Energy Harvesting Techniques for Wireless Sensor Networks/Radio-Frequency Identification: A Review
Abstract
:1. Introduction
- Presents an overview of popular research topics on green WSNs and RFID ecosystem, covering the recent industry development in the main areas of application, challenges and key players;
- Addresses several substantial design choices and features for WSNs and RFID, both of which are considered the top priorities of green IoT technologies. These features are deeply investigated on the basis of their respective sub-domains to achieve a precise, concrete and concise conclusion;
- Provides new references to other researchers who need insights into enabling hardware green IoT which provides eco-sustainability.
2. Green Wireless Sensor Network
2.1. Radio Optimisation Techniques
- Joint rate and power control are modelled as two distinct games (i.e., an uplink transmission rate allocation and an uplink transmission power allocation problem), which are based on the game theoretic perspective. Users determine first their uplink transmission rate and then given their uplink transmission rate, they apply power control to allocate their uplink transmission powers [51,52]. The main drawback of this approach is that the optimisation problem is solved asynchronously and separately considering the two systems’ resources. Thus, the combined outcome of the two distinct optimisation problems is less efficient than jointly solving the problem [53].
- The joint rate and power control problem is amended in a single-variable problem of the ratio of uplink transmission rate to the uplink transmission power [54]. However, this approach is limited in realistic cases and can only be applied in specific studies where simplified forms of utility functions are assumed (i.e., where the ratio of uplink transmission rate to power appears). As a result, the use of this approach strongly depends on problem formulation. The single variable problem is solved with respect to the substituted ratio. To determine users’ optimal pair of uplink transmission rate and power, the maximum value of one resource is assumed and the other one is determined, so the ratio is equal to the optimal one. Although users update their uplink transmission rate and power in the same step, the obtained solution remains inferior compared with the corresponding solution of the actual joint two-variable optimisation problem discussed in [53].
2.2. Sleep/Wake Up Techniques
2.3. Energy Harvesting and Wireless Charging Techniques
2.4. Energy-Efficient Routing and WSN Architecture
- Reduction of transmitting distance of cluster members requiring lower transmission power.
- Cluster heads limiting the transmissions frequency as a result of fusion.
- Mandating the cluster head to perform all the energy-sapping functions, such as coordination and aggregation.
- Permit to power-off some cluster members while the cluster head assumes the forwarding roles.
- Alternate the choice of cluster head among the nodes so as energy consumption in the network.
2.5. Aggregation and Reduction of the Data
3. Green Radio-Frequency Identification
3.1. Passive RFID Systems
3.2. Active RFID System
- Low-end devices. These devices are similar to traditional watches or pacemakers that can be powered for a long time (years) by button type batteries, given their low energy consumption (usually under 100 µW).
- Mid-range devices. These devices consume an average of 500 mW, considering their use of wireless communications transceivers. They usually last less than a day (often just several hours) when transmitting continuously, although certain technologies make use of sleep modes or periodic transmissions (e.g., BLE beacons) to last long. This kind of device requires bulkier batteries than low-end devices (e.g., AA or AAA batteries), making them less appropriate to be embedded into smart clothing.
- High-end devices. These are devices similar to smartphones or laptops that consume up to 50 W. They usually utilise Li-ion batteries, which can be bulky and add weight to a garment.
4. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Parameters | LoRa | Bluetooth | LR-WPAN | Mobile Communication | WiMAX | WiFi |
---|---|---|---|---|---|---|
Standard | LoRaWAN R1.0 | IEEE 802.15.1 | IEEE 802.15.4 (ZigBee) | 2G-GSM, CDMA 3G-UMTS, CDMA2000 4G-LTE-A | IEEE 802.16 | IEEE 802.11 a/c/b/d/g/n |
Energy consumption | Very Low | Bluetooth: Medium; BLE: Very Low | Low | Medium | Medium | High |
Frequency band | 868/900 MHz | 2.4 GHz | 868/915 MHz, 2.4 GHz | 865 MHz–2. GHz | 2–66 GHz | 5–60 GHz |
Data rate | 0.3–50 Kb/s | 1–24 Mb/s | 40–250 Kb/s | 200 kb/s–1 Gb/s | 1 Mb/s–1 Gb/s (Fixed) 50–100 Mb/s (mobile) | 1 Mb/s–6.75 Gb/s |
Transmission range | <30 Km | 8–10 m | 10–20 m | Entire cellular area | <50Km | 20–100 m |
Cost | High | Low | Low | Medium | High | High |
Wireless Technology | Healthcare | Smart Cities | Smart Building | Automotive | Industry | Local Network (M2M) |
---|---|---|---|---|---|---|
Bluetooth (BLE) | very high | low | low | very low | very high | medium |
LR-WPAN | medium | high | low | very low | low | high |
LoRa | low | high | high | high | high | high |
WiFi | low | high | medium | medium | low | high |
WiMAX | low | very high | high | high | very high | high |
Mobile communication | low | high | high | high | medium | very low |
Environment | Power Harvester | |||
---|---|---|---|---|
Solar Panel | Wind Generator | Thermoelectric | Electromagnetic | |
Power density of the indoor environment | 100 µW/cm2 | 35 µW/cm2 @ wind speed < 1m/s | 100 µW/cm2 @ 5 oC | 4 µW/cm3 @ human motion (Hz) 800 µW/cm3 @ machine (kHz) |
Power density of the outdoor environment | 10 mW/cm2 | 3.5mW/cm2 @ wind speed ≤ 8.4m/s | 3.5 mW/cm2 @ 30 oC |
IoT Applications | Energy Harvesting Source | ||||
---|---|---|---|---|---|
Solar Panel | Wind Generator | Electromagnetic | Thermoelectric | ||
Smart Home | Outdoor sensor | ✔ | ✔ | ✔ | |
Smart thermostat | ✔ | ✔ | |||
Air quality monitor | ✔ | ✔ | |||
Lighting | ✔ | ✔ | |||
Security monitor | ✔ | ||||
Smart door lock | ✔ | ||||
Wearables | Smartwatch | ✔ | |||
Monitoring and tracking | ✔ | ||||
Health | Medical patch | ✔ | ✔ | ||
Fitness band/monitor | ✔ | ||||
Industrial | Factory automation | ✔ | ✔ | ✔ | |
Machine monitor | ✔ | ✔ | |||
Vehicles | Wireless parking meter | ✔ | ✔ |
Technique | Advantages | Disadvantages | Charging Distance |
---|---|---|---|
Magnetic inductive coupling |
|
| From a few millimetres to a few centimeters. |
Magnetic resonance coupling |
|
| From a few centimeters to a few meters. |
Non-directive RF radiation |
|
| Typically, within several tens of meters, up to several kilometers. Suitable for mobile applications. |
Issues | Battery | Capacitor |
---|---|---|
Advantages |
|
|
Challenges |
|
|
Improvements |
|
|
Device | Power | Battery Type | Operating Period | Weight (g) | Size (l × w × h/d × h, mm) |
---|---|---|---|---|---|
Watches | 3–10 µW | Silver oxide button | 1–2 years | 2.4 | 11.6 × 5.4 |
Pacemakers | 25–80 µW | Lithium button | 7–10 years | 2.83 | 20 × 3.2 |
Hearing aids | N/A | Zinc-mercury oxide | 25–30 days | 0.3–1.85 | (5.8−11.6) × (3.6–5.4) |
Digital clocks | 13 mW | Silver oxide button | 6–10 months | 15–25 | (5.8–11.8) × (1.65–5.4) |
LEDs | 25–100 mW | Silver oxide button | 6–12 months (depends on the usage frequency) | 15–25 | (5.8–11.8) × (1.65–5.4) |
Pedometers | 250 mW | Silver oxide button | 1–2 years (depends on the usage frequency) | 15–25 | (5.8–11.8) × (1.65–5.4) |
Portable radio | 500 mW | AAA | 3–6 months (depends on the usage frequency) | 8.5–11 | 10.5 × 44.5 |
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Alsharif, M.H.; Kim, S.; Kuruoğlu, N. Energy Harvesting Techniques for Wireless Sensor Networks/Radio-Frequency Identification: A Review. Symmetry 2019, 11, 865. https://doi.org/10.3390/sym11070865
Alsharif MH, Kim S, Kuruoğlu N. Energy Harvesting Techniques for Wireless Sensor Networks/Radio-Frequency Identification: A Review. Symmetry. 2019; 11(7):865. https://doi.org/10.3390/sym11070865
Chicago/Turabian StyleAlsharif, Mohammed H., Sunghwan Kim, and Nuri Kuruoğlu. 2019. "Energy Harvesting Techniques for Wireless Sensor Networks/Radio-Frequency Identification: A Review" Symmetry 11, no. 7: 865. https://doi.org/10.3390/sym11070865