angle based information aggregation and routing in wsn: multi agent approach

7/29/2019 ANGLE BASED INFORMATION AGGREGATION AND ROUTING IN WSN: MULTI AGENT APPROACH

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International Journal of Smart Sensors and Ad Hoc Networks (IJSSAN) ISSN No. 2248-9738 Volume-1, Issue-3, 2012

1

Angle based Aggregation and Routing in WSN: Multi-Agent approach

Prashant Sangulagi1, A. V. Sutagundar

1, S. S. Manvi

2, B. S. Halakarnimath

3

Department of Electronics and Communication Engineering1Basaveshwar Engineering College, Bagalkot-587102, INDIA2Reva Institute of Technology and Management, Bangalore, INDIA.

3S. G. Balekundri Institute of Technology, Belgaum, INDIAEmail: (psangulgi, basaprabhu97)@gmail.com, (ashok_ec, agentsun_2002)@yahoo.com,

AbstractEnergy, delay and bandwidth are the main issues

in the sensor network as nodes are operated by battery

power, utilizing the energy in the efficient way is challenging

task. By making efficient routing and aggregating the

information one can consume the less energy and

bandwidth. Selection of optimal routing and aggregation

technique is the challenging task in WSN. In this paper we

are propose the zonal based data aggregation and routing in

WSN. Proposed scheme gets the users interest and

depending on that it sink manager agent finds the angle at

which the sectors are to be created. Based on the number ofnodes, SMA decides the number of zones in a sector. SMA

triggers aggregation agent for data aggregation in the

respective zones. To validate the proposed scheme, we have

simulated the scheme for various performance parameters.

Keywords: Information Aggregation, Mobile Agent, Routing,

Static Agent, WSN, Energy, Angle.

I. INTRODUCTIONWireless Sensor Network (WSN) consist of spatially

distributed sensor nodes, which have the capability to sense

the environment, process it and send the sensed information to

the sink node or base station. WSN mainly consist of fourunits sensing unit, processing unit, transceiver unit and power

unit [1]. The sensor network can be used in various

applications like health monitoring, environmental parametermonitoring, battle field monitoring, and industrial application,

tracking the parameters, home application and mainly militaryapplication. WSN are low cost, low power (operated by

battery), multifunctional, small in size and communicate in a

small distance. In WSN nodes can be deployed in a random

manner or manually depends upon user requirements. Each

node has a capability to communicate with the other node or

directly to the sink node.The characteristics of WSN are explained in [2]. WSN can be

used for scalar applications and multimedia application, scalar

data like temperature, humidity, pressure, fire detection etc.multimedia data like image capturing, video recording and

audio. Routing is WSN is challenging thing as nodes are

operated by battery power and have lower bandwidth. Routingapproaches for ad-hoc networks proved not to be suitable to

sensors networks. This is due to different routing requirements

for ad-hoc and sensor networks in several aspects. Forinstance, communication in sensor networks is from multiple

sources to a single sink, which is not the case in ad-hoc

networks [16].Routing the data is one of the challenging tasks in WSN.

Energy required transmitting the data from one point to

another is equal to square of the distance so efficient routing ismust in sensor network because sensor nodes have low energy

and bandwidth. Mainly routing protocols in WSN are dividedin three types flat based routing [3]; hierarchical-based routing

[4, 5] and location based routing [3].

Data aggregation has been put forward as an essential

paradigm for wireless routing in sensor networks. The idea isto combine the data coming from different sources enrouteeliminating redundancy, minimizing the number of

transmissions and thus saving energy. This paradigm shifts the

focus from the traditional address-centric approaches for

networking (finding short routes between pairs of addressableend-nodes) to a more data-centric approach (finding routes

from multiple sources to a single destination that allows in-

network consolidation of redundant data). There are manytypes of aggregation techniques in WSN namely centralized

approach, In-network aggregation [6], Tree-based approach

[7] and Cluster based approach [8] etc. Software agents are the

suitable for data aggregation and routing in WSN. Software

agents can used for the data aggregation for the following

reasons.A) Software Agents

Agent is a piece of software that can achieve a specific task inan autonomous way, in other words agents are autonomous

program situated within an environment which sense the

environment and act upon it to achieve the goals. The agentsperform the following functions: (1) eliminating data

redundancy among sensors by application context-aware local

processing at the node level; (2) eliminating spatialredundancy among closely-located sensors by data

aggregation at the task level; (3) reducing communication

overhead by concatenating data at the combined task level.Agents have some special properties such as mandatory

property and orthogonal property [11, 12]. Orthogonal

properties provide strong notion of the agents such as:mobility, collaborative, learning. Mandatory properties such

as: autonomy, decision-making, temporal continuity, goal

oriented. Agents are classified into two type static agents andmobile agent [13]. Static agent is stationary in nature, which is

usually used to find the path between the nodes and creating a

routing table. On the other hand mobile agents which alwaysmigrate from one node to another node (Mobility


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characteristic) gets the information from it and again jumps to

another node till it reaches the destination.

