Energy Sustainability in Wireless Sensor Networks: Comparison
View Latest Version
Please note this is a comparison between Version 2 by Beatrix Zheng and Version 1 by Dimitris Antonios Rountos.

Wireless Sensor Networks (WSNs) are considered to be among the most important scientific domains. Yet, the exploitation of WSNs suffers from the severe energy restrictions of their electronic components. For this reason there are numerous scientific methods that have been proposed aiming to achieve the extension of the lifetime of WSNs, either by energy saving or energy harvesting or through energy transfer. 

  • wireless sensor networks
  • energy sustainability
  • energy efficiency
  • energy saving

1. Consumption and Waste of Energy in WSNs

As meIntioned above, in most cases sensor nodes in WSNsireless Sensor Networks (WSNs) have a limited lifetime because of their restricted energy residues. For this reason, the achievement of energy conservation during the obligatory tasks of nodes (i.e., sensing, receiving, transmitting, and processing) is necessitated. Even more so, the elimination of every cause of energy waste is imperative.
Actually, the main causes of energy waste in WSNs are [20][1]:
  • Idle listening, i.e., listening to a communication channel, which is idle, with the intention of receiving possible incoming messages;
  • Overhearing, i.e., when a node takes delivery of packets that are intended to be received by other nodes;
  • Packet collision, i.e., the conflict caused to the messages that arrive at a node simultaneously which necessitates the rejection of them and their retransmission;
  • Interference, i.e., the signals intended to be wirelessly received by a node are modified in a disruptive way due to the addition of other unwanted signals;
  • Control packet overhead, i.e., the overhead caused by the excessive use of packets that synchronize data transmission without having data themselves;
  • Over-emitting, i.e., the case that a node transmits data packets while the corresponding receiver node is not available to receive them.

2. Hardware-Based Energy Sustainability in WSNs

2.1. The Architecture of Wireless Sensor Nodes

Each sensor node of a WSN is a Micro Electromechanical system (MEMS) [1[2][3],26], which is composed of four main and two optional subsystems, as illustrated in Figure 31. The basic subsystems of a node are:
/media/item_content/202212/63ae844cc6961jlpea-12-00065-g003.png
Figure 31.
Typical architecture of a wireless sensor node.
  • The power unit, of which the battery is the main and most commonly used part. Solar panels could also be used as a secondary energy source to a node [3][4];
  • The sensing unit that contains one or more analog or digital sensors and an analog to digital converter (ADC);
  • The central processing unit (CPU), which comprises a microprocessor or microcontroller, along with its memory and its main purpose is to aggregate, store and process the data recorded from sensors;
  • The communication unit, which is responsible for the transmission of the produced data to other nodes or to the base station. The communication unit usually contains a wireless radiofrequency (RF) transceiver. Moreover, devices for the communication through optical, or infrared signals may be used.
A sensor node may also contain, as optional subsystems, a position tracking unit, which monitors the current location of this node, and a mobility unit, which provides the node the ability to be transportable [2][5].
Summarily, the sensing unit of a sensor node is triggered by an occurring event in its adjacent environment. The ADC converts the signals to electric signals that are handled by the processing unit. Once the processing procedure is completed, the produced data can be wirelessly transmitted to neighboring nodes or/and the BS.

2.2. Hardware-Based Methods for Energy Sustainability

As illustrated in Figure 42, Hardware-based approaches for energy sustainability focus on the selection of the optimum hardware components that should be embedded in a sensor node, the management of their operation, and the use of energy harvesting and transference methods [19,20,21,22,23,24,25][1][6][7][8][9][10][11].
/media/item_content/202212/63ae846bd4c53jlpea-12-00065-g004.png
Figure 42.
Categorization of hardware-based methods for energy sustainability in WSNs.

