SIR: A New Wireless Sensor Network Routing Protocol Based on Artificial Intelligence

Currently, Wireless Sensor Networks (WSNs) are formed by hundreds of low energy and low cost micro-electro-mechanical systems. Routing and low power consumption have become important research issues to interconnect this kind of networks. However, conventional Quality of Service routing models, are not suitable for ad hoc sensor networks, due to the dynamic nature of such systems. This paper introduces a new QoS-driven routing algorithm, named SIR: Sensor Intelligence Routing. We have designed an artificial neural network based on Kohonen self organizing features map. Every node implements this artificial neural network forming a distributed intelligence and ubiquitous computing system

A new QoS routing algorithm based on self-organizing maps for wireless sensor networks

For the past ten years, many authors have focused their investigations in wireless sensor networks. Different researching issues have been extensively developed: power consumption, MAC protocols, self-organizing network algorithms, data-aggregation schemes, routing protocols, QoS management, etc. Due to the constraints on data processing and power consumption, the use of artificial intelligence has been historically discarded. However, in some special scenarios the features of neural networks are appropriate to develop complex tasks such as path discovery. In this paper, we explore and compare the performance of two very well known routing paradigms, directed diffusion and Energy- Aware Routing, with our routing algorithm, named SIR, which has the novelty of being based on the introduction of neural networks in every sensor node. Extensive simulations over our wireless sensor network simulator, OLIMPO, have been carried out to study the efficiency of the introduction of neural networks. A comparison of the results obtained with every routing protocol is analyzed. This paper attempts to encourage the use of artificial intelligence techniques in wireless sensor nodes

Using artificial intelligence in routing schemes for wireless networks

For the latest 10 years, many authors have focused their investigations in wireless sensor networks. Different researching issues have been extensively developed: power consumption, MAC protocols, self-organizing network algorithms, data-aggregation schemes, routing protocols, QoS management, etc. Due to the constraints on data processing and power consumption, the use of artificial intelligence has been historically discarded. However, in some special scenarios the features of neural networks are appropriate to develop complex tasks such as path discovery. In this paper, we explore the performance of two very well-known routing paradigms, directed diffusion and Energy-Aware Routing, and our routing algorithm, named SIR, which has the novelty of being based on the introduction of neural networks in every sensor node. Extensive simulations over our wireless sensor network simulator, OLIMPO, have been carried out to study the efficiency of the introduction of neural networks. A comparison of the results obtained with every routing protocol is analyzed. This paper attempts to encourage the use of artificial intelligence techniques in wireless sensor nodes

EYES - Energy Efficient Sensor Networks

The EYES project (IST-2001-34734) is a three years European research project on self-organizing and collaborative energy-efficient sensor networks. It will address the convergence of distributed information processing, wireless communications, and mobile computing. The goal of the project is to develop the architecture and the technology which enables the creation of a new generation of sensors that can effectively network together so as to provide a flexible platform for the support of a large variety of mobile sensor network applications. This document gives an overview of the EYES project

Improved Fair-Zone technique using Mobility Prediction in WSN

The self-organizational ability of ad-hoc Wireless Sensor Networks (WSNs) has led them to be the most popular choice in ubiquitous computing. Clustering sensor nodes organizing them hierarchically have proven to be an effective method to provide better data aggregation and scalability for the sensor network while conserving limited energy. It has some limitation in energy and mobility of nodes. In this paper we propose a mobility prediction technique which tries overcoming above mentioned problems and improves the life time of the network. The technique used here is Exponential Moving Average for online updates of nodal contact probability in cluster based network.Comment: 10 pages, 7 figures, Published in International Journal Of Advanced Smart Sensor Network Systems (IJASSN

Snapshots of the EYES project

The EYES project (IST-2001-34734) is a three years European research project on self-organizing and collaborative energy-efficient sensor networks. It addresses the convergence of distributed information processing, wireless communications, and mobile computing. The goal of the project is to develop the architecture and the technology which enables the creation of a new generation of sensors that can effectively network together so as to provide a flexible platform for the support of a large variety of mobile sensor network applications. This paper provides a broad overview of the EYES project and highlights some approaches and results of the architecture

SELF ORGANIZING WIRELESS SENSOR NETWORKS

This dissertation is concerned with the properties of self-organizing network systems, where a large number of distributed sensor nodes with limited sensing, processing and communication capability organize themselves into a cooperative network without any centralized control or management. Due to the distributed nature of the management and lack of global information for in-node decision making, sensor management in such networks is a complicated task. The dynamics of such networks are characterized by constraints and uncertainty, and the presence of disturbances that significantly affect aggregate system behavior. In this dissertation we examine several important topics in the management of self-organizing wireless sensor networks. The first topic is a statistical analysis to determine the minimum requirements for the deployment phase of a random sensor network to achieve a desired degree of coverage and connectivity. The second topic focuses on the development of a viable online sensor management methodology in the absence of global information. We consider consensus based sensor data fusion as a motivating problem to demonstrate the capability of the sensor management algorithms. The approach that has been widely investigated in the literature for this problem is the fusion of information from all the sensors. It does not involve active control of the sensors as part of the algorithm. Our approach is to control the operations of the nodes involved in the consensus process by associating costs with each node to emphasize those with highest payoff. This approach provides a practical, low complexity algorithm that allows the nodes to optimize their operations despite the lack of global information. In the third topic we have studied sensor networks that include "leaders," "followers," and "disrupters." The diffusion of information in a network where there are conflicting strategies is investigated through simulations. These results can be used to develop algorithms to manage the roles in the network in order to optimize the diffusion of information as well as protect the network against disruption