Minimization Of Congestion Research Articles

A Rule-Based Approach with Fuzzy Logic for Adaptive Network Optimization

This study introduces an adaptive network optimization framework grounded in fuzzy logic and rule-based machine learning for cognitive radio systems. The primary objective is the concurrent minimization of interference, congestion, and bit error rate, coupled with enhancements in throughput and signal-to-noise ratio. A comprehensive set of rules, guided by linguistic variables for qualitative network aspects, is established alongside membership functions for quantitative analysis.The incorporation of machine learning into our approach enables adaptability to diverse network conditions, resulting in overall improved efficiency. The positive impact of machine learning is particularly evident in the reduction of congestion during specific timeframes. Specifically, on Saturday at hour 6, congestion decreases from the conventional 3.055 to 2.86. This notable improvement underscores the efficacy of machine learning in expediting the sensing mechanism of secondary users, facilitating the rapid identification of unused channels from primary users. The findings contribute to the advancement of cognitive radio systems, providing a robust and adaptable solution to address the intricate dynamics of modern wireless networks.

Evolutionary Minimization of Traffic Congestion

Evolutionary Minimization of Traffic Congestion

A Traffic Management System for Minimization of Intersection Traffic Congestion: Case Bengkong Junction, Batam

Purpose: An increase in the number of vehicles that are not matched by the growth of road area is one of the causes of road Traffic congestion in Batam City. Traffic congestion occurs, especially in business and densely populated residential areas. The government has carried out various engineering and traffic management to keep the road service level under control. This study aims to analyze traffic management's efficiency in reducing Traffic congestion on roads in Batam City.
 Design/methodology/approach: This study uses a case study at the Bengkong red light intersection, Batam City. The analysis was carried out using The Indonesian Highway Capacity Manual-1997.
 Findings: Traffic management and engineering must be implemented gradually to get a suitable implementation model. Actual conditions in the field become essential in determining the right choice of traffic management and engineering.
 Research limitations/implications: Traffic management and engineering analyzes were conducted locally at the case study site.
 Practical implications: The analysis of traffic management reduces traffic congestion's impact.
 Originality/value: Original paper
 Paper type: A case study

Application of Assignment Problem and Traffic Intensity in Minimization of Traffic Congestion

In our daily life, we are facing several problems; Traffic congestion is one of them, which is becoming serious day by day. High number of vehicles on road, insufficient infrastructure, unreasonable developments are some reasons for increasing traffic congestion. The aim of this paper is to introduce a mathematical model which can be applied to traffic control system having fixed time signal with preset time to minimize traffic congestion at an intersection. In this paper, we use the concept of traffic intensity and Assignment model for traffic congestion minimization.

The Impact of PV Power Plants Implementation on Electricity Cost and Shadow Price Minimization

The increase reliance on competitive electricity market has led to widespread research to reallocate energy sources and minimize the price of energy and the services related to it. The main issues that faces the design of any energy market, is thehigh cost of generation and the high shadow pricesthat highly impacts the consumers.Also,achieving the supply-demand balance and minimization of the transmission congestion is a vital goal while planning. In this paper, a transparent and open competitive market is attained. In order to control the electricity market and reduce the market clearing price, this study proposed introducing renewable energy power plants which has lower electricity generation cost in comparison with the conventional power plants.Minimization of the shadow price is achieved by dividing the electricity grid into multiple regions. Every region has a different shadow price depending on the load demand and the power plants available to supply the demand at this region. Where,the market clearing price of each region is set as the price of generation of the highest power plant sharing in supplying the load demand at this region. This methodology is applied on the Egyptian unified power network.Sizing and allocation of the renewable energy power plantsis studied carefully from the technical and economical point of view to maximize the benefit and minimize the overall cost function and shadow price.

