Solar-Based Energy Harvesting and Low-Power Wireless Networks

Abstract

In modern industrial applications, machines and facilities, more and more sensors are used to control and optimise the processes. To be flexible and reduce cost, wireless sensors can be used in a broad range of applications. To prevent regular battery replacement, there is the possibility to supply wireless sensors by energy harvesting. In this chapter, we investigate the possibility to use solar-based energy harvesting to supply wireless sensors. For this, we consider four wireless network protocols and evaluate the power consumption using a simple sensor use case with different communication parameters. We further measure how much power can actually be harvested in a typical office environment with natural light. To provide realistic results, different sensor locations as well as seasonal changes are considered. The evaluations are combined to understand what size of solar cell is needed to supply the considered wireless protocols with different configurations.

L. B. Hörmann and J. Karoliny contributed equally to this work.

1 Introduction

Modern industrial processes and machines require more and more data to control the operation and optimise it regarding, e.g., quality, speed, or energy consumption. This data has to be captured by sensors applied at the machines and facilities, and thus, more and more sensors are integrated. These sensors can be connected using Industrial Internet of Things (IIoT). The advances in ultra-low power electronics and wireless communication are the key enabling technologies to integrate computational power and wireless communication functionality directly into the sensors. Such intelligent sensors can be applied not only in industrial use cases, but also in typical Internet of Things (IoT) use cases, e.g., smart buildings, home automation, precision agriculture, and health care applications [1, 2]. Using wireless communication technology, sensors communicate with each other and form so-called wireless sensor networks (WSNs). As part of such a network, they are typically referred to as sensor nodes measuring physical quantities and transmitting them wirelessly towards a gateway using a certain communication protocol [3, 4]. Frequently used protocols to connect sensor nodes are Bluetooth Low Energy (BLE) and OpenThread [5]. If special requirements are necessary, specialised communication protocols may be necessary. One example would be ultra-wideband (UWB)-based communication if wireless localisation is necessary in the applications. Another example would be highly synchronised measurements utilised by a large number of energy-constrained sensor nodes. Here, highly specialised protocols like the Energy and Power Efficient Synchronous Sensor Network (EPhESOS) communication protocol [4] need to be considered.

Most wireless sensor nodes are not powered via the grid. Thus, they can be powered in two different ways: by batteries and by energy harvesting [6]. By using batteries, the energy is supplied already with the sensor node itself, with an operational time that is limited but guaranteed. In a lot of applications, this is acceptable since it is a relatively cheap solution and the cost is transferred to the customer. In this chapter, we discuss the second possibility. Using Energy Harvesting Devices (EHDs), the electrical power to supply the sensor nodes is converted from the power available in the environment. This could be for example solar or artificial light or temperature gradients. The theoretical operational time is not limited by using energy harvesting. This is of great interest if regular maintenance (e.g., for battery replacement) is not possible. Examples are industrial processes or applications where an interruption is unfeasible and cable-based measurement is impractical, e.g., rotating parts or high-voltage applications. In the following, we will describe the most common energy harvesting technology to transform environmental energy into electrical energy in order to supply embedded devices.

1.1 Solar-Based Energy Harvesting

Solar-based energy harvesting is very often used to power embedded devices because of its simplicity. Using solar cells, radiation available in the environment (e.g., visible light) is converted directly into electrical energy. However, typically environmental energy sources do not provide a continuous power [7, 8]. To guarantee the continuous operation of the supplied device, some kind of energy storage must be used. For example, supercapacitors or rechargeable batteries can be applied. The needed capacity depends on the expected variability of the energy source and the average power consumption of the supplied device. If the application area of the supplied device is exposed to natural light from the sun (also indirectly inside buildings), the available power is still discontinuous but fortunately periodically accessible. This periodicity makes it possible to estimate the needed capacity of the energy storage and the size of the solar cells. A perpetual operation is achievable [9]. However, also weather conditions and seasonal changes must be considered in the calculations. In [10], we have presented real-world measurement results of solar-based harvestable energy in an office building. In addition to the good availability of light in most use cases, also simple conversion is an advantage of solar-based energy harvesting. In the simplest form, only a well-selected solar cell is necessary to supply embedded devices. In contrast to other energy harvesting methods (like electromechanical generators), there are no moving or rotating parts which can cause a malfunction. Furthermore, there are also off-the-shelf components to optimise the harvestable power by decoupling the solar cell and the supplied device with integrated maximum power point trackers (MPPTs). These are the reasons why solar-based energy harvesting is a highly favourable solution to supply embedded devices, and thus wireless sensor nodes. This chapter extends the already presented measurement results by combining them with real-world measurement results of the power consumption needed for the discussed communication protocols, and estimating the necessary size of the supplying solar cells for different protocol parameters.

