Power Aware Routing Protocol for MANETs Based on Swarm Algorithm

Creating any standard protocol having 2 or more QoS limitations might be described as an NP-complete. Swarm technology is used to solve such problem. Even so, to fix difficult problems using swarm algorithms, how many iterations essential is going to be proportional for you to problem complexity. In this a standard protocol is presented depending on hybrid swarm algorithm standard protocol. This protocol has higher bundle overheads which more often than not bring about devouring higher battery power. In the present work to reduce the higher battery power , power aware routing protocol is developed based on Swarm algorithm

RFDMRP: River Formation Dynamics based Multi-hop Routing Protocol for Data Collection in Wireless Sensor Networks

AbstractIn Wireless sensor networks, sensor nodes sense the data from environment according to its functionality and forwards to its base station. This process is called Data collection and it is done either direct or multi-hop routing. In direct routing, every sensor node directly transfers its sensed data to base station which influences the energy consumption from sensor node due to the far distance between the sensor node and base station. In multi-hop routing, the sensed data is relayed through multiple nodes to the base station, it uses less energy. This paper introduces a new mechanism for data collection and routing based on River Formation Dynamics. The proposed algorithm is termed as RFDMRP: River Formation Dynamics based Multi-hop Routing Protocol. This algorithm is explained and implemented using MATLAB. The performance results are compared with LEACH and MODLEACH. The comparison reveals that the proposed algorithm performs better than LEACH and MODLEACH

Optimising routing and trustworthiness of ad hoc networks using swarm intelligence

This thesis was submitted for the degree of Doctor of Philsophy and awarded by Brunel UniversityThis thesis proposes different approaches to address routing and security of MANETs using swarm technology. The mobility and infrastructure-less of MANET as well as nodes misbehavior compose great challenges to routing and security protocols of such a network. The first approach addresses the problem of channel assignment in multichannel ad hoc networks with limited number of interfaces, where stable route are more preferred to be selected. The channel selection is based on link quality between the nodes. Geographical information is used with mapping algorithm in order to estimate and predict the links’ quality and routes life time, which is combined with Ant Colony Optimization (ACO) algorithm to find most stable route with high data rate. As a result, a better utilization of the channels is performed where the throughput increased up to 74% over ASAR protocol. A new smart data packet routing protocol is developed based on the River Formation Dynamics (RFD) algorithm. The RFD algorithm is a subset of swarm intelligence which mimics how rivers are created in nature. The protocol is a distributed swarm learning approach where data packets are smart enough to guide themselves through best available route in the network. The learning information is distributed throughout the nodes of the network. This information can be used and updated by successive data packets in order to maintain and find better routes. Data packets act like swarm agents (drops) where they carry their path information and update routing information without the need for backward agents. These data packets modify the routing information based on different network metrics. As a result, data packet can guide themselves through better routes. In the second approach, a hybrid ACO and RFD smart data packet routing protocol is developed where the protocol tries to find shortest path that is less congested to the destination. Simulation results show throughput improvement by 30% over AODV protocol and 13% over AntHocNet. Both delay and jitter have been improved more than 96% over AODV protocol. In order to overcome the problem of source routing introduced due to the use of the ACO algorithm, a solely RFD based distance vector protocol has been developed as a third approach. Moreover, the protocol separates reactive learned information from proactive learned information to add more reliability to data routing. To minimize the power consumption introduced due to the hybrid nature of the RFD routing protocol, a forth approach has been developed. This protocol tackles the problem of power consumption and adds packets delivery power minimization to the protocol based on RFD algorithm. Finally, a security model based on reputation and trust is added to the smart data packet protocol in order to detect misbehaving nodes. A trust system has been built based on the privilege offered by the RFD algorithm, where drops are always moving from higher altitude to lower one. Moreover, the distributed and undefined nature of the ad hoc network forces the nodes to obligate to cooperative behaviour in order not to be exposed. This system can easily and quickly detect misbehaving nodes according to altitude difference between active intermediate nodes

Parviälykkyys optimointiongelmien ratkaisemisessa

Parviälykkyys optimointiongelmien ratkaisemisessa on saavuttanut viimeisten vuosien aikana alan tutkimuksessa huomattavan aseman. Parviälykkyysalgoritmien toimintaperiaatteet perustuvat luonnossa havaittavaan hyönteisten ja muiden eläinten parvikäyttäytymiseen ja ne tarjoavat useita etuja perinteisiin optimointimenetelmiin nähden. Tässä tutkielmassa esitellään parviälykkyysoptimointia yleisesti ja kuvataan lisäksi tarkemmin joukko valittuja parviälykkyysalgoritmeja

