Breath monitoring helps assess the general personal health and gives clues to chronic diseases. Yet current breath monitoring technologies are inconvenient and intrusive. For instance, typical breath monitoring devices need to attach nasal probes or chest bands to users. Wireless sensing technologies have been applied to monitor breathing using radio waves without physical contact. Those wireless sensing technologies however require customized radios which are not readily available. More importantly, due to interference, such technologies do not work well with multiple users. With multiple users in presence, the detection accuracy of existing systems decreases dramatically. In this paper, we propose to monitor users' breathing using commercial-off-the-shelf (COTS) RFID systems. In our system, passive lightweight RFID tags are attached to users' clothes and backscatter radio waves, and commodity RFID readers report low level data (e.g., phase values). We track periodic body movement due to inhaling and exhaling by analyzing the low level data reported by commodity readers. To enhance the measurement robustness, we synthesize data streams from an array of multiple tags to improve the monitoring accuracy. Our design follows the standard EPC protocol which arbitrates collisions in the presence of multiple tags. We implement a prototype the breath monitoring system with commodity RFID systems. The experiment results show that the prototype system can simultaneously monitor breathing with high accuracy even with the presence of multiple users.

Tree-based RFID identification adopts a binary-tree structure to collect IDs of an unknown set. Tag IDs locate at the leaf nodes and the reader queries through intermediate tree nodes and converges to these IDs using feedbacks from tag responses. Existing works cannot function well under random ID distribution as they ignore the distribution information hidden in the physical-layer signal of colliding tags. Different from them, we introduce PHY-Tree, a novel tree-based scheme that collects two types of distribution information from every encountered colliding signal. First, we detect if all colliding tags send the same bit content at each bit index by looking into inherent temporal features of the tag modulation schemes. If such resonant states are detected, either left or right branch of a certain subtree can be trimmed horizontally. Second, we estimate the number of colliding tags in a slot by computing a related metric defined over the signal's constellation map, based on which nodes in the same layers of a certain subtree can be skipped vertically. Evaluations from both experiments and simulations demonstrate that PHY-Tree outperforms state-of-the-art schemes by at least 1.79×.

Estimating the number of RFID tags is a fundamental operation in RFID systems and has recently attracted wide attentions. Despite the subtleties in their designs, previous methods estimate the tag cardinality from the slot measurements, which distinguish idle and busy slots and based on that derive the cardinality following some probability models. In order to fundamentally improve the counting efficiency, in this paper we introduce PLACE, a physical layer based cardinality estimator. We show that it is possible to extract more information and infer integer states from the same slots in RFID communications. We propose a joint estimator that optimally combines multiple sub-estimators, each of which independently counts the number of tags with different inferred PHY states. Extensive experiments based on the GNURadio/USRP platform and the large-scale simulations demonstrate that PLACE achieves approximately 3 ~ 4× performance improvement over state-of-the-art cardinality estimation approaches.

Estimating the number of RFID tags is a fundamental operation in RFID systems and has recently attracted wide attentions. Despite the subtleties in their designs, previous methods estimate the tag cardinality from the slot measurements, which distinguish idle and busy slots and based on that derive the cardinality following some probability models. In order to fundamentally improve the counting efficiency, in this paper we introduce PLACE, a physical layer based cardinality estimator. We show that it is possible to extract more information and infer integer states from the same slots in RFID communications. We propose a joint estimator that optimally combines multiple sub-estimators, each of which independently counts the number of tags with different inferred PHY states. Extensive experiments based on the GNURadio/USRP platform and the large-scale simulations demonstrate that PLACE achieves approximately 3~4× performance improvement over state-of-the-art cardinality estimation approaches.

We consider the problem of allocating two network resources (wireless bandwidth and backhaul router buffer) to support fair QoS in WLAN. We transform the QoS requirements to multi-resource demands and apply the DRF scheme to achieve fair allocation of the two different types of resources. We define a QoS utility function which measures the user QoS satisfaction. We briefly prove the property of resource-based strategy proofness of DRF, the corresponding mathematical expression is used to prove QoS-based strategy-proofness in terms of both resource share and user QoS. Finally our simulation results confirm the properties of DRF and demonstrate that our DRF-based approach performs better than several multi-resource allocation schemes with respect to both fairness and utility.