To overcome the limited endurance of traditional unmanned aerial vehicles (UAVs), we propose a network of robotic aerial base stations (RABSs) that can energy-efficiently anchor into tall urban landforms, such as lampposts. This approach enables the creation of a hyper-flexible wireless multi-hop network, designed to support green, densified, and dynamic network requirements, thereby ensuring reliable long-term coverage for the whole observed region. The proposed network infrastructure can concurrently address the backhaul link capacity bottleneck and support access link traffic demand in the millimeter-wave (mmWave) frequency band. Specifically, the RABSs grasping locations, resource blocks (RBs) assignment, and route flow control are simultaneously optimized to maximize the served traffic demands. The group of RABSs capitalizes on the fact that traffic distribution varies considerably across both time and space within a given geographical area. Hence, they are able to relocate to suitable locations, i.e., ‘follow’ the traffic demand as it unfolds to increase the overall network efficiency. To tackle the curse of dimensionality of the proposed mixed-integer problem, we propose a greedy algorithm to obtain a competitive solution with low computational complexity. A wide set of numerical investigations reveals that RABSs could improve the served traffic demand. For instance, compared to networks with randomly deployed fixed small cells, the proposed mode serves at most 65% more traffic demand.

In this work, the problem of training the secure federated learning (FL) algorithm over a multicell wireless network is investigated. FL is indeed a learning method that can protect users’ privacy, but it has also been shown to be vulnerable to gradient leakage attacks, which can leak users’ private data. Therefore, we propose a defense method to prevent gradient leakage attacks by uploading the selected layers to attend global model updates. Moreover, we add the differential privacy (DP) noise to the model during the local training to strengthen the defense further. Furthermore, to minimize the total privacy leakage for users, we formulate an optimization problem that minimizes this leakage by jointly optimizing resource block (RB) allocation, layer selection, and DP noise. To find a locally optimal solution to this problem, we divide it into two subproblems: 1) initially solving for RB allocation and 2) layer selection using successive convex approximation (SCA) for convex approximation, followed by optimizing DP noise. The simulation results demonstrate that our proposed optimization algorithm outperforms the other two benchmarks.

This paper proposes efficient ways for constructing the energy-delay Pareto front in cache-enabled integrated access and backhauling (IAB) heterogeneous wireless network. More specifically, an improved non-dominated sorting genetic algorithm II (NSGA-II) is proposed which is coupled with the operator parameter control ability based on reinforcement learning, to solve the energy-delay trade-off in the multi-objective optimization problem. To estimate the effectiveness of the proposed scheme, key performance indicators that cover the convergence and distribution of the Pareto front solution set are conducted and analyzed. A wide set of numerical investigations show that the proposed algorithm can provide a more evenly distributed result than the state of the art techniques with a 15% gain compared to the nominal case which is the weighted-sum method. Furthermore, and maybe more importantly, the undesirable large gaps between solutions in the Pareto front which are caused by the weighting coefficient choices are avoided. By enabling the operator parameter control ability, the exploration and exploitation process of the proposed algorithm can be balanced, which prevents the frequently faced problems of early convergence and being trapped at a local optimum in the genetic algorithm. The proposed technique can have significant implications in improving the avoidable choices regarding the network operation, and compared with the traditional NSGA-II, the proposed algorithm can provide a near-optimal solution set with 20% more diversity.

This paper presents a new approach to formation forming of multiple mobile robots with decentralized motion planning. When the robots enter the required formation, there exists the formation-structure constraint, which causes disorder or even deadlock of the formation. A dynamic priority strategy is developed to solve the problem of the formation-structure constraint, and coordinate the robots to form the formation in a proper order. Simulations are performed on a group of mobile robots to demonstrate the validity of the proposed strategy to the formation system.

In this paper, we present a synchronization approach to trajectory tracking of multiple mobile robots while maintaining time-varying formations. The main idea is to control each robot to track its desired trajectory while synchronizing its motion with those of other robots to keep relative kinematics relationships, as required by the formation. First, we pose the formation-control problem as a synchronization control problem and identify the synchronization control goal according to the formation requirement. The formation error is measured by the position synchronization error, which is defined based on the established robot network. Second, we develop a synchronous controller for each robot's translation to guarantee that both position and synchronization errors approach zero asymptotically. The rotary controller is also designed to ensure that the robot is always oriented toward its desired position. Both translational and rotary controls are supported by a centralized high-level planer for task monitoring and robot global localization. Finally, we perform simulations and experiments to demonstrate the effectiveness of the proposed synchronization control approach in the formation control tasks.

As a probabilistic localization algorithm, Monte Carlo localization (MCL) method has been widely used for mobile robot localization over the past decade. In this paper, an extended MCL method (EMCL) is developed by incorporating two different resampling processes, namely importance resampling and sensor-based resampling, to conventional MCL for improvement of localization performance. Different resampling processes are utilized based on a matching of sample distribution and observations. Two additional processes for validating over-convergence and uniformity are introduced for examination of such matching. A visual based EMCL is further implemented using a triangulation-based resampling from visual features recognized by Bayesian networks. Experiments are conducted to demonstrate the validity of the proposed approach

Recognition of environmental features, which is now the central research topic both in computer vision and mobile robot fields, is prerequisite for vision-guided mobile robot navigation. Perceptual organization is a powerful tool for object recognition through grouping low-level features into objects. In this paper, a perceptual organization algorithm based-on Bayesian networks is proposed to recognize 3D polyhedrons, e.g. compartments and doors, from 2D image in office environment. The algorithm makes full use of knowledge representation and probability inference characteristics of Bayesian networks, thus generating robust recognition results. Moreover, mobile robot active ability is developed to enhance recognition effects. Experimental results demonstrate the validity of the algorithm.