Systematic Review Analysis on Smart Building: Challenges and Opportunities
Abstract
:1. Introduction
2. Materials and Methods
3. Review Planning
Overview of SBEMS
4. Conducting Literature Review
4.1. Taxonomy of Literature Reviews
4.1.1. Literature Search Process
4.1.2. Inclusion and Exclusion Criteria
4.1.3. Quality Assessment Criteria
5. Literature Review Analysis
5.1. Publication Trends of the Selected Literature
5.2. Literature Classification Based on Domain of SBEMS
5.3. Literature Classification Based on Control Approach
5.4. Classification Based on SBEMS Technology
5.5. Classification Based on Quality Attribute
6. Discussion and Future Recommendations
6.1. Reporting Finding
6.2. Implications of the Study
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Author | ID | Type | QA1 | QA2 | QA3 | QA4 | Total | |
1 | [24] | S1 | Journal | N | N | N | N | 0.0 |
2 | [49] | S2 | Journal | Y | Y | Y | Y | 4.0 |
3 | [130] | S3 | Journal | Y | Y | Y | Y | 4.0 |
4 | [81] | S4 | Journal | P | P | P | P | 2.0 |
5 | [50] | S5 | Journal | Y | Y | Y | P | 3.5 |
6 | [35] | S6 | Journal | Y | Y | Y | Y | 4.0 |
7 | [45] | S7 | Journal | Y | Y | Y | Y | 4.0 |
8 | [131] | S8 | Journal | Y | Y | Y | Y | 4.0 |
9 | [72] | S9 | Journal | Y | Y | Y | P | 3.5 |
10 | [70] | S10 | Journal | Y | Y | Y | Y | 4.0 |
11 | [73] | S11 | Journal | Y | Y | Y | P | 3.5 |
12 | [76] | S12 | Journal | Y | Y | Y | P | 3.5 |
13 | [77] | S13 | Journal | Y | Y | Y | P | 3.5 |
14 | [68] | S14 | Journal | Y | Y | Y | P | 3.5 |
15 | [132] | S15 | Journal | Y | Y | Y | Y | 4.0 |
16 | [65] | S16 | Journal | Y | Y | Y | Y | 4.0 |
17 | [133] | S17 | Journal | Y | Y | Y | Y | 4.0 |
18 | [104] | S18 | Journal | Y | Y | Y | Y | 4.0 |
19 | [36] | S19 | Journal | Y | Y | Y | Y | 4.0 |
20 | [37] | S20 | Conference | Y | Y | Y | P | 3.5 |
21 | [120] | S21 | Journal | Y | Y | P | P | 3.0 |
22 | [134] | S22 | Journal | Y | Y | P | P | 4.0 |
23 | [10] | S23 | Journal | Y | Y | Y | P | 3.5 |
24 | [46] | S24 | Journal | Y | Y | Y | Y | 4.0 |
25 | [135] | S25 | Journal | Y | Y | Y | Y | 4.0 |
26 | [82] | S26 | Journal | Y | Y | Y | P | 3.5 |
27 | [66] | S27 | Conference | Y | Y | Y | P | 3.5 |
28 | [67] | S28 | Journal | Y | Y | P | P | 3.0 |
29 | [17] | S29 | Journal | Y | Y | Y | Y | 4.0 |
30 | [101] | S30 | Journal | Y | Y | Y | Y | 4.0 |
31 | [136] | S31 | Journal | Y | Y | Y | Y | 4.0 |
32 | [137] | S32 | Journal | Y | Y | Y | Y | 4.0 |
33 | [38] | S33 | Journal | Y | Y | Y | P | 3.5 |
34 | [122] | S34 | Journal | Y | Y | P | P | 3.0 |
35 | [138] | S35 | Journal | Y | Y | Y | P | 3.5 |
36 | [139] | S36 | Journal | Y | Y | Y | Y | 4.0 |
37 | [140] | S37 | Journal | Y | Y | Y | P | 3.5 |
38 | [93] | S38 | Journal | Y | Y | Y | P | 3.5 |
39 | [141] | S39 | Conference | Y | Y | P | P | 3.0 |
40 | [8] | S40 | Journal | Y | Y | P | P | 3.0 |
41 | [142] | S41 | Journal | Y | Y | Y | Y | 4.0 |
42 | [143] | S42 | Journal | Y | Y | P | P | 3.0 |
43 | [144] | S43 | Journal | Y | Y | P | P | 3.0 |
44 | [39] | S44 | Journal | Y | Y | Y | Y | 4.0 |
45 | [145] | S45 | Journal | Y | Y | Y | Y | 4.0 |
46 | [119] | S46 | Journal | Y | Y | Y | Y | 4.0 |
47 | [102] | S47 | Journal | Y | Y | Y | P | 3.5 |
48 | [126] | S48 | Conference | P | P | P | P | 2.0 |
49 | [62] | S49 | Journal | P | P | Y | Y | 3.0 |
50 | [74] | S50 | Journal | Y | Y | Y | Y | 4.0 |
51 | [146] | S51 | Journal | Y | Y | Y | Y | 4.0 |
52 | [147] | S52 | Journal | Y | Y | P | P | 3.0 |
53 | [51] | S53 | Conference | Y | Y | P | P | 3.0 |
54 | [116] | S54 | Journal | Y | Y | Y | Y | 4.0 |
55 | [105] | S55 | Journal | Y | Y | Y | Y | 4.0 |
56 | [52] | S56 | Journal | Y | Y | Y | Y | 4.0 |
57 | [148] | S57 | Journal | Y | Y | Y | Y | 4.0 |
58 | [83] | S58 | Journal | Y | Y | Y | Y | 4.0 |
59 | [123] | S59 | Journal | Y | Y | Y | P | 3.5 |
60 | [84] | S60 | Journal | Y | N | P | P | 2.0 |
61 | [40] | S61 | Journal | Y | Y | Y | P | 3.5 |
62 | [149] | S62 | Journal | Y | Y | Y | Y | 4.0 |
63 | [41] | S63 | Journal | Y | Y | Y | Y | 4.0 |
64 | [53] | S64 | Journal | Y | Y | Y | P | 3.5 |
65 | [61] | S65 | Journal | Y | Y | Y | P | 3.5 |
66 | [78] | S66 | Journal | Y | Y | Y | Y | 4.0 |
67 | [42] | S67 | Journal | Y | Y | Y | Y | 4.0 |
68 | [150] | S68 | Journal | P | P | P | P | 2.0 |
69 | [87] | S69 | Journal | P | P | P | P | 2.0 |
70 | [43] | S70 | Journal | Y | Y | Y | P | 3.5 |
71 | [151] | S71 | Journal | Y | Y | Y | P | 3.5 |
72 | [152] | S72 | Journal | Y | Y | Y | P | 3.5 |
73 | [153] | S73 | Journal | Y | Y | Y | P | 3.5 |
74 | [94] | S74 | Journal | Y | Y | Y | Y | 4.0 |
75 | [154] | S75 | Journal | Y | Y | Y | P | 3.5 |
76 | [60] | S76 | Journal | Y | Y | Y | P | 3.5 |
77 | [96] | S77 | Journal | Y | Y | Y | P | 3.5 |
78 | [155] | S78 | Journal | Y | Y | Y | Y | 4.0 |
79 | [156] | S79 | Journal | Y | Y | Y | Y | 4.0 |
80 | [157] | S80 | Journal | Y | Y | Y | Y | 4.0 |
81 | [97] | S81 | Journal | Y | Y | Y | P | 3.5 |
82 | [64] | S82 | Journal | Y | Y | Y | P | 3.5 |
83 | [158] | S83 | Journal | Y | Y | Y | Y | 4.0 |
84 | [159] | S84 | Journal | Y | Y | Y | P | 3.5 |
85 | [160] | S85 | Journal | P | P | P | P | 2.0 |
86 | [161] | S86 | Journal | Y | Y | Y | Y | 4.0 |
87 | [54] | S87 | Journal | Y | Y | Y | Y | 4.0 |
88 | [162] | S88 | Journal | Y | Y | Y | Y | 4.0 |
89 | [163] | S89 | Journal | Y | Y | Y | Y | 4.0 |
90 | [55] | S90 | Journal | Y | Y | Y | Y | 4.0 |
91 | [88] | S91 | Journal | Y | Y | Y | Y | 4.0 |
92 | [164] | S92 | Journal | Y | Y | Y | P | 3.5 |
93 | [103] | S93 | Journal | Y | Y | Y | Y | 4.0 |
94 | [32] | S94 | Journal | Y | Y | P | P | 3.0 |
95 | [165] | S95 | Journal | Y | Y | Y | Y | 4.0 |
96 | [166] | S96 | Journal | Y | Y | Y | Y | 4.0 |
97 | [167] | S97 | Journal | Y | Y | Y | Y | 4.0 |
98 | [168] | S98 | Journal | Y | Y | Y | P | 3.5 |
99 | [107] | S99 | Journal | Y | Y | Y | Y | 4.0 |
100 | [169] | S100 | Journal | Y | Y | Y | Y | 4.0 |
101 | [108] | S101 | Journal | Y | Y | P | P | 3.0 |
102 | [109] | S102 | Conference | Y | Y | P | P | 3.0 |
103 | [57] | S103 | Journal | Y | Y | Y | Y | 4.0 |
104 | [170] | S104 | Journal | Y | Y | Y | Y | 4.0 |
105 | [171] | S105 | Journal | Y | Y | Y | Y | 4.0 |
106 | [89] | S106 | Journal | Y | Y | Y | Y | 4.0 |
107 | [85] | S107 | Journal | Y | Y | Y | Y | 4.0 |
108 | [47] | S108 | Journal | Y | Y | Y | Y | 4.0 |
109 | [98] | S109 | Journal | Y | Y | Y | Y | 4.0 |
110 | [172] | S110 | Journal | Y | Y | P | P | 3.0 |
111 | [173] | S111 | Journal | Y | Y | Y | Y | 4.0 |
112 | [174] | S112 | Journal | Y | Y | Y | Y | 4.0 |
113 | [175] | S113 | Journal | Y | Y | Y | Y | 4.0 |
114 | [110] | S114 | Journal | Y | Y | Y | Y | 4.0 |
115 | [56] | S115 | Journal | Y | Y | Y | Y | 4.0 |
116 | [176] | S116 | Journal | Y | Y | Y | Y | 4.0 |
117 | [177] | S117 | Journal | Y | Y | Y | Y | 4.0 |
118 | [44] | S118 | Journal | Y | Y | P | P | 3.0 |
119 | [86] | S119 | Journal | Y | Y | Y | Y | 4.0 |
120 | [99] | S120 | Journal | Y | Y | Y | Y | 4.0 |
121 | [178] | S121 | Journal | Y | Y | Y | Y | 4.0 |
122 | [100] | S122 | Conference | Y | Y | P | P | 3.0 |
123 | [48] | S123 | Journal | Y | Y | Y | Y | 4.0 |
124 | [111] | S124 | Journal | Y | Y | P | P | 3.0 |
125 | [95] | S125 | Journal | Y | Y | Y | Y | 4.0 |
126 | [179] | S126 | Journal | Y | Y | Y | Y | 4.0 |
127 | [58] | S127 | Journal | Y | Y | Y | Y | 4.0 |
128 | [180] | S128 | Journal | Y | Y | Y | Y | 4.0 |
129 | [181] | S129 | Journal | Y | Y | Y | Y | 4.0 |
