A Hybrid Complex Network Model for Wireless Sensor Networks and Performance Evaluation

A Hybrid Complex Network Model for Wireless Sensor Networks and Performance Evaluation

Peppino Fazio (University of Calabria, Italy), Mauro Tropea (University of Calabria, Italy), Salvatore Marano (University of Calabria, Italy) and Vincenzo Curia (University of Calabria, Italy)
Copyright: © 2016 |Pages: 17
DOI: 10.4018/978-1-4666-9964-9.ch016
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This chapter proposes a new approach, based on Complex Networks modeling, to alleviate the limitations of wireless communications. In fact, many recent studies have demonstrated that telecommunication networks can be well modeled as complex ones, instead of using the classic approach based on graph theory. The study of Complex Networks is a young and active area of scientific research, inspired largely by the empirical study of real-world networks, such as computers and social networks. The chapter contributes to the improvement of distributed communication, quantifying it in terms of clustering coefficient and average diameter of the entire network. The main idea consists in the introduction of Hybrid Data Mules (HDMs) that are able to enhance the whole connectivity of the entire network. The considered HDMs are equipped by “special” wireless devices, using two different transmission standards. The introduction of special nodes contributes to the improvement of network scalability, without substantial changes to the structure of the network.
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1. Introduction And Background

The Internet architecture is today part of everybody’s life, thanks to the great progress done in the technologies that allow the use of networks through different type of devices. Despite their different nature, technologies are born with the purpose of ensuring connectivity every-where and every-time: to send information, for example, we can use electromagnetic radiations in infrared frequency band, electric transmission lines or wireless devices, which take advantage of the ether. Since the ether is present everywhere in the world, it is reasonable to exploit the potentiality of this medium in order to use it as a mean of communication, on which information can travel.

This is a very good choice, although there are some limitations, due to its intrinsic nature and physical barriers to be overcame. It is a still open challenge for modern engineering: the limited coverage radius that the current IEEE’s standards fail to ensure and the supply management of these devices give the majority of limitations. Other important issues are strictly related to higher-level management, such as data link and network operations (Fazio, 2012), that inherently are subject to interference and physical undesired phenomena. Reasonably, Wireless Sensor Networks (WSNs) (De Rango, 2013) are the subject to some kind of recent studies, given that they are applied for many kinds of applications in real life (Fazio, 2013).

Many recent research efforts have confirmed that, given the natural evolution of telecommunication systems, they can be approached by a new modeling technique, not based yet on traditional approach of graphs theory. The branch of complex networking (Yan, 2010), although young, is able to introduce a new and strong way of networks modeling, nevertheless they are social, telecommunication or friendship networks. Each network present in nature, whether artificial (as the national water supply network) or based on telecommunications, natural (such as brain synapses network) or relative to molecular interactions can be seen as a Complex Network (CN), if appropriately modeled. CNs represent a new paradigm to which the world (seen in its various disciplines of humanities, physical and scientific studies) is shifting.

It is necessary to find a modeling technique which allows us to model a real network as a Small World Network (SWN, a particular branch of CNs) (Guidoni, 2008; Huang, 2012; Xiaojuan & Huiqun, 2010), while maintaining a low network diameter and a high CC. Such networks are highly connected, because of their largest connected sub-graph contains a huge proportion of the vertices. For example, the Internet represents a SWN: we know that IP-packets cannot use more than a precise amount of physical links (equal to the value of their Time-To-Live field), thus the structure of the Internet has evolved in a graph with relatively small distances, even though it is rather large.

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