Chapter 6
Energy in the IoT Ecosystem
Internet of things (IoT) is a revolutionizing technology which aims to create an ecosystem of connected objects and embedded devices and provide ubiqui- tous connectivity between trillions of not only smart devices but also simple sensors and actuators. Although recent advancements in miniaturization of devices with higher computational capabilities and ultra-low power communi- cation technologies have enabled the vast deployment of sensors and actuators everywhere, such an evolution calls for fundamental changes in hardware design, software, network architecture, data analytic, data storage and power sources. A large portion of IoT devices cannot be powered by batteries only anymore, as they will be installed in hard to reach areas and regular battery replacement and maintenance is infeasible. A viable solution is to scavenge and harvest energy from environment and then provide enough energy to the devices to per- form their operations. This will significantly increase the device life time and eliminate the need for the battery as an energy source. This chapter aims at providing a review on energy sources for powering IoT devices.
71
72 CHAPTER 6. ENERGY IN THE IOT ECOSYSTEM
Figure 6.1: Energy consumers in the IoT ecosystem.
6.1 Energy in the IoT Ecosystem
IoT consists of several layers of devices, ranging from tiny devices to giant data centers. Fig. 6.1 shows some of energy consumers in the IoT ecosystem. In the large scale, there are data centers and network infrastructure, which consume huge amount of energy; expecting to dramatically increase in near future. In the small scale, there are traditional network devices, such as mobile phones and tablets, computers and home entertainment, and IoT specific devices, which are somehow new to the market and are expected to grow exponentially in terms of the numbers. Only a small fraction of these devices are powered by connecting to the main power, and most of them would be battery operated or self-powered and use energy harvesting technologies. These were enabled through technological advancements in miniaturization of electronic devices, low power processing, low power wireless communications, and battery and energy storage, and the continuing fall in their prices and sizes. However, the unique characteristics of IoT systems open new challenges and problems in terms of energy, which are needed to be solved to enable sustainable and autonomous IoT systems and applications.
6.1.1 Characteristics of IoT Energy Sources
Here, we list these unique challenges of IoT systems and devices which must be carefully considered when designing the devices and communications protocols.
Scalability
The energy source for IoT devices must be scalable. For the vast deployment of IoT services and applications, the devices need to be placed in all kinds of locations to collect data and communicate with the gateways. The devices may be located in hard-to-reach areas, so they need to work autonomously with- out human intervention. They also require minimum maintenance; therefore, batteries are not viable solutions.
Traditional
Network Devices
Mobile devices (smart phones and tablets)
Computers (PC, laptops)
Home Entertainment (smart TVs, set-top boxes,…)
IoT Devices
Bettery powered
Self powered
Main connected
(Switches, routers, access points, …)
Cloud (data centers)
Network Infrustructure
6.1. ENERGY IN THE IOT ECOSYSTEM 73 Maintenance-free
The energy source for most IoT applications must be maintenance-free. IoT and MTC use cases usually involve a large number of devices. Connecting these devices through wiring to the main power is not feasible, as it restrict device movements and also increase the total deployment cost. Batteries are not feasible either as regular battery maintenance is impractical due to the large number of devices, the cost associated with batteries and also the enormous scale of maintenance expenses.
Mobility support
Energy sources for many IoT application must support mobility. Many IoT devices are mobile, and the constant movement of the devices must be carefully considered when designing the devices and power management systems.
Long life-time
Many IoT applications required energy sources that supports long life-time with minimal maintenance. In some IoT applications, for example in struc- tural health monitoring, embedded wireless sensors must be deployed inside the buildings, bridges, etc., and they are supposed to wok for several decades. In these applications, both the energy storage and wireless connectivity must be optimized in order to maximize the lifetime of the device.
Flexibility
IoT energy sources must be flexible in size and capacity due to wide variety of IoT applications and services. For example in health monitoring applications, a tiny device will be implanted inside the human body to sense vital information and send it to a personal device. This requires long-lifetime tiny batteries or energy-harvesting enabled batteries. Many other applications, such as parking meters, may be connected to main power or can benefit from large photovoltaic cells due to their size and locations.
Low-cost
Many IoT applications will require a large number of devices to be installed. These devices, and accordingly their power sources, should be of low cost; oth- erwise the application will provide low revenue or is very expensive, which limit its popularity.
Sustainability
IoT will include trillions of devices, in different scales, and all of them con- sume power, which will affect our environment. IoT power solutions must be sustainable to avoid the depletion of natural resources in order to maintain an
74 CHAPTER 6. ENERGY IN THE IOT ECOSYSTEM
ecological balance. This further emphasizes the importance of energy harvest- ing power sources for IoT as alternatives to conventional non-environmentally friendly power sources.
Environment-friendly
The expansion of IoT services will negatively impact the environment due to a large number of battery discarding, e.g., more than 125,000 tons of batteries are discarded in USA every year. Discarded AA batteries would circle the earth six times which worsen the problem. Addressing this problem is of urgent priority and energy harvesting techniques provide several solutions.
6.1.2 Powering IoT Devices
Authors in [1] have divided the IoT devices into five categories as follows:
• Type I devices is wearable devices, such as smart watches and fitness monitors, which have longevity requirement of several days as the number of them per user is small and they can be regularly recharged.
