1 Introduction

The efficient management of utilities such as natural gas, electricity, or water is a challenge faced by modern societies. It is also a focus of interest for smart cities that deal with the problems generated by rapid urbanization and population growth [1, 2]. According to the United Nations [3], water usage worldwide is increasing by about 1% per year since the 1980s and is expected to rise, at a similar rate, up to 30% above the current level by 2050. Predictions suggest that the demand in industrial and domestic sectors will be the primary drivers for demand increase.

Water losses in supply systems can reach an impressive amount of about 50% on a global scale, being one of the most relevant problems affecting water distribution efficiency worldwide. They are usually related to infrastructure deterioration, ineffective billing systems, inaccurate metering, and unauthorized consumption [4, 5]. Water distribution monitoring and automatic metering are valuable instruments to improve water distribution efficiency.

Traditionally, the water management companies used fully mechanical water meters, which have low cost and good reliability but allow neither real-time nor automatic water consumption monitoring. As such, the monitoring activities involve periodic visits to the water meter installation site to retrieve readings manually, while billing brings about estimations based on past consumption profiles. This process has considerable operational costs, is time-consuming, and prone to errors [6].

The introduction of the automatic metering reading technologies transforms the sensors into smart water meters—devices that can measure water consumption and store usage data, becoming part of a widespread sensor network that communicates with a central hub, which bills based on real data rather than estimates [6, 7]. The potential of water metering is enormous. For end users can be an effective way to raise consumer awareness, potentially leading to a considerable reduction in consumption [6,7,8]. Water management companies can reduce operational costs and uncertainty associated with human intervention, better control the distribution network, limit water losses, and improve overall efficiency [6, 7].

An increasing interest in sustainability is driving the development of the smart metering market to combat water waste. Leakage is the most important cause of losses in water distribution systems, and numerous studies and practical solutions have already been proposed to address this problem [5,6,7,8,9]. In recent years, there is a growing interest to investigate the leakages located after the metering point, which can reach up to 13% of household water consumption. For example, a battery-powered visual smart device used to detect post meter leakages and generate alarms at the household level is proposed in [10].

Despite the considerable development of the smart metering market, there is still a lot of potential to explore, and this topic is thoroughly discussed in numerous studies. The potential for generating new products and services, as well as offering more efficient processes is explored in [8] aiming to identify the benefits of digital water metering, for both water providers and costumers. Fettermann et al. [7] explore household preferences, defining how much the users are willing to pay for different smart meter configurations.

Less effort has been dedicated to the investigation of smart metering solutions for large-scale deployments. The water meter, presenting in all consumer connections to the distribution network, is a critical element of this smart metering market. Smart water meters (SWM) can be retrofitted or fully integrated solutions. A retrofitted SWM approach implies that a new electronic device or module must be attached to a pre-existing mechanical water meter, perform automated water flow measurement, record water usage data, and communicate it via wireless transmission. Recent fully integrated water meters already include these automatic metering reading (AMR) capabilities in the integrated measurement electronics.

The current and growing interest in AMR solutions for water meters is mostly associated with the emerging communication protocols and technologies dedicated to the Internet of Things (IoT), such as low-power wide-area networks (LPWAN) with long-range and effective penetration. Nowadays the AMR solutions with LPWAN technologies make it possible to transmit detailed meter reading data for more than ten years using a single long-life battery [11].

Several methods are proposed in the literature to minimize node energy consumption in wireless sensor networks, which have an effective impact on the sensor lifetime, such as scheduling of duty cycle, energy-efficient medium access control, and energy-efficient routing [12]. Another approach consists in recharging sensor nodes by harvesting energy from the environment (water/wind flow, light, thermal gradients, vibrations, etc.) and converting it into electric energy to power the electronic components [13]. There is a great potential in the integration of energy harvesting technologies, which could lead to environmental and business advantages by increasing lifetime or reduction of battery capacities needed. Notably, the existing commercial smart water meters still rely exclusively on the use of internal batteries rather than benefit from energy harvesting technologies and, to the best of our knowledge, no commercial water meter incorporates or explores energy harvesting technologies. Recently, motivating research has been carried out in this area, mainly related to the study of small-sized micro-turbines designed to collect energy from a water stream [14,15,16,17]. These studies have presented interesting results proving that a small water turbine can provide a considerable amount of power, which could result in a self-powered system. It worth to note that most of this research still present many challenges regarding the integration of the proposed solution into the AMR units and the proof of concept prototypes have to address several scientific and engineering problems before the large-scale deployment.

