References
- Mercer D. Global Connected and IoT Device Forecast Update Report May. Strategy Analytics; 2019.
- IoT Analytics. Cellular IoT & LPWA tracker report(Q4 2020). IoT Analytics; 2020.
- IEA.org [Online]. Paris: international energy agency; [cited 2022 Oct 17]. Available from: https://www.iea.org/reports/electricity-information-2019.
- Hilty LM, Lohmann W, Huang EM. Sustainability and ICT – an overview of the field. Notizie Di Politeia. 2011;27(104):13–28.
- EPA.gov [Online]. United States Environmental Protection Agency. Washington D.C. [cited 2022 Oct 17]. Available from: https://www.epa.gov/ghgemissions/sources-greenhouse-gas-emissions.
- Griffor ER, Greer C, Wollman DA, et al. Framework for cyber-physical systems: overview - report. Gaithersburg, MD: National Institute of Standards and Technology; 2017. NIST publication; no. 1500-201.
- Shi J, Wan J, Yan H, et al. A survey of cyber-physical systems. In: Proceedings of the 2011 International Conference on Wireless Communications and Signal Processing (WCSP); 2011 Nov 25–29; Nanjing, China. Institute of Electrical and Electronics Engineers (IEEE); 2011. p. 1–6.
- Xiao Y, Nazarian S, Bogdan P. Self-optimizing and self-programming computing systems: a combined compiler, complex networks, and machine learning approach. IEEE Trans Very Large Scale Int (VLSI) Sys. 2019;27(6):1416–1427.
- Hu L, Xie N, Kuang Z, et al. Review of cyber-physical system architecture. In: Proceedings of the IEEE 15th International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing Workshops; 2012 Apr 11; Shenzhen, Guangdong, China. IEEE; 2012. p. 25–30.
- Xue Y, Rodriguez S, Bogdan P A spatio-temporal fractal model for a CPS approach to brain-machine-body interfaces. In: Proceedings of the 2016 Design, Automation & Test in Europe Conference & Exhibition; 2016 Mar 14-18; Singapore; Research Publishing Services; 2016. p. 642–647.
- Ghorbani M, Bogdan P A cyber-physical system approach to artificial pancreas design. In: Proceedings of the International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS); 2013 Sep 29—Oct 4; Montreal, Canada. IEEE; 2013. p. 1–10.
- Gupta G, Pequito S, Bogdan P Re-Thinking EEG-based non-invasive brain interfaces: modeling and analysis. In: 2018 ACM/IEEE 9th International Conference on Cyber- Physical Systems (ICCPS); 2018 Apr 11–13; Porto, Portugal. IEEE; 2018. p. 275–286.
- Pratt M What is ICT (Information and Communications Technology)? TechTarget.com [Online]; 2019; [cited 2022 Jul 22]. Available from: https://www.techtarget.com/searchcio/definition/ICT-information-and-communications-technology-or-technologies.
- ISO/IEC. Information technology – open systems interconnection – basic reference model: the basic model. 1994; Standard No. ISO/IEC.
- Sanchez M, Exposito E, Aguilar J. Industry 4.0: survey from a system integration perspective. Int J Comput Integr Manuf. 2020;33(10– 11):1–25.
- Letichevsky AA, Letychevskyi OO, Skobelev VG, et al. Cyber-Physical Systems. Cybern Syst Analy. 2017;53(6):821–834.
- Sadiku MNO, Wang Y, Cui S, et al. Cyber-physical systems: a literature review. Eur Sci J. 2017;13(36):52.
- Wan K, Hughes D, Lok Man K, et al. Investigation on composition mechanisms for cyber physical sytems. Int J Circuit Theory Appl. 2011;2(1):33–89.
- Xue Y, Li J, Nazarian S, et al. Fundamental challenges toward making the iot a reachable reality: a model-centric investigation. ACM Trans Des Autom Electron Syst. 2017;22(3):1–25.
- La HJ, Kim SD A service-based approach to designing cyber physical systems. In: Proceedings of the IEEE/ACIS 9th International Conference on Computer and Information Science; 2010 Aug 25–29; Yamagata, Japan. IEEE; 2010. p. 895–900.
- Sanislav T, Mois G, Folea S, et al. A cloud-based cyber-physical system for environmental monitoring. In: Proceedings of the 3rd Mediterranean Conference on Embedded Computing (MECO); 2014 Jun 15–19; Budva, Montenegro. IEEE; 2014. p. 6–9.
- Hahn A, Thomas RK, Lozano I, et al. A multi-layered and kill-chain based security analysis framework for cyber-physical systems. Int J Crit Infrastruct Prot. 2015 December;11:39–50.