130 | [63] | S130 | Conference | Y | Y | P | P | 3.0 |
131 | [182] | S131 | Journal | Y | Y | Y | P | 3.5 |
132 | [183] | S132 | Journal | Y | Y | P | P | 3.0 |
133 | [184] | S133 | Journal | Y | Y | Y | Y | 4.0 |
134 | [69] | S134 | Journal | Y | Y | Y | P | 3.5 |
135 | [185] | S135 | Journal | Y | Y | P | P | 3.0 |
136 | [186] | S136 | Journal | Y | Y | Y | P | 3.5 |
137 | [187] | S137 | Journal | Y | Y | Y | Y | 4.0 |
138 | [188] | S138 | Journal | Y | Y | Y | Y | 4.0 |
139 | [128] | S139 | Conference | Y | Y | Y | P | 3.5 |
140 | [1] | S140 | Journal | Y | Y | Y | Y | 4.0 |
141 | [2] | S141 | Journal | Y | Y | Y | Y | 4.0 |
142 | [3] | S142 | Journal | Y | Y | Y | Y | 4.0 |
143 | [4] | S143 | Conference | Y | Y | P | P | 3.0 |
144 | [5] | S144 | Journal | Y | Y | Y | Y | 4.0 |
145 | [6] | S145 | Journal | Y | Y | Y | Y | 4.0 |
146 | [7] | S146 | Conference | Y | Y | Y | P | 3.5 |
147 | [9] | S147 | Journal | Y | Y | Y | Y | 4.0 |
148 | [11] | S148 | Journal | Y | Y | Y | Y | 4.0 |
149 | [12] | S149 | Journal | Y | Y | Y | Y | 4.0 |
150 | [13] | S150 | Conference | Y | Y | Y | P | 3.5 |
151 | [14] | S151 | Journal | Y | Y | Y | Y | 4.0 |
152 | [15] | S152 | Journal | Y | Y | Y | Y | 4.0 |
153 | [16] | S153 | Journal | Y | Y | Y | Y | 4.0 |
154 | [18] | S154 | Conference | Y | Y | Y | P | 3.5 |
155 | [19] | S155 | Conference | Y | Y | P | P | 3.0 |
156 | [20] | S156 | Journal | Y | Y | Y | Y | 4.0 |
157 | [21] | S157 | Journal | Y | Y | Y | Y | 4.0 |
158 | [22] | S158 | Journal | Y | Y | Y | Y | 4.0 |
159 | [23] | S159 | Journal | Y | Y | Y | Y | 4.0 |
160 | [25] | S160 | Journal | Y | Y | Y | Y | 4.0 |
161 | [26] | S161 | Journal | Y | Y | Y | Y | 4.0 |
162 | [27] | S162 | Journal | Y | Y | Y | Y | 4.0 |
163 | [28] | S163 | Journal | Y | Y | Y | Y | 4.0 |
164 | [29] | S164 | Journal | Y | Y | Y | Y | 4.0 |
165 | [30] | S165 | Journal | Y | Y | Y | Y | 4.0 |
166 | [31] | S166 | Journal | Y | Y | Y | Y | 4.0 |
167 | [33] | S167 | Journal | Y | Y | Y | Y | 4.0 |
168 | [34] | S168 | Journal | Y | Y | Y | Y | 4.0 |
169 | [59] | S169 | Conference | Y | Y | P | P | 3.0 |
170 | [71] | S170 | Journal | Y | Y | Y | Y | 4.0 |
171 | [75] | S171 | Journal | Y | Y | Y | Y | 4.0 |
172 | [79] | S172 | Journal | Y | Y | Y | Y | 4.0 |
173 | [80] | S173 | Journal | Y | Y | Y | Y | 4.0 |
174 | [90] | S174 | Journal | Y | Y | Y | Y | 4.0 |
175 | [91] | S175 | Journal | Y | Y | Y | Y | 4.0 |
176 | [92] | S176 | Journal | Y | Y | Y | Y | 4.0 |
177 | [106] | S177 | Journal | Y | Y | Y | Y | 4.0 |
178 | [112] | S178 | Journal | Y | Y | Y | Y | 4.0 |
179 | [113] | S179 | Journal | Y | Y | Y | Y | 4.0 |
180 | [114] | S180 | Journal | Y | Y | Y | Y | 4.0 |
181 | [115] | S181 | Journal | Y | Y | Y | Y | 4.0 |
182 | [117] | S182 | Journal | Y | Y | Y | Y | 4.0 |
183 | [118] | S183 | Journal | Y | Y | Y | Y | 4.0 |
184 | [121] | S184 | Journal | Y | Y | Y | Y | 4.0 |
185 | [124] | S185 | Journal | Y | Y | Y | Y | 4.0 |
186 | [125] | S186 | Journal | Y | Y | Y | Y | 4.0 |
187 | [127] | S187 | Journal | Y | Y | Y | Y | 4.0 |
188 | [129] | S188 | Journal | Y | Y | Y | Y | 4.0 |
References
- Simsek, Y.; Santika, W.G.; Anisuzzaman, M.; Urmee, T.; Bahri, P.A.; Escobar, R. An analysis of additional energy requirement to meet the sustainable development goals. J. Clean. Prod. 2020, 272, 122646. [Google Scholar] [CrossRef]
- Qudrat-Ullah, H. Introduction: Dynamics of Energy, the Environment and Economy: A Sustainability Perspective. In Dynamics of Energy, Environment and Economy; Springer: Cham, Switzerland, 2020; pp. 3–4. [Google Scholar]
- Dai, X.; Liu, J.; Zhang, X. A review of studies applying machine learning models to predict occupancy and window-opening behaviours in smart buildings. Energy Build. 2020, 223, 110159. [Google Scholar] [CrossRef]
- Alrashed, F.; Asif, M. Analysis of critical climate related factors for the application of zero-energy homes in Saudi Arabia. Renew. Sustain. Energy Rev. 2015, 41, 1395–1403. [Google Scholar] [CrossRef]
- Asif, M. Growth and sustainability trends in the buildings sector in the GCC region with particular reference to the KSA and UAE. Renew. Sustain. Energy Rev. 2016, 55, 1267–1273. [Google Scholar] [CrossRef]
- Sittón-Candanedo, I.; Alonso, R.S.; García, Ó.; Muñoz, L.; Rodríguez-González, S. Edge Computing, IoT and Social Computing in Smart Energy Scenarios. Sensors 2019, 19, 3353. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Iqbal, A.; Ullah, F.; Anwar, H.; Kwak, K.S.; Imran, M.; Jamal, W.; Rahman, A.U. Interoperable Internet-of-Things platform for smart home system using Web-of-Objects and cloud. Sustain. Cities Soc. 2018, 38, 636–646. [Google Scholar] [CrossRef]
- Baig, F.; Mahmood, A.; Javaid, N.; Razzaq, S.; Khan, N.; Saleem, Z. Smart home energy management system for monitoring and scheduling of home appliances using zigbee. J. Basic. Appl. Sci. Res. 2013, 3, 880–891. [Google Scholar]
- Han, J.; Choi, C.-S.; Lee, I. More efficient home energy management system based on Zig, Bee communication and infrared remote controls. IEEE Trans. Consum. Electron. 2011, 57, 85–89. [Google Scholar]
- Han, J.; Choi, C.-S.; Park, W.-K.; Lee, I.; Kim, S.-H. Smart home energy management system including renewable energy based on Zig, Bee and PLC. IEEE Trans. Consum. Electron. 2014, 60, 198–202. [Google Scholar] [CrossRef]
- Aliero, M.S.; Qureshi, K.N.; Pasha, M.F.; Jeon, G. Smart Home Energy Management Systems in Internet of Things networks for green cities demands and services. Environ. Technol. Innov. 2021, 22, 101443. [Google Scholar] [CrossRef]
- Chellamani, G.K.; Chandramani, P.V. An Optimized Methodical Energy Management System for Residential Consumers Considering Price-Driven Demand Response Using Satin Bowerbird Optimization. J. Electr. Eng. Technol. 2020, 15, 955–967. [Google Scholar] [CrossRef]
- Liu, Y.; Xiao, L.; Yao, G.; Bu, S. Pricing-Based Demand Response for a Smart Home with Various Types of Household Appliances Considering Customer Satisfaction. IEEE Access 2019, 7, 86463–86472. [Google Scholar] [CrossRef]
- Lin, Y. Novel smart home system architecture facilitated with distributed and embedded flexible edge analytics in demand-side management. Int. Trans. Electr. Energy Syst. 2019, 29, 29. [Google Scholar] [CrossRef]
- Dong, J.; Winstead, C.; Nutaro, J.; Kuruganti, T. Occupancy-Based HVAC Control with Short-Term Occupancy Prediction Algorithms for Energy-Efficient Buildings. Energies 2018, 11, 2427. [Google Scholar] [CrossRef] [Green Version]
- Lee, S.; Joe, J.; Karava, P.; Bilionis, I.; Tzempelikos, A. Implementation of a self-tuned HVAC controller to satisfy occupant thermal preferences and optimize energy use. Energy Build. 2019, 194, 301–316. [Google Scholar] [CrossRef]
- Sehar, F.; Pipattanasomporn, M.; Rahman, S. Integrated automation for optimal demand management in commercial buildings considering occupant comfort. Sustain. Cities Soc. 2017, 28, 16–29. [Google Scholar] [CrossRef] [Green Version]
- Becker, V.; Kleiminger, W.; Coroamă, V.C.; Mattern, F. Estimating the savings potential of occupancy-based heating strategies. Energy Inform. 2018, 1, 52. [Google Scholar] [CrossRef]
- Trivedi, D.; Badarla, V. Occupancy detection systems for indoor environments: A survey of approaches and methods. Indoor Built Environ. 2020, 29, 1053–1069. [Google Scholar] [CrossRef]
