In recent years, within large cities with a high population density, traffic congestion has become more and more serious, resulting in increased emissions of vehicles and reducing the efficiency of urban operations. Many factors have caused traffic congestion, such as insufficient road capacity, high vehicle density, poor urban traffic planning and inconsistent traffic light cycle configuration. Among these factors, the problems of traffic light cycle configuration are the focal points of this paper. If traffic lights can adjust the cycle dynamically with traffic data, it will reduce degrees of traffic congestion significantly. Therefore, a modified mechanism based on Q-Learning to optimize traffic light cycle configuration is proposed to obtain lower average vehicle delay time, while keeping significantly fewer processing steps. The experimental results will show that the number of processing steps of this proposed mechanism is 11.76 times fewer than that of the exhaustive search scheme, and also that the average vehicle delay is only slightly lower than that of the exhaustive search scheme by 5.4%. Therefore the proposed modified Q-learning mechanism will be capable of reducing the degrees of traffic congestions effectively by minimizing processing steps.

In mobile networks, handover mechanisms provide fast and smooth access service for mobile users. However, one of the main challenges in mobile networks is the handover management with increased mobility and bandwidth demand of the required network services. Therefore, in this paper, we propose a MOS-aware (mean opinion score-aware) mobile network handover mechanism based on deep learning to determine the appropriate handover time for real-time video conference services in mobile networks. We construct a wireless network topology with LTE characteristics in a Mininet-WiFi simulation. User equipment (UE) can determine the service-required MOS (Mean Opinion Score) from the proposed deep-learning-based handover mechanism with appropriate handover time. Simulation results show that the proposed scheme provides higher performance than the original A3 handover mechanism. The contribution of this paper is to combine the real-time video conferencing services with a deep-learning-based handover mechanism by predicting MOS values to improve the quality of service for users in mobile networks.

6L0WPAN is an IPv6 adaptation layer that defines Internet Protocols over low power, low data rate devices such as IEEE 802.15.4. It is difficult to implement because the size of IPv6 packet is much larger than the packet size of IEEE 802.15.4 data link layer. In order to solve this problem, the IETF 6LoWPAN working group introduces the adaptation layer between network and data link layers. It provides header compression to reduce transmission overhead, fragmentation and reassembly of IPv6 packet. In addition, the routing schemes in 6LoWPAN can be divided into two categories: mesh-under and route-over. The main difference between these two schemes depends on how the packets or fragments are processed before being forwarded. In this paper, we present an analysis between these two routing schemes and comparing their IP packet successful transmission probabilities. Moreover, while the number of competing nodes increases, the probability of successful transmission will decrease due to the CSMA/CA mechanism.