Our approach to the problem is developing a new retrofitted SWM prototype, for the segment of mechanical water meters with no AMR capabilities that respond to the scientific and engineering challenges arising in the smart metering market. Despite recent evolution, most water meters in operation today are still mechanic units that need refurbishment to enable AMR. Reality shows that water companies are retrofitting existing and continue to invest in new mechanical units because of their low cost, proven record of efficiency, and reliability. The investigation of retrofitting opportunities is still an area of great potential, where one will be able to see improvements in the near future.

The proposed modernized SWM meets some of the most important requisites of today’s market: compact battery-powered units without cables or external connections; low power consumption ensuring a service life of several years; low-power, long-range and efficient penetration LPWAN communications. The design of the prototype takes into account the mechanical and production details that can provide real-life deployment and allows the integration of an external energy harvester, used to recover energy from the environment, as well as managing and storing energy in an efficient way. We believe this feature can become a business benefit, driving down costs and reducing the ecological footprint of the devices. To demonstrate the feasibility and efficiency of integrating energy harvesting in the SWM prototype, we consider the design process and implementation of two proof-of-concept energy-harvesting prototypes, light and water-turbine based.

The structure of this paper is as follows. Section 2 presents the relevant work, an overview of the technologies involved in SWM retrofitting, and introduces reported energy harvesting research in the context of water metering applications. Section 3 presents the major steps involved in the design and implementation of the proposed retrofitted SWM prototype, namely the power management system, the flow-rate measurement, and the LPWAN communication block. This chapter also includes the design and integration of two energy-harvesting modules (light and water-turbine based) aiming to demonstrate the feasibility of energy harvesting in the prototype, followed by experimental testing and discussion of the results. We present the conclusion and future work in Sect. 4.

2 Related Work

In this section, we provide an overview of the key components of the retrofitted SWM and technologies for harvesting energy from the environment.

2.1 Water flow-rate readings

A water meter is a device that measures and records the volume of water consumed in water distribution networks. Mechanical registers display water consumption for visual reading, expressed in one of the internationally recognized volumetric measuring units (e.g., cubic meters or their fractions). The retrofitted SWM uses such a mechanical device for measurement and reads its data instead of a human. The measuring method depends on the specific characteristics of the meter. In the last decades, most manufacturers supply their mechanical water meters with add-ons that allow some form of data output, anticipating future AMR applications. For example, it may be a magnet attached permanently to one of the dials or gear inside the meter, which rotates proportionally to the flow rate. Different magnetic sensors such as reed switches or hall effect sensors are then used to detect and assess the magnet rotation, extra electronics generates a voltage pulse proportional to the water flow [6, 18, 19]. Others designs harness a metal target connected to a meter dial; with an induction emitter, it is possible to detect the target movement and to derive the volume of water flow.

Such water meters are ready for operation with external pulse emitters — digital devices that can be attached to the water meter to read the water flow from the meter and generate a proportional voltage pulse. As a rule, the water meters under consideration also have dedicated connecting points or mechanical interfaces specifically designed to attach the emitters. Some of them, mainly those dedicated to the commercial and industrial segments of the market, have the pulse emitter technology already integrated into the scheme. At the same time, there are a plethora of water meters that cannot be equipped with magnetic pulse or inductive emitter sensors. In this case, photodiodes can be used to detect a wheel's rotation in the meter [18, 19], and image processing can be used to capture the water meter display for processing [6].

Normally, the pulse emitters are connected to external AMR units through an external cable, which can be a problem since it can be easily damaged, especially in outdoor environments. The AMR technique evolution towards the battery-powered, compact units directly connected to the water meter eliminates this kind of vulnerabilities.

2.2 Wireless communication

Although there are several smart water metering solutions available on the market, the application of SWM in water distribution is still a relatively recent practice. There are no widely accepted communication standards, and SWM companies typically propose proprietary solutions [20].

