Ultra-dense networks (UDNs) are considered as key 5G technologies. They provide mobile users a high transmission rate and efficient radio resource management. However, UDNs lead to the dense deployment of small base stations (BSs) that can cause stronger interference and subsequently increase the handover management complexity. At present, the conventional handover triggering mechanism of user equipment (UE) is only designed for macro mobility and thus could result in negative effects such as frequent handovers, ping-pong handovers, and handover failures on the handover process of UE at UDNs. These effects degrade the overall network performance. In addition, a massive number of BSs significantly increase the network maintenance system workload. To address these issues, this paper proposes an intelligent handover triggering mechanism for UE based on Q-learning frameworks and subtractive clustering techniques. The input metrics are first converted to state vectors by subtractive clustering, which can improve the efficiency and effectiveness of the training process. Afterward, the Q-learning framework learns the optimal handover triggering policy from the environment. The trained Q table is deployed to UE to trigger the handover process. The simulation results demonstrate that the proposed method can ensure the stronger mobility robustness of UE that is improved by 60%–90% compared to the conventional approach with respect to the number of handovers, ping-ping handover rate, and handover failure rate while maintaining other key performance indicators (KPIs), that is, a relatively high level of throughput and network latency. In addition, through integration with subtractive clustering, the proposed mechanism is further improved by an average of 20% in terms of all the evaluated KPIs.

VANET routing aims to interconnect vehicular nodes via wireless links to transmit network packets. The goal of VANET routing is to reduce the communication cost, decrease the latency and increase the interoperability of the network. In this paper, a cluster-based routing protocol for VANETs called CNN is proposed. It takes advantage of the Hamming distance technique to partition a vehicular network into information-centric clusters based on the mobility of vehicular nodes. However, the proposed approach uses a named data network technique to forward network transmissions according to a hybrid communication model of Dedicated Short Range Communication and Mobile Agent. The former focuses on reactive intra-cluster link establishment, while the latter proactively forwards inter-cluster transmissions. A simulation measures the performance of the proposed approach and compares the results with two well-known VANET routing protocols: AODV and A- STAR. According to the simulation results, CNN outperforms the benchmarks in average end-to-end delay, path length, data delivery ratio, and total transmitted traffic, especially when the network is dense and the nodes are highly mobile.

Mobility management is an important feature in modern wireless networks that can provide seamless and ubiquitous connectivity to mobile users. Due to the dense deployment of small cells and heterogeneous network topologies, the traditional handover control method can lead to various mobility-related problems, such as frequent handovers and handover failures. On the other hand, the mobility management’s maintenance and operation cost is also increased due to increasing node density. In this paper, an autonomous mobility management control approach is proposed to increase the mobility robustness of user equipment (UE) mobility and minimize the operational cost of mobility management. The proposed method is based on reinforcement learning, which can autonomously learn an optimal handover control policy by interacting with the environment. The function approximation approach is adopted to allow reinforcement learning to process a large state and action space. A linear function approximator is used to approximate the state-action value function. Finally, the semi-gradient state-action-reward-state-action (Sarsa) method is implemented to update the approximated state-action function and learn the optimal handover control policy. The simulation results show that the proposed method can effectively improve the mobility robustness of UE under different speed ranges. Compared with the conventional reference signal received power (RSRP) based approach, the proposed approach can reduce unnecessary handovers by about 20% and latency by 58%, while achieving near zero handover failure rate, and increasing throughput by 12%.

In order to achieve accurate and robust indoor position estimation in a wireless sensor network using particle filtering (PF)-based positioning algorithms, particle impoverishment, a problem that aroused from the traditional replicating-and-replacing resampling operation, must be well addressed. The loss of particle diversity not only degrades positioning accuracy but can also result in filtering divergence. To address this problem, this article proposes an adaptive neighbor-guided particle optimization strategy to substitute the traditional resampling operation. The strategy optimizes the distribution of the posterior particles through three steps: neighbor radius calculation, true neighbor identification, and neighbor-guided attraction. The proposed strategy is then integrated into the PF framework to form a novel positioning algorithm referred to as adaptive neighbor-guided particle optimization-based PF algorithm (ANPOPF). The test results show that the integration of the proposed particle optimization strategy considerably enhances the robustness of the PF algorithm, mitigating the effects of particle impoverishment. With the aid of the strategy, the ANPOPF algorithm achieves higher positioning accuracy compared to several existing positioning algorithms. Moreover, the ANPOPF algorithm owns an affordable computation load for most real-time applications.

The container terminal is a key node in global trade and logistics, where trucks connect quay cranes, storage yards, and vessels. Optimizing truck scheduling is crucial for enhancing port efficiency by addressing issues such as low truck utilization, excessive quay crane waiting times, and extended equipment completion times. This paper develops a container terminal simulation model based on graph theory, with the objective of minimizing the maximum completion time of terminal equipment. A collaborative scheduling algorithm for truck fleets, based on Deep Double Q-Networks (DDQN), is proposed. The algorithm designs five heuristic rules as the action space and refines state features and reward functions to optimize scheduling effectively. Experimental results indicate that this algorithm consistently identifies optimal scheduling strategies, outperforming both the five heuristic rules and the Deep Q-Network (DQN) algorithm. It significantly reduces quay crane waiting times and equipment completion times, improves truck utilization, and enhances overall container terminal efficiency.