With wide deployments of bluetooth low energy (BLE) infrastructures and recent advances in backscatter communications, BLE backscatter-based indoor localization becomes a promising solution for asset management and object tracking due to its low cost and near-zero power consumption. Despite extensive localization techniques, none can meet the high accuracy, protocol compatibility, and low-power consumption requirements in BLE backscatter localization. To fill this gap, this article presents B2Loc, the first BLE backscatter localization system that enables decimeter-level localization for low-power backscatter tags with existing BLE infrastructures. The key insight is to design a low-power backscatter modulation to create a frequency-constant and phase-continuous constant tone extension (CTE) field for backscatter localization while guaranteeing communication compatibility with the commodity BLE. In addition, B2Loc also fully exploits the signal propagation signatures and the BLE backscatter characteristic to improve localization performance. We fabricate$\mu $W-level backscatter tags with off-the-shelf components and evaluate the performance using commodity BLE infrastructures. The results show that B2Loc achieves decimeter-level median localization accuracy even deployed in multipath-rich indoor environments.

Offloading computing to edge servers is a promising solution to support growing video understanding applications at resource-constrained IoT devices. Recent efforts have been made to enhance the scalability of such systems by reducing inference costs on edge servers. However, existing research is not directly applicable to pixel-level vision tasks such as video semantic segmentation (VSS), partly due to the fluctuating VSS accuracy and segment bitrate caused by the dynamic video content. In response, we present Penance, a new edge inference cost reduction framework. By exploiting softmax outputs of VSS models and the prediction mechanism of H.264/AVC codecs, Penance optimizes model selection and compression settings to minimize the inference cost while meeting the required accuracy within the available bandwidth constraints. We implement Penance in a commercial IoT device with only CPUs. Experimental results show that Penance consumes a negligible 6.8% more computation resources than the optimal strategy while satisfying accuracy and bandwidth constraints with a low failure rate.

A long-standing vision of indoor localization is to eliminate infrastructure and deployment costs. Recent innovations make it possible to enable device-to-device (D2D) localization while requiring multiple antennas for the systems. We ask the following question: can we localize the more generally used single-antenna devices (e.g., IoT) using another single-antenna device (e.g., smartphone or smartwatch) in a smart environment where low-cost backscatter tags are widely deployed on walls or smart objects? In this article, we present TagLoc, a lightweight system that enables D2D localization without relying on large antenna arrays. Our observation is that the reflected signals from the ambient smart environment can be exploited to eliminate the requirement of bulky antenna arrays that are unachievable for the simple-designed IoT devices. Specifically, TagLoc creates multiple direction signatures using backscatter arrays in smart environments. Then, the receiver can accurately estimate the direction signatures from the transmitter to the arrays and then localize the target by cooperating all tag arrays. We prototype TagLoc using two single-antenna Intel NUCs with off-the-shelf Intel 5300 WiFi cards and customized backscatter tags. The results show TagLoc can achieve robust performance in a real indoor environment with a median localization error of 0.82 m.

Miniature radar has demonstrated its great potential in smart homes, such as understanding the wellness of the residents and providing ubiquitous interactions. While it has many promising applications, it also results in congested RF (radio frequency) environments as there is an unprecedented amount of traffic in a smart home. To ease the strain on the limited spectrum, we ask the question that, can we reuse the sensing signals for data communication? With such a capability, we can improve the spectrum utilization by sharing the spectrum between sensing and communication systems. However, radar signals are customized for the sensing purpose and are incompatible with legacy communication standards. To address this challenge, we have an observation that, non-linearity effect in RF circuits can convert wideband radar signals into a LoRa signal. Based on this observation, in this paper, we present LoRadar, which enables an FMCW (Frequency-Modulated Continuous Wave) radar to carry LoRa signals in sensing waves. We present both the downlink and uplink design, enabling a LoRadar device to communicate with LoRa nodes in a bi-directional way. We implement LoRadar and evaluation results show that LoRadar can achieve home-level coverage with 3.4kbps data rate while it preserves the sensing resolution of the radar.

The low-power radio technologies open up many opportunities to facilitate Internet-of-Things (IoT) into our daily life, while their minimalist design also makes IoT devices vulnerable to many active attacks. Recent advances use an antenna array to extract fine-grained physical-layer signatures to identify the attackers, which adds burdens in terms of energy and hardware cost to IoT devices. In this paper, we present ShieldScatter, a lightweight system that attaches low-cost tags to single-antenna devices to shield the system from active attacks. The key insight of ShieldScatter is to intentionally create multi-path propagation signatures with the careful deployment of tags. These signatures can be used to construct a sensitive profile to identify the location of the signals’ arrival, and thus detect the threat. In addition, we also design a tag-random scheme and a multiple receivers combination approach to detect a powerful attacker who has the strong priori knowledge of the legitimate user. We prototype ShieldScatter with USRPs and tags to evaluate our system in various environments. The results show that even when the powerful attacker is close to the legitimate device, ShieldScatter can mitigate 95 percent of attack attempts while triggering false alarms on just 7 percent of legitimate traffic.

The lightweight protocols and low-power radio technologies open up many opportunities to facilitate Internet-of-Things (IoT) into our daily life, while their minimalist design also makes IoT devices vulnerable to many active attacks due to the lack of sophisticated security protocols. Recent advances advocate the use of an antenna array to extract fine-grained physical-layer signatures to mitigate these active attacks. However, it adds burdens in terms of energy consumption and hardware cost that IoT devices cannot afford. To overcome this predicament, we present ShieldScatter, a lightweight system that attaches battery-free backscatter tags to single-antenna devices to shield the system from active attacks. The key insight of ShieldScatter is to intentionally create multi-path propagation signatures with the careful deployment of backscatter tags. These signatures can be used to construct a sensitive profile to identify the location of the signals' arrival, and thus detect the threat. We prototype ShieldScatter with USRPs and ambient backscatter tags to evaluate our system in various environments. The experimental results show that even when the attacker is located only 15 cm away from the legitimate device, ShieldScatter with merely three backscatter tags can mitigate 97% of spoofing attack attempts while at the same time trigger false alarms on just 7% of legitimate traffic.