2.2.1. Energy Saving Methods Applied in Submodules

When referring to the main submodules of nodes (i.e., sensors, processors and transceivers) the utilization of low-power MEMS is necessitated in order to achieve energy saving [1,2,3,26][2][3][4][5]. Moreover, the power of a sensor node can be managed by hardware scaling methods, which are used to handle the settings and the configuration of the hardware in nodes’ submodules. When engaging with such methods, the voltage, the frequency, and the rate can be adjusted according to the application’s requirements to limit energy consumption. Furthermore, methods such as system power optimization, aim at putting the node in sleep mode while not in operation in order to avoid energy depletion. Actually, several methods may be applied in each one of the submodules of nodes:
  • While designing the Sensing Unit, the type of the application WSN is intended to be used in, needs to be considered in order to choose the appropriate sensors and converters [21,24][7][10].
    ○
    The selection of low power sensor units contributes to the energy conservation of the overall sensor node;
    ○
    The ability to promptly control the operations of sensors (e.g., turning on and off), as well as its quick response time to irritations and its low duty cycle can lead to energy saving;
    ○
    Additionally, instead of active sensors, passive sensors may be used. Such devices do not contain any piece of active circuits. For this reason, they use not exterior energy supplies. Actually, they are not powered at all. Instead, they receive incoming signals that they are reflected backwards along with the sensed information [27][12].
  • The design of the central Processing Unit is related to the choice of the optimum microprocessors and microcontrollers (MCUs) [19,21][6][7].
    ○
    Low-power processors offer low frequency clock choices, consume lower currents and are able to operate using lower voltages. In addition, it is critical to avoid implementing a huge number of features and peripherals, since the greater the amount is, the higher the power consumption becomes;
    ○
    Microprocessors, mostly support different modes of operation, such as, active, idle and sleep mode for clearer power management objectives;
    ○
    Furthermore, dynamic voltage scaling (DVS) method frequently applies in processors during their operation in active status in order to lessen the energy consumption levels [28,29][13][14]. Usually, microprocessors do not operate continually at their highest computational power, due to the fact that the work load of each task varies. Thus, the use of DVS method provides energy efficiency to sensor nodes by adjusting both the voltage of the processor and operating frequency dynamically according to the demands of the momentary processing tasks.
  • The selection of appropriate transceivers to be integrated in the communication unit of the sensor nodes is extremely helpful in order to achieve energy conservation.
    ○
    The use of low power transceivers is extremely helpful in order to reduce energy consumption [19][6];
    ○
    Putting the transceiver in sleep mode while there are no communication needs, or using Adaptive Transmission Power Control can also save energy;
    ○
    The use of Cognitive Radio (CR), i.e., an intelligent radio that enables the dynamic selection of the most suitable radio channel can lead to a network energy conservation [21][7]. This selection depends on the transmit power, the data rate, the duty cycle, and the modulation required by the existing conditions;
    ○
    In the so-called Adaptive Transmission Power Control method, the power required for data transmission is estimated based on the distances among nodes [19][6]. Additionally, the power levels of the transmitter are adjusted according to the needs of each application, in order to limit the energy consumption [24][10];
    ○
    In addition, directional antennas may be used. Such antennas are able to both send and receive signals in one direction. Subsequently, they consume lower amounts of power comparatively to omnidirectional antennas that transmit towards many and probably undesired directions and consequently cause higher energy consumption [21][7];
    ○
    Moreover, energy conservation depends on the way the nodes are deployed, the distance between them and the power needed for data transmission. In fact, in networks with dense deployment, nodes can communicate with nearby allocated nodes by using small communication links. This way, the transferred data reach their final destination by exploiting multi-hop paths, which results in the consumption of low power levels of each node. Contrariwise, in networks with sparse deployment in which single-hop communication applies, the transmission power and consequently the overall energy dissipation is greater [21][7].
  • Regarding the power supply unit of sensor nodes, small batteries with restricted capacity [22][8] are typically used as power sources. The amount of the stored energy while a battery is fully charged is characterized as its capacity. There are different types of batteries used in WSNs, and some of the most commonly used are the Alkaline, the Lithium-Ion (Li-ion) and the Nickel Metal Hydride (NiMH) batteries. Of course, all types of batteries have an extremely limited lifetime. For this reason, the use of rechargeable batteries or supercapacitors is a better alternative.
    ○
    In WSNs where the recharge of the batteries of the nodes is feasible, the usage of rechargeable batteries can considerably prolong the operational lifespan of the nodes and the overall network. Additionally, due to their high energy density, rechargeable batteries are suitable for WSNs utilizing energy harvesting implementations. Specifically, the density of NiMH batteries is 60–80 Wh/kg and that of lithium batteries is 120–140 Wh/kg, while their lifetime varies between 300–500 and 500–1000 recharge cycles, respectively [19][6]. In the cases where battery recharge is difficult to perform, techniques that aim at either estimating [30][15] or prolonging [31][16] the remaining battery lifetime may be used;
    ○
    Supercapacitors are capacitors having higher capacitance with lower voltage limits when compared to typical capacitors. They have grown into practical alternatives of power sources in WSNs nodes due to their energy density levels that range between 1–10 Wh/kg, and their smaller size in comparison with batteries. Thus, an even long-lasting lifespan of the sensor nodes could be achieved by replacing the non-rechargeable batteries of sensor nodes used in harvesting systems with supercapacitors as means of energy storage [19][6].