The remaining part of this chapter is organised as follows. Section 2 describes the analysed communication protocols which can be used to connect wireless sensor nodes. Section 3 presents measurement results of the power consumption needed for the discussed communication protocols at various settings. Section 4 summarises the measurement results of the available energy in real-world scenarios. Finally, Sect. 5 presents the key results and Sect. 6 concludes the chapter and gives the direction for future work.

2 Low-Power Network Protocols

In this section, we will introduce different wireless protocols that are relevant for industrial applications and provide energy efficiency to combine with solar-based energy harvesting. We consider BLE, Thread, EPhESOS, and UWB in this work. BLE is one of the key enablers for IoT solutions and, with continuously added features like LE Audio, the number of BLE devices is drastically increasing. Thread or OpenThread is a representative of the home automation wireless protocols. With the huge support of major tech companies and the Matter project [11], Thread can be expected to be the main wireless protocol in this field. EPhESOS shall represent one proprietary wireless solution specifically tailored for industrial use cases. In detail, it supports a high degree of configurability, allows lots of sensor nodes, and determinism while maintaining low-power features. UWB gained a lot of momentum in the recent years, especially in indoor localisation topics. The high bandwidth of this technology allows high throughput and provides the time resolution for Time of Arrival (ToA) or Time Difference of Arrival (TDoA) measurements needed in localisation tasks. While there are many more protocols and standards worth considering, the presented ones will give a good overview of the different wireless sensor topics.

In the following, we summarise the key information of the proposed wireless protocols. We focus on the link layer parts of the protocols, i.e., how the access to the channel and thus the communication with other devices is managed. Although the physical (PHY) layer also plays a role in the comparison of wireless protocols regarding their power consumption, the major part that determines the low-power capability will be the link layer. To maintain the low power consumption needed to combine with energy harvesting, it is important to avoid transmitting messages without the target listening and going into receive mode without expecting a message at an exact time. As a result, we will focus on data exchange and channel access of the proposed protocols.

2.1 Bluetooth Low Energy

BLE is a Wireless Personal Area Network (WPAN) technology that operates in the 2.4 GHz industrial, scientific and medical (ISM) band. It is defined in the same standard as the classical version of Bluetooth BR/EDR, however, they are incompatible in terms of communication. Bluetooth Mesh uses the same PHY layer as BLE but builds different layers on top to support mesh functionalities. Due to the high energy efficiency of BLE, we will use it as a benchmark and representative of the Bluetooth communication protocols. BLE uses Gaussian Frequency Shift Keying (GFSK) modulation with a time-bandwidth-product of 0.5 as PHY layer. In this work, we will use the LE 1M PHY layer of BLE [12]. The specification defines 40 BLE channels with a bandwidth of 2 MHz in the 2.4 GHz ISM band, where channels 0 to 36 are used for general communication and channels 37–39 for advertisement. Before a BLE connection between a central and peripheral is formed, the peripheral uses these advertisement channels to announce its presence and supported features. The central requests for connection and, if the peripheral agrees, they switch to the general communication channel, exchange configuration parameters, and start communicating.

The communication is organised in so-called connection events, which happen periodically with a defined connection interval. The interval can be chosen between 7.5 ms and 4 s and is an important parameter defining the power consumption. Each connection event starts with a message from the central which is immediately answered by the peripheral. As long as there is data available or until the end of the connection event, they continue to exchange data. With the next connection event (the length of the connection interval later) the procedure starts again. To further save energy, the peripheral is also allowed to skip connection events, i.e., no reply to the central if no data is available. To reduce the chance of packet loss due to obstructed channels, BLE applies frequency hopping spread spectrum (FHSS). For each connection event, a new channel is calculated where both devices will communicate to exchange data. Besides reducing the probability of collisions, the channel-hopping itself has no effect on the consumed energy and is not further explained in this work. For the general channel access procedure we refer to [13]. Though both the central and peripheral are able to maintain a very low power consumption, we will use the peripheral for the energy harvesting considerations due to the additional low-power features. Parameters important for low-power operations are the connection interval and the number of connection events that the peripheral is allowed to skip.

