Internet of Things-Based Energy Management, Challenges, and Solutions in Smart Cities
Abstract
Smart cities have an attracted extensive and emerging interest from both science and industry with an increasing number of international examples emerging from all over the world. The promising and increasing trend in current computing, where it focuses on proper energy consumption leading to an increased life span of networks that uses interconnected devices that are technically referred to as the Internet of Things (IoT). These devices facilitate close resource availability on the edge of the network with resource pooling. This study presents a comprehensive survey on the proper Internet of Things-Based Energy Management in Smart Cities. The study shows that the IoTs have increased energy consumption, further the summarized table presented shows the state-of-the-art proposed methods in managing energy on different girds like smart home, smart building, and smart networks among others.
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“A Unified Smart City Model (USCM) for Smart City Conceptualization and Benchmarking: Science & Engineering Book Chapter | IGI Global.†[Online]. Available: https://www.igi-global.com/chapter/a-unified-smart-city-model-uscm-for-smart-city-conceptualization-and-benchmarking/211294. [Accessed: 24-Jan-2020].
“Smart City Operations: Modeling Challenges and Opportunities | Manufacturing & Service Operations Management.†[Online]. Available: https://pubsonline.informs.org/doi/abs/10.1287/msom.2019.0823. [Accessed: 24-Jan-2020].
“Smart innovative cities: The impact of Smart City policies on urban innovation - Science Direct.†[Online]. Available: https://www.sciencedirect.com/science/article/pii/S0040162517318346. [Accessed: 24-Jan-2020].
“A Cloud and Fog based Architecture for Energy Management of Smart City by using Meta-heuristic Techniques - IEEE Conference Publication.†[Online]. Available: https://ieeexplore.ieee.org/abstract/document/8766702/. [Accessed: 24-Jan-2020].
“Energy Management For Electric Vehicles in Smart Cities: A Deep Learning Approach - IEEE Conference Publication.†[Online]. Available: https://ieeexplore.ieee.org/abstract/document/8766580/. [Accessed: 24-Jan-2020].
“Fusing TensorFlow with building energy simulation for intelligent energy management in smart cities - ScienceDirect.†[Online]. Available: https://www.sciencedirect.com/science/article/pii/S2210670718314380. [Accessed: 24-Jan-2020].
“Challenges for adopting and implementing IoT in smart cities: An integrated MICMAC-ISM approach | Emerald Insight.†[Online]. Available: https://www.emerald.com/insight/content/doi/10.1108/INTR-06-2018-0252/full/html. [Accessed: 24-Jan-2020].
“HybridIoT: Integration of Hierarchical Multiple Access and Computation Offloading for IoT-Based Smart Cities - IEEE Journals & Magazine.†[Online]. Available https://ieeexplore.ieee.org/abstract/document/8675166. [Accessed: 24-Jan-2020].
“Optimal Edge Resource Allocation in IoT-Based Smart Cities - IEEE Journals & Magazine.†[Online]. Available: https://ieeexplore.ieee.org/abstract/document/8675169. [Accessed: 24-Jan-2020].
“Towards optimal resource management for IoT based Green and sustainable smart cities - ScienceDirect." [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0959652619302082. [Accessed: 24-Jan-2020].
“A transactive energy modeling and assessment framework for demand response business cases in smart distributed multi-energy systems - ScienceDirect.†[Online]. Available: https://www.sciencedirect.com/science/article/pii/S0360544218303177. [Accessed: 24-Jan-2020].
“Algorithms | Free Full-Text | Total Optimization of Energy Networks in a Smart City by Multi-Population Global-Best Modified Brain Storm Optimization with Migration.†[Online]. Available: https://www.mdpi.com/1999-4893/12/1/15. [Accessed: 24-Jan-2020].
“Modeling and Operational Optimization Based on Energy Hubs for Complex Energy Networks With Distributed Energy Resources | Journal of Energy Resources Technology | ASME Digital Collection.†[Online].
Available: https://asmedigitalcollection.asme.org/energyresources/article-abstract/141/2/022002/474669. [Accessed: 24-Jan-2020].
"A review of a smart building sensing system for better indoor environment control - ScienceDirect." [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0378778819309302. [Accessed: 24-Jan-2020].
“A testbed for a smart building | Proceedings of the Fourth Workshop on International Science of Smart City Operations and Platforms Engineering.†[Online]. Available: https://dl.acm.org/doi/abs/10.1145/3313237.3313296. [Accessed: 24-Jan-2020].
“Leveraging Machine Learning and Big Data for Smart Buildings: A Comprehensive Survey - IEEE Journals & Magazine.†[Online]. Available: https://ieeexplore.ieee.org/abstract/document/8754678. [Accessed: 24-Jan-2020].
"A low-cost smart power meter for IoT - ScienceDirect.†[Online]. Available: https://www.sciencedirect.com/science/article/pii/S0263224118312144. [Accessed: 24-Jan-2020].
"Context-Aware Traffic Scheduling Algorithm for Power Distribution Smart Grid Network - IEEE Journals & Magazine.†[Online]. Available: https://ieeexplore.ieee.org/abstract/document/8779637. [Accessed: 24-Jan-2020].
“Internet of things application in smart grid: A brief overview of challenges, opportunities, and future trends - ScienceDirect.†[Online]. Available: https://www.sciencedirect.com/science/article/pii/B9780128121542000134. [Accessed: 24-Jan-2020].
“An Energy-Efficient and Deadline-Aware Task Offloading Strategy Based on Channel Constraint for Mobile Cloud Workflows - IEEE Journals & Magazine.†[Online]. Available: https://ieeexplore.ieee.org/abstract/document/8723368. [Accessed: 24-Jan-2020].
