Designing Reliable Communication Networks with a Genetic Algorithm using a Repair Heuristic

This paper investigates GA approaches for solving the reliable communication network design problem. For solving this problem a graph with minimum cost must be found that satisfies a given network reliability constraint. To consider the additional reliability constraint different approaches are possible. We show that existing approaches using penalty functions can result in invalid solutions and are therefore not appropriate for solving this problem. To overcome these problems we present a repair heuristic, which is based on the number of spanning trees in a graph. This heuristic always generates a valid solution, which when compared to a greedy cheapest repair heuristic shows that the new approach finds better solutions with less computational effort

Designing reliable communication networks with a genetic algorithm using a repair heuristic

This paper investigates GA approaches for solving the reliable communication network design problem. For solving this problem a graph with minimum cost must be found that satisfies a given network reliability constraint. To consider the additional reliability constraint different approaches are possible. We show that existing approaches using penalty functions can result in invalid solutions and are therefore not appropriate for solving this problem. To overcome these problems we present a repair heuristic, which is based on the number of spanning trees in a graph. This heuristic always generates a valid solution, which when compared to a greedy cheapest repair heuristic shows that the new approach finds better solutions with less computational effort.In geänderter Fassung erschienen als : Working papers / Lehrstuhl für ABWL und Wirtschaftsinformatik ; 2003,14 Mannheim: Universität Mannheim / Fakultät für Betriebswirtschaftslehre, 200

Reliable Communication Network Design with Evolutionary Algorithms

For the reliable communication network design (RCND) problem links are unreliable and for each link several options are available with different reliabilities and costs. The goal is to find a cost-minimal communication network design that satisfies a pre-defined overall reliability constraint. This paper presents two new EA approaches, LaBORNet and BaBORNet, for the RCND problem. LaBORNet uses an encoding that represents the network topology as well as the used link options and repairs infeasible solutions using an additional repair heuristic (CURE). BaBORNet encodes only the network topology and determines the link options by using the repair heuristic CURE as a local search method. The experimental results show that the new EA approaches using repair heuristics outperform existing EA approaches from the literature using penalties for infeasible solutions and find better solutions for existing problems from the literature as well as for new and larger test problems

Coverage Protocols for Wireless Sensor Networks: Review and Future Directions

The coverage problem in wireless sensor networks (WSNs) can be generally defined as a measure of how effectively a network field is monitored by its sensor nodes. This problem has attracted a lot of interest over the years and as a result, many coverage protocols were proposed. In this survey, we first propose a taxonomy for classifying coverage protocols in WSNs. Then, we classify the coverage protocols into three categories (i.e. coverage aware deployment protocols, sleep scheduling protocols for flat networks, and cluster-based sleep scheduling protocols) based on the network stage where the coverage is optimized. For each category, relevant protocols are thoroughly reviewed and classified based on the adopted coverage techniques. Finally, we discuss open issues (and recommend future directions to resolve them) associated with the design of realistic coverage protocols. Issues such as realistic sensing models, realistic energy consumption models, realistic connectivity models and sensor localization are covered

A RELIABLE ROUTING MECHANISM WITH ENERGY-EFFICIENT NODE SELECTION FOR DATA TRANSMISSION USING A GENETIC ALGORITHM IN WIRELESS SENSOR NETWORK

Energy-efficient and reliable data routing is critical in Wireless Sensor Networks (WSNs) application scenarios. Due to oscillations in wireless links in adverse environmental conditions, sensed data may not be sent to a sink node. As a result of wireless connectivity fluctuations, packet loss may occur. However, retransmission-based approaches are used to improve reliable data delivery. These approaches need a high quantity of data transfers for reliable data collection. Energy usage and packet delivery delays increase as a result of an increase in data transmissions. An energy-efficient data collection approach based on a genetic algorithm has been suggested in this paper to determine the most energy-efficient and reliable data routing in wireless sensor networks. The proposed algorithm reduced the number of data transmissions, energy consumption, and delay in network packet delivery. However, increased network lifetime. Furthermore, simulation results demonstrated the efficacy of the proposed method, considering the parameters energy consumption, network lifetime, number of data transmissions, and average delivery delay

