Wireless sensor network (WSN) is to monitor the surroundings with reliable data collection. Usually, observed terms in WSN can be pressure, wind speed and direction, power line voltage, pollutant stages, humidity, the temperature of the body, illumination intensity, chemical attention, and sound concentration. Our knowledgeable research team supports your wireless sensor network project topics with the help of many advanced technologies and algorithm parameters. The sensor is a tiny node that has the capability to perform specific tasks namely, sensing, transmitting, and receiving. Each sensor node is fully furnished with the transducer, minicomputer, transceiver, and power source. On this page, we converse the contemporary research WSN and the uses of the wireless applications and sensor nodes.Here we have highlighted the significant wireless sensor network project topics.

1. Energy Conservation in
wireless sensor networks
Kshitij Desai, Mayuresh Randive,
Animesh Nandanwar

2. Basic Design
• Sensor Network Architecture
Internet
Sensor
Network
Sink

3. Architecture of a Sensor Node
• Ref: Energy Conservation in Wireless Sensor Networks – a
Survey

4. Observations
• Communication Sub-system consumes more energy than
computation sub-system
• Energy to transmit one bit = Energy for execution 1000
. instructions
• Radio component requires same order of energy for
reception, transmission and idle states
• Sensing sub-system might also require significant amount
of energy based on the type of sensor node.

5. Three Main enabling Techniques
• Duty-cycling
• Data-Driven approaches
• Mobility

6. Duty-cycling
• Topology Control
• Power Management
• Sleep/Wake Protocols
• On-demand, scheduled rendezvous and Async
• MAC Protocols with low Duty-cycle
• TDMA, Contention-based and hybrid

7. Data-driven approaches
• Data reduction
• In-Network Processing
• Data-Compression
• Data-prediction
• Stochastic, Time-series Forecasting and algorithmic
approaches
• Energy-efficient data acquisition
• Adaptive Sampling
• Hierarchical Sampling
• Model-Driven active sampling

8. Mobility-basedapproaches
• Mobile-sink
• Mobile-relay

9. ATPC: Adaptive Transmission
Power Control for Wireless
Sensor Networks

10. Main Points
• What is this paper about?
• Power saving for wireless communication
• Paper style?
• Empirical study + a little theory work
• What is the contribution?
• Study of spatial-temporal impact on communication
• Mechanism to adaptively achieve an optimal transmission power
consumption

11. Motivation
11
TP1
TP2
TP2

12. Motivation
12
TP1
TP1
T1
T2
The minimum transmission power level
to save energy and maintain specified link quality
TP2
T2

13. DesignGoals
• Achieve energy efficiency
• The minimum transmission power
• Maintain Link Quality
• Reliable links
• In runtime systems, dynamic environments
• Spatial impact
• Temporal impact
13

14. Roadmap
Data Analysis
Empirical Observation
Algorithm Design
Algorithm Evaluation
PART 1
PART 2

15. Part1-TransmissionPowervs. LinkQuality
• Link Quality Metrics
• RSSI (Received Signal Strength Indication), LQI (Link
Quality Indication), and PRR (Packet Reception Ratio)
• Transmission Power Level Index (3~31)
• Experiments on Spatial Impact
• 5 pairs of motes, 3 environments
• 100 packets at each transmission power level
• RSSI/LQI/PRR measured at different distances
15

16. Part1-InvestigationofSpatialImpact
-95
-90
-85
-80
-75
-70
-65
-60
-55
-50
3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
Transmission Power Level Index
RSSI
(dbm)
2 ft
6 ft
12 ft
18 ft
24 ft
28 ft
-95
-90
-85
-80
-75
-70
-65
-60
-55
-50
3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
Transmission Power Level Index
RSSI
(dbm)
3 ft
6 ft
12 ft
18 ft
24 ft
30 ft
16
(a) RSSI measured on a grass field
-95
-90
-85
-80
-75
-70
-65
-60
-55
-50
3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
Transmission Power Level Index
RSSI
(dbm)
3 ft
6 ft
12 ft
18 ft
24 ft
30 ft
(c) RSSI measured in a parking lot
1. Different shapes at the same distance in
different environments
2. Different degree of variation in different
environments
3. Approximately linear
(b) RSSI measured in a corridor

