Recent advances in mobile technologies (like WiFi 6 and 5G) do not seem to deliver the promised access bandwidth. To effectively characterize mobile access bandwidth in the wild, we work with a major commercial mobile bandwidth testing app to conduct a long-term (2020-2023) and large-scale (involving 4.76M users) measurement study in China, based on coarse-grained general statistics and fine-grained sampling diagnostics. Our study presents distinct facts as to WiFi, 5G, and 4G: in the past few years, the average WiFi download bandwidth exhibits a considerable rise (by 119.7% ), the average 5G download bandwidth constantly decreases (by a total of 20.2% ) despite the enormous infrastructure investments, while the average 4G download bandwidth first declines (by 22.1% ) and then increases (by 22.5% ). The situations of upload bandwidths are generally similar to those of download bandwidths, except that 5G upload bandwidths manifestN-shaped$(\nearrow \searrow \nearrow )$fluctuations. Our cross-layer and cross-technology analysis reveals a variety of impact factors as well as their complicated interplay as the root causes, such as the bottlenecks in underlying infrastructure (e.g., communication devices and wired Internet access), the traffic offloading from one access technology to another, the influence of the COVID-19 pandemic, and the side effects of aggressively migrating radio resources from 4G to 5G. With the longitudinal, holistic picture of today's mobile access bandwidth, we finally provide multifold practical implications on closing the technology gaps.

UWB (Ultra-wideband) has been shown to be a promising technology to provide accurate positioning for the Internet of Things. However, its performance significantly degrades in practice due to Non-Line-Of-Sight (NLOS) issues. Various approaches have implicitly or explicitly explored the problem. In this article, we propose RefLoc , which leverages the unique benefits of UWB to address the NLOS problem. While we find that NLOS links can vary significantly in the same environment, LOS links possess similar features that can be captured by the high bandwidth of UWB. Specifically, the high-level idea of RefLoc is to first identify links among anchors with known positions and leverage those links as references for tag link identification. To achieve this, we address the practical challenges of deriving anchor link status, extracting qualified link features, and inferring tag links with anchor links. We implement RefLoc on commercial hardware and conduct extensive experiments in different environments. The evaluation results show that RefLoc achieves an average NLOS identification accuracy of 96% in various environments, improving the state-of-the-art by 10%, and reduces 80% localization error with little overhead.

LoRa, as a representative Low-Power Wide-Area Network (LPWAN) technology, holds tremendous potential for various city and industrial applications. However, as there are few real large-scale deployments, it is unclear whether and how well LoRa can eventually meet its prospects. In this paper, we demystify the real performance of LoRa by deploying and measuring a citywide LoRa network, named CityWAN, which consists of 100 gateways and 19,821 LoRa end nodes, covering an area of 130 km2 for 12 applications. Our measurement focuses on the following perspectives: (i) Performance of applications running on the citywide LoRa network; (ii) Infrastructure efficiency and deployment optimization; (iii) Physical layer signal features and link performance; (iv) Energy profiling and cost estimation for LoRa applications. The results reveal that LoRa performance in urban settings is bottlenecked by the prevalent blind spots, and there is a gap between the gateway efficiency and network coverage for the infrastructure deployment. Besides, we find that LoRa links at the physical layer are susceptible to environmental variations, and LoRa and other LPWANs show diverse costs for different scenarios. Our measurement provides insights for large-scale LoRa network deployment and also for future academic research to fully unleash the potential of LoRa.

Low-Power Wide-Area Networks (LPWANs) have been shown as a promising technique to provide long-range low-power communication for large-scale IoT devices. In this paper, however, we show the poor performance of LoRa network due to its link diversity in macro- and micro- scope through one-month measurements in an area of$2.2\ km\times 1.5\ km$. We present FerryLink, which exploits such link diversity and leverages peer nodes to ferry data of weak links, to combat performance degradation. Traditional arts (e.g., building multi-hop networks) are inefficient or too heavyweight for the current star-topology-based LoRa network. FerryLink thus proposes a novel ferry mechanism combining RSSI sampling and Channel Activity detection(CAD) to suit multiple orthogonal transmission parameters of LoRa. To reduce energy overhead, FerryLink leverages convention windows for coarse-grained transmission synchronization between two coupled nodes. Finally, FerryLink utilizes the orthogonality of uplink and downlink signals to avoid data redundancy due to the ferry mechanism, maintaining comparable capacity with original LPWANs. We build FerryLink on top of LoRaWANwith commercial off-the-shelf hardware. The extensive evaluation results show that FerryLink effectively improves the packet delivery rate (PDR) of LoRa nodes (to over 95%), achieves 2x less energy overhead, and increases communication range by 50% compared with the original LoRaWAN.