Agent-based approaches have been a source of technologies to

a number of research areas, both theoretical and applied

[9].An agent is anything that can be viewed as perceiving its

environment through sensors and acting upon thatenvironment through effectors [10]. Advantages of mobile

agents explained in [14]. Agents can be written in Java, Tcl,Perl and XML languages. By using mobile agents in WSN wecan consume nodes energy as it takes very less amount of

energy for computation and transportation.

B) Our Contributions

1. User specified area is selected by using Semi Definite

Program.

2. In-network aggregation and cluster based aggregationtechniques are used to eliminate redundancy in the data.

3. Using Multi-Agents to aggregate the sensed data from the

different sensor nodes.

4. Different routing techniques for different applications (Sink

driven, Time driven and Emergency information driven).

In this paper we propose angle based aggregation and

routing in WSN (A2RWSN) using multi-agent system thatprovides better energy efficiency, reduced delay, increases

network lifetime. The organization of paper is as follows:

section II presents proposed work and section III depicts

Simulation and Results.

II. PROPOSED WORKThis section provides the complete model of the proposed

work. It starts with the representation of the system

environment and then describes different mathematical

models. Then different agencies with their models and

interactions are presented.

A. System environment

The system environment mainly consists of sensor nodes and

Sink Node (SN). Sink Node is fixed and its position is alsoprefixed. Scalar sensor nodes are deployed in the given

network randomly. Depends upon user interest SN creates

angle through user prescribed direction by using agent

technology. Once the angle is created next step is to createzones and subzones (subzones are created when

user/controller requested area is large).

Zones are created in such way that more number of nodesshould be covered in it. Zone Head (ZH) is elected depends

upon its residual energy and distance from sink node. ZH

exchanges message between all its zonal members telling that

Im the ZH of this zone and updates its data base that is ZBB

(Zone Black Board).Upon receiving request from controller, SN) sends mobile

agent into the zone called as Aggregator Agent (AA). Systemenvironment is as shown below figure1.

B. Agencies

In this section, the sink agency, zone agency and node agency

used in this proposes schemes are discussed.

1) Sink Node AgencyThe sink agency comprises of Sink Black Board (SBB), Sink

Manager Agent (SMA), Angle Construction Agent (ACA) and

Aggregator Agent (AA). Sink node agency is as shown below

figure2.

Figure1. System Environment

Sink Black Board (SBB): SBB is a sink nodes knowledgebase which can be read and it is updated by SMA which is

static agent. It stores all information about the present node

and all its members, like its node id, GPS location information

(x, y), battery left, status, signal strength, previously sensed

data with time, and presently sensed data with time.

Figure2: Sink Node AgencySink Manager Agent (SMA): SMA is a static agent and itmanages whole sensor node activity. SMA monitors and

updates SBB continuously. SMA creates mainly two agents,

ACA and AA both are mobile agents. When SN receives

interest from the user/controller, SMA generates ACA whichconstructs angle depends upon user required area. After the

selection of area, zones are created in it. Now SMA creates

AA (in both time driven and sink driven) which is going tomigrate into the zones and collects data, aggregates them and

at last comes back to the SN with aggregated high quality

information about the environment.

Angle Construction Agent (ACA): ACA is a mobile agentgenerated by SMA. ACA is mainly used to construct the angle

depending upon user interested area. In case of time drivensink node simply draw the angle which is prefixed. When SNreceives the interest or when the time stamp completes SMA

draws the angle in a required direction and constructed angle

and is updated to SBB. Angle is constructed in such a manner

that first node finds its next node which is located exactly orapproximately to the given angle. Same process continues

with the next nodes, if a node does not find any nodes in given


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angle then that node acts as a final node. Formulae used for

constructing angle is

= tan-1

((y2-y1) / (x2-x1));

(x1, y1)= node 1st position

(x2, y2)= node 2nd position

Aggregator Agent (AA): AA is a mobile agent generated bySMA [15]. The AA is mainly used to aggregate the

information from the nodes in the network or zones; AAreduces redundancy in the information and extract onlyrequired information from the sensed data. In both sink driven

and time driven AA is used.