In the first generation of SWM, remote communication was implemented using low-power short-range wireless technologies, such as wireless M-Bus (WM-Bus) [20] or ZigBee [6], and operated over unlicensed bands. In these wireless sensor networks, each network node (smart meter) sends data to a specific node, the gateway, which can be fixed or mobile and responsible for retransmitting the collected data to the management center. With a mobile gateway, operators equipped with portable receivers collect data in the proximity of the SWM, usually in a drive-by mode. This solution eliminates the need for physical access or visual inspection of the water meter but does not allow either real-time or automated water consumption monitoring. With a fixed gateway, data collection is fully automated: the primary nodes periodically transmit water consumption information to the gateway devices that gather data from the in-range meters and retransmit it to the correspondent utility in the management center.

This is now a real AMR solution, which however still faces significant drawbacks: in addition to the smart meters, the water management companies must also provide gateways, which often require careful positioning to maximize their effectiveness and range. In some cases, there is a need for appropriate cabinets or cases for protection and external power to work that brings about additional costs.

Later some AMR solutions evolved towards using long-range communication (GSM, GPRS), no longer requiring the external gateway. However, such a communication scheme is not suitable for the SWM market due to the energy consumption requirements, as it needs considerable power to operate and therefore requires large batteries or frequent battery replacement.

Recently, the expansion of the IoT resulted in a new set of communication standards and technologies, which has led to the emergence of low-power wide-area networks (LPWAN), giving a big boost to the smart metering market from the communications perspective. In contrast to short-range technologies, the LPWAN approach allows for long-range (kilometer-scale) communications and provides a connectivity solution that attractive in both range and energy consumption viewpoints. Based on the wave spectrum usage, different LPWAN technologies can be classified into two major groups [11, 21,22,23]. Technologies like LoRaWAN or SigFox use unlicensed ISM-based bands while others like Mobile IoT (NB-IoT) use licensed radio bands.

For the smart water metering market, which requires relatively low data payloads, both SigFox and LoRaWAN offer the range, coverage, and power consumption needed. However, these technologies have significant differences in terms of the operational model. LoRaWAN requires external gateways to operate, others like SigFox rely on operator base stations and do not have global coverage. Although due to the modulation schemes, data rate, and the need of synchronization with the base stations [22], NB-IoT-based devices have higher power consumption than their ISM-based counterparts, their use still presents certain advantages due to one-hop data transmission, especially in terms of the availability, coverage, throughput and security [21,22,23]. At the data transmission through the unlicensed ISM band, others are likely to listen to communications and systems can experience high levels of interference as the number of connected devices increases [22].

2.3 Energy harvesting

A challenge for the wireless sensor networks in general and for SWMs in particular, is how they get access to and manage the energy required for their regular operation. From the service provider point of view, devices that require external power sources are not a good option. Usually, the water meter is located in an area with no on the spot electricity supply, which means that further adaption work has to be carried out at the installation site. This is contrary to the natural provider's desire to achieve the lowest possible installation cost, with minimum work to be carried out on-site, preferably with no wires or external power supplies.

With the predictable proliferation of self-powered SWM installations, the problems associated with battery use and disposal will be exacerbated. Energy-saving technologies can present significant advances in extending battery life, reducing battery volumes and associated waste. Recent research in this field has been focused on renewable energy sources and energy harvesting [13,14,15,16,17]. In water distribution systems, particularly in the water meter surrounding environment, there exist several possibilities to harvest energy, such as light, kinetic energy from the water flow, temperature differences, mechanical vibrations, and electromagnetic radiations [24].

Water flow is the most obvious source of energy. Although water turbines are commonly associated with large installations and high-power systems, several state-of-the-art micro-turbines designed to harvest energy from a water flow have been reported [14,15,16,17]. Various types of fluid-to-mechanical conversions have been tested, indicating that a water micro-turbine can provide a considerable amount of power, ranging from a few to several hundreds of milliwatts [15], which is more than sufficient to power the SWM unit.

Solar energy is one of the most popular, environmentally friendly renewable energy sources for powering wireless sensors and embedded systems [24, 25]. A solar cell provides high power density, making it the preferred option for powering embedded units in wireless sensor networks. It should be noted that dust, dirt, or tampering can be a problem and prevent sunlight energy absorption [24]. Solar energy is unreliable and ineffective in indoor applications, and in many cases, the water meters are in locations with no light exposure. However, there are situations where the meters are installed in an outdoor environment or exposed to artificial light.