- Haider, H.T.; See, O.H.; Elmenreich, W. A review of residential demand response of smart grid. Renew. Sustain. Energy Rev. 2016, 59, 166–178. [Google Scholar] [CrossRef]
- Alaa, M.; Zaidan, A.; Zaidan, B.; Talal, M.; Kiah, M. A review of smart home applications based on Internet of Things. J. Netw. Comput. Appl. 2017, 97, 48–65. [Google Scholar] [CrossRef]
- Ullah, F.; Al-Turjman, F.; Nayyar, A. IoT-based green city architecture using secured and sustainable android services. Environ. Technol. Innov. 2020, 20, 101091. [Google Scholar] [CrossRef]
- Haseeb, K.; Lee, S.; Jeon, G. EBDS: An energy-efficient big data-based secure framework using Internet of Things for green environment. Environ. Technol. Innov. 2020, 20, 101129. [Google Scholar] [CrossRef]
- Kitchenham, B.; Brereton, O.P.; Budgen, D.; Turner, M.; Bailey, J.; Linkman, S. Systematic literature reviews in software engineering—A systematic literature review. Inf. Softw. Technol. 2009, 51, 7–15. [Google Scholar] [CrossRef]
- Aliero, M.S.; Qureshi, K.N.; Pasha, M.F.; Ghani, I.; Yauri, R.A. Systematic Mapping Study on Energy Optimization Solutions in Smart Building Structure: Opportunities and Challenges. Wirel. Pers. Commun. 2021, 119, 2017–2053. [Google Scholar] [CrossRef]
- White, G.; Nallur, V.; Clarke, S. Quality of service approaches in IoT: A systematic mapping. J. Syst. Softw. 2017, 132, 186–203. [Google Scholar] [CrossRef]
- Rad, M.M.; Rahmani, A.M.; Sahafi, A.; Qader, N.N. Social Internet of Things: Vision, challenges, and trends. Hum. Cent. Comput. Inf. Sci. 2020, 10, 1–40. [Google Scholar]
- Pérez-Lombard, L.; Ortiz, J.; Pout, C. A review on buildings energy consumption information. Energy Build. 2008, 40, 394–398. [Google Scholar] [CrossRef]
- Aliero, M.S.; Qureshi, K.N.; Pasha, M.F.; Ghani, I.; Yauri, R.A. Systematic Review Analysis on SQLIA Detection and Prevention Approaches. Wirel. Pers. Commun. 2020, 112, 2297–2333. [Google Scholar] [CrossRef]
- Cavalcante, E.; Pereira, J.; Alves, M.P.; Maia, P.; Moura, R.; Batista, T.; Delicato, F.C.; Pires, P.F. On the interplay of Internet of Things and Cloud Computing: A systematic mapping study. Comput. Commun. 2016, 89–90, 17–33. [Google Scholar] [CrossRef]
- Zhang, Z.; Wang, J.; Zhong, H.; Ma, H. Optimal scheduling model for smart home energy management system based on the fusion algorithm of harmony search algorithm and particle swarm optimization algorithm. Sci. Technol. Built Environ. 2019, 26, 42–51. [Google Scholar] [CrossRef]
- Lu, X.; Zhou, K.; Chan, F.T.S.; Yang, S. Optimal scheduling of household appliances for smart home energy management considering demand response. Nat. Hazards 2017, 88, 1639–1653. [Google Scholar] [CrossRef]
- Aliero, M.S.; Pasha, M.F.; Toosi, A.N.; Ghani, I. The COVID-19 impact on air condition usage: A shift towards residential energy saving. Environ. Sci. Pollut. Res. 2022, 1–15. [Google Scholar] [CrossRef] [PubMed]
- Jung, W.; Jazizadeh, F. Human-in-the-loop HVAC operations: A quantitative review on occupancy, comfort, and energy-efficiency dimensions. Appl. Energy 2019, 239, 1471–1508. [Google Scholar] [CrossRef]
- De Carvalho Curi, T.M.R.; Conti, D.; do Amaral Vercellino, R.; Massari, J.M.; de Moura, D.J.; de Souza, Z.M.; Montanari, R. Positioning of sensors for control of ventilation systems in broiler houses: A case study. Sci. Agric. 2017, 74, 101–109. [Google Scholar] [CrossRef] [Green Version]
- Aswani, A.; Master, N.; Taneja, J.; Culler, D.; Tomlin, C. Reducing Transient and Steady State Electricity Consumption in HVAC Using Learning-Based Model-Predictive Control. Proc. IEEE 2012, 100, 240–253. [Google Scholar] [CrossRef]
- Bujdei, C.; Moraru, S.A. Ensuring Comfort in Office Buildings: Designing a KNX Monitoring and Control System. In Proceedings of the 2011 Seventh International Conference on Intelligent Environments, Nottingham, UK, 25–28 July 2011; pp. 222–229. [Google Scholar]
- Salamone, F.; Danza, L.; Meroni, I.; Pollastro, M.C. A Low-Cost Environmental Monitoring System: How to Prevent Systematic Errors in the Design Phase through the Combined Use of Additive Manufacturing and Thermographic Techniques. Sensors 2017, 17, 828. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Moreno, M.V.; Zamora, M.A.; Skarmeta, A.F. User-centric smart buildings for energy sustainable smart cities. Trans. Emerg. Telecommun. Technol. 2014, 25, 41–55. [Google Scholar] [CrossRef]
- Giuseppina, N.; Salvatore, T. WSN system design by using an innovative neural network model to perform thermals forecasting in a urban canyon scenario. AIP Conf. Proc. 2015, 1702, 180012. [Google Scholar]
- Hang-Yat, L.A.; Wang, D. Carrying My Environment with Me. In Proceedings of the BuildSys 2013: 5th ACM Workshop on Embedded Systems for Energy-Efficiency in Buildings, Rome, Italy, 14–15 November 2013; pp. 1–8. [Google Scholar]
- Altayeva, A.B.; Omarov, B.S.; Cho, Y.I. Intelligent Microclimate Control System Based on IoT. Int. J. Fuzzy Log. Intell. Syst. 2016, 16, 254–261. [Google Scholar] [CrossRef] [Green Version]
- Mansur, V.; Carreira, P.; Arsenio, A. A Learning Approach for Energy Efficiency Optimization by Occupancy Detection. In Internet of Things. User-Centric IoT; Springer: Berlin/Heidelberg, Germany, 2015; pp. 9–15. [Google Scholar]
- Al-Ali, A.; Zualkernan, I.A.; Rashid, M.; Gupta, R.; AliKarar, M. A smart home energy management system using IoT and big data analytics approach. IEEE Trans. Consum. Electron. 2017, 63, 426–434. [Google Scholar] [CrossRef]
- Hafeez, K.; Chandio, Y.; Bakar, A.; Ali, A.; Syed, A.A.; Jadoon, T.M.; Alizai, M.H. Inverting HVAC for energy efficient thermal comfort in populous emerging countries. In Proceedings of the BuildSys 2017: 4th ACM International Conference on Systems for Energy-Efficient Built Environments, Delft, The Netherlands, 8–9 November 2017; p. 18. [Google Scholar]
- Zeiler, W.; Vissers, D.; Maaijen, R.; Boxem, G. Occupants’ behavioural impact on energy consumption: ‘human-in-the-loop’ comfort process control. Archit. Eng. Des. Manag. 2013, 10, 108–130. [Google Scholar] [CrossRef]
- Roselyn, J.P.; Uthra, R.A.; Raj, A.; Devaraj, D.; Bharadwaj, P.; Kaki, S.V.D.K. Development and implementation of novel sensor fusion algorithm for occupancy detection and auto-mation in energy efficient buildings. Sustain. Cities Soc. 2019, 44, 85–98. [Google Scholar] [CrossRef]
- Cao, N.; Ting, J.L.; Sen, S.; Raychowdhury, A. Smart Sensing for HVAC Control: Collaborative Intelligence in Optical and IR Cameras. IEEE Trans. Ind. Electron. 2018, 65, 9785–9794. [Google Scholar] [CrossRef]
- Arnesano, M.; Revel, G.M.; Pietroni, F.; Frick, J.; Reichert, M.; Schmitt, K.; Huber, J.; Ebermann, M.; Battista, U.; Alessi, F. Cost-Effective Technologies to Control Indoor Air Quality and Comfort in Energy Efficient Building Retrofitting. Environ. Eng. Manag. J. 2015, 14, 1487–1494. [Google Scholar]
- Rossi, A.; Vila, Y.; Lusiani, F.; Barsotti, L.; Sani, L.; Ceccarelli, P.; Lanzetta, M. Embedded smart sensor device in construction site machinery. Comput. Ind. 2019, 108, 12–20. [Google Scholar] [CrossRef]
- Ciabattoni, L.; Ferracuti, F.; Ippoliti, G.; Longhi, S.; Turri, G. IoT based indoor personal comfort levels monitoring. In Proceedings of the 2016 IEEE International Conference on Consumer Electronics, Las Vegas, NV, USA, 7–11 January 2016; pp. 125–126. [Google Scholar]
