In this paper, a hybrid adaptive MCB-PSO node localization algorithm is proposed for three-dimensional mobile wireless sensor networks (MWSNs), which considers the random mobility of both anchor and unknown nodes. An improved particle swarm optimization (PSO) approach is presented with Monte Carlo localization boxed (MCB) to locate mobile nodes. It solves the particle degeneracy problem that appeared in traditional MCB. In the proposed algorithm, a random waypoint model is incorporated to describe random movements of anchor and unknown nodes based on different time units. An adaptive anchor selection operator is designed to improve the performance of standard PSO for each particle based on time units and generations, to maintain the searching ability in the last few time units and particle generations. The objective function of standard PSO is then reformed to make it obtain a better rate of convergence and more accurate cost value for the global optimum position. Furthermore, the moving scope of each particle is constrained in a specified space to improve the searching efficiency as well as to save calculation time. Experiments are made in MATLAB software, and it is compared with DV-Hop, Centroid, MCL, and MCB. Three evaluation indexes are introduced, namely, normalized average localization error, average localization time, and localization rate. The simulation results show that the proposed algorithm works well in every situation with the highest localization accuracy, least time consumptions, and highest localization rates.

In this paper, a quantitative ground risk assessment mechanism is proposed in which urban ground features are extracted based on high-resolution data in a satellite image when unmanned aerial vehicles (UAVs) operate in urban areas. Ground risk distributions are estimated and a risk map is constructed with a multi-layer method considering the comprehensive risk imposed by UAV operations. The urban ground feature extraction is first implemented by employing a K-Means clustering method to an actual satellite image. Five main categories of the ground features are classified, each of which is composed of several sub-categories. Three more layers are then obtained, which are a population density layer, a sheltering factor layer, and a ground obstacle layer. As a result, a three-dimensional (3D) risk map is formed with a high resolution of 1 m × 1 m × 5 m. For each unit in this risk map, three kinds of risk imposed by UAV operations are taken into account and calculated, which include the risk to pedestrians, risk to ground vehicles, and risk to ground properties. This paper also develops a method of the resolution conversion to accommodate different UAV operation requirements. Case study results indicate that the risk levels between the fifth and tenth layers of the generated 3D risk map are relatively low, making these altitudes quite suitable for UAV operations.

In this study, an urban airspace assessment mechanism is proposed and validated using the actual urban building data, offering a systematic approach to airspace selection for unmanned aerial vehicle (UAV) operations. Two metrics are involved to assess the urban airspace accurately, which are the airspace availability and risk to ground population. The former is measured by analyzing the connectivity of the urban airspace which particularly emphasizes the impact of urban features like buildings and obstacles. The latter is quantized by using a previously proposed risk estimation model, with which an urban risk map can be generated. Quadrant analysis and Pareto ranking are then employed to evaluate the available airspace for UAVs. Quadrant analysis maps the urban airspace availability and risk to ground population onto a two-dimensional space. Additionally, Pareto ranking determines a set of Pareto-optimal solutions wherein no objective can be improved without compromising at least one other objective. The topology of urban airspace could be constructed by using the top 50% of grids ranked by Pareto ranking based on the actual building data. A case study is conducted in a densely populated urban area in Changqing District, Jinan, Shandong Province, China. The connectivity of the airspace topology is verified by employing the A-star algorithm to generate a feasible path for UAVs.

In this paper, an improved particle swarm optimization - artificial bee colony (PSO-ABC) path planning algorithm for unmanned aerial vehicle (UAV) operations based on a construction of urban airspace topology is proposed. A mechanism is introduced first to build an airspace topology by assessing the feasibility of specified urban airspace. In this mechanism, a complete urban airspace is discretized into many voxels and two indexes are incorporated which are urban airspace availability and urban ground risk. The former is quantified by analyzing the connectivity of urban voxels focusing on impacts of urban buildings and other features. The latter uses an existing risk estimation model to generate an risk map that describes ground risk distributions imposed by UAV operations. According to these two indexes, the feasibility of the airspace is evaluated by a quadrant analysis and a Pareto sorting successively to produce an airspace topology with the real urban GIS data. In this constructed airspace topology, by considering the safety levels of ground areas, performance constraints of UAVs and no-fly zones defined in the city, a global and local search strategy is proposed by fusing two heuristic algorithms, which are PSO for a global search and ABC algorithm for a local search. An improved PSO-ABC planning algorithm for UAV operations is formed using an urban airspace topology. An urban area in Changqing District, Jinan City is taken as the experimental scenario to carry out simulation analysis. The experimental results show that the performances of the proposed algorithm is the best of all with high path smoothness and fast convergence speed. The proposed method significantly improves both the stability and robustness of the path planning solution.