Drones can be used for many assistance roles in complex communication scenarios and play as the aerial relays to support terrestrial communications. Although a great deal of emphasis has been placed on the drone-assisted networks, existing work focuses on routing protocols without fully exploiting the drones superiority and flexibility. To fill this gap, this paper proposes a collaborative communication scheme for multiple drones to assist the urban vehicular ad-hoc networks (VANETs). In this scheme, drones are distributed regarding the predicted terrestrial traffic condition in order to efficiently alleviate the inevitable problems of conventional VANETs, such as building obstacle, isolated vehicles, and uneven traffic loading. To effectively coordinate multiple drones, this issue is modeled as a multimodal optimization problem to improve the global performance on a certain space. To this end, a succinct swarm-based optimization algorithm, namely Multimodal Nomad Algorithm (MNA) is presented. This algorithm is inspired by the migratory behavior of the nomadic tribes on Mongolia grassland. Based on the floating car data of Chengdu China, extensive experiments are conducted to examine the performance of the MNA-optimized drone-assisted VANET. The results demonstrate that our scheme outperforms its counterparts in terms of hop number, packet delivery ratio, and throughput.

Mobile CrowdSensing (MCS) is an emerging paradigm that employs massive mobile devices (MDs) to complete sensing tasks cooperatively. To provide ubiquitous MCS services, Unmanned Aerial Vehicle (UAV), featured by high agility and flexibility, becomes increasingly attractive as a powerful assistant for MCS to collect sensing data in hard-to-reach and infrastructure-restrained areas. Focusing on urban MCS scenarios where a tremendous amount of data needs to be uploaded by massive mobile devices, we propose a Three-Dimensional Multi-UAV assisted crowdsensing, termed 3DM, to collect sensing data efficiently in an infrastructure-free manner. Different from the existing methods, 3DM has two unique advantages: 1) removing the assumption of the ideal distributions of mobile devices and 2) fully exploiting the 3D flexibility to optimize the device matching and data transmission between UAVs and MDs. By employing a joint optimization metric that incorporates both energy efficiency and collection latency, 3DM dynamically maintains cost-effective UAV-MD links and 3D UAVs trajectories thus completes the collection tasks with less time and energy. Compared with the baseline algorithm and two state-of-the-art counterparts, extensive simulations demonstrate that 3DM saves at least 50% energy and 25% time of baseline while achieving 76% improvement of the sub-optimal competitor on overall utility.

Benefiting from the large-scale commercial use of 5G, smart campuses have attracted increasing research attention in recent years and are expected to revolutionize traditional campus activities. However, there are some obstacles that hinder the practical deployment of MEC (multi-access edge computing). First, traditional information infrastructures on campus cannot support latency-sensitive and computing-intensive smart applications, such as AR/VR, live interactive lectures and digital twin experiments. In addition, the mixture of old and new applications, isolated data islands and heterogeneous equipment management introduce more challenges. Moreover, the existing MEC framework proposed by ETSI and 3GPP cannot meet the specific deployment requirements of smart campuses, e.g., educational data security, real-time interactive applications, heterogeneous connections, and others. In this paper, we propose a 5G-based architecture for smart education information infrastructure; a new dedicated cloud architecture eMEC (educational multi-access edge computing) is defined. It consists of a UGW (universal access gateway) and an eMEP (educational multi-access edge computing platform), making it possible to satisfy education-specific requirements and long-term evolution. Furthermore, we implement the framework and conduct real-world field tests for eMEC in a university campus. Based on the framework and practical field tests, we also conduct a measurement study to unveil the spatial-temporal characteristics of mobile users in the smart campus and discuss exploiting them for better network performance. The experimental results show that the system achieves satisfactory performance in terms of both throughput and latency.

The recent breakthrough in Wireless Power Transfer (WPT) provides a promising way to prolong network lifetime by employing a charging vehicle to replenish energy. Data transmissions from nodes typically happen in response to physical sensory events, leading to time-varying energy consumption. To improve charging efficiency, the existing schemes collect energy information by employing a data-gathering vehicle or data collection protocol. However, in duty cycle networks, these schemes either incur extra vehicles or high data collection delay. To solve this problem, we propose an mobile adaptive charging scheme with rapid data sharing (rShare), which establishes multi-layer collection trees and collects overall energy data to the vehicle. A spatial predicted active sending (SPAS) algorithm is proposed for distant nodes to actively estimate the future position and transmit their data to cover potential positions of the charging vehicle, which significantly reduces data collection delay. We also propose an estimated time of arrival (ETA)-aware scheme based on the TSP Nearest Neighbor algorithm that updates the charging path based on the collected data. Extensive simulation results demonstrate that our scheme outperforms the state-of-the-arts in terms of dead node avoidance with less communication overhead.

In an Internet-of-Things system supported by Internet Protocol version 6 (IPv6), the Routing Protocol for Low-Power and Lossy Networks (RPL) presents extensive applications in various network scenarios. In these novel scenarios characterized by the access of massive devices, path recovery, which reconstructs the complete path of the packet transmission, plays a vital role in network measurement, topology inference, and information security. This paper proposes a Lightweight Path recovery algorithm (LiPa) for multi-hop point-to-point communication. The core idea of LiPa is to make full use of the spatial and temporal information of the network topology to recover the unknown paths iteratively. Specifically, spatial and temporal information refer to the potential correlations between different paths within a time slot and path status during different time slots, respectively. To verify the effect of our proposal, we separately analyze the performance of leveraging temporal information, spatial information, and their composition by extensive simulations. We also compare LiPa with two state-of-the-art methods in terms of the recovery accuracy and the gain–loss ratio. The experiment results show that LiPa significantly outperforms all its counterpart algorithms in different network settings. Thus, LiPa can be considered as a promising approach for packet-level path recovery with minor loss and great adaptability.