Novel backscatter communication techniques enable battery-free sensor tags to interoperate with unmodified standard IoT devices, extending a sensor network’s capabilities in a scalable manner. Without requiring additional dedicated infrastructure, the battery-free tags harvest energy from the environment, while the IoT devices provide them with the unmodulated carrier they need to communicate. A schedule coordinates the provision of carriers for the communications of battery-free devices with IoT nodes. Optimal carrier scheduling is an NP-hard problem that limits the scalability of network deployments. Thus, existing solutions waste energy and other valuable resources by scheduling the carriers suboptimally. We present DeepGANTT, a deep learning scheduler that leverages graph neural networks to efficiently provide near-optimal carrier scheduling. We train our scheduler with optimal schedules of relatively small networks obtained from a constraint optimization solver, achieving a performance within 3% of the optimum. Without the need to retrain, our scheduler generalizes to networks 6 × larger in the number of nodes and 10 × larger in the number of tags than those used for training. DeepGANTT breaks the scalability limitations of the optimal scheduler and reduces carrier utilization by up to compared to the state-of-the-art heuristic. As a consequence, our scheduler efficiently reduces energy and spectrum utilization in backscatter networks.

New battery-free sensor tags that interoperate with unmodified standard IoT devices and protocols can extend a sensor network's capabilities in a scalable and cost-effective manner. The tags achieve battery-free operation through backscatterrelated techniques, while the standard IoT devices avoid additional dedicated infrastructure by providing the unmodulated carrier that tags need to communicate. However, this approach requires coordination between devices transmitting, receiving and generating carrier, adds extra latency and energy consumption to already constrained devices, and increases interference and contention in the shared spectrum. We present a scheduling mechanism that optimizes the use of carrier generators, minimizing any disruptions to the regular nodes. We employ time slots to coordinate the unmodulated carrier while minimizing latency, energy consumption and overhead radio emissions. We propose an efficient scheduling algorithm that parallelizes communications with battery-free tags when possible and shares carriers among multiple tags concurrently. In our evaluation we demonstrate the feasibility and reliability of our approach in testbed experiments. We find that we can significantly reduce the excess latency and energy consumption caused by the addition of sensor tags when compared to sequential interrogation. We show that the gains tend to improve with the network size and that our solution is close to optimal on average.

New battery-free sensor tags that interoperate with unmodified standard IoT devices can extend a sensor network’s capabilities in a scalable and cost-effective manner. The tags achieve battery-free operation through backscatter-related techniques, while the standard IoT devices can provide the necessary unmodulated carrier, avoiding additional dedicated infrastructure. However, this approach presents multiple challenges: It requires coordination between nodes transmitting, receiving and generating carrier, adds extra latency and energy consumption to already constrained devices, and increases interference and contention in shared spectrum. We present TagAlong, a medium access mechanism for interoperable sensor tags that, besides coordinating, optimizes the use of carrier generators, minimizing the disruption caused to the operation of the regular nodes. We accomplish this by parallelizing communications with battery-free tags when possible, sharing carriers for multiple tags concurrently and synchronizing communications with tags that share carrier generators. We demonstrate the feasibility of TagAlong in a testbed deployment. In our evaluation we find that it can reduce the duration of the tags’ schedule by 60% while improving the energy and spectrum usage by 30% when compared to sequential interrogation with no difference in reliability.

New battery-free backscatter tags that integrate with unmodified standard IoT devices can extend the latter's sensing capabilities in a scalable and cost effective way. Existing IoT nodes can provide the unmodulated carrier needed by the new nodes, avoiding the need for additional infrastructure. This, however, puts extra energetic demands on constrained IoT nodes while increasing interference and contention in the network. We use a slotted MAC protocol to guarantee synchronization between transmitters, receivers and carrier generators. We then express the slot allocation problem as a Constraint Optimization Problem (COP) that parallelizes interrogations to battery-free tags when they do not collide with each other and reuses carriers for multiple tags looking to minimize the total time and the number of carrier generators needed to interrogate a set of tags. In networks with sufficient battery-free nodes we obtain a 25% reduction in the number of necessary carriers and a 50% decrease in interrogation time in most cases; leading to significant energy savings, reduced collisions and improved latency.

We present the architecture for an 802.15.4 receiver that enables battery-free operation. To reach micro-power consumption, the architecture diverges from that of commodity receivers in the following ways: First, similar to backscatter transmitters, it offloads the power-hungry local oscillator to an external device. Second, we avoid the energy cost of demodulating a phase-modulated signal by treating 802.15.4 as a frequency-modulated one, allowing us to receive with a simple passive detector and an energy-efficient thresholding circuit. We demonstrate an off-the-shelf prototype of our receiver receives 802.15.4 from a distance of 470 cm with the carrier generator 30 cm away. This range is sufficient to integrate with deployed wireless sensor networks (WSNs). We demonstrate this integration by pairing our receiver with a 802.15.4 backscatter transmitter and integrating it with unmodified commodity sensor nodes running the TSCH protocol.

