Efficient Energy Management for the Internet of Things in Smart Cities
Open accessAbstract
The drastic increase in urbanization over the past few years requires sustainable, efficient, and smart solutions for transportation, governance, environment, quality of life, and so on. The Internet of Things offers many sophisticated and ubiquitous applications for smart cities. The energy demand of IoT applications is increased, while IoT devices continue to grow in both numbers and requirements. Therefore, smart city solutions must have the ability to efficiently utilize energy and handle the associated challenges. Energy management is considered as a key paradigm for the realization of complex energy systems in smart cities. In this article, we present a brief overview of energy management and challenges in smart cities. We then provide a unifying framework for energy-efficient optimization and scheduling of IoT-based smart cities. We also discuss the energy harvesting in smart cities, which is a promising solution for extending the lifetime of low-power devices and its related challenges. We detail two case studies. The first one targets energy-efficient scheduling in smart homes, and the second covers wireless power transfer for IoT devices in smart cities. Simulation results for the case studies demonstrate the tremendous impact of energy- efficient scheduling optimization and wireless power transfer on the performance of IoT in smart cities.
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The current training program for students in electrical engineering and its automation is inadequate in the introduction of various functions and types of sensors, which will lead to the inability to carry out maintenance and other work on the sensors in the application.
“The current training program for students in electrical engineering and its automation is inadequate in the introduction of various functions and types of sensors, which will lead to the inability to carry out maintenance and other work on the sensors in the application [65].”
From Reform of Electrical Engineering Undergraduate Teaching and the Curriculum System in the Context of the Energy Internet (2023) · cites this paper as [65]
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Experimental — accuracy not yet measuredIn applications for precision agriculture, smart cities, and Industry 4.0, it is common to deploy IoT nodes and gateways in large areas where a power outlet is not available and electricity has to be harvested to charge the batteries that power every device.
“in applications for precision agriculture [8], smart cities [9], and Industry 4.0 [10,11], it is common to deploy IoT nodes and gateways in large areas where a power outlet is not available and electricity has to be harvested to charge the batteries that power every device.”
From A Practical Evaluation of a High-Security Energy-Efficient Gateway for IoT Fog Computing Applications (2017) · cites this paper as [9]
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Cites
Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications
Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2444095 · Cited by 8,630 · see citing works
The Internet of Things: How the Next Evolution of the Internet Is Changing Everything
Evans, D. (2011) · Cited by 2,685 · see citing works
Wireless energy harvesting for the Internet of Things
Kamalinejad, P., Mahapatra, C., Sheng, Z., et al. (2015) · IEEE Communications Magazine · doi:10.1109/mcom.2015.7120024 · Cited by 676 · see citing works
Wireless Sensor Networks for Cost-Efficient Residential Energy Management in the Smart Grid
Erol‐Kantarci, M., & Mouftah, H. T. (2011) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2011.2114678 · Cited by 549 · see citing works
Demand Response Optimization for Smart Home Scheduling Under Real-Time Pricing
Tsui, K. M., & Chan, S. C. (2012) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2012.2218835 · Cited by 527 · see citing works
Enabling smart cities through a cognitive management framework for the internet of things
Vlacheas, P. T., Giaffreda, R., Stavroulaki, V., Kelaidonis, D., Foteinos, V., Poulios, G., Demestichas, P., Somov, A., Biswas, A. R., & Moessner, K. (2013) · IEEE Communications Magazine · doi:10.1109/mcom.2013.6525602 · Cited by 408 · see citing works
Smart RF energy harvesting communications: challenges and opportunities
Mishra, D., De, S. K., Jana, S., et al. (2015) · IEEE Communications Magazine · doi:10.1109/mcom.2015.7081078 · Cited by 219 · see citing works
Appliance Scheduling Optimization in Smart Home Networks
Qayyum, F., Naeem, M., Khwaja, A. S., et al. (2015) · IEEE Access · doi:10.1109/access.2015.2496117 · Cited by 181 · see citing works
Self-citationNaderi, M. Y., Nintanavongsa, P., & Chowdhury, K. R. (2014) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2014.2315211 · Cited by 177 · see citing works
Energy-Centered and QoS-Aware Services Selection for Internet of Things
Khanouche, M. E., Amirat, Y., Chibani, A., Kerkar, M., & Yachir, A. (2016) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2016.2539240 · Cited by 112 · see citing works
RERUM: Building a reliable IoT upon privacy- and security- enabled smart objects
Pöhls, H. C., Angelakis, V., Suppan, S., Fischer, K., Oikonomou, G., Τράγος, Η., Rodriguez, R. D., & Mouroutis, T. (2014) · IEEE Wireless Communications and Networking Conference Workshops (WCNCW) · doi:10.1109/wcncw.2014.6934872 · Cited by 100 · see citing works
Energy and throughput efficient cooperative spectrum sensing in cognitive radio sensor networks
Ejaz, W., Shah, G. A., Hasan, N. U., et al. (2014) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.2803 · Cited by 85 · see citing works
Self-citationALMANAC: Internet of Things for Smart Cities
Bonino, D., Alizo, M. T. D., Alapetite, A., et al. (2015) · International Conference on Future Internet of Things and Cloud (FiCloud) · doi:10.1109/ficloud.2015.32 · Cited by 82 · see citing works
Reconfigurable Wireless Networks
Mougy, A. E., Ibnkahla, M. A., Hattab, G., & Ejaz, W. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2014.2376812 · Cited by 63 · see citing works
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Flagged first, then by citations
Research works printed but not linked: 1
- [7]7. T. Ozawa et al., "Smart Cities and Energy Management," Fujistu Scientific and Technical J., vol. 50, no. 2, Apr. 2015, pp. 49-57.
