Energy Harvesting Solution for Wireless Sensors in IoT Systems ( - - PowerPoint PPT Presentation

Energy Harvesting Solution for Wireless Sensors in IoT Systems (Internet of Things (IoT) solutions for Smart )

R & D Department

Environment) ا تا ا د ءا إ !" 15/8/2017 –14/8/2019 Report 1

http://ntraeri-ehiot.com

Project Team

Name Institute

• Prof. Esmat Abdel – Fattah Abdallah

Electronics Research Institute (ERI), PI Prof . Hala Abdel Monem El-sadek Electronics Research Institute (ERI), C-PI A .Prof . Dalia Mohamed Nashaat Electronics Research Institute (ERI) A . Prof . Ahmed Khattab Fathi Khattab Faculty of Engineering , Cairo University

2

Khattab A . Prof . Shereen Aly Mohamed Taie Faculty of Computers and Information, Fayoum University. Eng . Nermeen Ahmed El-Tresy Electronics Research Institute (ERI) Eng . Osama Mohammad Dardeer Electronics Research Institute (ERI) Eng . Esraa Mohammed Hashem ELhariri Faculty of Computers and Information, Fayoum University. Eng . Ghada Hussien Alsuhly Faculty of Engineering , Cairo University

M1 Planned Tasks

OUTCOME #1 LITERATURE SURVEY ON

ENERGY HARVESTING IN IOT SYSTEMS AND PROPOSED SPECIFICATIONS

ACHIEVEMENT PERCENTAGE: 100 %

OUTCOME #2 THE DESIGN OF THE

RECTENNA CIRCUITS FOR SINGLE ANTENNA ELEMENT

ACHIEVEMENT PERCENTAGE: 50 %

3

OUTCOME #3 LISTS OF PURCHASES OF

COMPONENTS AND MATERIALS

ACHIEVEMENT PERCENTAGE: 100 %

OUTCOME #4 INTERNET OF THINGS

(IOT) SOLUTIONS FOR SMART

ENVIRONMENT

ACHIEVEMENT PERCENTAGE 25%

OUTCOME #5 INTERNET OF THINGS AND

DATA ANALYSIS

ACHIEVEMENT PERCENTAGE: 25%

List of contents

IoT and Energy Harvesting Survey

Different Definitions of IoT Applications of IoT Indoor Proposed Application Energy harvesting Types Selected RF EH

Project Activities Project Activities Technical Achievements

System Block Diagram Rectenna design and implementation

Three single element antenna designs Rectifier Rectenna specifications

IoT system survey and specifications Data Analysis survey and specifications

Lists of Purchases

4

Project Activities

Junior RAs Training at Cisco Academy

Nermeen Ahmed Mohammed Eltresy, “Introduction to IoT” Osama Dardeer, “Programming Essentials in C++”

Attending webinars

8 August 2017 , “Enabling the real power of IoT using machine learning” 7 November 2017, “ Smart cities the E2E opportunity”

• Project IoT Lab. permanent place in new ERI premises was selected.
• Project IoT Lab. permanent place in new ERI premises was selected.

The establishment of the Lab. as central laboratory for IoT solutions as well as trainings. Negotiations with Cisco Company in Egypt for help in establishing the Lab were done. Educational kits were gifted to start the Lab with.

• Project placards for IoT Lab. were designed and implemented. Also a

project roll up was designed and printed.

• The project team shares in the Egypt IoT forum. ERI is the

responsible entity for the regulations track in the forum.

5

Project Activities (cont.)

Submitted and published papers

1-“Multi-Bandwidth CPW-Fed Open End Square Loop Monopole Antenna for Energy Harvesting, Presented in 2018, (International Applied Computational Electromagnetics Society (ACES) Symposium), Denver, Colorado, USA on March 24-29, 2018. Colorado, USA on March 24-29, 2018. 2- “Tri-Band Compact CPW-Fed PIFA Antenna for Energy Harvesting”, Accepted in: 2018 IEEE International Symposium on Antenna and Propagation (AP-S), 8-13 July 2018, Boston, Massachusetts, USA. 3-CPW-Fed Multiband Antenna for Various Wireless Communications”, Accepted in: 2018 IEEE International Symposium on Antenna and Propagation (AP-S), 8-13 July 2018, Boston, Massachusetts, USA.

6

Project Activities (cont.)

Junior RAs registration for degrees in universities

• Eng. Ghada Alsuhly: registered for PhD degree at Communication

and Electronics Dept., Faculty of Engineering, Cairo University.

• Eng. Esraa Elhariri: registered for PhD degree at Computer Science

Dept., Faculty of Computers and Information, Fayoum University. Dept., Faculty of Computers and Information, Fayoum University. Eng. Nermeen Eltresy: registered for PhD degree at Communication and Electronics Dept., Faculty of Engineering, Ain Shams University.

• Eng. Osama Dardeer: registered for PhD degree at Communication

and Electronics Dept., Faculty

• f

Engineering, Ain Shams University.

7

Project Activities (cont.)

Project website: http://ntraeri-ehiot.com

8

System Overview

Data Analysis & Prediction

Collected Data Relations

Energy Harvesting System

• IoT System

Sensor node. Actuator node.

9

between data parameters Prediction Time Series Classification Integration Fuzzy Logic System

Operating frequency bands. Antenna. Substrate materials. Rectifier. node. Gateway. Wireless communicati

• n.

Power Management Unit.

System Overview

Data Analysis & Prediction

Collected Data Relations

Energy Harvesting System

• IoT System

Sensor node. Actuator node.

10

between data parameters Prediction Time Series Classification Integration Fuzzy Logic System

Operating frequency bands. Antenna. Substrate materials. Rectifier. node. Gateway. Wireless communicati

• n.

Power Management Unit.

System Block Diagram for Energy Harvesting

11

IoT Overview: General Self IoT Overview: General Self-Powered IoT System Powered IoT System

WSN 12 Gateway

Self-powered Sensor Node Actuator Node

Host

Definitions of IoT

There aren't any standard definitions for IoT. Various definitions are evolved: 1) The Cluster of European Research Projects,

• Tech. Rep..

13

• Tech. Rep..

2) IoT European Research Cluster (IERC). 3) European Technology Platform

• n

Smart Systems Integration (ETP EPoSS). 4) Institute of Electrical and Electronics Engineers (IEEE). 5) Oxford English Dictionary.

Definitions of IoT (cont.)

1) The Cluster of European Research Projects, Tech. Rep.. (broader vision of IoT).

14

(broader vision of IoT).

“IoT allows things and people to be connected anywhere, anytime, with anyone, ideally using any path

• r

network and providing any service”.

2) IoT European Research Cluster (IERC).

Definitions of IoT (cont.)

15

3.1) focus on seamless integration: “ Interconnected objects having an active role in what might be called the Future

3) European Technology Platform on Smart Systems Integration (ETP EPoSS):

Definitions of IoT (cont.)

16

role in what might be called the Future Internet ” 3.2) states the semantic of the expression: “ The semantic origin of the expression is composed by two words and concepts: ‘Internet’ and ‘Thing’ ” ……… cont.

3) European Technology Platform on Smart Systems Integration (ETP EPoSS):

Definitions of IoT (cont.)

17

Cont.

Semantically, ’Internet of Things’ means ‘a world-wide network

• f

interconnected

• bjects

uniquely addressable, based

• n

standard communication protocols ”.

