The architecture for the Beyond 3rd Generation (B3G) or 4th Generation (4G) wireless networks aims at integrating various heterogeneous wireless access networks. One of the major design issues is the support of vertical handoff. Vertical handoff occurs when a mobile terminal switches from one network to another (e.g., from wireless local area network to code-division multiple-access 1x radio transmission technology). The objective of this paper is to determine the conditions under which vertical handoff should be performed. The problem is formulated as a Markov decision process with the objective of maximizing the total expected reward per connection. The network resources that are utilized by the connection are captured by a link reward function. A signaling cost is used to model the signaling and processing load incurred on the network when vertical handoff is performed. The value iteration algorithm is used to compute a stationary deterministic policy. For performance evaluation, voice and data applications are considered. The numerical results show that our proposed scheme performs better than other vertical handoff decision algorithms, namely, simple additive weighting, the technique for order preference by similarity to ideal solution, and Grey relational analysis.

In a fourth generation (4G) wireless environment, the need for an user to be always best connected (ABC) anywhere at anytime leads to execute a vertical handoff decision for guaranteeing service continuity and quality of service (QoS). Several strategies have been proposed in the literature for addressing this problem, being multiple attribute decision making (MADM) one of the most promising methods. A comparative analysis of these methods including SAW, MEW, TOPSIS, ELECTRE, VIKOR, GRA, and WMC is illustrated with a numerical simulation, showing their performance for different applications such as: voice and data connections, in a 4G wireless system.

The 4th generation (4G) wireless communication systems aim to provide users with the convenience of seamless roaming among heterogeneous wireless access networks. To achieve this goal, the support of vertical handoff in mobility management is crucial. This paper focuses on the vertical handoff decision algorithm, which determines under what criteria vertical handoff should be performed. The vertical handoff decision problem is formulated as a constrained Markov decision process (CMDP). The objective is to maximize the expected total reward of a connection subject to the expected total access cost constraint. In our model, a benefit function is used to assess the quality of the connection, and a penalty function is used to model signaling and call dropping. The user's velocity and location information are considered when making the handoff decisions. The value iteration and Q-learning algorithms are used to determine the optimal policy. Numerical results show that our proposed vertical handoff decision algorithm outperforms another scheme which does not consider the user's velocity.

One of the major design issues in heterogeneous wireless networks is the support of vertical handoff. Vertical handoff occurs when a mobile terminal switches from one network to another (e.g., from WLAN to CDMA 1timesRTT). The objective of this paper is to determine the conditions under which vertical handoff should be performed. The problem is formulated as a Markov decision process. A link reward function and a signaling cost function are introduced to capture the tradeoff between the network resources utilized by the connection and the signaling and processing load incurred on the network. A stationary deterministic policy is obtained when the connection termination time is geometrically distributed. Numerical results show good performance of our proposed scheme over two other vertical handoff decision algorithms, namely: SAW (simple additive weighting) and GRA (grey relational analysis).

In the forthcoming heterogeneous wireless environment, the mobility management of users roaming between different wireless access technologies is a challenging and important technical issue. New mobile devices such as netbooks, smartphones and tablets allow users to perform vertical handoffs among different wireless networks. The multiple attribute decision making (MADM) methods are suitable tools to model and study the vertical handoff process. Hence, recently several MADM methods such as SAW, MEW, TOPSIS, GRA, ELECTRE, VIKOR and WMC have been proposed for vertical handoff. In this paper, we present an extensive performance evaluation and comparative study of the seven MAMD methods by means of numerical simulations in MATLAB. We evaluate the performance of each vertical handoff method under different applications such as voice, data, and cost-constrained connections. We also perform a sensitivity analysis and evaluate the computational complexity of each method in terms of number of floating point operations.

In this paper, we consider a mobile wireless sensor network where both sensor nodes and the seeds are moving. We propose and analyze two variations of the Monte Carlo localization (MCL) algorithms, namely: dual MCL and mixture MCL, for mobile sensor networks. We conduct simulation experiments to evaluate the performance of these two algorithms by varying the number of seeds, number of nodes, number of samples, velocity of nodes, and radio pattern degree of irregularity. Results show that both dual MCL and mixture MCL are more accurate than the original MCL algorithm. In terms of the trade off between the computational time and estimated location accuracy, the mixture MCL has a better performance than both dual MCL and the original MCL algorithms.