Heat transfer is another possible source of energy. Because water pipes are usually laid underground, where the temperature tends to be constant, the temperature difference between the ambient air and water can be used to generate electricity [24]. In [26], a prototype of a thermoelectric energy collection system was proposed, consisting of a thermoelectric module and a heatsink mounted at the bottom side of the water meter. The internal DC-DC converter was able to generate the potential of 3.7 V and charge a capacitor with 2 μA current (7.4 μW power) with air to flowing water temperature difference of 8 ºC, rising up to 40 μA (148 μW) at higher temperature differences.

Since energy sources provided by the environment are not stable, a typical energy harvester architecture includes an energy storage subsystem and appropriate power control [13]. It is very important to match the input impedance of the power converter to the output impedance of the energy source because the maximum power output is achieved under these conditions. To extract the full power under variable conditions, a maximum power point tracking (MPPT) algorithm can be used [27]. A large number of MPPT techniques, circuits, and algorithms have been developed specifically for solar energy systems to efficiently track the variations of the maximum power point [27]. Some of these techniques require considerable computing resources and are not suitable for automatic measurement reading applications. Smart water monitoring imposes low energy consumption, and therefore limited computing resources, making low-complexity MPPT methods more preferable.

3 Prototype design and evaluation

The proposed prototype was designed to operate with a commercial mechanical water meter that can be found in the Portuguese household products market. This water meter has a half-metal wheel that spins when water flows through the meter, and it is the rotation of this wheel that we use as a means for water flow measurements.

We developed a mechanical enclosure that fits the selected water meter (as shown in Fig. 1) and incorporates the internal battery and customized PCB (printed circuit board) electronics, maintaining the water flow register visible to maintain the availability of mandatory human inspections. Although the wheel position and the connection points (screws) depend on the water meter, only minor changes are required to the enclosure and PCB to adapt the solution to other models.

Fig. 1
figure 1

Retrofitted smart water meter

In the following subsections, we present a detailed description of the main components of the proposed retrofitted SWM.

3.1 Power management

Figure 2 presents the power management flow chart comprising three major blocks: power management, energy harvesting system, and storage.

Fig. 2
figure 2

Power management flow chart

The storage block represents the primary battery — or a rechargeable battery if energy harvesting is selected. The element should have a very low self-discharge, providing a safe and reliable performance over a wide range of environmental conditions for long periods.

The power management block generates and manages all internal voltages necessary for the SWM operation and controls battery voltage (and state of charge), which is sampled by the internal microcontroller.

When installed, the energy harvesting system is an external module connected to the SWM. This type of design approach enables the operation with or without the energy harvest functionality, favoring modularity and reconfiguration flexibility. The energy harvesting system includes a generator—the element responsible for collecting energy from the environment, which can be a solar panel or a micro-turbine, among others. The energy harvesting circuit is an electronic PCB, connected to the PCB of the SWM (inside the mechanical enclosure). It is responsible for the conditioning of the output voltage of the generator and includes a circuit accountable for charging the internal battery (storage device) in an efficient way. The battery charger should implement an MPPT algorithm, whose role as described in Sect. 2.3, is to check the input voltage and current to define how much power is available and limit the current consumption from the source, always keeping the condition of maximum production.

3.2 Water flow-rate measurements

For water flow measurement, several technologies and sensors can be used, as described in Sect. 2.1, as long as the characteristics of the "dumb" water meter architecture permit. In the proposed prototype we use an induction emitter that detects the position and movement of a metal target located on the wheel of the mechanical water meter. The choice of the sensor was mainly due to its low power consumption and the fact that a similar target can be found in many of the mechanical water meters available on the market. This enables the proposed solution to be used with other water meter models/brands just with minor adaptions.

The induction emitter uses an inductor and a capacitor to form a tank oscillator. After the circuit begins to oscillate at its resonant frequency, the inductor generates a magnetic field. When the metal part of the wheel intercepts the magnetic field, the amplitude of the vibration signal decreases. The internal microcontroller detects these changes, observed at each complete revolution of the wheel. By counting the number of revolutions with respect to elapsed time, the volume of water flow is determined and a total volume counter is incremented accordingly.

