Use of unmanned aerial vehicles (UAVs) as flying base stations (BSs) has been gaining significant attention because they can provide connections to ground users efficiently during temporary events (e.g., sports events) owing to their flexible 3D-mobility. However, the complicated air-to-ground channel characteristics and interference among UAVs hinder the dynamic optimization of 3D-deployment of UAVs for spatially and temporally varying users. In this paper, we propose a novel distributed 3D-deployment method for UAV-BSs in a downlink millimeter-wave network for on-demand coverage. Our method consists mainly of two parts: sensing-aided crowd density estimation part; and distributed push-sum algorithm part. Since it is unrealistic to obtain all the specific positions users, the first part estimates the user density based on partial information obtained from on-ground sensors that can detect ground users around them. With the second part, each UAV dynamically updates its 3D-position by collaborating with its neighbors so that the total coverage of users is maximized. By employing a distributed push-sum protocol framework, we also prove the convergence of our algorithm. Simulation results demonstrate that our method can improve the coverage with a limited number of sensors and is applicable to a dynamic network.

Unmanned aerial vehicles (UAVs) is being widely used in wireless communication, e.g., data collection from ground nodes (GNs), and energy is critical. Existing works combine speed scheduling with trajectory design for UAVs, which is complicated to be optimally solved and lose trace of the fundamental nature of speed scheduling. We focus on speed scheduling by considering straight line flights, having applications in monitoring power transmission lines, roads, pipes or rivers/coasts. By real-world flight tests, we disclose a speed-related flight energy consumption model, distinct from typical distance-related or duration-related models. Based on such practical energy model, we develop the 'look before cross' (LBC) algorithm: on the time-distance diagram, we construct rooms representing GNs, and the goal is to design a room crossing walking trajectory, uniquely mapping to a speed scheduling. Such trajectory is determined by looking before crossing rooms. It is proved to be optimal for the offline scenario, in which information about GNs is available before scheduling. For the online scenario, we proposed a heuristic based on LBC. Simulation shows it performs close to the optimal offline solution. Our study on the speed scheduling and practical flight energy model shed light on a new direction on UAV aided wireless communication.

Networks of Unmanned Aerial Vehicles will take a vital role in future Internet of Things and 5G networks. However, how to control UAV networks in an automated and scalable fashion in distributed, interference-prone, and potentially adversarial environments is still an open research problem.

We introduce SwarmControl, a new software-defined control framework for UAV wireless networks based on distributed optimization principles. In essence, SwarmControl provides the Network Operator (NO) with a unified centralized abstraction of the networking and flight control functionalities. High-level control directives are then automatically decomposed and converted into distributed network control actions that are executed through programmable software-radio protocol stacks. SwarmControl (i) constructs a network control problem representation of the directives of the NO; (ii) decomposes it into a set of distributed sub-problems; and (iii) automatically generates numerical solution algorithms to be executed at individual UAVs.

We present a prototype of an SDR-based, fully reconfigurable UAV network platform that implements the proposed control framework, based on which we assess the effectiveness and flexibility of SwarmControl with extensive flight experiments. Results indicate that the SwarmControl framework enables swift reconfiguration of the network control functionalities, and it can achieve an average throughput gain of \(159%\) compared to the state-of-the-art solutions.

Unmanned aerial vehicles (UAVs) are being investigated to substitute for labor in many indoor applications, e.g., asset tracking and surveillance, where the global positioning system (GPS) is not available. Emerging autonomous UAVs are also expected to land in indoor or canopied aprons automatically. Such GPS-denied environments require alternative non-GPS positioning methods. Though there have been some vision-based solutions for UAVs, they perform poorly in the scenes with bad illumination conditions, or estimate only relative locations but not global positions. Other common indoor localization methods do not cover UAV factors, such as low power and flying behaviors. To this end, we propose a practical non-GPS positioning system for UAVs, named WPF-PS, using low-power, off-the-shelf WiGig devices. We formulate a 3-dimensional beam fingerprint by leveraging the diversity of available TX/RX beams and the link quality. To augment accuracy, we use the weighted k-nearest neighbors algorithm to overcome partial fingerprint inaccuracy, and applies the particle filtering technique into considering the UAV motion. We prototype the WBF-PS on our UAV platform, and it yields a 90th percentile positioning error of below 1m with both small and large velocity estimation errors.

