Design of Wireless Sensors for IoT with Energy Storage and Communication Channel Heterogeneity
Abstract
:1. Introduction
- Definition and adoption of an appropriate structure and topology for the WSN;
- Decision on the parameters that must be optimized from the energetic perspective;
- Development of an AWS prototype in order to simulate, using real components, several use cases and highlight the relationship between data and energy consumption in accordance with the application’s requirements (e.g., spectrum and storage system life span).
2. Related Work
2.1. Transceivers, Standards and Parameters
- Communication protocol. Energy consumption, latency and throughput for different Medium Access Control (MAC) protocols for WSNs may have a significant impact on the sensor’s performance [8]. A significant reduction in energy consumption (i.e., 18%–45%) was obtained for MAC protocols based on Bluetooth (BT) nodes with increased throughput and lower latency. Experimental data proves that Bluetooth Low Energy (BLE) is more energy efficient when compared to the ZigBee protocol. Translated in power consumption, an improvement from 35–40 mW to 12–16 mW can be gained, as illustrated in Table 1. The possibility to develop smart applications with BLE is reviewed in Reference [9]. Solutions based on BLE are more efficient than the Wi-Fi based implementations. Comparing Wi-Fi with BLE in terms of power consumption, Table 1 and Table 2 illustrate BLE’s advantages.
- Components cost. While most commercial devices for WSNs are expensive and proprietary, and as IoT continues to grow, more resources are needed for building smart WSNs with lower costs. The performance of built-for-purpose devices against open-source devices is analyzed in Reference [10]. Based on the analysis, the most expensive proprietary devices for WSNs are based on the ZigBee standard.
- Sensor lifetime. BLE can increase the lifetime of the system for up to 5 years in some cases [11]. Recently, novel BLE mesh topology with improved scalability, sustainability, and coverage was explored [12]. A systematic review of BLE’s performance and limitations is presented in Reference [13]. Unfortunately, studies on network coverage and energy consumption for different operations or models that follow real-world power consumption based on bit rate and topology variations are absent.
- WSN topology. A different topology may be employed for achieving optimal performance, when attenuation and interference sources are present. The star topology is based on peer-to-peer communications among the gateway and the WSNs. Hybrid and mesh topology are more adaptable to the environment’s radio settings and nodes failure, by enabling new density of network nodes.
- Range. The BT/BLE transceivers have a short range compared to RFM transceivers that work at more than several hundred meters. In case of ZigBee and Wi-Fi, there are medium range transceivers. Wi-Fi consumes high energy when communicating at the range limits.
- Communication reliability. An important aspect, less investigated in the research literature, is the reliability of the WSN in terms of transceiver antenna and band coexistence. BLE based transceivers allow a much shorter range, gain, and sensitivity threshold than ZigBee and Wi-Fi, as illustrated in Table 1. It is possible to use a directional antenna instead of an omnidirectional one, commonly implemented by ZigBee and Wi-Fi [14]. The benefits are: Improved energy efficiency, transmission range, and fewer collisions. The coexistence in the 2.4 GHz band is still controversial, especially between ZigBee and Wi-Fi [15].
- Security. WSNs communicate sensitive data, thus security concerns must be addressed at the beginning of the system design [16,17,18,19,20,21,22,23]. The main aspects deal with: Limited resources [16], unreliable communication [16,17], unattended operation [16], data integrity and confidentiality [18,19], authentication [19], time synchronization [19], secure localization [19], traffic analysis attacks [20], and countermeasures to attacks [21], like cryptography and key establishment [22,23]. Due to the resource, space, and cost constraints placed on the sensor nodes in a WSN [24], many of the traditional security solutions are not suitable. The large number of threats makes it very difficult to build security solutions for WSNs.