- Sánchez L, Elicegui I, Cuesta J, et al. On the energy savings achieved through an internet of things enabled smart city trial. In: Proceedings of the 2014 IEEE International Conference on Communications (ICC); 2014 Jun 10–14; Sydney, New South Wales, Australia. IEEE; 2014. p. 3836–3841.
- Kocabas O, Soyata T, Aktas MK. Emerging security mechanisms for medical cyber physical systems. IEEE/ACM Trans Comput Biol Bioinform. 2016 may;13(3):401–416.
- Rad CR, Hancu O, Takacs IA, et al. Smart monitoring of potato crop: a cyber-physical system architecture model in the field of precision agriculture. J Agric Sci Procedia. 2015 December;6:73–79.
- Wurm J, Jin Y, Liu Y, et al. Introduction to cyber-physical system security: a cross-layer perspective. IEEE Trans Multi-Scale Comput Syst. 2017;3(3):215–227.
- Agency Danish energy. Denmark’s energy and climate outlook 2019. Copenhagen Denmark: Danish Ministry of Climate, Energy & Utilities; 2019.
- Kitchenham B, Brereton P, Budgen D, et al. Systematic literature reviews in software engineering – a systematic literature review. Inf Software Technol. 2009 01; 51(1): 7–15.
- Zhou B, Li W, Chan KW, et al. Smart home energy management systems: concept, configurations, and scheduling strategies. Renew Sust Energ Rev. 2016;61:30–40.
- Sundar Prasad S, Kumar C An energy efficient and reliable internet of things. In: Proceedings of the 2012 International Conference on Communication, Information & Computing Technology (ICCICT); 2012 Oct 19–20; Mumbai, India. IEEE; 2012. p. 1–4.
- Han J, Choi C-S, Lee I. More efficient home energy management system based on ZigBee communication and infrared remote controls. IEEE Trans Consum Electron. 2011;57(1):85–89.
- Choi K, Ahn Y, Park YC, et al. Architectural design of home energy saving system based on realtime energy-awareness. In: Proceedings of the 4th International Conference on Ubiquitous Information Technologies & Applications; 2009 Dec 20–22; Fukuoka, Japan. IEEE; 2009. p. 1–5.
- Agarwal Y, Savage S, Gupta R. SleepServer: a software-only approach for reducing the energy consumption of PCs within enterprise environments. In: Proceedings of the 2010 USENIX Annual Technical Conference; Boston, MA, USA. USENIX Association; 2010. p. 285–299.
- Parolini L, Sinopoli B, Krogh BH, et al. A cyber–physical systems approach to data center modeling and control for energy efficiency. Proceedings of the IEEE. 2012; 100(1):254–268.
- Parolini L, Tolia N, Sinopoli B, et al. A cyber-physical systems approach to energy management in data centers. In: Proceedings of the 1st ACM/IEEE International Conference on Cyber-Physical Systems - ICCPS ‘10; 2010 Apr 13–15; Stockholm, Sweden. New York: ACM Press; 2010. p. 168.
- Qi L, Chen Y, Yuan Y, et al. A QoS-aware virtual machine scheduling method for energy conservation in cloud-based cyber-physical systems. World Wide Web. 2020;23(2):1275–1297.
- Dou W, Xu X, Meng S, et al. An energy-aware virtual machine scheduling method for service QoS enhancement in clouds over big data. Concurr Comput Pract Exp. 2017;29(14):e3909.
- Wang H, Smys S. Secure and optimized cloud-based cyber-physical systems with memory-aware scheduling scheme. J Trends Comput Sci Smart Technol. 2020;2(3):141–147.
- Liang Y, Lu X, Li W, et al. Cyber physical system and big data enabled energy efficient machining optimisation. J Clean Prod. 2018;187:46–62.
- Shakshuki EM, Malik H, Sheltami T. WSN in cyber physical systems: enhanced energy management routing approach using software agents. Future Gener Comput Syst. 2014;31(1):93–104.
- Deng C, Guo R, Liu C, et al. Data cleansing for energy-saving: a case of cyber-physical machine tools health monitoring system. Int J P Res. 2018;56(1–2):1000–1015.
- Lin M, Pan Y, Yang LT, et al. Scheduling co-design for reliability and energy in cyber-physical systems. IEEE Trans Emerging Top Comput. 2013;1(2):353–365.
- Leitão J, Gil P, Ribeiro B, et al. Improving household’s efficiency via scheduling of water and energy appliances. In: Proceedings of the 13th APCA International Conference on Control and Soft Computing, CONTROLO; 2018 Jun 4–6; Ponta Delgada, Portugal. IEEE; 2018. p. 253–258.