4) Institute

• f

Electrical and Electronics Engineers (IEEE):

IEEE described the phrase “Internet

• f

Things” as: “A network of items—each embedded with

Definitions of IoT (cont.)

18

“A network of items—each embedded with sensors—which are connected to the Internet.” This statement is written as a description of the “Internet of Things,” not as an official definition of the concept.

5) Oxford English Dictionary:

Definitions of IoT (cont.)

19

“A proposed development

• f

the Internet in which everyday objects have network connectivity, allowing them to send and receive data.”

IoT Overview: Applications IoT Overview: Applications

20

[IERC- European Research Cluster].

Applications of IoT

21

IoT Overview: Growth of IoT IoT Overview: Growth of IoT

22

Growth Growth of IoT

• f IoT [cisco.com].

IoT Market Opportunity

23

Projected market share for different IoT applications by 2025 McKinsey Global Instit., San Francisco, CA, USA: 2013.

Museum Ambience Control (Proposed Application)

IoT can be very powerful for museums. Sensors that will be used: For monitoring purposes:

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For monitoring purposes: Temperature, humidity, ambient light and pollutants (CO, CO2) sensors. For security purposes: Vibration and motion sensors.

Energy Harvesting Sources

Energy Harvesting Classifications Thermal Energy Mechanical Energy Radiant Energy

25

Body heat External heat Visible Light Infrared RF waves Body motion Heel strikes Air flow Blood flow The different sources of the energy harvesting

Survey on Energy Harvesting

The energy harvesting means reaping the ambient and wasted power which is in the surrounding environment without any detriment to the environment The energy harvesting has main merits of the portability, reduce the dependence

• n battery power and it also presents long-term solutions.

26

Complete vision of how the energy from various sources is used

Survey on Energy Harvesting (cont.)

Two main categories for energy harvesting: 1) Ambient EH: ambient sources are already exist in the surrounding environment. 2) Dedicated EH: dedicated sources are placed intentionally for these IoT devices.

27

The level of the energy harvested by each IoT device depends on:

• Distance between the IoT device and the

energy source.

• Sensitivity of harvesting circuit.
• Environment.

Survey on Energy Harvesting (cont.)

Challenges that are facing the energy harvesting for IoT devices: Energy harvesting receiver design. Energy arrival rate.

28

Energy arrival rate. Minimum number

• f

dedicated energy sources. Scheduling of energy transmitters. Multi-path energy routing.

RF EH Block Diagram

29

The main components of the RF energy harvesting system

Advantages of Using Planar Antennas

Low profile. Light weight.

• 30

Can be fixed on the building window. Easy to be integrated with the rectifier and the matching circuit.

Antennas for IoT Systems

Depending on the substrate material and the conductive layer, there are: 1) Opaque antennas: opaque dielectric material and opaque conducting material. 2) Transparent antennas: Semi-transparent:

• paque

conductive

31

Semi-transparent:

• paque

conductive material is etched on a transparent substrate such as polymers. Fully-transparent: transparent conductive

• xides

are deposited

• n

a transparent substrate and the final deposited traces seems to be invisible to the human eye.

Our Proposed Antennas for EH

The antennas are the main device in the RF energy harvesting. This is due to the fact that the antennas are used to collect the ambient electromagnetic power.

Energy Harvesting Frequency Bands

32

Other bands include Wi-Fi hotspots (and other 2.4GHz sources), and WiMax (2.3/3.5 GHz) network transmitters and WLAN (5.2/5.8GHz).

GSM 900 Mobile transmit 880 - 915 MHz Base transmit 925 - 960 MHz GSM 1800 Mobile transmit 1710 - 1785 MHz Base transmit 1805 - 1880 MHz UMTE 2100 Mobile transmit 1920 - 1980 MHz Base transmit 2110 - 2170 MHz

Three Proposed Antennas for EH

W L

W L1 L2 L3

33

L Lg Wf

L L Lg Wf

A triple band coplanar waveguide fed planar inverted-F antenna (CPIFA). A quad band multi arm coplanar waveguide (CPW) fed. A quad band CPW monopole antenna loaded with double E- shaped stubs.

Triple Band Antenna for Energy Harvesting

Compact three resonant frequencies coplanar waveguide fed planar inverted- F antenna (PIFA). The PIFA antenna is a better choice for reducing the space

• f the antenna.

W

Substrate length L 60 mm Substrate width W 70 mm

34

L Lg Wf Y X The detailed structure of the CPIFA antenna.

Substrate width W 70 mm feeding line width Wf 3.5 mm feeding line length Lf 18 mm Separation gap g 0.35 mm Ground plane length Lg 18 mm

The Proposed Antenna Reduction Size

The total area of the traditional antenna is 70×90 mm2. At 900 MHz the CPIFA antenna reduced the total area by 33%. Moreover, the traditional PIFA antenna has only one band at 900 MHz, however the CPIFA antenna has three resonate bands.

35

Triple Band Antenna Design Steps

Step (c) Step (a) Step (b) 36

Step (a) Step (b) Step (c)

|S11|of design steps of the CPIFA. Step (c) Step (a) Step (b)

Triple Band Antenna Results

37

HFSS CST Measurement

Comparison between simulated and measured results. Photo of fabricated antenna

Triple Band Antenna Results

10.40 12.80 15.20 17.60 90 60 30

• 30
• 60
• 90
• 120
• 150
• 180

150 120

• 5.20
• 2.40

0.40 3.20 90 60 30

• 30
• 60
• 90
• 120
• 150
• 180

150 120

• 7.20
• 4.40
• 1.60

1.20 90 60 30

• 30
• 60
• 90
• 120
• 150
• 180

150 120

H-plane (XZ plane) E-plane (XY plane) (a) F=0.9 GHz (b) F=1.8 GHz (c) F=2.4 GHz The radiation pattern of the antenna in E-plane, and H-plane at different frequencies 0.9, 1.8, and 2.4 GHz.

The values of directivity, gain, and radiation efficiency for the CPIFA antenna.

38

The values of directivity, gain, and radiation efficiency for the CPIFA antenna.

Frequency (GHz) Directivity (dBi) Gain (dBi) Radiation efficiency % 0.9 GHz 2.2 1.5 90.9 1.8 GHz 3.73 3.4 90.1 2.4 GHz 2.8 2.12 76.1

“Tri-Band Compact CPW-Fed PIFA Antenna for Energy Harvesting”, Accepted in: 2018 IEEE International Symposium on Antenna and Propagation (AP-S), 8-13 July 2018, Boston, Massachusetts, USA.

Quad Band Antenna for Energy Harvesting

The antenna was designed to operate at the Egypt cellular frequency bands

• f GSM 1800, UMTS 2100, the 2.4 GHz Wi-Fi, and the 5.2 GHz WLAN

frequency bands.

W L1

2

Substrate length L 60 mm Substrate width W 40 mm feeding line width Wf 5 mm feeding line length Lf 18 mm

39

L L1 L2 L3 Lg Wf X Y

The detailed structure of the quad band antenna.

