Different from using human-centric mobile devices like smartphones, unmanned aerial vehicles (UAVs) can be utilized to form a new UAV crowdsensing paradigm, where UAVs are equipped with build-in high-precision sensors, to provide data collection services especially for emergency situations like earthquakes or flooding. In this paper, we aim to propose a new deep learning based framework to tackle the problem that a group of UAVs energy-efficiently and cooperatively collect data from low-level sensors, while charging the battery from multiple randomly deployed charging stations. Specifically, we propose a new deep model called "j-PPO+ConvNTM" which contains a novel spatiotemporal module "Convolution Neural Turing Machine" (ConvNTM) to better model long-sequence spatiotemporal data, and a deep reinforcement learning (DRL) model called "j-PPO", where it has the capability to make continuous (i.e., route planing) and discrete (i.e., either to collect data or go for charging) action decisions simultaneously for all UAVs. Finally, we perform extensive simulation to show its illustrative movement trajectories, hyperparameter tuning, ablation study, and compare with four other baselines.

The advances in compact and agile micro aerial vehicles (MAVs) have shown great potential in replacing human for labor-intensive or dangerous indoor investigation, such as warehouse management and fire rescue. However, the design of a state estimation system that enables autonomous flight in such dim or smoky environments presents a conundrum: conventional GPS or computer vision based solutions only work in outdoor or well-lighted texture-rich environments. This paper takes the first step to overcome this hurdle by proposing Marvel, a lightweight RF backscatter-based state estimation system for MAVs in indoors. Marvel is nonintrusive to commercial MAVs by attaching backscatter tags to their landing gears without internal hardware modifications, and works in a plug-and-play fashion that does not require any infrastructure deployment, pre-trained signatures, or even without knowing the controller's location. The enabling techniques are a new backscatter-based pose sensing module and a novel backscatter-inertial super-accuracy state estimation algorithm. We demonstrate our design by programming a commercial-off-the-shelf MAV to autonomously fly in different trajectories. The results show that Marvel supports navigation within a range of 50 m or through three concrete walls, with an accuracy of 34 cm for localization and 4.99° for orientation estimation, outperforming commercial GPS-based approaches in outdoors.

Social media sensing has emerged as a new disaster response application paradigm to collect real-time observations from online social media users about the disaster status. Due to the noisy nature of social media data, the task of identifying trustworthy information (referred to as "truth discovery") has been a crucial task in social media sensing. However, existing truth discovery solutions often fall short of providing accurate results in disaster response applications due to the spread of misinformation and difficulty of an efficient verification in such scenarios. In this paper, we present SocialDrone, a novel closed-loop social-physical active sensing framework that integrates social media and drones for reliable disaster response applications. SocialDrone introduces several unique challenges: i) how to drive the drones using the unreliable social media signals? ii) How to ensure the system is adaptive to the high dynamics from both the physical world and social media? iii) How to incorporate real-world constraints into the framework? The SocialDrone addresses these challenges by developing new models that leverage techniques from game theory, constrained optimization, and reinforcement learning. The evaluation results on a real-world disaster response application show that SocialDrone significantly outperforms the state-of-the-art baselines by providing more rapid and accurate disaster response.

Natural disaster can cause unpredictable losses to human life and property. In such an emergency post-disaster rescue situation, unmanned aerial vehicles (UAVs) can enter some dangerous areas to perform disaster recovery missions due to its high mobility and development flexibility. However, the intensive computation tasks generated by UAVs cannot be performed locally due to their limited batteries and computational capabilities. To solve this issue, in this paper, we first introduce the vehicular fog computing (VFC) that makes the unmanned ground vehicles (UGVs) perform the computation tasks offloaded from UAVs by sharing the idle computing resources. Due to the competitions and cooperations among UAVs and UGVs, we propose a stable matching algorithm to transform the computation task offloading problem into a two-sided matching problem. Both sides structure the preference lists based on the preference of the profit, whereby a profit based algorithm is devised to solve the problem by matching each UAV with the UGV that benefits the UAV most in an iterative way. Finally, extensive simulations are conducted to evaluate the performance of the proposed scheme. Numerical results demonstrate that the proposed scheme can effectively improve the utility of UAVs and reduce the average delay, compared with the conventional schemes.

