R. Harikrishnan

A Comparative Analysis of Intelligent Algorithms for Localization in Wireless Sensor Networks

Abstract In a smart and decision making environment the location information of the sensors and devices under monitoring and control, is very much important, otherwise the sensed data becomes meaningless. This paper proposes three intelligent algorithms namely differential evolution localization algorithm, firefly localization algorithm, and a hybrid firefly differential evolution localization algorithm for wireless sensor networks localization problem. The proposed algorithms are range based and distributed localization algorithms. The algorithms are studied, analyzed and compared with respect to time complexity, convergence and accuracy of the estimated location information.

Firefly Algorithm Approach for Localization in Wireless Sensor Networks

Development of sensor technology has led to low power, low cost and small sized distributed wireless sensor networks (WSN). The self organizable and distributed characteristics of wireless sensor networks had made it for monitoring and control applications at home and other environment. In most of these applications location information plays a crucial role in increasing the performance and reliability of the network. This paper makes an attempt in analyzing and implementation of a novel nature based algorithm known as firefly localization algorithm. This is a distributed algorithm which uses range based trilateration method for distance measurement required for estimating the location of the sensor node. The algorithm is simple to implement and has better convergence and accuracy.

Hybrid Genetic Algorithm–Differential Evolution Approach for Localization in WSN

Nature-inspired algorithms have the characteristics to learn and decide and to be adaptable, intelligent, and robust, and so they can be used for solving complex problems. This paper deals with one such algorithm named hybrid genetic algorithm–differential evolution for localization in wireless sensor network. This algorithm is used to estimate the position of sensor node. A novel hybrid algorithm is analyzed, designed, and implemented. This algorithm provides better accuracy and is simple to implement.

An Integrated Xbee Arduino and Flower Pollen Algorithm Approach for Localization in Wireless Sensor Networks

The wireless sensor networks are used for smart intelligent applications which involve monitoring of various events and parameters of the environment. For most of these applications, location information of the sensors or devices used for monitoring is considered to be the fundamental requirement of sensed data. Localization improves the performance and reliability of wireless sensor networks and also supports the network management. So there is a need of a localization algorithm that is simple to implement, less complex, has less computation time and does not require extra hardware to implement. This could be solved by a nature inspired algorithms which takes idea from the nature’s optimization. This paper introduces an approach of one such algorithm known as flower pollen localization algorithm integrated with Xbee arduino sensor network.