Separation gap g 0.3 mm Ground plane length Lg 18 mm L1 29.5 mm F1=1.8 GHz GSM1800 L2 23 mm F2=2.2 GHz UMTS2100 L3 15.8 mm F3=2.4 GHz Wi-Fi 2.4 L4 8.9 mm F4=5.2 GHz WLAN 5.2 GHz

• 10
• 5

5

Coefficient (dB)

Quad Band Antenna Design Steps

40 Antenna 1

0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0

• 30
• 25
• 20
• 15

Reflection Co Frequency (GHz)

L1=29.5 mm and gives the first resonance frequency

• f 1.8 GHz with a frequency band from 1.76 GHz to

1.9 GHz which includes almost the GSM 1800.

Quad Band Antenna Design Steps

• 10
• 5

5

• n Coefficient (dB)

41 Antenna 2

The second arm which has length of L2=23 mm and is responsible for the second resonance frequency 2.2 GHz with a frequency band 2 GHz to 2.31 GHz which contains the downlink frequencies of the UMTS 2100.

0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0

• 25
• 20
• 15

Reflection Frequency (GHz)

Quad Band Antenna Design Steps

• 10
• 5

5

n Coefficient (dB) 42 Antenna 3

The third arm with length L3=15.8 mm to get the 2.4 GHz Wi-Fi band with a wide band.

0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0

• 25
• 20
• 15

Reflection C Frequency (GHz)

Quad Band Antenna Design Steps

• 10

Coefficient (dB)

43 Antenna 4

The fourth arm L4= 8.9 mm to obtain the WLAN 5.2 GHz resonance.

0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0

• 30
• 20

Reflection C Frequency (GHz)

Quad Band Antenna Results

(a) F=1.8 GHz (b) F=2.2 GHz (c) F=2.45 GHz (d) F=5.2 GHz

44

The current density distribution on the antenna at different frequencies. A photo of the fabricated antenna.

HFSS CST Measurement

Comparison between the simulated results using HFSS, CST and the measurement results for |S11|.

Quad Band Antenna Results

• 8.00
• 6.00
• 4.00
• 2.00

0.00 2.00 90 60 30

• 30
• 60
• 90
• 120
• 150
• 180

150 120

• 8.00
• 6.00
• 4.00
• 2.00

0.00 2.00 90 60 30

• 30
• 60
• 90
• 120
• 150
• 180

150 120

• 8.00
• 6.00
• 4.00
• 2.00

0.00 2.00 90 60 30

• 30
• 60
• 90
• 120
• 150
• 180

150 120

H-plane (XZ plane) E-plane (XY plane)

(a) F=1.8 GHz (b) F=2.2 GHz (c) F=2.4 GHz Radiation pattern in E-plane, and H-plane at different frequencies 1.8, 2.2, and 2.4 GHz. The values of directivity, gain, and radiation efficiency for the quad band antenna.

45

Frequency (GHz) Directivity (dBi) Gain (dBi) Radiation efficiency % 1.8 GHz 3.12 3.03 97 1.9 GHz 2.57 2.42 94 2.15 GHz 1.95 1.86 95.1 2.45 GHz 2.1 1.6 76.2 5.2 GHz 5.4 4.93 90.4

“Multi-Bandwidth CPW-Fed Open End Square Loop Monopole Antenna for Energy Harvesting, Presented in 2018, (International Applied Computational Electromagnetics Society (ACES) Symposium), Denver, Colorado, USA on March 24-29, 2018.

Rectangular CPW Monopole Antenna with Double E-shaped Stubs

Material: Rogers RT6002 with ε =2.94, h= 0.76 mm,

46

The rectangular monopole alone generates the 2.45 GHz band. with εr=2.94, h= 0.76 mm, and tanδ=0.0012.

The two E-shaped stubs,

• perating

as monopole radiators,

Rectangular CPW Monopole Antenna with Double E-shaped Stubs

47

monopole radiators, generate the 3.5 GHz band for the WiMAX system.

Rectangular CPW Monopole Antenna with Double E-shaped Stubs

48

(a) 0.94 GHz. (b)2.45

Rectangular CPW Monopole Antenna with Double E-shaped Stubs

49

(b)2.45 GHz. (c) 3.5 GHz. (d)5.2 GHz.

The

• verall radiation pattern seems to be an

CPW Monopole Antenna (cont.)

50

The

• verall radiation pattern seems to be an

Omni-directional pattern which fulfills the requirements

• f

RF energy harvesting applications since the received ambient RF field may come from different directions.

CPW-Fed Multiband Antenna for Various Wireless Communications”, Accepted in: 2018 IEEE International Symposium on Antenna and Propagation (AP-S), 8-13 July 2018, Boston, Massachusetts, USA.

Design for Harvesting Antenna

Directive radiation patterns are preferred The positions of source and receiving antennas are known. (Dedicated RF energy harvesting source) if

51

The positions of source and receiving antennas are relatively uncertain. (Ambient RF energy harvesting source) if Omni- directional radiation patterns are preferred

The Measurement of the Ambient Power in the RF Spectrum

52

This survey was done in two places one of them is outdoor measurements in the street and the other is indoor in our Electronics Research Institute buildings. The spectrum measurements were done using the Agilent Technology VNA N9918A which works as a spectrum analyzer.

Survey on Egypt RF Spectrum.

53 Picture of the horn which is used in the RF spectrum measurements Picture taken during the measurements of the received ambient power at 10 am using our proposed antenna (indoor measurements).

The aim of using two different antennas is to study the effect of the antenna gain on the received RF power. Because the more the antenna received power the more the system overall efficiency increases.

Survey on Egypt RF Spectrum (cont.)

The measured RF spectrum was (indoor measurements) in the ERI at 11 am at ERI Dokki building

54

0.7 1.4 2.1 2.8 3.5 4.2 4.9 5.6 6

• 60
• 50
• 40
• 30
• 20
• 10

Frequency (GHz) Power (dBm)

Picture taken from the

Survey on Egypt RF spectrum (cont.)

55 Picture taken from the Agilent Technology N9918A during the RF spectrum study using (indoor measurements)

The measured received power of the antenna in reality at different times using the quad band multi-arm CPW antenna in our Electronics Research Institute buildings (indoor) at ERI Dokki building.

Survey on Egypt RF Spectrum (cont.)

• 65
• 60
• 55
• 50
• 45
• 40
• 35
• 30
• 25

Pow er (dBm )

Spectrum at 5 pm Spectrum at 11am

Peak1 Peak2 Peak3 Peak4 Peak5

56

The maximum values of the measured received power using the quad band multi-arm CPW antenna in our Electronics Research Institute buildings (indoor) at 11 am at ERI Dokki building. Peak Frequency power (dBm) Power (micro watt) Peak1 0.9 GHz

• 32 dBm

0.63 µw Peak2 1.8 GHz

• 42.88 dBm

0.05 µw Peak3 2.1 GHz

• 43.3 dBm

0.05 µw Peak4 2.4 GHz

• 26.6 dBm

2.19 µw Peak5 5.18 GHz

• 45.2 dBm

0.03 µw

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6

• 65

Frequency (GHz)

Survey on Egypt RF Spectrum (cont.)

Outdoor measurement at 1 pm. 57

Value of the power at GSM 900 band is the highest value comparing to other bands, which means that there was a GSM 900 base station tower near to us during the outdoor measurements. Value of the ambient RF power at Wi-Fi band is very low at the street comparing to the indoor measurements in the ERI because the street does not

contain hot spots

The AC to DC Converter Unit

The energy harvested by the antenna is integrated with the matching circuit and the AC to DC converter (rectifier) to maximize the stored power. The next stage after the antenna in the ambient RF energy harvesting system is the AC to DC converter unit which consists of a rectifier circuit.

