Energy Harvesting Techniques for Wireless Sensor Networks/Radio-Frequency Identification: A Review
Abstract
:1. Introduction
- Presents an overview of popular research topics on green WSNs and RFID ecosystem, covering the recent industry development in the main areas of application, challenges and key players;
- Addresses several substantial design choices and features for WSNs and RFID, both of which are considered the top priorities of green IoT technologies. These features are deeply investigated on the basis of their respective sub-domains to achieve a precise, concrete and concise conclusion;
- Provides new references to other researchers who need insights into enabling hardware green IoT which provides eco-sustainability.
2. Green Wireless Sensor Network
2.1. Radio Optimisation Techniques
- Joint rate and power control are modelled as two distinct games (i.e., an uplink transmission rate allocation and an uplink transmission power allocation problem), which are based on the game theoretic perspective. Users determine first their uplink transmission rate and then given their uplink transmission rate, they apply power control to allocate their uplink transmission powers [51,52]. The main drawback of this approach is that the optimisation problem is solved asynchronously and separately considering the two systems’ resources. Thus, the combined outcome of the two distinct optimisation problems is less efficient than jointly solving the problem [53].
- The joint rate and power control problem is amended in a single-variable problem of the ratio of uplink transmission rate to the uplink transmission power [54]. However, this approach is limited in realistic cases and can only be applied in specific studies where simplified forms of utility functions are assumed (i.e., where the ratio of uplink transmission rate to power appears). As a result, the use of this approach strongly depends on problem formulation. The single variable problem is solved with respect to the substituted ratio. To determine users’ optimal pair of uplink transmission rate and power, the maximum value of one resource is assumed and the other one is determined, so the ratio is equal to the optimal one. Although users update their uplink transmission rate and power in the same step, the obtained solution remains inferior compared with the corresponding solution of the actual joint two-variable optimisation problem discussed in [53].
2.2. Sleep/Wake Up Techniques
2.3. Energy Harvesting and Wireless Charging Techniques
2.4. Energy-Efficient Routing and WSN Architecture
- Reduction of transmitting distance of cluster members requiring lower transmission power.
- Cluster heads limiting the transmissions frequency as a result of fusion.
- Mandating the cluster head to perform all the energy-sapping functions, such as coordination and aggregation.
- Permit to power-off some cluster members while the cluster head assumes the forwarding roles.
- Alternate the choice of cluster head among the nodes so as energy consumption in the network.
2.5. Aggregation and Reduction of the Data
3. Green Radio-Frequency Identification
3.1. Passive RFID Systems
3.2. Active RFID System
- Low-end devices. These devices are similar to traditional watches or pacemakers that can be powered for a long time (years) by button type batteries, given their low energy consumption (usually under 100 µW).
- Mid-range devices. These devices consume an average of 500 mW, considering their use of wireless communications transceivers. They usually last less than a day (often just several hours) when transmitting continuously, although certain technologies make use of sleep modes or periodic transmissions (e.g., BLE beacons) to last long. This kind of device requires bulkier batteries than low-end devices (e.g., AA or AAA batteries), making them less appropriate to be embedded into smart clothing.
- High-end devices. These are devices similar to smartphones or laptops that consume up to 50 W. They usually utilise Li-ion batteries, which can be bulky and add weight to a garment.
4. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- Arshad, R.; Zahoor, S.; Shah, M.A.; Wahid, A.; Yu, H. Green IoT: An investigation on energy saving practices for 2020 and beyond. IEEE Access 2017, 5, 15667–15681. [Google Scholar] [CrossRef]