- Patti, E.; Acquaviva, A.; Macii, E. Enable sensor networks interoperability in smart public spaces through a service oriented approach. In Proceedings of the 5th IEEE International Workshop on Advances in Sensors and Interfaces IWASI, Bari, Italy, 13–14 June 2013; pp. 2–7. [Google Scholar]
- Harfield, A.; Rattanongphisat, W. Towards an open monitoring platform for improving energy efficiency and thermal comfort in public buildings. In Proceedings of the 2017 9th International Conference on Knowledge and Smart Technology (KST), Pattaya, Thailand, 1–4 February 2017; pp. 150–155. [Google Scholar]
- Jia, Q.-S.; Zhang, Y.; Zhao, Q. Controlling the Internet of Things—From Energy Saving to Fast Evacuation in Smart Buildings. In Advances in Industrial Control; Springer: Berlin/Heidelberg, Germany, 2017; pp. 293–310. [Google Scholar]
- Singh, A.; Pandey, Y.; Kumar, A.; Singh, M.K.; Kumar, A.; Mukhopadhyay, S.C. Ventilation Monitoring and Control System for High Rise Historical Buildings. IEEE Sens. J. 2017, 17, 7533–7541. [Google Scholar] [CrossRef]
- Elkhoukhi, H.; NaitMalek, Y.; Bakhouya, M.; Berouine, A.; Kharbouch, A.; Lachhab, F.; Hanifi, M.; el Ouadghiri, D.; Essaaidi, M. A platform architecture for occupancy detection using stream processing and machine learning approaches. Concurr. Computat. Pract. Exper. 2020, 32, e5651. [Google Scholar] [CrossRef]
- Ain, Q.U.; Iqbal, S.; Khan, S.A.; Malik, A.W.; Ahmad, I.; Javaid, N. IoT Operating System Based Fuzzy Inference System for Home Energy Management System in Smart Buildings. Sensors 2018, 18, 2802. [Google Scholar] [CrossRef]
- Khalid, R.; Javaid, N.; Rahim, M.H.; Aslam, S.; Sher, A. Fuzzy energy management controller and scheduler for smart homes. Sustain. Comput. Inform. Syst. 2019, 21, 103–118. [Google Scholar] [CrossRef]
- Chagas, B.A.; Redmiles, D.F.; de Souza, C.S. End-user development for the Internet of Things OR How can a (smart) light bulb be so complicated? In Proceedings of the 2017 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), Raleigh, NC, USA, 11–14 October 2017. [Google Scholar]
- Gomes, L.; Sousa, F.; Vale, Z. An Intelligent Smart Plug with Shared Knowledge Capabilities. Sensors 2018, 18, 3961. [Google Scholar] [CrossRef] [Green Version]
- Oh, J. IoT-Based Smart Plug for Residential Energy Conservation: An Empirical Study Based on 15 Months’ Monitoring. Energies 2020, 13, 4035. [Google Scholar] [CrossRef]
- Lee, S.-H.; Yang, C.-S. An intelligent power monitoring and analysis system for distributed smart plugs sensor networks. Int. J. Distrib. Sens. Netw. 2017, 13, 1550147717718462. [Google Scholar] [CrossRef]
- Chen, M.-T.; Lin, C.-M. Standby Power Management of a Smart Home Appliance by Using Energy Saving System with Active Loading Feature Identification. IEEE Trans. Consum. Electron. 2018, 65, 11–17. [Google Scholar] [CrossRef]
- Zhai, S.; Wang, Z.; Yan, X.; He, G. Appliance Flexibility Analysis Considering User Behavior in Home Energy Management System Using Smart Plugs. IEEE Trans. Ind. Electron. 2019, 66, 1391–1401. [Google Scholar] [CrossRef]
- Ghazal, M.; Akmal, M.; Iyanna, S.; Ghoudi, K. Smart plugs: Perceived usefulness and satisfaction: Evidence from United Arab Emirates. Renew. Sustain. Energy Rev. 2016, 55, 1248–1259. [Google Scholar] [CrossRef] [Green Version]
- Songkittirote, N.; Setthapun, W.; Sintuya, H. Smart Plug Control System Development with My, SQL Database and MQTT Protocol. In Proceedings of the 2018 International Symposium on Computer, Consumer and Control (IS3C), Taichung, Taiwan, 6–8 December 2018; pp. 76–79. [Google Scholar]
- Wang, L.; Peng, D.; Zhang, T. Design of Smart Home System Based on Wi, Fi Smart Plug. Int. J. Smart Home 2015, 9, 173–182. [Google Scholar] [CrossRef]
- Blanco-Novoa, Ó.; Fernández-Caramés, T.M.; Fraga-Lamas, P.; Castedo, L. An Electricity Price-Aware Open-Source Smart Socket for the Internet of Energy. Sensors 2017, 17, 643. [Google Scholar] [CrossRef] [Green Version]
- Ge, D.; Lee, E.; Yang, L.; Cho, Y.; Li, M.; Gianola, D.S.; Yang, S. A Robust Smart Window: Reversibly Switching from High Transparency to Angle-Independent Structural Color Display. Adv. Mater. 2015, 27, 2489–2495. [Google Scholar] [CrossRef]
- Tsui, K.M.; Chan, S.C. Demand Response Optimization for Smart Home Scheduling Under Real-Time Pricing. IEEE Trans. Smart Grid 2012, 3, 1812–1821. [Google Scholar] [CrossRef]
- Martinez-Molina, A.; Alamaniotis, M. Enhancing Historic Building Performance with the Use of Fuzzy Inference Systems to Control the Electric Cooling System. Sustainability 2020, 12, 5848. [Google Scholar] [CrossRef]
- Meana-Llorián, D.; García, C.G.; Bustelo, B.C.P.; Lovelle, J.M.C.; Garcia-Fernandez, N. IoFClime: The fuzzy logic and the Internet of Things to control indoor temperature regarding the outdoor ambient conditions. Futur. Gener. Comput. Syst. 2017, 76, 275–284. [Google Scholar] [CrossRef] [Green Version]
- Talebi, A.; Hatami, A. Online fuzzy control of HVAC systems considering demand response and users’ comfort. Energy Sources Part B Econ. Plan. Policy 2020, 15, 403–422. [Google Scholar] [CrossRef]
- Kontes, G.D.; Giannakis, G.I.; Horn, P.; Steiger, S.; Rovas, D.V. Using Thermostats for Indoor Climate Control in Office Buildings: The Effect on Thermal Comfort. Energies 2017, 10, 1368. [Google Scholar] [CrossRef] [Green Version]
- Pan, J.; Jain, R.; Paul, S.; Vu, T.; Saifullah, A.; Sha, M. An Internet of Things Framework for Smart Energy in Buildings: Designs, Prototype, and Experiments. IEEE Internet Things J. 2015, 2, 527–537. [Google Scholar] [CrossRef] [Green Version]
- Brundu, F.G.; Patti, E.; Osello, A.; Del Giudice, M.; Rapetti, N.; Krylovskiy, A.; Jahn, M.; Verda, V.; Guelpa, E.; Rietto, L.; et al. IoT Software Infrastructure for Energy Management and Simulation in Smart Cities. IEEE Trans. Ind. Inform. 2017, 13, 832–840. [Google Scholar] [CrossRef]
- Feldmeier, M.; Paradiso, J.A. Personalized HVAC control system. In Proceedings of the 2010 Internet of Things (IoT), Tokyo, Japan, 29 November–1 December 2010; pp. 1–8. [Google Scholar]
- Zhu, J.; Li, Y. Hesitant Fuzzy Linguistic Aggregation Operators Based on the Hamacher t-norm and t-conorm. Symmetry 2018, 10, 189. [Google Scholar] [CrossRef] [Green Version]
- Curry, E.; Derguech, W.; Hasan, S.; Kouroupetroglou, C.; Hassan, U.U. A Real-time Linked Dataspace for the Internet of Things: Enabling “Pay-As-You-Go” Data Management in Smart Environments. Futur. Gener. Comput. Syst. 2019, 90, 405–422. [Google Scholar] [CrossRef] [Green Version]
- Javaid, N.; Ahmed, A.; Iqbal, S.; Ashraf, M. Day Ahead Real Time Pricing and Critical Peak Pricing Based Power Scheduling for Smart Homes with Different Duty Cycles. Energies 2018, 11, 1464. [Google Scholar] [CrossRef] [Green Version]
- Kaushik, V.; Venkitaramanan, D.; Vasudevan, S.K.; Periasamy, P.; Arumugam, B. Internet of things in cloud. J. Eng. Appl. Sci. 2013, 8, 304–313. [Google Scholar]
- Kumar, S. Ubiquitous Smart Home System Using Android Application. Int. J. Comput. Netw. Commun. 2014, 6, 33–43. [Google Scholar] [CrossRef]
- Jahn, M.; Jentsch, M.; Prause, C.R.; Pramudianto, F.; Al-Akkad, A.; Reiners, R. The Energy Aware Smart Home. In Proceedings of the 2010 5th International Conference on Future Information Technology, Busan, Korea, 20–24 May 2010; pp. 1–8. [Google Scholar]
- Tereshchenko, T.; Nord, N. Future Trends in District Heating Development. Curr. Sustain. Energy Rep. 2018, 5, 172–180. [Google Scholar] [CrossRef] [Green Version]