Cited by
Sorted by citations
A Comprehensive Survey on Internet of Things (IoT) Toward 5G Wireless Systems
Chettri, L., & Bera, R. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2948888 · Cited by 1,737 · see citing works
A Survey on 5G Networks for the Internet of Things: Communication Technologies and Challenges
Akpakwu, G. A., Silva, B. J., Hancke, G. P., & Abu-Mahfouz, A. M. (2018) · IEEE Access · doi:10.1109/access.2017.2779844 · Cited by 1,293 · see citing works
Intelligence in IoT-Based 5G Networks: Opportunities and Challenges
Javaid, N., Sher, A., Nasir, H., & Guizani, N. (2018) · IEEE Communications Magazine · doi:10.1109/mcom.2018.1800036 · Cited by 189 · see citing works
Intelligent Security Planning for Regional Distributed Energy Internet
Lv, Z., Kong, W., Zhang, X., Jiang, D., Lv, H., & Lu, X. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2914339 · Cited by 152 · see citing works
Greening internet of things for greener and smarter cities: a survey and future prospects
Alsamhi, S. H., Ma, O., Ansari, M. S., & Meng, Q. (2019) · Telecommunication Systems · doi:10.1007/s11235-019-00597-1 · Cited by 124 · see citing works
Suárez-Albela, M., Fernández-Caramés, T., Fraga-Lamas, P., & Castedo, L. (2017) · Sensors · doi:10.3390/s17091978 · Cited by 125 · see citing works
Edge Computing, IoT and Social Computing in Smart Energy Scenarios
Sittón-Candanedo, I., Alonso, R. S., García, Ó., Muñoz, L., & Rodríguez-González, S. (2019) · Sensors · doi:10.3390/s19153353 · Cited by 114 · see citing works
A survey of blockchain applications in sustainable and smart cities
Makani, S., Pittala, R., Alsayed, E., Aloqaily, M., & Jararweh, Y. (2022) · Cluster Computing · doi:10.1007/s10586-022-03625-z · Cited by 66 · see citing works
Muhammad, K., Lloret, J., & Baik, S. W. (2019) · IEEE Communications Magazine · doi:10.1109/mcom.2018.1800371 · Cited by 46 · see citing works
Suárez-Albela, M., Fraga-Lamas, P., Castedo, L., & Fernández-Caramés, T. M. (2018) · Sensors · doi:10.3390/s19010015 · Cited by 35 · see citing works
Zhang, D., Rong, C., Goh, H. H., Liu, H., Li, X., Zhu, H., & Wu, T. (2023) · Sustainability · doi:10.3390/su15065280 · Cited by 8 · see citing works
- Authors
- Ejaz, W., Naeem, M., Shahid, A., Anpalagan, A., & Jo, M.
- Affiliations
- Toronto Metropolitan University (CA)
- COMSATS University Islamabad (PK)
- Ghent University (BE)
- Korea University (KR)
- Venue
- IEEE Communications Magazine
- Published
- 2017-01
- Volume
- 55
- Issue
- 1
- Pages
- 84-91
- ISSN
- 0163-6804 (ISSN-L)
- OpenAlex
- W2577854396
- Cited by
- 492
- References
- 14
- Citations by year
- 2026: 8 · 2025: 36 · 2024: 39 · 2023: 48 · 2022: 70 · 2021: 79 · 2020: 72 · 2019: 60 · 2018: 58 · 2017: 22
- FWCI
- 38.62
- Type
- journal article
- Language
- en
- Open access
- green
- biblio.ugent.be
- Locations
- IEEE Communications Magazine (publishedVersion)
- Ghent University Academic Bibliography (Ghent University) (submittedVersion, other-oa)
- Funders
- Lehigh University
- University of Engineering and Technology, Lahore
- University of Cambridge
- National Research Foundation
- Chosun University
- Sejong University
- Yonsei University
- Korea University
- University of Toronto
- iMinds
- Simon Fraser University
- Universiteit Gent
- Taif University
- Asian Institute of Technology
- University of Engineering and Technology, Taxila
- Samsung
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Energy Harvesting in Wireless Networks
- Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
- Keywords
- energy management, Internet of Things, smart cities, energy harvesting, energy-efficient scheduling, wireless power transfer, home automation, low-power devices
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