An experimental test bench was built, which includes a water circulation loop as presented in Fig. 3. A pump provides water circulation from a water tank and feeds the water meters under test. The bench was used in the laboratory conditions (relevant environment) to validate the measurements and assess the precision of the designed solution. Later, the tests were performed in the operation environment, with water meters of a real water distribution network.

Fig. 3
figure 3

Test bench for water meters with AMR

At this point, it was also possible to measure the average power consumption of the unit in normal operation, with continuous water flow readings and communication disabled. The measured average power consumption was 195 µW.

3.3 Wireless communication

As was mentioned in Sect. 2.2, there are several protocols and technologies available in the LPWAN market for remote communication. For the SWM prototype, our choice was NB-IoT.

Although NB-IoT has higher power consumption than other LPWAN solutions, this technology is a modified LTE design that aims to serve IoT traffic. For this reason, NB-IoT has the advantage of availability and coverage (since is more likely to be available worldwide than ISM-based LPWAN), throughput, and security. We selected an NB-IoT transceiver with low power consumption and defined the communications protocol, providing periodic transmissions of flow measurements and alarms.

Several tests to evaluate LPWAN communications performance in the smart water meters were performed, which revealed that an NB-IoT data transmission requires an average of 78 mW of electric power and takes about 15 s to synchronize with the base station and transmit the metering data packet. Because the prototype has a typical power consumption of 195 µW, the energy associated with data transmission reveals, as expected, that the frequency of communications is a crucial element that defines the operational time of the solution.

3.4 Energy harvesting system

The selection of an optimal source for energy harvesting depends on the surrounding environment. Some options may require changes to the water supply piping (e.g., installation of a water turbine), while others depend on the location and installation conditions (e.g., solar, indoor light, and thermal), and some even require the use of external cables, which can lead to problems, especially in outdoor environments.

The present section describes the design process and the implementation of two energy-harvesting subsystems prototypes, whose objective is to demonstrate the feasibility of energy harvesting integration in the main SWM prototype.

3.4.1 Light energy harvesting prototype

Although water meters are often operating in locations with no light exposure, there are situations where they are exposed to light (as illustrated in Fig. 4), either in outdoor or indoor (typically in commercial and industrial installations) environments, with natural or artificial illumination.

Fig. 4
figure 4

Example of water meter installations: a outdoor environment with natural light; b indoor environment with artificial light

A polycrystalline solar cell (3 V, 100 mA) was selected to validate the light energy harvesting concept (presented in Fig. 5a).

Fig. 5
figure 5

a Polycrystalline solar cell; b SWM with light energy harvesting integration

The proposed solution consists of integrating this solar cell in the outer cover of the retrofit SWM prototype. The small size (60 × 48 × 3 mm3), significant power output, low cost, and surface finishing with epoxy resin (making it waterproof) of this solar cell makes it an attractive option for the concept validation. Figure 5b shows the modified SWM enclosure, which accommodates the selected solar cell.

Rigorous laboratory tests were performed to properly evaluate the performance of this solar cell in different situations, which include: outdoor environment in two different daylight conditions and indoor environment with LED lamps (artificial light of 900 lx). Although there are cells more suitable for indoor environments (for example, amorphous silicon cells), because they are more susceptible to artificial light, the chosen polycrystalline solar cell can be successfully used for this preliminary study.

The generated current and power plotted versus the output voltage for the outdoor environment are presented in Fig. 6. Table 1 summarizes the results regarding the open-circuit voltage (VOC), maximum power output, and maximum power point voltage.

Fig. 6
figure 6

Solar cell evaluation in the outdoor environment at 2 p.m

Table 1 Solar panel evaluation results

As expected, the results reveal that the output voltage and power of the solar panel depend on the level of incident radiant flux, reflected in variations of VOC and the maximum power point voltage. It confirms the usefulness of the MPPT algorithm to maximize the extracted power over different light conditions. In the outdoor environment, with good sunlight exposure, the daily collected energy is more than enough to supply the SWM and refill the energy spent during periods with no light exposure. Indoors, the generated energy is much smaller and depend on environmental conditions (solar radiation or artificial light). However, it can lead to considerable reductions in the required battery capacity.