- Nadeem, Z.; Javaid, N.; Malik, A.W.; Iqbal, S. Scheduling Appliances with GA, TLBO, FA, OSR and Their Hybrids Using Chance Constrained Optimization for Smart Homes. Energies 2018, 11, 888. [Google Scholar] [CrossRef] [Green Version]
- Liu, Y.; Yang, C.; Jiang, L.; Xie, S.; Zhang, Y. Intelligent Edge Computing for IoT-Based Energy Management in Smart Cities. IEEE Netw. 2019, 33, 111–117. [Google Scholar] [CrossRef]
- Huang, C.-C.; Yang, R.; Newman, M.W. The potential and challenges of inferring thermal comfort at home using com-modity sensors. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2015), Osaka, Japan, 7–8 September 2015; pp. 1089–1100. [Google Scholar]
- Matsui, K. An information provision system to promote energy conservation and maintain indoor comfort in smart homes using sensed data by IoT sensors. Futur. Gener. Comput. Syst. 2018, 82, 388–394. [Google Scholar] [CrossRef]
- Hussain, H.M.; Javaid, N.; Iqbal, S.; Hasan, Q.U.; Aurangzeb, K.; Alhussein, M. An Efficient Demand Side Management System with a New Optimized Home Energy Management Controller in Smart Grid. Energies 2018, 11, 190. [Google Scholar] [CrossRef] [Green Version]
- Saeed, W.; Ahmad, Z.; Jehangiri, A.I.; Mohamed, N.; Umar, A.I.; Ahmad, J. A Fault Tolerant Data Management Scheme for Healthcare Internet of Things in Fog Computing. KSII Trans. Internet Inf. Syst. (TIIS) 2021, 15, 35–57. [Google Scholar]
- Ding, W.-L.; Zhu, X.-J.; Xu, B.; Xu, Y.; Chen, K.; Wan, Z.-X. The Interactive Modeling Method of Virtual City Scene Based on Building Codes. KSII Trans. Internet Inf. Syst. 2021, 15, 74–89. [Google Scholar]
- Thomas, A.M.; Moore, P.; Shah, H.; Evans, C.; Sharma, M.; Xhafa, F.; Mount, S.; Pham, H.V.; Wilcox, A.J.; Patel, A.; et al. Smart care spaces: Needs for intelligent at-home care. Int. J. Space-Based Situated Comput. 2013, 3, 35. [Google Scholar] [CrossRef]
- Kathiravelu, P.; Sharifi, L.; Veiga, L. Cassowary: Middleware Platform for Context-Aware Smart Buildings with Software-Defined Sensor Networks. In Proceedings of the M4IoT 2015: Proceedings of the 2nd Workshop on Middleware for Context-Aware Applications in the IoT, Vancouver, BC, Canada, 7–11 December 2015; pp. 1–6. [Google Scholar]
- Ko, H.; Kim, J.H.; An, K.; Mesicek, L.; Marreiros, G.; Pan, S.B.; Kim, P. Smart home energy strategy based on human behaviour patterns for transformative computing. Inf. Process. Manag. 2020, 57, 102256. [Google Scholar] [CrossRef]
- Tila, F.; Kim, D.H. Semantic IoT System for Indoor Environment Control—A Sparql and SQL based Hybrid Model. Green Smart Technol. 2015, 2015, 678–683. [Google Scholar]
- Peffer, T.; Pritoni, M.; Fierro, G.; Kaam, S.; Kim, J.; Raftery, P. Writing controls sequences for buildings: From HVAC industry enclave to hacker’s weekend project. In Proceedings of the ACEEE Summer Study on Energy Efficiency in Buildings, Pacific Grove, CA, USA, 21–26 August 2016. [Google Scholar]
- Tomé, M.D.C.; Nardelli, P.H.J.; Hussain, H.M.; Wahid, S.; Narayanan, A. A Cyber-Physical Residential Energy Management System via Virtualized Packets. Energies 2020, 13, 699. [Google Scholar] [CrossRef] [Green Version]
- Jo, H.; Yoon, Y.I. Intelligent smart home energy efficiency model using artificial Tensor, Flow engine. Hum. Cent. Comput. Inf. Sci. 2018, 8, 1–18. [Google Scholar] [CrossRef]
- Yuliansyah, H.; Corio, D.; Yunmar, R.; Aziz, M.R.K. Energy Monitoring System Based on Internet of Things Toward Smart Campus in Institut Teknologi Sumatera. IOP Conf. Ser. Earth Environ. Sci. 2019, 258, 012008. [Google Scholar] [CrossRef]
- Barman, A.D.; Halder, A. Indoor visible light communication with smart lighting technology. Smart Photonic Optoelectron. Integr. Circuits XIX 2017, 10107, 101070W. [Google Scholar]
- Chen, X.; Wei, T.; Hu, S. Uncertainty-Aware Household Appliance Scheduling Considering Dynamic Electricity Pricing in Smart Home. IEEE Trans. Smart Grid 2013, 4, 932–941. [Google Scholar] [CrossRef]
- Shakeri, M.; Shayestegan, M.; Abunima, H.; Reza, S.S.; Akhtaruzzaman, M.; Alamoud, A.; Sopian, K.; Amin, N. An intelligent system architecture in home energy management systems (HEMS) for efficient demand response in smart grid. Energy Build. 2017, 138, 154–164. [Google Scholar] [CrossRef]
- Asif, S.; Ambreen, K.; Iftikhar, H.; Khan, H.N.; Maroof, R.; Javaid, N. Energy Management in Residential Area using Genetic and Strawberry Algorithm. In Advances in Network-Based Information Systems; Springer: Berlin/Heidelberg, Germany, 2018; pp. 165–176. [Google Scholar]
- Rehman, A.U.; Aslam, S.; Abideen, Z.U.; Zahra, A.; Ali, W.; Junaid, M.; Javaid, N. Efficient Energy Management System Using Firefly and Harmony Search Algorithm. In Advances in Network-Based Information Systems; Springer: Berlin/Heidelberg, Germany, 2018; pp. 37–49. [Google Scholar]
- Alyass, A.; Turcotte, M.; Meyre, D. From big data analysis to personalized medicine for all: Challenges and opportunities. BMC Med Genom. 2015, 8, 33. [Google Scholar] [CrossRef] [Green Version]
- Hakimi, S.M.; Hasankhani, A. Intelligent energy management in off-grid smart buildings with energy interaction. J. Clean. Prod. 2020, 244, 118906. [Google Scholar] [CrossRef]
- Chamandoust, H.; Derakhshan, G.; Hakimi, S.M.; Bahramara, S. Tri-objective scheduling of residential smart electrical distribution grids with optimal joint of responsive loads with renewable energy sources. J. Energy Storage 2020, 27, 101112. [Google Scholar] [CrossRef]
- Choo, Y.Y.; Ha, Y.J.; Kim, Y.B.; Lee, S.J.; Choi, H.D. Development of CoAP-based IoT Communication System for Smart Energy Storage System. In Proceedings of the 2nd International Symposium on Computer Science and Intelligent Control, Stockholm, Sweden, 21–23 September 2018; pp. 1–5. [Google Scholar]
- Pawar, P.; TarunKumar, M.; Vittal, K.P. An IoT based Intelligent Smart Energy Management System with accurate fore-casting and load strategy for renewable generation. Measurement 2020, 152, 107187. [Google Scholar] [CrossRef]
- Paudyal, P.; Ni, Z. Smart home energy optimization with incentives compensation from inconvenience for shifting electric appliances. Int. J. Electr. Power Energy Syst. 2019, 109, 652–660. [Google Scholar] [CrossRef]
- Sha, K.; Wei, W.; Yang, T.A.; Wang, Z.; Shi, W. On security challenges and open issues in Internet of Things. Futur. Gener. Comput. Syst. 2018, 83, 326–337. [Google Scholar] [CrossRef]
- Aliero, M.S.; Ghani, I.; Qureshi, K.N.; Rohani, M.F. An algorithm for detecting SQL injection vulnerability using black-box testing. J. Ambient Intell. Humaniz. Comput. 2020, 11, 249–266. [Google Scholar] [CrossRef]
- Alaba, F.A.; Othman, M.; Hashem, I.A.T.; Alotaibi, F. Internet of Things security: A survey. J. Netw. Comput. Appl. 2017, 88, 10–28. [Google Scholar] [CrossRef]
- Granjal, J.; Monteiro, E.; Silva, J.S. Security for the Internet of Things: A Survey of Existing Protocols and Open Research Issues. IEEE Commun. Surv. Tutor. 2015, 17, 1294–1312. [Google Scholar] [CrossRef]
- Hu, M.; Xiao, F.; Jørgensen, J.B.; Wang, S. Frequency control of air conditioners in response to real-time dynamic electricity prices in smart grids. Appl. Energy 2019, 242, 92–106. [Google Scholar] [CrossRef]
- Castro, D.; Coral, W.; Rodriguez, C.; Cabra, J.; Colorado, J. Wearable-Based Human Activity Recognition Using an IoT Approach. J. Sens. Actuator Netw. 2017, 6, 28. [Google Scholar] [CrossRef] [Green Version]
