A Chaos-Based Data Gathering Scheme Using Chaotic Oscillator Networks
Akihide Utani, Arata Miyauchi, Hisao Yamamoto, Hidehiro Nakano · InTech eBooks · 2010
Recently, wireless sensor networks have been studied extensivelywith a great amount of interest. In wireless sensor networks, many wireless sensor nodes are deployed in an observation area, and monitor status information such as temperature around them. Sensing information is transmitted to and gathered by one or more sink nodes. Each wireless sensor node not only transmits own sensing data but also relays the sensing data from the other wireless sensor nodes. By such a multi-hop wireless communication, the wireless sensor networks are available to observation for large-scale area, and have various applications including natural environmental monitoring. Since wireless sensor nodes generally operate by batteries, efficient data gathering schemes with saving energy consumption of each wireless sensor node are needed for prolonging wireless sensor network lifetime. Ant-based algorithms (Caro et al., 2004; Marwaha et al., 2002; Ohtaki et al., 2006; Subramanian et al., 1998) and cluster-based algorithms (Dasgupta et al., 2003; Heinzelman et al., 2000) have been proposed as routing algorithms. They are more scalable, efficient and robust than the other conventional routing algorithms (Clausen & Jaquet, 2003; Johnson et al., 2003; Ogier et al., 2003; Perkins & Royer, 1999). Sink node allocation schemes based on particle swarm optimization algorithms (Kumamoto et al., 2009; Yoshimura et al., 2009) aim to minimize total hop counts in wireless sensor networks and to reduce energy consumption in each wireless sensor node. Forwarding node set selection schemes (Nagashima et al., 2009; Sasaki et al., 2009) can significantly reduce the number of transmissions of duplicate query messages as compared with original flooding schemes. Secure communication schemes considering energy savings (Li et al., 2009; Wang et al., 2009) have also been proposed. Common purpose of these studies is to prolong wireless sensor network lifetime by saving energy consumption of each wireless sensor node. Along this line, this study focuses on control schemes for timings of transmissions and receptions of sensing data, proposed as a synchronization-based data gathering scheme (Wakamiya & Murata, 2005). In this scheme, each wireless sensor node has a timer characterized by an integrate-and-fire neuron (Keener et al., 1981). Coupling the timers of wireless sensor nodes which can directly communicate to each other, they construct a pulse-coupled neural network. It is known that pulse-coupled neural networks can exhibit various synchronous and asynchronous phenomena (Catsigeras & Budelli, 1992; Mirollo & Strogatz, 1990). The conventional synchronization-based data gathering scheme is based on the synchronization in pulse-coupled neural networks. As synchronization is achieved, the following control for timings of transmissions and receptions of sensing data is possible: wireless sensor nodes turn 21