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

Mobicom Poster: Evaluating Location Predictors with Extensive Wi-Fi Mobility Data

A fundamental problem in mobile computing and wireless networks is the ability to track and predict the location of mobile devices. An accurate location predictor can significantly improve the performance or reliability of wireless network protocols, the wireless network infrastructure itself, and many applications in pervasive computing. These improvements lead to a better user experience, to a more cost-effective infrastructure, or both. Location prediction has been proposed in many areas of wireless cellular networks as a means of enhancing performance, including better mobility management, improved assignment of cells to location areas, more efficient paging, and call admission control. To the best of our knowledge, no other researchers have evaluated location predictors with extensive mobility data from real users. In this poster we compare the most significant domain-independent predictors using a large set of user mobility data collected at Dartmouth College. In this data set, we recorded for two years the sequence of wireless cells (Wi-Fi access points) frequented by more than 6000 users. We found that the simple Markov predictors performed as well or better than the more complicated LZ predictors, with smaller data structures

Evaluating Next Cell Predictors with Extensive Wi-Fi Mobility Data

Location is an important feature for many applications, and wireless networks can better serve their clients by anticipating client mobility. As a result, many location predictors have been proposed in the literature, though few have been evaluated with empirical evidence. This paper reports on the results of the first extensive empirical evaluation of location predictors, using a two-year trace of the mobility patterns of over 6,000 users on Dartmouth\u27s campus-wide Wi-Fi wireless network. The surprising results provide critical evidence for anyone designing or using mobility predictors. \par We implemented and compared the prediction accuracy of several location predictors drawn from four major families of domain-independent predictors, namely Markov-based, compression-based, PPM, and SPM predictors. We found that low-order Markov predictors performed as well or better than the more complex and more space-consuming compression-based predictors

Controllability for a Wave Equation with Moving Boundary

We investigate the controllability for a one-dimensional wave equation in domains with moving boundary. This model characterizes small vibrations of a stretched elastic string when one of the two endpoints varies. When the speed of the moving endpoint is less than 1 − 1/√ , by Hilbert uniqueness method, sidewise energy estimates method, and multiplier method, we get partial Dirichlet boundary controllability. Moreover, we will give a sharper estimate on controllability time that only depends on the speed of the moving endpoint

Controllability for a Wave Equation with Moving Boundary

We investigate the controllability for a one-dimensional wave equation in domains with moving boundary. This model characterizes small vibrations of a stretched elastic string when one of the two endpoints varies. When the speed of the moving endpoint is less than 1-1/e, by Hilbert uniqueness method, sidewise energy estimates method, and multiplier method, we get partial Dirichlet boundary controllability. Moreover, we will give a sharper estimate on controllability time that only depends on the speed of the moving endpoint

Update of hadronic decays of J/ψJ/\psi and ψ(2S)\psi(2S) though virtual photons

The hadronic decay branching ratios of $J/\psi$ and $\psi(2S)$ through virtual photons, $B(J/\psi, \psi(2S) \rightarrow \gamma^*\rightarrow \text{hadrons})$, are updated by using the latest published measurements of the $R$ value and the branching ratios of $J/\psi, \psi(2S) \rightarrow l^+l^-$. Their respective precision increases by about 4 and 3 times.Comment: 3 pages, 1 figur

Mobicom Poster Abstract: Bandwidth Reservation Using Wlan Handoff Prediction

Many network services may be improved or enabled by successful predictions of users\u27 future mobility. The success of predictions depend on how much accuracy can be achieved on real data and on the sensitivity of particular applications to this achievable accuracy. We investigate these issues for the case of advanced bandwidth reservation using real WLAN traces collected on the Dartmouth College campus

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