2) Zone Agency

Zone agency comprises of static agent, mobile agents and

Zone Black Board (ZBB). Static agent used in it are ZoneManager Agent (ZMA), Mobile agents used in it are Node

Discovery Agent (NDA) and Aggregator Agent (AA). ZH is

selected depends upon its residual energy and distance from

SN. Zone agency block diagram as shown below figure3.

Figure 3: Zone Agency

Zone Black Board (ZBB): ZBB is a Knowledge base of the

ZH and which is always updated by agents. ZBB comprises of

Node ID, its location, power in milliwatts, bandwidth inpercentage, signal strength, previously aggregated data with

time, presently aggregated data with time, status and it also

contains nodes information which are comes under the zonelike their id, location, signal strength, status and power in

milliwatts.

Zone Manager Agent (ZMA): ZMA is a static agent andwhich is responsible for creating ZBB and NDA. ZMA

manages whole zone and continuously updates ZBB. ZMA is

also used for aggregation purpose. Initially it receives theinformation from different nodes in the zone and aggregates

them; aggregated data is transferred to the SN. In case of sink

driven AA meets ZMA and access ZBB and collect latest

information, if latest updated data is not available means AA

just migrates to all the nodes in the Zone, collect andaggregates the information.

Node Discovery Agent (NDA): Node Discovery Agent is a

mobile agent generated by ZMA. After the creation of zone,ZH is selected depends upon its residual energy and distance

from SN. Now ZMA creates NDA for finding the nodes in the

zone. NDA migrates up to two hops and gets the information

about neighbor nodes and at last comes back to the ZH and

updates ZBB and stores all their information in the section

named as Zone members table. Once it finds all the members

in the zone means NDA task completes and it dies.

3) Node Agency

Node agency comprises of static agent, mobile agents andNode Black Board (NBB). Static agent is Node Manager

Agent (NMA) and mobile agents are Emergency InformationAgent (EIA) and Aggregator Agent (AA). Node agency is asshown in below figure4.

Node Black Board (NBB): It is the database/ knowledge base

of the normal sensor node. NBB usually updated by agentsand if anyone want to fetch the information about the node

they must have to contact NBB. NBB contains information

about the node and its neighbors like node id, position, battery,power, status, signal strength, available bandwidth, previously

sensed data with time, and present sensed data with time.

Figure4. Node AgencyNode Manager Agent (NMA): Node Manager Agent is a

static agent and which is present in all the sensor nodes. NMA

creates EA and NBB and is responsible for synchronizing the

actions of the agents within themselves and outside

world/agents. NMA is responsible for comparing the sensedinformation with the predefined information, if the sensedinformation is much greater than or less than threshold level

then NMA create EIA. In non active mode NMA does notsend any information to the ZH or SN.

Emergency Information Agent (EIA): Emergency agent or

Emergency Information Agent is mobile agent generated by

NMA. This agent is used to transfer emergency data to sinknode in a quick manner and updates NBB. This EIA uses more

energy to transfer data to the sink node as the information is

very critical and it has to be transferred in a short time;basically EIA will reach to the SN within two or three hops.

C. Agent Interactions

Agent interaction is mainly divided into three steps, 1) SinkDriven 2) Time Driven and 3) Emergency Data Driven. Agentinteractions are as shown below figure5.

1) Sink Driven

1.1 In case of Sink driven user/controller send area of interestto the sink node, depends upon user request SN draws angle

on the prescribed direction using mobile agent called ACA


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which is generated by SMA. After the construction of area,

zones are created and ZH is elected.

1.2 Now SMA creates AA which migrates to all nodes in the

zone. At first AA moves towards ZH of 1st zone, if latest

information is available means it just gathers the information

and migrate to next ZH. If latest information is not availablemeans AA will meet all the nodes in the zone and gathers the

information and also aggregate them.1.3 AA will move forward from 1st zone to 2nd zone orsubzone and gathers latest information from it. In 1.4 AA now

moves forward and meets 3rd subzone and continues same

operation. After aggregating the information from 3rd zone,AA moves to SN through 1st zone as it is very near to SN

which was shown through 1.5.