2.2 IEEE 802.15.4 and Thread

IEEE 802.15.4 is a wireless standard that describes the operation of a low-rate wireless personal area network (LR-WPAN). It defines a PHY layer and media access control (MAC) layer on which many wireless protocols like ZigBee [14], WirelessHART [15], and Thread build on top. There are different PHY layer versions and frequency bands available, however, we focus on the 2.4 GHz ISM band PHY layer. Here the standard defines 16 channels with a bandwidth of 2 MHz and Offset Quadrature Phase Shift Keying (O-QPSK) for modulation. Additionally, direct-sequence spread spectrum (DSSS) is used which results in a transfer rate of 250 kbps. The MAC layer defines two operation modes, a communication purely based on Carrier Sense Multiple Access - Collision Avoidance (CSMA-CA) and a synchronised version with a central coordinator that sends out periodic beacons. We will focus on the unsynchronised version since it is the one used by the Thread specification. Thread is a low-power wireless mesh networking protocol which builds on IEEE 802.15.4 [5]. It supports IPv6 and is therefore perfectly tailored for IoT applications. Thread gained momentum in the home automation domain and major tech companies contribute to the specification. In a Thread-based network, there can be two types of nodes, Full Thread Devices and Minimal Thread Devices. Full Thread Devices are used to maintain the mesh features and the communication to other networks. These devices usually stay in receive mode and are not suitable for energy harvesting considerations. In this work, we focus on the Minimal Thread Devices, especially on the low-power end devices. For communication with the network, these devices always need a corresponding parent node for packet forwarding. The downlink communication to the node happens periodically to save energy on the low-power end device, while the uplink communication to the parent node can be performed at any time using CSMA-CA.

Unlike BLE, the uplink messages, e.g., sensor data, are not scheduled. However, since our applications are in the WSN domain, data will be provided periodically to the network similar to the other discussed communication protocols. For this, we are using User Datagram Protocol (UDP) messages which are directly supported by Thread. We will not consider the network establishment but only the data communication of a low-power end device to the parent. Thus, important energy-related parameters are the update period of sensor data and the downlink interval of the parent node. For the evaluation, we will use the open-source implementation of the Thread protocol, denoted as OpenThread, which is directly supported by various wireless transceivers.

2.3 EPhESOS Protocol

In industrial environments, WSNs have to fulfil stringent requirements such as tight synchronisation, low power consumption, and deterministic latency. One way to meet these conditions is to guarantee deterministic channel access with time division multiple access (TDMA)-based network protocols and a centralised network coordinator. In TDMA-based networks, individual transmission timeslots are assigned to each sensor node by the network coordinator to prevent collisions between them. One such protocol is the EPhESOS protocol introduced in [4, 16]. A major advantage of EPhESOS is the independence of the PHY layer, which allows the application in different use cases and with various hardware. In this chapter, we choose one implementation of the EPhESOS protocol using the BLE PHY layer, however also others like IEEE 802.15.4 are possible choices. Low-power sensor nodes can join a network, i.e., register with a centralised network coordinator, during the so-called sporadic mode, further described in [4]. For the evaluation, we focus on the energy consumption during the operational state, the continuous mode.

In the continuous mode, all communication is performed highly synchronised in a TDMA structure. For this, every node in the network is only allowed to transmit in a specific timeslot assigned by the network coordinator in the previous steps. All possible timeslots are collected in a so-called superframe which is periodically transmitted with a certain period. Each superframe starts with a beacon, sent out by the network coordinator for synchronisation and as acknowledgements to the individual sensor nodes. The superframe structure of the EPhESOS network is depicted in Fig. 1. Due to the TDMA structure, collisions within the network are prevented and sensor nodes know when to transmit and receive at any time, which allows to maintain very low power consumption. Additionally, nodes only listen to beacons for acknowledgements and synchronisation if they transmitted data in the previous superframe. Otherwise, they can remain in sleep mode during the whole superframe, which additionally saves energy [16]. For the energy harvesting purpose, we will only consider the continuous mode of EPhESOS and the sensor nodes. The network coordinator is not suitable for low-power applications since it needs to stay in receive mode.

