Abstract

Currently, adverse wireless and mobile networks including Worldwide Interoperability for Microwave Access (WiMAX), Wireless Local Area Network (WLAN), Third Generation (3G) mobile communications such as Universal Mobile Telecommunications Systems (UMTS), Wideband Code Division Multiple Access (WCDMA) and Bluetooth as shown in Fig. 1, have emerged and continuously developed to achieve high-speed transmission. The network characteristics are summarized in Table 1. However, no one network can provide all types of desired services, e.g. wide coverage, high bandwidth and low access costs. For example, WLAN provides high data rates within limited coverage areas, e.g. hotel, airport, campus and other hotspots whereas UMTS provides lower data rates over a larger coverage area. Therefore, one of the challenges in the next generation of wireless communications (McNair & Fang, 2004) ; (Frattasi et al., 2006) ; (Boudriga et al., 2008) is the integration of existing and future wireless technologies and supporting transparent and seamless vertical handoffs without degrading quality of services (QoS) between these heterogeneous networks (Kassar et al., 2008) ; (Haibo et al., 2009). This will need a multi-interfaced terminal which can change connections during inter-network movement. Received Signal Strength (RSS) based handoff scheme is commonly used to initiate a handover (Pollini, 1996) ; (Pahlavan et al., 2000) ; (Majlesi & Khalaj, 2002). In heterogeneouswireless networks, RSS is not however sufficient for a vertical handoff decision because the RSS of different networks cannot be compared directly, and moreover, RSS cannot reflect network conditions adequately. In order to develop vertical handoff decisions, new metrics such as service types, monetary cost, network conditions, mobile terminal conditions and user preference should be used in conjunction with RSS measurement. In policy-based approaches, multi-criteria are needed not only for decision when the handover occurs but also determinewhich network should be chosen for user choice and intervention (Nkansah-Gyekye & Agbinya, 2008) ; (Stevens-Navarro et al., 2008) ; (Sun et al., 2008) ; (Nay & Zhou, 2009) ; (Haibo et al., 2009). In (Song & Jamalipour, 2008), a merit function is proposed to evaluate network performance based on user preferences and adopted to find the best possible network for users. However, the counter to ensure the conditions in handoff policy consistently true is fixed which is not adjusted to the mobile computing and network environment. The approach proposed in (Chang & Chen, 2008) determines the optimal target network in two phases, i.e., RSS prediction and Markov decision process (MDP). Predicting RSS can minimize the dropping 14

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