• Type II devices are set-and-fort devices, such as home security, with the longevity requirement of several years and a user may have dozens of them, so regular replacement of batteries is inconvenient.
• TypeIIIdevicesaresemi-permanentdevices,whicharedeployedinbridges, buildings, and infrastructures for monitoring purposes, and expected to work for more than a 10 years. Regular replacement of batteries infeasible.
• Type IV devices are battery-less and self powered devices, such as RFID tags and smart cards.
• The last type, i.e., Type V devices, are powered appliances, such as smart refrigerators, that are always connected to the main power.
Type II, III, and IV devices usually require long lifetime energy sources as regular battery replacement is infeasible due to the large number of devices or hard-to-reach areas where the devices are deployed. In fact, most of IoT devices need small-sized and high-energy density batteries for longer lifetime. This calls for major technological improvements in battery development. En- ergy harvesting (EH) techniques are interesting alternatives to batteries, which promise to enable autonomous and deployable IoT applications, in energy-rich environments [2]. Extracting energy from the environment, enables the devices to reincarnate once they have accumulated enough energy from the ambience. Energy harvesting is then become an excellent choice for applications, which require increased lifetime, battery-less functionality and ease of maintenance [3].
6.1. ENERGY IN THE IOT ECOSYSTEM 75 6.1.3 Energy Harvesting Use Cases in IoT
EH techniques use different sources of energy in the surrounding environment to harvest enough energy which is used later by the device for sensing, actuat- ing, and communicating with the server. Solar, thermal, vibration, RF signals, and human body are only few examples of the available energy sources in the environment commonly used for energy harvesting, There are several scenarios where EH technologies can significantly enhance the system wide performance, in terms of energy, network life time, cost and maintenance. We divide these scenarios into three categories as detailed below.
First, energy harvesting is mostly useful in applications where devices are deployed in hard-to-reach areas, therefore, replacing the batteries for sensor nodes is almost impossible [4]. Wireless sensor networks (WSNs) have been widely studied for structural health monitoring, where the damage in aerospace [5], buildings [6], bridges [7], and mechanical infrastructures is detected by sen- sor nodes. The goal is to replace qualitative visual inspection and time-based maintenance procedures with a more autonomous condition-based damage as- sessment processes [8]. The aircraft health monitoring system using WSNs and energy harvesting techniques, including thermoelectric and vibration sources, was studied in [9]. Energy harvesting using piezoelectric and inductive devices was also proposed to monitor the health of a real railroad track in [10]. Another example where energy harvesting is crucial is body sensor networks, where the sensors are required to harvest energy for autonomous operation [11–14].
Second, energy harvesting can be used in applications which usually require too many devices and replacing their batteries is almost impossible or cost in- effective. Examples include electronic shelf labeling [15], body sensor networks, and massive IoT applications.
Third, in many applications there is no steady supply of electricity available. An example was discussed in [16], where energy harvesting was used in an agricultural setting to enable a delay tolerant wireless sensor network. In many IoT applications, such as smart home or smart offices, intelligence transportation systems, smart grids, and industrial monitoring, a large number of devices will be installed everywhere, sometimes in hard-to-reach areas. These devices will be the major consumer of energy in near future due to the rapid growth of their numbers, and the use of energy harvesting will decrease our dependencies on fossil fuels and other traditional energy sources which are depleting very fast. Energy harvesting can also promote environment-friendly, clean technology that saves energy and reduces CO2 emissions, which is a promising solution for achieving the next generation smart city and sustainable society [4].
On the other hand, energy harvesting could save lots of energy in a wider scope, as it could hugely reduce the cost of modifications in the buildings and industries for wiring and maintenances. Energy rich environment, such as in- dustries and vehicles, must be capitalized on this available energy, where the small amounts of energy from the environment would be sufficient to run sensor nodes. Advanced sensing techniques in industries will significantly increase the performance and competitiveness, as constant monitoring through embedded
76
CHAPTER 6.
ENERGY IN THE IOT ECOSYSTEM
Hours
Battery Run-time
Power Consumption
μP desktop μP Laptop
Bluetooth LE Zigbee
Zigbee Pro
smoke alarm Beacon
smartwatch Hearing aid
Thermostat smartcard
NB-IoT, EC-GSM, LTE-M, LoRA, SigFox
Mechanical Sources
Thermal Sources
Radiant Sources
GSM
Mobile Phone
WiFi
MP3 LED
Years
Vibration sesor
32kHz quartz oscillator
Thermal variation Thermal gradient
RFID tags
calculator
RF
Stress-strains Vibration
0.1 1
10 100 1000 10000 100000
Energy Harvesting μW/cm2 or μW/cm3
Indoor sun
Figure 6.2: Power consumption for various applications and power densities for various energy sources (data obtained from [17]). Power requirement for wireless technologies.
devices reduces the cost and energy consumption associated with system failure and maintenance [18]. This however cannot be achieved if the system requires cables or if the battery have to be regularly replaced [19, 20]. Wireless solutions and energy harvesting techniques provide a wide range of solutions for the sen- sors to become maintenance free. Some examples are the use of piezoelectric energy harvesters for vibration monitoring [21], solar energy harvesting for au- tonomous field sensors [22], and recently proposed multi-source energy harvester strategies for wireless sensor networks [23].
