5,077 research outputs found

    Context Aware Computing for The Internet of Things: A Survey

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    As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201

    A caregiver support platform within the scope of an ambient assisted living ecosystem

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    The Ambient Assisted Living (AAL) area is in constant evolution, providing new technologies to users and enhancing the level of security and comfort that is ensured by house platforms. The Ambient Assisted Living for All (AAL4ALL) project aims to develop a new AAL concept, supported on a unified ecosystem and certification process that enables a heterogeneous environment. The concepts of Intelligent Environments, Ambient Intelligence, and the foundations of the Ambient Assisted Living are all presented in the framework of this project. In this work, we consider a specific platform developed in the scope of AAL4ALL, called UserAccess. The architecture of the platform and its role within the overall AAL4ALL concept, the implementation of the platform, and the available interfaces are presented. In addition, its feasibility is validated through a series of tests.Project โ€œAAL4ALLโ€, co-financed by the European Community Fund FEDER, through COMPETEโ€”Programa Operacional Factores de Competitividade (POFC). Foundation for Science and Technology (FCT), Lisbon, Portugal, through Project PEst-C/CTM/LA0025/2013. Project CAMCoFโ€”Context-Aware Multimodal Communication Framework funded by ERDFโ€”European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCTโ€”Fundaรงรฃo para a Ciรชncia e a Tecnologia (Portuguese Foundation for Science and Technology) within project FCOMP-01-0124-FEDER-028980. This work is part-funded by National Funds through the FCT - Fundaรงรฃo para a Ciรชncia e a Tecnologia (Portuguese Foundation for Science and Technology) within project PEst-OE/EEI/UI0752/201

    Business strategies in sustainable energy

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    Cross-Layer Energy Optimization for IoT Environments: Technical Advances and Opportunities

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    [EN] Energy efficiency is a significant characteristic of battery-run devices such as sensors, RFID and mobile phones. In the present scenario, this is the most prominent requirement that must be served while introducing a communication protocol for an IoT environment. IoT network success and performance enhancement depend heavily on optimization of energy consumption that enhance the lifetime of IoT nodes and the network. In this context, this paper presents a comprehensive review on energy efficiency techniques used in IoT environments. The techniques proposed by researchers have been categorized based on five different layers of the energy architecture of IoT. These five layers are named as sensing, local processing and storage, network/communication, cloud processing and storage, and application. Specifically, the significance of energy efficiency in IoT environments is highlighted. A taxonomy is presented for the classification of related literature on energy efficient techniques in IoT environments. Following the taxonomy, a critical review of literature is performed focusing on major functional models, strengths and weaknesses. Open research challenges related to energy efficiency in IoT are identified as future research directions in the area. The survey should benefit IoT industry practitioners and researchers, in terms of augmenting the understanding of energy efficiency and its IoT-related trends and issues.Kumar, K.; Kumar, S.; Kaiwartya, O.; Cao, Y.; Lloret, J.; Aslam, N. (2017). Cross-Layer Energy Optimization for IoT Environments: Technical Advances and Opportunities. Energies. 