An Energy-Efficient and Delay-AwareWireless Computing System for

Industrial Wireless Sensor Networks

CONTENTS

1. INTRODUCTION2. TECHNOLOGY & ARCHITECTURE 3. OPERATION PROCEDURE4. SYSTEM RATING & DATA SPECIFICATION5. IMPROVEMENT IN POWER CONSUMPTION6. DELAY SATISFACTION RATIO7. CONCLUSION8. FUTURE SCOPE9. REFERENCE

INTRODUCTION

It is a wireless computation system.

It can be used in different industries.

It is based on fog-based industrial wireless sensor

networks.

Different process can be controlled and monitored using

this system.

The basic element used is wireless sensors.

The principle used here is wireless telemetry.

It allows user to interfere with a process which is far

located.

It ensures efficiency as well as acceurate sensing and

monitoring without any physical contact with the

process.

It is the most advanced energy saving wireless

computation system(WCS).

ARCHITECTURE

It consists of spherical network of servers.

Each servers are interconnected.

It uses both time division multiplexing and space

division multiplexing.

Fixed timeslot is allowed for each server.

Server to server communication is used.

It uses advanced Low power consumption technology.

Fig: network topology in story v0 from top view

Power management is obtained by proper arrangement

of sleep mode of servers.

Internal delay is used to obtain this.

Internal delay can be avoided by maintaining a proper

timeslot for each server.

Number of sleeping servers is properly managed by

mathematical analysis.

As far as, it is concerned that proper energy

management is necessary. This system brings

satisfactory to this.

Fig:- effect of the number of servers in sleep state on the system power consumption.

Fig:-effect of the number of servers in sleep state on the internal delay.

The average delay can be mathematically represented as,

The total power consumption can be represented as,

OPERATION PROCEDURE

Its operation is carried out in 3 different steps:

i. Ratio management of servers in sleep state &

servers in active state.

ii. Delay calculation.

iii. Monitoring at the finial server.

SYSTEM RATING & DATA SPECIFICATION

IMPROVEMENT IN POWER CONSUMPTION

The power consumption of the system can be explained

in scenarios.

The duration is 100secs.

During this time, the change in power consumption can

be noted.

The power can varies with respect to time.

The system satisfies acceptable internal delay with

maximum number of servers in sleep state until 7secs.

It starts to increase in number of servers in active state

after 8secs.

The number of active servers is set to maximum value

until 37secs to satisfy required internal delay.

After 37secs, the system starts to increase servers in

sleep state.

It is done to reduce system power consumption.

Fig: change in system power consumption in scenario 1

Fig: change in system power consumption in scenario 2

It is achieved by a proposal, DSCD, it dynamically

controls the number of servers in sleep state.

It ensures the power consumption CSCD constant.

It is achieved by altering DSCD depending on the value

of acceptable internal delay.

DELAY SATISFACTION RATIO

It can be expressed as,

Delay Satisfaction= Psatisfaction / Ptotal

CONCLUSION

In this paper, the proposed project is an efficient wireless

computation system.

In this modern world, the available energy resources are limited.

This project mainly focused on 2 important factors. The first one

is wireless computation and second one is power efficient system.

It ensures reduced time consumption and power consumption.

The delay in communication can be reduced by this system.

It is highly durable and a simple computation method.

Wireless data transfer ensures less physical linkage and

make sure that there is less data loss.

Information's are readily available at every instant at the

monitoring server.

This system provides direct access to the process

whether to control and monitor.

From this project, it is clear that it provides an efficient,

durable and feasible WCS.

FUTURE SCOPE

Using Nano technology, the size of the equipment can be

reduced.

The time consumption and manufacturing cost can be reduced by

increasing the number of multiplexes.

Number of servers can be increased to increase data

acceptability.

Data transfer rate can be increased by using optical fiber linkage.

Efficiency in installation will contribute to total efficiency of the

system.

REFERENCE

[1] S. Savazzi, V. Rampa, and U. Spagnolini, “Wireless cloud networks for the factory of things: connectivity modeling and layout design,” IEEE Internet of Things Journal, vol. 1, no. 2, pp. 180-195, Mar. 2014.[2] E. Shellshear, R. Berlin, and J. S. Carlson, “Maximizing smart factory systems by incrementally updating point clouds,” IEEE Computer Graphics and Applications, vol. 35, no. 2, pp. 62-69, Mar.-Apr. 2015.

[3] F. Wang, C. Xu, L. Song, and Z. Han, “Energy-efficient resource allocation for device-to-device underlay communication,” IEEE Trans.on Wireless Communications, vol. 14, no. 4, pp. 2082-2092, Apr. 2015.[4] Accenture, “Driving Unconventional Growth through the Industrial Internetof Things,” 2014 [Online], Available: http://www.accenture.com/SiteCollectionDocuments/PDF/Accenture-Driving-Unconventional-Growth-through-IIoT.pdf

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