Maximizing the Flow Reliability in Cellular IP Network Using PSO

A Cellular IP (CIP) network involves a bulk of data transmission. It is highly reliable and guarantees the safe delivery of the packets required in such systems. Reliable traffic performance leads to efficient and reliable connectivity in Cellular IP network. CIP network, which consists of mobile hosts, base stations, and links, are often vulnerable and prone to failure. During the routing operation in the network, the base station, which works as router for the transmitted packets, may fail to perform. Reliable transmission is desirable, in terms of services of the base stations in the network, reliable routing, and processing the data. In this paper, the authors design a reliability model to increase the reliability of a flow, consisting of packets, passing through routers in a Cellular IP network. Particle Swarm Optimization (PSO) is able to solve a class of complex optimization problems. PSO is used to improve the reliability of the flow in CIP network. The proposed model studies the effects of packet processing rate (), packet arrival rate (), and the number of packets per flow on the reliability of the system. A simulation experiment is conducted and results reveal the effectiveness of the model.

Buffer Management in Cellular IP Networks using Evolutionary Algorithms

Real-time traffic in Cellular IP network is considered to be important and therefore given priority over non-real-time. Buffer is an important but scarce resource and to optimize Quality of Service by managing buffers of the network is an important and complex problem. Evolutionary Algorithms are quite useful in solving such complex optimization problems, and in this regard, a two-tier model for buffer, Gateway and Base Station, management in Cellular IP network has been proposed. The first tier applies a prioritization algorithm for prioritizing real-time packets in the buffer of the gateway with a specified threshold. Packets which couldn’t be served, after the threshold, is given to the nearest cells of the network to be dealt with in the second tier, while Evolutionary Algorithm (EA) based procedures are applied in order to optimally store these packets in the buffer of the base stations. Experiments have been conducted to observe the performance of the proposed models and a comparative study of the models, GA based and PSO based, has been carried out to depict the advantage and disadvantage of the proposed models.

A Comparative Study of Evolutionary Algorithms for Maximizing Reliability of a Flow in Cellular IP Network

The rapid development in technology, witnessed in daily communication, especially in wireless communication, is a good motivation for performance improvement in this field. Cellular IP access network is a suitable environment where a micro mobility of mobile users is implemented and managed. The reliability of Cellular IP network during the communication is an important characteristic measure and must be considered while designing a new model. Evolutionary Algorithms are powerful tools for optimization and problem solving, which require extracting the best solution from a big search space. This chapter explores the reliability issue in Cellular IP of a flow of packets passing through the route from a source to a destination. The main aim of the chapter is to maximize the reliability of the flow passing through a route having number of routers. Two Evolutionary Algorithms (EAs), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), have been used for this purpose, and a comparative study between the two is performed. Experimental studies of the proposed work have also been performed.

On Demand Bandwidth Reservation for Real-Time Traffic in Cellular IP Network using Particle Swarm Optimization

Cellular IP network deals with micro mobility of the mobile devices. An important challenge in wireless communication, especially in cellular IP based network, is to provide good Quality of Service (QoS) to the users in general and to the real-time users (users involved in the exchange of real-time packets) in particular. Reserving bandwidth for real time traffic to minimize the connection drop (an important parameter) is an activity often used in Cellular IP network. Particle Swarm Optimization (PSO) algorithm simulates the social behavior of a swarm or flock to optimize some characteristic parameter. PSO is effectively used to solve many hard optimization problems. The work, in this paper, proposes an on demand bandwidth reservation scheme to improve Connection Dropping Probability (CDP) in cellular IP network by employing PSO. The swarm, in the model, consists of the available bandwidth in the seven cells of the cellular IP network. The anytime bandwidth demand for real-time users is satisfied by the available bandwidth of the swarm. The algorithm, used in the model, searches for the availability of the bandwidth and reserves it in the central cell of the swarm. Eventually, it will allocate it on demand to the cell that requires it. Simulation experiments reveal the efficacy of the model.