Termite-Hill: From Natural to Artificial Termites in Sensor Networks

Termite-Hill: From Natural to Artificial Termites in Sensor Networks

Adamu Murtala Zungeru (School of Electrical and Electronic Engineering, University of Nottingham, Jalan Broga, Semenyih, Selangor Darul Ehsan, Malaysia), Li-Minn Ang (School of Engineering, Edith Cowan University, Joondalup, WA, Australia) and Kah Phooi Seng (School of Computer Technology, Sunway University, Bandar Sunway, Petaling Jaya, Selangor, Malaysia)
Copyright: © 2012 |Pages: 22
DOI: 10.4018/jsir.2012100101
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Termites present a good natural metaphor to evolutionary computation. While each individual’s computational power is small compared to more evolved species, it is the power of their colonies that inspires communication engineers. This paper presents a study of artificial termites in sensor networks for the purpose of solving its’ routing problem. The behaviours of each of the termites in their colony allow their simulation in a restricted environment. The simulating behaviour demonstrates how the termites make use of an autocatalytic behaviour to collectively find a solution for a posed problem in reasonable time. The derived algorithm termed Termite-hill demonstrates the principle of termites’ behavior to routing problem solving in the real applications of sensor networks. The performance of the algorithm was tested on static and dynamic sink scenarios. The results as compared with other routing algorithms and with varying network density show that Termite-hill is scalable and improved on network energy consumption with a control over best-effort-service.
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1. Introduction

Termites are relatively simple beings. With their small size and small number of neurons, they are incapable of dealing with complex tasks individually. The termite colony on the other hand is often seen as an intelligent entity for its great level of self-organization and the complexity of tasks it performs. In this paper, we will focus on one of the resources termite colonies use for their achievements, pheromone trails, and furthermore, show the similarity between termite colonies and sensor networks. We also try to show some relationship between the stigmergic behaviour facilitated by pheromones and the process of representation in a complex system (sensor network). One way termites communicate is by secreting chemical agents that will be recognized by receptors on the bodies of other termites. For example, a termite is capable of determining if another termite is a member of its own colony by the “smell” of its body. One of the most important of such chemical agents is the pheromone. Pheromones are molecules released from glands on the termite’s body. Once deposited on the ground they start to evaporate, releasing molecules of that chemical agent into the air. Individual termites leave a trail of such scent, which stimulates other termites to follow that trail, dropping pheromones while doing so (Matthews & Mattheus, 1942). This use of the environment as a medium for indirect communication is called stigmergy. This process will continue until a trail from the termite colony to the food source is established. The creation of a trail with the shortest distance from nest to food source is a side effect of their behaviour, which is not something they have as an a priori goal. While following very basic instincts, termites accomplish complex tasks for their colonies in a perfect demonstration of emergent behaviour. In the foraging example, one of the characteristics of the pheromone trail is that it is highly optimized, tending toward the shortest highway between the food source and the termites’ nest (hill).

However, a sensor network is an infrastructure composed of sensing, computing and communication elements that give a user or administrator the ability to instrument, observe and react to events and phenomena in a specific environment (Saleem, Di Caro, & Farooq, 2010; Zungeru, Ang, & Seng, 2012b; Akyildiz, Su, Sankarasubramaniam, & Cayirci, 2002). Wireless Sensor Networks (WSNs) are collections of compact-size, relatively inexpensive computational nodes that measure local environmental conditions, or other parameters and forward such information to a central point for appropriate processing using radio frequency (RF) transceivers attached to them. Each sensor node is equipped with embedded processors, sensor devices, storage devices and radio transceivers. Nevertheless, the sensor nodes typically have limited resources in terms of battery supplied energy, processing capability, communication bandwidth, and storage. WSN nodes can sense the environment, communicate with neighboring nodes and in many cases perform basic computations on the data being collected. WSNs applications range from commercial applications such as healthcare, target tracking, monitoring, smart homes, surveillance applications and intrusion detection. The main problem in WSN is how to design a routing protocol which is not only energy efficient, scalable, robust and adaptable, but also provides the same or better performance than that of the existing state-of-the-art routing protocols.

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