International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, Volume 2, Issue 5, May 2012)
Mobile Agent Technology and its repercussion in Wireless
Sensor Networks
Madhuri Rao1, K.Vinod Kumar2
Department of Computer Science & Engineering,
Institute of Technical Education & Research,
S.O.A University, Bhubaneswar, Orissa
RPC laid the path for Process Migration which allows a
partially executed process to be relocated to another node.
In this scenario execution state of the process is migrated,
i.e Stack, memory, program counter, state of open files.
Locus (UCLA), Sprite (UC Berkeley), Condor (Wisconsin)
is some such examples which with the help of process
migration could achieve load balancing. Remote Evaluation
model by Stamos and Gifford (MIT) (1990) presented yet
another technique of Remote programming and Code
Mobility. This was further enriched by Java Sun
Microsystems in 1995 as they presented code migration
across heterogeneous platforms.
Abstract— Mobile Agent is the computer program which can
automatically move among different network hosts and
perform the user's operations. A Mobile Agent characteristic
is being automatic, cooperative, mobile, secure and intelligent.
These are agents that can physically travel across a network,
and perform tasks on machines that provide agent hosting
capability. It allows processes to migrate from computer to
computer, for processes to split into multiple instances that
execute on different machines, and to return to their point of
origin. Unlike remote procedure calls, where a process
invokes procedures of a remote host, process migration allows
executable code to travel and interact with databases, file
systems, information services and other agents. Significant
research and development into mobile agency has been
conducted in recent years, and there are many mobile agent
architectures available today. However, mobile agency has
failed to become a sweeping force of change, and now faces
competition in the form of message passing and remote
procedure call (RPC) technologies. The strategies it flavors
could be an alternate choice for routing mechanism in various
networks especially wireless sensors networks which could
have a specific function only to be invoked and then migrate
while intelligently compressing and processing data that is
collected for the sink to receive. This certainly would reduce
network traffic and congestion in wireless sensor networks.
In section II some early category of Process migration is
outlined. Section III briefs the aspects of Mobile Agent
Technology research .Mobile Agents in Wireless Sensor
network is explained in Section IV. In section V two
algorithms are proposed for Sensor Nodes which are
designed on the basis of Mobile Agent Technology.
II. PROCESS MIGRATION :
Process migration is achieved to fulfill the following goals.
1.
Exploitation of resource locality
2.
Accessing more processing power
3.
Resource sharing
Keywords: Mobile Agent, Process Migration, Wireless Sensor
Network, Load Sharing
4.
Fault resilience
5.
System administration
I. INTRODUCTION
6.
Mobile computing
An Agent is a software component (object) which can
perform one or more tasks in some predefined manner. The
characteristics of an agent are mobility, autonomy, being
deliberative and it possesses an element of learning. It is a
loosely coupled process having cooperation in catering to a
common goal. It borrows its evolution from what is known
as Remote Procedure Call (RPC), where a client invokes a
code that resides on a server which then manipulates and
returns results to the client. Some widely known examples
of RPC is Courier at Xerox PARC in 1980, Sun RPC 1984,
DCE, CORBA late 1980’s.
Table 1. below outlines some examples of process
migration conceptualized so far. Viruses is also sometimes
understood as a code that could migrate. Mobile agents
derived from two fields namely Artificial Intelligence &
Distributed Systems which is an area of immense potential.
Some Aspects of Mobile Agent research is explained in the
next section.
423
International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, Volume 2, Issue 5, May 2012)
Sl.
No
Category
Example
a.
Code mobility, safety, programming
constructs
1.
Early Work
XOS, Worm, Butler,
DEMOS/MP
b.
Agent communication languages
2.
Transparent
migration in Unixlike systems
Locus, OSF/1 AD,
MOSIX, Sprite
3
OS with Messagepassing interface
Charlotte, Accent, V
Kernel
4
Microkernels
RHODOS, Arcade,
Chorus, Amoeba,
Birlix, Mach
5
User-space
migrations
Condor, Migratory
PVM, LSF
6
Applicationspecific migrations
Freeman, Skordos,
Bharat & Cardelli
(Migratory
Applications
7
Mobile objects
Emerald, SOS, COOL
8
Mobile agents:
derived from 2
fields- AI and
Distributed
Systems.
Telescript, Agent Tcl,
TACOMA, Mole
3) The element of intelligence is imbibed in it by the
possibilities in Artificial Intelligence Research
with:
a.