- Al-Fuqaha, A.; Guizani, M.; Mohammadi, M.; Aledhari, M.; Ayyash, M. Internet of things: A survey on enabling technologies, protocols, and applications. IEEE Commun. Surv. Tutor. 2015, 17, 2347–2376. [Google Scholar] [CrossRef]
- Zanella, A.; Bui, N.; Castellani, A.; Vangelista, L.; Zorzi, M. Internet of things for smart cities. IEEE Internet Things J. 2014, 1, 22–32. [Google Scholar] [CrossRef]
- Almotiri, S.H.; Khan, M.A.; Alghamdi, M.A. Mobile Health (m-Health) System in the Context of IoT. In Proceedings of the 4th IEEE International Conference on in Future Internet of Things and Cloud Workshops (FiCloud), Vienna, Austria, 22–24 August 2016; pp. 39–42. [Google Scholar]
- Alsharif, M.H.; Nordin, R. Evolution towards fifth generation (5G) wireless networks: Current trends and challenges in the deployment of millimetre wave, massive MIMO, and small cells. Telecommun. Syst. 2017, 64, 617–637. [Google Scholar] [CrossRef]
- Lee, C.-S.; Kim, D.-H.; Kim, J.-D. An energy efficient active RFID protocol to avoid overhearing problem. IEEE Sens. J. 2014, 14, 15–24. [Google Scholar] [CrossRef]
- Tsai, C.-W.; Lai, C.-F.; Chiang, M.-C.; Yang, L.T. Data mining for Internet of Things: A survey. IEEE Commun. Surv. Tutor. 2014, 16, 77–97. [Google Scholar] [CrossRef]
- Mukherjee, A.; Paul, H.S.; Dey, S.; Banerjee, A. Angels for Distributed Analytics in IoT. In Proceedings of the IEEE World Forum on Internet of Things (WF-IoT), Seoul, Korea, 6–8 March 2014; pp. 565–570. [Google Scholar]
- Gelenbe, E.; Caseau, Y. The impact of information technology on energy consumption and carbon emissions. Ubiquity 2015, 2015, 1. [Google Scholar] [CrossRef]
- Shaikh, F.K.; Zeadally, S.; Exposito, E. Enabling technologies for green internet of things. IEEE Syst. J. 2017, 11, 983–994. [Google Scholar] [CrossRef]
- Zhu, C.; Leung, V.C.; Shu, L.; Ngai, E.C.-H. Green internet of things for smart world. IEEE Access 2015, 3, 2151–2162. [Google Scholar] [CrossRef]
- Miorandi, D.; Sicari, S.; de Pellegrini, F.; Chlamtac, I. Internet of things: Vision, applications and research challenges. Ad Hoc Netw. 2012, 10, 1497–1516. [Google Scholar] [CrossRef] [Green Version]
- Baliga, J.; Ayre, R.W.; Hinton, K.; Tucker, R.S. Green cloud computing: Balancing energy in processing, storage, and transport. Proc. IEEE 2011, 99, 149–167. [Google Scholar] [CrossRef]
- Shaikh, F.K.; Zeadally, S. Energy harvesting in wireless sensor networks: A comprehensive review. Renew. Sustain. Energy Rev. 2016, 55, 1041–1054. [Google Scholar] [CrossRef]
- Akkaya, K.; Guvenc, I.; Aygun, R.; Pala, N.; Kadri, A. IoT-Based Occupancy Monitoring Techniques for Energy-Efficient Smart Buildings. In Proceedings of the IEEE in Wireless Communications and Networking Conference Workshops (WCNCW), New Orleans, LA, USA, 9–12 March 2015; pp. 58–63. [Google Scholar]
- Alsharif, M.H.; Nordin, R.; Abdullah, N.F.; Kelechi, A.H. How to make key 5G wireless technologies environmental friendly: A review. Trans. Emerg. Telecommun. Technol. 2018, 29, e3254. [Google Scholar] [CrossRef]
- Rault, T.; Bouabdallah, A.; Challal, Y. Energy efficiency in wireless sensor networks: A top-down survey. Comput. Netw. 2014, 67, 104–122. [Google Scholar] [CrossRef]
- Sinha, R.S.; Wei, Y.; Hwang, S.-H. A survey on LPWA technology: LoRa and NB-IoT. ICT Express 2017, 3, 14–21. [Google Scholar] [CrossRef]
- Ahmed, N.; Rahman, H.; Hussain, M.I. A comparison of 802.11 ah and 802.15.4 for IoT. ICT Express 2016, 2, 100–102. [Google Scholar] [CrossRef]
- Alsharif, M.H.; Nordin, R.; Ismail, M. Energy optimisation of hybrid off-grid system for remote telecommunication base station deployment in Malaysia. EURASIP J. Wirel. Commun. Netw. 2015, 2015, 1–15. [Google Scholar] [CrossRef]
- Chu, X.; Sethu, H. Cooperative topology control with adaptation for improved lifetime in wireless sensor networks. Ad Hoc Netw. 2015, 30, 99–114. [Google Scholar] [CrossRef]
- Lin, S.; Miao, F.; Zhang, J.; Zhou, G.; Gu, L.; He, T. ATPC: Adaptive transmission power control for wireless sensor networks. ACM Trans. Sens. Netw. (TOSN) 2016, 12, 6. [Google Scholar] [CrossRef]
- Cui, S.; Goldsmith, A.J.; Bahai, A. Energy-efficiency of MIMO and cooperative MIMO techniques in sensor networks. IEEE J. Sel. Areas Commun. 2004, 22, 1089–1098. [Google Scholar] [CrossRef]