Considering the preliminary results, we conceived a solar energy harvester circuit, including a power management integrated component (PMIC), an ultralow-power energy harvester with an embedded MPPT algorithm (SPV1050 by STMicroelectronics), a battery charger, and a power manager. This PMIC integrates a DC-DC converter that can be configured to operate in buck or buck-boost mode, with a wide input voltage range (0.15 V to 18 V). This versatility allows the use of different solar panels and storage technologies, including lithium-based batteries or super-capacitors, by strictly monitoring the end of the charge. The MPPT is programmable by a resistor input divider, sustained on periodic VOC sampling.

Several evaluation tests were performed, which allowed the battery charger operation's validation with the MPPT tracking. As illustrated in Fig. 7, the PMIC periodically tracks the solar panel VOC voltage and adapts the charging current while tries to find the maximum power point.

Fig. 7
figure 7

MPPT tracking with indoor light energy harvesting

3.4.2 Water-flow energy harvesting prototype

A micro-hydro turbine (presented in Fig. 8) was selected to evaluate the concept of water flow energy harvesting. The device is a commercial 12 V, 10 W generator with Ø0.5″ water inlet and outlet, which facilitates its connection to existing standard water distribution pipes. The hydro turbine dimensions and its power output makes this unit the right choice for the energy harvesting proof of concept.

This type of turbine generator was proposed in recent studies [16, 17], where the micro-turbines were used as water flow sensors and power generators. The generated power is proportional to the speed of the turbine rotation, which in its turn is defined by the flow rate and pressure drop between the inlet and the outlet [17]. The output of this generator corresponds to a standard scheme for a three-phase voltage generator whose windings are coupled to a small PCB with a three-phase rectifier.

Specific tests were carried out to define the exact power harvested by this generator on a typical residential water installation. Output voltage and power output were measured as a function of the water flow measured with the flow meter YF-S201. Figure 9 represents the generated voltage versus output power for different flow rates. As one might expect, the power output increases proportionally to the flow rate, and there is an optimal regime (maximum power point) for each water flow value. As in the previous case, due to the variable amount of energy generated by the turbine (depending on the flow rate), there is a necessity to implement an MPPT algorithm.

Fig. 8
figure 8

Micro water generator

The preliminary tests revealed the SPV1050 harvester cannot be interfaced with this generator. We observed that the impedance of the interface circuit prevents reaching its VOC during the sampling time. Considering this, a new PMIC was selected, LTC3129 by Linear Technology Corporation, which is a highly efficient 200 mA synchronous buck-boost DC-DC converter with a voltage range up to 18 V. This device includes MPPC (maximum power point control) capability that ensures maximum power extraction from unstable power sources. The MPPC voltage is programmable by a voltage divider: if the input voltage falls below the programmed level, it reduces the charging current until it regains the programmed voltage level.

Several evaluation tests were performed, which included the validation of the battery charging and MPPC operation. Figure 10 illustrates the setup built to test water flow energy harvesting with the selected water turbine generator.

Fig. 9
figure 9

Water generator voltage vs. power output for different flow rates

3.5 Energy management evaluation

The proposed SWM power architecture allows operation with different types of batteries. Considering the standard SWM configuration (without energy harvesting), the preferred storage option is to use a primary lithium battery. Several lithium-based primary batteries are available in the market, fitted for long-term deployments due to their high capacity, very low self-discharge (less than 3% per year) and wide temperature range.

If the SWM is provided with energy harvesting, one should use rechargeable batteries. Such a battery stores the energy gathered during the harvesting periods. A lithium rechargeable battery is a good choice for energy harvesting integration. Although consumer-grade batteries are not suitable for SWM applications, due to the limited number of recharge cycles (< ~ 500) and the narrow temperature ranges (0ºC to 40ºC), the industrial-grade rechargeable lithium-ion batteries can withstand up to 5000 rechargeable cycles, and operate in a wider temperature range (-40º to 85º C).