With the maturation of wireless charging technology, Wireless Rechargeable Sensor Networks (WRSNs) has become a promising solution for prolong network lifetimes. Recently studies propose to employ a mobile charger (MC) to simultaneously charge multiple sensors within the same charging range, such that the charging performance can be improved. In this paper, we aim to jointly optimize the number of dead sensors and the energy usage effectiveness in such multi-node charging scenarios. We achieve this by introducing the partial charging mechanism, meaning that instead of following the conventional way that each sensor gets fully charged in one time step, our work allows MC to fully charge a sensor by multiple times. We show that the partial charging mechanism causes minimizing the number of dead sensors and maximizing the energy usage effectiveness to conflict with each other. We formulate this problem and develop a multi-node temporal spatial partial-charging algorithm (MTSPC) to solve it. The optimality of MTSPC is proved, and extensive simulations are carried out to demonstrate the effectiveness of MTSPC.

This paper proposes a self-sustaining broadband long-range maritime communication as an alternative to the expensive and slow satellite communications in offshore areas. The proposed system, named Marinet, consists of many buoys. Each of the buoys has two units: an energy harvesting unit and a wireless communication unit. The energy harvesting unit extracts energy from ocean waves to support the operation of the wireless communication unit. The wireless communication unit at each buoy operates in a TV white space frequency band and connects to each other and wired high-speed gateways on land or islands to form a mesh network. The resulting mesh network provides wireless access services to marine users in their range. A prototype of the energy harvesting unit and the wireless communication unit are built and tested in the field. In addition, to ensure Marinet will maintain stable communications in rough sea states, an ocean-link-state prediction algorithm is designed. The algorithm predicts ocean link-states based on ocean wave movements. A realistic ocean simulator is designed and used to evaluate how such a link-state prediction algorithm can improve routing algorithm performance.

Benefitting from the recent breakthrough of wireless power transfer technology, Wireless Rechargeable Sensor Networks (WRSNs) have become an important research topic. Most prior arts focus on system performance enhancement in an ideal environment that ignores impacts of obstacles. This contradicts with practical applications in which obstacles can be found almost anywhere and have dramatic impacts on energy transmission. In this paper, we concentrate on the problem of charging a practical WRSN in the presence of obstacles to maximize the charging utility under specific energy constraints. First, we propose a new theoretical charging model with obstacles based on Fresnel diffraction model, and conduct experiments to verify its effectiveness. Then, we propose a spatial discretization scheme to obtain a finite feasible charging position set for MC, which largely reduces computation overhead. Afterwards, we re-formalize charging utility maximization with energy constraints as a submodular function maximization problem and propose a cost-efficient algorithm with approximation ratio \(\frac{(e-1)}{2e}(1-\varepsilon)\) to solve it. Lastly, we demonstrate that our scheme outperforms other algorithms by at least \(14.8%\) in terms of charging utility through test-bed experiments and extensive simulations.

This paper studies the problem of Placing directional wIreless chargers with Limited mObiliTy (PILOT), that is, given a budget of mobile directional wireless chargers and a set of static rechargeable devices on a 2D plane, determine deployment positions, stop positions and orientations, and portions of time for all chargers such that overall charging utility of all devices can be maximized. To the best of our knowledge, we are the first to study placement of mobile chargers. To address PILOT, we propose a (1/2−ε)-approximation algorithm. First, we present a method to approximate nonlinear charging power of chargers, and further propose an approach to construct Maximal Covered Set uniform subareas to reduce the infinite continuous search space for stop positions and orientations to a finite discrete one. Second, we present geometrical techniques to further reduce the infinite solution space for candidate deployment positions to a finite one without performance loss, and transform PILOT to a mixed integer nonlinear programming problem. Finally, we propose a linear programming based greedy algorithm to address it. Simulation and experimental results show that our algorithm outperforms five comparison algorithms by 23.11% ∼ 281.10%.

Long Range (LoRa), more generically Low-Power Wide Area Network (LPWAN), is a promising platform to connect Internet of Things. It enables low-cost low-power communication at a few kbps over upto tens of kilometers with 10-year battery lifetime. However, practical LPWAN deployments suffer from collisions given the dense deployment of devices and wide coverage area.