- Jayaweera, S.K. Virtual MIMO-based cooperative communication for energy-constrained wireless sensor networks. IEEE Trans. Wirel. Commun. 2006, 5, 984–989. [Google Scholar] [CrossRef]
- Cui, S.; Goldsmith, A.J.; Bahai, A. Energy-constrained modulation optimization. IEEE Trans. Wirel. Commun. 2005, 4, 2349–2360. [Google Scholar]
- Costa, F.M.; Ochiai, H. A Comparison of Modulations for Energy Optimization in Wireless Sensor Network Links. In Proceedings of the IEEE in Global Telecommunications Conference (GLOBECOM 2010), Maiami, FL, USA, 6–10 December 2010; pp. 1–5. [Google Scholar]
- Misra, S.; Kumar, M.P.; Obaidat, M.S. Connectivity preserving localized coverage algorithm for area monitoring using wireless sensor networks. Comput. Commun. 2011, 34, 1484–1496. [Google Scholar] [CrossRef]
- Karasabun, E.; Korpeoglu, I.; Aykanat, C. Active node determination for correlated data gathering in wireless sensor networks. Comput. Netw. 2013, 57, 1124–1138. [Google Scholar] [CrossRef] [Green Version]
- Anastasi, G.; Conti, M.; di Francesco, M.; Passarella, A. Energy conservation in wireless sensor networks: A survey. Ad Hoc Netw. 2009, 7, 537–568. [Google Scholar] [CrossRef]
- Carrano, R.C.; Passos, D.G.; Magalhães, L.C.S.; Vinicius, N.C. Survey and taxonomy of duty cycling mechanisms in Wireless Sensor Networks. IEEE Commun. Surv. Tutor. 2014, 16, 181–194. [Google Scholar] [CrossRef]
- de Paz Alberola, R.; Pesch, D. Duty cycle learning algorithm (DCLA) for IEEE 802.15.4 beacon-enabled wireless sensor networks. Ad Hoc Netw. 2012, 10, 664–679. [Google Scholar] [CrossRef]
- Wan, Z.; Tan, Y.; Yuen, C. Review on Energy Harvesting and Energy Management for Sustainable Wireless Sensor Networks. In Proceedings of the IEEE 13th International Conference on Communication Technology (ICCT), Jinan, China, 25–28 September 2011; pp. 362–367. [Google Scholar]
- Sudevalayam, S.; Kulkarni, P. Energy harvesting sensor nodes: Survey and implications. IEEE Commun. Surv. Tutor. 2011, 13, 443–461. [Google Scholar] [CrossRef]
- Nintanavongsa, P.; Naderi, M.Y.; Chowdhury, K.R. Medium Access Control Protocol Design for Sensors Powered by Wireless Energy Transfer. In Proceedings of the IEEE in INFOCOM, Turin, Italy, 14–19 April 2013; pp. 150–154. [Google Scholar]
- Shi, Y.; Xie, L.; Hou, Y.T.; Sherali, H.D. On Renewable Sensor Networks with Wireless Energy Transfer. In Proceedings of the IEEE in INFOCOM, Shanghai, China, 10–15 April 2011; pp. 1350–1358. [Google Scholar]
- Li, K.; Luan, H.; Shen, C.-C. Qi-ferry: Energy-Constrained Wireless Charging in Wireless Sensor Networks. In Proceedings of the IEEE in Wireless Communications and Networking Conference (WCNC), Paris, France, 1–4 April 2012; pp. 2515–2520. [Google Scholar]
- Erol-Kantarci, M.; Mouftah, H.T. Suresense: Sustainable wireless rechargeable sensor networks for the smart grid. IEEE Wirel. Commun. 2012, 19. [Google Scholar] [CrossRef]
- Kumar, D.; Aseri, T.C.; Patel, R. EEHC: Energy efficient heterogeneous clustered scheme for wireless sensor networks. Comput. Commun. 2009, 32, 662–667. [Google Scholar] [CrossRef]
- Li, H.; Liu, Y.; Chen, W.; Jia, W.; Li, B.; Xiong, J. COCA: Constructing optimal clustering architecture to maximize sensor network lifetime. Comput. Commun. 2013, 36, 256–268. [Google Scholar] [CrossRef]
- Radi, M.; Dezfouli, B.; Bakar, K.A.; Lee, M. Multipath routing in wireless sensor networks: Survey and research challenges. Sensors 2012, 12, 650–685. [Google Scholar] [CrossRef] [PubMed]
- Lu, Y.M.; Wong, V.W.S. An energy-efficient multipath routing protocol for wireless sensor networks. Int. J. Commun. Syst. 2007, 20, 747–766. [Google Scholar]
- Ergen, S.C.; Varaiya, P. Optimal Placement of Relay Nodes for Energy Efficiency in Sensor Networks. In Proceedings of the IEEE International Conference on Communications (ICC’06), Istanbul, Turkey, 11–15 June 2006; pp. 3473–3479. [Google Scholar]
- Misra, S.; Majd, N.E.; Huang, H. Constrained Relay Node Placement in Energy Harvesting Wireless Sensor Networks. In Proceedings of the IEEE 8th International Conference on Mobile Adhoc and Sensor Systems (MASS), Valencia, Spain, 17–22 October 2011; pp. 25–34. [Google Scholar]