- Aksanli B, Rosing TS. Human behavior aware energy management in residential cyber-physical systems. IEEE Trans Emerging Top Comput. 2016;8(1):45–57.
- Jithish J, Sankaran S, Achuthan K. A decision-centric approach for secure and energy- efficient cyber-physical systems. J Ambient Intell Humaniz Comput. 2021;12(1):417–441.
- Wang W, Hong T, Li N, et al. Linking energy-cyber-physical systems with occupancy prediction and interpretation through WiFi probe-based ensemble classification. Appl Energy. 2019 February;236:55–69.
- Qasim L, Jankovic M, Olaru S, et al. Model-based system reconfiguration: a descriptive study of current industrial challenges. In: Bonjour E, Krob D, Palladino L, et al., editors. Complex systems design & management. Proceedings of the Ninth international conference on complex systems design & management, CSD&M. Paris France: Springer International Publishing; 2018. 97–108.
- Koc H, Madupu PP Optimizing energy consumption in cyber physical systems using multiple operating modes. In: Proceedings of the 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC); 2018 Jan 8–10; Las Vegas, NV, USA. IEEE; 2018. p. 520–525.
- Horcas JM, Pinto M, Fuentes L. Context-aware energy-efficient applications for cyber- physical systems. Ad Hoc Netw. 2019;82(8):15–30.
- Baldi S, Korkas CD, Lv M, et al. Automating occupant-building interaction via smart zoning of thermostatic loads: a switched self-tuning approach. Applied Energy, Elsevier. 2018;231:1246–1258.
- US Department of Energy. EnergyPlus™ [software]; 2020 [cited 2020 Jun 02]. Available from: https://energyplus.net/.
- Liu Y, Liu A, Guo S, et al. Context-aware collect data with energy efficient in Cyber–physical cloud systems. Future Gener Comput Syst. 2020;105:932–947.
- Li XX, He FZ, Li WD. A cloud-terminal-based cyber-physical system architecture for energy efficient machining process optimization. J Ambient Intell Humaniz Comput. 2019;10(3):1049–1064.
- Lin X, Bogdan P, Chang N, et al. Machine learning-based energy management in a hybrid electric vehicle to minimize total operating cost. In: Proceedings of the 2015 IEEE/ACM International Conference on Computer-Aided Design (ICCAD); 2015 Nov 2–6; Austin TX, USA. IEEE; 2015. p. 627–634.
- Liu T, Tian B, Ai Y, et al. Parallel reinforcement learning-based energy efficiency improvement for a cyber-physical system. IEEE/CAA J AUTOMATICA SINICA. 2020;7(2):617–626.
- Ahmad RW, Gani A, Hamid SHA, et al. A survey on virtual machine migration and server consolidation frameworks for cloud data centers. J Network Comput Appl. 2015;52:11–25.
- Kleissl J, Agarwal Y Cyber-physical energy systems: focus on smart buildings. In: Proceedings of the 47th Design Automation Conference on - DAC ‘10; 2010 Jun 13 – 18; Anaheim California, USA. New York: ACM; 2010. p. 749–754.
- Simonin M, Feller E, Orgerie AC, et al. Snooze: an autonomic and energy-efficient management system for private clouds. In: Proceedings of the european conference on energy efficiency in large scale distributed systems. Vol. 8046. Vienna, Austria: Springer; 2013. p. 114–117.
- Xu X, Zhang X, Khan M, et al. A balanced virtual machine scheduling method for energy-performance trade-offs in cyber-physical cloud systems. Future Gener Comput Syst. 2020;105:789–799.
- Zeng D, Gu L, Yao H. Towards energy efficient service composition in green energy powered cyber–physical fog systems. Future Gener Comput Syst. 2020;105:757–765.
- Sanford F, Dori D, Mordecai Y. Why model?. Guide to the Systems Engineering Body of Knowledge (SEBoK). BKCASE. v1.4. Hoboken NJ: The Trustees of the Stevens Institute of Technology; 2022 86–88.
- Aughenbaugh JM, Paredis CJJ The role and limitations of modeling and simulation in systems design. In: Proceedings of the International Mechanical Engineering Congress and Exposition; —19; 2004 Nov 13 Anaheim, California, USA. ASMEDC; 2004. p. 13–22.
- Manotas I, Bird C, Zhang R, et al. An empirical study of practitioners’ perspectives on green software engineering. In: Proceedings of the 38th International Conference on Software Engineering - ICSE ‘16; Vol. May; 2016 May 14–22; Austin, Texas, USA. ACM Press; 2016. p. 237–248.