The Age-of-Information (AoI) is a newly introduced metric for capturing information updating timeliness, as opposed to the network throughput. While considerable work has addressed either optimal AoI or throughput individually, the inherent relationships between the two metrics are yet to be explored. In this paper, we explore their relationships in multi-hop networks for the very first time, particularly focusing on the impacts of flexible routes on the two metrics. By developing a rigorous mathematical model with interference, channel allocation, link scheduling, and routing path selection taken into consideration, we build the interrelation between AoI and throughput in multi-hop networks. A multi-criteria optimization problem is formulated with the goal of simultaneously minimizing AoI and maximizing network throughput. To solve this problem, we resort to a novel approach by transforming the multi-criteria problem into a single objective one so as to find the weakly Pareto-optimal points iteratively, thereby allowing us to screen all Pareto-optimal points for the solution. From simulation results, we identify the tradeoff points of the optimal AoI and throughput, demonstrating that one performance metric improves at the expense of degrading the other, with the routing path found as one of the key factors in determining such a tradeoff.

We address energy-efficient placement of data and analytics components of composite analytics services on a wireless network to minimize execution-time energy consumption (computation and communication) subject to compute, storage and network resource constraints.

We introduce an expressive analytics-service-hypergraph model for representing k-ary composability relationships between various analytics and data components and leverage binary quadratic programming(BQP) to minimize the total energy consumption of a given placement of the hypergraph nodes on the network subject to resource availability constraints. Then, after defining a potential-energy functional P(.) to model the affinities of analytics components and network resources using analogs of attractive and repulsive forces in physics, we propose a decentralized Metropolis-Monte-Carlo(MMC) sampling method which seeks to minimize P by moving analytics and data on the network. Although P is non-convex, using a potential game formulation, we identify conditions under which the algorithm provably converges to a local minimum energy equilibrium configuration.

Trace-based simulations of the placement of a deep-neural-network analytics service on a realistic wireless network show that for smaller problem instances our MMC algorithm yields placements with total energy within a small factor of BQP and more balanced workload distributions; for larger problems, it yields low-energy configurations while the BQP approach fails.

Research on Cross-technology communication (CTC) has made rapid progress in recent years, but how to estimate the quality of a CTC link remains an open and challenging problem. Through our observation and study, we find that none of the existing approaches can be applied to estimate the link quality of CTC. Built upon the physical-level emulation, transmission over a CTC link is jointly affected by two factors: the emulation error and the channel distortion. We in this paper propose a new link metric called C-LQI and a joint link model that simultaneously takes into account the emulation error and the channel distortion in the process of CTC. We further design a light-weight link estimation approach to estimate C-LQI and in turn the PRR over the CTC link. We implement C-LQI and compare it with two representative link estimation approaches. The results demonstrate that C-LQI reduces the relative error of link estimation respectively by 46% and 53% and saves the communication cost by 90%.

Low Power Wide Area Networks (LPWAN) are an emerging well-adopted platform to connect the Internet-of-Things. With the growing demands for LPWAN in IoT, the number of supported end-devices cannot meet the IoT deployment requirements. The core problem is the transmission collisions when large-scale end-devices transmit concurrently. The previous research mainly focuses on traditional wireless networks, including scheduling strategies, collision detection and avoidance mechanism. The use of these traditional techniques to address the above limitations in LPWAN may introduce excessive communication overhead, end-devices cost, power consumption, or hardware complexity. In this paper, we present S-MAC, an adaptive MAC-layer scheduler for LPWAN. The key innovation of S-MAC is to take advantage of the periodic transmission characteristics of LPWAN applications and also the collision behaviour features of LoRa PHY-layer to enhance the scalability. Technically, S-MAC is capable of adaptively perceiving clock drift of end-devices, adaptively identifying the join and exit of end-devices, and adaptively performing the scheduling strategy dynamically. Meanwhile, it is compatible with native LoRaWAN, and adaptable to existing Class A, B and C devices. Extensive implementations and evaluations show that S-MAC increases the number of connected end-devices by 4.06X and improves network throughput by 4.01X with PRR requirement of > 95%.