- Dandekar, D.R.; Deshmukh, P. Energy Balancing Multiple sink Optimal Deployment in Multi-Hop Wireless Sensor Networks. In Proceedings of the IEEE 3rd International on Advance Computing Conference (IACC), Ghaziabad, India, 22–23 February 2013; pp. 408–412. [Google Scholar]
- Rajagopalan, R.; Varshney, P.K. Data aggregation techniques in sensor networks: A survey. IEEE Commun. Surv. Tutor. 2006, 8, 48–63. [Google Scholar] [CrossRef]
- Fasolo, E.; Rossi, M.; Widmer, J.; Zorzi, M. In-network aggregation techniques for wireless sensor networks: A survey. IEEE Wirel. Commun. 2007, 14. [Google Scholar] [CrossRef]
- Wang, S.; Vasilakos, A.; Jiang, H.; Ma, X.; Liu, W.; Peng, K. Energy Efficient Broadcasting Using Network Coding Aware Protocol in Wireless Ad Hoc Network. In Proceedings of the IEEE International Conference on Communications (ICC), Kyoto, Japan, 5–9 June 2011; pp. 1–5. [Google Scholar]
- Correia, L.H.; Macedo, D.F.; Santos, A.L.d.; Loureiro, A.A.; Nogueira, J.M.S. Transmission power control techniques for wireless sensor networks. Comput. Netw. 2007, 51, 4765–4779. [Google Scholar] [CrossRef]
- Nosratinia, A.; Hunter, T.E.; Hedayat, A. Cooperative communication in wireless networks. IEEE Commun. Mag. 2004, 42, 74–80. [Google Scholar] [CrossRef]
- Jung, J.W.; Wang, W.; Ingram, M.A. Cooperative Transmission Range Extension for Duty Cycle-Limited Wireless Sensor Networks. In Proceedings of the 2nd International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace & Electronic Systems Technology (Wireless VITAE), Chennai, India, 28 February–3 March 2011; pp. 1–5. [Google Scholar]
- Musku, M.R.; Chronopoulos, A.T.; Popescu, D.C. Joint Rate and Power Control Using Game Theory. In Proceedings of the 3rd IEEE Consumer Communications and Networking Conference (CCNC 2006), Las Vegas, NV, USA, 8–10 January2006; pp. 1258–1262. [Google Scholar]
- Zhou, P.; Liu, W.; Yuan, W.; Cheng, W. Energy-Efficient Joint Power and Rate Control Via Pricing in Wireless Data Networks. In Proceedings of the IEEE Wireless Communications and Networking Conference, Las Vegas, NV, USA, 31 March–3 April 2008; pp. 1091–1096. [Google Scholar]
- Tsiropoulou, E.E.; Vamvakas, P.; Papavassiliou, S. Joint utility-based uplink power and rate allocation in wireless networks: A non-cooperative game theoretic framework. Phys. Commun. 2013, 9, 299–307. [Google Scholar] [CrossRef]
- Musku, M.R.; Chronopoulos, A.T.; Popescu, D.C.; Stefanescu, A. A game-theoretic approach to joint rate and power control for uplink CDMA communications. IEEE Trans. Commun. 2010, 58, 923–932. [Google Scholar] [CrossRef]
- Alsharif, M.H.; Kim, J. Optimal Solar Power System for Remote Telecommunication Base Stations: A Case Study Based on the Characteristics of South Korea’s Solar Radiation Exposure. Sustainability 2016, 8, 942. [Google Scholar] [CrossRef]
- Tutuncuoglu, K.; Yener, A. Communicating using an energy harvesting transmitter: Optimum policies under energy storage losses. IEEE Trans. Wirel. Commun. 2012. Available online: https://arxiv.org/abs/1208.6273 (accessed on 1 July 2019).
- Zhang, F.; Hackworth, S.A.; Liu, X.; Chen, H.; Sclabassi, R.J.; Sun, M. Wireless Energy Transfer Platform for Medical Sensors and Implantable Devices. In Proceedings of the Annual International Conference of the IEEE in Engineering in Medicine and Biology Society (EMBC 2009), Minneapolis, MN, USA, 3–6 September 2009; pp. 1045–1048. [Google Scholar]
- Jonah, O.; Georgakopoulos, S.V. Efficient Wireless Powering of Sensors Embedded in Concrete Via Magnetic Resonance. In Proceedings of the IEEE International Symposium on Antennas and Propagation (APSURSI), Boston, MA, USA, 18–13 July 2011; pp. 1425–1428. [Google Scholar]
- Griffin, B.; Detweiler, C. Resonant Wireless Power Transfer to Ground Sensors from a UAV. In Proceedings of the IEEE international conference on Robotics and automation (ICRA), St. Paul, MN, USA, 14–18 May 2012; pp. 2660–2665. [Google Scholar]
- Ho, S.; Wang, J.; Fu, W.; Sun, M. A comparative study between novel witricity and traditional inductive magnetic coupling in wireless charging. IEEE Trans. Magn. 2011, 47, 1522–1525. [Google Scholar] [CrossRef]
- Kurs, A.; Karalis, A.; Moffatt, R.; Joannopoulos, J.D.; Fisher, P.; Soljačić, M. Wireless power transfer via strongly coupled magnetic resonances. Science 2007, 317, 83–86. [Google Scholar] [CrossRef] [PubMed]