Fig. 6.2 shows power requirements of different applications and the power densities for various energy sources. As can be seen selecting the energy source for IoT applications, depends mainly on the application and the specific require- ments of that. Silicon Lab [24] has recently identified 5 fundamental consider- ations for powering wireless IoT sensor products. These include, 1) the target market and its specific requirements on cost, reliability, and network lifetime, 2) energy efficiency and the choice of wireless connectivity, 3) the required trans- mission strength, duration and duty-cycle between active and sleep states, 4) the sensor node and its power requirement and cost, and 5) the space constraints and storage energy.
Outdoor sun
Operational range of many IoT devices
10nW 100nW 1μW 10μW 100μW 1mW 10mW 100mW 1W
10W 100W
6.2.
POWERING THE INTERNET OF THINGS
Constant supply
RF Solar Flow Thermal
Vibration
77
Battery
Ambient environment
External sources
Mechanical
Human
Activity Physiological
6.2
Figure 6.3: Energy sources for IoT applications.
Powering the Internet of Things
Energy sources for massive IoT applications
Pressure
Fig. 6.3 shows the taxonomy of energy sources for IoT devices. They can be mainly categorized into four groups. The first and second groups are respectively main power and battery. The third group is the ambient environment, where the energy is harvested from the environment through solar, thermal, wind, and RF signal energy. The fourth group is the external sources, where the energy is extracted from mechanical movements or from the human body. It is important to note that an energy source may be categorized under different categories depending on the application. For example, mechanical vibration may be considered as an energy source from the ambient environment, when the sensors are attached to vibration-rich environment structures such as bridges. In this section, we review different energy sources for massive IoT applications, present their pros and cons, and the applications where they can be used.
6.2.1 Main Power
The devices in IoT, may be connected to a wired power supply. This is more suitable for IoT applications with fixed-location devices, where a constant power supply can be connected to the device through wires or cables. This however makes the devices immobile, which limit its application in massive IoT. More- over, it is impractical to connect every device to the power supply through wires when the number of devices is very large. This option is only feasible when the number of devices is very small, and due to specific requirements of the devices is the only way to power the devices. Examples include video surveillance and monitoring applications, where a specific area is constantly monitored by a few high-quality cameras and the data is continuously sent to the security units. An- other major application is smart appliances, which are places in fixed-locations in homes, so they can be connected to the main power.
6.2.2 Battery and Super-capacitors
Battery is the most common energy source which has been widely used in our everyday devices. The stored energy in batteries however is limited, therefore
Stress-strain
78 CHAPTER 6. ENERGY IN THE IOT ECOSYSTEM
the battery-driven systems have a finite lifetime. It is also difficult and costly to regularly maintain and replace batteries especially when the nodes are remotely located or the number of nodes is very large. These are the main problems of battery-driven systems, which limit the use of batteries in some massive IoT applications.
There are two major approaches to solve these problems for IoT systems. First, one could consider higher battery capacity. But this indeed implies in- creased cost, which is of course not suitable for massive IoT applications. Sec- ond, low-duty cycle operation modes can be used to extend battery life time. This has been recently considered in the recent standards for cellular IoT ap- plications, including LTE-M and NB-IoT [25], where the battery life-time was shown to extend to up to 10 years by introducing new duty-cycle operation modes to traditional LTE standard. Further modifications in the transmission have been considered in these standards, such as lower data rates, lower trans- mit peak power, and half-duplexing, to minimize energy wastage and optimize battery usage [25].
Table 6.1 shows different battery technologies which are commonly used and their respective efficiencies. Lithium batteries are the most efficient batter- ies which have the highest power densities and efficiencies which can provide higher battery lifetime, suitable for some IoT applications. However, massive IoT applications require the devices to be tiny and autonomous, which put strict limitations on the energy storage and power management of IoT devices. These make the batteries not viable solutions for them [3]. Non-rechargeable batteries cannot be solely used for many IoT applications due to ecological implications and the fact that they have only limited storage [3, 26]. Imprint Energy [27] de- veloped 3D printed Zinc rechargeable batteries were developed by for powering IoT devices, which do not require heavy installation and can be formed into any shape; allowing for customized applications. These batteries are slim and flexi- ble and customizations ensures the required capacity and voltage to avoid extra power conditioning [3]. Another solution which has low power density and high energy density is solid-state thin-film batteries suitable for long-term deploy- ment of IoT devices. The flexibility of these batteries and the fact that they can be manufactured in IC packages have made them suitable candidates for many IoT applications that targeting low cost and ting device implementation [28]. Super-capacitors have been also considered to replace rechargeable batteries, which have unlimited charge-discharge cycle, but suffer from high self-discharge (up to 20% per day) [3].
Amongst those Lithium batteries shown in Table 6.1, a popular battery for the IoT applications is Lithium Thionyl Chloride (Li-SOCl2) battery. The features of this type of battery include long lifespan with low self-discharge rates, and most importantly the highest energy density comparing with other types of Lithium batteries. However, the battery charging techniques for this type of battery are tough due to the high output resistance of it and limited output current. Moreover, the energy efficient scheme often optimises the electronic circuit such that the batteries of IoT devices would be capable to burst to wake up and stabilise the voltage of the devices in a tiny time slot. But this operation
6.2.
