Predictive mobility support for QoS provisioning in mobile wireless environments

Aljadhai, A.; Znati, Taieb

doi:10.1109/49.957307

Cited by 147 publications

(108 citation statements)

References 17 publications

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“…These are all based on the order-k Markov predictor [34], and include Global Path Cache [14], Selective Scan with Caching [13], Movement Model [38], Two-Tier prediction [39,40], and a technique based on the use of directional vectors [28]. However, they do not consider other characteristics of mobile users, such as group, time-of-day, and duration.…”

Section: Related Workmentioning

confidence: 99%

“…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%

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 BSs exchange information on the predicted bandwidth demands for future time intervals in order to determine the feasibility of admitting new call requests. In [9], an advance reservation is kept valid for a limited period of time starting at the Earliest Arrival Time and ending at the Latest Arrival…”

Section: Prior Related Workmentioning

confidence: 99%

Proactive Resource Management for the Mitigation of Service Discontinuation in Mobile Networks

Alyfantis

Hadjiefthymiades

Merakos

2006

Proceedings of 15th International Conference on Computer Communications and Networks

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Abstract-A scheme for the proactive allocation of network resources to mobile users, based on a pricing framework, is proposed. The objective is the reduction of service discontinuation events attributed to handovers in the cellular infrastructure. The future base station, where network resources have to be reserved proactively, is determined by means of a path prediction algorithm. The network receives a fee for providing the advance reservation service to the user. The exact price is determined after a sequential bargaining procedure, modeled as a two-person non-cooperative game between the mobile user and the network. The efficiency of the proposed scheme is evaluated through simulations.

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“…Mobility prediction of wireless devices (Aljadhai and Znati, 2001) not only helps the users in smart access but also helps the service provider in planning the infrastructure and provide better QOS. Mobility prediction is not only applicable for GSM mobile networks but also for wireless networks comprising many access points and routers.…”

Section: Introductionmentioning

confidence: 99%

A Hidden Genetic Layer Based Neural Network for Mobility Prediction

Mahmoud¹

2012

American Journal of Applied Sciences

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Problem statement: With numerous wireless devices increasingly connecting to the internet, WLAN infrastructure planning becomes very important to maintain desired quality of services. For maintaining desired quality of service it is desirable to know the movement pattern of users. Mobility prediction involves finding the mobile device's next access point as it moves through the wireless network. Hidden Markov models and Bayesian approach have been proposed to predict the next hop. Approach: In this study we propose a new method for feature extraction and propose a novel neural network classifier based on a hidden Genetic Algorithm layer-GA-SOFM Neural Network. We evaluate our hypotheses by using one month syslog data of Dartmouth college mobility traces available online to extract mobility features and use this features to find the classification accuracy of the proposed model. Results and Conclusion: Proposed methodology was implemented and obtained accuracy in mobility prediction was 83.68%. The output obtained is better than Naive Bayesian method by 15.56% and over CART by 11.94%.

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Predictive mobility support for QoS provisioning in mobile wireless environments

Cited by 147 publications

References 17 publications

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

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

Proactive Resource Management for the Mitigation of Service Discontinuation in Mobile Networks

A Hidden Genetic Layer Based Neural Network for Mobility Prediction

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