- Application requirements. WSNs are used in many domains, e.g., military, industrial, environmental, residential, and health care [25,26]. Applications include smart homes (systems based on own Wi-Fi platforms [27], or commercial: ESP8266 [28,29]) to smart cities (including smart transportation [30], smart governance [31,32], and smart grid [32])smart utilities(especially water [33,34,35] and energy management [33,35] systems) to smart cars (including software defined networks [36], automotive applications [37], smart parking systems based on ZigBee platforms [38], and car security-based on Arduino Uno board [39]), and precision agriculture (mainly smart farming and irrigation with Wi-Fi platforms, such as ESP8266 [40] and ZigBee platforms, such as eZ430 [41] or 3G/4G/Wi-Fi connections [42] to e-health solutions (mainly patient monitoring and support with Raspberry Pi board [43], or with ZigBee platforms, such as Xbee [44], or with Bluetooth [45]). Depending on their requirements and sensor capabilities, one can define WSNs in terms of size (small to very large scale), sensors’ capacity (homogeneous to heterogeneous), topology, and mobility (static, mobile, and hybrid) [46]. Many types of WSN architectures are presented in literature, such as these: Based on DAQ boards [47], for indoor localization [48], based on intelligent gateways [49], industrial [50], and global/ heterogeneous sensor data networks [51]. IoT-based architectures for WSNs are reviewed in Reference [52]. A flexible architecture can be achieved, as discussed inReference [53]. All applications can benefit from new, low-power WSN standards and platforms, as illustrated in References [47,48,49,50,51]. By taking them into account, a modular IoT architecture is proposed in Reference [4]. While LoRa and ZigBee [48,50,51] are perceived as more suitable, most implementations do not consider, in their analysis, IoT-based requirements such as connectivity and cost (illustrated in the last columns of Table 1 and Table 2). Different WSN deployment strategies can be adapted in this sense to solve coverage, network connectivity, deployment cost, energy efficiency, life span, data fidelity, and load balancing issues. The cost of Zigbee solutions, especially Xbee-based, is still high enough for low-cost IoT implementations. On the other hand, Lora has adopted a very efficient modulation, respectively, chirp spread spectrum modulation for achieving low power, simultaneously increasing the range. At the same time, this protocol shows a higher robustness to interference. The costs for transceivers are kept low and are able to support high data rates. Mentioned features make this protocol very attractive for implementing a large spectrum of IoT applications [54,55].
2.2. Energy Sources and Storage for AWS
- Battery. The specificity of WSN-related applications requires the use of energy sources that have to meet constraints such as: Being mechanically robust, having high energy/power densities, and exceptional lifespan. Recent developments in micro batteries are related to the development of controlled 3D atomic structures that generate exceptional properties and high performance [59]. LiPO (Lithium Polymer) batteries, or other new implementation like NiSn-LMO (Nickel tin-anode, Lithiated Manganese Oxide-cathode) reach ~440 Whkg−1. In the case of Li-air batteries, the energy density is higher and can reach 700 Whkg−1 [60]. These values are comparable with the liquid fuel energy density. Despite the batteries technological progress, two main issues still remain: The relatively high internal resistance and reduced cycle-ability and life span of batteries.
- Super-capacitors (SC). Therecent evolution of the SC domain shows a significant extension of the temperature domain (−40 °C at more than +150 °C), in parallel with an increase in capacity (more than 550 Fg−1 theoretical value, at huge specific surface more than 2675 m2g−1), power (10 Wg−1), and energy density (more than 10 mWhg−1) comparable with Li-Ion batteries 100 mWhg−1). The significant increase of energy density at values similar to lead-acid batteries, make these solutions very attractive for future developments. An actual manifested trend illustrates the research and development of a fully integrated solid-state device that merges transceivers and storage elements on the same system.
- Hybrid Energy Storage System (HESS)—as a combination of batteries and SC. In this case, the high-power density of SC will be in accordance with the transceiver needs. Moreover, hybrid SCs have one electrode based on Faradaic phenomena (chemical), and a second one based on non-Faradaic phenomena (electrostatic).
- Harvesting-based Systemsassure an infinite life span for the AWSs, if the harvesting generator is properly integrated with the storage element. The sizing of the storage element must shadow the attributes of the energy harvesting system (e.g., solar, mechanical, thermal, electromagnetic, or piezoelectric). Various AWSs were proposed, employing ZigBee and energy harvesting mechanisms [58,59,60,61,62], however, these implementations not only lack detailed lifetime analysis based on environment and spectrum information, but also lack relevant cost estimations. Additionally, we show that it is crucial to perform analyses based on the energy consumed over a transmitted bit so as to precisely determine the impact of operation phases on the wireless transceiver consumption. A similar solution with a harvesting system consists of building a WSN that uses both wireless transfer of signal (information) and also energy. This solution is investigated in Reference [63]. In References [64,65], various mathematical models are proposed as topology and organization of WSNs are redesigned [66,67]. For improved autonomy, the strict control of the AWS’s energy state becomes of crucial importance. Current trends propose the replacement of classic batteries with new storage solutions (e.g., micro super-capacitors) that present many advantages (e.g., weight, extended temperature domain, life span, robustness, power, and energy density).
