A data mining approach for location prediction in mobile environments

Yavaş, Gökhan; Katsaros, Dimitrios; Ulusoy, Özgür; Manolopoulos, Yannis

doi:10.1016/j.datak.2004.09.004

Cited by 276 publications

(168 citation statements)

References 14 publications

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“…Many different mobility prediction techniques have been proposed for a variety of wireless networks, such as cel lular [27][28][29][30][31][32], WLANs [10-13, 33, 34], ad hoc networks [35,36], and mesh networks [5], and applied to reduce handoff latency [8,12,13,37], provide efficient resource reservation [27][28][29][30][31][32][33], improve routing protocols [35], and conserve power [36]. However, these methods tend to be general and thus do not consider the special characteristics of WLANs, such as highly overlapped cell coverage, MAC contention, and variations in link quality.…”

Section: Related Workmentioning

confidence: 99%

Behavior-based mobility prediction for seamless handoffs in mobile wireless networks

Wanalertlak

Liu

et al. 2010

Wireless Netw

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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 stan dard 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 applica tions. 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.

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Section: Related Workmentioning

confidence: 99%

Behavior-based mobility prediction for seamless handoffs in mobile wireless networks

Wanalertlak

Liu

et al. 2010

Wireless Netw

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“…Yavas et al [47] propose a similar movement matching method to [49], but uses vehicle regional movements instead of trajectories. The method divides the roads into small, discrete segments, and then compares a vehicle's recent segment history to previous vehicle segment history.…”

Section: Probability Analysismentioning

confidence: 99%

“…Yavas et al [47] propose a similar movement matching method to [49], but use over the network to be compared to a database. The database is then used to determine the closest matching vehicle, and return the next most-likely AP.…”

Section: Pattern Matching and Hybridmentioning

confidence: 99%

Predictive mobile IP handover for vehicular networks

Magnano

Xin

Boukerche

2015

2015 IEEE 40th Conference on Local Computer Networks (LCN)

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Vehicular networks are an emerging technology that offer potential for providing a variety of new services. However, extending vehicular networks to include IP connections is still problematic, due in part to the incompatibility of mobile IP handovers with the increased mobility of vehicles. The handover process, consisting of discovery, registration, and packet forwarding, has a large overhead and disrupts connectivity. With increased handover frequency and smaller access point dwell times in vehicular networks, the handover causes a large degradation in performance. This thesis proposes a predictive handover solution, us-

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“…The NIM service helps reduce handover-delay and battery energy consumption. It may also use analytics to provide even more refined network selection capabilities [21]. The network-based PM service enables incremental deployment of the system by converting the enhanced networking stack packets into the traditional stack.…”

Section: Network Servicesmentioning

confidence: 99%

Unified Heterogeneous Networking Design

Singh¹,

Ormazabal²,

Schulzrinne³

et al. 2013

Proceedings of Principles, Systems and Applications on IP Telecommunications

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The Internet was designed under the assumption that end-hosts are stationary and have one interface. Current mobile devices have multiple network interfaces, such as Wi-Fi, LTE, WiMAX, and possibly Ethernet. Such diverse network connectivity can be used to increase both reliability and performance by running applications over multiple links sequentially, for a seamless user experience, or in parallel, for bandwidth and performance enhancements. Users are also consuming Internet services from multiple locations and devices, such as smartphones, tablets, laptops, and IP-enabled TVs. The existing networking stack, however, offers almost no support for intelligently exploiting such network, location and device diversity.Since, most Internet devices today are mobile, we propose a unified networking architecture that makes optimal use of a heterogeneous dynamic environment, both in terms of networks and user devices. The system core functionalities include mobility, multi-homing, multipath, and disruption tolerance. The system enables mobile nodes to make decisions about how and when to use each or a combination of networks, in a secure manner. With this new architecture, we envision a shift from current applications supporting a single network, location, and device at a time, to applications that can support multiple networks, multiple locations, and multiple devices.

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A data mining approach for location prediction in mobile environments

Cited by 276 publications

References 14 publications

Behavior-based mobility prediction for seamless handoffs in mobile wireless networks

Behavior-based mobility prediction for seamless handoffs in mobile wireless networks

Predictive mobile IP handover for vehicular networks

Unified Heterogeneous Networking Design

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