# Internet of Things Energy Consumption Optimization in Buildings: A Step toward Sustainability

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## Abstract

**:**

## 1. Introduction

## 2. Related Works

## 3. Optimization Model

#### 3.1. Particle Swarm Optimization Algorithm

#### 3.2. Chaotic Particle Swarm Optimization Algorithm

#### 3.3. Fractional Chaotic Particle Swarm Optimization Algorithm

#### 3.4. Interface Node to Send Data Selection

_{ijBS}shows the energy used to send data from the i node through the j interface node to reach the base station. d(s

_{i},s

_{j}) shows the distance from node i to node j and d(s

_{i},BS) represents the distance of node i to the base station. The energy used to send information to the base station is obtained directly from Equation (8).

## 4. Evaluation of the Proposed Method

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- Etx, Erx: the energy used to transmit and receive data in nodes, respectively.
- -
- Dij: distance between node i and j.
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- Eelec: the available energy in each node.
- -
- Fij: data transmission rate between two nodes.
- -
- CS, CR, CB: base station node cost, sensor node cost, and amplifier node cost, respectively.

## 5. Conclusions and Suggestions for Future Work

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- Practical implementation of the proposed method in a smart environment that uses the IoT, such as smart buildings.
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- Using the proposed method for multicast routing, additional research can be conducted in this area, such as how to select routes.
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- Utilizing alternative evolutionary algorithms and evaluating their outcomes to increase the reduction of current overheads and reach more ideal results.
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- Combining the proposed method with other algorithms, such as tree-based algorithms, and presenting it within the IoT domain.

