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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.

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.

6LoWPAN technology has attracted extensive attention recently. It is because 6LoWPAN is one of Internet of Things standard and it adapts to IPv6 protocol stack over low-rate wireless personal area network, such as IEEE 802.15.4. One view is that IP architecture is not suitable for low-rate wireless personal area network. It is a challenge to implement the IPv6 protocol stack into IEEE 802.15.4 devices due to that the size of IPv6 packet is much larger than the maximum packet size of IEEE 802.15.4 in data link layer. In order to solve this problem, 6LoWPAN provides header compression to reduce the transmission overhead for IP packets. In addition, two selected routing schemes, mesh-under and route-over routing schemes, are also proposed in 6LoWPAN to forward IP fragmentations under IEEE 802.15.4 radio link. The distinction is based on which layer of the 6LoWPAN protocol stack is in charge of routing decisions. In route-over routing scheme, the routing distinction is taken at the network layer and, in mesh-under, is taken by the adaptation layer. Thus, the goal of this research is to understand the performance of two routing schemes in 6LoWPAN under error-prone channel condition.

Location estimation for object tracking is one of the important topics in the research of wireless sensor networks (WSNs). Recently, many location estimation or position schemes in WSN have been proposed. In this paper, we will propose the procedure and modeling of location estimation for object tracking in WSN. The designed modeling is a simple scheme without complex processing. We will use Matlab to conduct the simulation and numerical analyses to find the optimal modeling variables. The analyses with different variables will include object moving model, sensing radius, model weighting value α, and power-level increasing ratio k of neighboring sensor nodes. For practical consideration, we will also carry out the shadowing model for analysis.

Recently, the applications of Internet of Things (IoTs) are growing rapidly. Wireless Sensor Network (WSN) becomes an emerging technology to provide the low power wireless connectivity for IoTs. The IPv6 over low-power wireless personal area networks (6LoWPAN) has been proposed by IETF, which gives each WSN device an IPv6 address to connect with the Internet. The transmission congestion in IoTs could be a problem when a large numbers of sensors are deployed in the field. Therefore, it is important to consider whether the WSN devices have be completely integrated into the Internet with proper quality of service (QoS) requirements. The Software Defined Network (SDN) is a new architecture of network decoupling the data and control planes, and using the logical centralized control to manage the forwarding issues in large-scale networks. In this research, the SDN-based 6LoWPAN Border Router (6LBR) is proposed to integrate the transmission fromWSNs to Internet. The proposed SDN-based 6LBR communicating between WSNs and the Internet will bring forward the requirements of end-to-end QoS with bandwidth guarantee. Based on our experimental results, we have observed that the selected 6LoWPAN traffic flows achieve lower packet loss rate in the Internet. Therefore, the 6LoWPAN traffic flows classified by SDN-based 6LBR can be reserved for the required bandwidth in the Internet to meet the QoS requirements. [ABSTRACT FROM AUTHOR]

A series of nitrogen-modified Li4Ti5O12 (N-LTO) nanomaterials with hierarchical micro/nanoporous structures are first synthesized via a facile one-step combustion process using thermal decomposition of urea. Successful deposition of a TiN thin layer onto the LTO surface was confirmed by transmission electron microscopy with energy-dispersive X-ray analysis, X-ray photoelectron spectroscopy, Raman spectroscopy, and thermogravimetric measurements. The electrochemical performances of the N-LTO nanomaterials are also investigated in this work. Compared with pristine LTO, the N-LTO nanomaterial with 1.1 wt % nitrogen exhibits a higher rate capability and better reversibility. At charge/discharge rates of 1, 2, 8, and 15 C, the discharge capacities of the N-LTO electrode were 159, 150, 128, and 108 mAh g(-1), respectively. After 200 cycles at 1 C, its capacity retention was 98.5% with almost no capacity fading.

A wavelength-division-multiplexing (WDM) visible light communiction (VLC) system employing red and green laser pointer lasers (LPLs) with directly modulating data signals is proposed and experimentally demonstrated. With the assistance of preamplifier and adaptive filter at the receiving sites, low bit error rate (BER) at 10 m/500 Mbps operation is obtained for each wavelength. The use of preamplifier and adaptive filter offer significant improvements for free-space transmission performance. Improved performance of BER of <10(-9), as well as better and clear eye diagram were achieved in our proposed WDM VLC systems. LPL features create a new category of good performance with high-speed data rate, long transmission length (>5m), as well as easy handling and installation. This proposed WDM VLC system reveals a prominent one to present its advancement in simplicity and convenience to be installed., (© 2012 Optical Society of America)