10(12):1-40. https://doi.org/10.3390/en10122073S1401012Zanella, A., Bui, N., Castellani, A., Vangelista, L., & Zorzi, M. (2014). Internet of Things for Smart Cities. IEEE Internet of Things Journal, 1(1), 22-32. doi:10.1109/jiot.2014.2306328Kamalinejad, P., Mahapatra, C., Sheng, Z., Mirabbasi, S., M. Leung, V. C., & Guan, Y. L. (2015). Wireless energy harvesting for the Internet of Things. IEEE Communications Magazine, 53(6), 102-108. doi:10.1109/mcom.2015.7120024Kaiwartya, O., Abdullah, A. H., Cao, Y., Altameem, A., Prasad, M., Lin, C.-T., & Liu, X. (2016). Internet of Vehicles: Motivation, Layered Architecture, Network Model, Challenges, and Future Aspects. IEEE Access, 4, 5356-5373. doi:10.1109/access.2016.2603219Grieco, L. A., Rizzo, A., Colucci, S., Sicari, S., Piro, G., Di Paola, D., & Boggia, G. (2014). IoT-aided robotics applications: Technological implications, target domains and open issues. Computer Communications, 54, 32-47. doi:10.1016/j.comcom.2014.07.013Aijaz, A., & Aghvami, A. H. (2015). Cognitive Machine-to-Machine Communications for Internet-of-Things: A Protocol Stack Perspective. IEEE Internet of Things Journal, 2(2), 103-112. doi:10.1109/jiot.2015.2390775Lin, Y.-B., Lin, Y.-W., Chih, C.-Y., Li, T.-Y., Tai, C.-C., Wang, Y.-C., โ€ฆ Hsu, S.-C. (2015). EasyConnect: A Management System for IoT Devices and Its Applications for Interactive Design and Art. IEEE Internet of Things Journal, 2(6), 551-561. doi:10.1109/jiot.2015.2423286Bello, O., & Zeadally, S. (2016). Intelligent Device-to-Device Communication in the Internet of Things. IEEE Systems Journal, 10(3), 1172-1182. doi:10.1109/jsyst.2014.2298837Atzori, L., Iera, A., & Morabito, G. (2010). The Internet of Things: A survey. Computer Networks, 54(15), 2787-2805. doi:10.1016/j.comnet.2010.05.010Kaur, N., & Sood, S. K. (2017). An Energy-Efficient Architecture for the Internet of Things (IoT). IEEE Systems Journal, 11(2), 796-805. doi:10.1109/jsyst.2015.2469676Erol-Kantarci, M., & Mouftah, H. T. (2015). Energy-Efficient Information and Communication Infrastructures in the Smart Grid: A Survey on Interactions and Open Issues. IEEE Communications Surveys & Tutorials, 17(1), 179-197. doi:10.1109/comst.2014.2341600Machine-to-Machine Communications (M2M). M2M Service Requirementshttp://www.etsi.org/deliver/etsi_ts/102600_102699/102689/01.01.01_60/ts_102689v010101p.pdfKhan, M., Silva, B. N., & Han, K. (2016). Internet of Things Based Energy Aware Smart Home Control System. IEEE Access, 4, 7556-7566. doi:10.1109/access.2016.2621752Huang, S.-C., Chen, B.-H., Chou, S.-K., Hwang, J.-N., & Lee, K.-H. (2016). Smart Car [Application Notes]. IEEE Computational Intelligence Magazine, 11(4), 46-58. doi:10.1109/mci.2016.2601758Kant, K., & Pal, A. (2017). Internet of Perishable Logistics. IEEE Internet Computing, 21(1), 22-31. doi:10.1109/mic.2017.19Roopaei, M., Rad, P., & Choo, K.-K. R. (2017). Cloud of Things in Smart Agriculture: Intelligent Irrigation Monitoring by Thermal Imaging. IEEE Cloud Computing, 4(1), 10-15. doi:10.1109/mcc.2017.5Trรถster, G. (2011). Smart Clothesโ€”The Unfulfilled Pledge? IEEE Pervasive Computing, 10(2), 87-89. doi:10.1109/mprv.2011.32Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015). Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications. IEEE Communications Surveys & Tutorials, 17(4), 2347-2376. doi:10.1109/comst.2015.2444095Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H., & Zhao, W. (2017). A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications. IEEE Internet of Things Journal, 4(5), 1125-1142. doi:10.1109/jiot.2017.2683200Perera, C., Liu, C. H., Jayawardena, S., & Min Chen. (2014). A Survey on Internet of Things From Industrial Market Perspective. IEEE Access, 2, 1660-1679. doi:10.1109/access.2015.2389854Kamilaris, A., & Pitsillides, A. (2016). Mobile Phone Computing and the Internet of Things: A Survey. IEEE Internet of Things Journal, 3(6), 885-898. doi:10.1109/jiot.2016.2600569Arcadius Tokognon, C., Gao, B., Tian, G. Y., & Yan, Y. (2017). Structural Health Monitoring Framework Based on Internet of Things: A Survey. IEEE Internet of Things Journal, 4(3), 619-635. doi:10.1109/jiot.2017.2664072Razzaque, M. A., Milojevic-Jevric, M., Palade, A., & Clarke, S. (2016). Middleware for Internet of Things: A Survey. IEEE Internet of Things Journal, 3(1), 70-95. doi:10.1109/jiot.2015.2498900Luong, N. C., Hoang, D. T., Wang, P., Niyato, D., Kim, D. I., & Han, Z. (2016). Data Collection and Wireless Communication in Internet of Things (IoT) Using Economic Analysis and Pricing Models: A Survey. IEEE Communications Surveys & Tutorials, 18(4), 2546-2590. doi:10.1109/comst.2016.2582841Perera, C., Zaslavsky, A., Christen, P., & Georgakopoulos, D. (2014). Context Aware Computing for The Internet of Things: A Survey. IEEE Communications Surveys & Tutorials, 16(1), 414-454. doi:10.1109/surv.2013.042313.00197Khan, A. A., Rehmani, M. H., & Rachedi, A. (2017). Cognitive-Radio-Based Internet of Things: Applications, Architectures, Spectrum Related Functionalities, and Future Research Directions. IEEE Wireless Communications, 24(3), 17-25. doi:10.1109/mwc.2017.1600404Ahmed, E., Yaqoob, I., Gani, A., Imran, M., & Guizani, M. (2016). Internet-of-things-based smart environments: state of the art, taxonomy, and open research challenges. IEEE Wireless Communications, 23(5), 10-16. doi:10.1109/mwc.2016.7721736Cao, Y., Jiang, T., & Han, Z. (2016). A Survey of Emerging M2M Systems: Context, Task, and Objective. IEEE Internet of Things Journal, 3(6), 1246-1258. doi:10.1109/jiot.2016.2582540Rajandekar, A., & Sikdar, B. (2015). A Survey of MAC Layer Issues and Protocols for Machine-to-Machine Communications. IEEE Internet of Things Journal, 2(2), 175-186. doi:10.1109/jiot.2015.2394438Botta, A., de Donato, W., Persico, V., & Pescapรฉ, A. (2016). Integration of Cloud computing and Internet of Things: A survey. Future Generation Computer Systems, 56, 684-700. doi:10.1016/j.future.2015.09.021Risteska Stojkoska, B. L., & Trivodaliev, K. V. (2017). A review of Internet of Things for smart home: Challenges and solutions. Journal of Cleaner Production, 140, 1454-1464. doi:10.1016/j.jclepro.2016.10.006Liu, C. H., Fan, J., Branch, J. W., & Leung, K. K. (2014). Toward QoI and Energy-Efficiency in Internet-of-Things Sensory Environments. IEEE Transactions on Emerging Topics in Computing, 2(4), 473-487. doi:10.1109/tetc.2014.2364915Du, R., Gkatzikis, L., Fischione, C., & Xiao, M. (2015). Energy Efficient Sensor Activation for Water Distribution Networks Based on Compressive Sensing. IEEE Journal on Selected Areas in Communications, 33(12), 2997-3010. doi:10.1109/jsac.2015.2481199Chen, Y., Chiotellis, N., Chuo, L.-X., Pfeiffer, C., Shi, Y., Dreslinski, R. G., โ€ฆ Kim, H. S. (2016). Energy-Autonomous Wireless Communication for Millimeter-Scale Internet-of-Things Sensor Nodes. IEEE Journal on Selected Areas in Communications, 34(12), 3962-3977. doi:10.1109/jsac.2016.2612041Akgรผl, ร–. U., & Canberk, B. (2016). Self-Organized Things (SoT): An energy efficient next generation network management. Computer Communications, 74, 52-62. doi:10.1016/j.comcom.2014.07.004Ahn, J. H., & Lee, T.