6

58

0.5 1.0 1.5 2.0 2.5 0.0 3.0

• 4
• 2

2 4

• 6

time, nsec in, V

• ut, V

Diode R AC input signal Output volt

(a) (b) (a) Half wave rectifier circuit and (b) comparison between the AC input volt and the half wave rectified output volt.

The AC to DC Converter Unit

AC input signal Output volt

Bridge rectifier

59

D2 RL AC input signal Output volt D1 C1 C2 Time (µS) Voltage (V) Vin (AC) Vout

Voltage doubler rectifier

The schottky didoes are used in the ambient RF energy harvesting systems because of their high sensitivity to the very low ambient power. The schottky diodes have very fast switching action which is suitable for the high frequencies.

Schottky Diode

60

Diode HSMS 2850 HSMS 2860 Operating frequency range 915 MHz-5.8 GHz 915 MHz-5.8 GHz Forward voltage VF 150-250 mV 250-350 mV Total device dissipation PT 75 mW 250 mW Saturation current Is 3×10-6 A 5×10-8A

Voltage Doubler Circuit

61

vo vin R R1 R=700 Ohm C C2 C=100 pF di_hp_HSMS2850_20000301 D1 di_hp_HSMS2850_20000301 D3 P_1Tone PORT1 Freq=2.41 GHz P=dbmtow(pin) Z=50 Ohm Num=1 C C3 C=100 pF

Voltage Doubler Output Volt and Efficiency

RL=1000 ohm RL=700 ohm RL=500 ohm

HSMS 2860

RL=1000 ohm RL=700 ohm RL=500 ohm

HSMS 2850

62

RL=1000 ohm RL=700 ohm RL=500 ohm

HSMS 2850

RL=1000 ohm RL=700 ohm RL=500 ohm

HSMS 2860

• The 500 ohm load resistance gives the maximum efficiency that reaches to 95% at 15 dBm input

power.

• The rectifier conversion efficiency using the HSMS2850 reaches to 85% at input power of 15

dBm.

Comparison between using HSMS 2850 and HSMS 2860 in the rectifier circuit

HSMS 2860 HSMS 2850

63

Diode Turn on voltage Conversion efficiency HSMS 2850 150 mV High conversion efficiency at low values of the input power less than 5 dBm HSMS 2860 250 mV High conversion efficiency at the input power levels higher than 5 dBm

The efficiency variation versus the input power using two different schottky diodes (HSMS2850, HSMS2860), RL=500 ohm at 2.41 GHz.

Rectifier with (Open Ended Stub) Matching Circuit

Simulation ADS Measurement

64 The rectifier circuit integrated with the

• pen ended matching stub reflection

coefficient measurement using the VNA and HSMS2850 schottky diode. Comparison between the simulated ADS and the measured reflection coefficient variation versus frequency to the rectifier attached to the open ended matching stub using HSMS2850 schottky diode.

Photo of the fabricated rectifier circuit using HSMS2850 schottky diode integrated with the short ended matching stub. Comparison between the simulation ADS and measurements for the reflection coefficient variation versus frequency when the rectifier attached to the short ended matching stub using HSMS2850 schottky diode.

Rectifier with (Short Ended Stub) Matching Circuit

65

Simulation ADS Measurement

Rectifier Circuit Testing

66

13 mV obtained for -20 dBm input power

Rectifier Circuit Testing

67

624 mV obtained for 0 dBm input power

Rectifier Circuit Efficiency Measurement

Simulation Measurement

• 1. The output DC volt of 13

mV using -20 dBm RF input power

• 2. The output DC volt of 47

mV for -15 dBm RF input

68 The simulated and measured rectifier conversion efficiency at different RF input power at 2.41 GHz. .

mV for -15 dBm RF input power.

• 3. The output DC volt of 154

mV DC output volt using

• 10 dBm RF input power.

4. The DC

• utput

volt reaches 624 mV when the input power is 0 dBm

Operating frequency bands:

The receiving antenna may be operating on the following commercial bands for ambient RF energy harvesting:

0.94 GHz (GSM 900 Downlink: 925 MHz to 960 MHz) 1.84 GHz (GSM 1800 Downlink: 1805 MHz to 1880 MHz) 2.1 GHz (3G UMTS Downlink: 2110 MHz to 2170 MHz) 2.45 GHz (WiFi, IEEE 802.11 b&g: 2.4 GHz to 2.5 GHz) 3.5 GHz (Licensed WiMAX: 3.4 GHz to 3.6 GHz) 5.55 GHz (Unlicensed WiMAX: 5.25 GHz to 5.85 GHz)

Rectenna Specifications:

Gain: ─ High gain antennas are preferred if the positions of source and receiving antennas are known. ─ Low gain antennas are preferred if the positions of source and receiving ─ Low gain antennas are preferred if the positions of source and receiving antenna are relatively uncertain in order to collect signals from various directions simultaneously. ─ About 5 dBi gain: single element & conductor on both sides. ─ About 10 dBi gain: 4-elements array & conductor on both sides. ─ About 2 dBi gain: single element & conductor on single side. About 5 dBi gain: 4-elements array & conductor on single side. Polarization: Dual linearly polarized because the incident electromagnetic waves

• f

all polarizations (arbitrary LP, LHCP, and RHCP) can be entirely collected at its two ports (circular polarized antenna is employed to receive linear polarized wave, there will be 3-dB polarization mismatch loss). Weight: Light weight and low profile.

69

Substrate materials specifications:

─ Opaque materials: conductivity of about 5x107 S/m for the coated conductor & loss tangent of about 0.003 for the substrate material. ─ Transparent materials: suitable for museums and on the window glass since they don't affect the place decorations. Expected lower gains than opaque materials since the conductivity for ITO is 1.3x106 S/m and for FTO is 0.0917x106 S/m.

Rectifier Section specifications:

Power conversion efficiency: About 30% at Pin of -20 dBm and about 40 % at Pin of -15 dBm at 2.4 About 30% at Pin of -20 dBm and about 40 % at Pin of -15 dBm at 2.4 GHz. Rectifying element: Schottky diode is used because of its low threshold voltage. For input RF power greater than -20 dBm, HSMS 282x diode is used. For the retrieved power less than −30 dBm (1 µW), the low-barrier SMS7630 diode is recommended. High detection sensitivity: Up to 50 mV/µW at 915 MHz. Rectifier topology: Half wave, full wave, and bridge.

70

System Overview

Data Analysis & Prediction

Collected Data Relations

Energy Harvesting System

• IoT System

Sensor node. Actuator node.

71

between data parameters Prediction Time Series Classification Integration Fuzzy Logic System

Operating frequency bands. Antenna. Substrate materials. Rectifier. node. Gateway. Wireless communicati

• n.

Power Management Unit.

IoT System: Survey Outlines IoT System: Survey Outlines

IoT-based Ambient Monitoring in Smart Buildings

• Ambient Monitoring and Control.
• Occupancy Monitoring.

IoT Applications in Museums and Heritage Buildings 72

• Environmental Parameters Monitoring and Control.
• Interactive Museums.
• Historic Buildings Health Monitoring.
• Museum and Artifacts Security.

IoT Wireless Communication Protocols. Proposed IoT System.