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

- Ray, P.P. A Survey on Internet of Things Architectures. J. King Saud Univ. Comput. Inf. Sci.
**2018**, 30, 291–319. [Google Scholar] - Motlagh, N.H.; Mohammadrezaei, M.; Hunt, J.; Zakeri, B. Internet of Things (IoT) and the Energy Sector. Energies
**2020**, 13, 494. [Google Scholar] [CrossRef] [Green Version] - Lee, J.; Ruy, W.S. Multi-Objective Parametric Optimization of FPSO Hull Dimensions. Int. J. Nav. Archit. Ocean Eng.
**2021**, 13, 734–745. [Google Scholar] [CrossRef] - Shaheen, M.A.M.; Hasanien, H.M.; Alkuhayli, A. A Novel Hybrid GWO-PSO Optimization Technique for Optimal Reactive Power Dispatch Problem Solution. Ain Shams Eng. J.
**2021**, 12, 621–630. [Google Scholar] [CrossRef] - Alayi, R.; Mohkam, M.; Seyednouri, S.R.; Ahmadi, M.H.; Sharifpur, M. Energy/Economic Analysis and Optimization of on-Grid Photovoltaic System Using CPSO Algorithm. Sustainability
**2021**, 13, 12420. [Google Scholar] [CrossRef] - Humayun, M.; Jhanjhi, N.Z.; Alsayat, A.; Ponnusamy, V. Internet of Things and Ransomware: Evolution, Mitigation and Prevention. Egypt. Inform. J.
**2021**, 22, 105–117. [Google Scholar] [CrossRef] - Hasan, M.Z.; Al-Rizzo, H. Task Scheduling in Internet of Things Cloud Environment Using a Robust Particle Swarm Optimization. Concurr. Comput. Pract. Exp.
**2020**, 32, e5442. [Google Scholar] [CrossRef] - Kabalci, Y.; Kabalci, E.; Padmanaban, S.; Holm-Nielsen, J.B.; Blaabjerg, F. Internet of Things Applications as Energy Internet in Smart Grids and Smart Environments. Electronics
**2019**, 8, 972. [Google Scholar] [CrossRef] [Green Version] - Lee, J.; Kim, B.C.; Ruy, W.S.; Han, I.S. Parametric Optimization of FPSO Hull Dimensions for Brazil Field Using Sophisticated Stability and Hydrodynamic Calculations. Int. J. Nav. Archit. Ocean Eng.
**2021**, 13, 478–492. [Google Scholar] [CrossRef] - Li, F.; Zhang, Z.; Armaou, A.; Xue, Y.; Zhou, S.; Zhou, Y. Study on ADRC Parameter Optimization Using CPSO for Clamping Force Control System. Math. Probl. Eng.
**2018**, 2018, 1–8. [Google Scholar] [CrossRef] [Green Version] - Hasan, M.Z.; Al-Rizzo, H. Beamforming Optimization in Internet of Things Applications Using Robust Swarm Algorithm in Conjunction with Connectable and Collaborative Sensors. Sensors
**2020**, 20, 2048. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Wadood, A.; Kim, C.H.; Khurshiad, T.; Farkoush, S.G.; Rhee, S.B. Application of a Continuous Particle Swarm Optimization (CPSO) for the Optimal Coordination of Overcurrent Relays Considering a Penalty Method. Energies
**2018**, 11, 869. [Google Scholar] [CrossRef] [Green Version] - Elsisi, M.; Tran, M.Q.; Mahmoud, K.; Lehtonen, M.; Darwish, M.M.F. Deep Learning-Based Industry 4.0 and Internet of Things towards Effective Energy Management for Smart Buildings. Sensors
**2021**, 21, 1038. [Google Scholar] [CrossRef] [PubMed] - Al-Turjman, F.; Hasan, M.Z.; Al-Rizzo, H. Task Scheduling in Cloud-Based Survivability Applications Using Swarm Optimization in IoT. Trans. Emerg. Telecommun. Technol.
**2019**, 30, e3539. [Google Scholar] [CrossRef] - Li, Y.; Miao, S.; Luo, X.; Wang, J. Optimization Scheduling Model Based on Source-Load-Energy Storage Coordination in Power Systems. In Proceedings of the 2016 22nd International Conference on Automation and Computing, ICAC 2016: Tackling the New Challenges in Automation and Computing, Colchester, UK, 7–8 September 2016; pp. 120–125. [Google Scholar]
- Rana, B.; Singh, Y.; Singh, P.K. A Systematic Survey on Internet of Things: Energy Efficiency and Interoperability Perspective. Trans. Emerg. Telecommun. Technol.