- Yang, B.; Li, X.; Hou, Y.; Meier, A.; Cheng, X.; Choi, J.H.; Li, H. Non-invasive (non-contact) measurements of human thermal physiology signals and thermal comfort/discomfort poses—A review. Energy Build. 2020, 224, 110261. [Google Scholar] [CrossRef]
- Luo, M.; de Dear, R.; Ji, W.; Bin, C.; Lin, B.; Ouyang, Q.; Zhu, Y. The dynamics of thermal comfort expectations: The problem, challenge and impication. Build. Environ. 2016, 95, 322–329. [Google Scholar] [CrossRef]
- Dikel, E.E.; Li, Y.E.; Vuotari, M.; Mancini, S. Evaluating the standby power consumption of smart LED bulbs. Energy Build. 2019, 186, 71–79. [Google Scholar] [CrossRef]
- Seyedolhosseini, A.; Masoumi, N.; Modarressi, M.; Karimian, N. Daylight adaptive smart indoor lighting control method using artificial neural networks. J. Build. Eng. 2020, 29, 101141. [Google Scholar] [CrossRef]
- Salamone, F.; Belussi, L.; Danza, L.; Galanos, T.; Ghellere, M.; Meroni, I. Design and Development of a Nearable Wireless System to Control Indoor Air Quality and Indoor Lighting Quality. Sensors 2017, 17, 1021. [Google Scholar] [CrossRef]
- Pan, J.; Jain, R.; Biswas, P.; Wang, W.; Addepalli, S. A framework for smart location-based automated energy controls in a green building testbed. In Proceedings of the 2012 IEEE Energytech, Cleveland, OH, USA, 29–31 May 2012; pp. 1–6. [Google Scholar]
- Ammar, M.; Russello, G.; Crispo, B. Internet of Things: A survey on the security of IoT frameworks. J. Inf. Secur. Appl. 2018, 38, 8–27. [Google Scholar] [CrossRef] [Green Version]
- Zhang, K.; Ni, J.; Yang, K.; Liang, X.; Ren, J.; Shen, X.S. Security and Privacy in Smart City Applications: Challenges and Solutions. IEEE Commun. Mag. 2017, 55, 122–129. [Google Scholar] [CrossRef]
- Kim, Y.P.; Yoo, S.; Yoo, C. DAoT: Dynamic and energy-aware authentication for smart home appliances in Internet of Things. In Proceedings of the 2015 IEEE International Conference on Consumer Electronics, Las Vegas, NV, USA, 9–12 January 2015; pp. 196–197. [Google Scholar]
- Paukstadt, U.; Becker, J. Uncovering the business value of the internet of things in the energy domain—A review of smart energy business models. Electron. Mark. 2021, 31, 51–66. [Google Scholar] [CrossRef]
- Schmid, S.; Ziegler, J.; Corbellini, G.; Gross, T.R.; Mangold, S. Using consumer LED light bulbs for low-cost visible light communication systems. In Proceedings of the 1st ACM MobiCom Workshop on Visible Light Communication Systems, Maui, Hawaii, 7 September 2014; pp. 9–14. [Google Scholar]
- Orfanos, V.A.; Kaminaris, S.D.; Piromalis, D.; Papageorgas, P. Smart home automation in the IoT era: A communication technologies review. AIP Conf. Proc. 2020, 2307, 020054. [Google Scholar]
- Zakirullin, R.S. A Smart Window for Angular Selective Filtering of Direct Solar Radiation. J. Sol. Energy Eng. 2020, 142, 011001. [Google Scholar] [CrossRef]
- Stojkoska, B.R.; Trivodaliev, K.; Davcev, D. Internet of Things Framework for Home Care Systems. Wirel. Commun. Mob. Comput. 2017, 2017, 8323646. [Google Scholar] [CrossRef] [Green Version]
- Khan, M.; Silva, B.N.; Han, K. Internet of Things Based Energy Aware Smart Home Control System. IEEE Access 2016, 4, 7556–7566. [Google Scholar] [CrossRef]
- Moreno, M.V.; Santa, J.; Zamora, M.A.; Skarmeta, A.F. A holistic IoT-based management platform for smart environments. In Proceedings of the 2014 IEEE International Conference on Communications (ICC), Sydney, Australia, 10–14 June 2014. [Google Scholar]
- Park, S.; Jun, S. Technology Analysis of Global Smart Light Emitting Diode (LED) Development Using Patent Data. Sustainability 2017, 9, 1363. [Google Scholar] [CrossRef] [Green Version]
- Barata, F.A.; Silva, R.N. Distributed Model Predictive Control for Housing with Hourly Auction of Available Energy. In Technological Innovation for the Internet of Things; Springer: Berlin/Heidelberg, Germany, 2013; pp. 469–476. [Google Scholar]
- Uribe, O.H.; Martin, J.P.S.; Garcia-Alegre, M.C.; Santos, M.; Guinea, D. Smart Building: Decision Making Architecture for Thermal Energy Management. Sensors 2015, 15, 27543–27568. [Google Scholar] [CrossRef] [Green Version]
- Salamone, F.; Belussi, L.; Danza, L.; Ghellere, M.; Meroni, I. An Open Source “Smart Lamp” for the Optimization of Plant Systems and Thermal Comfort of Offices. Sensors 2016, 16, 338. [Google Scholar] [CrossRef] [Green Version]
- Robles, R.J.; Kim, T.H. Applications, systems and methods in smart home technology: A review. Int. J. Adv. Sci. Technol. 2010, 15, 37–48. [Google Scholar]
- Fischer, D.; Madani, H. On heat pumps in smart grids: A review. Renew. Sustain. Energy Rev. 2017, 70, 342–357. [Google Scholar] [CrossRef] [Green Version]
- Darby, S.J. Smart technology in the home: Time for more clarity. Build. Res. Inf. 2018, 46, 140–147. [Google Scholar] [CrossRef] [Green Version]
- Piyare, R.; Lee, S.R. Smart home-control and monitoring system using smart phone. ICCA ASTL 2013, 24, 83–86. [Google Scholar]
- Rabbani, A.; Keshav, S. The SPOT* Personal Thermal Comfort System. In Proceedings of the BuildSys 2016: 3rd ACM International Conference on Systems for Energy-Efficient Built Environments, Palo Alto, CA, USA, 16–17 November 2016; pp. 75–84. [Google Scholar]
- Zheng, S.; Xiong, X.; Vause, J.; Liu, J. Real-time measurement of wind environment comfort in urban areas by Environmental Internet of Things. Int. J. Sustain. Dev. World Ecol. 2013, 20, 254–260. [Google Scholar] [CrossRef]
- Yamauchi, T.; Kondo, H.; Nii, K. Automotive low power technology for IoT society. In Proceedings of the 2015 Symposium on VLSI Technology (VLSI Technology), Kyoto, Japan, 16–18 June 2015. [Google Scholar]
- Javed, A.; Larijani, H.; Ahmadinia, A.; Gibson, D. Smart Random Neural Network Controller for HVAC Using Cloud Computing Technology. IEEE Trans. Ind. Inform. 2016, 13, 351–360. [Google Scholar] [CrossRef] [Green Version]
- Walker, G.; Brown, S.; Neven, L. Thermal comfort in care homes: Vulnerability, responsibility and ‘thermal care’. Build. Res. Inf. 2016, 44, 135–146. [Google Scholar] [CrossRef] [Green Version]
- Chen, S.-Y.; Lai, C.-F.; Huang, Y.-M.; Jeng, Y.-L. Intelligent home-appliance recognition over IoT cloud network. In Proceedings of the 2013 9th International Wireless Communications and Mobile Computing Conference (IWCMC), Sardinia, Italy, 1–5 July 2013; pp. 639–643. [Google Scholar]
- Talari, S.; Shafie-Khah, M.; Siano, P.; Loia, V.; Tommasetti, A.; Catalão, J.P.S. A Review of Smart Cities Based on the Internet of Things Concept. Energies 2017, 10, 421. [Google Scholar] [CrossRef] [Green Version]
- Serra, J.; Pubill, D.; Antonopoulos, A.; Verikoukis, C. Smart HVAC control in IoT: Energy consumption minimization with user comfort constraints. Sci. World J. 2014, 2014, 161874. [Google Scholar] [CrossRef] [Green Version]
- Lockton, D.; Bowden, F.; Greene, C.; Brass, C.; Gheerawo, R. People and Energy: A design-led approach to understanding everyday energy use behaviour. In Ethnographic Praxis in Industry Conference Proceedings; Wiley: Hoboken, NJ, USA, 2013; Volume 2013, pp. 348–362. [Google Scholar]
- Langevin, J.; Wen, J.; Gurian, P. Modeling thermal comfort holistically: Bayesian estimation of thermal sensation, acceptability, and preference distributions for office building occupants. Build. Environ. 2013, 69, 206–226. [Google Scholar] [CrossRef]
