Effectiveness in Packet Data Transmission in the Reserved Channel Bandwidth Wireless Sensor Network

8/12/2019 Effectiveness in Packet Data Transmission in the Reserved Channel Bandwidth Wireless Sensor Network

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International Journal of Computer Trends and Technology (IJCTT) – volume 11 number 2 – May 2014

ISSN: 2231-2803 http://www.ijcttjournal.org Page65

Effectiveness in Packet Data Transmission in

the Reserved Channel Bandwidth Wireless

Sensor Network

Miss.Lavanya Y1, Ms.SowmyaDevi V2

1(M.Tech student, Computer Science Technology, Gitam University/Hyderabad, India)

2(Assistant Professor, Computer Science Engineering, Gitam University/Hyderabad, India)

ABSTRACT:

In the Trend of wireless sensor network, where

nodes plays the most important role in implementing the

wave based communication. These days if we look the

Telecom Industry where IT plays the crucial role in

implementing the path of communication has changed a lot,

Considering those factors in this paper we described Short

communication range requires a number of sensor nodes

working together to cover a large region to obtain data

about the environment. Because of the number of nodes,

interference increases. When excessive nodes are deployed,

radio from one node disturbs those from the others

communicating with the same channel. On the other hand,

connection could be lost when not enough nodes are

deployed to the task requiring a larger number of nodes.

Optimization of the number of nodes, management of

channels, and transmission scheduling issues arise here.

Depending on the application, a higher degree of coveragemay be required to increase the accuracy of the sensed data.

Keywords: Aggregation of Nodes, MAC, Packet Data,

Dedicated Bandwidth.

1. INTRODUCTION

Technologically wireless sensor network has

its own way of taking the concept into new level of

communication, when heterogeneous wireless

networks are constructed over sensor networks; we

call them heterogeneous wireless sensor networks.

Similar to typical wireless networks, heterogeneouswireless sensor networks suffer from limited battery

lifetime, excessive contention between wireless

nodes, and insufficient network throughput capacity,

often heterogeneous wireless sensor network

applications. The middle layer is a group of child

cluster heads which convey the data from sensor

nodes to the parent cluster through a faster wireless

connection, mainly IEEE 802.11b. This middle layer

is responsible for handling the heavy traffic of the

underlying sensors. In this layer, the issues such as

contention and interference between adjacent

wireless nodes result in insufficient throughput

capacity.

Fig. 1.1 Showing the Cluster of in the Medium of

Communication in the various angles

In the above fig. 1.1 if the network resources do not

sufficiently satisfy all the requirements, applications

suffer from low throughput, delayed arrival times,

and other performance problems. Relaxing the

network contention can lessen performance

problems. By reducing the network contention

between nodes, the number of packet retransmissionsdecrease, which results in lower communication

power consumption. In addition, reducing conflicts in

packet transmission often improves throughput.

Therefore, we propose a novel distributed scheduling

algorithm to drastically lower power consumption

and to improve network performance at the level of

child cluster heads.





2. RELATED WORK

Technology of Data aggregation in time and

space is proposed to save overall energy consumption

of the network. Due to the high density of sensor

deployment, the degree of "similarity" among

spatially proximal sensor observations increases as

the inter-node distance decreases. Also, the degree of"similarity" between consecutive sensor

measurements varies according to the temporal

characteristics of the phenomenon's manifestation.

Energy-efficient estimation of the phenomenon can

be performed by leveraging the above mentioned

spatial-temporal correlation among the sensor data. In

this research effort, adaptive distributed estimation

techniques will be developed to prolong the lifetime

of 3D WSNs and/or to ensure effective utilization of

resources, whereas existing solutions perform spatial

and temporal adaptive sampling separately. By

grouping sensors in clusters and electing cluster

heads that will report the data on behalf of the nodes

in their clusters, communication cost can be

minimized.

Fig. 2.1 Showing the Related Hierarchical flow

When more wireless nodes are added into a network,there is a higher probability that nodes transmit

packets at the same time, and they consequently

cause more collisions. This leads to packet

retransmissions and an increased size, which means

that nodes could wait longer before accessing a

wireless channel. Given that all nodes have data

packets to send, MAC layer queues are always full.

In that case, as there are more nodes in a wireless

network, the chance of successfully transmitting a

packet through the wireless channel decreases

dramatically as the probability of collisions increases.

In that case, nodes spend a lot of time waiting for the

channel to become idle while no node can

successfully transmit a packet. Therefore, the total

aggregate throughput of a wireless network drops

because the utilization of wireless channel decreases.

It shows how much the aggregate throughput drops

as the number of wireless nodes increases. In this

simulation, we have one base station (access point)

and a lot of wireless nodes around the base station.

Wireless nodes generate UDP data traffic.

3. METHODS

As of the technology and demand increasing, it leads

to last during the beginning of the commercialization

of the Internet, organizations and individuals

connected without concern for the security of their

system or network. The degree of interference is

dependent on an actual network topology and the size

of carrier-sensing range. When carrier sensing is

Fig 3.1 provisioning Model of Node based

communication

Cell-level and node-level scheduling

algorithms run at every scheduling interval. By

running the cell-level scheduling algorithm, each

base station decides if its cell is active in the nextscheduling slot. The sensitive, a node defers packet

transmission more often because it senses much more

radio activity. Channel utilization in the network

drops further. Therefore, in designing a scheduling

algorithm for multi-cell wireless networks, it is very

important to reduce the interference from neighbor

cells. The purpose of cell-level scheduling is to





reduce the interference between nodes in adjacent

cells. Contention between nodes in the same cell is

already managed by node-level scheduling. Cell-level

scheduling decides the active cells such that only the

child nodes in the active cells are considered in node-

level scheduling algorithm. Any child nodes in the

cells not scheduled by cell-level scheduling algorithm

are not active in a given time slot.

nodes also run the node-level scheduling algorithm.