We propose CoLoRa, a protocol to decompose large numbers of concurrent transmissions from one collision in LoRa networks. At the heart of CoLoRa, we utilize packet time offset to disentangle collided packets. CoLoRa incorporates several novel techniques to address practical challenges. (1) We translate time offset, which is difficult to measure, to frequency features that can be reliably measured. (2) We propose a method to cancel inter-packet interference and extract accurate feature from low SNR LoRa signal. (3) We address frequency shift incurred by CFO and time offset for LoRa decoding. We implement CoLoRa on USRP N210 and evaluate its performance in both indoor and outdoor networks. CoLoRa is implemented in software at the base station and it can work for COTS LoRa nodes. The evaluation results show that CoLoRa improves the network throughput by 3.4\(\times\) compared with Choir and by 14\(\times\) compared with LoRaWAN.

LoRa has been shown as a promising platform to provide low-power long-range communication with a low data rate for connecting IoT devices. LoRa can adjust transmission parameters including transmission power and spreading factor, leading to different noise resilience, transmission range and energy consumption. Existing LoRa transmission control approaches can hardly achieve optimal energy efficiency. This leads to a gap to the optimal solution. In this paper, we propose DyLoRa, a dynamic LoRa transmission control system to optimize energy efficiency. The main challenge is very limited data rate of LoRa, making it time- and energy-consuming to obtain link statistics. We show that the demodulation symbol error rate can be stable and thus derive the model for symbol error rate. We further derive the energy efficiency model based on the symbol error model. DyLoRa can derive parameter settings for optimal energy efficiency even from a single packet. We also adapt the model to different hardware to compensate the deviation. We implement DyLoRa based on LoRaWAN 1.0.2 with SX1276 LoRa node and SX1301 LoRa gateway. We evaluate DyLoRa with 11 real deployed nodes. The evaluation results show that DyLoRa improves the energy efficiency by up to 103% compared with the state-of-the-art LoRaWAN ADR.

This paper presents LiteNap which improves the energy efficiency of LoRa by enabling LoRa nodes to operate in a downclocked `light sleep' mode for packet reception. A fundamental limit that prevents radio downclocking is the Nyquist sampling theorem which demands the clock-rate being at least twice the bandwidth of LoRa chirps. Our study reveals under-sampled LoRa chirps suffer frequency aliasing and cause ambiguity in symbol demodulation. LiteNap addresses the problem by leveraging an empirical observation that the hardware of LoRa radio can cause phase jitters on modulated chirps, which result in frequency leakage in the time domain. The timing information of phase jitters and frequency leakages can serve as physical fingerprints to uniquely identify modulated chirps. We propose a scheme to reliably extract the fingerprints from under-sampled chirps and resolve ambiguities in symbol demodulation. We implement LiteNap on a software defined radio platform and conduct trace-driven evaluation. Experiment results show that LiteNap can downclock LoRa nodes to sub-Nyquist rates for energy savings (\eg, 1/8 of Nyquist rate), without substantially affecting packet reception performance (\eg, $>$95% packet reception rate).

Long Range (LoRa) communication, thanks to its wide network coverage and low energy operation, has attracted extensive attentions from both academia and industry. However, existing LoRa-based Wide Area Network (LoRaWAN) suffers from severe inter-network interference, due to the following two reasons. First, the densely-deployed LoRa ends usually share the same network configurations, such as spreading factor (SF), bandwidth (BW) and carrier frequency (CF), causing interference when operating in the vicinity. Second, LoRa is tailored for low-power devices, which excludes LoRaWAN from using the listen-before-talk (LBT) mechanisms- LoRaWAN has to use the duty-cycled medium access policy and thus being incapable of channel sensing or collision avoidance. To mitigate the inter-network interference, we propose a novel solution achieving the online concurrent transmissions at LoRa gateway, called OCT, which can be easily deployed at LoRa gateway. We have implemented/evaluated OCT on USRP platform and commodity LoRa ends, showing OCT achieves: (i) >90% packet reception rate (PRR), (ii) 3× 10−3 bit error rate (BER), (iii) 2x and 3x throughput in the scenarios of two- and three- packet collisions respectively, and (iv) reducing 67% latency compared with state-of-the-art.