Figure5. Agent Interaction

1.6 From 1st Zone total aggregated data is given to the SN

using the same AA. AA updates SBB.

1.7 From SN the required/interested data is dispatched to the

controller/ user side.

2) Time Driven2.1 When given time stamp completes all the sensor nodes in

the zone transmits their sensed data to the ZH. ZH aggregates

all the informations.2.2, 2.3 by using AA the aggregated data is transferred to the

1st ZH.

2.4 1st ZH now has three aggregated datas one its own,second and third are from 2nd and 3rd zone respectively. By

using AA they all are aggregated and made them in a single

data. Now the data is given to the SN using AA.1.7 From SN the data is transferred to the controller.

3) Emergency Driven

In some cases, if any unaccepted event occurs in the

environment means the sensed node immediately send that

information to the SN (information should reach SN within 2or 3 hops).

3.1 At first NMA create EIA which is a mobile agent, sends

the event occurred information with time to the nearest ZH.3.2 From ZH the information is given to the SN without using

any intermediate nodes as the information priority is very

high.1.7 From SN Emergency information is given to the

controller/user.

D. Algorithms

1) Sink Agency

Nomenclature: Tth= threshold level, Z=zone, a=area, AA=

Aggregator Agent, SBB= Sink Black Board, PAD= Presently

Aggregated Data, PaD= previously aggregated Data, Tot=

Total Aggregated data, ACA= Angle Construction Agent,

ZH= Zone Head, SN= Sink Node, SMA: Sink Manager Agent.

Begin

For automatic time driven basis1. From SN draw 45degree angles thorough all directions.2. Draw angle using ACA, which is generated by SMA

If A is small

Then create only one Z.Else

Create more than one Z.

3. Now create AA using SMA4. AA will migrate to ZH and collect the aggregated data.

If (PAD > Tth)

Get the data.

Else

Discard the data.5. Move to another zone continue step 4.

6. Aggregate datas from different Zs using AA

Tot = (Z1s data+Z2 data)/2 or (Z1s data, Z2s data)7. Tot is passed to the SN using AA

8. Update SBB, goto step 13

For Sink driven basis

9. depends upon user interested area draw angle thorough thatarea using ACA. ACA is created by SMA.

10. The angles can be = 30, 45, 60, 90 degree. Depend uponusers area of interest angle is selected and updates SBB.

Angle limitation range => 30< 40% of Tth))

Get the Sif with time and send it to the ZH, update

NBBElse

Dont send the Sif (if Sif is equal to Tth then noneed to send the information), update NBB.3. ZH receives all nodes information, aggregates them and

waits for AA. Go to step 5.

4. If (Sif >> Tth) or (if (Sif > 60% of Tth))

Then that information considered as emergencyinformation Now NMA creates EIA


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EIA gets the information with time and send that

information to the SN within 2 or 3 hops.

Else

Send the information with time to ZH.

5. Update NBB.

6. Stop

End

3) Zone Agency AlgorithmNomenclature: Ergy= Nodes Energy, Epre= Nodes initial

energy, SBB= Sink Black Board, ZBB= Zone Black Board,

ZMA= Zone Manager Agent, AA=Aggregator Agent, Tth=Predefined threshold information, ZH= Zone Head, SN= Sink

Node.

Begin

1. Zones are created where more number of nodes present.

2. ZH is elected depends upon its residual energy and distance

from SN.

If (Ergy = 60% of Epre)

Then goto next stepElse

Discard that node and search node which is near to

it.3. If (nodes distance from SN is small)

Elect that node as ZH

Else

Discard that node and choose another node andfollow up step 2.

4. If (ZH got request form SN)Then ZH allows AA to migrate into the Zone and

gathers and aggregate the sensed information.

Else if (Time stamp completes (in case of time driven))

All nodes in the zones send their data to ZH

5. ZMA now allows AA to aggregate all the information in thezone. Upon completion of aggregation AA goes back to SN

using shortest path routing, updates ZBB.