-J. (2018). ALLYS: All You can Send for Energy Harvesting Networks. IEEE Transactions on Mobile Computing, 17(4), 775-788. doi:10.1109/tmc.2017.2740929Mondal, S., & Paily, R. (2017). Efficient Solar Power Management System for Self-Powered IoT Node. IEEE Transactions on Circuits and Systems I: Regular Papers, 64(9), 2359-2369. doi:10.1109/tcsi.2017.2707566Qureshi, F. F., Iqbal, R., & Asghar, M. N. (2017). Energy efficient wireless communication technique based on Cognitive Radio for Internet of Things. Journal of Network and Computer Applications, 89, 14-25. doi:10.1016/j.jnca.2017.01.003Nguyen, T. D., Khan, J. Y., & Ngo, D. T. (2017). Energy harvested roadside IEEE 802.15.4 wireless sensor networks for IoT applications. Ad Hoc Networks, 56, 109-121. doi:10.1016/j.adhoc.2016.12.003Khanouche, M. E., Amirat, Y., Chibani, A., Kerkar, M., & Yachir, A. (2016). Energy-Centered and QoS-Aware Services Selection for Internet of Things. IEEE Transactions on Automation Science and Engineering, 13(3), 1256-1269. doi:10.1109/tase.2016.2539240Afzal, B., Alvi, S. A., Shah, G. A., & Mahmood, W. (2017). Energy efficient context aware traffic scheduling for IoT applications. Ad Hoc Networks, 62, 101-115. doi:10.1016/j.adhoc.2017.05.001Song, L., Chai, K. K., Chen, Y., Schormans, J., Loo, J., & Vinel, A. (2017). QoS-Aware Energy-Efficient Cooperative Scheme for Cluster-Based IoT Systems. IEEE Systems Journal, 11(3), 1447-1455. doi:10.1109/jsyst.2015.2465292Energy-Efficient Probabilistic Routing Algorithm for Internet of Thingshttp://www.ietf.org/rfc/rfc3561.txtMachado, K., Rosรกrio, D., Cerqueira, E., Loureiro, A., Neto, A., & de Souza, J. (2013). A Routing Protocol Based on Energy and Link Quality for Internet of Things Applications. Sensors, 13(2), 1942-1964. doi:10.3390/s130201942Chelloug, S. A. (2015). Energy-Efficient Content-Based Routing in Internet of Things. Journal of Computer and Communications, 03(12), 9-20. doi:10.4236/jcc.2015.312002Zhao, M., Ho, I. W.-H., & Chong, P. H. J. (2016). An Energy-Efficient Region-Based RPL Routing Protocol for Low-Power and Lossy Networks. IEEE Internet of Things Journal, 3(6), 1319-1333. doi:10.1109/jiot.2016.2593438Qiu, T., Lv, Y., Xia, F., Chen, N., Wan, J., & Tolba, A. (2016). ERGID: An efficient routing protocol for emergency response Internet of Things. Journal of Network and Computer Applications, 72, 104-112. doi:10.1016/j.jnca.2016.06.009Liu, Y., Liu, A., Hu, Y., Li, Z., Choi, Y.-J., Sekiya, H., & Li, J. (2016). FFSC: An Energy Efficiency Communications Ap-proach for Delay Minimizing in Internet of Things. IEEE Access, 1-1. doi:10.1109/access.2016.2588278Qiu, S., Haselmayr, W., Li, B., Zhao, C., & Guo, W. (2017). Bacterial Relay for Energy-Efficient Molecular Communications. IEEE Transactions on NanoBioscience, 16(7), 555-562. doi:10.1109/tnb.2017.2741669Biason, A., Pielli, C., Rossi, M., Zanella, A., Zordan, D., Kelly, M., & Zorzi, M. (2017). EC-CENTRIC: An Energy- and Context-Centric Perspective on IoT Systems and Protocol Design. IEEE Access, 5, 6894-6908. doi:10.1109/access.2017.2692522Huang, Z., Lin, K.-J., Yu, S.-Y., & Hsu, J. Y. (2014). Co-locating services in IoT systems to minimize the communication energy cost. Journal of Innovation in Digital Ecosystems, 1(1-2), 47-57. doi:10.1016/j.jides.2015.02.005Kwak, J., Kim, Y., Lee, J., & Chong, S. (2015). DREAM: Dynamic Resource and Task Allocation for Energy Minimization in Mobile Cloud Systems. IEEE Journal on Selected Areas in Communications, 33(12), 2510-2523. doi:10.1109/jsac.2015.2478718Abu Sharkh, M., & Shami, A. (2017). An evergreen cloud: Optimizing energy efficiency in heterogeneous cloud computing architectures. Vehicular Communications, 9, 199-210. doi:10.1016/j.vehcom.2017.02.004Bui, D.-M., Yoon, Y., Huh, E.