3. Transceiver Testing Methodology
4. AWS Design and Implementation
- Parameters associated with the transceiver performance (e.g., range, band, and power consumption).
- Parameters that describe the energy stored as well as the static and dynamic performances.
- Parameters inter-related with the communication protocol.
- Parameters influenced by the sensor’s physical placement and environmental conditions.
4.1. Hardware Implementation
4.2. Software Components
- Connecting and transferring data to another AWS that has similar interfaces, respectively, BT (BLE) and RFM (2.4 GHz-24L01) interfaces.
- Preprocessing of the acquired data: Mean values calculation, histogram of data acquired, and conversion from binary to ASCII in order to improve the telegram transfer visibility.
- Data transfer initialization through the chosen transceiver, as well as triggering the current acquisition signals of the transceivers on the AWS. The recording time is limited by the microcontroller memory (i.e.,8 KB).
- Offline transfer of the data files with the recorded currents through a serial interface at the initiative of the network data collector (UART).
- Allows star and mesh topology implementations.
- Scalable, flexible and re-configurable routines allowing quick modification of the initial setup of each AWS node.
- A limited set of ASCII commands transmitted through the UART serial interface, ensures system control during experiments.
5. Band Coexistence for Short to Medium Range Communication
- In the first scenario, the free spectrum can be observed for the entire ISM band: [2400–2480 MHz], which is almost at noise level (around −100 dBm)
- In the second scenario, the spectrum is occupied: The best case is for [2400–2420 MHz], and the worst case for [2430–2450 MHz], as seen in Figure 6a,b where Wi-Fi interference is less visible.
6. 3D Visualization of the AWS Emission Fields and Power Consumption
6.1. 3D Representation for Power Consumption Evaluation—Static Systems
6.2. Current Drawn by Medium to Short Range Transceivers
6.3. Power Consumption for Medium to Short Range Communications
- Before the pairing stage,
- Transmission/reception (echo mode) of a character with minimum transition stages (the ‘Null’ character 00H) and
- Transmission/reception (echo mode) of a character with maximum transition stages (‘U’ character, 55H).
7. Methodology for Sizing Hybrid Storage Systems and Optimization
- (a)
- The transceiver operates usually in the voltage (supply) interval: [Vmin,Vmax], where Vmin = 1.6 V and Vmax = 3.6 V. We consider the variation of the supply voltage (for the voltage windows interval) as half of Vmax (= 1/2 × 3.6 V = 1.8 V). Therefore, the new levels are: Vmin = 1.8 V and Vmax = 3.6 V.
- (b)
- For Li-Ion batteries the voltage window is [3.6 V,4.2 V], where 3.6 V represents SoC = 0%, and 4.2 V represents SoC = 100%. (SoC = state of charge).
- (c)
- We consider for the analogue switch. The control voltage interval is [1.6 V,3.6 V]
- If then operation based only on battery is sufficient
- If then operation based only on battery is not sufficient, and . The calculated value for Cequiv reaches 0.6 µF.
- The maximum data flow of transceiver in accordance with the process or applications requirements;
- The actual palette of BT implementations that can satisfy a large variety of applications;
- The environmental conditions that can play a significant role on design strategies.
8. IoT Applications
8.1. 3D Thermal (+Other Parameters: Humidity, Light etc.) Maps
8.2. RFM Based Application
9. Conclusions and Future Work
- There are dependencies between different data payload flows (with command, from 50 to 500 characters), stages (disconnected, no command/command), modes (echo/no echo), and distance between transceivers and transceiver type.
