A location-based fog computing optimization of energy management in smart buildings: DEVS modeling and design of connected objects

doi:10.1007/s11704-021-0375-z

Frontiers of Computer Science

2023, Vol. 17

Issue (2): 172501 https://doi.org/10.1007/s11704-021-0375-z

本期目录

A location-based fog computing optimization of energy management in smart buildings: DEVS modeling and design of connected objects

Abdelfettah MAATOUG^1,²(

), Ghalem BELALEM¹, Saïd MAHMOUDI³

¹. Computer Science Department, Faculty of Exact and Applied Sciences, University of Oran 1 Ahmed Ben Bella, Oran 31000, Algeria
². Science and Technology Department, Faculty of Science and Technology, University of TIARET, Tiaret 14000, Algeria
³. Computer Science Department, Faculty of Engineering, University of Mons, Mons 7000, Belgium

全文: PDF(45328 KB) HTML

Abstract：

Nowadays, smart buildings rely on Internet of things (IoT) technology derived from the cloud and fog computing paradigms to coordinate and collaborate between connected objects. Fog is characterized by low latency with a wider spread and geographically distributed nodes to support mobility, real-time interaction, and location-based services. To provide optimum quality of user life in modern buildings, we rely on a holistic Framework, designed in a way that decreases latency and improves energy saving and services efficiency with different capabilities. Discrete EVent system Specification (DEVS) is a formalism used to describe simulation models in a modular way. In this work, the sub-models of connected objects in the building are accurately and independently designed, and after installing them together, we easily get an integrated model which is subject to the fog computing Framework. Simulation results show that this new approach significantly, improves energy efficiency of buildings and reduces latency. Additionally, with DEVS, we can easily add or remove sub-models to or from the overall model, allowing us to continually improve our designs.

Key words： smart building energy consumption IoT fog computing Framework DEVS simulation models

收稿日期: 2020-07-25 出版日期: 2022-03-01

Corresponding Author(s): Abdelfettah MAATOUG

引用本文:

. [J]. Frontiers of Computer Science, 2023, 17(2): 172501.
Abdelfettah MAATOUG, Ghalem BELALEM, Saïd MAHMOUDI. A location-based fog computing optimization of energy management in smart buildings: DEVS modeling and design of connected objects. Front. Comput. Sci., 2023, 17(2): 172501.

链接本文:

https://academic.hep.com.cn/fcs/CN/10.1007/s11704-021-0375-z
https://academic.hep.com.cn/fcs/CN/Y2023/V17/I2/172501