POWERING THE INTERNET OF THINGS 79 Table 6.1: Different battery technologies and their efficiencies [29–34].
Battery type Nominal voltage
(V)
Weight En- Self-
ergy Density discharging (Wh/kg) rate level
SLA 6 26
NiCd 1.2 42 NiMH 1.2 100 Li-ion 3.7 165 Li-polymer 3.7 156 Li-MnO2 3 280 Li-(CF)x 3 360∼500 Li-FeS2 1.4∼1.6 297 Li-SOCI2 3.5 500∼700 BrCl, Li-BCX 3.7∼3.8 350 Li-SO2 CI2 3.7 330 Li-SO2 2.85 250
Li-I2 2.8 330
high
low
high
Low
Low
Low
High Very Low Low
Low Low Low Low
would not only sag down the voltage of Lithium Thionyl Chloride battery but also reduce its lifespan.
Battery storage is a mature technology when compared with energy har- vesting technologies, and the fact that batteries are available in different sizes and shapes, make them strong candidate for many massive IoT applications, which are expected to operate with ultra-low power and have limited life time, up to 10 years. Therefore, the battery technology still plays an important role in the IoT ecosystem for many years. The unique requirements of many IoT applications, open new challenges for battery providers.
6.2.3 RF Energy
In RF energy harvesting, the electricity is generated as a result of magnetic inductive coupling effect [35]. It is basically based on the induction of an open circuit voltage around the receive loop from a loop which carries a time varying current. The flux and the open-circuit voltage are mainly determined by the distance between the turns of the loops, the amplitude of the transmit loop current, and the dimension and distance between the loops [35]. The induced voltage at the recieve loop can be used to power a passive RFID tag or stored in a rechargeable battery.
Currently, we use this technology in electronic ID tags and smart cards, which are embedded with passive electronic devices and will be triggered when they are exposed to nearby energy rich sources which are transmitting RF sig- nals. Considering the vast deployment of the devices in massive IoT, this so- lution may not be scalable as the environment needs to be flooded with RF radiation to power the nodes. Such radiations of RF signals would probably presents health risks for human beings.
Wireless energy harvesting (WEH) has been considered for powering IoT devices in [36] and improvements in terms of being wireless, availability of the RF energy, low cost and relatively easy implementation were shown. Sensor nodes which are powered by WEH usually consist of a transceiver and antenna
80
CHAPTER 6. ENERGY IN THE IOT ECOSYSTEM
RF Signal Antenna
Micro-Control Unit
Match Circuit RF-DC Charge Tank Chanrge Pump
Figure 6.4: RF energy harvesting circuit [38].
element, a WEH unit which is responsible for scavenging RF energy and deliver- ing a stable output power, a power management unit, and possibly an onboard battery (see Fig. 6.4). Recently, Freevolt proposed an innovative technology, called Low Energy Internet of Things (LE-IoT) devices, which can harvests RF energy from both short-range and cellular wireless networks, such 4G, WiFi and Digital TV [37].
Washington University [39] has developed a novel communication system Wi-Fi Backscatter, which aims to establish Internet connectivity for the IoT RF-powered devices. Nonetheless, the energy harvested from the RF signals is far less comparing with many harvesters that based on other energy sources. Therefore, the scalability of this type of harvesters is limited and these would be more suitable for powering auxiliary devices like Wi-Fi Backscatter.
Another emerging technology that enables the RF energy harvesting is us- ing metamaterials instead of antennas. The concept of metamaterial is to use an architecture consists of many small-sized elements to manipulate the elec- tromagnetic waves. This array of all elements called metamaterial which can be engineered and designed to behave very different compared to other known materials. For instance, a metamaterial can be designed to have a very high ab- sorption coefficient and very low transmission and reflection coefficients. This leads to higher conversion efficiencies for metamaterial energy harvesters. In [40], an RF energy harvester was designed for 900MHz frequency consists of 5 elements reaching the efficiency of 36% at 70Ω load. The unique characteristics of metamaterials made them very attractive for scavenging energy from high frequency electromagnetic waves, optical beams and even acoustic waves [41].
Efficiency is a key factor for a WEH system, where it is reported that higher efficiencies are achieved when higher power signals are transmitted. However, the maximum power should be limited to avoid health risks and also avoid in- terference on other networks. Recent developments of RF harvesting systems
6.2. POWERING THE INTERNET OF THINGS 81
Hot Plate
Heat Sink
Th
Tc
Figure 6.5: Thermal energy generator.
are reported to achieve efficiencies as high as 50% at sub-milliwatt power levels. Harvested energy from RF signals depends on the relevant distance between the transmitter-receiver pair, but the received energy is adversely affected in the presence of multipath and fading [42, 43]. Energy beamforming is shown to significantly improve the RF energy harvesting efficiency, and further improve- ments in efficiency can be achieved by using energy beamforming and a mixture of high gain antennas and multi-band harvesting [36]. Another approach to im- prove efficiency is introducing Duty-cycling aware MAC and power management in physical layer [36].