- Isasa JAE, Larsen PG, Hansen FO. Energy-aware model-driven development of a wearable healthcare device. In: Huhn M, Williams L, editors. Software engineering in health care. sehc 2014, fhies 2014. lecture notes in computer science. Vol. 9062. Washington: Springer; 2017. p. 44–63.
- Georg H, Muller SC, Rehtanz C, et al. Analyzing cyber-physical energy systems: the INSPIRE Cosimulation of Power and ICT systems using HLA. IEEE Trans Ind Inform. 2014;10(4):2364–2373.
- Faruque MAA, Ahourai F A model-based design of cyber-physical energy systems. In: Proceedings of the 19th Asia and South Pacific Design Automation Conference (ASP- DAC); 2014 Jan 20–23; SunTec, Singapore. IEEE; 2014. p. 97–104.
- Bordin C, Hakansson A, Mishra S. Smart energy and power systems modelling: an iot and cyber-physical systems perspective, in the context of energy informatics. Procedia Comput Sci. 2020;176:2254–2263.
- Akkad MZ, Haidar S, Bányai T. Design of cyber-physical waste management systems focusing on energy efficiency and sustainability. Designs. 2022;6(2):39.
- Agostinelli S, Cumo F, Guidi G, et al. Cyber-physical systems improving building energy management: digital twin and artificial intelligence. Energies. 2021;14(8):1–25.
- Ghadge A, Mogale DG, Bourlakis M, et al. Link between Industry 4.0 and green supply chain management: evidence from the automotive industry. Comput Ind Eng. 2022 June;169:108303.
- Wan J, Yan H, Li D, et al. Cyber-physical systems for optimal energy management scheme of autonomous electric vehicle. The Computer Journal. 2013;56(8):947–956.
- Kurpick T, Pinkernell C, Look M, et al. Modeling cyber-physical systems: model-driven specification of energy efficient buildings. In: Proceedings of the Modelling of the Physical World Workshop on - MOTPW ‘12; 2012 Oct 1–5; Innsbruck, Austria. ACM Press; 2012. p. 1–6.
- Basile D, Di Giandomenico F, Gnesi S Statistical model checking of an energy-saving cyber-physical system in the railway domain. In: Proceedings of the Symposium on Applied Computing (SAC ‘17); 2017 Apr 3–7; Marrakech, Morocco; 2017. p. 1356–1363.
- Wu W, Li W, Law D, et al. Improving data center energy efficiency using a cyber- physical systems approach: integration of building information modeling and wireless sensor networks. Procedia Eng. 2015;118:1266–1273.
- Wang S, Zhang G, Shen B, et al. An integrated scheme for cyber-physical building energy management system. Procedia Eng. 2011;15:3616–3620.
- Bogdan P, Marculescu R Towards a science of cyber-physical systems design. In: Proceedings of the ACM/IEEE Second International Conference on Cyber-Physical Systems; 2011 Apr 12–14; Chicago, IL, USA. IEEE; 2011. p. 99–108.
- Bogdan P, Marculescu R. Cyberphysical systems: workload modeling and design optimization. IEEE Des Test Comput. 2011;28(4):78–87.
- Ma S, Zhang Y, Lv J, et al. Energy-cyber-physical system enabled management for energy-intensive manufacturing industries. J Clean Prod. 2019;226:892–903.
- Park KT, Kang YT, Yang SG, et al. Cyber physical energy system for saving energy of the dyeing process with industrial internet of things and manufacturing big data. Int J Precis Eng Manuf Green Technol. 2020;7(1):219–238.
- Zhang C, Hindle A, German DM. The Impact of User Choice on Energy Consumption. IEEE Software. 2014;31(3):69–75.
- Sowe SK, Simmon E, Zettsu K, et al. Cyber-physical-human systems: putting people in the loop. IT Prof. 2016;18(1):10–13.
- Daniel M, Rivière G, Couture N CairnFORM: a shape-changing ring chart notifying renewable energy availability in peripheral locations. In: Proceedings of the Thirteenth International Conference on Tangible, Embedded, and Embodied Interaction TEI 2019; 2019 Mar 17–20; Tempe, Arizona, USA. ACM; 2019. p. 275–286.
- Francisco A, Taylor JE. Understanding citizen perspectives on open urban energy data through the development and testing of a community energy feedback system. Appl Energy. 2019 September;256:113804.
- Konstantakopoulos IC, Barkan AR, He S, et al. A deep learning and gamification approach to improving human-building interaction and energy efficiency in smart infrastructure. Appl Energy. 2019 September;237(2018):810–821.