Fig. 1

Format of the EPhESOS superframe including the beacon for synchronisation and the timeslots of the individual nodes

2.4 UWB Localisation

In recent years, the UWB technologies gained increasing popularity, especially in location-based topics like indoor tracking and access control. Many smartphone and car manufacturers are currently adding this technology to their products. UWB is a general term for radio communications with a bandwidth of 500 MHz or more. The current focus is on Impulse Radio UWB (IR-UWB) solutions which use radio frequency pulses with a very short time duration and thus a high bandwidth. This enables high timing resolution with steep edges that allows a very accurate ToA detection of received signals. With this, centimetre-level localisation utilising different ranging techniques like Two Way Ranging (TWR) or TDoA is possible. However, there exist many different UWB standards for the PHY and MAC layer. We focus here mainly on IEEE 802.15.4 and IEEE 802.15.4z since these are widely used. Similar to other protocols, we consider the access and communication scheme the most critical part for low-power considerations. However, the channel access scheme in UWB is not defined by any standard which causes many different proprietary solutions. Many chip suppliers leave the implementation of the MAC layer to the host system controlling the chip [17]. Additionally, the access scheme and communication strongly depend on the localisation approach. For the localisation, we differentiate between two different kinds of devices in the network, such as Anchors and Tags. Anchors are in most scenarios devices with a known reference location, while Tags are the mobile devices that will be localised. In this work, we consider the power consumption for the Tag, since this will be the device potentially powered by utilising energy harvesting. Here the most energy-efficient approach is TDoA, where the Tag sends out periodic beacons for the localisation. This beacon will be received by multiple anchors at different times due to the different distances to the Tag. If the Anchors are synchronised, the location of the Tag can be derived based on this time difference. Compared to the other discussed protocols, the location information is not in the transmitted packet, while it is the packet itself. This aspect, together with the completely different hardware makes a fair comparison challenging, however, we include also UWB measurements in our evaluation.

3 Power Consumption in Different Scenarios

In this section, we evaluate if the available energy in the different scenarios is enough to supply the proposed protocols. Here we focus on the WSN use case, i.e., the device-under-test shall provide its sensor data periodically to the network. We evaluate the energy consumption of the protocols introduced in Sect. 2 for the parts that are suitable for low-power sensor applications. As an example, it would be challenging to supply router nodes in a Thread network by energy harvesting since it is required to continuously activate their receiver unit.

In our example use case, a sensor node has to periodically transmit 2-byte sensor data to the network. We want to evaluate how the energy consumption depends on the transmission period including the overhead of the network protocol. Additionally, we want to investigate how much the energy consumption decreases if larger packets are transmitted, although less frequently. Since the beacon itself is the sensor value in the UWB case, we cannot transmit more location information less frequently. However, we still increase the data similar to the other protocols and see the localisation as a side product of the communication. To focus the evaluation only on the communication protocol, we will only transmit dummy data. This ensures that the evaluations are independent of the measurement acquisition.

3.1 Measurement Setup and Hardware

In our measurement setup, we use the NRF52840 transceiver [18], specifically, the corresponding development kit of Nordic Semiconductors. Since the NRF52840 supports multiple wireless protocols, we can cover all proposed network protocols but the UWB scenario. For this, we use the same hardware as described in [10] which combines the NRF52832 with the Qorvo DW1000 UWB transceiver unit. We assume that the power consumption in low-power sleep mode of both boards are comparable and differences in the measurements are due to the additional UWB transceiver unit and different network protocols. To make the individual measurements comparable, no acknowledgements or retransmissions are considered in the evaluations. For the energy consumption measurements, we use the Power Profiler Kit II [19] developed by Nordic Semiconductor. It is a low-cost solution for power profiling, which achieves a considerable measurement resolution of down to 0.2 $\upmu$A and a sampling speed of 100 kS/s. The Power Profiler Kit II is used in source mode to simultaneously power the device and measure the power consumption, which is averaged over 60 s. The power consumption measurements acquired within this work are published as open dataset under [20].

As BLE and OpenThread network stack, we use the implementation of the NRF Connect SDK [21] and adapted the provided example programs. For the BLE measurements, we evaluate the power consumption of a peripheral device. Here we choose a connection interval of 45 ms and allowed the device to skip up to 40 connection events if no data is available. For OpenThread, we implemented a Sleepy End Device which can transmit data anytime and periodically polls its parent node with a configured interval of 1 s. EPhESOS is implemented as described in [4] and for UWB we simply transmit periodic beacons to the anchor nodes for localisation. Here we performed our measurements with a centre frequency of 4.5 GHz and a bandwidth of 499.2 MHz. We choose a PRF of 64 MHz and 64 preamble symbols.

