Predictability of Wlan Mobility and Its Effects on Bandwidth Provisioning

Wireless local area networks (WLANs) are emerging as a popular technology for access to the Internet and enterprise networks. In the long term, the success of WLANs depends on services that support mobile network clients. \par Although other researchers have explored mobility prediction in hypothetical scenarios, evaluating their predictors analytically or with synthetic data, few studies have been able to evaluate their predictors with real user mobility data. As a first step towards filling this fundamental gap, we work with a large data set collected from the Dartmouth College campus-wide wireless network that hosts more than 500 access points and 6,000 users. Extending our earlier work that focuses on predicting the next-visited access point (i.e., location), in this work we explore the predictability of the time of user mobility. Indeed, our contributions are two-fold. First, we evaluate a series of predictors that reflect possible dependencies across time and space while benefiting from either individual or group mobility behaviors. Second, as a case study we examine voice applications and the use of handoff prediction for advance bandwidth reservation. Using application-specific performance metrics such as call drop and call block rates, we provide a picture of the potential gains of prediction. \par Our results indicate that it is difficult to predict handoff time accurately, when applied to real campus WLAN data. However, the findings of our case study also suggest that application performance can be improved significantly even with predictors that are only moderately accurate. The gains depend on the applications\u27 ability to use predictions and tolerate inaccurate predictions. In the case study, we combine the real mobility data with synthesized traffic data. The results show that intelligent prediction can lead to significant reductions in the rate at which active calls are dropped due to handoffs with marginal increments in the rate at which new calls are blocked

Evaluating Mobility Predictors in Wireless Networks for Improving Handoff and Opportunistic Routing

We evaluate mobility predictors in wireless networks. Handoff prediction in wireless networks has long been considered as a mechanism to improve the quality of service provided to mobile wireless users. Most prior studies, however, were based on theoretical analysis, simulation with synthetic mobility models, or small wireless network traces. We study the effect of mobility prediction for a large realistic wireless situation. We tackle the problem by using traces collected from a large production wireless network to evaluate several major families of handoff-location prediction techniques, a set of handoff-time predictors, and a predictor that jointly predicts handoff location and time. We also propose a fallback mechanism, which uses a lower-order predictor whenever a higher-order predictor fails to predict. We found that low-order Markov predictors, with our proposed fallback mechanisms, performed as well or better than the more complex and more space-consuming compression-based handoff-location predictors. Although our handoff-time predictor had modest prediction accuracy, in the context of mobile voice applications we found that bandwidth reservation strategies can benefit from the combined location and time handoff predictor, significantly reducing the call-drop rate without significantly increasing the call-block rate. We also developed a prediction-based routing protocol for mobile opportunistic networks. We evaluated and compared our protocol\u27s performance to five existing routing protocols, using simulations driven by real mobility traces. We found that the basic routing protocols are not practical for large-scale opportunistic networks. Prediction-based routing protocols trade off the message delivery ratio against resource usage and performed well and comparable to each other

EVEREST IST - 2002 - 00185 : D23 : final report

Deliverable públic del projecte europeu EVERESTThis deliverable constitutes the final report of the project IST-2002-001858 EVEREST. After its successful completion, the project presents this document that firstly summarizes the context, goal and the approach objective of the project. Then it presents a concise summary of the major goals and results, as well as highlights the most valuable lessons derived form the project work. A list of deliverables and publications is included in the annex.Postprint (published version

Client-based SBM layer for predictive management of traffic flows in heterogeneous networks

In a heterogeneous networking environment, the knowledge of the time before a vertical handover (TBVH) for any network is vital in correctly assigning connections to available channels. In this paper, we introduce a predictive mathematical model for calculating the estimated TBVH component from available network parameters and discuss the different scenarios that arise based on a mobile host’s trajectory. We then introduce the concept of an intelligent Stream Bundle Management Layer (SBM) which consists of a set of policies for scheduling and mapping prioritised traffic streams on to available channels based on their priority, device mobility pattern and prevailing channel conditions. The layer is also responsible for the maintenance of connections during vertical handovers to avoid their forced termination

Behavior-Based Mobility Prediction for Seamless Handoffs in Mobile Wireless Networks

The field of wireless networking has received unprecedented attention from the research community during the last decade due to its great potential to create new horizons for communicating beyond the Internet. Wireless LANs (WLANs) based on the IEEE 802.11 standard have become prevalent in public as well as residential areas, and their importance as an enabling technology will continue to grow for future pervasive computing applications. However, as their scale and complexity continue to grow, reducing handoff latency is particularly important. This paper presents the Behavior-based Mobility Prediction scheme to eliminate the scanning overhead incurred in IEEE 802.11 networks. This is achieved by considering not only location information but also group, time-of-day, and duration characteristics of mobile users. This captures short-term and periodic behavior of mobile users to provide accurate next-cell predictions. Our simulation study of a campus network and a municipal wireless network shows that the proposed method improves the next-cell prediction accuracy by 23~43% compared to location-only based schemes and reduces the average handoff delay down to 24~25 ms

Behavior-Based Mobility Prediction for Seamless Handoffs in Mobile Wireless Networks

The field of wireless networking has received unprecedented attention from the research community during the last decade due to its great potential to create new horizons for communicating beyond the Internet. Wireless LANs (WLANs) based on the IEEE 802.11 standard have become prevalent in public as well as residential areas, and their importance as an enabling technology will continue to grow for future pervasive computing applications. However, as their scale and complexity continue to grow, reducing handoff latency is particularly important. This paper presents the Behavior-based Mobility Prediction scheme to eliminate the scanning overhead incurred in IEEE 802.11 networks. This is achieved by considering not only location information but also group, time-of-day, and duration characteristics of mobile users. This captures short-term and periodic behavior of mobile users to provide accurate next-cell predictions. Our simulation study of a campus network and a municipal wireless network shows that the proposed method improves the next-cell prediction accuracy by 23~43% compared to location-only based schemes and reduces the average handoff delay down to 24~25 ms

A survey of network coverage prediction mechanisms in 4G heterogeneous wireless networks.

Seamless connectivity in 4G wireless networks requires the development of intelligent proactive mechanisms for efficiently predicting vertical handovers. Random device mobility patterns further increase the complexity of the handover process. Geographical topologies such as indoor and outdoor environments also exert additional constraints on network coverage and device mobility. The ability of a device to acquire refined knowledge about surrounding network coverage can significantly affect the performance of vertical handover prediction and QoS management mechanisms. This paper presents a comprehensive survey of research work conducted in the area of 4G wireless network coverage prediction for the optimisation of vertical handovers. It discusses different coverage prediction approaches and analyses their ability to accurately predict network coverage