2.2.2. Energy Harvesting

Generally, energy harvesting is the process by which energy is captured and stored in order to empower small electronic devices. In WSNs, energy harvesting is achieved using energy scavenging systems that can be attached in the sensor nodes [32,33][17][18]. Power management modules (PMM) are usually integrated in these energy harvesting systems in order to increase the harvested power level and to restrict the energy mismatches between the harvester and the node. Typically, the harvesting process entails an energy source, a harvester or harvesting system, and standalone nodes or nodes with embedded energy storage devices [19,34][6][19]. The overall energy harvesting process is illustrated in Figure 53.
/media/item_content/202212/63ae848982afejlpea-12-00065-g005.png
Figure 53.
Overview of energy harvesting process.
Specifically, energy harvesting can be performed by taking advantage of either ambient or external sources. Ambient sources of energy are almost permanently available in the surrounding environment of the nodes, while external sources of energy are especially set up for energy scavenging purposes [35,36][20][21].
[42][27]. Energy wireless transfer can be achieved in three ways:
  • Inductive coupling: energy can be wirelessly transferred from a primary to a secondary coil that is placed in close distance. The amount of generated energy is proportional to the size of the coil. This method is simple and safe to apply [19][6];
  • Magnetic resonant coupling: power is transferred from a main coil (source) to a secondary (receiver). This can be accomplished through the utilization of resonant coils that have the same resonant frequency and are either loosely or strongly coupled [42][27]. Compared to inductive coupling, this method provides the power transfer over longer distances, and it is not a radiative method. So, it causes almost no harm to humans and does not have need of line of sight;
  • Electromagnetic (EM) radiation: a source device transmits energy via electromagnetic waves through its antenna to another device’s receiving antenna. There are two types of electromagnetic radiation: omnidirectional and unidirectional. By using EM, energy can be transmitted over long distances [43][28].
  • According to the specific type of the physical quantity that is used, energy harvesting via ambient sources can be further classified as: RF-based, light-based, thermal-based, flow-based, and biomass-based [33,34][18][19].
    ○
    RF-based energy harvesting makes use of radio frequency (RF) waves that may derive from wirelessly emitted signals coming from the BS, television, radio, Wi-Fi, or mobile devices. Such RF waves are initially captured by the nodes via either the receiver that they use for their wireless communication or another radio antenna that is dedicated only for energy scavenging. Next, the RF waves captured are converted into DC electricity [35,37][20][22].
    ○
    In case there is the ability to capture light energy from either sunlight, or indoors light, light sensitive devices may be used. Specifically, photovoltaic (PV) cells may be incorporated into the sensor nodes in order to capture and absorb photons that are emitted by light. Actually, PV cells contain semiconducting materials, such as silicon, which are able to convert the energy of light that is captured into a flow of electrons [38,39][23][24];
    ○
    Thermal-based energy harvesting is based on the generation of energy due to the existence of either heat or variations in temperature. The conversion of thermal energy to electric energy is achieved via either pyroelectric transducers or Thermo Electric Generators (TEGs). The former produce electricity from charge changes that are created on the surface of pyroelectric crystals due to temperature fluctuations, while TEGs take advantage of either Seebeck, or Joule, or Peltier, or Thomson effects [33,34,36][18][19][21];
    ○
    Flow-based energy harvesting uses the transformation of the energy produced by wind and water into electric energy. Specifically, the energy harvesting via wind in WSNs is based on the use of propellers, triboelectric, and piezoelectric devices of small dimensions for the exploitation of rotations, and the vibrations caused by the flow of wind. The existence of moving or falling water near by the nodes is very useful. Specifically, small sized hydrogenerators, which convert mechanical energy created by water movement into electricity, are used. Additionally, the use of seawater batteries, consisting of electrodes, is another alternative for WSNs located in sea [33][18];
    ○
    Biomass-based energy harvesting is performed by piezoelectric and triboelectric nanogenerators that scavenge energy from decomposable wastage, organic constituents, chemical substances, human urine, and other types of biological material. In this way, WSNs can be powered in environmental, biomedical, and various other applications [33,35][18][20].
  • According to the specific type of the quantity that it is used, energy harvesting via external sources of energy can be further classified as: mechanical-based and human-based.
    ○
    Mechanical-based energy harvesting is achieved by using the so called Mechanical-to-Electrical Energy Generators (MEEGs). Such devices include piezoelectric, electromagnetic, or electrostatic mechanisms in order to scavenge energy created by vibrations, stress–strain and pressure [33,36][18][21];
    ○
    Human-based energy harvesting is performed in Wireless Body Area Networks (WBANs) in which nodes are either deployed on human bodies or implanted in human bodies. In such networks of this type, human-based energy harvesting is ideal for energy supply. It refers to the scavenging of the energy created during various activities or processes of human body, such as walking, finger movements, blood flow, and body heat. Electroactive materials, miniscule thermoelectric, piezoelectric, or triboelectric generators, and tiny rotary devices may be used for this purpose [34,39,40][19][24][25].

