Hindawi Publishing Corporation
International Journal of Distributed Sensor Networks
Volume 2012, Article ID 182561, 9 pages
doi:10.1155/2012/182561
Research Article
Cooperative Data Processing Algorithm Based on Mobile
Agent in Wireless Sensor Networks
Shukui Zhang,
1, 2
Yong Sun,
1
Jianxi Fan,
1
and He Huang
1
1
School of Computer Science and Technology, Soochow University, Suzhou 215006, China
2
State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China
Correspondence should be addressed to Shukui Zhang, zhangsk2000@163.com
Received 7 January 2012; Accepted 25 March 2012
Academic Editor: Hongli Xu
Copyright © 2012 Shukui Zhang et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Mobile agent (MA) systems provide new capabilities for energy-efficient data processing by flexibly planning its itinerary for
facilitating agent-based data collection and aggregation. In this paper, we present a cooperative data processing algorithm based
on mobile agent (MA-CDP), and considers MA in multihop environments and can autonomously clone and migrate themselves in
response to environmental changes. MA accounts for performing data processing and making data aggregation decisions at nodes
rather than bringing data back to a central processor, and redundant sensory data will be eliminated. The results of our s imulation
show that MA-based cooperative data processing provides better performance than directed diffusion in terms of end-to-end
delivery latency, packet delivery ratio, and energy consumption.
1. Introduction
The advances in Microelectromechanical System (MEMS)
and w ireless communication have enabled the development
of a new kind of network—the wireless sensor network
(WSN). One of the unique features of WSN applications
is the necessity of cooperation. Each sensor node normally
has limited sensing and processing capabilities, constrained
power resources, and reduced communication bandwidth.
Therefore, cooperation among sensor nodes is important in
order to compensate for each other’s capabilities as well as
to improve the degree of fault tolerance, and the key to an
effective cooperation is a combination of low-level sensor
processing and local exchange of data to reach consensus in
the neighborhood of the occurring e vent. This characteristic
of WSNs brings up some important issues for cooperation
communication, including energy efficiency, scalability, and
reliability [1].
To address such challenges, most of researches focus
on prolonging the network lifetime, allowing scalability
for a large number of sensor nodes, or supporting fault
tolerance (e.g., sensor’s failure and battery depletion) [2, 3].
Most energy-efficient proposals are based on the traditional
client/server computing model, where each sensor node
sends its sensory data to a processing center or a sink node.
Because the link bandwidth of a wireless sensor network is
typically much lower than that of a wired network, a sensor
network’s data traffic may exceed the network capacity. To
solve the problem of the overwhelming data traffic, Qi et
al. [2] proposed the mobile-agent-(MA-) based distributed
sensor network (MADSN) for collaborative signal and
information processing.
Generally speaking, an MA is a special kind of software
that can execute autonomously, with identification, itinerary,
data space, and method as its attributes. An MA [4]isa
computational process which has several characteristics: (1)
“reactivity” (allowing agents to perceive and respond to a
changing environment), (2) “social ability” (by which agents
interact with other agents), and (3) “proactiveness” (through
which agents behave in a goal-directed way). An MA may
need to cooperate in order to achieve better and more accu-
rate performance or need additional capabilities that it does
not have. This cooperation takes place by doing a coalition
formation which it is created by the fusion center agent. By
cooperation we m ean sharing data and resolving conflicts.
By transmitting the software code, namely, mobile agent
(MA) to sensor nodes, the large amount of sensory data can
be reduced or t ransformed into small data by eliminating