- Meinke, A.; Hawighorst, M.; Wagner, A.; Trojan, J.; Schweiker, M. Comfort-related feedforward information: Occupants’ choice of cooling strategy and perceived comfort. Build. Res. Inf. 2016, 45, 222–238. [Google Scholar] [CrossRef]
- Moreno, M.V.; Ramos, J.L.H.; Skarmeta, A.F. User role in IoT-based systems. In Proceedings of the 2014 IEEE World Forum on Internet of Things (WF-IoT), Seoul, Korea, 6–8 March 2014. [Google Scholar]
- Alan, A.T.; Shann, M.; Costanza, E.; Ramchurn, S.D.; Seuken, S. It is too Hot: An In-Situ Study of Three Designs for Heating. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, San Jose, CA, USA, 7–12 May 2016; pp. 5262–5273. [Google Scholar]
- Pritoni, M.; Salmon, K.; Sanguinetti, A.; Morejohn, J.; Modera, M. Occupant thermal feedback for improved efficiency in university buildings. Energy Build. 2017, 144, 241–250. [Google Scholar] [CrossRef]
- Taleghani, M.; Tenpierik, M.; Kurvers, S.; van den Dobbelsteen, A. A review into thermal comfort in buildings. Renew. Sustain. Energy Rev. 2013, 26, 201–215. [Google Scholar] [CrossRef]
- Brager, G.; Zhang, H.; Arens, E. Evolving opportunities for providing thermal comfort. Build. Res. Inf. 2015, 43, 274–287. [Google Scholar] [CrossRef] [Green Version]
- Royapoor, M.; Roskilly, T. Building model calibration using energy and environmental data. Energy Build. 2015, 94, 109–120. [Google Scholar] [CrossRef] [Green Version]
- Michailidis, I.T.; Schild, T.; Sangi, R.; Michailidis, P.; Korkas, C.; Fütterer, J.; Müller, D.; Kosmatopoulos, E.B. Energy-efficient HVAC management using cooperative, self-trained, control agents: A real-life German building case study. Appl. Energy 2018, 211, 113–125. [Google Scholar] [CrossRef]
- Wei, F.; Li, Y.; Sui, Q.; Lin, X.; Chen, L.; Chen, Z.; Li, Z. A Novel Thermal Energy Storage System in Smart Building Based on Phase Change Material. IEEE Trans. Smart Grid 2018, 10, 2846–2857. [Google Scholar] [CrossRef]
- Khandelwal, H.H.; Schenning, A.P.H.J.; Debije, M.G. Infrared Regulating Smart Window Based on Organic Materials. Adv. Energy Mater. 2017, 7, 1602209. [Google Scholar] [CrossRef]
- Park, H.; Rhee, S.B. IoT-Based Smart Building Environment Service for Occupants’ Thermal Comfort. J. Sens. 2018, 2018, 1757409. [Google Scholar] [CrossRef]
- AlFaris, F.; Juaidi, A.; Manzano-Agugliaro, F. Intelligent homes’ technologies to optimize the energy performance for the net zero energy home. Energy Build. 2017, 153, 262–274. [Google Scholar] [CrossRef]
- Ejaz, W.; Naeem, M.; Shahid, A.; Anpalagan, A.; Jo, M. Efficient Energy Management for the Internet of Things in Smart Cities. IEEE Commun. Mag. 2017, 55, 84–91. [Google Scholar] [CrossRef] [Green Version]
- Rodriguez, C.M.; Medina, J.M.; Pinzón, A. Thermal Comfort and Satisfaction in the Context of Social Housing: Case Study in Bogotá, Colombia. J. Constr. Dev. Ctries. 2019, 24, 101–124. [Google Scholar] [CrossRef]
- Samadi, A.; Saidi, H.; Latify, M.A.; Mahdavi, M. Home energy management system based on task classification and the resident’s requirements. Int. J. Electr. Power Energy Syst. 2020, 118, 105815. [Google Scholar] [CrossRef]
- Jung, W.; Jung, W.; Hong, T.; Oh, J.; Kang, H.; Lee, M. Development of a prototype for multi-function smart window by integrating photovoltaic blinds and ventilation system. Build. Environ. 2019, 149, 366–378. [Google Scholar] [CrossRef]
- Li, Z.; Xu, Y.; Feng, X.; Wu, Q. Optimal Stochastic Deployment of Heterogeneous Energy Storage in a Residential Multi-Energy Microgrid with Demand-Side Management. IEEE Trans. Ind. Inform. 2020, 17, 991–1004. [Google Scholar] [CrossRef]
- Kerboua, A.; Boukli-Hacene, F.; Mourad, K.A. Particle Swarm Optimization for Micro-Grid Power Management and Load Scheduling. Int. J. Energy Econ. Policy 2020, 10, 71–80. [Google Scholar] [CrossRef]
- Ullah, I.; Hussain, I.; Singh, M. Exploiting Grasshopper and Cuckoo Search Bio-Inspired Optimization Algorithms for Industrial Energy Management System: Smart Industries. Electronics 2020, 9, 105. [Google Scholar] [CrossRef] [Green Version]
- Hassan, N.U.; Pasha, M.A.; Yuen, C.; Huang, S.; Wang, X. Impact of Scheduling Flexibility on Demand Profile Flatness and User Inconvenience in Residential Smart Grid System. Energies 2013, 6, 6608–6635. [Google Scholar] [CrossRef] [Green Version]
- Aftab, M.; Chen, C.; Chau, S.C.-K.; Rahwan, T. Automatic HVAC control with real-time occupancy recognition and simulation-guided model predictive control in low-cost embedded system. Energy Build. 2017, 154, 141–156. [Google Scholar] [CrossRef] [Green Version]
- Jamaludin, A.A.; Keumala, N.; Ariffin, A.R.M.; Hussein, H. Satisfaction and perception of residents towards bioclimatic design strategies: Residential college buildings. Indoor Built Environ. 2014, 23, 933–945. [Google Scholar] [CrossRef]
- Revel, G.M.; Arnesano, M.; Pietroni, F. Development and validation of a low-cost infrared measurement system for re-al-time monitoring of indoor thermal comfort. Meas. Sci. Technol. 2014, 25, 085101. [Google Scholar] [CrossRef]
- Khemakhem, S.; Rekik, M.; Krichen, L. A collaborative energy management among plug-in electric vehicle, smart homes and neighbors’ interaction for residential power load profile smoothing. J. Build. Eng. 2020, 27, 100976. [Google Scholar] [CrossRef]
- Arya, A.K.; Chanana, S.; Kumar, A. Energy Saving in Distribution System using Internet of Things in Smart Grid environment. Int. J. Comput. Digit. Syst. 2019, 8, 158–165. [Google Scholar] [CrossRef]
- Ray, P.P. Internet of Things cloud enabled MISSENARD index measurement for indoor occupants. Measurement 2016, 92, 157–165. [Google Scholar] [CrossRef]
- Feng, W.; Zou, L.; Gao, G.; Wu, G.; Shen, J.; Li, W. Gasochromic smart window: Optical and thermal properties, energy simulation and feasibility analysis. Sol. Energy Mater. Sol. Cells 2016, 144, 316–323. [Google Scholar] [CrossRef]
- Khemakhem, S.; Rekik, M.; Krichen, L. Double layer home energy supervision strategies based on demand response and plug-in electric vehicle control for flattening power load curves in a smart grid. Energy 2019, 167, 312–324. [Google Scholar] [CrossRef]
- Wei, P.; Ning, Z.; Ye, S.; Sun, L.; Yang, F.; Wong, K.C.; Westerdahl, D.; Louie, P.K.K. Impact Analysis of Temperature and Humidity Conditions on Electrochemical Sensor Response in Ambient Air Quality Monitoring. Sensors 2018, 18, 59. [Google Scholar] [CrossRef] [Green Version]
- Guo, B.; Wang, X.; Zhang, X.; Yang, J.; Wang, Z. Research on the Temperature & Humidity Monitoring System in the Key Areas of the Hospital Based on the Internet of Things. Int. J. Smart Home 2016, 10, 205–216. [Google Scholar]
- Chen, C.-F.; de Rubens, G.Z.; Xu, X.; Li, J. Coronavirus comes home? Energy use, home energy management, and the social-psychological factors of COVID-19. Energy Res. Soc. Sci. 2020, 68, 101688. [Google Scholar] [CrossRef] [PubMed]
- Li, Y.; Kumar, M.; Shi, W.; Wan, J. Falcon: An ambient temperature aware thermal control policy for IoT gateways. Sustain. Comput. Inform. Syst. 2017, 16, 48–55. [Google Scholar] [CrossRef]
- Ke, Y.; Zhou, C.; Zhou, Y.; Wang, S.; Chan, S.H.; Long, Y. Emerging Thermal-Responsive Materials and Integrated Techniques Targeting the Energy-Efficient Smart Window Application. Adv. Funct. Mater. 2018, 28, 1800113. [Google Scholar] [CrossRef]
- Kwon, H.-Y.; Lee, K.-Y.; Hur, K.; Moon, S.H.; Quasim, M.M.; Wilkinson, T.D.; Han, J.-I.; Ko, H.; Han, I.-L.; Park, B.; et al. Optically Switchable Smart Windows with Integrated Photovoltaic Devices. Adv. Energy Mater. 2015, 5, 1401347. [Google Scholar] [CrossRef]
