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.

Cellular IP networks deal with the concepts of micro-mobility. Buffer management in Cellular IP networks is very crucial as its proper usage not only increases the throughput of the network but also results in the reduction of the call drops. This article proposes a model for buffer management in Cellular IP network using Particle Swarm Optimization (PSO), an evolutionary computational method often used to solve hard problems. The model considers two kinds of buffers; Gateway buffer and Base Station buffer. In the proposed two-tier model, the first tier applies a prioritization algorithm for prioritizing real-time packets in the buffer. In the second tier PSO algorithm is used on a swarm of cells in the network. PSO is applied for a given time slot, called window. In each window period the swarm can store number of packets depending on the window size and the total number of packets. The effect of various parameters e.g. number of packets, size of packets, window size, and a threshold value on buffer utilization has been studied by conducting the simulation experiments.

Cellular IP networks deal with the concepts of micro-mobility. Buffer management in Cellular IP networks is very crucial as its proper usage not only increases the throughput of the network but also results in the reduction of the call drops. This article proposes a model for buffer management in Cellular IP network using Particle Swarm Optimization (PSO), an evolutionary computational method often used to solve hard problems. The model considers two kinds of buffers; Gateway buffer and Base Station buffer. In the proposed two-tier model, the first tier applies a prioritization algorithm for prioritizing real-time packets in the buffer. In the second tier PSO algorithm is used on a swarm of cells in the network. PSO is applied for a given time slot, called window. In each window period the swarm can store number of packets depending on the window size and the total number of packets. The effect of various parameters e.g. number of packets, size of packets, window size, and a threshold value on buffer utilization has been studied by conducting the simulation experiments.

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.

Providing good quality of service (QoS) in cellular IP networks is an important requirement for performance improvement of the cellular IP network. Resource reservation is one of the methods used in achieving this goal and is proven to be effective. The main resources to be reserved in a cellular IP network are bandwidth, buffer and central processing unit (CPU) cycles. Router CPU cycle is the time taken by the router to process the packet of the flow before forwarding it to the next router (hop). This paper proposes a model for CPU cycle optimization of routers for real-time flows in a cellular IP network. The model applies both genetic algorithm (GA) and particle swarm optimization (PSO) as soft computing tools to optimize the CPU cycles and reduces the flow processing time at each router in the route taken by a flow. Simulation experiments illustrate a comparative study of the model. peer-reviewed conferences. He has contributed chapters in many edited books. He is in the editorial board of two international journals and in the reviewer's panel of many international journals. Also, he has co-authored a book (research monograph) entitled 'Scheduling in Distributed Computing Systems: Design, Analysis and Models' published by Springer, USA released in December,