2.2.3. Wireless Energy Transfer

Wireless energy transfer (WET) is another method used to increase energy residues of the nodes in WSNs. Actually, this method, is described as the ability of wirelessly transferring electrical energy among nodes by using appropriate hardware components [19][6]. When exploiting this method, energy may be transferred from the segments of the network with higher energy levels to segments having lower amounts of energy residues so as to balance the energy levels of the network [41][26]. Power transfer in a WSN can be accomplished using either stationary sources or mobile chargers. Energy is provided to the nodes via charging vehicles and robots, or energy transmitters. Furthermore, sensor nodes are capable of transferring energy to their neighboring nodes

References

  1. Rezaei, Z. Energy Saving in Wireless Sensor Networks. Int. J. Comput. Sci. Eng. Surv. 2012, 3, 23–37.
  2. Akyildiz, I.F.; Su, W.; Sankarasubramaniam, Y.; Cayirci, E. Wireless Sensor Networks: A Survey. Comput. Netw. 2002, 38, 393–422.
  3. Warneke, A.; Pister, J. MEMS for distributed wireless sensor networks. In Proceedings of the 9th IEEE International Conference on Electronics, Circuits, and Systems, Dubrovnik, Croatia, 15–18 November 2002; pp. 291–294.
  4. Wang, Q.; Balasingham, I. Wireless sensor networks-an Introduction. In Wireless Sensor Networks: Application-Centric Design; InTechOpen: London, UK, 2010; pp. 1–14.
  5. Yick, J.; Mukherjee, B.; Ghosal, D. Wireless Sensor Network Survey. Comput. Netw. 2008, 52, 2292–2330.
  6. Engmann, F.; Katsriku, F.A.; Abdulai, J.-D.; Adu-Manu, K.S.; Banaseka, F.K. Prolonging the Lifetime of Wireless Sensor Networks: A Review of Current Techniques. Wirel. Commun. Mob. Comput. 2018, 2018, 8035065.
  7. Rault, T.; Bouabdallah, A.; Challal, Y. Energy Efficiency in Wireless Sensor Networks: A Top-down Survey. Comput. Netw. 2014, 67, 104–122.
  8. Khan, J.A.; Qureshi, H.K.; Iqbal, A. Energy Management in Wireless Sensor Networks: A Survey. Comput. Electr. Eng. 2015, 41, 159–176.
  9. Anastasi, G.; Conti, M.; Di Francesco, M.; Passarella, A. Energy Conservation in Wireless Sensor Networks: A Survey. Ad Hoc Netw. 2009, 7, 537–568.
  10. Patel, H.; Shah, V. A review on energy consumption and conservation techniques for sensor node in WSN. In Proceedings of the IEEE 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), Paralakhemundi, India, 3–5 October 2016; pp. 594–599.
  11. Stankovic, J.A.; He, T. Energy Management in Sensor Networks. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 2012, 370, 52–67.
  12. Gamba, P.; Goldoni, E.; Savazzi, P.; Arpesi, P.G.; Sopranzi, C.; Dufour, J.F.; Lavagna, M. Wireless passive sensors for remote sensing of temperature on aerospace platforms. IEEE Sens. J. 2014, 14, 3883–3892.
  13. Pouwelse, J.; Langendoen, K.; Sips, H. Dynamic voltage scaling on a low-power microprocessor. In Proceedings of the 7th Annual International Conference on Mobile Computing and Networking—MobiCom’01, Rome, Italy, 16–21 July 2001.