The application of WEH is however limited, as the devices need to harvest wireless energy from the RF signal which then needs to be constantly provided to the devices. This may however increase the overall power consumption of the networks, especially at the base stations, which should be carefully consid- ered. Another important issue is that the RF power harvesting circuits and communication circuits are different, so the received signal must be either split- ted between the circuits or be used by only one of them. This may add to the complexity of the system, as a complex power management system is required. Power splitting at the receive may also reduce the effective transmission rate, as the communication circuit is needed to work under lower power as some power is used for energy harvesting. These issues have been considered in the liter- ature and different simultaneous information and power techniques have been proposed to mitigate these issues.
6.2.4 Thermal energy
A thermal energy generator (TEG) converts temperature differences into elec- trical energy. A TEG usually suffers from low efficiency (5-10%) which limits its widespread adoption [44, 45]. However it has a long life cycle and station- ary parts. To extract the energy from a thermal source, a thermal difference is required (see Fig. 6.5); e.g, 30 degree difference in the temperature of hot and cold surfaces of the device in the room temperature, results in only up to 10% conversion efficiency [46].
As shown in [47], about 22 μW can be harvested from a human body which was used to derive a Seiko thermic wrist watch and charge a lithium-ion battery. Authors in [44, 48, 49] showed that efficiencies more than 10 can be achieved by using new thermoelectric materials and efficient modules. It was also shown
82 CHAPTER 6. ENERGY IN THE IOT ECOSYSTEM
that the power density can be scaled by the square of the temperature difference [50], which makes thermal energy harvesting more suitable for environments with large temperature differences, such as buildings with heaters.
In [51], a comparison between different thermal energy harvesting devices was provided and it was shown that 150 mW, 100 mW, and 0.026 mW thermal power can be harvested for room heater at 34 degree temperature difference [47], Flex thermal EH for 6 degree temperature difference [52], and wearable thermal EH for 6 degree temperature difference [53]. This study shows that existing commercial thermal energy generators are not sufficient at a low difference of temperature. Lead Zirconate Titanate (PZT) and Poly-Vinylidene Fluoride (PVDF) films were also used to develop pyroelectric cells to power low power RF transmitters; however, low efficiency was observed in these applications [54– 56].
6.2.5 Solar and Photovoltaic Energy
Photovoltaic (PV) is considered as one of the most effective EH techniques to power IoT devices due to its power density, efficiency, and the flexibility in terms of different output voltage and current [57, 58]. When sunlight is directed to certain semiconductor materials, solar energy will be converted into DC power. This is called the photovoltaic (PV) effect. A solar cell is usually composed of silicon and when it is stroke by sunlight with enough energy, the electron and holes are separated and using an input an output regulator, electrons start to move towards the load [51]. To control the charging current to a battery or super-capacitor a maximum power point tracking (MPPT) unit is necessary, which also maximize the efficiency of the PV cells.
Outdoor environments are exposed to sunlight and are generally more ap- propriate for PV energy harvesting [51], where for a typical outdoor illumination level of 500 W/m2, efficiencies of 15% to 25% can be achieved using polycrys- talline and amorphous silicon cells [59]. Other indoor environments such as hospitals and stadiums can also benefit from this technology as they have many lightings. Authors in [59] reported PV energy efficiencies of 2% to 10% for indoor applications at illumination level of 10 W/m2.
Jiangtao et al. [60] proposed bidirectional visible light communication (VLC) technology named Retro-VLC, that enables battery-free IoT applications. The main ideas here include the low-power optimisation techniques, the bidirectional communication which is established on the shared light carrier between the up- link and the downlink and battery-free realised by energy harvesting utilising Photovoltaic cells. The implemented physical system essentially consists of a reader and a tag, which are integrated in the lighting infrastructure and IoT devices respectively. According to the circuit diagram, the receiving circuit of the tag, or ViTag-RX, contains light sensor that captures incoming light sig- nals, amplifier that amplifies those signals, demodulator and comparator that demodulate and digitise the analog signals for later decoding in the microcon- troller. On the other hand, the transmitting circuit of the ViTag, or ViTag-TX, includes the aforementioned microcontroller and a LCD driver that encodes and
6.2. POWERING THE INTERNET OF THINGS 83
transmits the signals respectively. This design enables battery-free IoT applica- tions with ultra-low power consumption that only a 12W bulb on the upstream (ViReader) is enough to power the downstream (ViTag) circuit. However, the range of this technology is very limited, that the maximum continuous effec- tive working range is only around 2.5 meters according to their tests, which limited the scalability of this design. Moreover, the requirements on interfer- ence is highly restricted. Nonetheless, this design is still desirable for many IoT applications such as smart home IoT, intelligent traffic systems IoT, etc.
There are several problems with solar and PV energy harvesting technologies, which make them not suitable for the wide adoption in many IoT applications. First of all, the solar panel must receive enough light to be able to generate enough power for the device. Therefore, when there is no light or the light intensity is low and varying, the PV energy cannot be properly harvested and used. Second, to harvest solar or PV energy, a relatively large (compared to the device size) PV cell must be installed to harvest enough energy, even if the light intensity is large. This maybe the biggest issue with the PV energy harvesting which limit its application in massive IoT, which mainly require tiny devices to be installed in different places, regardless of the light intensity. Third, when the light intensity is varying, a battery must be also considered to store energy and provide that energy to the device, when the light intensity is low. This is quite common in applications which are powered by solar cells, where the energy is stored in batteries during the day and then used at night for different applications.
