The fifth-generation (5G) has empowerd various applications. Effective bandwidth and delay prediction in 5G cellular networks are essential for many applications, such as virtual reality and holographic video streaming. However, accurate bandwidth and delay prediction in 5G networks remains a challenging task due to the short-distance coverage and frequent handover properties of 5G base stations. In this paper, we propose HYPER, a hybrid bandwidth and delay prediction approach that uses an Auto Regressive Moving Average (ARMA) time series predictive model for intra-cell prediction and a Random Forest (RF) regression model for cross-cell prediction. Our ARMA model takes prior information as its input, while the RF model further uses related network and physical features to predict future performance. We conduct a measurement study in commercial 5G networks to analyze the relationship between these features and bandwidth/delay. Moreover, we also propose a handover window adaptation algorithm to automatically adjust the handover window size and determine which model to use during handover for accurate bandwidth and delay prediction. We use commercial 5G smartphones for data collection and conducted extensive experiments in diverse urban environments. Experimental results show that HYPER can reduce the prediction error by more than 13% compared to state-of-the-art prediction approaches.

Bluetooth Low Energy (BLE) is one of the most popular wireless protocols for building IoT applications because of its low energy, low cost, and wide compatibility nature. However, BLE communication performance can be easily affected by interference and blockages because of its low transmission power. This paper presents BLEW, a technique to improve the BLE communication performance over weak links by exploiting adaptive symbol extension and DNN-based demodulator to combat channel interference and maximize network throughput. First, we propose a phase peak clustering-based preamble detection method that coherently adds up the phase difference of preambles to combat the interference. We then propose a multidomain DNN-based demodulator to fully extracts the temporal and spectrum features of the signal and enhance the demodulation performance. Finally, we model the throughput of Commercial Off-The-Shelf (COTS) BLE chips transmitting extended packets, which can be used to optimize the symbol length in an adaptive manner. We implement BLEW with USRP B210 and COTS nRF52840 platform. Experiments show that BLEW can increase throughput by up to 157.39 Kb/s compared with native BLE over typical weak links. Compared with existing approaches, BLEW has up to 25.70% higher preamble detection rate and up to 3.37 dB demodulation gain.

In recent years, Bluetooth Low Energy (BLE) has become one of the most wildly used wireless protocols and it is common that users carry one or more BLE devices. With the extensive deployment of BLE devices, there is a significant privacy risk if these BLE devices can be tracked. However, the common wisdom suggests that the risk of BLE location tracking is negligible. The reason is that researchers believe there are no stable BLE fingerprints that are stable across different scenarios (e.g., temperatures) for different BLE devices with the same model. In this paper, we introduce a novel physical-layer fingerprint named Transient Dynamic Fingerprint (TDF), which originated from the negative feedback control process of the frequency synthesizer. Because of the hardware imperfection, the dynamic features of the frequency synthesizer are different, making TDF unique among different devices, even with the same model. Furthermore, TDF keeps stable under different thermal conditions. Based on TDF, we propose BTrack, a practical BLE device tracking system and evaluate its tracking performance in different environments. The results show BTrack works well once BLE beacons are effectively received. The identification accuracy is 35.38%-57.41% higher than the existing method, and stable over temperatures, distances, and locations.

Recent years have witnessed the rapid growth of IoT (Internet of Things). Bluetooth Low Energy (BLE) is one of the most popular wireless protocols to implement IoT applications because of its energy efficiency and low-cost properties. However, the development of BLE applications is time-consuming and exhausting. Users are required to write programs for both sides of a BLE connection using complicated low-level APIs. Moreover, it needs much expertise for developers to set appropriate parameters in accordance to different application requirements, especially when there exist multiple concurrent BLE connections. To address these problems, we propose BLEdge , an edge-centric programming approach for BLE applications with multi-connection optimization. First, we propose a wireless bus abstraction for BLE programming. With this, users can write BLE applications in an edge-centric way, as if the BLE-connected peripherals are physically attached to the edge node. Second, we advocate an optimization approach for BLE connection parameters. This optimization approach considers the time slot collision problem under a multi-connection scenario. We conduct extensive experiments with the nRF52840DK platform. Experiment results show that BLEdge can reduce 62.50% to 90.55% LOC (Lines of Code) when developing BLE applications. Furthermore, our parameter optimization approach can reduce up to 42.23% energy consumption.

Bluetooth Low Energy (BLE) is one of the popular wireless protocols to build IoT applications. However, the BLE suffers from three major issues that make it unable to provide reliable service to time-critical IoT applications. First, the BLE operates in the crowded 2.4GHz frequency band, which can lead to a high packet loss rate. Second, it is common for one device to connect with multiple BLE Peripherals, which can lead to severe collision issue. Third, there is a long delay to re-allocate time resource. In this paper, we propose RT-BLE: a real-time multi-connection scheduling scheme for BLE. We first formulate the BLE transmission latency in noisy RF environments considering the BLE retransmission mechanism. With this, RT-BLE can get a set of initial connection parameters. Then, RT-BLE uses collision tree based time resource scheduling technology to efficiently manage time resource. Finally, we propose a subrating-based fast connection re-scheduling method to update the connection parameters and the position of anchor points. The result shows RT-BLE can provide reliable service and the error of our model is less than 0.69%. Compare with existing works, the re-scheduling delay is reduced by up to 86.25% and the capacity is up to 4.33× higher.

The Internet of Things (IoT) interconnects massive cyber-physical devices (CPD) to provide various applications, such as smart home and smart building. Bluetooth Mesh is an emerging networking technology, which can be used to organize a massive network with Bluetooth Low Energy (BLE) devices. Managed-flooding protocol is used in Bluetooth Mesh to route the data packets. Although it is a highly desirable option when data transmission is urgent, it is inefficient in a larger and denser mesh network due to the collisions of broadcast data packets. In this paper, we introduce ACE: a Routing Algorithm based on Autonomous Channel Scheduling for Bluetooth Mesh Network. ACE relies on the existing Bluetooth Mesh messages to distribute routes without additional traffic overhead and conducts a beacon-aware routing update adaptively as the topology evolves. In ACE, BLE channel resources can be efficiently utilized by a channel scheduling scheme for each node locally and autonomously without any neighborly negotiation. We implement ACE on the nRF52840 from Nordic Semiconductor and evaluate its effectiveness on our testbed. Compared to the Bluetooth Mesh, our experiments proved that ACE could reduce the end-to-end latency by 16%, alleviate packets collisions issues, and increase the packet delivery ratio (PDR) by 30% under heavy traffic. Moreover, simulation results verified that ACE has better scalability when the size and density of networks become larger and denser.