IoT IoT-based Ambient Monitoring in Smart Buildings: based Ambient Monitoring in Smart Buildings: 1-

• Ambient Monitoring

Ambient Monitoring

Indoor Air Quality: [Saad2013], [wu2017]. Monitored parameters:

• Gaseous pollutants (CO, CO2, methane, …).
• Particulate Matter (dust).
• Temperature.

73

• Temperature.
• Relative humidity

Building automation purposes: [Shah2016] Monitored parameters:

• Temperature.
• Relative humidity.
• Light

IoT IoT-based Ambient Monitoring in Smart Buildings: based Ambient Monitoring in Smart Buildings: 2-

• Occupancy Monitoring

Occupancy Monitoring

Occupancy monitoring purposes:[Kleiminger2015]

• Security issues (intruder detection)
• Economical and environmental issues (occupancy-driven

lighting, heating,….) Using: 74 Using:

• Passive infrared (PIR) detectors (low accuracy) [Kleiminger2015]

[Agarwal2010].

• Camera (privacy, cost, power) [Teixeira 2008].
• CO2-based occupancy detection (low accuracy) [Gruber2014].
• Ultrasonic array sensor [Caicedo2012].
• Electricity meters [Santini2015].

IoT System: Survey Outlines IoT System: Survey Outlines

IoT-based Ambient Monitoring in Smart Buildings

• Ambient Monitoring and Control.
• Occupancy Monitoring.

IoT Applications in Museums and Heritage Buildings 75

• Environmental Parameters Monitoring and Control.
• Interactive Museums.
• Historic Buildings Health Monitoring.
• Museum and Artifacts Security.

IoT Wireless Communication Protocols. Proposed IoT System.

IoT Applications in Museums and Heritage Buildings : IoT Applications in Museums and Heritage Buildings : 1-

• Environmental Parameters Monitoring

Environmental Parameters Monitoring

Environmental parameters: [Camuffo2001][Gennusa2008] [Brito2008, Pestana2008a, Peralta2009, Peralta2010, Peralta2010a Peralta2013] [Chianese2014] [Zonta2010] [Xiao2016] [Aderohunmu2014] [Viani2014] [Viani2012] [Shah2016a]….. 76

• Relative Humidity
• Temperature
• Light
• Pollutants (CO, CO2)

IoT Applications in Museums and Heritage Buildings : IoT Applications in Museums and Heritage Buildings : 2-

• Interactive Museums

Interactive Museums

Example: [Chianese2014], [Chianese2014a], [Alletto2016] 77

IoT based Interactive Museum, [Chianese2014], [Chianese2014a].

IoT Applications in Museums and Heritage Buildings : IoT Applications in Museums and Heritage Buildings : 3-

• Historic Buildings Health

Historic Buildings Health

Example: [Zonta2010], [Ceriotti2009]

• FOS node
• Acceleration node
• Environmental node

Accelerometer node

78

• Sink node

Accelerometer node Environmental node FOS node A historic tower structure monitoring, [Zonta2010][Ceriotti2009].

IoT Applications in Museums and Heritage Buildings : IoT Applications in Museums and Heritage Buildings : 4-

• Museum Security

Museum Security

Example: [Viani2012], [Xiao2016]

• Purpose: Early warning!
• Video: Detection of replacement or unexpected movement
• Vibration sensor: To detect the touch.

79

• Vibration sensor: To detect the touch.

A Museum or an exhibition security monitoring, [Viani2012].

IoT System: Survey Outlines IoT System: Survey Outlines

IoT-based Ambient Monitoring in Smart Buildings

• Ambient Monitoring and Control.
• Occupancy Monitoring.

IoT Applications in Museums and Heritage Buildings 80

• Environmental Parameters Monitoring and Control.
• Interactive Museums.
• Historic Buildings Health Monitoring.
• Museum and Artifacts Security.

IoT Wireless Communication Protocols. Proposed IoT System.

IoT Wireless Communication Protocols: IoT Wireless Communication Protocols:

Wireless IoT connectivity technologies,[Mahmoud2016].

For indoor app.

81

Bluetooth ZigBee Wi-Fi Cellular Z-Wave Thread Suitable for indoor IoT applications

IoT Wireless Communication Protocols: IoT Wireless Communication Protocols:

Z-Wave Thread NFC SigFox Neul LoRaWAN ……… 82

IoT Wireless Communication Protocols: IoT Wireless Communication Protocols: A Comparative Study A Comparative Study

ZigBee 3.0 BLE* Z-Wave Thread Wi-Fi GPRS Standard

IEEE 802.15.4 IEEE 802.15.1 ZAD12837 / ITU-T G.9959 IEEE 802.15.4 and 6LowPAN IEEE 802.11 GPRS

Max no. devices

65000 8 232 250-300 2007 1000

Data rate

250 Kbps 1Mbps 9.6-100 kbps 250 Kbps Up to 1Gbps 35-170kps 2.4GHz and 5GHz 850-900-1800-

* The Bluetooth mesh, was announced in July 2017, supports mesh topology and larger number of devices (32000).

83

Frequency

2.4GHz (ISM) 2.4GHz (ISM) 916 MHz (ISM) 2.4GHz (ISM) 2.4GHz and 5GHz (ISM) 850-900-1800- 1900MHz

Network Topology

Star/Mesh Star Star/Mesh Star/Mesh Star/Mesh Star

Operating range

10-100m 10m 30m 30m 100m 26km

Power consumption

Very Low Very Low Very Low Very Low Medium High

IP Compatible

Yes No No Yes Yes No

IoT System: Survey Outlines IoT System: Survey Outlines

IoT-based Ambient Monitoring in Smart Buildings

• Ambient Monitoring and Control.
• Occupancy Monitoring.

IoT Applications in Museums and Heritage Buildings 84

• Environmental Parameters Monitoring and Control.
• Interactive Museums.
• Historic Buildings Health Monitoring.
• Museum and Artifacts Security.

IoT Wireless Communication Protocols. Proposed IoT System.

Sensor Node

Proposed IoT System Proposed IoT System

85 Sensor Node Actuators Node

Host

Gateway

Proposed IoT System: Proposed IoT System: 1- Sensor Node Sensor Node

Node 1 (EGP) 86 Node 2 (USA)

Proposed IoT System: Proposed IoT System: 1- Sensor Node Sensor Node

Sensors

• Temperature sensor.
• Relative Humidity sensor.
• Ambient Light sensor.
• CO,CO2 level sensors.

87

• CO,CO2 level sensors.
• Vibration sensor.
• Passive infrared (PIR) detectors (occupancy).