IEEE 802.11ax is a promising standard for the next-generation WiFi network, which uses orthogonal frequency division multiple access (OFDMA) to segregate the wireless spectrum into time-frequency resource units (RUs). In this paper, we aim at designing an 802.11ax-based dense WiFi network to provide WiFi services to a large number of users within a given area with the following objectives: (1) to minimize the number of access points (APs); (2) to fulfil the user's throughput requirement; and (3) to be resistant to AP failures. We formulate the above into a joint AP placement and power-channel-RU assignment optimization problem, which is NP-hard. To tackle this problem, we first derive an analytical model to estimate each user's throughput under the mechanism of OFDMA and a widely used interference model. We then design a heuristic algorithm to find high-quality solutions with polynomial time complexity. Simulation results show that our algorithm can achieve the optimal performance for a small area of 50 x 50 m2. For a larger area of 100 x 80 m2 where we cannot find the optimal solution through an exhaustive search, our algorithm can reduce the number of APs by 32 - 55% as compared to the random and Greedy solutions.

Spoofing attacks pose a serious threat to wireless communications. Exploiting physical-layer features to counter spoofing attacks is a promising technology. Although various physical-layer spoofing attack detection (PL-SAD) techniques have been proposed for conventional 802.11 networks in the sub-6GHz band, the study of PL-SAD for 802.11ad networks in 5G millimeter wave (mmWave) 60GHz band is largely open. In this paper, we propose a unique physical layer feature in IEEE 802.11ad networks, i.e., the signal-to-noise-ratio (SNR) traces in the sector level sweep (SLS) of beam pattern selections, to achieve PL-SAD. The proposed schemes are based on the observation that each 802.11ad device presents distinctive beam patterns in the beam sweeping process, which results in distinguishable SNR traces. Based on these observations, we present a novel neural network framework, named BNFN-framework, that can tackle small samples learning and allow for quick construction. The BNFN-framework consists of a backpropagation neural network and a fast forward propagation neural network. Generative adversarial networks (GANs) are introduced to optimize these neural networks. We conduct experiments using off-the-shelf 802.11ad devices, Talon AD7200s and MG360, to evaluate the performance of the proposed PL-SAD scheme. Experimental results confirm the effectiveness of the proposed PL-SAD scheme under different scenarios.

Multi-user identification could facilitate various large-scale identity-based services such as access control, automatic surveillance system, and personalized services, etc. Although existing solutions can identify multiple users using cameras, such vision-based approaches usually raise serious privacy concerns and require the presence of line-of-sight. Differently, in this paper, we propose MU-ID, a gait-based multi-user identification system leveraging a single commercial off-the-shelf (COTS) millimeter-wave (mmWave) radar. Particularly, MU-ID takes as input frequency-modulated continuous-wave (FMCW) signals from the radar sensor. Through analyzing the mmWave signals in the range-Doppler domain, MU-ID examines the users' lower limb movements and captures their distinct gait patterns varying in terms of step length, duration, instantaneous lower limb velocity, and inter-lower limb distance, etc. Additionally, an effective spatial-temporal silhouette analysis is proposed to segment each user's walking steps. Then, the system identifies steps using a Convolutional Neural Network (CNN) classifier and further identifies the users in the area of interest. We implement MU-ID with the TI AWR1642BOOST mmWave sensor and conduct extensive experiments involving 10 people. The results show that MU-ID achieves up to 97% single-person identification accuracy, and over 92% identification accuracy for up to four people, while maintaining a low false positive rate.

Dynamic bandwidth operation in IEEE 802.11ac helps wireless access points to tune channel widths based on carrier sensing and bandwidth requirements of associated wireless stations. However, wide channels result in a reduction in the carrier sensing range, which leads to the problem of channel sensing asymmetry. As a consequence, access points face hidden channel interference that may lead to as high as 60% reduction in the throughput under certain scenarios of dense deployments of access points. Existing approaches handle this problem by detecting the hidden channels once they occur and affect the channel access performance. In a different direction, in this paper, we develop a method for avoiding hidden channels by meticulously predicting the channel width that can reduce interference as well as can improve the average communication capacity. The core of our approach is a deep probabilistic machinery based on point process modeling over the evolution of channel width selection process. The proposed approach, SmartBond, has been implemented and deployed over a testbed with 8 commercial wireless access points. The experiments show that the proposed model can significantly improve the channel access performance although it is lightweight and does not incur much overhead during the decision making process.