Wireless channels are vulnerable to eavesdroppers due to their broadcast nature. One approach to thwart an eavesdropper (Eve) is to decrease her SNR, e.g., by reducing the signal in her direction. Unfortunately, such methods are vulnerable to (1) a highly directional Eve that can increase her received signal strength and (2) Eve that is close to the receiver, Bob, or close to the transmitter, Alice. In this paper, we design and experimentally evaluate Multipath Multicarrier Misinformation to Adversaries (M3A), a system for Alice to send data to Bob while simultaneously sending misinformation to Eve. Our approach does not require knowledge of Eve's channel or location and, with multipath channels, randomly transforms Eve's symbols even if Eve is located one wavelength-scale distance from Bob (approximately 10 cm) or if Eve is located between Alice and Bob in their direct path (Eve is approximately 1/3 closer to Alice). In particular, our approach is to move each of Eve's received symbols (over time and across subcarriers), to an independently random transformation as compared to Bob, without Alice or Bob knowing Eve's location or channel. We realize this by modulating Alice's per-subcarrier beamforming weights with an i.i.d. random binary sequence, as if Alice had a separate antenna array for each subcarrier, and could randomly turn antennas in each array on and off. We implement M3A on a real-time Massive MIMO testbed and show that M3A can increase Eve's bit error rate more than two hundredfold compared to beamforming, even if she is positioned approximately a wavelength away, whether above, below, or beside Bob. Finally, to ensure reliability at Bob, we show that with M3A, Bob's bit error rate is approximately an order of magnitude lower than achieved with prior work.

To handle the impact of the explosive growth of data traffic generated by smart industrial applications in the Industrial Internet of Things (IIoT) scenario, edge caching is commonly used in the IIoT to reduce the content access delay and to release the backhaul load. However, due to the mobility of IIoT devices and temporal dependence of popularity, traditional content caching policies, such as least frequently used (LFU) and least recently used (LRU), cannot cache the required contents accurately within the coverage of edge server. Therefore, by exploiting the moving trajectory of the IIoT devices and the temporal dependence of the content popularity, we propose a multiagent cooperative caching policy, in which each edge server acts as an agent to cooperatively learn the optimal caching decision, then each edge server caches the corresponding contents to reduce the content access delay. Especially, we first apply the$K$-order Markov chain to predict the moving trajectory of the IIoT devices to get the IIoT device set within the coverage of each edge server. Second, we use long short-term memory (LSTM) to get the prior knowledge of the content requests by using the moving trajectory prediction results. Finally, we obtain the optimal caching decision by deep reinforcement learning to improve the Quality of Service (QoS) of the IIoT applications. Experimental results demonstrate that the proposed caching policy can efficiently improve the cache-hit ratio and content access delay.

Low-power wireless mesh networks (LPWMNs) have been widely used in wireless monitoring and control applications. Although LPWMNs work satisfactorily most of the time thanks to decades of research, they are often complex, inelastic to change, and difficult to manage once the networks are deployed. Moreover, the deliveries of control commands, especially those carrying urgent information such as emergency alarms, suffer long delay, since the messages must go through the hop-by-hop transport. Recent studies show that adding low-power wide-area network radios such as LoRa onto the LPWMN devices (e.g., ZigBee) effectively overcomes the limitation. However, users have shown a marked reluctance to embrace the new heterogeneous communication approach because of the cost of hardware modification. In this article, we introduce LoRaBee, a novel LoRa to ZigBee cross-technology communication (CTC) approach, which leverages the energy emission in the Sub-1 GHz bands as the carrier to deliver information. Although LoRa and ZigBee adopt distinct modulation techniques, LoRaBee sends information from LoRa to ZigBee by putting specific bytes in the payload of legitimate LoRa packets. The bytes are selected such that the corresponding LoRa chirps can be recognized by the ZigBee devices through sampling the received signal strength. Experimental results show that our LoRaBee provides reliable CTC communication from LoRa to ZigBee with the throughput of up to 281.61 bps in the Sub-1 GHz bands.

This article presents a 1st-order noise-shaping (NS) successive approximation register (SAR) analog-to-digital converter (ADC) with a hybrid error-feedback (EF) and cascaded-integrator-feed-forward (CIFF) structure assisted by a unity-gain buffer (UGB). Without using a multi-input comparator which is widely adopted in conventional 1st-order passive NS structures, the proposed hybrid EF-CIFF structure realizes a more ideal 1st-order noise transfer function (NTF) with a reasonable capacitance ratio, so as to obtain better NS effect. Fabricated in a 28-nm CMOS technology, the prototype NS-SAR ADC consumes 150$\mu \text{W}$under a 0.9-V supply voltage when operating at 40-MS/s sampling rate. A 75-dB signal-to-noise-and-distortion ratio (SNDR) is measured for a 2.47-MHz sinusoid input under an oversampling ratio (OSR) of 8. It achieves a peak Schreier figure-of-merit (FoM) of 177.2 dB and the core circuit occupies 0.012-mm2 area.