- There is a difference in power consumption, from 7% to 30%,for data payload content at extremes (null vs. “U” characters), for the actual transmission period (2.5 ms),
- Energy efficiency can be optimized by taking into account the above observations.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Short to Medium Range | Long Range | Proprietary | ||||
---|---|---|---|---|---|---|
Metric per technology | ZigBee/ *802.15.4e | Bluetooth/ *BLE | Wi-Fi/ *802.11ah | LoRa | MIOTY | nRF24 |
Radio Spectrum Performance | ||||||
Main Freq. bands | 868/915 MHz & 2.4 GHz | 2.4 GHz | 2.4–5 GHz/770, 868,915 MHz | 868/915 MHz | 868 MHz | 2.4 GHz |
Spreading sequence & Ch. bandwidth | DSSS/+TSCH | FHSS | MC-DSSS, CCK | CSS | CSS | DSSS |
2 MHz | 1 MHz | 22 MHz/1–16 MHz | <500 KHz | 200 KHz | 1 MHz | |
RF channels &IF band resist. | 1,10 & 16 | 79/40 | 11 to 24 | 10 EU, 8US | unknown | 126 |
modest | good | best | good | best | poor | |
Power Consumption | ||||||
Sleep & Peak current | 4.18 μA | 0.78 μA | 50–70/≅1 μA | 1 μA | 1 μA–10 μA | 26 μA |
30–40 mA | 30/15 mA | 116/22 mA | 32 mA | unknown | 18 mA | |
Power cons. watts | low | med/low | high/low | low | low | low |
36.9 mW | 215 mW/10 mW | 835 mW/≅200 mW | 100 mW | unknown | 60 mW | |
Pow. Efficiency | 0.15 μW/bit | 186 μW/bit | 0.005/50 μW/bit | 1.5 μW/bit | unknown | 2.48 μW/bit |
Data Flow | ||||||
Data rate &Max. throughput | 250 Kbps | 1–25 Mbps/3 Mbps | 11,54,300 Mbps 0.15–346 Mbps | 50 Kbps | 0.4 Kbps | 2 Mbps |
150 Kbps | 2 Mbps/300 Kbps | 7,25,100 Mbps/≅40 Mbps | 22 Kbps | unknown | 372–512 Kbps | |
Latency | 20–30 ms | 100 ms/6 ms | 50 ms/≅1 ms | >1 s | unknown | 20–30 ms |
Coverage | 10–300 m | 10–30 m/10 m | 100–500 m/1 km | 5 km | <15 km | 10–50 m |
Connectivity | Possible w. 6 LP | yes | yes | Possible w. 6LP | Possible, no IP cnct. | yes (limited) |
WSN IoTDevelopment Platforms and Modules | ||||||||
---|---|---|---|---|---|---|---|---|
Transceiver | ESP8266 | NRF24L01 | HM-10 | HC-05 | AMS001/002 | LM811 | MicaZ | Xbee |
Standard | Wi-Fib/g/n | Nrf24 | BLE | BT | BLE | BLE/Wi-Fi | ZigBee | ZigBee |
Supply | 3.3 V | 3.3 V | 2–3.7 V | 3.6–6 V | 1.8–3.6 V | 3.3/5 V | 2.7–3.3 V | 3.3 V |
Current draw Tx and Rx | 100–150 mA | Tx 7–11.3Ma Rx 9–13.5 mA | 8.5–9 mA | ~30 mA | Tx 13/23 mA Rx 11/25 mA | 150 mA | Tx17.5 mA Rx19.7 mA | Tx 45 mA Rx 50 mA |
Max range | 100 m | 10–50 m | 10–20 m | 10–20 m | 10–20 m | 10–20 m | 20–70 m | 10–100 m |
Size (mm) Weight(g) | (10–18) × (20–24), 2–20 g | (12–18) × (18–40), 10–20 g | 13 × 27, 8 g | (13–15) × (27–28), 15–20 g | 11.4 × 17.6, 20 g | 12 × 25, 25 g | 32 × 58, 20 g | 23× (27–33), 40–70 g |
Cost | 5–10$ | ≅5$ | 5–10$ | ≅5$ | 5–10$ | 10–20$ | 300$ | 30–200$ |
Characteristic | HC-05 | JDY-30 | HM-10 | NRF24 |
---|---|---|---|---|
Indoor scenario range (same floor level) | 10–15 m | 10 m | 5 m | 15–25 m, 100–200 m 1 |
(between floors) | 6–10 m | 5 m | 2–3 m | 15 m, 100 m 1 |
Throughput loss under interference | Severe: 30–50%, Average: 15–20% | Severe: 45–60%, Average: 20% | Severe: 70–80%, Average: 25% | Not higher than 20% |