6. StopEnd

IV. SIMULATION and RESULTS

Our proposed work is simulated in Turbo C, by consideringarea as 100X100 meters, number of nodes is varying from 30

to 100, communication range is 15meters, and nodes

predefined energy is 1joules. Nodes are deployed in randommanner in the network; size of sensed data is 25bytes, type of

aggregation used in this is Average. Angle varies from 30 to

180 degrees. Nodes are randomly deployed in the network and

sink node is fixed at the center. Area is selected depending

upon user request for sink driven application and in case oftime driven angle is fixed which is 45

0(45

0each to all eight

directions). The area covered by the angle is called sector andin the sector zones are created where number of nodes are

more. ZH is elected depends upon its residual energy anddistance from SN. Every node in the zone sends data to the ZH

only (in all three sink driven, time driven and emergency

driven applications). Performance parameters considered inthe proposed scheme are average energy, energy consumption,

network lifetime, delay Vs angle.

1. Energy: Each node consumes some energy for receiving/

transmitting, computation, zone formation, angle creation and

for aggregation process.

2. Delay: Time to transmit data to ZH and from ZH to SN.

Time also consumes for creation of sector and zone.

3. Network lifetime: This metric gives the time of the firstnode running out of its energy.

4. Node Battery Usage:It is defined as the battery depletedwith the usage of a node.

5. Data aggregation: instead of sending raw of data to the SN

which consumes lot of energy, nodes compute data

aggregation method in it to reduce redundancy in the data. Forexample if more than one node have same information means

only one nodes data is considered and transferred to the SN,

discarding other nodes data.The lifetime of the sensor node mainly depends on the

Residual Energy of the node. Sensor nodes are in active mode

whenever the nodes have the information otherwise node is in

inactive (sleep mode). Node consumes more energy in

transmitting data then receiving the data. Data aggregationconsumes some amount of energy which is in terms of

nanojoules. Data aggregation cost will increase linearly if

number of nodes are increased. But aggregation processreduces overall energy consumption. If we increase the angle

then number of nodes increases and overall energy

consumption also increases.

The following graphs shows how energy varies as there is anincrease in number of nodes and for different angles in both

Sink Driven and Time Driven applications. In figure6 depictsthat as the number of nodes increases energy consumption also

increases taking angle in the third quadrant. This result we

have got through Sink Driven application. In Time driven

(figure7)

Figure6. Energy consumption Vs Number of Nodes

Figure7. Energy consumption Vs Number of Nodes

Instead of increasing angle we have increased its

communication range because angle is fixed in case of time


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driven. The results shows as the communication range

increases the energy consumption also increases.

Next graph (figure8) is based on delay. As the number of

nodes increases delay also increases because for more number

of nodes more computation is needed and reaching time also

increases. Due to aggregation process also delay increases.Following graph shows how delay is related to number of

nodes. Next graph shows aggregation cost for one session.

Figure8. Delay Vs Number of Nodes

Basically aggregation cost is very low which is in terms ofnano-joules. Figure9 shows how aggregation cost varies in

sink driven application. In case of sink driven aggregator

agent have to meet all the members of the ZH, collect andaggregate the updated information from them and lastly comes

back to SN with a single data which is high quality

information about the environment. As the number of nodes

increases the amount of aggregation increases hence energyrequirement is slight high. In case of time driven, when the

given time stamp completes all the nodes in the zone send

their information to the ZH and ZH aggregates the all the data.Node use in-network aggregation for aggregation of data. The

energy required for this process is slight lower then sink

driven application as shown in figure10.

Figure9. Aggregation cost Vs Number of Nodes

Figure10. Aggregation cost Vs Number of Nodes

The network lifetime means, a metric which gives the time of

the first node running out of its energy. Generally senor nodes

lifetime is very less as nodes are operated by battery power.

But by the use of agents one can easily prolong the sensor

network lifetime. The network lifetime of our approach is asshown in below figure11.

VII. CONCLUSION

By using A2RWSN concept one can easily prolong network

life time, because for different applications we have differentrouting methods with multi-agent system involved in it.

Aggregation and routing the data is done by using agents only,

so there is no wastage of nodes energy, hence nodes lives forlong time. Creating the angle also plays an important role

here, because only user directed areas nodes are in active

mode at that time, rest all nodes are in non-active mode, hence

lifetime of network increases.

Figure11. Number of rounds Vs Number of NodesResults shows that both sink driven and time driven saves

energy, especially time driven application holds good as its

delay is less, saves energy and aggregation cost is also very

low. This A2RWSN holds good in all applications whether it

may be static application or multimedia application. In ourcase we have done simulation considering static application.

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angle based information aggregation and routing in wsn: multi agent approach

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