Smart data packet ad hoc routing protocol

This paper introduces a smart data packet routing protocol (SMART) based on swarm technology for mobile ad hoc networks. The main challenge facing a routing protocol is to cope with the dynamic environment of mobile ad hoc networks. The problem of finding best route between communication end points in such networks is an NP problem. Swarm algorithm is one of the methods used solve such a problem. However, copping with the dynamic environment will demand the use of a lot of training iterations. We present a new infrastructure where data packets are smart enough to guide themselves through best available route in the network. This approach uses distributed swarm learning approach which will minimize convergence time by using smart data packets. This will decrease the number of control packets in the network as well as it provides continues learning which in turn provides better reaction to changes in the network environment. The learning information is distributed throughout the nodes of the network. This information can be used and updated by successive packets in order to maintain and find better routes. This protocol is a hybrid Ant Colony Optimization (ACO) and river formation dynamics (RFD) swarm algorithms protocol. ACO is used to set up multi-path routes to destination at the initialization, while RFD mainly used as a base algorithm for the routing protocol. RFD offers many advantages toward implementing this approach. The main two reasons of using RFD are the small amount of information that required to be added to the packets (12 bytes in our approach) and the main idea of the RFD algorithm which is based on one kind of agent called drop that moves from source to destination only. This will eliminate the need of feedback packets to update the network and offers a suitable solution to change data packet into smart packets. Simulation results shows improvement in the throughput and reduction in end to end delay and jitter compared to AODV and AntHocNet protocols. © 2013 Elsevier B.V. All rights reserved

Generalization and Completeness of Evolutionary Computation

The need of a structured framework for evolutionary computation has been acknowledged. In order to achieve this we designed a set of operational semantics and defined a “general form” of evolutionary computation. Our second approach towards a generalization was to study the relationship between different algorithms and the problems they solve from a performance standpoint. Lastly, we tried to analyze the convergence and complexity of evolutionary algorithms. This led to a set of computability results, the main one being that evolutionary computation is Turing-complete

Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations

In recent algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. This paper addresses this problem by proposing two comprehensive, principle-based taxonomies that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm. Using these taxonomies we review more than three hundred publications dealing with nature- inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper. From our analysis we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-third of the reviewed bio-inspired solvers are versions of classical algorithms. Grounded on the conclusions of our critical analysis, we give several recommendations and points of improvement for better methodological practices in this active and growing research field

Otimização do Processo de Receção e Processamento de Uvas em Lagares

Dissertação de Mestrado em Engenharia InformáticaDurante o período de vindimas os produtores de uvas enfrentam longos peíodos de espera para realizar a descarga das suas uvas nas empresas produtoras de vinhos. Estes tempos de espera têm um impacto negativo na qualidade dos vinhos. O objetivo deste projeto consiste em estudar e utilizar algoritmos de otimização para resolver este problema. Neste sentido, foi desenvolvido um Algoritmo Genético, que através de um conjunto de inputs iniciais, encontrar a melhor solução para a otimização do escalonamento dos camiões dos produtores, assim como otimizar o processamento das uvas. Após o Algoritmo Genético obter a solução mais apta para um certo cenário, os colaboradores responsáveis pela receção dos camiões de uvas poderão então visualizar informações sobre a mesma através de dashboards criados no Kibana e que permitem verificar várias informações, tais como: as ações que cada entidade (camiões, tegões e prensas) deve efetuar e quando, a quantidade de uvas de cada entidade ao longo do tempo e tempos médias de espera por tipo de uva. Com os resultados obtidos e com a ajuda das visualizações gráficas dos mesmos, haverá um melhor uso da capacidade de produção disponível, otimização do espaço utilizado durante todo o processo e diminuição do tempo de espera dos produtores, evitando assim, a perda de qualidade dos vinhos.During the grape harvest period, producers face long waiting periods to unload their grapes at the winemaking companies. There waiting times have negative impact on wine quality. The aim of this project is to study and use optimization algorithms to solve this problem. In this project Genetic Algorithms were chosen and one was developed. This algorithm is capable of through a set of initial inputs, find the best solution for optimizing the truck scheduling of the producers of these companies, as well as optimizing grape processing. After the Genetic Algorithm gets the most suitable solution for a given scenario, users can then view the information about it through dashboards created in Kibana, that allows them to check various information, such as: the actions that each entity (truck, grain-tank and press) should take and when, quantity of grapes from each entity over time and average waiting times per grape type. With the results obtained by the Genetic Algorithm and with the help of their graphic visualizations, there will be a better use of the available production capacity, optimization of the space used throughout the process and reduction of the waiting time of the producers, thus avoiding the loss of quality of the wines