Backscatter
WiFi backscattering can enable direct connectivity of IoT devices with commodity WiFi hardware at low power. However, most existing work in this area has overlooked the importance of synchronization and have, as a result, accepted either limited range between the transmitter and the IoT device, reduced throughput via bit repetition, or both. In this paper, we present SyncScatter, which achieves accurate synchronization to incident signals at the IoT device level, while also achieving sensitivity commensurate with the maximum possible afforded by a backscattering link budget. SyncScatter creates a novel modeling framework, and derives the maximal optimal range and synchronization error that can be achieved without major performance compromises. Next, SyncScatter builds a novel hierarchical wake-up protocol, which together with a custom ASIC, achieves a range of 30+ meters at 2Mbps, with an average power consumption of 25.2µW.
Manideep Dunna, Miao Meng, Po-Han Wang, Chi Zhang, Patrick Mercier, and Dinesh Bharadia
Contact force is a natural way for humans to interact with the physical world around us. However, most of our interactions with the digital world are largely based on a simple binary sense of touch (contact or no contact). Similarly, when interacting with robots to perform complex tasks, such as surgery, richer force information that includes both magnitude and contact location is important for task performance. To address these challenges, we present the design and fabrication of WiForce which is a ‘wireless’ sensor, sentient to contact force magnitude and location. WiForce achieves this by transducing force magnitude and location, to phase changes of an incident RF signal of a backscattering tag. The phase changes are thus modulated into the backscattered RF signal, which enables measurement of force magnitude and contact location by inferring the phases of the reflected RF signal. WiForce’s sensor is designed to support wide-band frequencies all the way up to 3 GHz. We evaluate the force sensing wirelessly in different environments, including through phantom tissue, and achieve force accuracy of 0.3 N and contact location accuracy of 0.6 mm.
Agrim Gupta, Cédric Girerd, Manideep Dunna, Qiming Zhang, Raghav Subbaraman, Tania Morimoto, and Dinesh Bharadia
[Ubiquitous IoT Project Page] [slides]
More portable, fully wireless smart home setups. Lower power wearables. Batteryless smart devices. These could all be made possible thanks to a new ultra-low power Wi-Fi radio developed by electrical engineers at the University of California San Diego. The device, which is housed in a chip smaller than a grain of rice, enables Internet of Things (IoT) devices to communicate with existing Wi-Fi networks using 5,000 times less power than today’s Wi-Fi radios. It consumes just 28 microwatts of power. And it does so while transmitting data at a rate of 2 megabits per second (a connection fast enough to stream music and most YouTube videos) over a range of up to 21 meters.
P-H. P. Wang, C. Zhang, H. Yang, D. Bharadia, P. P. Mercier
Press cover by UCSD News, Tech Explorist, ACM News, Hacker News
In the last decade, the bandwidth expansion and MIMO spatial multiplexing have promised to increase data throughput by orders of magnitude. However, we are yet to enjoy such improvement in real-world environments, as they lack rich scattering and preclude effective MIMO spatial multiplexing. In this paper, we present ScatterMIMO, which uses smart surface to increase the scattering in the environment, to provide MIMO spatial multiplexing gain. Specifically, smart surface pairs up with a wireless transmitter device say an active AP and re-radiates the same amount of power as any active access point (AP), thereby creating virtual passive APs. ScatterMIMO avoids the synchronization, interference, and power requirements of conventional distributed MIMO systems by leveraging virtual passive APs, allowing its smart surface to provide spatial multiplexing gain, which can be deployed at a very low cost. We show that with optimal placement, these virtual APs can provide signals to their clients with power comparable to real active APs, and can increase the coverage of an AP. Furthermore, we design algorithms to optimize ScatterMIMO’s smart surface for each client with minimal measurement overhead and to overcome random per-packet phase offsets during the measurement. Our evaluations show that with commercial off-the-shelf MIMO WiFi (11ac) AP and unmodified clients, ScatterMIMO provides a median throughput improvement of 2x over the active AP alone.
Manideep Dunna, Chi Zhang, Daniel Sievenpiper, Dinesh Bharadia
Press cover by UCSD News, Hackster News
Internet-of-things (IoT) devices are becoming widely adopted, but they increasingly suffer from limited power, as power cords cannot reach the billions and batteries do not last forever. Existing systems address the issue with ultra-low-power designs and energy scavenging, which inevitably limit functionality. To unlock the full potential of ubiquitous computing and connectivity, our solution uses capacitive power transfer (CPT) to provide battery-like wireless power delivery, henceforth referred to as “Capttery”. Capttery presents the first room-level (~5 m) CPT system, which delivers continuous milliwatt-level wireless power to multiple IoT devices concurrently. Unlike conventional one-to-one CPT systems that target kilowatt power in a controlled and potentially hazardous setup, Capttery is designed to be human-safe and invariant in a practical and dynamic environment. Our evaluation shows that Capttery can power end-to-end IoT applications across a typical room, where new receivers can be easily added in a plug-and-play manner.