The node is active in the next scheduling slot only if

it is scheduled by the result of node-level scheduling

algorithm and if its cell is active. The schedule of a

cell is announced to a new node when the node

enters. In our combined scheduling, we use the same

size of scheduling interval for both cell-level

scheduling and node-level scheduling. It is also

possible to use different slot sizes for two scheduling

algorithms. For example, if we set the scheduling

interval of cell-level scheduling

Fig3.2 showing the packet transmission through

the periodic step of channel bandwidth

The scheduling problem we cover in this research has

been introduced and defined. We describe an optimal

scheduling algorithm and show that this optimal

algorithm is NP-complete. We have presented the

centralized node-level scheduling algorithm for a

maximal scheduling. Next, we generalize it to make

it work in a distributed manner. We illustrate how we

can reduce the interference from neighbor cells by

running a cell-level scheduling algorithm. Finally, we

show how to combine the cell-level scheduling and

the analyze the performance of our scheduling

algorithms

Node-level scheduling algorithms for multi-cell

networks. In the next chapter, we as a double of

node-level scheduling interval, we can schedule the

nodes twice in active cells. In that case, the overhead

is that the delay of application traffic in the nodes of

inactive cells increases. Therefore, there is a tradeoff

between more scheduling opportunities and longer

delay.

4. ALGORITHM DESCRIPTION

Algorithm for protocol Based Approach for

effective Transmission of Packet

1. If Start IDLE:

2. Then Start Mode

3. do{

4. handle route ad(Packet p)

5. {

6. for each Route r in p do7. handle update(r);

8. }

9. Set Pr BT to max BT power observed while

sensing

10. }while(no Data Generated and no RPTS

Received)

11. if(Data Generated){

12. Goto TX State

13. }

The scheduler is an application process that

determines when a node can access the radio channel.Our proposed scheduling algorithm resides in the

scheduler. The scheduler is tightly integrated with a

proxy. We have two types of schedulers. The cell

level scheduler running on base stations determineswhen the corresponding cell is scheduled. The

schedule of a node is decided by the node-level

scheduler running over a proxy layer of nodes.

5. PERFORMANCE EVOLUTION

Simulations are performed on two types of

network topologies; hexagonal cells and square cells.

The topology and the deployment of cells used in our

simulations are shown in fig 3.2. The sizes of

network topologies are 533m by 550m for hexagonal

multi-cell networks, and 524m by 524m for square

multi-cell networks respectively. The hexagonal

topology has 39 cells, and the square topology 49

cells.





Fig.5.1 Destination range distribution for 802.11

showing the packet Transmission

In this section, the simulation with real data traffic

shows how our scheduling performs in the case

where a network contains a large number of the child

cluster heads. The results on Communication power,average throughput per cell and MAC layer

transmission delay is presented.

6. CONCLUSION

Technology in the domain of networking ischanging environment making things simpler. NP-

complete algorithm at runtime is not practical in

large-scale multi-cell wireless networks; we proposed

the centralized node-level scheduling algorithm that

is a solution for the simpler maximal scheduling

problem. Next, we converted this algorithm into a

distributed node-level scheduling algorithm. Multi-

cell heterogeneous wireless networks. Our schedulingmechanism provides the control of channel access

and throughput allocation to the sensor node cluster

heads. Finally, we combined the cell-level scheduling

and the node-level scheduling algorithms to run ourscheduling in a wireless network consisting of large

number of cells.

7.REFERENCES

[1] IEEE 802.11Working Group.

http://grouper.ieee.org/groups/802/11/index.html.[2] www.intersil.com and www.ti.com for information on IEEE

802.11g.[3] C. Grinstead and J. Snell , Introduction to Probability, 2nd ed.Providence, RI: Amer. Math. Soc., 1997.

[4] A. P. Jardosh, K. N. Ramachandran, K. C. Almeroth, and E. M.Belding-Royer, “Understanding congestion in IEEE 802.11b

wireless networks,” in Proc. USENIX IMC , Oct. 2005, p. 25.

[5] G. Bianchi, “Performance analysis of the IEEE 802.11

distributed coordination function,” IEEE J. Sel. Areas Commun.,vol. 18, no. 3, pp. 535–547, Mar. 2000.

[6] V. Bharghavan, “MACAW: A media access protocol forwireless LAN’s,” in Proc. ACM SIGCOMM , 1994, pp. 212–225.[7] P. M. Soni and A. Chockalingam, “Analysis of link-layer

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Resilient Security in Wireless Sensor Networks,” Proc. Sixth ACMInt’l Symp. Mobile Ad Hoc Networking and Computing (MobiHoc’05), 2005.

[9] V. Bharghavan, A. Demers, S. Shenker, and L. Zhang,

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SIGCOMM, Oct. 1994.[10] IEEE Std 802.11 - 1997, Wireless LAN Medium AccessControl (MAC) and Physical Layer (PHY) Specifications, 1997.

[11] C. Fullmer and J. Garcia-Luna-Aceves, “Floor acquisitionmultiple access (FAMA)

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AUTHORS PROFILE:

1.Ms.Lavanya yegireddi student in

GITAM university,Hyderabad pursuingher MTech in department of CST and research area in

the area of Networking.

2. Vallala Sowmya Devi, B.Tech.

M.Tech, (Ph.D.), MIEEE, MISTE, MIETE.

Currently, she is a Assistant professor in CSE

department in GITAM University, Hyderabad. Her

specializations include networking, MANET,

Network Security. Her current research interests are

wireless communications and networking, MANET,

Network Security.

Documents

Effectiveness in Packet Data Transmission in the Reserved Channel Bandwidth Wireless Sensor Network