6.2.6 Mechanical Energy
As shown in Fig. 6.3, electrical energy can be harvested from vibrations, pres- sure and stress-strain. Electromagnetic, electrostatic, and piezoelectric are three main mechanisms to generate electricity from mechanical sources [51]. In elec- tromagnetic energy harvesting, the electric current is generated when a magnet moves across a coil. In piezoelectric materials, an electric potential is induced at the terminals of a piezoelectric material due to the polarization of ions in the crystal as a result of the strain. In electrostatic converters, the plates of a charged capacitor are pulled using the vibration, which then results in electrical energy due to the change in the capacitance. Piezoelectric energy harvesters has the highest energy density, that is higher energy can be produced for a given surface area, which is very important in micro scales, where most IoT devices are supposed to operate. Electrostatic mechanism requires separates voltage source and electromagnetic usually generate low voltages.
in [61], vibrational energy harvesting was used to power wireless sensors at- tached to the bridge, where the vibrations from the passing traffic on a bridge was used to generate electricity using linear electromagnetic generators. Electro- static micro generators were also suggested in [62], where about 50 μW energy can be harvested from 0.1cm2 surface area. Also in [63] a peak power of 3.25V was reported to be delivered for an electromagnetic transducer at the mechanical resonate frequency of 10.4Hz.
84 CHAPTER 6. ENERGY IN THE IOT ECOSYSTEM
The authors in [46] have categorized the mechanical sources into, steady state sources, intermittent sources, human and machine motion, and Gyroscopic motion. The vibration source in steady state mechanical sources are based on the constant flow/motion of a liquid/object in either natural channel or pipes. This energy sources are usually used to generate electrical energy on a macro scale. However, similar concepts can be used to harvest energy on a micro scale, for example using blood flow and inhalation/exhalation in human to harvest energy as reported in [64]. Intermittent mechanical sources on the other hand are not steady and usually require an external object to trigger an event and thus produce the mechanical vibration. For example, the energy can be harvested from human activities such as walking, sitting, and sleeping [46]. Human body is considered as a mechanically-rich environment where different motions and forces can be effectively converted to power wireless devices. A comprehensive summary of the human-based energy harvesting systems was presented in [46].
We will provide more details on vibrational energy harvesting based on piezo- electric materials in the next section, where we explain the basic concept, the available piezoelectric materials and their characteristics, and a brief explana- tion of the newly developed material with high electrical properties.
6.2.7 Human Body
Human body is considered as a rich environment to scavenge energy to power wearable electronics [64, 65]. Wearable devices are very important in health monitoring applications, where sensor nodes are deployed on or implanted in- side the human body, which form a network called wireless body are network (WBAN). As the battery replacement for wearable devices is inconvenient for people and sometimes impossible in cases when the devices are deployed in- side the human body, the sensor nodes in WBAN must have very long lifetime. Therefore, energy harvesting from human body is favorable in these applications [51].
All the aforementioned energy harvesting techniques can be used to harvest energy from the human body. The main challenge is then to miniaturize them for the ease of human adoption [51]. The energy can be harvested from blood pressure and breath, finger motion, paddling and walking, and even body heat. For example, in [64, 66] the energy was harvested during heel strike and in the bending of the ball of the foot using piezoelectric materials and also in [67, 68] the energy harvested from piezoelectric shoe insert was used to power RFIC tags. A summary of different applications and use cases of piezoelectric energy harvesting for wearable bio-sensors was provided in [69].
The power scavenging techniques from human body can be generally cat- egorized to passive and active techniques. In passive human powered devices, the energy is harvested from normal activities of a person, such as orthopedic implants [70], motion of the heart, lungs and diaphragm [71]. In active en- ergy harvesting, the person needs to perform some special activities or power generating motions. Flashlights which are powered by squeezing a lever is an example of active human powered devices [69]. As these activities may not be
6.2. POWERING THE INTERNET OF THINGS 85
Figure 6.6: Implanting integrated PMN-PT energy harvesting device and a pacemaker on the living heart [72].
Figure 6.7: Medical experiment on a living rat to verify the harvesting device [72].
convenient, active human powering is not very practical for IoT applications, where the data is sent seamlessly and sometimes randomly to the server.
Artificial cardiac pacemaker is a device which can be implanted in human body to regulate the heartbeat using electrical impulses. This device is vital for people who suffer from sick sinus syndrome which causes abnormal heart rate. So far, batteries were the only options to power such implantable devices, however, limited lifespan of the batteries have made the surgical replacement inevitable. The replacement period can be as short as 3 to 6 years, which can be very harmful for probable infection or bleeding during surgery.