Meng, Sa, Peng Sun, Jie Luo, and Han Xu. "An Energy Prediction Model for Cloud Data Centers Through Performance Counter." International Journal of Performability Engineering 11 (2019).
L. Parra, J. Rocher, S. Sendra, and J. Lloret, “An Energy-Efficient IoT Group-Based Architecture for Smart Cities,†in Energy Conservation for IoT Devices : Concepts, Paradigms and Solutions, M. Mittal, S. Tanwar, B. Agarwal, and L. M. Goyal, Eds. Singapore: Springer, 2019, pp. 111–127.
T. Cioara et al., "Exploiting data centers energy flexibility in smart cities: Business scenarios,†Inf. Sci., vol. 476, pp. 392–412, Feb. 2019, doi: 10.1016/j.ins.2018.07.010.
M. Min, L. Xiao, Y. Chen, P. Cheng, D. Wu, and W. Zhuang, “Learning-Based Computation Offloading for IoT Devices With Energy Harvesting,†IEEE Trans. Veh. Technol., vol. 68, no. 2, pp. 1930–1941, Feb. 2019, doi: 10.1109/TVT.2018.2890685.
H. Chen, M. Liu, Y. Wang, W. Fang, and Y. Ding, “A Markov Approximation Algorithm for Computation Offloading and Resource Scheduling in Mobile Edge Computing,†in Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health, Singapore, 2019, pp. 3–20, doi: 10.1007/978-981-15-1925-3_1.
I. Sittón-Candanedo, R. S. Alonso, Ó. GarcÃa, A. B. Gil, and S. RodrÃguez-González, “A Review on Edge Computing in Smart Energy by means of a Systematic Mapping Study,†Electronics, vol. 9, no. 1, p. 48, Jan. 2020, doi: 10.3390/electronics9010048.
“GreenEdge: Greening Edge Datacenters with Energy-Harvesting IoT Devices,†in 2019 IEEE 27th International Conference on Network Protocols (ICNP), 2019, pp. 1–6, doi: 10.1109/ICNP.2019.8888103.
“Electronics | Free Full-Text | LoBEMS—IoT for Building and Energy Management Systems.†[Online]. Available: https://www.mdpi.com/2079-9292/8/7/763. [Accessed: 24-Jan-2020].
“Information and resource management systems for Internet of Things: Energy management, communication protocols and future applications - ScienceDirect.†[Online]. Available: https://www.sciencedirect.com/science/article/pii/S0167739X18329194. [Accessed: 24-Jan-2020].
“Research on intelligent city energy management based on Internet of things | SpringerLink.†[Online]. Available: https://link.springer.com/article/10.1007/s10586-018-1742-x. [Accessed: 24-Jan-2020].
F. Li, K.-Y. Lam, X. Li, Z. Sheng, J. Hua, and L. Wang, “Advances and Emerging Challenges in Cognitive Internet of Things,†IEEE Trans. Ind. Inform., pp. 1–1, 2019, doi: 10.1109/TII.2019.2953246.
S. Lima and L. Terán, “Cognitive Smart Cities and Deep Learning: A Classification Framework,†in 2019 Sixth International Conference on eDemocracy eGovernment (ICEDEG), 2019, pp. 180–187, doi: 10.1109/ICEDEG.2019.8734346.
J. Cuenca, F. Larrinaga, L. Eciolaza, and E. Curry, “Towards Cognitive Cities in the Energy Domain,†in Designing Cognitive Cities, E. Portmann, M. E. Tabacchi, R. Seising, and A. Habenstein, Eds. Cham: Springer International Publishing, 2019, pp. 155–183.
“Capsule Network Assisted IoT Traffic Classification Mechanism for Smart Cities - IEEE Journals & Magazine.†[Online]. Available: https://ieeexplore.ieee.org/abstract/document/8651277. [Accessed: 24-Jan-2020].
“Cloud, network and sensing in a smart city: toward a cloud of meshed cooperative heterogeneous things : Smart Cities in the Post-algorithmic Era.†[Online]. Available: https://www.elgaronline.com/view/edcoll/9781789907049/9781789907049.00014.xml. [Accessed: 24-Jan-2020].
N. Shoaib and J. A. Shamsi, “Understanding Network Requirements for Smart City Applications: Challenges and Solutions,†IT Prof., vol. 21, no. 3, pp. 33–40, May 2019, doi: 10.1109/MITP.2018.2883047.
D. Li, L. Deng, M. Lee, and H. Wang, “IoT data feature extraction and intrusion detection system for smart cities based on deep migration learning,†Int. J. Inf. Manag., vol. 49, pp. 533–545, Dec. 2019, doi: 10.1016/j.ijinfomgt.2019.04.006.
Y. Qian, D. Wu, W. Bao, and P. Lorenz, “The Internet of Things for Smart Cities: Technologies and Applications,†IEEE Netw., vol. 33, no. 2, pp. 4–5, Mar. 2019, doi: 10.1109/MNET.2019.8675165.
“Data Integration for Smart Cities: Opportunities and Challenges | SpringerLink.†[Online]. Available: https://link.springer.com/chapter/10.1007/978-981-15-0058-9_38. [Accessed: 24-Jan-2020].
S. Mostafavi and W. Shafik, “Fog Computing Architectures, Privacy and Security Solutions,†J. Commun. Technol. Electron. Comput. Sci., vol. 24, pp. 1–14, 2019.
W. Shafik, S. M. Matinkhah, and M. Ghasemazade, “Fog-Mobile Edge Performance Evaluation and Analysis on Internet of Things,†J. Adv. Res. Mob. Comput., vol. 1, no. 3.
DOI: http://dx.doi.org/10.22385/jctecs.v27i0.302