International Journal of Hybrid Information Technology
Vol. 5, No. 2, April, 2012
123
An Agent Based Routing Algorithm for Ubiquitous Sensor Networks
Jin Wang
1
, Ho-Chan Kim
2
, Jeong-Uk Kim
3
, Yunjie Chen
4
and Jianwei Zhang
4
1
Jiangsu Engineering Center of Network Monitoring, Nanjing University of
Information Science & Technology, Nanjing, China
2
Department of Electrical Engineering, Jeju National University, Jeju, Korea
3
Department of Energy Grid, Sangmyung University, Seoul, Korea
4
School of Math and Statistic, Nanjing University of Information Science &
Technology, Nanjing, China
{wangjin, yjchen, zhangjw}@nuist.edu.cn; hckim@jejunu.ac.kr; jukim@smu.ac.kr
Abstract
An autonomic computing system has four basic characteristics, namely self-configuration,
self-optimization, self-healing and self-protection. Autonomic computing can be viewed as a
new computing paradigm and it is becoming a hot research topic in distributed and
ubiquitous computing area. In this paper, we not only discuss the four basic aspects of
autonomic computing comprehensively based on our own understanding but also proposed
autonomic agent based power-aware routing approach for ubiquitous sensor networks which
is a distributed and localized routing approach. Besides, we provide an application scenario
to the sensor network among which the power consumption is one of the most critical issues.
The amount of agent is carefully selected and network performance such as packet delivery
rate and power consumption is also compared in the simulation part.
Keywords: Autonomic Computing, Routing, Sensor Networks, Power Consumption
1. Introduction
Autonomic computing [1] has recently attracted much attention as a novel computing
paradigm. Basically, it is a concept of self-managed computing systems with minimum
human consciousness or involvement, deriving from the human autonomic nervous
system. In [2], the essence of autonomic computing, engineering and scientific
challenges are thoroughly analyzed. Opportunities and possible research directions of
autonomic computing in the system engineering field are well explained in [3].
Swarm Intelligence (SI) is an Artificial Intelligence technique involving the study of
collective behaviour in decentralized systems. Such systems are made up by a
population of simple agents interacting locally with one another and with their
environment. Although there is typically no centralized control dictating the behaviour
of these agents, local interactions among them often cause a global intelligent pattern to
emerge. Swarm-like algorithms, such as Particle Swarm Optimization (PSO) [4] and
Ant Colony Optimization (ACO) [5], have already been applied successfully to solve
real-world optimization problems.
It is a good alternative to combine autonomic computing with swarm intelligence and
to apply them to some distributed applications such as ubiquitous sensor networks and
complex networks etc. Through localized collaboration among autonomic agents, better
performance like power consumption, packet delivery rate and communication overhead
can be achieved in a dynamic and distributed environment.