3.2 Power Consumption with Increasing Update Period

One of the key factors for power consumption is the communication period of the network protocol. This period refers to the time between the transmission of two messages. If the sensor data is transmitted less frequently, the device can stay longer in low-power sleep mode and thus save energy. The variation in the individual network protocols is due to the different overhead to maintain the connection. We define a data acquisition interval of 100 ms, where we acquire 2 bytes of sensor data that needs to be transmitted. This interval is kept constant while increasing the communication period. As an example, for a communication period of 400 ms we will transmit 8 bytes of data. Figure 2 and Table 1 depict the results of the power consumption measurements for different communication periods, where they clearly show how power consumption decreases if the devices transmit the data less frequently. However, this decrease is not linear since the amount of data is increased for lower communication rates. Additionally, the overhead of the network protocol does not always scale with the update period. For example, the poll period in OpenThread for our configuration is constant and thus its part in the overall power consumption stays the same. Finally, even in low-power sleep mode, the power consumption is not zero.

Fig. 2

Measured power consumption of the individual protocols with increasing update period

Table 1 Measured power consumption of the individual wireless network protocols with increasing transmit period

Also between the network protocols, a difference in the power consumption can be observed due to the different PHY layer and communication procedure. BLE is a highly-optimised low-power protocol and returns the lowest power consumption in our measurements. However, BLE only supports communication between two devices in the standard configuration. OpenThread and EPhESOS support mesh features with multiple nodes, though having a higher power consumption compared to BLE for our configuration. EPhESOS has a slightly lower power consumption and allows highly synchronised communication with multiple nodes, while OpenThread supports easy network authentication and higher network layer features like IPv6. UWB shows the highest power consumption, although we do not consider bidirectional communication. However, the results for the given use case are still comparable and UWB additionally provides high-accuracy localisation capabilities.

4 Available Energy in Real-World Scenarios

As already stated in Sect. 1, we have performed a measurement campaign inside an office building with solar cells. This section summarises the results, which are necessary to estimate the supply possibilities of the discussed communication protocols. The measurement campaign has been performed in Linz, Austria, at the coordinates N 48.335902 and E 14.322516 in July 2021 (summer). Figure 3 shows the four different locations for short-term measurements.

Fig. 3

Floor plan of the relevant part of the building showing the orientation of the rooms and indicating the locations of the measurements

The measurements have been performed with a self-developed measurement device as presented in [22]. The connected solar cell is regularly characterised by measuring the voltage-current trace of the solar cell from open-circuit to short-circuit. Based on the recorded voltage-current trace, the maximum power point can be calculated. In this chapter, we are considering the three best performing solar cells, which are the YH-57$\,\times \,$65 from Conrad components, with an active area of 30.87 cm$^{2}$ [23], SM141K10LV from IXYS, with an active area of 16.2 cm$^{2}$ [24], and SM141K09L from IXYS, with an active area of 14.49 cm$^{2}$ [25]. The maximum power point of the three solar cells has been averaged for the four measurement locations.

Figure 4 shows the harvestable power density of the solar cells mounted directly on the window glass. It compares the power in two different rooms in sunny and cloudy weather conditions. The results clearly show a difference in the harvestable power depending on the alignment of the room. Additionally, for changing weather conditions, e.g., cloudy weather, the degradation is more in the north-aligned room.

Fig. 4

Harvestable power density of the solar cells placed directly on the window at two rooms

Figure 5 shows the harvestable power density of the solar cells mounted on the doorframe opposite the window side, though directed towards the window. The measurement compares the power in two different rooms in sunny and cloudy weather conditions. Although a similar ratio between the different conditions can be observed, the absolute values are considerably lower. While for the measurements on the window, we could achieve for the best condition a harvestable power density of 381.79 $\upmu$W/cm$^2$, on the doorframe only 13.997 $\upmu$W/cm$^2$ could be achieved. This additionally shows the huge impact of the location within the considered rooms.

Fig. 5

Harvestable power density of the solar cells placed on the doorframe at two rooms

Table 2 summarises the measurement results, including a factor for each value comparing it to the maximum value measured on the south-aligned window. The maximum power density $S_{\text {EH},\text {max},\text {loc}}$ refers to the measured daily maximum harvestable power density and the average power density $S_{\text {EH},\text {avg},\text {loc}}$ refers to the average value which considers also periods with no illumination during the night. This average value is estimated with 1/3 of the daily maximum harvestable power.

