Buffer Management in Cellular IP Networks using Evolutionary Algorithms

2010 ◽  
Vol 1 (4) ◽  
pp. 1-22 ◽  
Author(s):  
Mohammad Anbar ◽  
Deo P. Vidyarthi
Keyword(s):  
Evolutionary Algorithms ◽  
Real Time ◽  
Optimization Problems ◽  
Buffer Management ◽  
Complex Problem ◽  
Base Station ◽  
Base Stations ◽  
Real Time Traffic ◽  
Ip Network ◽  
Cellular Ip

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 propsed. 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.

Buffer Management in Cellular IP Networks using Evolutionary Algorithms

2012 ◽  
pp. 254-275
Author(s):  
Mohammad Anbar ◽  
Deo P. Vidyarthi
Keyword(s):  
Evolutionary Algorithms ◽  
Real Time ◽  
Optimization Problems ◽  
Buffer Management ◽  
Base Stations ◽  
Real Time Traffic ◽  
Ip Network ◽  
Cellular Ip ◽  
Second Tier ◽  
Time Buffer

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.

Maximizing the Flow Reliability in Cellular IP Network Using PSO

2011 ◽  
Vol 3 (1) ◽  
pp. 1-19
Author(s):  
Mohammad Anbar ◽  
Deo P. Vidyarthi
Keyword(s):  
Optimization Problems ◽  
Arrival Rate ◽  
Base Station ◽  
Base Stations ◽  
Safe Delivery ◽  
Mobile Hosts ◽  
Model Studies ◽  
Packet Arrival ◽  
Ip Network ◽  
Cellular Ip

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.

Maximizing the Flow Reliability in Cellular IP Network Using PSO

2013 ◽  
pp. 1-19
Author(s):  
Mohammad Anbar ◽  
Deo P. Vidyarthi
Keyword(s):  
Optimization Problems ◽  
Arrival Rate ◽  
Base Station ◽  
Base Stations ◽  
Safe Delivery ◽  
Mobile Hosts ◽  
Model Studies ◽  
System A ◽  
Ip Network ◽  
Cellular Ip

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 Network using PSO

2011 ◽  
pp. 80-92
Author(s):  
Mohammad Anbar ◽  
Deo Prakash Vidyarthi
Keyword(s):  
Buffer Management ◽  
Window Size ◽  
Threshold Value ◽  
Pso Algorithm ◽  
Ip Networks ◽  
Base Station ◽  
Computational Method ◽  
Ip Network ◽  
Cellular Ip ◽  
Second Tier

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.

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

2011 ◽  
pp. 215-228 ◽  
Author(s):  
Mohammad Anbar ◽  
D.P. Vidyarthi
Keyword(s):  
Particle Swarm Optimization ◽  
Real Time ◽  
Particle Swarm ◽  
Bandwidth Reservation ◽  
Swarm Optimization ◽  
Available Bandwidth ◽  
On Demand ◽  
Real Time Traffic ◽  
Ip Network ◽  
Cellular Ip

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.

The Debris Flow Disaster Faster-Than-Early Forecast System (DFS) with Multi-Sensors Integrated Wireless Sensor Networks

2013 ◽  
Vol 347-350 ◽  
pp. 975-979
Author(s):  
Rong Zhao ◽  
Cai Hong Li ◽  
Yun Jian Tan ◽  
Jun Shi ◽  
Fu Qiang Mu ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Debris Flow ◽  
Real Time ◽  
Debris Flows ◽  
Base Station ◽  
Base Stations ◽  
Wireless Sensor ◽  
Forecast System ◽  
Debris Flow Disaster

This paper presents a Debris Flow Disaster Faster-than-early Forecast System (DFS) with wireless sensor networks. Debris flows carrying saturated solid materials in water flowing downslope often cause severe damage to the lives and properties in their path. Faster-than-early or faster-than-real-time forecasts are imperative to save lives and reduce damage. This paper presents a novel multi-sensor networks for monitoring debris flows. The main idea is to let these sensors drift with the debris flow, to collect flow information as they move along, and to transmit the collected data to base stations in real time. The Raw data are sent to the cloud processing center from the base station. And the processed data and the video of the debris flow are display on the remote PC. The design of the system address many challenging issues, including cost, deployment efforts, and fast reaction.

scholarly journals Edge Computing in an IoT Base Station System: Reprogramming and Real-Time Tasks

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Huifeng Wu ◽  
Junjie Hu ◽  
Jiexiang Sun ◽  
Danfeng Sun
Keyword(s):  
Mathematical Model ◽  
Real Time ◽  
Base Station ◽  
Base Stations ◽  
Management Systems ◽  
Dynamic Programming Method ◽  
Programmable Logic ◽  
Seamless Integration ◽  
Edge Response ◽  
Support Devices

There are millions of base stations distributed across China, each containing many support devices and monitoring sensors. Conventional base station management systems tend to be hosted in the cloud, but cloud-based systems are difficult to reprogram and performing tasks in real-time is sometimes problematic, for example, sounding a combination of alarms or executing linked tasks. To overcome these drawbacks, we propose a hybrid edge-cloud IoT base station system, called BSIS. This paper includes a theoretical mathematical model that demonstrates the dynamic characteristics of BSIS along with a formulation for implementing BSIS in practice. Embedded programmable logic controllers serve as the edge nodes; a dynamic programming method creates a seamless integration between the edge nodes and the cloud. The paper concludes with a series of comprehensive analyses on scalability, responsiveness, and reliability. These analyses indicate a possible 60% reduction in the number of alarms, an edge response time of less than 0.1s, and an average downtime ratio of 0.66%.

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

2012 ◽  
pp. 247-257
Author(s):  
Mohammad Anbar ◽  
Deo P. Vidyarthi
Keyword(s):  
Evolutionary Algorithms ◽  
Comparative Study ◽  
Rapid Development ◽  
Experimental Studies ◽  
Access Network ◽  
Search Space ◽  
Ip Network ◽  
Cellular Ip ◽  
Reliability Issue ◽  
Characteristic Measure

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.

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