Reliable and efficient data dissemination schemein VANET: a review

Vehicular ad-hoc network (VANET), identified as a mobile ad hoc network MANETs with several added constraints. Basically, in VANETs, the network is established on the fly based on the availability of vehicles on roads and supporting infrastructures along the roads, such as base stations. Vehicles and road-side infrastructures are required to provide communication facilities, particularly when enough vehicles are not available on the roads for effective communication. VANETs are crucial for providing a wide range of safety and non-safety applications to road users. However, the specific fundamental problem in VANET is the challenge of creating effective communication between two fast-moving vehicles. Therefore, message routing is an issue for many safety and non-safety of VANETs applications. The challenge in designing a robust but reliable message dissemination technique is primarily due to the stringent QoS requirements of the VANETs safety applications. This paper investigated various methods and conducted literature on an idea to develop a model for efficient and reliable message dissemination routing techniques in VANET

An intelligent framework and prototype for autonomous maintenance planning in the rail industry

This paper details the development of the AUTONOM project, a project that aims to provide an enterprise system tailored to the planning needs of the rail industry. AUTONOM extends research in novel sensing, scheduling, and decision-making strategies customised for the automated planning of maintenance activities within the rail industry. This paper sets out a framework and software prototype and details the current progress of the project. In the continuation of the AUTONOM project it is anticipated that the combination of techniques brought together in this work will be capable of addressing a wider range of problem types, offered by Network rail and organisations in different industries

A Genetic Algorithm based Approach for Topological Optimization of Interconnection Networks

AbstractThe paper addresses the two terminal reliability while designing the interconnection networks. Thus a topological optimization problem is defined as the existence of at least a reliable path between a pair of nodes satisfying the predefined cost of the network. A new method based on Genetic Algorithm is proposed to solve the above said problem. In the proposed method the chromosome as well as the genes are efficiently encoded so that the cross over provides the optimal solution with better convergence rate. The reliability of some benchmark interconnection networks are evaluated by the proposed method. The population size and the computational time of the said networks as reported in this paper ensures that the proposed method converges to it's optimal solution in very few cpu secondss, while maximizing the value of the reliability of the said network to a greater extent

Predicting expected TCP throughput using genetic algorithm

Predicting the expected throughput of TCP is important for several aspects such as e.g. determining handover criteria for future multihomed mobile nodes or determining the expected throughput of a given MPTCP subflow for load-balancing reasons. However, this is challenging due to time varying behavior of the underlying network characteristics. In this paper, we present a genetic-algorithm-based prediction model for estimating TCP throughput values. Our approach tries to find the best matching combination of mathematical functions that approximate a given time series that accounts for the TCP throughput samples using genetic algorithm. Based on collected historical datapoints about measured TCP throughput samples, our algorithm estimates expected throughput over time. We evaluate the quality of the prediction using different selection and diversity strategies for creating new chromosomes. Also, we explore the use of different fitness functions in order to evaluate the goodness of a chromosome. The goal is to show how different tuning on the genetic algorithm may have an impact on the prediction. Using extensive simulations over several TCP throughput traces, we find that the genetic algorithm successfully finds reasonable matching mathematical functions that allow to describe the TCP sampled throughput values with good fidelity. We also explore the effectiveness of predicting time series throughput samples for a given prediction horizon and estimate the prediction error and confidence.Peer ReviewedPostprint (author's final draft

Solution of minimum spanning forest problems with reliability constraints

We propose the reliability constrained k-rooted minimum spanning forest, a relevant optimization problem whose aim is to find a k-rooted minimum cost forest that connects given customers to a number of supply vertices, in such a way that a minimum required reliability on each path between a customer and a supply vertex is satisfied and the cost is a minimum. The reliability of an edge is the probability that no failure occurs on that edge, whereas the reliability of a path is the product of the reliabilities of the edges in such path. The problem has relevant applications in the design of networks, in fields such as telecommunications, electricity and transports. For its solution, we propose a mixed integer linear programming model, and an adaptive large neighborhood search metaheuristic which invokes several shaking and local search operators. Extensive computational tests prove that the metaheuristic can provide good quality solutions in very short computing times