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.

Abstract-This paper proposes a technique called Global PathCache (GPC) that provides fast handoffs in WLANs. GPC maintains a history of mobile stations' mobility patterns in a network to assist in the prediction of the next point-of-attachment. GPC properly captures the dynamic behavior of the network and mobile stations, and provides accurate next AP predictions. 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.

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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