Proposed IoT System: Proposed IoT System: 1- Sensor Node (EGP) Sensor Node (EGP)

Sensors: Specifications

Module Type Company Output Voltage IActive Isleep Accuracy Response DHT11 Temperature and Humidity Aosong Digital 3.3-6 2.5mA

• Humid.±5

% Temp.±2º C 2s TSL2561 Light AMS AG Digital 2.7-3.6 0.24- 3.2-15µA

• 4s

88

TSL2561 Light AMS AG Digital 2.7-3.6 0.24- 0.6mA 3.2-15µA

• 4s

MQ-7 CO

• Analog

1.2-5 70 mA

• ±3%

90s LSM303DLHC Accelerometer ST Digital 2.16-3.6 110 µA 1 µA HC-SR501 PIR

• Digital

4.5-20 65mA 50 µA

• 0.3-5min

Proposed IoT System: Proposed IoT System: 1- Sensor Node (USA) Sensor Node (USA)

Sensors: Specifications

Module Type Company Output Voltage IActive Isleep Accuracy Response

HDC1010 Temperature and Humidity TI Digital 2.7-5.5 1.3 µA 100 nA Humid.±2 % Temp.±0.2º C 8s OPT3001 Light TI Digital 1.6-3.6 1.8 µA 0.25 µA ±15% 880ms

89

OPT3001 Light TI Digital 1.6-3.6 1.8 µA 0.25 µA ±15% 880ms ULPSM-CO 968- 001 CO spec- sensors Analog 2.6-3.6 15 µA

• ±3 %

30s COZIR (GC-0012) CO2, temp, humidity CO2 Meter Analog 3.25 -5.5 1.5mA

• ±3%

10s LIS3DH Accelerometer ST Digital 1.71-3.6 11 µA 2 µA

• BOOSTXL-

TLV8544PIR PIR TI Digital 3.3 <10 µA 1.7 µA

• 30s

Proposed IoT System: Proposed IoT System: 1- Sensor Node (EGP) Sensor Node (EGP)

MCU: Specifications

Company Module MCU MCU role Protocol Vdd ITX IRX Isleep Bit Rate ESPRESSIF ESP32 Tensilica LX6 stack +app. Wi-Fi, Bluetooth 2.7-3.6 190 mA 95 mA 5 µA 72.2Mbps

90

Proposed IoT System: Proposed IoT System: 1- Sensor Node (USA) Sensor Node (USA)

MCU: Specifications

Company Module MCU MCU role Protocol Vdd ITX IRX Isleep Bit Rate

TEXAS INSTRUMENTS CC2650 ARM Cortex- M3 stack +app. ZigBee, BLE, …. 1.8-3.8 6.1-9.1mA 5.9 mA 1 µA 2 Mbps

91

Wireless MC

Proposed IoT System: Proposed IoT System: 2- Actuator Node Actuator Node

Buzzer Actuator LED lamb Actuator Dehumidifier Actuator Air Conditioner Actuator Fan Actuator

92

Proposed IoT System: Proposed IoT System: 3- Gateway (EGP) Gateway (EGP)

Module Company wireless Ethernet CPU Memory Raspberry pi 3 Raspberry 802.11n, Bluetooth 4.0 yes quad- Cortex A53@1.2 GHZ 1 GB SDRAM

93

Proposed IoT System: Proposed IoT System: 3- Gateway (USA) Gateway (USA)

Module Company wireless Ethernet Wi-Fi X2E-Z3C-W1-W Di-Gi ZigBee yes yes

94

Power Management Unit (PMU)

• 1. The voltage harvested is extremely low in the range of few milli-Volts

This is not enough to power any IC Proposed Solution: To develop a low-voltage step-up demo kit with rechargeable batteries This demo kit will boost the input voltage up then it will either supply system or recharge the battery to save extra energy

95

TEGPuck Module

• 1. Input voltage: 40–400 mV
• 2. Output Voltage: 3-5 V
• 3. Max. output Current: 4.2 mA
• 4. Efficiency: 80%
• 5. Price: $8
• 6. Size: 2*2*1 cm

96

Ultralow Voltage Step-Up Converter

• 1. Chip Used: LTC3108
• 2. Input voltage: 50–500 mV
• 3. Output Voltage: 2.35-5 V
• 4. Max. output Current: 7 mA
• 5. Price: $18.90
• 6. Size: 47mm x 42mm

97

Boost Converter

• 1. Chip Used: 25504
• 2. Input voltage: 80–330 mV
• 3. Output Voltage: 2.5-5.25 V
• 4. Price: $18.85

98

Rechargeable Battery

• 1. Maximum Output Voltage: 3.2V
• 2. Battery Capacity: 600 mAh
• 3. Price: $22.99

99

Battery Charger

• 1. LiFePO4 Battery Charger Module Protection PCB Board
• 2. Batteries supply voltage: 3.2V~3.6V
• 3. Maximum output 2A
• 4. 5V Micro USB Interface
• 5. Price: $4.99
• 6. 3.3 cm x 2 cm

100

Battery Holder

• 1. 2*AA Parallel Batteries Holder Case Box with Leads
• 2. Price: $1.34
• 3. 5.8 cm x 3.2 cm

101

System Overview

Data Analysis & Prediction

Collected Data Relations

Energy Harvesting System

• IoT System

Sensor node. Actuator node.

102

between data parameters Prediction Time Series Classification Integration Fuzzy Logic System

Operating frequency bands. Antenna. Substrate materials. Rectifier. node. Gateway. Wireless communicati

• n.

Power Management Unit.

Data Analysis: Data Analysis: Survey Outlines Survey Outlines

Data Analysis Definition. Data Analysis Objectives. Data Analysis Techniques.

103

Data Analysis Techniques. IoT Data Analysis. Data Analysis Examples. Specifications.

Data Analysis

Definition Data analysis is the process

• f

examining, cleansing, transforming, and modeling raw data of various types to discover hidden patterns, other useful information and drawing conclusions about useful information and drawing conclusions about that information via applying a specific process to derive insights.

104

Data Analysis Objectives

Making better and faster decisions using previously inaccessible or unusable data. The identification of important (and often mission- critical) trends critical) trends Identifying performance problems that require some sort of action Identifying any gaps

105

Data analysis techniques

Techniques 106 Statistical Analysis Visualization Text Analysis Video & Audio Analysis Machine Learning

We will focus on:

Statistical Analysis Machine Learning

Mean, Standard

Supervised learning Unsupervised learning

107

Mean, Standard Deviation Correlation Regression and etc.

Classification Clustering Prediction, etc

IoT Data Analysis

IoT data alone has no meaning, Data analysis brings IoT to the life. Data provided by IoT enables organizations to generate real-time insights that benefit them in the present.

• But by using predictive data analysis it also helps them to:
• Predict future business trends in advance.

Tell when physical components are likely to fail, enabling them to carry out vital maintenance work before disruption occurs. Take preventive actions. Make better decision. Optimize processes. 108

IoT Data Analysis Example 1:

109

IoT Data Analysis Example 2:

110

Data Analysis Survey Data Analysis Survey

111

Accurate occupancy detection of an office room using statistical learning models [Candanedo 2016]

Used data:

Data from Light, Temperature, Humidity and CO2 sensors. Derived data (humidity ratio)

Using camera for building truth table of occupancy. Compute correlation between different variables. Compute correlation between different variables. Used techniques:

Linear Discriminant Analysis, Classification and Regression Trees) and Random Forest.

Results:

• Selecting features together with classification models

appropriately has an important impact on prediction accuracy.

• Accuracies ranging from 95 to 99%

112

SCRMS: An RFID and Sensor Web-Enabled Smart Cultural Relics Management System [Xiao 2016]

Their aim is to manage the cultural relics and preserve them from damage and loss. RFID is used for identification and tracking of identification and tracking of cultural relics. Video sensor with infrared imaging and motion detection features, vibration and displacement are used for security. Temperature, Humidity.

113

SCRMS: An RFID and Sensor Web-Enabled Smart Cultural Relics Management System [Xiao 2016]

They used a set of rules. The proposed system was successfully applied to a museum in China, demonstrating its feasibility demonstrating its feasibility and advantages for smart and efficient management and preservation of cultural relics.