Many researches have been conducted to harvest energy from piezoelectric materials to supply the power required by pacemaker. For example, using dif- ferent types of piezoelectric materials such as BaTiO3 thin film [73] and lead Zirconatate Titanate (PZT) [74] thin films have been used as flexible energy harvester to be installed on heart tissues for converting mechanical stress to electrical energy. However, their output current hardly exceeds a few micro amperes and therefore, they were not operable in powering a cardiac pacemaker which needs inputs of 100μA and 3V. In [72] a new approach to make self- powered artificial pacemaker has been reported which can supply maximum current and voltage as 145μA and 8.2V respectively. By installing such device on a moving tissue, i.e. heart muscle Fig. 6.6, it can produce enough energy
86 CHAPTER 6. ENERGY IN THE IOT ECOSYSTEM
to supply implantable devices in body. As shown in Fig. 6.7, it is successfully implanted on a live rate to verify the output rate, where approximately 133.9 W of energy was generated by kinetic energy from an adult. This amount of energy is a good source to power wearable sensors and personal services such as tracker, GPS, health care devices, etc. Over past few years, regarding the advancement of electronic technology to decrease the power consumption of electronic de- vices, harvesting electricity from human power is becoming intriguing. Thus, extracting electricity from everyday activities is an option based on which so many researches has been conducted.
6.2.8 Biofuels and Biobatteries
Biofuel cells has introduced a promising alternative source of sustainable electri- cal energy. These cells are working based on bio catalyzed chemical reactions to change chemical energy into electricity. Biofuel cells can be implanted in living organisms as micro-power sources, however, providing the adequate amount of voltage and connectivity of such cells to the electronic devices has been intrigu- ing for researchers.
In [75], it is reported that electrical energy has been extracted from im- planted biofuel cell in lobsters body. Enzyme modified electrodes in a living lobster results producing electricity by biocatalytic oxidation of glucose and re- duction of oxygen inside the body. Most biofuel cells can provide an open circuit voltage of 0.5V which is subject to deduction by drawing current from the cell. The low voltage problem can be addressed by two approaches: (i) assembling cells in series electrically (ii) collecting the produced energy in a capacitor and then release it as pulse.
Implantable biofuel cell can be used to power many IoT devices. Environ- mental monitoring applications where microelectronic devices sense and send data wirelessly, or wireless transmitting devices can be carried by animals while taking energy from implanted biofuel cells in their body. More importantly, it can be widely used in biomedical applications, where the energy can be taken from human body to power implanted devices, e.g. pacemakers, eliminating the need for batteries. Millions of patients using implanted pacemaker suffer from the necessary surgeries for replacing batteries. The risk and cost of frequent surgeries can be mitigated by serving an implanted biofuel cell providing energy as long as the patient is alive.
6.3 Evaluating Energy Efficiency of IoT Systems
When designing an IoT system, the amount of energy that each node, especially sensors, is consuming must be carefully evaluated in order to have a precise estimation on the system lifetime and therefore plan for the maintenance. While this topic is very wide and studying different aspects of energy analysis is very difficult, here we only focus on evaluating the energy consumption of basic devices in IoT which have limited capabilities.
6.3. EVALUATING ENERGY EFFICIENCY OF IOT SYSTEMS 87 6.3.1 Energy consumers in an IoT Device
As shown in Fig. 6.8, a communication or a sensor node is composed of supply and demand sides. The demand side consists of energy consuming units such as a sensor, a signal processing unit (DSP), a wireless transceiver and a buffer, either to store the sensed data and/or for the data to be transmitted/received. A sensor node differs from a communication node by the presence of a sen- sor. Transceivers typically use Bluetooth or Zigbee protocols to communicate within a range of maximum 30m and require output power levels in the order of 2100mW. Hence, power levels needed by a sensor node may be in the order of a few 100mWs. However, the energy consumption in transceivers may be decreased by reducing the data to be transmitted/received, choosing adaptive coding and modulation strategies, using energy efficient transmission schedul- ing, routing and medium access control as well as exploiting power saving modes (sleep/listen).
Energy to
be harvested Information to be sent
Information to be transmitted
Figure 6.8: Block diagram of a sensor/communication node from energy per- spective.
As can be seen in Fig. 6.8 to gather information by a sensor and send it to the gateway, several units will perform operation over the data. Each of these units will consume power and therefore energy. Table 6.2 lists the most important energy consumption elements in an IoT devices:
Energy harvester
Energy storage
Supply side
Sensor
DSP
Buffer
Table 6.2: Energy consumers in an IoT device
Transceiver
Demand side
Operation Sensing Processing
Tx/Rx Circuit Transmit power
Unit
Etx(r) = βr2
Definition
Energy consumed per sensing operation Energy consumed per processing a packet of information
Energy consumed to perform circuit op- erations to receive or transmit per bit Energy consumed to transmit a bit of information to a receiver at distance r
Es
Ep
Ec
88 CHAPTER 6. ENERGY IN THE IOT ECOSYSTEM Evaluating energy consumption at a sensor node
Let us consider a simple wireless network as depicted in Fig. 6.9 which consists of a sensor node and a sink node (or gateway). The sensor node is located at the distance r from the sink node and performs temperature sensing. Each temperature is converted to a packet of length b bits and sent to the sink node.
Scenario 1. Scenario 2.
The sensors send the temperature sample directly to the sink and the sink perform the average operation;
sensor 1 and sensor 2 send their sample to sensor 3, sensor 3 performs the average of the three samples (from 1, 2 and its own) and sends a single packet to the sink (in this case, the energy consumed by sensor 3 for processing is 3Ep)
Sensor
Sink node
r
Figure 6.9: A simple wireless network.
