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In universal mobile telecommunications system (UMTS), session management (SM) maintains a communication session between a user equipment (UE) and the core network. 3GPP TS 24.008 specifies the SM functions and the communication protocols between the UE and the core network. However, the interaction between the SM and other entities in the UE are not specified in detail. This paper designs and implements the SM software. We use a finite state machine (FSM) to model the SM functions. Based on the FSM and the primitives, we describe how to set up the development steps to implement the SM at the UE. Copyright © 2006 John Wiley & Sons, Ltd.

The mobile traffic has grown rapidly with the popularity of smart mobile devices. To accommodate increasing traffic, heterogeneous network integration is considered as a viable solution. By overlapping the coverage of heterogeneous networks (e.g., the long-term evolution (LTE) and Wi-Fi integrated network), the mobile operators can use the offloading service (e.g., Wi-Fi offloading) to reduce network congestion. In this approach, a proper network coordination mechanism is required for load balancing of the LTE and Wi-Fi integrated network. In this paper, we use access network discovery and selection function (ANDSF) to suggest selection of proper base stations (e.g., LTE evolved Node Bs or Wi-Fi access points) to user equipment (UE) for load balancing. We integrate the ANDSF with software-defined networking (SDN) to make the ANDSF-aided network more programmable, flexible, and dynamically manageable. Moreover, we propose a power-saving ANDSF (PSA) algorithm to appropriately assign network resource to UEs and reduce the power consumption of Wi-Fi access points (APs). We have implemented the SDN-based PSA and measured the delay times. We also conduct simulation experiments to show that the successful probability of UEs' resource requests to PSA is almost the same as the proposed schemes of the previous studies when the network traffic is unbalanced. Our study indicates that for unbalanced traffic, PSA can reduce 15.63 % more power consumption of Wi-Fi AP than the previous approaches.

Freeway short-term traffic flow prediction can lay a solid foundation for analyzing the road traffic operation status and making real-time traffic surveillance strategies. Firstly, because of the complex modeling process and off-line prediction, differential method together with zero equalization in the data pretreatment and recursive least square method with forgetting factors in the parameter identification are used in establishing improved time-series prediction model. Secondly, as the difficulty in determining the initial value, the statistical method is used to generate initial value in modeling of Kalman filtering prediction model. Thirdly, because of the low speed of convergence in the BP neural network and the local minimum problem existing in the BP algorithm, RBF neural network is used in establishing the traffic volume prediction model. The results indicate that predictability of various models is confirmed, and the characteristic of individual models is also obtained.