Without the energy harvesting module, the retrofitted SWM prototype requires 195 µW of power for the standard measuring operation, and the power demand rises to 78 mW during the communication periods, which can take on average 15 s, the time needed to synchronize with the base station and to transmit the data packet. In a scenario with one data transmission per day, the average power consumption is 208.5 µW. With this consumption, for a 20 Wh lithium battery, we have an estimated life expectancy of 10.9 years, which is in line with similar market solutions.

The expected life duration of a smart metering solution depends on several factors, such as the battery capacity, nominal power consumption, and the periodicity of data transmissions, but also on the selected battery type. Regarding batteries, several factors can affect the expected lifetime, such as the internal self-discharge rate, temperature effects, impedance changes during discharge (aggravated by the current pulses to which the battery is exposed during communication), and battery aging effects. The number of charging cycles is an important limitation of rechargeable batteries.

The radio signal coverage is another important factor that can influence the power consumption of the system. This is important because water meters are often located in buildings’ basements or other places with high radio signal constraints. These environments reduce the intensity of radio signals, which affect the efficiency of radio transmissions, the overall energy consumption, and the operational time of the solution.

Estimating the energy consumption of a device that is expected to operate for such a long period under different environmental conditions is a challenging process [28].

The two proposed and developed energy harvesting subsystems—light and water flow based—were subjected to evaluation tests to demonstrate their effect on the SWM operation. Since the data communication is the most critical mode in terms of energy consumption, the SWM was configured for communication at every 15 min, which is more frequent than is needed for SWM applications but is useful to give a better perception of the energy harvesting impact during the limited testing period. To get more illustrative results, we used a rechargeable lithium battery of low capacity (1.4 Wh).

We conducted some experiments to investigate the impact of radio signal strength on energy consumption, without energy harvesting integration. The signal strength is evaluated using the RSSI (received signal strength indicator) level, which is measured by the internal NB-IoT transceiver. Figure 11 shows the variations of the battery voltage during a 48 h test period, with different signal levels: very poor (- 107 dBm) and fair (- 81 dBm). As can be seen, the decrease in the battery voltage is much higher when the radio signal level weak.

Fig. 10
figure 10

Detailed view of the test setup for water-flow energy harvesting

Figure 12 illustrates the variations of the battery voltage during a one-day test period with different configurations: without and with energy harvesting based on indoor/outdoor light and water flow. During this period, the SWM transmitted messages that included the battery voltage information, essential for further analysing of the results.

Fig. 11
figure 11

Battery voltage evolution with different RSSI levels

Fig. 12
figure 12

Battery voltage evolution with different system configurations

With outdoor solar light energy harvesting, the battery voltage evolution shows that the energy collected is more than enough to satisfy daily energy requirements. During the one-day test period, the collected energy was enough to power the smart meter, recover the energy lost during the night, and store some reserve energy in the battery, even with the frequent, 15-min interval transmissions. With this energy harvesting add-on, the retrofitted SWM only needs a small rechargeable battery to store the energy required for the periods with no light exposure.

At indoor energy harvesting from 8-h exposure to artificial light (900 lx), the amount of energy collected is much smaller. The generated energy was insufficient to power the SWM during the one-day frequent-communication test period. The amount of energy recovered with artificial light energy harvesting depends on the environmental conditions. Still, these results show that it can generate significant power levels in appropriate environments, leading to a considerable reduction in the battery capacity needed, especially in a more realistic scenario of only one transmission per day.

As reported in Sect. 3.3.2, the output of water flow energy harvesting is high with respect to retrofitted SWM energy requirements. In this frequent-communication test, the energy gathered was also enough to power the smart meter and store some reserve energy in the battery. Bearing in mind that the recovered energy depends on the water flow rate and the user water consumption profile, let us consider the average household water consumption. According to the Portuguese Water and Waste Services Regulation Authority (ERSAR), the average daily water consumption per capita in Portugal is 187 L. Considering that an average private household in Portugal comprises 2.5 persons, we can estimate the average daily household water consumption at the level of 468 L. Taking to account the results reported in Sect. 3.3.2 and considering an average water flow of 10 L/min, we may conclude that, in average, the water turbine generator operates 46.75 min per day. In this scenario, the collected energy is more than 2 mWh, which is much more than the energy required for smart meter operation with one transmission per day, especially at operation in the considering commercial and industrial environments, where the water consumption is higher.