On the minimization of traffic congestion in road networks with tolls

Population growth and the massive production of automotive vehicles have lead to the increase of traffic congestion problems. Traffic congestion today is not limited to large metropolitan areas, but is observed even in medium-sized cities and highways. Traffic engineering can contribute to lessen these problems. One possibility, explored in this paper, is to assign tolls to streets and roads, with the objective of inducing drivers to take alternative routes, and thus better distribute traffic across the road network. This assignment problem is often referred to as the tollbooth problem and it is NP-hard. In this paper, we propose mathematical formulations for two versions of the tollbooth problem that use piecewise-linear functions to approximate congestion cost. We also apply a biased random-key genetic algorithm on a set of real-world instances, analyzing solutions when computing shortest paths according to two different weight functions. Experimental results show that the proposed piecewise-linear functions approximate the original convex function quite well and that the biased random-key genetic algorithm produces high-quality solutions.

Multipath Data Transmission with minimization of Congestion Using Ant Colony Optimization for MTSP and Total Queue Length

This paper presents a The Ant Colony Optimization for MTSP and Swarm Inspired Multipath Data Transmission with Congestion Control in MANET using Total Queue Length based on the behavioral nature in the biological ants. We consider the problem of congestion control for multicast traffic in wireless networks. MANET is multi hop wireless network in which the network components such as PC, PDA and mobile phones are mobile in nature. The components can communicate with each other without going through its server. One kind of agent (salesman) is engaged in routing. One is Routing agents, which collect the information about network congestion as well as link failure and same is message agent (salesman) that uses this information to get his destination nodes. Though a number of routing protocols exists, which aim to provide effecting routing but few provide a plausible solution to overall network congestion. We attempt to explore the property of the pheromone deposition by the real ant. The proposed algorithm using path pheromone scents constantly updates the goodness of choosing a particular path and measuring the congestion in the network using total queue length.

Collaborative Multiobjective Global Routing

This paper presents a collaborative procedure for multiobjective global routing. Our procedure takes multiple global routing solutions, which are generated independently (e.g., by one router that runs in different modes concurrently or by different routers running in parallel), as input. It then performs multiobjective optimization based on Pareto algebra and quickly generates multiple global routing solutions with a tradeoff between the considered objectives. The user can control the number of generated solutions and the degree of exploring the tradeoff between them by constraining the maximum allowable degradation in each objective. This paper then considers the following three multiobjective case studies: 1) minimization of interconnect power and wirelength; 2) minimization of routing congestion and wirelength; and 3) minimization of wirelength with respect to the (finite-capacity) routing resources. The maximum allowable degradation in wirelength is specified in all cases. Our multiobjective procedure runs in only a few minutes for each of the International Symposium on Physical Design 2008 benchmarks, even the unroutable ones, which imposes a tolerable overhead in the design flow. In our simulations, we demonstrate the effectiveness of our procedure using five modern academic global routers.

Local empathy provides global minimization of congestion in communication networks

We present a mechanism to avoid congestion in complex networks based on a local knowledge of traffic conditions and the ability of routers to self-coordinate their dynamical behavior. In particular, routers make use of local information about traffic conditions to either reject or accept information packets from their neighbors. We show that when nodes are only aware of their own congestion state they self-organize into a hierarchical configuration that delays remarkably the onset of congestion although leading to a sharp first-order-like congestion transition. We also consider the case when nodes are aware of the congestion state of their neighbors. In this case, we show that empathy between nodes is strongly beneficial to the overall performance of the system and it is possible to achieve larger values for the critical load together with a smooth, second-order-like, transition. Finally, we show how local empathy minimize the impact of congestion as much as global minimization. Therefore, here we present an outstanding example of how local dynamical rules can optimize the system's functioning up to the levels reached using global knowledge.

