The main purpose of this thesis is explore how to effectively detect a set of mobile devices which are moving as a group in both infrastructure based WiMAX / 3G systems, and Mobile Ad Hoc Networks (MANET) / Vehicles Ad Hoc Networks (VANET).
Due to the recent rapid development of wireless networks, there are more and more people using wireless mobile services through mobile devices nowadays. Today, wireless networks are classified into 2 categories, the infrastructure based networks, shown in Fig.1, and non-infrastructure based networks, shown in Fig.2. In Fig. 1, we show an infrastructure based wireless networks consists of a number of mobile devices that connect to the internet through the access to the fixed wireless based stations (BSs), such as WiMAX and3G. The non-infrastructure based networks consist of a number of mobile devices that connect with each other in the absence of fixed BS, such as MANET and VANET.
Fig. 1、Infrastructure based networks
Fig. 2、MANET / VANET
In infrastructure based WiMAX / 3G systems, a group of mobile devices move, the active connections established between those mobile devices and the present BS may need to be transferred or handoff to other BS. If the handoff request is initialed by each mobile device, the numbers of handoff messages that the BS need to handle are enormous. It may cause a handoff delay or even multiple calls drops due to limited messages processing capability of the BS. For example, the passengers in the same bus or train are traveling in the same direction. If some of them use the mobile devices, they will handoff to the same BS almost at the same time. Thus the BS has to process a large number of handoff messages simultaneously. It may cause a handoff delay or even a call drop, if the BS is overloaded with the excessive number of handoff messages. In order to reduce the number of handoff messages, we can treat a set of mobile nodes as a group instead of individual nodes, then handoff through the a group handoff method.
In addition, in the non-infrastructure based MANET / VANET, the knowledge of a set of mobile devices which travel together in a period of time such as a car fleet can help to distribute the information more efficiently. In order to ensure the information can be spread out in MANET, the information must be distributed to each mobile
device. Therefore, it will waste a lot of resources, and one mobile device may receive duplicate information. However if a group of mobile devices can be identified, we can send a copy of information only to one mobile device instead of a group of mobile devices. Today, most of devices have more than two network interfaces, for example, WiMAX / Wihi or WiMAX / Zigbee. If a mobile device can know its group member, it can communicate with its group member using short range interface instead of more power hunger long range interface. In addition, we can use the group knowledge to implement a better cluster based routing algorithm for efficient resource utilization in MANET / VANET. A cluster based routing relies on the connectivity of the cluster heads (CH). With group knowledge we can have a better choice of cluster heads and maintain more stability of the clusters.
The purpose of this study is to explore how to detect a set of mobile device by their characteristic of movement and appropriate threshold. For example, in the bus, we regard a set of mobile device carried by the passengers as the same group.
Therefore, we can improve handoff overhead by performing group handoff seamless.
In addition, in MANET/VANET we can distribute data more efficiently with group knowledge. For example, we can spread the information to a specific MN instead of all MNs..
Fig. 3、(a)The BS has the bus information;(b) The BS has no the bus information
In the WiMAX / 3G systems, we make use of mobility information of mobile devices such as position, speed and moving direction to detect a group of mobile devices which are travel together. The issues of group detection in infrastructure-based wireless network can be classified into two categories: with group leader and without group leader. As shown as Fig.3(a), the bus has a mobile device which can be identified by the BSs as a group leader. Opposite, as shown as Fig.3(b), the bus carry no mobile device, thus the BSs have no information of the group leaders. Suppose that the BS has the information of the group leaders, the BS can detect a group by using the group leader’s mobility information as benchmarks to match up with the mobility information of other mobile devices. However, if the BS has no information of the group leaders; there are no benchmarks for comparison.
Therefore, the mobility information of all mobile devices must be compared with each other which will increase the complexity of detection.
In the MANET / VANET, mobile device is unable to obtain global information such as positions, direction and velocities of all nodes. A mobile device detects its group members only by local information of its neighbored nodes.
According to the above descriptions, this thesis will explore how to detect a group of mobile devices by their mobility pattern in the infrastructure based networks and non-infrastructure networks. We employ statistical hypothesis test to obtain the suitable thresholds for an effective detection. In this thesis, we forces on the following three topics: (1) Deign a centralize algorithm to detect a set of nodes which is traveling together in infrastructure based networks with a group lead. (2) Deign a centralize algorithm to detect a set of nodes which is traveling together in infrastructure based networks without a group lead. (3) Deign a distributed algorithm to detect a set of nodes which is traveling together in non-infrastructure based
networks. First, we collect the positions of mobile nodes by signal strength. Then, we measure the difference of distance, moving angle and moving distance between two nodes. Calculate three confidence indexes by above measurements and appropriate thresholds. These thresholds are important. In order to improve the accuracy of the confidence index, we use the statistical hypothesis test to find the appropriate threshold. Finally, a probability of the same group between two nodes is obtained by confidence indexes and history data.
The remainder of the thesis is organized as follows. In chapter 2, we introduce some related works. In chapter 3, we present the existing group mobility models. In chapter 4, we describe how to calculate the confidence indexes and how to use the hypothesis testing to obtain the appropriate threshold. In chapter 5, we propose the mobility group detection. In chapter 6, we show the results of simulation experiments. Finally, in chapter 7, we summarize this paper.