Traditionally, satellite and cellular technologies have been used in establishing the long-distance links that collect real-time data from running vehicles to the base station. However, the systems that implement those technologies are often too costly for use in small communities, such as monitoring shuttles that circle a university campus. Recently, LoRa has been used as a low-cost alternative that provides the capability of long-range data collection for low data rate applications. In this paper, we present a low-cost LoRa-based wireless network that collects real-time data from six shuttles circling our university campus and has operated in the real world for more than a year. The selection of the LoRa Spreading Factor (SF) poses a significant challenge because of its effects on two conflicting network performance metrics. A larger SF provides higher network reliability at the cost of lower throughput. To address this challenge, we develop a runtime SF control solution that employs the K-Nearest Neighbors (KNN) algorithm to adapt the SF configuration based on the current link condition. Experimental results show that our approach significantly increases the data collection throughput while meeting the application reliability requirement compared to the state of the art.

The importance of real-time wireless data transfer is rapidly increasing for Internet of Things (IoT) applications. For example, smart glasses worn by a doctor need to transmit real-time data to a hospital information system, which performs face detection and recognition, for real-time interaction with recognized patients within a certain deadline, which is ideally a few hundred milliseconds. Other emerging IoT applications, e.g., structural health monitoring, clinical monitoring, and industrial process automation, also require real-time wireless data transfer. Those applications have critical demands for real-time and energy-efficient communication through wireless medium. However, it is very challenging to support stringent timing constraints energy-efficiently through wireless medium due to its inherent unreliability and timing-unpredictability. Fortunately, heterogeneous radios are becoming increasingly available in modern embedded devices, offering new opportunities to use multiple wireless technologies to accommodate the needs of real-time applications. In this paper, we first formulate the runtime radio selection and data partitioning for real-time IoT applications as an Integer Linear Programming (ILP) problem and then present (1) an optimal algorithm that makes quick and optimal decisions when selecting between two radios and (2) a heuristic algorithm for the platforms with more radios. Experimental results show that our heuristic algorithm provides optimal selections to 94.4% of the cases and makes the decisions 336~1412 times faster than an ILP problem solver.

Low-power wireless mesh networks (LPWMNs) have been widely used in wireless monitoring and control applications. Although LPWMNs work satisfactorily most of the time thanks to decades of research, they are often complex, inelastic to change, and difficult to manage once the networks are deployed. Moreover, the deliveries of control commands, especially those carrying urgent information such as emergency alarms, suffer long delay, since the messages must go through the hop-by-hop transport. Recent studies show that adding low-power wide-area network (LPWAN) radios such as LoRa onto the LPWMN devices (e.g., ZigBee) effectively overcomes the limitation. However, users have shown a marked reluctance to embrace the new heterogeneous communication approach because of the cost of hardware modification. In this paper, we introduce LoRaBee, a novel LoRa to ZigBee cross-technology communication (CTC) approach, which leverages the energy emission in the Sub-1 GHz bands as the carrier to deliver information. Although LoRa and ZigBee adopt distinct modulation techniques, LoRaBee sends information from LoRa to ZigBee by putting specific bytes in the payload of legitimate LoRa packets. The bytes are selected such that the corresponding LoRa chirps can be recognized by the ZigBee devices through sampling the received signal strength (RSS). Experimental results show that our LoRaBee provides reliable CTC communication from LoRa to ZigBee with the throughput of up to 281.61bps in the Sub-1 GHz bands.

Research efforts over the last few decades produced multiple wireless technologies, which are readily available to support communication between devices in various dynamic Internet of Things (IoT) and robotics applications. However, single radio technology can hardly deliver optimal performance across all critical quality of service (QoS) dimensions under the typically varying environmental conditions or under varying distance between communicating nodes. Using a single wireless technology therefore falls short of meeting the demands of varying workloads or changing environmental conditions. Instead of pursuing a one-radio-fits-all approach, we design ARTPoS , an Adaptive Radio and Transmission Power Selection system, which makes available at runtime multiple wireless technologies (e.g., WiFi and ZigBee) and selects the radio(s) and transmission power(s) most suitable for the current conditions and requirements. The principal components of ARTPoS include new empirical models of power consumption and packet reception ratio (the latter can also be refined online) and online optimization schemes. We have implemented our system and evaluate it on the physical testbed consisting of our new embedded platforms with heterogeneous radios. Experimental results show that ARTPoS can significantly reduce the power consumption, while maintaining desired link reliability, compared to standard baselines.

Research efforts over the last few decades produced multiple wireless technologies, which are readily available to support communication between devices in various Internet of Things (IoT) applications. However, none of the existing technologies delivers optimal performance across all critical quality of service (QoS) dimensions under varying environmental conditions. Using a single wireless technology therefore cannot meet the demands of varying workloads or changing environmental conditions. This problem is exacerbated with the increasing interest in placing embedded devices on the user's body or other mobile objects in mobile IoT applications. Instead of pursuing a one-radio-fits-all approach, we design ARTPoS, an adaptive radio and transmission power selection system, which makes available multiple wireless technologies at runtime and selects the radio(s) and transmission power(s) most suitable for the current conditions and requirements. Experimental results show that ARTPoS can significantly reduce the power consumption, while maintaining desired link reliability.