114

POEM: Power-efficient occupancy-based energy management system [Erickson 2013]

40% of US primary energy consumption and 72% of electricity. Of this total, 50% is used for Heating Ventilation and Air- Conditioning (HVAC) systems. Current HVAC systems only condition based on static schedules.

115

POEM: Power-efficient occupancy-based energy management system [Erickson 2013]

It uses one network of cameras, and another network of PIR sensors -> binary occupancy A nonparametric implementation of the Bayes filter as preprocessing step, K-nearest neighbors and image Processing. The system achieved savings of 26.0% while maintaining conditioning effectiveness.

116

Long-term load forecast modelling using a fuzzy logic approach [Ali 2016]

• Used data: weather data and historical load data for a

town to forecast a year-ahead load.

• Used technique: fuzzy Logic approach
• The model achieves a prediction accuracy of 93.1% and

capable of forecasting the load for a year-ahead load. capable of forecasting the load for a year-ahead load.

117

System Specifications

Energy Harvesting System Operating

IoT System Sensor node. Actuator node.

• Data Analysis &

Prediction

Collected Data Relations between data

118

Operating frequency bands. Antenna. Substrate materials. Rectifier.

Gateway. Wireless communicati

• n.

Power Management Unit.

between data parameter Prediction Time Series Classification Integration Fuzzy Logic System

ed Data

CO CO2 Temperature Humidity

Our Specifications

119

Collected

Humidity Light Motion Vibration Camera

Our Specifications

Occupancy detection Energy consumption prediction Environment quality monitoring Alarming System

120

Museum

Environment Relics

121 121

Motion Detection Approach

• The relations between T, H, L,

CO2 and Occupancy.

• Impact of current environmental

parameters on relics No Yes

Turn on camera To detect number

• f people

Temperature Humidity Light CO2 CO No . People

Pre-processing Outliers Missing values Input 122 Analysis Classification Time series prediction Automatic Control Alarm

Prediction Prediction

Current Readings

Pre-processing Feature Extraction

123

Exceeds predefined Threshold for safe relics

No Rules Automatic Control Alarm

Classification

No Yes

Our Specifications

As a start, due to our needs all experiments will be performed

• n a local host with the following specifications:

Intel Core i7 Q720@1.60 GHZ CPU, 6 GB memory. R, Java languages. Later, when data size becomes huge and there is a need for fast response, the system will be upgraded to cloud system and big data processing frameworks.

124

List of Purchases

Already purchased from local market Qty Item

1 Soldering Iron 40 W 4 Piezo Buzzer Module 6 LED 10 mm

125

6 LED 10 mm 4 Advanced Light Sensor Module 4 LSM303 DLHC Module 4 Esp32 2 Fan 2 BB power

List of Purchases

Already purchased from local market Qty Item

3 Battery 3 Crocodile cables (colors) 2 PCB 2 Holes 15x8

126

2 PCB 2 Holes 15x8 1 PCB 26 Double Holes 9x15 5 PH Male/Female Wires 30 cm 5 PH Arduino Male/Male Wires 30 cm 3 Battery Case 3xAA 1 Soldering Iron Stand

List of Purchases

Already purchased from local market Qty Item

1 Desoldering pump DY050503 20 PH Male/Female Wires 30 cm 4 BB.J65 cables

127

4 BB.J65 cables 4 PH62 – 20 cm Female/Female 2 Kit Resistor 450 Resistance 1 NT50 gm Flux 1 Solder wire Sn 70/30 1 Tweezer DY030307

List of Purchases

Already purchased from local market Qty Item

4 Gas Sensor MQ7 10 Transistor 2N2222 1

• Tel. wire 1 m

128

1

• Tel. wire 1 m

1 AVO UT890C unit 5 PH 1.1x40 Male 6 PH 2.1x40 Female 5 Jumper short 10 R3 Roseta 2 pin big

List of Purchases

Already purchased from local market Qty Item

5 IRF 1010 4 PIR Module Sensor 4 SEN.DHT11 Temp & Humidity Sensor

129

4 SEN.DHT11 Temp & Humidity Sensor 4 Kit M2 – 1 Relay Module 5V 1 Wire Cutter

List of Purchases

Already imported from abroad Qty Item

2 Rogers sheet RO4003C 100 HSMS-285C-BLKG(Schottky diode) SMS7630-061: Surface Mount, 0201

130

100 SMS7630-061: Surface Mount, 0201 Zero Bias Silicon, Schottky Diode 10 BQ25504RGTT:Energy harvesting IC for power management circuit 1 CAP KIT CERAMIC 1PF-47UF 6 SMA Female End Launch Connector (Up to 27 GHz)

List of Purchases

Already imported from abroad Qty Item

1 3.5mm Male to 3.5mm Female coaxial Cable 36 Inches 100 Diode HSMS 2860

131

100 Diode HSMS 282 10 Adapter Coaxial Connector SMA Plug, Male Pin To SMA Plug, Male Pin 50 Ohm 1 RES KIT 10-332K 1/10W 2200PCS 1 KIT-RMCF0603FT-04

List of Purchases

Already imported from abroad Qty Item

1 KIT RF INDUCTOR (0201-0402) 10 of 0.6nH ~ 120nH Surface Mount 2 CO Sensor , ULPSM-CO 968-001

132

5 Load switch TS5A3160DBV 3 MOSFET P-CH ,SI2323DS 5 2.4GHz Transceiver nRF 24L01 1 KIT RF INDUCTOR(0603-1008)

List of Purchases

Still being imported from abroad Qty Item

3 Transparent: FTO coated glass slide 2 LTE FDD 700 MHz – 2700 MHz Wide Band Log Periodic Yagi Antenna

133

Band Log Periodic Yagi Antenna 100 SMS7630-061: Surface Mount, 0201 Zero Bias Silicon Schottky Detector Diode 1 3.5 mm Male to 3.5 mm Female coaxial cable 18 inches 100 Diode HSMS 2820

List of Purchases

Still being imported from abroad Qty Item

100 Diode HSMS 2822 50 RS Pro Straight 50 Ω PCB Mount SMA End Launcher Connector, Solder

134

50 End Launcher Connector, Solder Termination 10 LTC5535 Details about 5 PCS LTC5535ES6 LBHK Precision 600 MHz to 7 GHz, RF Detector SC70-6 10 LTC5505 Details about Linear Technology, LTC5505-1ES5#PBF, RF Power Detector, 0.3-3.5 GHz

List of Purchases

Still being imported from abroad Qty Item

1 Professional Grade Glass Cutter Tool (Heavy Duty – 6 way cutting wheel) 2 Diamond Tipped Glass Cutter – Brass

135

2 Snapper – Hardwood Handle 2 65x22 Gauge Assorted Jumper Wires 3 Temperature and Humidity Sensor 3 Light Sensor 2 CO Sensor 2 CO2 Sensor

List of Purchases

Still being imported from abroad Qty Item

3 Accelerometer 3 Nano Timer 5 Load Switch

136

5 Load Switch 3 MOSFET P-CH 3 Development Board 1 PIR Sensor 1 Gateway 3 Zigbee Module

List of Purchases

Still being imported from abroad Qty Item

2 LiFePO4 Battery 1 Battery Charger 2 Battery Holder

137

2 Battery Holder 1 Dehumidifier 10 Chips of LTC3588EDD-1#TRPBF 10 Chips of LTC3588IDD-1#TRPBF 10 MAX17710

[Aderohunmu2014] [Alletto2016] [Brito 2008] [Ceriotti2009]

• F. Aderohunmu, D. Balsamo, G. Paci, and D. Brunelli, “Long term WSN monitoring for energy efficiency in EU cultural

heritage buildings,” in Real-World Wireless Sensor Networks, Springer, 2014, pp. 253–261.