- Liang, X.; Chen, M.; Wang, Q.; Guo, S.; Zhang, L.; Yang, H. Active and passive modulation of solar light transmittance in a hybrid thermochromic soft-matter system for energy-saving smart window applications. J. Mater. Chem. C 2018, 6, 7054–7062. [Google Scholar] [CrossRef]
- Connelly, K.; Wu, Y.; Ma, X.; Lei, Y. Transmittance and Reflectance Studies of Thermotropic Material for a Novel Building Integrated Concentrating Photovoltaic (BICPV) ‘Smart Window’ System. Energies 2017, 10, 1889. [Google Scholar] [CrossRef] [Green Version]
- Wang, Y.; Zhao, F.; Wang, J.; Li, L.; Zhang, K.; Shi, Y.; Gao, Y.; Guo, X. Tungsten-Doped VO2/Starch Derivative Hybrid Nanothermochromic Hydrogel for Smart Window. Nanomaterials 2019, 9, 970. [Google Scholar] [CrossRef] [Green Version]
- Raju, P.S.; Mahalingam, M.; Rajendran, R.A. Design, Implementation and Power Analysis of Pervasive Adaptive Resourceful Smart Lighting and Alerting Devices in Developing Countries Supporting Incandescent and LED Light Bulbs. Sensors 2019, 19, 2032. [Google Scholar] [CrossRef] [Green Version]
Criteria | Rationale |
---|---|
Published peer-review articles related to smart building energy consumption or optimization | A research article ensures a certain level of quality through a peer-review process with vital information. |
Published peer-reviewed articles related to models, frameworks, review methods, or experiences on SBEMS | specific energy research related to solutions, metrics, and analysis on smart building |
Published industrial or organizational reports related to smart building energy or world energy consumption and analysis | Scientific literature or reports on trends of global or region (Asia, Europe, Middle East, and Africa)-based energy consumption analysis |
Published peer-reviewed articles related to the conceptual framework or market analysis on SBEMS | To be informed on new trends and published SBEMS |
One peer-reviewed article not related to the energy management system | A research article that is not related to research work was conducted but used as guidelines throughout the study. |
A published peer-reviewed article on energy management not related to smart building | The objective is to focus on a study that is linked or related to SBEMS |
A published peer-reviewed article related to energy management on wireless sensor networks to maintain sustainable smart building operation | To eliminate study focuses on strategy and solution to maintain the power of network hardware/software in a smart building. |
Literature related to SBEMS that did not meet the criteria of journals indexing | To eliminate studies those are not indexed in Scopus or Scientific Journal Rankings. |
Non-English manuscripts | Non-English literature on SBEMS |
The Topic Domain | Definition | References | Paper Count |
---|---|---|---|
Architectures | Refers to high-level shape focused on defining views, perspective, roles together with their arrangement and way they should interact to achieving high energy saving potential | S5, S14, S15, S23, S27, S31, S39, S54, S55, S74, S102, S104, S121, S101, S76, S78, S81, S128 | 18 |
Platforms/Models | Refers to hardware/software infrastructure providing APIs to support real time improvement and execution of package for energy saving potential | S3, S26, S32, S33, S34, S40, S43, S45, S52, S53, S56, S58, S63, S69, S73, S83, S117, S11, S19, S20, S21, S22, S25, S44, S48, S83, S61, S65, S72, S85, S87, S88, S91, S98, S124, S129, S130, S135, S136, S137, S138 | 41 |
Framework | Refers to software infrastructure providing reusable additives or perspective to poster improvement of a package for energy-saving potential | S12, S64, S82, S98, S105, S109, S42, S59, S97, S133, S134, S140 | 12 |
Algorithms | Refers to the logical approach consisting of steps to arrive at a feasible solution to achieving high energy-saving potential | S2, S6, S7, S8, S13, S16, S18, S28, S29, S30, S41, S47, S49, S50, S55, S70, S77, S79, S84, S86, S89, S90, S92, S93, S94, S95, S96, S99, S100, S106, S107, S108, S110, S112, S113, S114, S115, S116, S118, S119, S123, S103, S66, S67, S71, S84, S120, S125, S126, S127, S131, S132, S139 | 53 |
Survey | Refers to the study that provides analytical review on platforms or models, frameworks, and algorithms | S1, S17, S35, S36, S37, S38, S46, S51, S57, S60, S62, S68, S75, S80, S111, S131 | 16 |
Total | 140 |
Category | ID (Components) |
---|---|
Smart HVAC systems | S6, S19, S20, S33, S34, S45, S54, S61, S63, S67, S69, S70, S116, S117, S25, S28, S29, S41, S42, S43, S50, S51, S84, S85, S132, S19, S31, S77, S95, S96, S102, S104, S105, S113, S126, S8, S7, S24, S108, S123, S2, S52, S53, S56, S64, S87, S90, S94, S103, S115, S127 (Temperature, Humidity, CO2 sensors, Infrared sensor) |
Smart lighting | S3, S15, S30, S22, S140 (LED technology) |
Smart plug loads | S5, S82, S130, S16, S27, S14, S49, S65, S76 (Temperature, Humidity sensor CO2) |
Smart window systems | S97, S121, S86, S133, 134, S135, S136, S137, S138, S139 (luminescent, solar concentrator, Temperature, battery and CO2) |
Smart energy optimization | S9, S11, S12, S13, S71, S32, S72, S78, S80, S83, S88, S89, S106, S112 (Temperature, humidity and occupancy data) |
Human operation in smart building | S4, S38, S44, S10, S26, S58, S59, S74, S79, S91, S118, S125, S21, S40, S66, S73, S81, S107, S109, S119, S120, S122, S128 (Passive Infrared, Light, smart phone, Temperature, Humidity microcontroller and cloud server) |
Distributed energy resources | S10, S23, S30, S47, S93, S129, S18, S55, S2, S98, S99, S100, S101, S114, S124 (renewable source, home Appliance and grid). |
Quality Factor | Study |
---|---|
Performance efficiency | S2, S6, S7, S9, S11, S13, S14, S15, S17, S18, S19, S20, S21, S22, S23, S24, S25, S29, S30, S31, S33, S38, S40, S42, S44, S45, S47, S49, S50, S51, S54, S61, S63, S64, S69, S70, S71, S72, S73, S78, S79, S80, S81, S82, S83, S84, S85, S87, S88, S89, S90, S91, S92, S93, S94, S95, S96, S99, S100, S101, S103, S104, 106, S107, S108, S112, S115, S116, S117, S119, S120, S122, S123, S125, S126, S127, S128, S132, S97, S133, S134, S135, S136, S137, S138, S139 |
Security | S48, S65 |
Privacy | S110 |
Interoperability | S12, S32, S34, S41, S55, S56, S58, S59, S66, S67, S74, S76, S77, S102, S113 |
Scalability | S3, S4, S5, S9, S10, S26, S27, S28, S39, S43, S52, S53, S98, S105, S109, S114, S118, S124, S129, S130 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Aliero, M.S.; Asif, M.; Ghani, I.; Pasha, M.F.; Jeong, S.R. Systematic Review Analysis on Smart Building: Challenges and Opportunities. Sustainability 2022, 14, 3009. https://doi.org/10.3390/su14053009
Aliero MS, Asif M, Ghani I, Pasha MF, Jeong SR. Systematic Review Analysis on Smart Building: Challenges and Opportunities. Sustainability. 2022; 14(5):3009. https://doi.org/10.3390/su14053009
Chicago/Turabian StyleAliero, Muhammad Saidu, Muhammad Asif, Imran Ghani, Muhammad Fermi Pasha, and Seung Ryul Jeong. 2022. "Systematic Review Analysis on Smart Building: Challenges and Opportunities" Sustainability 14, no. 5: 3009. https://doi.org/10.3390/su14053009
APA StyleAliero, M. S., Asif, M., Ghani, I., Pasha, M. F., & Jeong, S. R. (2022). Systematic Review Analysis on Smart Building: Challenges and Opportunities. Sustainability, 14(5), 3009. https://doi.org/10.3390/su14053009