For each sensing event, the sensor node consume Es to measure the tempera- ture, and then b×Ec to prepare each bit for the transmission, and then b×Etx(r) to transmit the packet to the sink node. The overall energy consumption in an sensor for each sensing event is then given by:
Esensor = Es + bEc + bEtx(r). (6.1) Evaluating energy consumption at the sink node
For each sensing event, the sink node consumes b × Ec to receive each bit sent by the sensor node and then Ep to perform processing (if any) on the data. The overall energy consumption in the sink node for each sensing event is then given by:
Esink = bEc + Ep. (6.2) 6.3.2 Example. Evaluating the energy consumption in a
wireless network
A personal area network (PAN) is shown in Fig. 6.10, which is used to gather periodical information on the average temperature of a given area. N = 3 sen- sors are geared with temperature sensors. Consider the following two scenarios:
Assuming that:
• the packets containing the temperature samples and the averaged temper- ature are b = 128 [bits].
6.3. EVALUATING ENERGY EFFICIENCY OF IOT SYSTEMS 89 Node 1
d = 5m
d = 5m
Node 3
Node 2
• the energy for operating sensing is Es = 40 [nJ].
• the energy for operating TX/RX circuitry is Ec = 50 [nJ/bit].
• the energy required to support sufficient transmission output power is Etx(r) = βr2 [J/bit], being β = 1 [nJ/bit/m2] and r the distance between the transmitter and receiver.
• the energy required to perform the average operation is Ep = 20 nJ for each temperature measure to be averaged.
We want to find out the energy consumed by each one of the three sensors and the sink node.
Scenario 1
We first calculate the energy consumption at Node 1 and Node 2, which are
equal as they perform the same operation and have the same distance to the
r = 10m
Sink node
Figure 6.10: Example wireless PAN.
sink node. The distance from Node 1 and Node 2 to the sink node is d =
√√
1,sink d2,sink = d2 + r2 = 125. The energy consumption at Node 1 and Node 2 is
then calculated as follows:
E1 = E2 = Es + bEc + bEtx(d1,sink)
= 40 + 128 × 50 + 128 × 1 × 125 = 0.022mJ. (6.3) Similarly, the energy consumption at Node 3 can be calculated as follows
considering that the distance of Node 3 to the sink node is r = 10m. E3 = Es + bEc + bEtx(r)
= 40 + 128 × 50 + 128 × 1 × 100 = 0.019mJ. (6.4) At the sink node, three packet are arrived from the sensors and are averaged.
The energy consumption at the sink node is then given by:
Esink =3×bEc +3×Ep =3×128×50+3×20=0.019mJ. (6.5)
90 CHAPTER 6. ENERGY IN THE IOT ECOSYSTEM The total energy consumption in Scenario 1 to obtain one averaged temper-
ature at the Sink node is given by:
ET1 =E1 +E2 +E3 +Esink =0.083mJ. (6.6)
Scenario 2
In this scenario Node 1 and Node 2 send their measurement to Node 3 which are located as distance d from them. The energy consumption at these nodes is given by:
E1 =E2 =Es +bEc +bEtx(d)
= 40 + 128 × 50 + 128 × 1 × 25 = 0.009mJ. (6.7)
Node 3 needs to perform its own measurement, receive the measurements from Node 1 and Node 2, and then averaged them and send the averaged value to the sink node. The energy consumption at Node 3 is given by:
E3 =Es +2×bEc +3Ep +bEc +bEtx(r)=0.032mJ (6.8) The sink node receives only one packet from Node 3 in this scenario, therefore
we have:
Esink = bEc = 0.006mJ. (6.9) The total energy consumption in Scenario 2 to obtain one averaged temper-
ature at the Sink node is given by:
ET2 = E1 + E2 + E3 + Esink = 0.057mJ. (6.10)
Comparing Scenario 1 and Scenario 2
In Scenario 1 that the sensors send the temperature sample directly to the sink, in total ET1 = 0.083 mJ is consumed to receive an averaged measurement at the sink node. In Scenario 2, that Sensor 1 and Sensor 2 send their samples to Sensor 3 and Sensor 3 performs the average of the three samples and sends a single packet to the sink , ET2 = 0.057 mJ is consumed. This shows that Scenario 2 is more energy efficient in terms of the total energy consumption.
Now, assume that the sensors use the same battery, therefore Node 3 in Scenario 2 depletes much faster than other nodes in both scenarios. This means that if all the measurements are necessary at the sink node, the lifetime of Scenario 2 is shorter than Scenario 1 although scenario 2 has less total energy consumption.
In some networks, the network will remain alive while the sink node is alive. As the sink node in Scenario 2 consume less energy than that in Scenario 1, we expect that Scenario 2 has longer lifetime.
6.4. FURTHER READING 91 6.4 Further Reading
1. Mahyar Shirvanimoghaddam, Kamyar Shirvanimoghaddam, Mohammad Mahdi Abolhasani, Majid Farhangi, Vaid Zahiri Barsari, Hangyue Liu, Mischa Dohler, and Minoo Naebe, “Paving the Path to a Green and Self- Powered Internet of Things”, https://arxiv.org/pdf/1712.02277.pdf.
92 CHAPTER 6. ENERGY IN THE IOT ECOSYSTEM