- Kline, M.; Izyumin, I.; Boser, B.; Sanders, S. Capacitive Power Transfer for Contactless Charging. In Proceedings of the Twenty-Sixth Annual IEEE in Applied Power Electronics Conference and Exposition (APEC), Fort Worth, TX, USA, 6–11 March 2011; pp. 1398–1404. [Google Scholar]
- Popovic, Z. Cut the cord: Low-power far-field wireless powering. IEEE Microw. Mag. 2013, 14, 55–62. [Google Scholar] [CrossRef]
- Hui, S. Planar wireless charging technology for portable electronic products and Qi. Proc. IEEE 2013, 101, 1290–1301. [Google Scholar] [CrossRef]
- Lu, X.; Wang, P.; Niyato, D.; Kim, D.I.; Han, Z. Wireless charging technologies: Fundamentals, standards, and network applications. IEEE Commun. Surv. Tutor. 2016, 18, 1413–1452. [Google Scholar] [CrossRef]
- Lu, X.; Wang, P.; Niyato, D.; Kim, D.I.; Han, Z. Wireless networks with RF energy harvesting: A contemporary survey. IEEE Commun. Surv. Tutor. 2015, 17, 757–789. [Google Scholar] [CrossRef]
- Shinohara, N. The wireless power transmission: Inductive coupling, radio wave, and resonance coupling. Wiley Interdiscip. Rev. Energy Environ. 2012, 1, 337–346. [Google Scholar] [CrossRef]
- Xie, L.; Shi, Y.; Hou, Y.T.; Lou, A. Wireless power transfer and applications to sensor networks. IEEE Wirel. Commun. 2013, 20, 140–145. [Google Scholar]
- Liu, H. Maximizing Efficiency of Wireless Power Transfer with Resonant Inductive Coupling; International Baccalaureate Program; Sir Winston Churchill Secondary School: Vancouver, BC, Canada, 2011; pp. 1–22. [Google Scholar]
- Jawad, A.M.; Nordin, R.; Gharghan, S.K.; Jawad, H.M.; Ismail, M. Opportunities and challenges for near-field wireless power transfer: A review. Energies 2017, 10, 1022. [Google Scholar] [CrossRef]
- Mur-Miranda, J.O.; Fanti, G.; Feng, Y.; Omanakuttan, K.; Ongie, R.; Setjoadi, A. Wireless Power Transfer Using Weakly Coupled Magnetostatic Resonators. In Proceedings of the IEEE in Energy Conversion Congress and Exposition (ECCE), Portland, OR, USA, 23–27 September 2010; pp. 4179–4186. [Google Scholar]
- Gurakan, B.; Ozel, O.; Yang, J.; Ulukus, S. Energy cooperation in energy harvesting communications. IEEE Trans. Commun. 2013, 61, 4884–4898. [Google Scholar] [CrossRef]
- Varshney, L.R. Transporting Information and Energy Simultaneously. In Proceedings of the IEEE International Symposium on Information Theory, Toronto, ON, Canada, 6–11 July 2008; pp. 1612–1616. [Google Scholar]
- Huang, K.; Zhou, X. Cutting the last wires for mobile communications by microwave power transfer. IEEE Commun. Mag. 2015, 53, 86–93. [Google Scholar] [CrossRef] [Green Version]
- Perera, T.D.P.; Jayakody, D.N.K.; Sharma, S.K.; Chatzinotas, S.; Li, J. Simultaneous wireless information and power transfer (SWIPT): Recent advances and future challenges. IEEE Commun. Surv. Tutor. 2017, 20, 264–302. [Google Scholar] [CrossRef]
- Gungor, V.C.; Hancke, G.P. Industrial wireless sensor networks: Challenges, design principles, and technical approaches. IEEE Trans. Ind. Electron. 2009, 56, 4258–4265. [Google Scholar] [CrossRef]
- Akyildiz, I.F.; Melodia, T.; Chowdhury, K.R. A survey on wireless multimedia sensor networks. Comput. Netw. 2007, 51, 921–960. [Google Scholar] [CrossRef]
- Peer, M.; Jain, N.; Bohara, V.A. A hybrid Spectrum Sharing Protocol for Energy Harvesting Wireless Sensor Nodes. In Proceedings of the 2016 IEEE 17th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Edinburgh, UK, 3–6 July 2016; pp. 1–6. [Google Scholar]
- Guo, S.; Wang, F.; Yang, Y.; Xiao, B. Energy-efficient cooperative for simultaneous wireless information and power transfer in clustered wireless sensor networks. IEEE Trans. Commun. 2015, 63, 4405–4417. [Google Scholar] [CrossRef]
- Tong, B.; Li, Z.; Wang, G.; Zhang, W. How Wireless Power Charging Technology Affects Sensor Network Deployment and Routing. In Proceedings of the IEEE 30th International Conference on Distributed Computing Systems, Genova, Italy, 21–25 June 2010; pp. 438–447. [Google Scholar]
- Nishimoto, H.; Kawahara, Y.; Asami, T. Prototype Implementation of Ambient RF Energy Harvesting Wireless Sensor Networks. In Proceedings of the SENSORS, 2010 IEEE, Waikoloa, HI, USA, 1–4 November 2010; pp. 1282–1287. [Google Scholar]