Indoor scenario in-band interference 2 | considerable | considerable | worst effect | negligible |
Outdoor scenario range | 30–40 m | 30 m | 20–30 m | 100 m, 1 km 1 |
Characteristic | MicaZ | HC-05 |
---|---|---|
Theoretical model: dBm range | −65.71 to −74.22 | −65.71 to −74.22 |
Model based on measurements: dBm range | −65.71 to −77.70 | −63.1 to −67.38 |
Difference in dBm between theoretical model and model based on measurements | Average: 0.5, Maximum: 3.48 | Average: 0.9, Maximum: 6.86 |
I(mA) | Tx&Rx | Mean | Spike | Nocmd |
---|---|---|---|---|
BLE [100] | 17.5 | 8.53 | 16 | 7.4 |
HM-10 | 20.60 | 10.47 | 18 1 | 8.80 |
JDY-30 | 31.98 | 14.40 | 60.43 | 8.53 |
HC-05 | 47.25 | 31.53 | 62.02 | 18.14 |
% | Tx&Rx | Mean | Spike | Nocmd |
---|---|---|---|---|
BLE [100] | 100 | 100 | 100 | 100 |
HM-10 | 117.71 | 122.77 | 112.5 1 | 118.88 |
JDY-30 | 182.74 | 168.86 | 377.69 | 115.22 |
HC-05 | 269.99 | 369.64 | 387.62 | 245.15 |
With Spikes | Energy [µJ]/2.5 ms | Energy/Byte [nJ/char] | ||||||
50 U | 50 Null | 100 U | 100 Null | 50 U | 50 Null | 100 U | 100 Null | |
HC-05 | 391.23 | 431.78 | 447.19 | 442.79 | 7.133 | 7.158 | 3.806 | 3.897 |
JDY-30 | 129.19 | 122.20 | 170.47 | 154.39 | 3.188 | 3.352 | 1.572 | 1.681 |
HM-10 1 | 103.07 | 106.73 | 132.51 | 125.43 | 1.916 | 1.989 | 1.060 | 1.029 |
Without Spikes | Energy [µJ]/2.5 ms | 50 | 100 | |||||
50 U | 50 Null | 100 U | 100 Null | U | Null | U | Null | |
HC-05 | 337.82 | 328.80 | 406.41 | 345.41 | 372% | 360% | 359% | 379% |
JDY-30 | 116.70 | 108.19 | 170.47 | 140.65 | 166% | 169% | 148% | 163% |
HM-10 1 | 103.07 | 106.73 | 132.51 | 125.43 | 100% | 100% | 100% | 100% |
T1 | T2 | T3 | T4 |
---|---|---|---|
204,703 | 30,205 | 55,683 | 106,703 |
94,484 | 0 1 | 19,219 | 15,490 1 |
80,501 2 | 0 2 | 22,574 | 0 2 |
Solution | Real-Time | Points of Representation | Cost | 2D or 3D, No. of Bits, Temp. Accuracy (°C) |
---|---|---|---|---|
Fluke TI 20 | yes | 12,288 | 1300 euro | 2D, 14 bits, 2 °C |
12 HC-05s+12 DHT22s with Arduino | no, 2 sets of measurements | 12 × 2 = 24 | 90 euro | 3D, 8 bits, 2 °C |
20 HC-05s+20 DHT22s with Arduino | yes | 20 | 150 euro | 3D, 8 bits, 2 °C |
Our solution with 3 AWSs | yes | 20–24 | 30–90 euro 1 | 3D, 10–14 bits, 2 °C |
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Borza, P.N.; Machedon-Pisu, M.; Hamza-Lup, F. Design of Wireless Sensors for IoT with Energy Storage and Communication Channel Heterogeneity. Sensors 2019, 19, 3364. https://doi.org/10.3390/s19153364
Borza PN, Machedon-Pisu M, Hamza-Lup F. Design of Wireless Sensors for IoT with Energy Storage and Communication Channel Heterogeneity. Sensors. 2019; 19(15):3364. https://doi.org/10.3390/s19153364
Chicago/Turabian StyleBorza, Paul Nicolae, Mihai Machedon-Pisu, and Felix Hamza-Lup. 2019. "Design of Wireless Sensors for IoT with Energy Storage and Communication Channel Heterogeneity" Sensors 19, no. 15: 3364. https://doi.org/10.3390/s19153364