Focus on intelligence, learning, and
cooperation
IV. MOBILE AGENTS IN WIRELESS SENSOR NETWORK
The wireless sensor network is a technology which
employs a large finite number of unattended, intelligent
sensor nodes with ubiquitous sensing, processing,
computing and with wireless communication capabilities,
to implement complicated tasks dynamically in the
specified sensing field or environment. As these are battery
powered sensor nodes functioning in hostile, unattended
sensing environment constrained in energy supply, it is of
tremendous significance to investigate energy optimization
methods to prolong the WSN lifetime. Target detection and
location tracking is a typical WSN application that calls for
effective and efficient energy management. It would also
be cost effective if the network could map to multiple sink
nodes with load and network sharing capabilities.
Computing capacity of each sensor node though limited,
when collaborated with multiple sensor nodes, offers much
more resources than an individual one. Distributed
computation in the sensor nodes is therefore always
encouraged. IN [6] an approach is suggested based on
behavior of mobile agents which could be a criteria to
choose a mobile agent. Mobile agents have been found
useful in facilitating efficient data fusion & dissemination
in Wireless Sensor Network [1-4]. The following scenarios
is considered in conceptualizing Mobile Agents in Wireless
Sensor Networks.
Table I: Examples of process migration
III. ASPECTS OF MOBILE AGENT RESEARCH:
There are three dimensions of Mobile Agent.
1) Its distributed nature cultivates much research
possibilities. Like as follows
a)
Focus on system architectures and protocols
for managing executions of mobile agent
objects.
b) Security, fault tolerance.
2) How one could program this technology makes an
avenue for - Programming Languages Research
and it could further be specific in :
Figure 1: Model of WSN with base station , data collectors
and sensor node.
424
International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, Volume 2, Issue 5, May 2012)
Here, the Base Station (BS) node receives the
surveillance report every few minutes. Based on the
coordinated information that is collected by Data Collector
(DC) nodes, a task is issued or a counter mechanism is
brought to happen. To ensure that nodes do not have to
communicate over long distances expending huge amount
of energy in routing, multiple Base Stations could be
placed. After the few initial minutes, these sink nodes
would also issue status packets that move in backward
position to the data packets and track the status of active
nodes, sleeping nodes and dead nodes. Its also finds the
most used path. This information in this status packet when
read by a sensor node is utilized to facilitate easy and quick
and dynamic routing. The beauty of this technique is
observed over a period of time, especially when the nodes
in a network are getting worn of energy. We introduce the
term Mobile multi agent for the piece of code that acts as
an agent in each of these sensor nodes. The agent is
triggered every clock cycle to compare with values noted in
every last cycle. The nodes are multi-capable and can read
more than one analog feeling. Each of the analog feeling is
considered as a separate entity [19]. Each entity needs to be
transmitted separately. The motivation for using Mobile
Agents in Wireless Sensor networks has been extensively
studied in [17].
Transmit to one of the six neighbors or the
cluster head node
Cluster head Reads status packet and know
nearest active neighbor and preferable path
End if
end for
end for
Algorithm 2: Status packet routing initiated and each
sink node
If data received in sink node is more than 5 entries
broadcast status packet with id and hop number to all
nearest neighbors
If battery of sensor node is less than 50%
Discard status packet and set to only transmit
mode
end if
If battery of sensor node is more than 50%
hop count ++
Forward packet to neighbor node with sink id and
new hop count
V. PROPOSED ALGORITHMS.
end if
The proposed Mobile agent here is expected to transmit its
state when there is change in any of its entities. Hence the
number of packets and the number of transmissions is
reduced, which obviously enhances the life time. This
Mobile agent checks for a status packet every clock cycle
when its power is more than 50%, reads the information of
closest sink node and number of hops to it. If the power
contained in a sensor node is less than 50% it would not
receive the status packet. In this manner the network holds
the information of nodes that should be involved in routing
by camouflaging the ones that should not. Therefore, our
heuristics form the basis of a simple and practical yet nearoptimal cooperation strategy for sensor nodes.
end if
VI. CONCLUSION:
Mobile agency has yet to achieve widespread acceptance,
and there are significant barriers to be overcome before it
does. Competing technologies, increasing network
bandwidth, and barriers to mobile agency make it unlikely
that mobile agency will leave the domain of research and
enter the domain of electronic commerce. This is not to say
that mobile agency does not have its place - specialized
situations and environments will continue to use and
develop mobile agents. Load sharing across a network of
agent hosts, and delegation of tasks to agents for offline
processing hold great potential. Additionally, as more
research and development into mobile agency is made, the
picture may become brighter for other applications. Some
recently developed mobile agent technology adds a new
dimension to distributed computing. Experts suggest that
mobile agents will be used in many Internet applications in
the years to come. However there still exist many technical
hurdles that need to be tackled, the most important of them
being security. Only when security issues are properly
addressed, will the mobile agent technology be widely
accepted.