4 Conclusions

In the condition of increasing water demand worldwide, automatic metering systems are essential tools to improve the efficiency of water distribution. The water meter, already installed in the majority of households and buildings, is a key element in the evolution of this smart metering market. However, despite the recent technical developments, most existing water meters in operation do not have automatic metering capabilities. Thus, retrofitting such devices with an add-on that provides automatic metering reading is an efficient and cost-effective approach, which allows providers to better control the water delivery and avoid water losses with minimum intrusion into the distribution system.

Supporting this approach, the article presents a retrofitted SWM solution for mechanical water meters that meets the most important requirements of today’s market. The approach is innovative because it enables the integration of energy harvesting, into a production-ready prototype, a technology with great potential to power small sensors and embedded devices. This technology can be used in SWM applications to extend battery life and reduce the volume of required batteries.

We have investigated the main components involved in retrofitted SWM design, including the sensors that allow collecting the water flow data from the mechanical water meters and LPWAN communications technologies. A retrofitted SWM prototype was presented, and the design choices for its major components were discussed. In particular, we analyzed the power management architecture that allows the integration of the energy harvester. The experimental test bench allowed to validate and assess the operation of the retrofitted SWM, especially such crucial parameters as the accuracy of the water flow readings, the stability of LPWAN communications, and the power consumption of the smart water meter in normal operation and communication mode. Results revealed a 195 µW standard power consumption that rises to an average of 78 mW during the communication periods. In a realistic scenario with one data transmission per day, the average power consumption is 208.5 µW.

Two energy harvesting proof-of-concept prototypes were conceived that explore water flow and light energy extraction from the environment surrounding the water meter. Our experimental results indicate the feasibility of this type of integration. The energy was recover and properly stored in the internal lithium rechargeable battery. With outdoor light and water flow energy harvesting integration, the energy gathered was enough to power the smart meter and store some reserve energy, which demonstrates that this type of integration has the potential to lead to a self-powered system. With indoor light energy, the amount of energy recovered depends on the environmental conditions and is normally much smaller. Although experimental results show that the energy recovered can lead to considerable reductions in the required battery capacity.

Several aspects can affect the lifetime of a battery in a typical IoT application and research shows that operational life in real-life applications drops significantly from the ideal results. The results presented in this paper prove that this type of integration can help to reduce the size and capacity of the internal batteries, enabling one to reduce the operation cost and mitigate long-term ecological problems associated with the use and disposal of batteries.

Several aspects still need to be addressed to integrate energy harvesting in commercial SWM applications. With light energy harvesting, there is no need to interfere with water distribution pipes, and the solar panel can be connected directly to the SWM case. However, such water meters need to be exposed to significant light, while, as a rule, the water meters are installed in places with no significant light exposure. Nerveless, there are situations where the meters are installed in environments where natural or artificial light is abundant, and in this case, the light harvesting is an easy-to-implement and efficient solution. Dust, dirt, or possible vandalism can be a problem, lowering or even stopping light energy absorption, and this must also be considered. Changes must be introduced in water distribution pipes to enable the use of turbine-driven energy harvesters. The turbine must be connected to water distribution pipes close to the water meter, with a cable connects the two devices.

The research on energy harvesting as a part of the retrofitting-based solutions can lead to substantial improvements for future upgrades of the water meter. In particular, the introduction of energy capturing turbines in future water meter designs, or other energy harvesting technologies, can lead to significant technological advancements. It has been shown that even with a small lithium-based rechargeable battery, the SWM with energy harvesting can operate during its entire lifetime. This can lead to a significant reduction of residues in the long term and enable several technologies and features—such as new communications modes, sensors, or valves, whose integration was not feasible due to the associated power consumption—to be adopted to the water metering devices.

Several improvements to this prototype are possible. For example, the implementation of other water flow measuring methods (such as magnetic or optical sensors), which will allow the use of this SWM prototype in other models of mechanical meters. Further work can be done developing a realistic model to estimate the required battery capacity based on energy harvesting options and water meter installation site conditions, such as levels of light exposure, water consumption profile, among others. Future research can also be directed towards investigating water leaks after the meter at the household level, using the prototype to detect post-meter leaks and generate alarms.