud (or the Internet) using direct or multi-hop communication. A gateway typically supports multiple wireless sensor networks (WSNs) and each WSN might use a different technology for communication. Hence gateways are designed to be heterogeneous, having necessary hardware support only for those technologies corresponding to their associated WSNs. Due to their heterogeneity, the technology specific channel access mechanisms need not provide fairness in data communication at each gateway. In this paper, we propose two centralized techniques that involve an efficient link selection per technology and time based scheduling among gateways. This improves fairness in data transmission for every gateway, resulting in an increased data transmission. The first technique uses a multinomial probability based link selection thereby reducing contention within each technology link for a given network topology. The second technique incorporates adaptation to the changing traffic patterns in the network. Our extensive simulation results show that the proposed techniques outperform default technology specific channel access mechanism in IoT networks in terms of total data transmitted, packet delivery ratio, and fairness.