Computational Intelligence Inspired Data Delivery for Vehicle-to-Roadside Communications

We propose a vehicle-to-roadside communication protocol based on distributed clustering where a coalitional game approach is used to stimulate the vehicles to join a cluster, and a fuzzy logic algorithm is employed to generate stable clusters by considering multiple metrics of vehicle velocity, moving pattern, and signal qualities between vehicles. A reinforcement learning algorithm with game theory based reward allocation is employed to guide each vehicle to select the route that can maximize the whole network performance. The protocol is integrated with a multi-hop data delivery virtualization scheme that works on the top of the transport layer and provides high performance for multi-hop end-to-end data transmissions. We conduct realistic computer simulations to show the performance advantage of the protocol over other approaches

Energy-Efficient Packet Forwarding Scheme Based on Fuzzy Decision-Making in Underwater Sensor Networks

Underwater Wireless Sensor Networks (UWSNs) are subjected to a multitude of real-life challenges. Maintaining adequate power consumption is one of the critical ones, for obvious reasons. This includes proper energy consumption due to nodes close to and far from the sink node (gateway), which affect the overall energy efficiency of the system. These wireless sensors gather and route the data to the onshore base station through the gateway at the sea surface. However, finding an optimum and efficient path from the source node to the gateway is a challenging task. The common reasons for the loss of energy in existing routing protocols for underwater are (1) a node shut down due to battery drainage, (2) packet loss or packet collision which causes re-transmission and hence affects the performance of the system, and (3) inappropriate selection of sensor node for forwarding data. To address these issues, an energy efficient packet forwarding scheme using fuzzy logic is proposed in this work. The proposed protocol uses three metrics: number of hops to reach the gateway node, number of neighbors (in the transmission range of a node) and the distance (or its equivalent received signal strength indicator, RSSI) in a 3D UWSN architecture. In addition, the performance of the system is also tested with adaptive and non-adaptive transmission ranges and scalable number of nodes to see the impact on energy consumption and number of hops. Simulation results show that the proposed protocol performs better than other existing techniques or in terms of parameters used in this scheme

Hierarchical distributed model predictive control based on fuzzy negotiation

This work presents a hierarchical distributed model predictive control approach for multiple agents with cooperative negotiations based on fuzzy inference. Specifically, a fuzzy-based two-layer control architecture is proposed. In the lower control layer, there are pairwise negotiations between agents according to the couplings and the communication network. The resulting pairwise control sequences are sent to a coordinator in the upper control layer, which merges them to compute the final ones. Furthermore, conditions to guarantee feasibility and stability in the closed-loop system are provided. The proposed control algorithm has been tested on an eightcoupled tank plant via simulation

Conficuration of enterprise networks

International audienceIn the study, we consider the methods for optimizing the configuration of the network structure of enterprises based on the theory of fuzzy sets. These methods allow customizing the value chain in such a way as to maximize the likelihood of the success of a joint project to create innovative products. A strategic decision to change the configuration of the network structure is made based on an analysis of deviations of the generalized capabilities from the generalized requirements for the enterprise and its closest neighbors along the value chain. This optimization principle allows changing the configuration, taking into account the interests of participants in the network structure as a whole. We have formulated the task of developing tools for enterprise engineering based on intelligent decision support technologies and multi-agent systems. The approach to justifying decisions in the conditions of lack and incompleteness of the initial data on the basis of soft models is an alternative to existing traditional methods. The proposed network structure optimization model will allow effective strategic planning, supporting flexible management mechanisms at the strategic and operational levels. The research results show that it is possible to improve the efficiency of interaction between enterprises united by common goals by using services that allow enterprises to find information about their potential partners

Application of Neuro-Fuzzy system to solve Traveling Salesman Problem

This paper presents the application of adaptive neuro-fuzzy inference system (ANFIS) in solving the traveling salesman problem. Takagi-Sugeno-Kang neuro-fuzzy architecture model is used for this purpose. TSP, although, simple to describ

Design and Development of Network Reliability based Secure Multicast Routing Protocol for MANET

In Mobile Ad hoc network (MANET), link quality and stability of links as well as nodes play a major role. In ad hoc network, links are often changing which could affect the node mobility and integrity of data packets. In this research work, Network Reliability based Secure Multicast Routing Protocol (NRSMRP) is proposed to achieve network reliability by means of creation of reliable multicast tree. This multicast tree is constructed based on link quality and reliability trust metric. In first phase, node categorization and reliability metric calculation are implemented with the help of link quality. In second phase, reliable multicast tree is formed based on parent node and child node. Parent node must have god capacity and signal strength to communicate with child node. In last phase, authentication based multicast routes are established based on the calculation of direct reputation of mobile nodes. From the results, proposed protocol achieves better performance than existing schemes

Edge detection algorithm based on fuzzy logic theory for a local vision system of robocup humanoid league

Este artículo presenta el desarrollo de un algoritmo para la extracción de bordes basado en la teoría de lógica difusa, mediante el cual es posible reconocer las marcas de un campo de juego para la liga humanoide de RoboCup. El algoritmo propuesto describe la creación de un sistema de inferencia difuso que permite evaluar la relación existente entre los pixeles de una imagen, encontrando así las variaciones en los niveles de gris para una vecindad de pixeles específica. Posteriormente se plantea la implementación del método OTSU para binarizar la imagen obtenida en el proceso difuso y así generar una imagen que contiene solo los bordes extraídos, validando el algoritmo en imágenes de la liga humanoide. Luego se analizan los resultados obtenidos evidenciando el buen desempeño del algoritmo, considerando que el tiempo de procesamiento de la propuesta es tan solo el 35% más del tiempo necesario en métodos tradicionales, mientras que los bordes extraídos son unos 52% menos susceptibles al ruido.At this paper we shown the development of an algorithm to perform edges extraction based on fuzzy logic theory. This method allows recognizing landmarks on the game field for Humanoid League of RoboCup. The proposed algorithm describes the creation of a fuzzy inference system that permit evaluate the existent relationship between image pixels, finding variations on grey levels of related neighbor pixels. Subsequently, it shows an implementation of OTSU method to binarize an image that was obtained from fuzzy process and so generate an imagecontaining only extracted edges, validating the algorithm with Humanoid League images. Later, we analyze obtained results that evidence a good performance of algorithm, considering that this proposal only takes an extra 35% processing time that will be required by traditional methods, whereas extracted edges are 52% less noise susceptible

Fuzzy tuned gossip algorithms in mobile ad hoc networks

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