Due to the high mobility of nodes and the complexity of the mission environment, mission-oriented UAV networks are not only subject to frequent topology changes, but also to the risk of being compromised, hijacked and corrupted. As a result, an operating UAV network is essentially a Byzantine distributed system whose physical structure and node trustworthiness change over time. How to implement the global management of UAV networks to achieve a rational allocation of UAV network resources and reconfiguration of trusted networks is a problem worthy of in-depth study. The method proposed in this paper introduces a lightweight storage blockchain in the UAV network through two-stage consensus, firstly performing data consensus on the local state records of the nodes, then performing decision consensus on the data consensus results using algorithms such as fuzzy K-Modes clustering and global trustworthiness assessment, and finally recording the decision consensus results into a new block as the new configuration information of the UAV network. A lightweight storage blockchain-assisted trusted zone routing protocol (BC_TZRP) is designed to dynamically and adaptively build configurable trusted networks in a way that the blockchain continuously adds new blocks. Using QualNet simulation experimental software, an experimental comparison between the classical routing protocol for mobile self-organizing networks and the traditional consensus algorithm for blockchains is conducted. The results show that the approach has significant advantages in terms of packet delivery rate, routing overhead and average end-to-end delay, and can effectively improve the overall working life and fault tolerance of the UAV network.

The UAV network composed of resource-constrained lightweight UAV swarms can efficiently accomplish mission with time critical requirements in dynamic and complex environments. However, the trusted authentication of network nodes poses a huge challenge due to its own resource constraints, the lack of trusted centralized support, frequent joining or departure of UAVs to or from the network, and the presence of cyber-attacks. In this paper, we propose a stateless blockchain based on triple aggregatable subvector commitment and present a dynamic proof of trust authorization consensus mechanism with a periodic random selection of authorized nodes to guarantee the trustworthiness of mutual authentication of UAV nodes. Our proposed triple vector authentication solution solves several of the challenges mentioned above very well. The extensive experiments demonstrate that our blockchain-based authentication scheme enjoins significant advantages over the four schemes currently available for UAV network authentication in terms of single authentication latency, speed of energy consumption, average computational cost, and end-to-end latency.

The complexity and difficulty of dynamic obstacle avoidance for AGVs are increased by the uncertainty in a dynamic environment. The adaptive speed obstacle method allows the size of the collision cone to be dynamically changed to solve this problem, but this method may cause the AGV to turn too much when it is close to obstacles, as the collision cone expands too fast, which may lead to unstable operations or even collision. In order to address these problems, we propose an improved speed obstacle algorithm. The proposed algorithm uses Kalman filtering to estimate the positions of dynamic obstacles and adopts the idea of forward simulation to build a speed obstacle buffer according to the estimated positions of obstacles, such that the AGV can use the predicted positions of obstacles in the next moment, instead of the current positions, to build a speed obstacle model. Finally, an objective function that balances efficiency and safety was established to score all the candidate speeds, such that the highest-rated speed could be selected as the candidate speed for the next moment.

Mission-oriented UAV networks operate in nonsecure, complex environments with time-varying network partitioning and node trustworthiness. UAV networks are thus essentially asynchronous distributed systems with the Byzantine General problem, whose availability depends on the tolerance of progressively more erroneous nodes in the course of a mission. To address the resource-limited nature of UAV networks, this paper proposes a lightweight asynchronous provable Byzantine fault-tolerant consensus method. The consensus method reduces the communication overhead by splitting the set of local trusted state transactions and then dispersing the reliable broadcast control transmission (DRBC), introduces vector commitments to achieve multivalue Byzantine consensus (PMVBA) for identity and data in a provable manner and reduces the computational complexity, and the data stored on the chain is only the consensus result (global trustworthiness information of the drone nodes), avoiding the blockchain’s “storage inflation” problem. This makes the consensus process lighter in terms of bandwidth, computation and storage, ensuring the longevity and overall performance of the UAV network during the mission. Through QualNet simulation platform, existing practical asynchronous consensus algorithms are compared, and the proposed method performs better in terms of throughput, consensus latency and energy consumption rate.

Narrowband-Internet of Things (NB-IoT) is an emerging cellular communication technology designed for low power wide area applications. Cell selection determines the channel of user device and hence is an important issue in cellular networks. In this paper, we take the first attempt to examine and optimize the the cell selection in NB-IoT networks by field measurement. We conduct measurements at 30 different locations which involve 5 typical application scenarios of NB-IoT. Two kinds of NB-IoT modules and two network operators are also involved in the measurements. We find four potential issues on the cell selection of the User Equipment (UE) through the measurements. We propose an adaptive cell selection approach to optimize the cell selection of UE. The simulation test based on real-world measurement data shows that the cell selected by the adaptive approach can improve the coverage level and reduce the power consumption for UE.