-N., Jun, S., & Lee, S. (2017). Energy efficiency for cloud computing system based on predictive optimization. Journal of Parallel and Distributed Computing, 102, 103-114. doi:10.1016/j.jpdc.2016.11.011Liu, A., Zhang, Q., Li, Z., Choi, Y., Li, J., & Komuro, N. (2017). A green and reliable communication modeling for industrial internet of things. Computers & Electrical Engineering, 58, 364-381. doi:10.1016/j.compeleceng.2016.09.005Kim, J. (2015). Energy-Efficient Dynamic Packet Downloading for Medical IoT Platforms. IEEE Transactions on Industrial Informatics, 11(6), 1653-1659. doi:10.1109/tii.2015.2434773Chiu, T.-C., Shih, Y.-Y., Pang, A.-C., & Pai, C.-W. (2017). Optimized Day-Ahead Pricing With Renewable Energy Demand-Side Management for Smart Grids. IEEE Internet of Things Journal, 4(2), 374-383. doi:10.1109/jiot.2016.2556006Gandotra, P., Jha, R. K., & Jain, S. (2017). Green Communication in Next Generation Cellular Networks: A Survey. IEEE Access, 5, 11727-11758. doi:10.1109/access.2017.2711784Li, J., Peng, M., Yu, Y., & Ding, Z. (2016). Energy-Efficient Joint Congestion Control and Resource Optimization in Heterogeneous Cloud Radio Access Networks. IEEE Transactions on Vehicular Technology, 65(12), 9873-9887. doi:10.1109/tvt.2016.2531184Kaiwartya, O., Abdullah, A. H., Cao, Y., Lloret, J., Kumar, S., Shah, R. R., โ€ฆ Prakash, S. (2018). Virtualization in Wireless Sensor Networks: Fault Tolerant Embedding for Internet of Things. IEEE Internet of Things Journal, 5(2), 571-580. doi:10.1109/jiot.2017.2717704Garcia, M., Sendra, S., Lloret, J., & Canovas, A. (2011). Saving energy and improving communications using cooperative group-based Wireless Sensor Networks. Telecommunication Systems, 52(4), 2489-2502. doi:10.1007/s11235-011-9568-3kaiwartya, omprakash, Abdullah, A., Cao, Y., Rao, R. S., Kumar, S., Lobiyal, D. K., โ€ฆ Shah, R. R. (2016). T-MQM: Testbed based Multi-metric Quality Measurement of Sensor Deployment for Precision Agriculture-A Case Study. IEEE Sensors Journal, 1-1. doi:10.1109/jsen.2016.2614748Alrajeh, N. A., Khan, S., Lloret, J., & Loo, J. (2013). Secure Routing Protocol Using Cross-Layer Design and Energy Harvesting in Wireless Sensor Networks. International Journal of Distributed Sensor Networks, 9(1), 374796. doi:10.1155/2013/374796Mehmood, A., Khan, S., Shams, B., & Lloret, J. (2013). Energy-efficient multi-level and distance-aware clustering mechanism for WSNs. International Journal of Communication Systems, 28(5), 972-989. doi:10.1002/dac.272

    A Critical Investigation into Whole System Transitions to Low Carbon Futures and New Sources of Energy Flexibility in Great Britain's Electricity Sector

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    Great Britainโ€™s (GB) electricity sector is transitioning to low carbon futures in response to various pressures including legally binding carbon emission targets while ensuring security of supply. Such transitions are likely to focus on a mix of inflexible low carbon generation and new sources of energy flexibility, e.g. demand side flexibility, storage and/or interconnection. Existing studies recognise that transitions are uncertain with actors across the whole sector playing a role. However, they suggest tidy and clearly delineated futures and fail to fully capture the messiness emerging from actor interactions. Drawing on transitions research concepts including the Multi-level Perspective, whole system analysis, architectural innovation, power and discourses, this study critically investigates whole system transitions to low carbon futures and new sources of energy flexibility in GBโ€™s electricity sector. Data were collected via semi-structured interviews with 28 senior figures across the sector and analysed using thematic coding and discourse analysis. This study shows that five futures are articulated representing five discourse coalitions (1) โ€˜Market-basedโ€™, (2) โ€˜Network-focussedโ€™, (3) โ€˜Policy-drivenโ€™, (4) โ€˜Consumer-centricโ€™; and (5) โ€˜Prosumer-ledโ€™. These futures are messy because actors hold a plurality of views and cannot be simply marshalled into discourse coalitions. This underscores the complexity of electricity sector transitions and reveals important issues such as different ontologies and framings of energy flexibility. By investigating contemporary energy transition discourses, the study argues that a system level understanding of transitions and changes in future making practices currently dominated by quantitative modelling analyses and fixed transition frameworks are essential to effectively manage transitions. Further research is needed to investigate and find ways to better attend to the messiness and multiplicity of energy transitions from a whole systems perspective. This exploratory study is situated in a broader landscape of transitions research about energy futures and provides useful recommendations for both industry and academic communities

    German and Israeli Innovation: The Best of Two Worlds

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    This study reviews โ€“ through desk research and expert interviews with Mittelstand companies, startups and ecosystem experts โ€“ the current status of the Israeli startup ecosystem and the Mittelstand region of North Rhine- Westphalia (NRW), Germany. As a case study, it highlights potential opportunities for collaboration and analyzes different engagement modes that might serve to connect the two regions. The potential synergies between the two economies are based on a high degree of complementarity. A comparison of NRWโ€™s key verticals and Israelโ€™s primary areas of innovation indicates that there is significant overlap in verticals, such as artificial intelligence (AI), the internet of things (IoT), sensors and cybersecurity. Israeli startups can offer speed, agility and new ideas, while German Mittelstand companies can contribute expertise in production and scaling, access to markets, capital and support. The differences between Mittelstand companies and startups are less pronounced than those between startups and big corporations. However, three current barriers to fruitful collaboration have been identified: 1) a lack of access, 2) a lack of transparency regarding relevant players in the market, and 3) a lack of the internal resources needed to select the right partners, often due to time constraints or a lack of internal expertise on this issue. To ensure that positive business opportunities ensue, Mittelstand companies and startups alike have to be proactive in their search for cooperation partners and draw on a range of existing engagement modes (e.g., events, communities, accelerators). The interviews and the research conducted for this study made clear that no single mode of engagement can address all the needs and challenges associated with German-Israeli collaboration

    Citizens Adoption and Intellectual Capital Approach

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ๊ธฐ์ˆ ๊ฒฝ์˜ยท๊ฒฝ์ œยท์ •์ฑ…์ „๊ณต, 2019. 2. Hwang, Junseok .The emergence of knowledge intensive industries gave rise to the issue of intellectual capital management which is used as an instrument to identify and measure the hidden sources of value creation at the firm, regional and national level. Knowledge-intensive companies are rated much higher than their book value suggests, and thus need to identify the intangible valuables of the company for the improvement and sustainability of their learning and capitalization system. Intellectual capital components are the key resources that can be leveraged for smart city development which intends to use information and communication technologies in order to bring efficiency and sustainability to