Minimization of Congestion in MPLS Networks by means of Flows Optimization Techniques

In this paper, we propose an optimized model that provides QoS by minimization of the congestion in the Label Switching Path (LSP) in a MPLS networks. We analyzed Capacity and Flow Allocation (CFA) model which doesn’t consider the size of the buffer for the calculation of capacities cost; this deficiency is solved by our model which we called Allocation of the Capacity of Buffer (ACB). The results of the simulations show that the proposed model minimizes the congestion in the LSP and uniformly distribute the flow in each link compared with the models: GFA and OCL

The multicast packing problem

This paper presents algorithms, heuristics and lower bounds for an optimal sharing of network resources among several multicast groups that coexist in the network. Group (i.e., many-to-many) multicasting is a demanding service since any member can become a sender independently from the others. We consider a shared tree as the backbone of a group multicasting session. Considering each multicast session in isolation and independently may cause congestion on some links and reduce network utilization. Thus, we define the multicast packing problem in which the network tries to accommodate simultaneously all the multicast groups while trying to avoid bottlenecks on the links for higher throughput (i.e., minimize the maximum link sharing among multicast groups). Minimization of maximum congestion is achieved at the expense of increasing the size of some multicast trees which in turn impacts the delay. This trade-off is addressed by adding a penalty term to the objective function of the optimal packing formulation. The penalty term is a function of the amount of dilation from the size of the optimal tree obtained for each group multicast independently from the others (i.e., in isolation). Since the mathematical programming formulation for the optimization problem is computationally intractable, we resort to suboptimal solutions with heuristics. Our heuristic method aims to reduce the sharing of a link while ensuring that the size of multicast trees will never exceed /spl alpha/OPT/sup k/ where OPT/sup k/ is the size of the optimum tree for multicast group k in isolation. Optimum multicast tree for each group (in isolation) is computed by using cutting-plane inequalities and the branch-and-cut algorithm. In order to evaluate the performance of our approximation, we derive lower bounds on the problem. Our first lower bound on the maximum congestion is a theoretical one and puts a cap on the following two constructive lower bounds. The lower bounds and the heuristic method are implemented and it is shown that the maximum congestion obtained by the heuristic method is quite close to the constructive lower bounds.

Are Minimization of Delay and Minimization of Freeway Congestion Compatible Ramp Metering Objectives?

Analysis of mechanisms by which ramp metering can reduce traffic delay, such as increasing bottleneck flow, expediting flow to exits upstream of the bottleneck, and diverting traffic around the bottleneck, suggests that minimization of delay and minimization of congestion on the freeway main line are not always compatible objectives. Simulations were performed to tentatively resolve this question. These simulations used a flow model based on Newell’s simplified kinematic wave theory and a control strategy that was a modification of that of Wattleworth and Berry. Simulation scenarios defined physical characteristics, demand patterns, and flow characteristics for hypothetical freeway sections on the basis of data from Blumentritt et al. and the San Diego area. Results of the simulations show that there are circumstances under which minimization of delay does not coincide with minimization of congestion on the main line and that relationships between delay and main-line congestion depend on the combination of the metering mechanisms in effect.

Multiobjective Urban Planning Using Genetic Algorithm

A genetic algorithm was used to search for optimal future land-use and transportation plans for a high-growth city. Millions of plans were considered. Constraints were imposed to ensure affordable housing for future residents. Objectives included the minimization of traffic congestion, the minimization of costs, and the minimization of change from the status quo. The genetic algorithm provides planners and decision makers with a set of optimal plans known as the Pareto set. The value of each plan in the Pareto set depends on the relative importance that decision makers place on the various objectives.

A neural network approach to routing without interference in multihop radio networks

The issues of routing and scheduling the activation of links in packet radio networks are highly interdependent. The authors consider a form of the problem of routing for the minimization of congestion as a step toward the study of the joint routing-scheduling problem. They formulate this as a combinatorial-optimization problem, and they use Hopfield neural networks (NN) for its solution. The determination of the coefficients in the connection weights is the most critical issue in the design and simulation of Hopfield NN models. They use the method of Lagrange multipliers, which permits these coefficients to vary dynamically along with the evolution of the system state. Extensive software simulation results demonstrate the capability of their approach to determine good sets of routes in large heavily congested networks.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>