We conduct the most comprehensive study of WLAN traces to date. Measurements collected from four major university campuses are analyzed with the aim of developing fundamental understanding of realistic user behavior in wireless networks. Both individual user and inter-node (group) behaviors are investigated and two classes of metrics are devised to capture the underlying structure of such behaviors. For individual user behavior we observe distinct patterns in which most users are 'on' for a small fraction of the time, the number of access points visited is very small and the overall on-line user mobility is quite low. We clearly identify categories of heavy and light users. In general, users exhibit high degree of similarity over days and weeks. For group behavior, we define metrics for encounter patterns and friendship. Surprisingly, we find that a user, on average, encounters less than 6 of the network user population within a month, and that encounter and friendship relations are highly asymmetric. We establish that number of encounters follows a biPareto distribution, while friendship indexes follow an exponential distribution. We capture the encounter graph using a small world model, the characteristics of which reach steady state after only one day. We hope for our study to have a great impact on realistic modeling of network usage and mobility patterns in wireless networks.
With these traces available, more recent research works focus on modeling user behaviors in wireless LANs. In @cite_11 the authors propose models to describe traffic flows generated by wireless LAN users, which is a different focus to this paper. In the first part of this paper we focus more on identifying metrics that capture important characteristics of user association behaviors. We understand user associations as coarse-grained mobility at per access point granularity. Similar methodology has been used in @cite_8 and @cite_3 . In @cite_3 the authors propose a mobility model based on association session length distribution and AP preferences. However, there are also other important metrics that are not included, such as user on-off behavior and repetitive patterns. We add these metrics to provide a more complete description for user behaviors in wireless networks.
@cite_8 @cite_3 @cite_11
2160494326 2002169759 2137688035 2042774345
Wireless local-area networks are becoming increasingly popular. They are commonplace on university campuses and inside corporations, and they have started to appear in public areas [17]. It is thus becoming increasingly important to understand user mobility patterns and network usage characteristics on wireless networks. Such an understanding would guide the design of applications geared toward mobile environments (e.g., pervasive computing applications), would help improve simulation tools by providing a more representative workload and better user mobility models, and could result in a more effective deployment of wireless network components.Several studies have recently been performed on wire-less university campus networks and public networks. In this paper, we complement previous research by presenting results from a four week trace collected in a large corporate environment. We study user mobility patterns and introduce new metrics to model user mobility. We also analyze user and load distribution across access points. We compare our results with those from previous studies to extract and explain several network usage and mobility characteristics.We find that average user transfer-rates follow a power law. Load is unevenly distributed across access points and is influenced more by which users are present than by the number of users. We model user mobility with persistence and prevalence. Persistence reflects session durations whereas prevalence reflects the frequency with which users visit various locations. We find that the probability distributions of both measures follow power laws. In this paper, we analyze the mobility patterns of users of wireless hand-held PDAs in a campus wireless network using an eleven week trace of wireless network activity. Our study has two goals. First, we characterize the high-level mobility and access patterns of hand-held PDA users and compare these characteristics to previous workload mobility studies focused on laptop users. Second, we develop two wireless network topology models for use in wireless mobility studies: an evolutionary topology model based on user proximity and a campus waypoint model that serves as a trace-based complement to the random waypoint model. We use our evolutionary topology model as a case study for preliminary evaluation of three ad hoc routing algorithms on the network topologies created by the access and mobility patterns of users of modern wireless PDAs. Based upon the mobility characteristics of our trace-based campus waypoint model, we find that commonly parameterized synthetic mobility models have overly aggressive mobility characteristics for scenarios where user movement is limited to walking. Mobility characteristics based on realistic models can have significant implications for evaluating systems designed for mobility. When evaluated using our evolutionary topology model, for example, popular ad hoc routing protocols were very successful at adapting to user mobility, and user mobility was not a key factor in their performance. Understanding user mobility is critical for simula- tions of mobile devices in a wireless network, but current mobility models often do not reflect real user movements. In this paper, we provide a foundation for such work by exploring mobility characteristics in traces of mobile users. We present a method to estimate the physical location of users from a large trace of mobile devices associating with access points in a wireless network. Using this method, we extracted tracks of always-on Wi-Fi devices from a 13-month trace. We discovered that the speed and pause time each follow a log-normal distribution and that the direction of movements closely reflects the direction of roads and walkways. Based on the extracted mobility characteristics, we developed a mobility model, focusing on movements among popular regions. Our validation shows that synthetic tracks match real tracks with a median relative error of 17 . In IEEE 802.11-based wireless mesh networks a user is associated with an access point (AP) in order to communicate and be part of the overall network. The association mechanism specified by the IEEE 802.11 standard does not consider the channel conditions and the AP load in the association process. Employing the mechanism in its plain form in wireless mesh networks we may only achieve low throughput and low user transmission rates. In this paper, we propose an association mechanism that is aware of the uplink and downlink channel conditions. We introduce a metric that captures the channel conditions and the load of the APs in the network. The users use this metric in order to optimally associate with the available APs. We then extend the functionality of this mechanism in a cross-layer manner taking into account information from the routing layer. The novelty of the mechanism is that the routing QoS information of the back haul is available to the end users. This information can be combined with the uplink and downlink channel information for the purpose of supporting optimal end-to-end communication and providing high end-to-end throughput values. We evaluate the performance of our system through simulations and we show that 802.11-based mesh networks that use the proposed association mechanism are more capable in meeting the needs of QoS-sensitive applications.