the urban functions. The role of intellectual capital components in smart city implementation needs to be studied due to the fact that attributes of intellectual capital components would have a distinguished impact on value creation and the increase in productivity and performance. Despite the existence of a significant number of literatures on intellectual capital, the role of its components in the success of smart city implementation has not been examined. This research aims to investigate the role of intellectual capital components towards smart city success using an analysis of experts preferences for human capital and structural capital. The research also includes the demand-side perspective towards smart city information services characteristics that influences the adoption decision. The analysis is performed using two methodologies: Analytics Hierarchy Process (AHP) for human capital and structural capital and discrete choice analysis using a mixed logit model for the adoption of smart city information services. The first study employs a multidimensional approach to the development of a model for human capital using individual-level characteristics and the collective behavior. The identification of the sources of value in human capital is critical to the success of smart city implementations as these capabilities can be leveraged and upgraded to improve productivity and performance. Human capital components have been categorized into personal qualifications, personal traits, culture and social factors. The findings reveal that the most important category is personal qualifications followed by culture. Moreover, the overall priority weights estimation shows that the existence of domain-specific tacit knowledge gained through experience, the multi-disciplinary scope of education and the density of R&D personnel are the top-three ranked attributes of human capital towards smart city success. The study on the structural capital examined 24 smart city cases across the globe to identify the structural capital elements valuable in the smart city development process. The different orchestration of these structural capital elements can influence the outcome of the development process and its impact on the efficiency of the urban systems. The identified structural capital elements have been categorized into process, relational and infrastructural dimensions. The findings reveal that the infrastructural dimension comprising communication and information system is most critical towards the smart city success, followed by the process category with the most dominant component of policy. The overall ranking of these elements suggest that the decision makers need to focus on city-level policies and the development and enforcement of procedures for innovation generation. Finally, the citizens preferences analysis was performed for the case of Islamabad city in Pakistan which is at the early stage of smart city development and can benefit from a better understanding of the demand-side perspective. The characteristics of smart city information services considered in the study comprise language, access mode, service ownership, interoperability and security. Willingness-to-pay was used to observe the price sensitivity of the end users choices. The findings reveal that citizens in Islamabad have a higher utility towards the use of the English language, a mobile access mode and a high level of security. In conclusion, the study provides guidelines for policy makers who are concerned with the early stage of smart city development. The demand-side study of Islamabad city provides valuable insights in to existing trends that affect the rapid adoption of smart city services.