  14. Kulau, U.; Büsching, F.; Wolf, L. A Node’s life: Increasing WSN lifetime by dynamic voltage scaling. In Proceedings of the 2013 IEEE International Conference on Distributed Computing in Sensor Systems, Cambridge, MA, USA, 8–11 July 2013; pp. 241–248.
  15. Nikolić, G.; Nikolić, T.; Stojčev, M.; Petrović, B.; Jovanović, G. Battery capacity estimation of wireless sensor node. In Proceedings of the IEEE 30th International Conference on Microelectronics (MIEL), Beirut, Lebanon, 9–11 October 2017; pp. 279–282.
  16. Narayanaswamy, S.; Schlueter, S.; Steinhorst, S.; Lukasiewycz, M.; Chakraborty, S.; Hoster, H. On Battery Recovery Effect in Wireless Sensor Nodes. ACM Trans. Des. Autom. Electron. Syst. 2016, 21, 2890501.
  17. Panatik, K.Z.; Kamardin, K.; Shariff, S.A.; Yuhaniz, S.S.; Ahmad, N.A.; Yusop, O.M.; Ismail, S. Energy harvesting in wireless sensor networks: A Survey. In Proceedings of the 2016 IEEE 3rd International Symposium on Telecommunication Technologies (ISTT), Kuala Lumpur, Malaysia, 28–30 November 2016.
  18. Shaikh, F.K.; Zeadally, S. Energy Harvesting in Wireless Sensor Networks: A Comprehensive Review. Renew. Sustain. Energy Rev. 2016, 55, 1041–1054.
  19. Singh, J.; Kaur, R.; Singh, D. Energy Harvesting in Wireless Sensor Networks: A Taxonomic Survey. Int. J. Energy Res. 2020, 45, 118–140.
  20. Tony, A.; Hiryanto, L. A Review on Energy Harvesting and Storage for Rechargeable Wireless Sensor Networks. IOP Conf. Ser. Mater. Sci. Eng. 2019, 508, 012120.
  21. Williams, A.J.; Torquato, M.F.; Cameron, I.M.; Fahmy, A.A.; Sienz, J. Survey of Energy Harvesting Technologies for Wireless Sensor Networks. IEEE Access 2021, 9, 77493–77510.
  22. Ruan, T.; Chew, Z.J.; Zhu, M. Energy-Aware Approaches for Energy Harvesting Powered Wireless Sensor Nodes. IEEE Sens. J. 2017, 17, 2165–2173.
  23. Sudevalayam, S.; Kulkarni, P. Energy Harvesting Sensor Nodes: Survey and Implications. IEEE Commun. Surv. Tutor. 2011, 13, 443–461.
  24. Sah, D.K.; Amgoth, T. Renewable Energy Harvesting Mechanisms in Wireless Sensor Networks: A Survey. Inf. Fusion 2020, 63, 223–247.
  25. Adu-Manu, K.S.; Adam, N.; Tapparello, C.; Ayatollahi, H.; Heinzelman, W. Energy-harvesting wireless sensor networks (EH-WSNs): A review. ACM Trans. Sens. Netw. 2018, 14, 1–50.
  26. Khriji, S.; El Houssaini, D.; Kammoun, I.; Kanoun, O. Energy-efficient methods in wireless sensor networks, technology, components and system design. In Energy Harvesting for Wireless Sensor Networks, 1st ed.; Kanoun, O., Ed.; De Gruyter: Berlin, Germany, 2018; pp. 287–304.
  27. Barman, S.D.; Reza, A.W.; Kumar, N.; Karim, M.E.; Munir, A.B. Wireless Powering by Magnetic Resonant Coupling: Recent Trends in Wireless Power Transfer System and Its Applications. Renew. Sustain. Energy Rev. 2015, 51, 1525–1552.
  28. Mou, X.; Sun, H. Wireless power transfer: Survey and roadmap. In Proceedings of the 2015 IEEE 81st Vehicular Technology Conference (VTC Spring), Glasgow, UK, 11–14 May 2015.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)
ScholarVision Creations
Feedback