**2021**, 32, e4166. [Google Scholar] [CrossRef] - Wu, Z.; Nie, Y.; Chen, S.; Zhang, H.; Wang, L. Double Layers Clustering Algorithm Based on CPSO for Wireless Sensor Networks. Inf. Technol. J.
**2012**, 11, 1737–1743. [Google Scholar] [CrossRef] [Green Version] - Ahmed, Z.E.; Hasan, M.K.; Saeed, R.A.; Hassan, R.; Islam, S.; Mokhtar, R.A.; Khan, S.; Akhtaruzzaman, M. Optimizing Energy Consumption for Cloud Internet of Things. Front. Phys.
**2020**, 8, 358. [Google Scholar] [CrossRef] - Ding, X.; Wu, J. Study on Energy Consumption Optimization Scheduling for Internet of Things. IEEE Access
**2019**, 7, 70574–70583. [Google Scholar] [CrossRef] - Fanian, F.; Kuchaki Rafsanjani, M.; Borumand Saeid, A. Fuzzy Multi-Hop Clustering Protocol: Selection Fuzzy Input Parameters and Rule Tuning for WSNs. Appl. Soft Comput.
**2021**, 99, 106923. [Google Scholar] [CrossRef] - Kadri, N.; Koudil, M. Multi-Objective Biogeography-Based Optimization and Reinforcement Learning Hybridization for Network-on Chip Reliability Improvement. J. Parallel Distrib. Comput.
**2022**, 161, 20–36. [Google Scholar] [CrossRef] - Lalitha, K.; Kamalam, G.K.; Priyan, R.; Rithanya, A.S.; Shanmugapriya, P. Optimizing the Sensor Deployment Strategy for Large-Scale Internet of Things (IoT) Using Artificial Bee Colony. AIP Conf. Proc.
**2021**, 2387, 140032. [Google Scholar] - Lan, K.; Fong, S.; Song, W.; Vasilakos, A.V.; Millham, R.C. Self-Adaptive Pre-Processing Methodology for Big Data Stream Mining in Internet of Things Environmental Sensor Monitoring. Symmetry
**2017**, 9, 244. [Google Scholar] [CrossRef] [Green Version] - Sani, A.S.; Yuan, D.; Jin, J.; Gao, L.; Yu, S.; Dong, Z.Y. Cyber Security Framework for Internet of Things-Based Energy Internet. Future Gener. Comput. Syst.
**2019**, 93, 849–859. [Google Scholar] [CrossRef] - Khare, V.; Nema, S.; Baredar, P. Optimisation of the Hybrid Renewable Energy System by HOMER, PSO and CPSO for the Study Area. Int. J. Sustain. Energy
**2017**, 36, 326–343. [Google Scholar] [CrossRef] - Hasan, M.Z.; Al-Rizzo, H.; Al-Turjman, F.; Rodriguez, J.; Radwan, A. Internet of Things Task Scheduling in Cloud Environment Using Particle Swarm Optimization. In Proceedings of the 2018 IEEE Global Communications Conference, GLOBECOM 2018—Proceedings, Abu Dhabi, United Arab Emirates, 9–13 December 2018. [Google Scholar]
- Rasheed, M.; Omar, R.; Sulaiman, M.; Halim, W.A. Particle Swarm Optimisation (PSO) Algorithm with Reduced Numberof Switches in Multilevel Inverter (MLI). Indones. J. Electr. Eng. Comput. Sci.
**2019**, 14, 1114–1124. [Google Scholar] [CrossRef] - Vadivel, R.; Sudalaimuthu, T. Cauchy Particle Swarm Optimization (CPSO) Based Migrations of Tasks in a Virtual Machine. Wirel. Pers. Commun.
**2021**, 127, 2229–2246. [Google Scholar] [CrossRef] - Li, J.; Kang, L.; Li, X.; Chen, Z.; Zhang, Y. Characterizing Cluster Formation in Wireless Sensor Networks: A Chaos Particle Swarm Optimization Approach. J. Comput. Inf. Syst.
**2015**, 11, 957–966. [Google Scholar] [CrossRef] - Peraza-Vázquez, H.; Peña-Delgado, A.F.; Echavarría-Castillo, G.; Morales-Cepeda, A.B.; Velasco-Álvarez, J.; Ruiz-Perez, F. A Bio-Inspired Method for Engineering Design Optimization Inspired by Dingoes Hunting Strategies. Math. Probl. Eng.
**2021**, 2021, 1–19. [Google Scholar] [CrossRef] - Peraza-Vázquez, H.; Peña-Delgado, A.; Ranjan, P.; Barde, C.; Choubey, A.; Morales-Cepeda, A.B. A Bio-Inspired Method for Mathematical Optimization Inspired by Arachnida Salticidade. Mathematics
**2021**, 10, 102. [Google Scholar] [CrossRef] - Osipov, M. Home Automation with Zigbee. In Lecture Notes in Computer Science; Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics; Springer: Berlin/Heidelberg, Germany, 2008; Volume 5174 LNCS, pp. 263–270. [Google Scholar]
- Gopalsamy, B.N. Communication Trends in Internet of Things; IGI Global: Hershey, PA, USA, 2017; pp. 284–305. [Google Scholar]