Table 2 Summary of harvestable power density at four locations and two different weather conditions. The average power density refers to the daily average value considering also night. A factor is given referring to the maximum harvestable power density (on window at south-aligned room)

Besides the location and the weather conditions, also seasonal changes influence the harvestable power density. Depending on the geo-location, the variation between different seasons is significant. In a long-term measurement campaign done in Graz, Austria, we have tracked the daily harvestable energy for almost two years [6]. The relevant results are summarised in Table 3 showing the harvestable energy on the south and north side of a building. For summer, the daily maximum harvestable energy, and for winter, the average harvestable energy (including the months December, January and February) is depicted. The measurements during summer are comparable with those previously presented based on sunny weather conditions. To consider the lower harvestable energy during winter, we calculate a factor between the daily maximum during summer and the daily average during winter. It can be seen that the difference between summer and winter is more significant at the south side. The reason is the direct sunlight during summer which is much stronger on the south side. For bad weather conditions in general, the clouds and fog act as giant diffusor which distribute the light almost equally.

Table 3 Summary of daily harvestable energy density at two locations in winter and summer

In order to estimate the harvestable energy, both location and seasonal changes have to be considered. The seasonal changes presented in Table 3 also include weather variations, since maximum and minimum values of nearly two years of records are used. As the factor depends on a specific location, these numbers should only indicate the magnitude and only be used for a rough estimation. The harvestable power density $S_{\text {EH}}$ can be estimated as follows:

$$\begin{aligned} S_{\text {EH}}= S_{\text {EH},\text {avg},\text {loc}}\cdot f_{\text {loc},\text {weather},\text {season}} \end{aligned}$$

(1)

Table 4 reports the numbers used for the estimation of the daily average harvestable power density at four different locations. This estimation considers the worst-case scenario of supplying a device during the winter months including December, January, and February without artificial light. Since the factor is based on the average value of three months, it includes also seasonal and weather effects. Please note that the device needs an energy storage device to bridge periods with insufficient illumination conditions.

Table 4 Estimation of the daily average harvestable power density at four different locations at the south and the north side of a building

5 Experimental Results

In Sect. 4 we evaluated how much energy can be harvested in a typical office environment using the available solar energy, while in Sect. 3 we evaluated how much energy is needed in common network protocols. Combining the results from both evaluations, we now investigate if the proposed wireless protocols are suitable to be operated by solar-based energy harvesting solutions. Specifically, we evaluate how big the solar cell needs to be at least to support the considered network protocols for the different update periods. This will help with the design and location choice of the sensor nodes.

Figure 6 depicts the needed solar cell area for each communication protocol for the deployment on the window in the south-aligned room. This evaluation also includes the factor for seasonal changes as depicted in Table 4. Depending on the update period and choice of network protocol the needed area for the solar cell can be estimated.

Fig. 6

Needed solar cell area in cm$^2$ of the different network protocols and update periods for the south-aligned room and window position. Here also the weather and seasonal factors are included

Tables 5 and 6 illustrate the remaining results for the window and doorframe location, respectively, with Fig. 6 showing the experimental results listed in Table 5, with regard to the needed area in the south collection area (highlighted in gray color in Table 5). These presented results can be used to properly select a wireless communication depending on the specific application requirements as well as to estimate the shortest possible communication period if a certain solar cell area is given. The results also include location-dependent influences which may be of great interest for certain applications. The main outcome is that a supply of wireless devices is easily possible at well-illuminated locations on or near a window independent of its orientation. At a location with bad illumination conditions inside a room, the supply of wireless devices is more challenging and the solar cells must be significantly larger considering only natural light. However, also here a supply is possible if the communication periods are long enough.

Table 5 Needed solar cell area of the different network protocols and update periods for the window position. Here also the weather and seasonal factors are included

Table 6 Needed solar cell area of the different network protocols and update periods for the doorframe position. Here also the weather and seasonal factors are included

6 Conclusion

This chapter discusses four well-suited communication protocols for wireless devices and presents measurement results of their power consumption at different communication periods. It further summarises the most relevant measurement results of the harvestable power at different locations in a building using solar cells including also the evaluation of weather and seasonal changes. Both measurement campaign results are combined in order to estimate the needed size of a solar cell depending on the location, the used communication protocol, and the selected communication periods considering only natural light. As presented, the supply using solar-based energy harvesting is easily possible at locations with good illumination conditions on or near a window. At locations with bad illumination conditions it is more challenging but also possible by using solar cells with reasonable size and adapted communication parameters.