Algorithm 1: Mobile agent at each sensor node
while True
for all nodes s in all quadrants in t clock cycle
for events e1 to e3
agent node senses the binary event
agent checks with the previous clock cycle values of
events
If (t event i ) ≠ t event i-1
425
International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, Volume 2, Issue 5, May 2012)
IEEE Global Telecommunications Conference, San Francisco, USA:
IEEE, PP. 1-5 2006.
REFERENCES:
[1] M.Chen et al,”Mobile agent-based Directed Diffusion in Wireless
Sensor Network”, EURASIPJ. Advances in Signal Processing, Vol.
2007.
[19] Garth V. Crosby, Niki Pissinou,” Evolution of Cooperation in MultiClass Wireless Sensor Networks” , IEEE , 32nd IEEE Conference on
Local Computer Networks
[2] Y. Yu, B. Krishnamachari, V. Prasanna Energy-latency tradeoff for
data gathering in wireless sensor networks. Proc. IEEE
INFOCOM’04, HongKong, China, pp. 244-255, 2004.
[3] Y.Xu & H.Qi,” Mobile Agent Migration Modelling & Design for
Target Tracking in Wireless Sensor Network”, Ad Hoc Networks,
Vol.6,N0.1,Jan 2007,pp.1-66
[4] Y.xu and H.Qi,” Distributed Computing Paradigms for Collaboration
Signals 7 information Processing in Sensor-Networks”,Int’l J. Parallel
& Distributed Comp.,Vol 64, No. 8, Aug 2004,pp.945-59.
[5] Ahmed A. D., Mohamed O. K., Lewis M. On balancing network traffic
in path-based multicast communication. Future Generation Computer
Systems, Vol. 22, pp. 805-811, 2006.
[6] T.Finin, Y.Labrou, and J.Mayfield, KQML as An Agent
Communication Language”, in Software Agents , MIT Press,1997.
[7] O. Powell, P. Leone, J. Rolim Energy optimal data propagation in
wireless sensor networks. Journal of Parallel and Distributed
Computing, Vol. 67, pp. 302-317, 2007.
[8] S. Olariu, I. Stojmenovic. Design guidelindes for maximizing lifetime
and avoiding energy holes in sensor networks with uniform
distribution and uniform reporting. In Porceedings of IEEE
Conference on Computer Communications (INFOCOM), pp. 1-12,
2006.
[9] X. Wu, G. Chen Avoiding energy holes in wireless sensor networks
with nonuniform node distribution. IEEE Transactions on Parallel and
Distributed Systems, Vol. 19, No. 5, pp. 710-720, 2008.
[10] H. Zhang, H. Shen., Balancing energy consumption to maximize
network lifetime in data-gather sensor networks. IEEE Transactions
on Parallel and Distributed Systems, Vol. 20, No. 10, pp. 1526-1539,
2009.
[11] B. Krishnamachari, The Impact of Data Aggregation in Wireless
Sensor Networks. In Proceedings of International Workshop on
Distributed Event-Based Systems. pp. 575-578, 2002.
[12] P.V. Rickenbach, R. Wattenhofer Gathering Correlated Data in
Sensor Networks. Proc. ACM Joint Workshop Foundations of Mobile
Computing (DIALM-POMC ’04), 2004.
[13] Goel A., Munagala K. Balancing Steiner Trees and Shortest Path
Trees Online. Proc. 11th Ann. ACM-SIAM Symp, Discrete
Algorithms (SODA ’00), 2000.
[14] Luo, H., Liu Y., and Das S.K. Distributed Algorithm for en route
[15] Anandkumar, A., Meng W., Lang T., Swami A. Prize-Collecting
Data for Cost-Performance Tradeoff Distributed Inference. IEEE
INFOCOM, pp.2150-2158, 2009.
[16] Luo H., Liu Y., Das S. K. Routing Correlated Data with Fusion Cost
in Wireless Sensor Networks. IEEE Transactions on Mobile
Computing, 5(11): pp.1620-1632, 2006.
[17] H.Qi, Y.Xu , and X.Wang, “ Mobile-Agent –Based Collaborative
Signal and Information Processing in Sensor Networks,” Proc IEEE,
Vol. 91, No. 8, Aug. 2003,pp. 1172-83.
[18] Liu, R., Rogers, G., Zhou, S. Honeycomb Architecture for Energy,
Conversation in Wireless Sensor Networks. Proceedings of the 2006
426