Evaluating Next-Cell Predictors with Extensive Wi-Fi Mobility Data

Abstract

Location is an important feature for many applications, and wireless networks may serve their clients better 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 more than 6,000 users on Dartmouth's campus-wide Wi-Fi wireless network. The surprising results provide critical evidence for anyone designing or using mobility predictors. We implemented and compared the prediction accuracy of several location predictors drawn from four major families of domain-independent predictors, namely, Markov-based, compressionbased, 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.

Perfilado inteligente de movilidad de usuarios

“…Los trabajos de Gambs [89], Song [139] y Yang [90] utilizan predictores basados en Markov. En el trabajo de Gambs [89] proponen cadenas de Markov de orden 2.…”

“…El principal problema de este predictor es que es incapaz de predecir cuando el usuario visita una secuencia de dos lugares por primera vez. En el trabajo de Song [139] proponen una combinación en cascada de cadenas de Markov, donde si las cadenas de Markov de orden N no pueden predecir un destino, recaen sobre cadenas de Markov de orden N-1, y así sucesivamente hasta llegar a Markov de orden 0.…”

Detecting and Reducing Biases in Cellular-Based Mobility Data Sets

“…Many kinds of data sets have been used so far, ranging from Global Positioning System (GPS) [6], cellular [7] or WiFi-based data [8], to the more recent data coming from location-based social networks (LBSNs) [9] or online games [10]. See [11] for a thorough survey of human mobility models and traces.…”

Mobility - based proactive caching models for addressing niche mobile demand and scalable ICN name resolution designs

“…The most important out of these protocols are the Mobile IP (MIP) protocol [38,39] and its most recent hierarchically extended version, the Hierarchical MIPv6 (HMIP) [40], which was introduced to enhance support in micro-mobility scenarios. Though achieving their goal of supporting host mobility, the performance of these as well as other protocols is known to suffer [41] from slow handovers, with corresponding efforts like the Fast Handover for MIPv6 [42] scheme ending up in failure [43]. Nevertheless, recent developments have allowed audio/video streaming and other applications to appear to run seamlessly in mobile scenarios, i.e.…”

“…The difference between the two schemes becomes more evident at lower tiers, where DONA increases state size by up to 3 or 4 orders of magnitude. Table 6.10 translates the observed state sizes into the corresponding hardware resource requirements, expressed as the number of 16 GB RAM servers required to hold the resolution state in RAM at each AS, per tier 43 . Clearly, both schemes require the deployment of large data centers at Tier-1 to cope with the state size; fortunately, this is required at only a few ASs.…”