This paper presents MobiScatter, which enhances capacity of CSS based backscatter networks for accommodating drone-carried access points (APs). CSS based backscatter design has favorable features including long range and high concurrency. However, the concurrency of the network can be reduced by 34% when the drone-carried AP is introduced due to the mobility and fast fading. In order to maintain high concurrency, MobiScatter presents a series of new designs. In particular, we propose to enhance concurrency of the CSS based backscatter network with symmetric upchirps and downchirps, which neutralizes the impact of imperfect frequency orthogonality. Then, to mitigate the impact of fast fading on decoding, we present a novel half-period chirp modulation scheme for crossed chirps. Finally, we provide a power management method for tags by controlling transmitting time of chirps. We construct a MobiScatter prototype, which contains a drone-carried AP implemented with a mobile USRP and 200 tags. We deploy those tags in an area of$200 m\times 180 m$on a meadow. Experimental results show that MobiScatter can support 160 concurrent backscatter transmissions when the AP moves at$15 m/s$.

Channel state information (CSI) based Wi-Fi localization can achieve admirable decimeter-level accuracy; however, such systems require labor-intensive site survey to calibrate the AP position and the antenna array direction, which hinders practical large-scale deployment. In this article, we reveal an interesting finding that the calibration efforts for deploying the CSI localization system can be significantly reduced by simply replacing the ordinary linear antenna layout of the AP with the non-linear layout. In particular, we first present an autonomous self-calibrating method to significantly facilitate site survey for deploying CSI localization systems. Then we propose a systematical evaluation mechanism to show the fundamental reason why linear antenna layout usually leads to serious errors and why non-linear antenna layout is better off. Finally, we build a testbed with COTS devices and conduct comprehensive experiments. Results show that triangular antenna layout can achieve 80% angle of arrival (AoA) measurement error within 9° for any direction in contrast to 16° based on linear antenna layout. Moreover, we can realize promising localization accuracy as previous works even without labor-intensive site survey, where 80% localization error is within$0.60m$.

In this paper, we for the first time demonstrate how to realize multi-frequency backscatter communication for low power IoT devices. Our key innovation is an encoding rule that works from O(100) MHz to 2.4 GHz to cover most commercial communication bands such as BLE, LoRa and Wi-Fi. Based on the proposed method, backscatter devices can adaptively select one appropriate wireless protocol according to application scenarios. To this end, we reveal the fundamental reason why the existing backscatter tags only work in a specific frequency range and propose a universal multi-frequency communication method. We build hardware prototype and conduct experiments at several frequencies from 150 MHz to 2.4 GHz. Experimental results show that UniTag significantly improves communication performance at different frequencies in contrast to the existing methods, where the average bit error rate is only 13% to 50% of existing methods.

Cellular network positioning is a mandatory requirement for localizing emergency callers, such as E911 in North America. Although smartphones are normally equipped with GPS modules, there are still a large number of users with cell phones only as basic devices, and GPS could be ineffective in urban canyon environments. To this end, the RF fingerprints based positioning mechanism is incorporated into LTE architecture by 3GPP, where the major challenge is to collect geo-tagged RF fingerprints in vast areas. This paper proposes to utilize the subspace identification approach for large-scale RF fingerprints prediction. We formulate the problem into the problem of finding the optimal subspace over Stiefel manifold, and redesign the Stiefel-manifold optimization method with fast convergence rate. Moreover, we propose a sliding window mechanism for the practical large-scale fingerprints prediction scenario, where recorded fingerprints are unevenly distributed in the vast area. Combining the two proposed mechanisms enables an efficient method of large-scale fingerprints prediction in the city level. Further, we validate our theoretical analysis and proposed mechanisms by conducting experiments with real mobile data, which shows that the resulted localization accuracy and reliability with our predicted fingerprints exceed the requirement of E911.

Channel state information (CSI) based Wi-Fi localization can achieve admirable decimeter-level accuracy; however, such systems require labor-intensive site survey to calibrate the AP position and the antenna array orientation, which hinders practical large-scale deployment. In this paper, we reveal an interesting finding that the calibration efforts for deploying the CSI localization system can be significantly reduced by simply replacing the ordinary linear antenna layout of the AP with the non-linear layout. In particular, we first present an autonomous self-calibrating method to significantly facilitate site survey for deploying CSI localization systems. Then we propose a systematical evaluation mechanism to show the fundamental reason why linear antenna layout usually leads to serious errors and why non-linear antenna layout is better off. Finally, we build a testbed with COTS devices and conduct comprehensive experiments. Results show that triangular antenna layout can achieve 80% angle of arrival (AoA) measurement error within 9◦for any direction in contrast to 16◦based on linear antenna layout. Moreover, we can realize promising localization accuracy as previous works even without labor-intensive site survey, where 80% localization error is within 0.60m.

Fingerprinting indoor localization systems have been studied from different perspectives in the past decades; however, a vitally important piece in the puzzle is still missing: how does the system scale with the number of users? In this paper, we study the issue from a theoretical perspective, where the upper and lower bound of the system's localization reliability with respect to the number of users are derived. Our theoretical results can be verified by experiments thus can provide meaningful guidance for practical system design, which is in contrast to the traditional scaling-law work utilizing asymptotical analysis that is valid only under unverifiable extreme conditions. The theoretical and experimental results of our work reveal two interesting observations that shed light on the insight into the scalability of the fingerprinting localization system: First, the localization reliability drops dramatically before the number of users increases to a critical point and then decreases smoothly, where the critical point tends to appear when the number of users equals the number of access points (APs) deployed in the service region; second, even if the number of users approaches to infinity, the fingerprinting localization system still retains certain level of reliability.