**Figure 4.**Comparison of (

**a**) energy consumption reduction, and (

**b**) the energy required for data transmission and reception of two normal methods and FCPSO optimization.

T_{p} | P | Cost | |
---|---|---|---|

PSO | O(1) | O(N) | O(N) |

CPSO | O(N/2) | O(N) | O(N^{2}/2) |

FCPSO | O(N^{1−x}) | O(N^{2}) | O(N log N) |

ID | Function | Interval |

F_{1} | $f(x)={\sum}_{i=1}^{n}{x}_{i}^{2}$ | $-\mathrm{\infty}\le {x}_{i}\le \mathrm{\infty}$ $1\le i\le n$ |

F_{2} | $f(x)=\sum _{i=1}^{n}{\left(\left[{x}_{i}+0.5\right]\right)}^{2}$ | $-10\le {x}_{i}\le 10$ |

F_{3} | $f\left(x\right)=An{\sum}_{i=1}^{n}\left[{x}_{i}^{2}-\mathrm{Acos}\left(2\pi {x}_{i}\right)\right]$ | $1\le i\le n$ |

F_{4} | $f(x)={\sum}_{i=1}^{n-1}\left[100{\left({x}_{i+1}-{x}_{i}^{2}\right)}^{2}+{\left({x}_{i}-1\right)}^{2}\right]$ | $-\mathrm{\infty}\le {x}_{i}\le \mathrm{\infty}$ $1\le i\le n$ |

F_{5} | $f(x)=418.9829-{\sum}_{i=1}^{d}{x}_{i}\mathrm{s}\mathrm{i}\mathrm{n}\left(\sqrt{\left|{x}_{i}\right|}\right)$ | $-\mathrm{\infty}\le {x}_{i}\le \mathrm{\infty}$ |

F_{6} | $f(x)=\sum _{i=1}^{n}\left[{x}_{i}^{2}-10\mathrm{c}\mathrm{o}\mathrm{s}\left(2\pi {x}_{i}+10\right)\right]$ | $-5.12\le {x}_{i}\le 5.12$ |

F_{7} | $f(x)=-20\mathrm{e}\mathrm{x}\mathrm{p}\left(-0.2\sqrt{1/n\sum _{i=1}^{n}{x}_{i}^{2}}\right)-\mathrm{e}\mathrm{x}\mathrm{p}\left(1/n\sum _{i=1}^{n}\mathrm{c}\mathrm{o}\mathrm{s}\left(2\pi {x}_{i}\right)\right)+20+e$ | $-5\le x,y\le 5$ |

F_{8} | $f(x)=1/4000\sum _{i=1}^{n}{x}_{i}^{2}-\prod _{i=1}^{n}\mathrm{c}\mathrm{o}\mathrm{s}\left(\frac{{x}_{i}}{\sqrt{i}}\right)+1$ | $-100\le {x}_{i}\le 100$ |

F_{9} | $f\left(x\right)=0.1\left\{{\mathrm{sin}}^{2}\left(3\pi {x}_{i}\right)+\sum _{i=1}^{n}{\left({x}_{i}-1\right)}^{2}\left[1+{\mathrm{sin}}^{2}\left(3\pi {x}_{i}+1\right)\right]+{\left({x}_{n}-1\right)}^{2}\left[1+{\mathrm{sin}}^{2}\left(2\pi {x}_{n}\right)\right]\right\}+\sum _{i=1}^{n}u\left({x}_{i},\mathrm{5,100,4}\right)$ | $-50\le {x}_{i}\le 50$ |

F_{10} | $f(x)=\pi /n\left\{10\mathrm{s}\mathrm{i}\mathrm{n}\left(\pi {y}_{1}\right)+\sum _{i=1}^{n-1}{\left({y}_{i}-1\right)}^{2}\left[1+10{\mathrm{s}\mathrm{i}\mathrm{n}}^{2}\left(\pi {y}_{i+1}\right)\right]+{\left({y}_{n}-1\right)}^{2}\right\}+\sum _{i=1}^{n}u\left({x}_{i},\mathrm{10,100,4}\right)$ ${y}_{i}=1+\left({x}_{i}+1/4\right)u\left({x}_{i},a,k,m\right)=\left\{\begin{array}{c}k{\left({x}_{i}-a\right)}^{m},{x}_{i}>a,\\ 0-a<{x}_{i}<a,\\ k{\left(-{x}_{i}-a\right)}^{m},{x}_{i}<-a.\end{array}\right.$ | $-50\le {x}_{i}\le 50$ |

**Table 3.**The result of PSO, CPSO and FCPSO algorithms in testbench functions for different orders of derivatives.

Method | The Order of the Fractional Derivative (α) | The Value of the Objective Function | Run time | ||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

F_{1} | F_{2} | F_{3} | F_{4} | F_{5} | F_{6} | F_{7} | F_{8} | F_{9} | F_{10} | F_{1} | F_{2} | F_{3} | F_{4} | F_{5} | F_{6} | F_{7} | F_{8} | F_{9} | F_{10} | ||

PSO | 0.98371 | 7 × 10^{−5} | 0.983 | 1.2 × 10^{−13} | 4.2 × 10^{−16} | 0.98371 | 7 × 10^{−5} | 0.983 | 2 × 10^{−14} | 3.99958 × 10^{−12} | 0.10098 | 0.09748 | 0.101 | 0.097 | 0.078 | 0.10098 | 0.09748 | 0.101 | 0.097 | 0.078 | |