We present the architecture of an 802.15.4 receiver that, for the first time, operates at a few hundred microwatts, enabling new battery-free applications. To reach the required micro-power consumption, the architecture diverges from that of commodity receivers in two important ways. First, it offloads the power-hungry local oscillator to an external device, much like backscatter transmitters do. Second, we avoid the energy cost of demodulating a phase-modulated signal by treating 802.15.4 as a frequency-modulated one, which allows us to receive with a simple passive detector and an energy-efficient thresholding circuit. We describe a prototype that can receive 802.15.4 frames with a power consumption of 361 μW. Our receiver prototype achieves sufficient communication range to integrate with deployed wireless sensor networks (WSNs).We illustrate this integration by pairing the prototype with an 802.15.4 backscatter transmitter and integrating it with unmodified 802.15.4 sensor nodes running the TSCH and Glossy protocols.

The sensing capabilities of most sensor networks are fixed at the time of deployment. Adding new sensing capabilities to such networks is a costly and cumbersome process. We present Passive Sensor Tags, battery-free sensing devices that could be used to extend the sensing capabilities of an existing network. Sensor tags feature our new 802.15.4 receiver design which is suitable for micro-power operation, making battery-free tags possible. Because our tags can both transmit and receive 802.15.4 frames there is no need for any modification to the deployed hardware. We present preliminary measurements of transmission and reception range.

There is the long-standing assumption that radio communication in the range of hundreds of meters needs to consume mWs of power at the transmitting device. In this paper, we demonstrate that this is not necessarily the case for some devices equipped with backscatter radios. We present LOREA an architecture consisting of a tag, a reader and multiple carrier generators that overcomes the power, cost and range limitations of existing systems such as Computational Radio Frequency Identification (CRFID). LOREA achieves this by: First, generating narrow-band backscatter transmissions that improve receiver sensitivity. Second, mitigating self-interference without the complex designs employed on RFID readers by keeping carrier signal and backscattered signal apart in frequency. Finally, decoupling carrier generation from the reader and using devices such as WiFi routers and sensor nodes as a source of the carrier signal. An off-the-shelf implementation of LOREA costs 70 USD, a drastic reduction in price considering commercial RFID readers cost 2000 USD. LOREA's range scales with the carrier strength, and proximity to the carrier source and achieves a maximum range of 3.4 km when the tag is located at 1 m distance from a 28 dBm carrier source while consuming 70 μW at the tag. When the tag is equidistant from the carrier source and the receiver, we can communicate upto 75 m, a significant improvement over existing RFID readers.

We present LOREA an architecture consisting of a backscatter tag, a reader and multiple carrier generators that overcomes the power, cost and range limitations of existing backscatter systems such as Computational Radio Frequency Identification (CRFID). LOREA achieves this by: First, generating narrow-band backscatter transmissions. Second, by mitigating self-interference without the complex designs employed on RFID readers by keeping carrier signal and backscattered signal apart in frequency. Finally, by decoupling carrier generation from the reader and using devices such as WiFi routers and sensor nodes as a source of the carrier signal. LOREA's communication range scales with the carrier strength, and proximity to the carrier source and achieves a maximum range of 3.4km when the tag is located 1 m from the carrier source while consuming 70 μWs at the backscatter tag. We present various ultra-low power and long-range features of the LoRea architecture.

Most existing indoor localization systems are battery-powered and use the changes in Radio Frequency (RF) signals to localize objects. In this paper, we present LocaLight: a battery-free indoor localization system that localizes objects using visible light by tracking the shadow they cast. By sensing a drop in the intensity of ambient light caused by the presence of a shadow, LocaLight localizes the object. Since the position of the shadow can be predicted, it is possible to localize the object in a sensitive area by carefully positioning the light sensors and the overhead lights. Our initial results suggest that LocaLight achieves an accuracy comparable to many of the state-of-the art solutions that use RF.

Ambient backscatter communication, where energy and wireless carrier are extracted from existing radio signals, are very attractive to the Internet of Things. This technology is emerging as an enabler for battery-less sensor nodes that can operate unattended for extended periods of time. Their capacity to operate without maintenance make them attractive for operation in situations where nodes might not be easily accessible. My research will help turn this vision into reality by advancing key areas that remain unexplored in this field.

The sensing capabilities of an Internet-of-Things (IoT) network are usually fixed at deployment. Adding new sensing modalities is a cumbersome process because it requires altering the deployed hardware. We introduce passive sensor tags that allow to easily and seamlessly add new sensors to existing IoT deployments without requiring hardware modifications or additional energy sources. Passive sensor tags employ backscatter communication to generate transmissions that can be decoded by the radio transceivers present in today's IoT devices. Furthermore, unlike recent works, our approach does not require dedicated infrastructure to generate the unmodulated carrier used for backscatter communication.