- Popović, Z.; Falkenstein, E.A.; Costinett, D.; Zane, R. Low-power far-field wireless powering for wireless sensors. Proc. IEEE 2013, 101, 1397–1409. [Google Scholar] [CrossRef]
- Zhang, X.; Jiang, H.; Zhang, L.; Zhang, C.; Wang, Z.; Chen, X. An energy-efficient ASIC for wireless body sensor networks in medical applications. IEEE Trans. Biomed. Circuits Syst. 2010, 4, 11–18. [Google Scholar] [CrossRef]
- Guo, S.; Wang, C.; Yang, Y. Mobile Data Gathering with Wireless Energy Replenishment in Rechargeable Sensor Networks. In Proceedings of the Proceedings IEEE INFOCOM, Turin, Italy, 14–19 April 2013; pp. 1932–1940. [Google Scholar]
- Guo, S.; Wang, C.; Yang, Y. Joint mobile data gathering and energy provisioning in wireless rechargeable sensor networks. IEEE Trans. Mob. Comput. 2014, 13, 2836–2852. [Google Scholar] [CrossRef]
- Wang, C.; Li, J.; Ye, F.; Yang, Y. NETWRAP: An NDN based real-time wireless recharging framework for wireless sensor networks. IEEE Trans. Mob. Comput. 2014, 13, 1283–1297. [Google Scholar]
- Zhang, S.; Wu, J.; Lu, S. Collaborative Mobile Charging for Sensor Networks. In Proceedings of the IEEE 9th International Conference on Mobile Ad-Hoc and Sensor Systems (MASS 2012), Las Vegas, NV, USA, 8–11 October 2012; pp. 84–92. [Google Scholar]
- Johnson, J.; Basha, E.; Detweiler, C. Charge Selection Algorithms for Maximizing Sensor Network Life with UAV-Based Limited Wireless Recharging. In Proceedings of the IEEE Eighth International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Melbourne, Australia, 2–5 April 2013; pp. 159–164. [Google Scholar]
- Naderi, M.Y.; Chowdhury, K.R.; Basagni, S.; Heinzelman, W.; De, S.; Jana, S. Experimental Study of Concurrent Data and Wireless Energy Transfer for Sensor Networks. In Proceedings of the IEEE Global Communications Conference, Austin, TX, USA, 8–12 December 2014; pp. 2543–2549. [Google Scholar]
- Wang, Z.; Bulut, E.; Szymanski, B.K. Energy Efficient Collision Aware Multipath Routing for Wireless Sensor Networks. In Proceedings of the IEEE International Conference on Communications (ICC’09), Dresden, Germany, 14–18 June 2009; pp. 1–5. [Google Scholar]
- Liu, A.; Ren, J.; Li, X.; Chen, Z.; Shen, X.S. Design principles and improvement of cost function based energy aware routing algorithms for wireless sensor networks. Comput. Netw. 2012, 56, 1951–1967. [Google Scholar] [CrossRef]
- Younis, M.; Akkaya, K. Strategies and techniques for node placement in wireless sensor networks: A survey. Ad Hoc Netw. 2008, 6, 621–655. [Google Scholar] [CrossRef]
- Yan, Z.; Subbaraju, V.; Chakraborty, D.; Misra, A.; Aberer, K. Energy-Efficient Continuous Activity Recognition on mobile Phones: An Activity-Adaptive Approach. In Proceedings of the 16th International Symposium on Wearable Computers (ISWC), Newcastle Upon Tyne, UK, 18–22 June 2012; pp. 17–24. [Google Scholar]
- Hou, I.-H.; Tsai, Y.-E.; Abdelzaher, T.F.; Gupta, I. Adapcode: Adaptive Network Coding for Code Updates in Wireless Sensor Networks. In Proceedings of the 27th IEEE Conference on Computer Communications (INFOCOM 2008), Phoenix, AZ, USA, 15–17 April 2008; pp. 1517–1525. [Google Scholar]
- Ray, P.P. A survey on Internet of Things architectures. J. King Saud Univ. Comput. Inf. Sci. 2018, 30, 291–319. [Google Scholar] [Green Version]
- Rida, A.; Yang, L.; Tentzeris, M.M. RFID-Enabled Sensor Design and Applications, 1st ed.; Artech House: Norwood, MA, USA, 2010. [Google Scholar]
- Khan, M.A.; Sharma, M.; Prabhu, B.R. A survey of RFID tags. Int. J. Recent Trends Eng. 2009, 1, 68. [Google Scholar]
- Farren, N.; Milou, C.; Volakos, P. The Evolution of Car Parking: Technology Creating Risk and Opportunity; The Transportation Research Board: Washington, DC, USA, 2015. [Google Scholar]
- Silva, B.N.; Khan, M.; Han, K. Towards sustainable smart cities: A review of trends, architectures, components, and open challenges in smart cities. Sustain. Cities Soc. 2018, 38, 697–713. [Google Scholar] [CrossRef]
- Abdulkader, O.; Bamhdi, A.M.; Thayananthan, V.; Jambi, K.; Alrasheedi, M. A Novel and Secure Smart Parking Management System (SPMS) Based on Integration of WSN, RFID, and IoT. In Proceedings of the 15th Learning and Technology Conference (L&T), Jeddah, Saudi Arabia, 25–26 February 2018; pp. 102–106. [Google Scholar]