Chi Zhang, Siddharth Kumar, and Dinesh Bharadia
Bluetooth Low Energy (BLE) tags have become very prevalent over the last decade for tracking applications in homes as well as businesses. These tags are used to track objects, navigate people, and deliver contextual advertisements. However, in spite of the wide interest in tracking BLE tags, the primary methods of tracking them are based on signal strength (RSSI) measurements. Past work has shown that such methods are inaccurate, and prone to multipath and dynamic environments. As a result, localization using Wi-Fi has moved to Channel State Information (CSI, includes both signal strength and signal phase) based localization methods. In this paper, we seek to investigate what are the challenges that prevent BLE from adopting CSI based localization methods. We identify fundamental differences at the PHY layer between BLE and Wi-Fi, that make it challenging to extend CSI based localization to BLE. We present our system, BLoc, that incorporates novel, BLE-compatible algorithms to overcome these challenges and enable an accurate, multipath-resistant localization system. Our empirical evaluation shows that BLoc can achieve a localization accuracy of 86 cm with BLE tags, a 3X improvement over a state-of-the-art baseline.
Roshan Ayyalasomayajula, Deepak Vasisht, and Dinesh Bharadia
We introduce the design and implementation of FreeRider, the first system that enables backscatter communication with multiple commodity radios, such as 802.11g/n WiFi, ZigBee, and Bluetooth, while these radios are simultaneously used for productive data communication.
Pengyu Zhang, Colleen Josephson, Dinesh Bharadia, Sachin Katti
We present HitchHike, a low power backscatter system that can be deployed entirely using commodity WiFi infrastructure. With HitchHike, a low power tag reflects existing 802.11b transmissions from a commodity WiFi transmitter, and the backscattered signals can then be decoded as a standard WiFi packet by a commodity 802.11b receiver. HitchHike’s key invention is a novel technique called codeword translation, which allows a backscatter tag to embed its information on standard 802.11b packets by just translating the original transmitted 802.11b codeword to another valid 802.11b codeword. This allows any 802.11b receiver to decode the backscattered packet, thus opening the doors for widespread deployment of low-power backscatter communication using widely available WiFi infrastructure. We show experimentally that HitchHike can achieve an uplink throughput of up to 300Kbps at ranges of up to 34m and ranges of up to 54m where it achieves a throughput of around 200Kbps
Pengyu Zhang, Dinesh Bharadia, Kiran Joshi, Sachin Katti
In this paper, we look at making backscatter practical for ultra-low power on-body sensors by leveraging radios on existing smartphones and wearables (e.g. WiFi and Bluetooth). The difficulty lies in the fact that in order to extract the weak backscattered signal, the system needs to deal with self interference from the wireless carrier (WiFi or Bluetooth) without relying on built-in capability to cancel or reject the carrier interference.
Pengyu Zhang, Dinesh Bharadia, Kiran Joshi, Sachin Katti
We present BackFi, a novel communication system that enables high throughput, long range communication between very low power backscatter IoT sensors and WiFi APs using ambient WiFi transmissions as the excitation signal. Specifically, we show that it is possible to design IoT sensors and WiFi APs such that the WiFi AP in the process of transmitting data to normal WiFi clients can decode backscatter signals which the IoT sensors generate by modulating information on to the ambient WiFi transmission
Dinesh Bharadia, Kiran Raj Joshi, Manikanta Kotaru, Sachin Katti
Could we build a motion tracing camera using wireless communication signals as the light source? This paper shows we can, we present the design and implementation of WiDeo, a novel system that enables accurate, high resolution, device free human motion tracing in indoor environments using WiFi signals and compact WiFi radios. The insight behind WiDeo is to mine the backscatter reflections from the environment that WiFi transmissions naturally produce to trace where reflecting objects are located and how they are moving. We invent novel backscatter measurement techniques that work in spite of the low bandwidth and dynamic range of WiFi radios, new algorithms that separate out the moving backscatter from the clutter that static reflectors produce and then trace the original motion that produced the backscatter in spite of the fact that it could have undergone multiple reflections. We prototype WiDeo using off-the-shelf software radios and show that it accurately traces motion even when there are multiple independent human motions occurring concurrently (up to 5) with a median error in the traced path of less than 7cm.
Kiran Joshi, Dinesh Bharadia, Manikanta Kotaru, Sachin Katti