Applications of Soft Computing in Mobile and Wireless Communications

Soft computing is a synergistic combination of artificial intelligence methodologies to model and solve real world problems that are either impossible or too difficult to model mathematically. Furthermore, the use of conventional modeling techniques demands rigor, precision and certainty, which carry computational cost. On the other hand, soft computing utilizes computation, reasoning and inference to reduce computational cost by exploiting tolerance for imprecision, uncertainty, partial truth and approximation. In addition to computational cost savings, soft computing is an excellent platform for autonomic computing, owing to its roots in artificial intelligence. Wireless communication networks are associated with much uncertainty and imprecision due to a number of stochastic processes such as escalating number of access points, constantly changing propagation channels, sudden variations in network load and random mobility of users. This reality has fuelled numerous applications of soft computing techniques in mobile and wireless communications. This paper reviews various applications of the core soft computing methodologies in mobile and wireless communications

Seamless Vertical Handoff using Invasive Weed Optimization (IWO) algorithm for heterogeneous wireless networks

AbstractHeterogeneous wireless networks are an integration of two different networks. For better performance, connections are to be exchanged among the different networks using seamless Vertical Handoff. The evolutionary algorithm of invasive weed optimization algorithm popularly known as the IWO has been used in this paper, to solve the Vertical Handoff (VHO) and Horizontal Handoff (HHO) problems. This integer coded algorithm is based on the colonizing behavior of weed plants and has been developed to optimize the system load and reduce the battery power consumption of the Mobile Node (MN). Constraints such as Receiver Signal Strength (RSS), battery lifetime, mobility, load and so on are taken into account. Individual as well as a combination of a number of factors are considered during decision process to make it more effective. This paper brings out the novel method of IWO algorithm for decision making during Vertical Handoff. Therefore the proposed VHO decision making algorithm is compared with the existing SSF and OPTG methods

A Seamless Vertical Handoff Protocol for Enhancing the Performance of Data Services in Integrated UMTS/WLAN Network

The Next Generation Wireless Network (NGWN) is speculated to be a unified network composed of several existing wireless access networks such as Wireless Local Area Network (WLAN), Global System for Mobile (GSM), Universal Mobile Telecommunications System (UMTS), Worldwide Interoperability for Microwave Access (WiMAX), and satellite network etc

Mobility and Handoff Management in Wireless Networks

With the increasing demands for new data and real-time services, wireless networks should support calls with different traffic characteristics and different Quality of Service (QoS)guarantees. In addition, various wireless technologies and networks exist currently that can satisfy different needs and requirements of mobile users. Since these different wireless networks act as complementary to each other in terms of their capabilities and suitability for different applications, integration of these networks will enable the mobile users to be always connected to the best available access network depending on their requirements. This integration of heterogeneous networks will, however, lead to heterogeneities in access technologies and network protocols. To meet the requirements of mobile users under this heterogeneous environment, a common infrastructure to interconnect multiple access networks will be needed. In this chapter, the design issues of a number of mobility management schemes have been presented. Each of these schemes utilizes IP-based technologies to enable efficient roaming in heterogeneous network. Efficient handoff mechanisms are essential for ensuring seamless connectivity and uninterrupted service delivery. A number of handoff schemes in a heterogeneous networking environment are also presented in this chapter.Comment: 28 pages, 11 figure

Hybridisation of genetic algorithm with simulated annealing for vertical-handover in heterogeneous wireless networks

To provide the seamless mobility in heterogeneous wireless networks two significant methods, simulated annealing (SA) and genetic algorithms (GAs) are hybrid. In this paradigm, vertical handovers (VHs) are necessary for seamless mobility. In this paper, the hybrid algorithm has the ability to find the optimal network to connect with a good quality of service (QoS) in accordance with the user's preferences. The intelligent algorithm was developed to provide solutions near to real time and to avoid slow and considerable computations according to the features of the mobile devices. Moreover, a cost function is used to sustain the chosen QoS during transition between networks, which is measured in terms of the bandwidth, BER, ABR, SNR and monetary cost. Simulation results presented that choosing the SA rules would minimise the cost function and the GA-SA algorithm could reduce the number of unnecessary handovers, and thereby avoid the 'Ping-Pong' effect

Multi-objective Network Opportunistic Access for Group Mobility in Mobile Internet

The integration of existing and emerging heterogeneous wireless networks in mobile Internet is a combination of diverse but complementary wireless access technologies. Satisfying a set of imperative constrains and optimization objectives, access network selection (ANS) for mobile node (MN) is an inherent procedure in mobility management that needs to be solved in a reasonable manner for the whole system to operate in an optimal fashion. However, ANS remains a significant challenge. Because many MNs with distinctive call characteristics are likely to have correlated mobility and may need to perform mobility management at the same time, this paper, with the goal of investigating group mobility solutions, proposes a network opportunistic access for group mobility (NOA-GM) scheme. By analyzing the directional patterns of moving MNs and introducing the idea of opportunistic access, this scheme first identifies underloaded access networks as candidates. Then, the candidates are evaluated using normalized models of objective and subjective metrics. On this basis, the ANS problem for group mobility can be conducted as a multiobjective combination optimization and then transferred to a signal-objective model by considering the optimization of the performance of the whole system as a global goal while still achieving each MN\u27s performance request. Using an improved genetic algorithm with newly designed evolutionary operators to solve the signal-objective model, an optimal result option for ANS for group mobility is achieved. Simulations conducted on the NS-2 platform show that NOA-GM outperforms the compared schemes in several critical performance metrics