- Rivera, J.A.; Fox, J.A.; Grimwood, D. Vehicle Location Tracking Systems and Methods. U.S. Patent Application No. 20180374365, 2018. [Google Scholar]
- Tsiropoulou, E.E.; Baras, J.S.; Papavassiliou, S.; Sinha, S. RFID-based smart parking management system. Cyber-Phys. Syst. 2017, 3, 22–41. [Google Scholar] [CrossRef]
- Debus, W. RF Path Loss & Transmission Distance Calculations; Axonn, LLC: New York, NY, USA, 2006. [Google Scholar]
- Ba, H.; Demirkol, I.; Heinzelman, W. Passive wake-up radios: From devices to applications. Ad Hoc Netw. 2013, 11, 2605–2621. [Google Scholar] [CrossRef]
- Massimo, A. Enabling the Internet of Things from Integrated Circuits to Integrated Systems, 1st ed.; Springer: Berlin, Germany, 2017; Chapter 15. [Google Scholar]
- Guan, M.; Liao, W.-H. Characteristics of energy storage devices in piezoelectric energy harvesting systems. J. Intell. Mater. Syst. Struct. 2008, 19, 671–680. [Google Scholar] [CrossRef]
- Carmo, J.; Rocha, R.; Silva, A.; Gonçalves, L.; Correia, J. Integrated Thin-Film Rechargeable Battery in a Thermoelectric Scavenging Microsystem. In Proceedings of the International Conference on Power Engineering, Energy and Electrical Drives (POWERENG’09), Lisboa, Portugal, 18–20 March 2009; pp. 359–362. [Google Scholar]
- Yeo, J.; Moon, S.G.; Jung, J.Y. Antennas for a battery-assisted RFID tag with thin and flexible film batteries. Microw. Opt. Technol. Lett. 2008, 50, 494–498. [Google Scholar] [CrossRef]
- Grbović, P.J.; Delarue, P.; le Moigne, P. Selection and Design of Ultra-Capacitor Modules for Power Conversion Applications: From Theory to Practice. In Proceedings of the 7th International Conference in Power Electronics and Motion Control (IPEMC), Harbin, China, 2–5 June 2012; pp. 771–777. [Google Scholar]
- Varley, J.; Martino, M.; Poshtkouhi, S.; Trescases, O. Battery and Ultra-Capacitor Hybrid Energy Storage System and Power Management Scheme for Solar-Powered Wireless Sensor Nodes. In Proceedings of the 38th Annual Conference on IEEE Industrial Electronics Society (IECON), Montreal, QC, Canada, 25–28 October 2012; pp. 4806–4811. [Google Scholar]
- Wang, Z.L. Toward self-powered sensor networks. Nano Today 2010, 5, 512–514. [Google Scholar] [CrossRef]
- Milici, S.; Lázaro, A.; Villarino, R.; Girbau, D.; Magnarosa, M. Wireless wearable magnetometer-based sensor for sleep quality monitoring. IEEE Sens. J. 2018, 18, 2145–2152. [Google Scholar] [CrossRef]
- Mardonova, M.; Choi, Y. Review of wearable device technology and its applications to the mining industry. Energies 2018, 11, 547. [Google Scholar] [CrossRef]
- Sharma, A.; Pande, T.; Aroul, P.; Soundarapandian, K.; Lee, W. Circuits and Systems for Energy Efficient Smart Wearables. In Proceedings of the IEEE International Electron Devices Meeting (IEDM), San Francisco, CA, USA, 3–7 December 2016; pp. 6.2.1–6.2.4. [Google Scholar]
- Klair, D.K.; Chin, K.-W.; Raad, R. A survey and tutorial of RFID anti-collision protocols. IEEE Commun. Surv. Tutor. 2010, 12, 400–421. [Google Scholar] [CrossRef]
- Namboodiri, V.; Gao, L. Energy-aware tag anticollision protocols for RFID systems. IEEE Trans. Mob. Comput. 2010, 9, 44–59. [Google Scholar] [CrossRef]
- Li, Z.; Zhao, H.; Su, X.; Wan, C. Asymmetric Cryptography Based Unidirectional Authentication Method for RFID. In Proceedings of the 2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), The Hague, The Netherlands, 19–23 July 2018; pp. 374–3743. [Google Scholar]
- Jadhao, A.; Ugale, S. Study of RFID Authentication Protocols. In Proceedings of the Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), Pimpri-Chinchwad, India, 17–18 August 2018; pp. 1–4. [Google Scholar]
- Sha, K.; Wei, W.; Yang, T.A.; Wang, Z.; Shi, W. On security challenges and open issues in Internet of Things. Future Gener. Comput. Syst. 2018, 83, 326–337. [Google Scholar] [CrossRef]
- Seliem, M.; Elgazzar, K.; Khalil, K. Towards Privacy Preserving IoT Environments: A Survey. Wirel. Commun. Mob. Comput. 2018, 2018. [Google Scholar] [CrossRef]
- Juels, A. RFID security and privacy: A research survey. IEEE J. Sel. Areas Commun. 2006, 24, 381–394. [Google Scholar] [CrossRef]
Parameters | LoRa | Bluetooth | LR-WPAN | Mobile Communication | WiMAX | WiFi |
---|---|---|---|---|---|---|
