The battery-powered Unmanned Aerial Vehicle (UAV) is a promising alternative to traditional logistics trucks. Using UAVs can achieve much more speedy, cost-effective, and environment-friendly delivery on an urban scale. However, UAVs suffer from insufficient delivery range and battery aging. This paper presents an innovative logistics UAV scheduling framework using public buses, in which logistics UAVs Land and Recharge its battery on Buses (ULRB) to extend its delivery range and decelerate its fading battery capacity. This work correlates physical layer parameters such as the energy consumption rate, the parcels weight, UAV velocity, the battery temperature to the UAVs path planning, the battery discharging, and the capacity fading models. Specifically, the ULRB framework consists of a single-UAV scheduling module and a multi-UAV dispatching module. In the single-UAV module, a Markov-based algorithm is utilized to plan the UAVs flying path to land and dynamically get recharged on the bus. The latter module optimized the delivery progress in a multi-UAV, multi-parcel, and multi-bus scenario. Finally, using a large-scale real-world bus trajectory dataset, extensive evaluations are conducted to verify ULRB. The results show that ULRB can extend the UAVs delivery range by 5.54 and decelerate the battery aging by 3.26 on average.

Multiagent routing problems, arising from practical applications, such as logistics, transportation, and emergency response, face challenges due to the exponential growth of the search space with increasing problem scales. This article proposes RouteMaker to address the often-overlooked multiagent routing problems involving dedicated multiple depots. RouteMaker leverages role-interaction-based graph neural network (RIGNN) to realize effective locations assignments and integrates an advanced planner to plan travel path for each agent. RouteMaker is trained on small-scale problems and can produce comparable or superior approximate optimal solutions compared with the best heuristic baselines. Notably, the learned RouteMaker generalizes seamlessly to large-scale problems and real-world problems without the need for fine-tuning, delivering significantly higher quality solutions in relatively less time. For scenarios involving 40 agents and 1000 locations, RouteMaker achieves over $600\times $ speed improvement and more than 88% cost reduction, compared with the representative classical heuristic solver (ORTools).

Unmanned aerial vehicles (UAVs) are widely applied in smart city applications such as urban sensing and delivery, due to the UAVs’ agility, low cost and not being restricted by ground road conditions. However, the limited battery capacity becomes one of the biggest obstacles to the application of UAVs. To address this issue, this article investigates an emergency response application, in which UAVs generally ride crowdsourced buses to save energy and respond to a stochastic emergency event (such as a traffic accident) when the event occurs. For the bus-based UAV response paradigm, a single UAV response process with the constraint of the bus mobility is first modeled. Subsequently, a data-driven UAV path planning algorithm is designed. Then two emergency response cases by multi-UAV are investigated. One case is irregular emergency response, whose objective is to maximize the temporal-spatial coverage of the urban area. The other case is predictable emergency response, which optimizes the response performance to these emergencies. Thereafter, the bus-stimulating problems for the two cases are formulated and solved. Finally, utilizing a real-world bus trajectory data set generated by a large-scale bus fleet and a traffic event data set, the emergency response performance of the bus-based UAV response paradigm is comprehensively evaluated. The results show that (1) with only 30 UAVs, 90% of Shenzhen city can be covered in the irregular emergency response case; (2) with only 50 UAVs, the average response delay to the emergencies is shorter than 1.5 min, which is 56% shorter than baselines, in the predictable emergencies response case.

Data dissemination in Internet of Things (IoT) networks provides a fundamental service for many critical applications. However, traditional dissemination schemes were investigated for an isolated IoT network, and did not fit the coexistence scenario of heterogeneous IoT networks. Motivated by the latest innovations in cross-technology communication (CTC) techniques, this article proposes CDD, a framework utilizing a WiFi-based access point (AP) to simultaneously conduct fast data diffusion in heterogeneous IoT networks (e.g., WiFi and ZigBee). We shed light on the network features in the coexistence scenario and then review the key challenges of coordinating dissemination based on CTC techniques. With these network features and challenges in mind, the CDD framework includes a series of designs to conduct efficient data dissemination in heterogeneous networks using a WiFi AP. The framework is implemented and evaluated on a real-world testbed. The results demonstrate that CDD can reduce the dissemination delay and significantly outperform the state-of-the-art approaches.

In recent years, a large number of commercial airplanes equipped with sensors have been contributing to collecting the atmospheric composition and meteorological data. Based on a worldwide commercial airplane dataset, we identify an inherent limitation of such an airplane-based sensing system as the airplanes fly through the airspace with spatiotemporal dynamic. As a result, the sensed data is coarse-grained and incomplete. Moreover, naive methods such as installing sensors on more airplanes, cannot effectively address the issue. In this paper, we propose a collaborative airspace sensing framework auSense to improve the sensing performance. In auSense, UAVs (Unmanned Aerial Vehicles) are employed to sense the airspace when the airspace is not sensed by airplanes. Particularly, auSense leverages the existing worldwide commercial airplane tracking infrastructure and fine-grained trajectory data to estimate when one airspace will be sensed by commercial planes. Then auSense schedules the UAV paths to improve the sensing performance. We implement our auSense in different airspaces based on the 1-month airplane trajectory data in different airspaces. The experiments demonstrate the superior performance of our data driven UAV and commercial airplane collaborative strategy over the ground truth and other benchmarks.data-driven UAV and commercial airplane collaborative strategy over the ground truth and other benchmarks.

Physical layer cross-technology communication (PHY-CTC) opens new horizons for spectrum utilization and wireless cooperation in the crowded ISM band. Current PHY-CTC technologies can be divided into two categories: one aims for high communication reliability, but sacrifices compatibility with commodity devices and the other maintains compatibility but suffers dramatically limited reliability. The latter mainly leverages the WiFi OFDM signal to emulate the ZigBee signal, while the Cyclic Prefix (CP) in OFDM brings inevitable signal disturbance and errors. To address these issues, we present WibZig, the first WiFi-To-ZigBee PHY-CTC technology that achieves high reliability and is fully compatible with commodity devices. By carefully selecting a cluster of CCK codewords that exhibit similar phase characteristics to ZigBee chips, we can emulate any given ZigBee symbol with great accuracy. In addition, WibZig adaptively controls the first CCK codeword of each cluster to eliminate inter-cluster phase discontinuity when emulating a ZigBee packet with multiple clusters. WibZig requires no hardware modification and is compatible with most commodity devices. We implement WibZig on both USRP and commercial devices and conduct extensive evaluations under various settings, which demonstrate a 15x improvement in reliability and a 7x increase in range compared to the latest PHY-CTC work.