- Lu CH. IoT-enabled adaptive context-aware and playful cyber-physical system for everyday energy savings. IEEE Trans Human-Mach Syst. 2018;48(4):380–391.
- Orland B, Ram N, Lang D, et al. Saving energy in an office environment: a serious game intervention. Energy Build. 2014 May;74:43–52.
- Shareef H, Ahmed MS, Mohamed A, et al. Review on home energy management system considering demand responses, smart technologies, and intelligent controllers. IEEE Access. 2018;6:24498–24509.
- Tham CK, Luo T. Sensing-driven energy purchasing in smart grid cyber-physical system. IEEE Trans Syst Man Cybern Syst. 2013;43(4):773–784.
- Zhao P, Simoes MG, Suryanarayanan S A conceptual scheme for cyber-physical systems based energy management in building structures. In: Proceedings of the 9th IEEE/IAS International Conference on Industry Applications - INDUSCON; 2010 Nov 8–10; Sao Paulo, Brazil. IEEE; 2010. p. 1–6.
- Wei W, Aziz MK, Huang H, et al. A real-time cyber-physical energy management system for smart houses. In: Proceedings of the 2011 IEEE PES Innovative Smart Grid Technologies; 2011 Nov 13–16; Perth, WA, Australia. IEEE; 2011. p. 1–8.
- Orumwense EF, Abo-al-ez K. On increasing the energy efficiency of wireless rechargeable sensor networks for cyber-physical systems. Energies. 2022;15(3):1204.
- Saber AY, Venayagamoorthy GK. Efficient utilization of renewable energy sources by gridable vehicles in cyber-physical energy systems. IEEE Syst J. 2010;4(3):285–294.
- Zhou Z, Wang B, Dong M, et al. Secure and efficient vehicle-to-grid energy trading in cyber physical systems: integration of blockchain and edge computing. IEEE Trans Syst Man Cybern Syst. 2020;50(1):43–57.
- Singh J, Naik K, Mahinthan V. Impact of developer choices on energy consumption of software on servers. Procedia Comput Sci. 2015;62:385–394. Scse.
- Acar H, Alptekin GI, Gelas JP, et al. The impact of source code in software on power consumption. Int J Electron Bus Manag. 2016;14:42–52.
- Oliveira W, Oliveira R, Castor F A study on the energy consumption of android app development approaches. In: Proceedings of the IEEE/ACM 14th International Conference on Mining Software Repositories (MSR); 2017 May 20–28; Buenos Aires, Argentina. IEEE/ACM; 2017. p. 42–52.
- Noureddine A, Bourdon A, Rouvoy R, et al. A preliminary study of the impact of software engineering on Green IT. Proceedings of the 1st International Workshop on Green and Sustainable Software, GREENS 2012; Zurich, Switzerland; 2012 Jun 3;21–27.
- Chen H, Wang S, Shi W Where does the power go in a computer system: experimental analysis and implications. In: Proceedings of the 2011 International Green Computing Conference and Workshops; 2011 Jul 25–28; Orlando, Florida, USA. IEEE; 2011. p. 1–6.
- Trobec R, Depolli M, Skala K, et al. Energy efficiency in large-scale distributed computing systems. In: Proceedings of the 36th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO 2013); 2013 May 20–24; Opatija, Croatia. IEEE; 2013. p. 253–257.
- Nassar M, Jarrett J, Saleh I, et al. Generating real-time profiles of runtime energy consumption for Java applications. In: Proceedings of the 26th International Conference on Software Engineering and Knowledge Engineering, SEKE; 2014 Jul 1–3; Vancouver, Canada. KSI Research Inc.; 2014. p. 592–597.
- Froehlich A, Bernstein C Wireless Ad hoc NETwork (WANET). TechTarget.com [Online]; 2021 [cited 2022 Oct 18]. Available from: https://www.techtarget.com/searchmobilecomputing/definition/ad-hoc-network.
- Awati R Delay-Tolerant Network (DTN). TechTarget.com [Online]. 2021 [cited 2022 Oct 18]. Available from: https://www.techtarget.com/searchnetworking/definition/delay-tolerant-network.
- Saborido R, Arnaoudova V, Beltrame G, et al. On the impact of sampling frequency on software energy measurements. PeerJ Pre Prints. 2015;3:e1219v2.
- Labiod H. Wireless Ad Hoc and sensor networks. Boston MA: Springer; 2010.Springer Series on Signals and Communication Technology
- Kephart J, Chess D. The vision of autonomic computing. J Comput. 2003;36(1):41–50.