Standard | LoRaWAN R1.0 | IEEE 802.15.1 | IEEE 802.15.4 (ZigBee) | 2G-GSM, CDMA 3G-UMTS, CDMA2000 4G-LTE-A | IEEE 802.16 | IEEE 802.11 a/c/b/d/g/n |
Energy consumption | Very Low | Bluetooth: Medium; BLE: Very Low | Low | Medium | Medium | High |
Frequency band | 868/900 MHz | 2.4 GHz | 868/915 MHz, 2.4 GHz | 865 MHz–2. GHz | 2–66 GHz | 5–60 GHz |
Data rate | 0.3–50 Kb/s | 1–24 Mb/s | 40–250 Kb/s | 200 kb/s–1 Gb/s | 1 Mb/s–1 Gb/s (Fixed) 50–100 Mb/s (mobile) | 1 Mb/s–6.75 Gb/s |
Transmission range | <30 Km | 8–10 m | 10–20 m | Entire cellular area | <50Km | 20–100 m |
Cost | High | Low | Low | Medium | High | High |
Wireless Technology | Healthcare | Smart Cities | Smart Building | Automotive | Industry | Local Network (M2M) |
---|---|---|---|---|---|---|
Bluetooth (BLE) | very high | low | low | very low | very high | medium |
LR-WPAN | medium | high | low | very low | low | high |
LoRa | low | high | high | high | high | high |
WiFi | low | high | medium | medium | low | high |
WiMAX | low | very high | high | high | very high | high |
Mobile communication | low | high | high | high | medium | very low |
Environment | Power Harvester | |||
---|---|---|---|---|
Solar Panel | Wind Generator | Thermoelectric | Electromagnetic | |
Power density of the indoor environment | 100 µW/cm2 | 35 µW/cm2 @ wind speed < 1m/s | 100 µW/cm2 @ 5 oC | 4 µW/cm3 @ human motion (Hz) 800 µW/cm3 @ machine (kHz) |
Power density of the outdoor environment | 10 mW/cm2 | 3.5mW/cm2 @ wind speed ≤ 8.4m/s | 3.5 mW/cm2 @ 30 oC |
IoT Applications | Energy Harvesting Source | ||||
---|---|---|---|---|---|
Solar Panel | Wind Generator | Electromagnetic | Thermoelectric | ||
Smart Home | Outdoor sensor | ✔ | ✔ | ✔ | |
Smart thermostat | ✔ | ✔ | |||
Air quality monitor | ✔ | ✔ | |||
Lighting | ✔ | ✔ | |||
Security monitor | ✔ | ||||
Smart door lock | ✔ | ||||
Wearables | Smartwatch | ✔ | |||
Monitoring and tracking | ✔ | ||||
Health | Medical patch | ✔ | ✔ | ||
Fitness band/monitor | ✔ | ||||
Industrial | Factory automation | ✔ | ✔ | ✔ | |
Machine monitor | ✔ | ✔ | |||
Vehicles | Wireless parking meter | ✔ | ✔ |
Technique | Advantages | Disadvantages | Charging Distance |
---|---|---|---|
Magnetic inductive coupling |
|
| From a few millimetres to a few centimeters. |
Magnetic resonance coupling |
|
| From a few centimeters to a few meters. |
Non-directive RF radiation |
|
| Typically, within several tens of meters, up to several kilometers. Suitable for mobile applications. |
Issues | Battery | Capacitor |
---|---|---|
Advantages |
|
|
Challenges |
|
|
Improvements |
|
|
Device | Power | Battery Type | Operating Period | Weight (g) | Size (l × w × h/d × h, mm) |
---|---|---|---|---|---|
Watches | 3–10 µW | Silver oxide button | 1–2 years | 2.4 | 11.6 × 5.4 |
Pacemakers | 25–80 µW | Lithium button | 7–10 years | 2.83 | 20 × 3.2 |
Hearing aids | N/A | Zinc-mercury oxide | 25–30 days | 0.3–1.85 | (5.8−11.6) × (3.6–5.4) |
Digital clocks | 13 mW | Silver oxide button | 6–10 months | 15–25 | (5.8–11.8) × (1.65–5.4) |
LEDs | 25–100 mW | Silver oxide button | 6–12 months (depends on the usage frequency) | 15–25 | (5.8–11.8) × (1.65–5.4) |
Pedometers | 250 mW | Silver oxide button | 1–2 years (depends on the usage frequency) | 15–25 | (5.8–11.8) × (1.65–5.4) |
Portable radio | 500 mW | AAA | 3–6 months (depends on the usage frequency) | 8.5–11 | 10.5 × 44.5 |
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Alsharif, M.H.; Kim, S.; Kuruoğlu, N. Energy Harvesting Techniques for Wireless Sensor Networks/Radio-Frequency Identification: A Review. Symmetry 2019, 11, 865. https://doi.org/10.3390/sym11070865
Alsharif MH, Kim S, Kuruoğlu N. Energy Harvesting Techniques for Wireless Sensor Networks/Radio-Frequency Identification: A Review. Symmetry. 2019; 11(7):865. https://doi.org/10.3390/sym11070865
Chicago/Turabian StyleAlsharif, Mohammed H., Sunghwan Kim, and Nuri Kuruoğlu. 2019. "Energy Harvesting Techniques for Wireless Sensor Networks/Radio-Frequency Identification: A Review" Symmetry 11, no. 7: 865. https://doi.org/10.3390/sym11070865
APA StyleAlsharif, M. H., Kim, S., & Kuruoğlu, N. (2019). Energy Harvesting Techniques for Wireless Sensor Networks/Radio-Frequency Identification: A Review. Symmetry, 11(7), 865. https://doi.org/10.3390/sym11070865