This document describes an energy-efficient and delay-aware wireless computing system for industrial wireless sensor networks. The system uses a fog-based architecture of interconnected servers that can communicate with each other to monitor and control industrial processes remotely. It aims to improve power efficiency by putting servers into sleep modes to reduce power consumption while still meeting delay requirements. The system dynamically adjusts the number of active and sleeping servers to maintain low power usage and acceptable communication delays. Evaluation shows the system is able to reduce power consumption over time while satisfying internal processing delays.

1. An Energy-Efficient and Delay-Aware
Wireless Computing System for
Industrial Wireless Sensor Networks

2. CONTENTS
1. INTRODUCTION
2. TECHNOLOGY & ARCHITECTURE
3. OPERATION PROCEDURE
4. SYSTEM RATING & DATA SPECIFICATION
5. IMPROVEMENT IN POWER CONSUMPTION
6. DELAY SATISFACTION RATIO
7. CONCLUSION
8. FUTURE SCOPE
9. REFERENCE

3. 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.

4.  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).

5. 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.

6. Fig: network topology in story v0 from top view

7.  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.

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

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

10. The average delay can be mathematically represented as,
The total power consumption can be represented as,

11. 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.

12. SYSTEM RATING & DATA SPECIFICATION

13. 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.

14.  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.

15. Fig: change in system power consumption in scenario 1

16. Fig: change in system power consumption in scenario 2

17.  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.

18. DELAY SATISFACTION RATIO
It can be expressed as,
Delay Satisfaction= Psatisfaction / Ptotal

19. 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.

20.  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.

21. 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.

22. 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.

23. [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

24. THANK YOU…