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 paper 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 dataset generated by a large-scale bus fleet and a traffic event dataset, 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 minutes, which is 56% shorter than baselines, in the predictable emergencies response case.

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.

Parcel delivery and Point of Interest (PoI) surveillance are two fundaches were conducted for an isolated application, separately. UAV-sharing in the two heterogeneous applications bears significant but unexplored benefit potentials, similar to current sharingmental applications of Unmanned Aerial Vehicles (UAVs) in city. Traditional resear economy such as taxi sharing. However, the inconsistency of the two heterogeneous applications in both temporal and spatial domains would impact the sharing performance. This work illustrates the first quantified studies of the UAV-sharing performance in the two heterogeneous applications. Specifically, some critical constraints of the UAV-sharing process are first discussed. Thereafter, a data-driven evaluation is conducted to understand the sharing process of the UAVs with a delivery dataset and a traffic accident dataset obtained from Shanghai city. Some inspiring results verify the particularly excellent UAV-sharing performance in the two heterogeneous applications.

Unmanned Aerial Vehicles (UAVs) are widely employed in smart city. However, the limited battery capacity is one of the UAV's most critical obstacles to monitoring mission. Recently, leveraging public buses to relay UAVs has been shown to be a promising solution to this critical issue. Existing works on this solution focused on pre-determined scenarios, namely the mission time and location of the UAVs are determined in advance. While in on-demand mission such as emergency response, mission time and location of the UAV is on-demand and stochastic. How the UAV riding on a bus perform in such stochastic missions remains open. In this paper, driven by the bus mobility data, a sampling-based algorithm is designed to navigate UAVs to land on buses in on-demand applications. A simple greedy algorithm is proposed to determine the appropriate buses to relay UAVs, so that the scheduling performance of the UAVs is optimized. Comprehensive evaluation using a large-scale bus trajectory data is conducted.

This paper presents ScaPSM (i.e., Scalable Power-Saving Mode Scheduler), a design that enables scalable competing background traffic scheduling in crowd event 802.11 deployments with Power-Saving Mode (PSM) radio operation. ScaPSM prevents the packet delay proliferation of previous study, if applied in the crowd events scenario, by introducing a new strategy of adequate competition among multiple PSM clients to optimize overall energy saving without degrading packet delay performance. The key novelty behind ScaPSM is that it exploits delay-aware load balance to control judiciously the qualification and the number of competing PSM clients before every beacon frame’s transmission, which helps to mitigate congestion at the peak period with increasing the number of PSM clients. With ScaPSM, the average packet delay is bounded and fairness among PSM clients is simultaneously achieved. ScaPSM is incrementally deployable due to only AP-side changes and does not require any modification to the 802.11 protocol or the clients. We theoretically analyze the performance of ScaPSM. Our experimental results show that the proposed design is practical, effective, and featuring with significantly improved scalability for crowd events.

Neighbor discovery is a fundamental service for initialization and managing network dynamics in wireless sensor networks and mobile sensing applications. In this paper, we present a novel design principle named Talk More Listen Less (TMLL) to reduce idle-listening in neighbor discovery protocols by learning the fact that more beacons lead to fewer wakeups. We propose an extended neighbor discovery model for analyzing wakeup schedules in which beacons are not necessarily placed in the wakeup slots. Furthermore, we are the first to consider channel occupancy rate in discovery protocols by introducing a new metric to trade off among duty-cycle, latency and channel occupancy rate. Guided by the TMLL principle, we have designed Nihao, a family of energy-efficient asynchronous neighbor discovery protocols for symmetric and asymmetric cases. We compared Nihao with existing state of the art protocols via analysis and real-world testbed experiments. The result shows that Nihao significantly outperforms the others both in theory and practice.

Data fusion or In-network processing methods were often adopted in Wireless Sensor Networks (WSNs) to reduce data communication and prolong network lifetime, which made WSNs application can be described as a set of tasks (sensing, processing) and dependencies among them. Task assignment has become a

The event-driven nature of wireless sensor networks (WSNs) leads to unpredictable network load. As a result, congestion may occur at sensors that receive more data than they can forward, which causes energy waste, throughput reduction and packet loss. In this paper, we propose a rate-based fairness-aware congestion control protocol (FACC), which controls congestion and achieves approximately fair bandwidth allocation for different flows. In FACC, we categorize intermediate relaying sensor nodes into near-source nodes and near-sink nodes. Near-source nodes maintain per-flow state, and allocate an approximately fair rate to each passing flow. On the other hand, near-sink nodes do not need to maintain per-flow state and use a light-weight probabilistic dropping algorithm based on queue occupancy and hit frequency. Our simulation results and analysis show that FACC provides better performance than previous approaches in terms of throughput, packet loss, and fairness.