## 2 Related Works  
Previously many network discovery approaches have been proposed. These network discovery approaches can be classified into RSS threshold-based methods and motion-aware methods. The RSS threshold-based methods include reactive methods and proactive methods. The proactive network discovery [6; 7; 8; 9; 10; 11; 12; 13; 14] uses a decision function based on a handoff mechanism. In the proactive scheme, an MN always turns on all of its wireless interfaces to monitor and scan the available access networks. If the MN finds out a better network, i.e., the RSS from the new AP is higher than that of the current serving AP, the MN triggers handoff process to switch its connection to the new AP. However, this method may cause ping-pong effect and reduce the performance of communication service. In [12], the authors use a dwell timer and an effective throughput ratio to optimize the RSS-based handoff algorithm while reducing the ping-pong effect. [PERSON] _et al._[14] propose an RSS prediction method and an adaptive cost-based competitive on-line (COL) method to trigger handoff and select a suitable wireless network. The predicted RSS is equal to the current RSS plus a difference value. The range of the predicted RSS is between the exponentially averaged RSS and the averaged RSS of the MN. If the predicted RSS is below a predefined threshold, the handoff will be triggered. However, these handoff algorithms do not consider power consumption. The reactive approaches [15; 16; 17; 18] use predefined thresholds to trigger an MN's network discovery so that the MN can preserve battery power. In [18], the authors discuss network discovery by using Link Going Down (LDG) trigger in the Media Independent Handover (MIH) framework [19]. LDG is an event message that is triggered by an MIH Information Service (IS) according to the threshold used. LDG can be invoked by various approaches to prevent ping-pong effect. [PERSON] _et al._[17] proposes an Application Signal Strength Threshold (ASST) mechanism, which defines different thresholds for applications. When the RSS slope estimation is smaller than ASST, network discovery is triggered. However, when many applications are active on an MN simultaneously, how to define thresholds becomes a problem. The motion-aware methods use the MN's location and motion information to trigger handoff. There are several location, tracking and motion detection technologies [20; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31], such as GPS, Assisted-GPS (A-GPS), time of arrival (TOA), time difference of arrival (TDOA), angle of arrival (AOA), OTDOA, cellular ID, smart antenna and pattern matching (network fingerprint [32]), have been proposed. However, these motion detection mechanisms require additional hardware and cost. The GPS and A-GPS method use the GPS information to track an MN's location and movement. An MN can receive GPS information from at least four satellites and the assistance data from network to accurately predict its location. However, the MN needs to equip a GPS receiver, which implies more monetary cost and more power consumption. Moreover, the GPS signal may not be received accurately in an indoor environment. The TOA method measures the travel time of a radio signal from an MN to three different APs. Each travel time is multiplied by the light speed to give the distance between the MN and each AP. The TOA method can estimate the MN's position and motion based on the intersection of these distance information. The TDOA method is an enhancement of TOA, which computes the MN's position based on trilateration. The TDOA method uses the time difference measurement in the location prediction. However, the TDOA method requires a synchronized clock for all communication systems to measure the time difference, which makes the TDOA method an expansive solution. On the other hand, the AOA method needs to use an antenna array or a smart antenna to estimate the angle of received signal, which is not easy to implement in many wireless systems.