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Fig.10

Fig.11

Fig.12

Fig.13

Fig.14

Tab.1

Tab.2

Fig.15

Fig.16

Tab.3

Tab.4

Fig.17

Fig.18

Fig.19

Fig.20

Tab.5

1	P Bellavista, J Berrocal, A Corradi, S K Das, L Foschini, A Zanni. A survey on fog computing for the Internet of Things. Journal of Pervasive and Mobile Computing, 2019, 52: 71– 99 https://doi.org/10.1016/j.pmcj.2018.12.007
2	F Jalali S Khodadustan C Gray K Hinton F Suits. Greening IoT with fog: a survey. In: Proceedings of the 2017 IEEE International Conference on Edge Computing (EDGE). 2017, 25– 31
3	H F Atlam, R J Walters, G B Wills. Fog computing and the internet of things: a review. Big Data and Cognitive Computing, 2018, 2( 2): 10 https://doi.org/10.3390/bdcc2020010
4	A Maatoug, G Belalem. Conception and validation of smart building energy management system BEMS using the discrete event system specification DEVS. Journal of Communications Software and Systems, 2014, 10( 2): 107– 113 https://doi.org/10.24138/jcomss.v10i2.131
5	B P L Lau, S H Marakkalage, Y Zhou, N U Hassan, C Yuen, M Zhang, U X Tan. A survey of data fusion in smart city applications. Information Fusion, 2019, 52: 357– 374 https://doi.org/10.1016/j.inffus.2019.05.004
6	J K Zao T T Gan C K You S J R Méndez C E Chung Y T Wang T Mullen T P Jung. Augmented brain computer interaction based on fog computing and linked data. In: Proceedings of the International Conference on Intelligent Environments. 2014, 374– 377
7	A Maatoug, G Belalem, S Mahmoudi. Fog computing framework for location-based energy management in smart buildings. Multiagent and Grid Systems, 2019, 15( 1): 39– 56 https://doi.org/10.3233/MGS-190301
8	M Etemad M Aazam M St-Hilaire. Using DEVS for modeling and simulating a Fog Computing environment. In: Proceedings of the 2017 International Conference on Computing, Networking and Communications (ICNC). 2017, 849– 854
9	Y Himeur, A Alsalemi, A Al-Kababji, F Bensaali, A Amira. Data fusion strategies for energy efficiency in buildings: overview, challenges and novel orientations. Information Fusion, 2020, 64: 99– 120 https://doi.org/10.1016/j.inffus.2020.07.003
10	B L R Stojkoska, K V Trivodaliev. A review of internet of things for smart home: challenges and solutions. Journal of Cleaner Production, 2017, 140: 1454– 1464 https://doi.org/10.1016/j.jclepro.2016.10.006
11	E Carrillo V Benitez C Mendoza J Pacheco. IoT framework for smart buildings with cloud computing. In: Proceedings of the 1st International Smart Cities Conference (ISC2). 2015, 1– 6
12	W Tushar, C Yuen, K Li, K L Wood, Z Wei, L Xiang. Design of cloud-connected IoT system for smart buildings on energy management (invited paper). EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, 2016, 3( 6): e3 https://doi.org/10.4108/eai.1-1-2016.150813
13	M A Al Faruque, K Vatanparvar. Energy management-as-a-service over fog computing platform. IEEE Internet of Things Journal, 2016, 3( 2): 161– 169 https://doi.org/10.1109/JIOT.2015.2471260
14	A Javed O Rana C Marmaras L Cipcigan. Fog paradigm for local energy management systems. In: Proceedings of the 2nd EAI International Conference on Cloud Infrastructures, Services, and IoT Systems for Smart Cities. 2018, 162– 176
15	P G V Naranjo, Z Pooranian, M Shojafar, M Conti, R Buyya. FOCAN: a Fog-supported smart city network architecture for management of applications in the Internet of Everything environments. Journal of Parallel and Distributed Computing, 2019, 132: 274– 283 https://doi.org/10.1016/j.jpdc.2018.07.003
16	H Lin, G Liu, F Li, Y Zuo. Where to go? Predicting next location in IoT environment.. Frontiers of Computer Science, 2021, 15( 1): 151306 https://doi.org/10.1007/s11704-019-9118-9
17	J Pan, R Jain, S Paul, T Vu, A Saifullah, M Sha. An internet of things framework for smart energy in buildings: designs, prototype, and experiments. IEEE Internet of Things Journal, 2015, 2( 6): 527– 537 https://doi.org/10.1109/JIOT.2015.2413397
18	M V Moreno, M A Zamora, A F Skarmeta. User-centric smart buildings for energy sustainable smart cities. Transactions on Emerging Telecommunications Technologies, 2014, 25( 1): 41– 55 https://doi.org/10.1002/ett.2771
19	T H Luan L Gao Z Li Y Xiang G Wei L Sun. Fog computing: focusing on mobile users at the edge. 2015, arXiv preprint arXiv: 1502.01815
20	M Albataineh, M Jarrah. DEVS-IoT: performance evaluation of smart home devices network. Multimedia Tools and Applications, 2021, 80( 11): 16857– 16885 https://doi.org/10.1007/s11042-020-09186-w
21	D Bonino F Corno. DogSim: a state chart simulator for domotic environments. In: Proceedings of the 8th IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops). 2010, 208– 213
22	S Yi Z Hao Z Qin Q Li. Fog computing: platform and applications. In: Proceedings of the 3rd IEEE Workshop on Hot Topics in Web Systems and Technologies (HotWeb). 2015, 73– 78
23	B P Zeigler H Praehofer T G Kim. Theory of Modelling and Simulation: Integrating Discrete Event and Continuous Complex Dynamic Systems. 2nd ed. San Diego: Academic Press, 2000
24	L Capocchi, F Bernardi, D Federici, P A Bisgambiglia. BFS-DEVS: a general DEVS-based formalism for behavioral fault simulation. Simulation Modelling Practice and Theory, 2006, 14( 7): 945– 970 https://doi.org/10.1016/j.simpat.2006.05.002
25	I Stojmenovic S Wen. The fog computing paradigm: scenarios and security issues. In: Proceedings of the 2014 Federated Conference on Computer Science and Information Systems (FedCSIS). 2014, 1– 8
26	B Varghese N Wang D S Nikolopoulos R Buyya. Feasibility of fog computing. 2017, arXiv preprint arXiv: 1701.05451
27	X Guo, N Ansari, F Hu, Y Shao, N R Elikplim, L Li. A survey on fusion-based indoor positioning. IEEE Communications Surveys & Tutorials, 2020, 22( 1): 566– 594 https://doi.org/10.1109/COMST.2019.2951036
28	J Cesconetto, Silva L Augusto, F Bortoluzzi, M Navarro-Cáceres, C A Zeferino, V R Q Leithardt. PRIPRO—privacy profiles: user profiling management for smart environments. Electronics, 2020, 9( 9): 1519 https://doi.org/10.3390/electronics9091519
29	A Y Abyaneh, V Pourahmadi, A H G Foumani. CSI-based authentication: extracting stable features using deep neural networks. Transactions on Emerging Telecommunications Technologies, 2020, 31( 2): e3795 https://doi.org/10.1002/ett.3795
30	A Belhadi, Y Djenouri, G Srivastava, D Djenouri, J C W Lin, G Fortino. Deep learning for pedestrian collective behavior analysis in smart cities: a model of group trajectory outlier detection. Information Fusion, 2021, 65: 13– 20 https://doi.org/10.1016/j.inffus.2020.08.003
31	L F Bittencourt, J Diaz-Montes, R Buyya, O F Rana, M Parashar. Mobility-aware application scheduling in fog computing. IEEE Cloud Computing, 2017, 4( 2): 26– 35 https://doi.org/10.1109/MCC.2017.27
32	J B Filippi, P Bisgambiglia. JDEVS: an implementation of a DEVS based formal framework for environmental modelling. Environmental Modelling & Software, 2004, 19( 3): 261– 274 https://doi.org/10.1016/j.envsoft.2003.08.016
33	GeoTools. Geotools Users Group, 2002. Available from Geotools.org. website
34	A S Shah, H Nasir, M Fayaz, A Lajis, A Shah. A review on energy consumption optimization techniques in IoT based smart building environments. Information, 2019, 10( 3): 108 https://doi.org/10.3390/info10030108

[1]

Highlights

Download

Viewed

Full text

Abstract

Cited

Shared

Discussed