CPSO | 1.00876 | 0.00088 | 1.009 | 1.8 × 10^{−12} | 3.5 × 10^{−14} | 1.00876 | 0.00088 | 1.009 | 6.4 × 10^{−12} | 1.765 × 10^{−12} | 0.09512 | 0.10276 | 0.095 | 0.102 | 0.12 | 0.09512 | 0.10276 | 0.095 | 0.102 | 0.12 | |

FCPSO | 0.2 | 0.0001802 | 0.00051 | 0.0000102 | 1 × 10^{−12} | 3.75 × 10^{−15} | 0.0001802 | 0.00051 | 1.02 × 10^{−5} | 1.5 × 10^{−12} | 4.10375 × 10^{−12} | 0.08104 | 0.09136 | 0.081 | 0.091 | 0.098 | 0.08104 | 0.09136 | 0.081 | 0.091 | 0.098 |

0.4 | 0.000189 | 0.00049 | 0.000031 | 1 × 10^{−12} | 4.02 × 10^{−15} | 0.000189 | 0.00049 | 3.1 × 10^{−5} | 7.9 × 10^{−12} | 4.90402 × 10^{−12} | 0.12399 | 0.08778 | 0.123 | 0.088 | 0.072 | 0.12399 | 0.08778 | 0.123 | 0.088 | 0.072 | |

0.6 | 0.00033989 | 0.00063 | 1.101 × 10^{−7} | 1 × 10^{−12} | 4.21 × 10^{−15} | 0.00033989 | 0.00063 | 1.10108 × 10^{−7} | 1.9 × 10^{−12} | 3.30421 × 10^{−12} | 0.10525 | 0.18101 | 0.105 | 0.181 | 0.163 | 0.10525 | 0.18101 | 0.105 | 0.181 | 0.163 | |

0.8 | 0.01136 | 0.00026 | 0.0109 | 1 × 10^{−12} | 3 × 10^{−15} | 0.01136 | 0.00026 | 0.0109 | 3.5 × 10^{−12} | 2.903 × 10^{−12} | 0.09274 | 0.09842 | 0.092 | 0.098 | 0.079 | 0.09274 | 0.09842 | 0.092 | 0.098 | 0.079 |

Optimization Algorithms | Mean Error ${\mathit{E}\mathit{R}}_{\mathit{A}}={\frac{\sum _{\mathit{n}=1}^{\mathit{N}}\sqrt{{\left({\mathit{Y}}_{\mathit{n}}-{\mathit{y}}_{\mathit{n}}\right)}^{2}}}{\mathit{N}}}^{\mathit{*}}$ |
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PSO | 0.0829 |

CPSO | 0.0971 |

FCPSO (α = 0.8) | 0.0029 |

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## Share and Cite

**MDPI and ACS Style**

Wang, W.-C.; Dwijendra, N.K.A.; Sayed, B.T.; Alvarez, J.R.N.; Al-Bahrani, M.; Alviz-Meza, A.; Cárdenas-Escrocia, Y.
Internet of Things Energy Consumption Optimization in Buildings: A Step toward Sustainability. *Sustainability* **2023**, *15*, 6475.
https://doi.org/10.3390/su15086475

**AMA Style**

Wang W-C, Dwijendra NKA, Sayed BT, Alvarez JRN, Al-Bahrani M, Alviz-Meza A, Cárdenas-Escrocia Y.
Internet of Things Energy Consumption Optimization in Buildings: A Step toward Sustainability. *Sustainability*. 2023; 15(8):6475.
https://doi.org/10.3390/su15086475

**Chicago/Turabian Style**

Wang, Wen-Cheng, Ngakan Ketut Acwin Dwijendra, Biju Theruvil Sayed, José Ricardo Nuñez Alvarez, Mohammed Al-Bahrani, Aníbal Alviz-Meza, and Yulineth Cárdenas-Escrocia.
2023. "Internet of Things Energy Consumption Optimization in Buildings: A Step toward Sustainability" *Sustainability* 15, no. 8: 6475.
https://doi.org/10.3390/su15086475