17. InvestigationofTemporalImpact
• Experiment on Temporal Impact
• In brushwood where human activity is rare, over 72 hours
• 9 MicaZ motes in a line, 3 feet apart
• A group of 20 packets at each power level every hour
-95
-93
-91
-89
-87
-85
-83
-81
-79
-77
-75
3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
Transmission Power Level Index
RSSI
(dbm)
9am 1st Day
10am 1st Day
11am 1st Day
12pm 1st Day
1pm 1st Day
2pm 1st Day
17
-95
-93
-91
-89
-87
-85
-83
-81
-79
-77
-75
3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
Transmission Power Level Index
RSSI
(dbm)
0am 1st Day
8am 1st Day
4pm 1st Day
0am 2nd Day
8am 2nd Day
4pm 2nd Day
1. Vary gradually but noticeably over time
2. Approximately parallel
(a) RSSI measured every 8-hour (b) RSSI measured every hour

18. Part1-LinkQualityThreshold
18
Binary link quality thresholds
Slight different in different environments
(a) RSSI Threshold on a grass field (b) LQI Threshold on a grass
field
0
20
40
60
80
100
120
-95 -90 -85 -80 -75 -70
RSSI (dbm)
PRR
(%)
0
20
40
60
80
100
120
50 60 70 80 90 100 110
LQI (Reading from MicaZ)
PRR
(%)

19. Part2-ModelDesignofATPC
• Use a linear model to approximate a non-linear correlation
• rssi(tp) = a · tp +b
• Least-square
approximation
• Dynamic model
• a and b vary
from time to time
19

20. Part2-ATPC
Overview
0.4TP+32
6
4
0.8TP+49
27
3
0.5TP+23
12
2
Control Model
Power Level
NodeID
0.4TP+32
6
4
0.8TP+49
27
3
0.5TP+23
12
2
Control Model
Power Level
NodeID
ATPC Table at Node 1
20
Initialization Phase: build models from linear approximation
Node
3
Node
5
Node
4
Node
1 Node
2
Transmission Range
Runtime Tuning Phase: pairwise closed loop control
Packet with
Transmission Power
Level 12
Notification
25
8

21. Part2–ClosedLoopControl
Start
here
RSSI, LQI
and PRR

22. Part 2- ExperimentSetup
22
• Current transmission power control algorithms
– A node-level non-uniform solution (Non-uniform)
– Network-level uniform solutions
» Max transmission power level (Max)
» The minimum transmission power level over nodes in a network
that allows them to reach their neighbors (Uniform)
• A 72-hour continuous experiment with MicaZ
– A spanning tree of 43 nodes, 24 leaf nodes
– Leaf nodes send 32 packets to the base every hour

23. Part2-Experimental
Setup
23
(a) Weather Conditions over 72 Hours
(b) Spanning Tree Topology (c) Experimental Site

24. Part2-PacketReceptionRatio
0.5
0.55
0.6
0.65
0.7
0.75
0.8
0.85
0.9
0.95
1
0 6 12 18 24 30 36 42 48 54 60 66 72
Time (hours)
End-to-end
PRR
ATPC
Max
Uniform
Non-Uniform
0
10
20
30
40
50
60
70
80
90
100
0 6 12 18 24 30 36 42 48 54 60 66 72
Time (hours)
PRR
(%)
Link with Static
Transmission
Power
Link with ATPC
24
(a) E2E packet reception ratio
Max ~ 100%
ATPC ~ 98.3%
Uniform ~ 98.3%
Non-Uniform ~ 58.8%
(b) PRR at a chosen link
ATPC ~ constantly 100%
Static transmission power
~ vary from 0% to 100%

25. Part2-TransmissionEnergyConsumption
0.4
0.45
0.5
0.55
0.6
0.65
0.7
0.75
0.8
0.85
0.9
0.95
1
6 12 18 24 30 36 42 48 54 60 66 72
Time (hours)
Relative
Transmission
Energy
Consumption
ATPC
Max
Uniform
Non-Uniform
25
Max ~ 100% ATPC ~ 58.3% (1% control overhead)
Uniform ~ 68.6% Non-Uniform ~ 43.2%
Relative energy consumption

26. ConclusionsandFutureWork
• Benefits of ATPC lie in three core aspects:
• ATPC maintains above 98% E2E PRR over time
• ATPC achieves significant energy savings
• 53.6% of the transmission energy of Max
• 78.8% of the transmission energy of Uniform
• ATPC accurately adjusts the transmission power
• Adapting to spatial and temporal factors
• Towards reliable and energy-efficient
routing
• Spatial reuse for concurrent transmissions
26

27. Questions?
27
Thank you very much!