• S. Alletto, R. Cucchiara, G. Del Fiore, L. Mainetti, V. Mighali, L. Patrono, and G. Serra, “An indoor location-aware

system for an IoT-based smart museum,” IEEE Internet of Things Journal, vol. 3, no. 2, pp. 244–253, 2016

• M. Ceriotti, L. Mottola, G. P. Picco, A. L. Murphy, S. Guna, M. Corra, M. Pozzi, D. Zonta, and P. Zanon, “Monitoring
• L. M. P. L. de Brito, L. M. R. Peralta, F. E. S. Santos, and R. P. R. Fernandes, “Wireless sensor networks applied to

museums’ environmental monitoring,” in The Fourth International Conference on Wireless and Mobile Communications (ICWMC 2008), IEEE Computer Society Press, 2008, pp. 364–369.

References References

[Ceriotti2009] [Chianese2014a] [Chianese2014a] [Caicedo2012]

• M. Ceriotti, L. Mottola, G. P. Picco, A. L. Murphy, S. Guna, M. Corra, M. Pozzi, D. Zonta, and P. Zanon, “Monitoring

heritage buildings with wireless sensor networks: The Torre Aquila deployment,” in Proceedings of the 2009 International Conference on Information Processing in Sensor Networks, 2009, pp. 277–288.

• A. Chianese and F. Piccialli, “SmaCH: A Framework for Smart Cultural Heritage Spaces,” in The Tenth International

Conference on Signal-Image Technology & Internet-Based Systems, IEEE, 2014.

• A. Chianese and F. Piccialli, “Designing a smart museum: When cultural heritage joins IoT,” in Next Generation Mobile

Apps, Eighth International Conference on Services and Technologies (NGMAST), 2014, pp. 300–306.

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• D. Caicedo and A. Pandharipande, “Ultrasonic Array Sensor for Indoor Presence Detection,” 20th European

Signal Processing Conference (EUSIPCO ), pp. 27–31, 2012.

[Gruber2014] [Kleiminger2015] [Peralta2013] [Peralta2010]

• W. Kleiminger, C. Beckel, and S. Santini, “Household occupancy monitoring using electricity meters,” in Proceedings of

the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2015, pp. 975–986.

• L. M. R. Peralta, A. M. Abreu, and L. M. Brito, “Environmental Monitoring based on Wireless Sensor Network via

Mobile Phone,” in The Seventh International Conference on Sensor Technologies and Applications, 2013, pp. 161–167.

• L. R. Peralta, L. P. L. de Brito, B. T. Gouveia, D. G. de Sousa, and C. D. S. Alves, “Automatic monitoring and control of

References References

• M. Gruber, A. Trüschel, and J.-O. Dalenbäck, “CO2 sensors for occupancy estimations: Potential in building automation

applications,” Energy and Buildings, vol. 84, pp. 548–556, 2014. [Peralta2010] [Peralta2009] [Peralta2010a] [Saad2013] [Santini2015]

• L. R. Peralta, L. De Brito, B. T. Gouveia, D. De Sousa, and C. Alves, “The wise-muse project: Environmental monitoring

and controlling of museums based on wireless sensors networks,” in Electronic Journal of Structural Engineering, 2009.

• L. R. Peralta, B. I. Gouveia, D. G. de Sousa, and C. da Silva Alves, “Enabling museum’s environmental monitorization

based on low-cost WSNs,” in 10th Annual International Conference on New Technologies of Distributed Systems (NOTERE), 2010, pp. 227–234.

• L. R. Peralta, L. P. L. de Brito, B. T. Gouveia, D. G. de Sousa, and C. D. S. Alves, “Automatic monitoring and control of

museums environment based on wireless sensor networks,” in Electronic Journal of Structural Engineering, 2010.

• S. M. Saad, A. R. M. Saad, A. M. Y. Kamarudin, A. Zakaria, and A. Y. M. Shakaff, “Indoor air quality monitoring system

using wireless sensor network (WSN) with web interface,” in Electrical, Electronics and System Engineering (ICEESE), 2013 International Conference on, 2013, pp. 60–64.

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• W. Kleiminger, C. Beckel, and S. Santini, “Household occupancy monitoring using electricity meters,” in Proceedings of

the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 975–986, 2015.

[Shah2016] [Shah2016a] [Teixeira 2008] [Viani2012]

• J. Shah and B. Mishra, “Customized IoT Enabled Wireless Sensing and Monitoring Platform for Smart Buildings,” in 3rd

International Conference on Innovations in Automation and Mechatronics Engineering, ICIAME, vol. 23, pp. 256–263, 2016.

• J. Shah and B. Mishra, “Customized IoT enabled Wireless Sensing and Monitoring Platform for preservation of artwork

in heritage buildings,” in Wireless Communications, Signal Processing and Networking International Conference (WiSPNET),IEEE, 2016, pp. 361–366.

References References

Teixeira and A. Savvides, “Lightweight people counting and localizing for easily deployable indoors wsns,” IEEE Journal of Selected Topics in Signal Processing, vol. 2, pp. 493–502, 2008. [Viani2012] [Wu2017] [Xiao2016] [Zonta2010]

• F. Wu, C. Rüdiger, and M. R. Yuce, “Real-Time Performance of a Self-Powered Environmental IoT Sensor Network

System,” in Sensors Journal, vol. 17, no. 2, p. 282, 2017.

• C. Xiao, N. Chen, D. Li, Y. Lv, and J. Gong, “SCRMS: An RFID and Sensor Web-Enabled Smart Cultural Relics

Management System ,” in Sensors Journal, vol. 17, no. 1, p. 60, 2016.

• F. Viani, M. Salucci, P. Rocca, G. Oliveri, and A. Massa, “A multi-sensor wsn backbone for museum monitoring and

surveillance,” in Antennas and Propagation (EUCAP), 2012 6th European Conference, IEEE, 2012, pp. 51–52.

• D. Zonta, H. Wu, M. Pozzi, P. Zanon, M. Ceriotti, L. Mottola, G. P. Picco, A. L. Murphy, S. Guna, and M. Corrá,

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• vol. 112, 2016, pp. 28-39

Xiao, Changjiang, Nengcheng Chen, Dandan Li, You Lv, and Jianya Gong, "SCRMS: An RFID and sensor

References References

Ali, Danladi, Michael Yohanna, M. I. Puwu, and B. M. Garkida, "Long-term load forecast modelling using a fuzzy logic approach," In Pacific Science Review A: Natural Science and Engineering, vol.18, no. 2, 2016, pp. 123-127. Erickson, Varick L., Stefan Achleitner, and Alberto E. Cerpa. "POEM: Power-efficient occupancy-based energy management system," In Proceedings of the 12th international conference on Information processing in sensor networks, ACM, 2013, pp. 203-216.

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[Xiao 2016] Xiao, Changjiang, Nengcheng Chen, Dandan Li, You Lv, and Jianya Gong, "SCRMS: An RFID and sensor web-enabled smart cultural relics management system," In Sensors, vol. 17, no. 1, 2016, pp. 60.

THANK YOU

NTRA R&D Workshop, Oct. ??, 2017

THANK YOU

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