B achelard, L a terre et les rêveries de la volonté.

In a real situation, user mobility has a high degree of predictability due to temporal and spatial localities [58]. The temporal locality indicates that many mobile users stay in or pass through an almost predictable set of cells during certain times. For example, many users remain at home early in the morning, then go to work and come back home along the same route in the evening, and stay at home until the morning of the next day. Consequently, we can predict the location and movement of a user based on the time of a day. Similarly, the spatial locality indicates that the paths along which a user can move are governed by geographical constraints, such as pathways, roads, or highways in the vicinity of the current location. The temporal and spatial locality information aggregated over time is referred to as the user mobility history. Then, some algorithms, such as one proposed in [59], are applied to the user mobility history to derive the most probable user mobility pattern (UMP). The UMP contains a list of cells/networks expected to be visited by mobile users starting from a given location/time.

Likewise, the service duration can be predicted in accordance with the service type, location, and time [60, 61]. Users access different types of services depending on their location and time. The mobile users commuting by the subways in Tokyo, for instance, mostly use only e-mail or web services, but do not use the voice service. Accordingly, different services have different call durations; video conferencing takes a longer amount of time than e-mail downloading, for

5.4. Analysis and Consideration of Access Network Selection 75

instance. If the user is downloading or streaming a video, the call duration depends on the file size and the average bandwidth of the network. Moreover, the call duration of the same type of services depends on the location/time of the service initiation and the correspondent host.

The user mobility history can be extended to include the type and duration of services ac- cessed by the user at a given location or time [60]. Then, similar to the derivation of user mobility pattern, a user service (call) duration model would be constructed from the mobil- ity history by using some statistical or heuristic approaches. Also, for some services, there are widely accepted call duration distributions such as an exponential distribution for voice services [56]. In such cases, we need to estimate only the mean call duration with respect to the user location and time coordinates. When making a service request, the user might tell the network selector what type of request it is making: telephony, web data, video, etc., so that the call dura- tion can be predicted for that type of service, depending on the model derived from the history for the current location, time, and correspondent host.

We now describe the issues related to bandwidth estimation. In cellular networks, estab- lishment of a connection guarantees the availability of a fixed amount of bandwidth for the whole connection duration. However, in a shared network such as wireless LAN, the available bandwidth depends on the number of users and their activities. In such cases, the bandwidth allocatable to a user depends on the bandwidth allocation policy [62], which guides how the net- work bandwidth is shared among users. The simplest scheme is to share the bandwidth equally among all users. That is, if B is the total bandwidth of a network shared by n number of users, the available bandwidth for each user is simply B/n. This scheme, however, does not work well when users use different types of applications requiring different amount of bandwidths. Other schemes can be used, such as sharing a portion of bandwidth among the users of the same type of services. In this scheme, the available bandwidth for a new user of a particular type of service is b = Bi/ni, where Bi is the bandwidth share assigned to all the users belonging to service type

i, and ni is the number of users currently using the service, including the new one. Bi can be

fixed or dynamically adjusted depending on user activities.

However, the bandwidth allocation mechanisms described above have a problem: they al- low the bandwidth of already-connected users to fluctuate when a new user joins or an old user leaves the network. This problem can be solved by using an admission control and resource reservation [63] method. Admission control prevents new users from accessing the network in order to maintain the performance quality of the existing users. Similarly, a bandwidth reser- vation scheme assures a connection guarantee to some classes of services, irrespective of the users’ movement across different cells of the network. Both of these mechanisms combined provide a notion that a connection once admitted would get the promised amount of bandwidth throughout the call duration. The promised bandwidth might be specified in terms of the sta-

76 Chapter 5. Optimal Wireless Network Selection tistical mean or worst-case minimum bandwidth. It is very logical to say that future shared networks will use these QoS assurance mechanisms because they are expected to support real- time applications. For instance, the wireless LAN is in the process of enhancement, in the form of the 802.11e standard, to support and maintain the network service quality.

5.5 Conclusion

In this chapter, our goal was to find a way to maximize user satisfaction in heterogeneous wireless overlay networks. We described the bandwidth utility functions for rigid, elastic, and adaptive applications; we used this bandwidth utility function and handover latency to derive the user satisfaction function. We found that selecting a high bandwidth access network does not guarantee higher satisfaction if the user happens to perform a handover to a lower bandwidth network. It is observed that after getting an estimate of the user’s movement, we should assign the network with most availability to the user’s call request, so that the user can remain in the same network throughout the call duration. By doing this, we can prevent the user satisfac- tion from being degraded. In accordance with the evaluation, we proposed an access network selection algorithm that maximizes user satisfaction in heterogeneous networks.

Chapter 6

Graceful Vertical Handover

6.1 Introduction

This chapter presents a new scheme for upward vertical handover management, which we call the graceful vertical handover scheme. This scheme provides graceful degradation of perfor- mance by smoothing the throughput change and reducing the possible packet losses during a handover. To achieve such a gracefulness in performance change, this scheme implements its functionalities in the link layer, IP layer, and TCP or application layer.

This chapter is organized as follows. Section 6.2 gives an overview of the problem state- ment. Section 6.3 presents the proposed graceful vertical handover scheme. Section 6.4 out- lines the simulation setup and discusses performance evaluation results. Finally, Section 6.5 concludes this chapter.