Scanless Fast Handoff Technique Based on Global Path Cache for WLANs

Wireless LANs (WLANs) have been widely adopted and are more convenient as they are inter-connected as wireless campus networks and wireless mesh networks. However, timesensitive 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 a technique called Global Path-Cache (GPC) that provides fast handoffs in WLANs.GPC properly captures the dynamic behavior of the network andMSs, and provides accurate next AP 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

Scanless Fast Handoff Technique Based on Global Path Cache for WLANs

Wireless LANs (WLANs) have been widely adopted and are more convenient as they are inter-connected as wireless campus networks and wireless mesh networks. However, timesensitive 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 a technique called Global Path-Cache (GPC) that provides fast handoffs in WLANs.GPC properly captures the dynamic behavior of the network andMSs, and provides accurate next AP 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

Behavior-Based Mobility Prediction for Seamless Handoffs in Mobile Wireless Networks

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 standard 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 applications. 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

Behavior-Based Mobility Prediction for Seamless Handoffs in Mobile Wireless Networks

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 standard 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 applications. 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