A Mobility Management Technique with Simple Handover Prediction for 3G LTE Systems

Kim, Tae Hyong; Yang, Qi-Ping; Park, Soon-Gi; Shin, Yeon-Seung

doi:10.1109/vetecf.2007.68

Cited by 38 publications

(18 citation statements)

References 3 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%

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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%

“…In addition, some techniques record geographic coordinates and directional movements of MSs from either GPS or triangulation of signal strengths [29,30]. The basic idea is to provide predictions by searching the database using user contexts stored in MSs.…”

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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“…There has been a plethora of work on mobility prediction for a variety of wireless networks, such as cellular [1,14,16,35,44,45], WLANs [8,10,15,24,26,33,36], sensor networks [47] ad hoc networks [38], and wireless mesh networks [3,13], and applied to reduce handoff latency [24,33], minimize handoff frequency [15], provide efficient resource reservation [1,14,16,35,36,44,45], improve routing protocols [38], and conserve power [47].…”

Section: Mobility Predictionmentioning

confidence: 99%

“…Third, stochastic techniques provide mobility predictions using probabilistic models. These techniques apply the knowledge of geographic coordinates of MSs from either GPS or triangulation of signal strengths to predicted future locations [1,16].…”

Section: Mobility Predictionmentioning

confidence: 99%

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Scanless fast handoff technique based on global Path-Cache for WLANs

Wanalertlak

Liu

et al. 2012

J Supercomput

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Wireless LANs (WLANs) have been widely adopted and are more convenient as they are interconnected as wireless campus networks and wireless mesh networks. However, time-sensitive multimedia applications, which have become more popular, could suffer from long end-to-end latency in WLANs. This is due mainly to handoff delay, which in turn is caused by channel scanning. This paper proposes (MSs), and provides accurate next-AP (access point) predictions to minimize the handoff latency. Moreover, the handoff frequencies are treated as time-series data, thus GPC calibrates the prediction models based on short-term and periodic behaviors of mobile users. Our simulation study shows that GPC virtually eliminates the need to scan for APs during handoffs and results in much better overall handoff delay compared to existing methods.

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Mobile Wireless Networks

Ayers¹

2012

Telecommunications System Reliability Engineering, Theory, and Practice

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A Mobility Management Technique with Simple Handover Prediction for 3G LTE Systems

Cited by 38 publications

References 3 publications

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

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

Scanless fast handoff technique based on global Path-Cache for WLANs

Mobile Wireless Networks

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