๊ตญ๋ฌธ์ดˆ๋ก ์ง€์‹์ง‘์•ฝ์  ์‚ฐ์—…์˜ ์ถœํ˜„์œผ๋กœ ๊ธฐ์—…, ์ง€์—ญ ๋ฐ ๊ตญ๊ฐ€ ์ฐจ์›์—์„œ ๊ฐ€์น˜ ์ฐฝ์ถœ์˜ ์ˆจ๊ฒจ์ง„ ์ถœ์ฒ˜๋ฅผ ํŒŒ์•…ํ•˜๊ณ  ์ธก์ •ํ•˜๋Š” ๋„๊ตฌ๋กœ ์‚ฌ์šฉ๋˜๋Š” ์ง€์  ์ž๋ณธ ๊ด€๋ฆฌ๊ฐ€ ์Ÿ์ ์œผ๋กœ ๋– ์˜ฌ๋ž๋‹ค. ์ง€์‹์ง‘์•ฝ์  ๊ธฐ์—…์€ ์ˆœ์ž์‚ฐ๋ณด๋‹ค ํ›จ์”ฌ ๋†’์€ ํ‰๊ฐ€๋ฅผ ๋ฐ›๊ณ  ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ๊ทธ๋“ค์˜ ํ•™์Šต๊ณผ ์ž๋ณธํ™” ์‹œ์Šคํ…œ์˜ ๊ฐœ์„ ๊ณผ ์ง€์† ๊ฐ€๋Šฅ์„ฑ์„ ์œ„ํ•ด ํšŒ์‚ฌ์˜ ๋ฌดํ˜• ๊ฐ€์น˜๋ฅผ ํ™•์ธํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ์ง€์  ์ž๋ณธ์š”์†Œ๋Š” ์ •๋ณดํ†ต์‹  ๊ธฐ์ˆ ์„ ์ด์šฉํ•ด ๋„์‹œ ๊ธฐ๋Šฅ์— ํšจ์œจ์„ฑ๊ณผ ์ง€์†์„ฑ์„ ๋†’์ด๋Š” ์Šค๋งˆํŠธ ์‹œํ‹ฐ ๊ฐœ๋ฐœ์— ํ™œ์šฉ๋  ์ˆ˜ ์žˆ๋Š” ํ•ต์‹ฌ ์ž์›์ด๋‹ค. ์ง€์  ์ž๋ณธ ์š”์†Œ์˜ ์†์„ฑ์€ ๊ฐ€์น˜ ์ฐฝ์ถœ๊ณผ ์ƒ์‚ฐ์„ฑ ๋ฐ ์„ฑ๋Šฅ ํ–ฅ์ƒ์— ๊ฐ€๋ณ€์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์น  ์ˆ˜ ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ์Šค๋งˆํŠธ ์‹œํ‹ฐ ๊ตฌํ˜„์—์„œ์˜ ์ง€์  ์ž๋ณธ ์š”์†Œ์˜ ์—ญํ• ์„ ์—ฐ๊ตฌํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ์ง€์  ์ž๋ณธ์— ๊ด€ํ•œ ์ค‘์š”ํ•œ ์—ฐ๊ตฌ ๋ฌธํ—Œ๋“ค์ด ์žˆ์ง€๋งŒ ์Šค๋งˆํŠธ ์‹œํ‹ฐ์˜ ์„ฑ๊ณต์ ์ธ ๊ตฌํ˜„์„ ์œ„ํ•œ๊ฐ ์š”์†Œ๋“ค์˜ ์—ญํ• ์€ ๊ฒ€ํ† ๋˜์ง€ ์•Š์•˜๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ์ธ์ ์ž๋ณธ๊ณผ ๊ตฌ์กฐ์ž๋ณธ์— ๋Œ€ํ•œ ์ „๋ฌธ๊ฐ€์˜ ์„ ํ˜ธ๋„ ๋ถ„์„์„ ์‚ฌ์šฉํ•˜์—ฌ ์Šค๋งˆํŠธ ์‹œํ‹ฐ์˜ ์„ฑ๊ณต์„ ์œ„ํ•œ ์ง€์  ์ž๋ณธ ์š”์†Œ์˜ ์—ญํ•  ์กฐ์‚ฌ๋ฅผ ๋ชฉ์ ์œผ๋กœ ํ•œ๋‹ค. ๋˜ํ•œ ์ˆ˜์šฉ ์˜์‚ฌ ๊ฒฐ์ •์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์Šค๋งˆํŠธ ์‹œํ‹ฐ ์ •๋ณด ์„œ๋น„์Šค ํŠน์„ฑ์— ๋Œ€ํ•œ ์ˆ˜์š” ์ธก๋ฉด์˜ ๊ด€์ ๋„ ์กฐ์‚ฌํ•œ๋‹ค. ๋ถ„์„์€ ์ธ์  ์ž๋ณธ ๋ฐ ๊ตฌ์กฐ์  ์ž๋ณธ์„ ์œ„ํ•œ ๋ถ„์„ ๊ณ„์ธต ํ”„๋กœ์„ธ์Šค(AHP)์™€ ์Šค๋งˆํŠธ ์‹œํ‹ฐ ์ •๋ณด ์„œ๋น„์Šค ์ฑ„ํƒ์„ ์œ„ํ•œ ํ˜ผํ•ฉ ๋กœ์ง“ ๋ชจ๋ธ์„ ์ด์šฉํ•œ ์ด์‚ฐ ์„ ํƒ ๋ถ„์„์ด๋ผ๋Š” ๋‘ ๊ฐ€์ง€ ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•œ๋‹ค. ์ฒซ ๋ฒˆ์งธ ์—ฐ๊ตฌ๋Š” ๋‹ค์ฐจ์›์  ์ ‘๊ทผ๋ฒ•์„ ์‚ฌ์šฉํ•ด ๊ฐœ์ธ ์ˆ˜์ค€์˜ ํŠน์„ฑ๊ณผ ์ง‘๋‹จ ํ–‰๋™์„ ์ด์šฉํ•œ ์ธ์  ์ž๋ณธ์— ๋Œ€ํ•œ ๋ชจ๋ธ์„ ๊ฐœ๋ฐœํ•œ๋‹ค. ์ธ์  ์ž๋ณธ์˜ ๊ฐ€์น˜์˜ ๊ทผ์›์„ ์‹๋ณ„ํ•˜๋Š” ๊ฒƒ์€ ์Šค๋งˆํŠธ ์‹œํ‹ฐ ๊ตฌํ˜„ ์„ฑ๊ณต์— ๋งค์šฐ ์ค‘์š”ํ•˜๋‹ค. ์ด๋Ÿฌํ•œ ๋Šฅ๋ ฅ๋“ค์ด ํ™œ์šฉ๋˜๊ณ  ๊ฐœ์„ ๋˜์–ด ์ƒ์‚ฐ์„ฑ๊ณผ ์„ฑ๋Šฅ์„ ํ–ฅ์ƒ์‹œํ‚ฌ ์ˆ˜ ์žˆ๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ์ธ์  ์ž๋ณธ ์š”์†Œ๋Š” ๊ฐœ์ธ์˜ ์ž๊ฒฉ, ์„ฑ๊ฒฉ, ๋ฌธํ™”, ์‚ฌํšŒ์  ์š”์ธ์œผ๋กœ ๋ถ„๋ฅ˜๋˜์—ˆ๋‹ค. ๊ทธ ๊ฒฐ๊ณผ, ์ฒซ๋ฒˆ์งธ๋กœ ์ค‘์š”ํ•œ ๊ฒƒ์€ ๊ฐœ์ธ์˜ ์ž๊ฒฉ์š”๊ฑด์ด๋ฉฐ ๋‘๋ฒˆ์งธ๋Š” ๋ฌธํ™”์ž„์„ ๋ฐํ˜€๋ƒˆ๋‹ค. ๋˜ํ•œ, ์ „์ฒด์ ์ธ ์šฐ์„ ์ˆœ์œ„ ๊ฐ€์ค‘์น˜ ์ถ”์ •์€ ๊ฒฝํ—˜์„ ํ†ตํ•ด ์–ป์€ ๋„๋ฉ”์ธ ๊ณ ์œ ์˜ ์•”๋ฌต์  ์ง€์‹์˜ ์กด์žฌ, ๋‹ค๋ถ„์•ผ์˜ ๊ต์œก ๋ฒ”์œ„ ๋ฐ R&D ์ธ๋ ฅ์˜ ๋ฐ€๋„๋Š” ์Šค๋งˆํŠธ ์‹œํ‹ฐ ์„ฑ๊ณต์„ ์œ„ํ•œ ์ธ์  ์ž๋ณธ์˜ ์ƒ์œ„ 3๋Œ€ ์†์„ฑ์ž„์„ ๋ณด์—ฌ์ค€๋‹ค. ๊ตฌ์กฐ์  ์ž๋ณธ์— ๊ด€ํ•œ ์—ฐ๊ตฌ๋Š” ์ „ ์„ธ๊ณ„ 24๊ฐœ ์Šค๋งˆํŠธ ์‹œํ‹ฐ ์‚ฌ๋ก€๋ฅผ ์กฐ์‚ฌํ•ด ์Šค๋งˆํŠธ ์‹œํ‹ฐ ๊ฐœ๋ฐœ ๊ณผ์ •์—์„œ ๊ฐ€์น˜ ์žˆ๋Š” ๊ตฌ์กฐ์  ์ž๋ณธ์˜ ์š”์†Œ๋ฅผ ํ™•์ธํ–ˆ๋‹ค. ์„œ๋กœ ๋‹ค๋ฅธ ๊ตฌ์กฐ์  ์ž๋ณธ ์š”์†Œ์˜ ์กฐ์ •์€ ๊ฐœ๋ฐœ ํ”„๋กœ์„ธ์Šค์˜ ๊ฒฐ๊ณผ์™€ ๋„์‹œ ์‹œ์Šคํ…œ์˜ ํšจ์œจ์„ฑ์— ์˜ํ–ฅ์„ ๋ฏธ์น  ์ˆ˜ ์žˆ๋‹ค. ํ™•์ธ๋œ ๊ตฌ์กฐ์  ์ž๋ณธ ์š”์†Œ๋Š” ํ”„๋กœ์„ธ์Šค, ๊ด€๊ณ„ ๋ฐ ๊ธฐ๋ฐ˜ ๊ตฌ์กฐ ์ฐจ์›์œผ๋กœ ๋ถ„๋ฅ˜๋˜์—ˆ๋‹ค. ์ด๋Š” ํ†ต์‹ ๊ณผ ์ •๋ณด ์‹œ์Šคํ…œ์„ ๊ตฌ์„ฑํ•˜๋Š” ๊ธฐ๋ฐ˜ ๊ตฌ์กฐ์˜ ์ฐจ์›์ด ์Šค๋งˆํŠธ ์‹œํ‹ฐ์˜ ์„ฑ๊ณต์— ๊ฐ€์žฅ ์ค‘์š”ํ•˜๋ฉฐ ๊ทธ ๋‹ค์Œ์œผ๋กœ ์ •์ฑ…์˜ ๊ฐ€์žฅ ์šฐ์„ธํ•œ ๊ตฌ์„ฑ ์š”์†Œ๋ฅผ ๊ฐ€์ง„ ํ”„๋กœ์„ธ์Šค ๋ฒ”์ฃผ๊ฐ€ ์ค‘์š”ํ•˜๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์—ฌ์ค€๋‹ค. ์ด๋“ค ์š”์†Œ์˜ ์ „์ฒด ์ˆœ์œ„๋Š” ์˜์‚ฌ๊ฒฐ์ •์ž๋“ค์ด ํ˜์‹  ์ƒ์„ฑ์„ ์œ„ํ•œ ๋„์‹œ ์ˆ˜์ค€์˜ ์ •์ฑ…๊ณผ ์ ˆ์ฐจ ๊ฐœ๋ฐœ๊ณผ ์ง‘ํ–‰์— ์ดˆ์ ์„ ๋งž์ถœ ํ•„์š”๊ฐ€ ์žˆ์Œ์„ ์‹œ์‚ฌํ•œ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ์Šค๋งˆํŠธ ์‹œํ‹ฐ ๊ฐœ๋ฐœ์˜ ์ดˆ๊ธฐ ๋‹จ๊ณ„์— ์žˆ์œผ๋ฉฐ ์ˆ˜์š” ์ธก๋ฉด ๊ด€์ ์—์„œ ์œ ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ํŒŒํ‚ค์Šคํƒ„์˜ ์ด์Šฌ๋ผ๋งˆ๋ฐ”๋“œ ๋„์‹œ์— ๋Œ€ํ•œ ์‹œ๋ฏผ์˜ ์„ ํ˜ธ ๋ถ„์„์ด ์ด๋ฃจ์–ด์กŒ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ ๊ณ ๋ คํ•œ ์Šค๋งˆํŠธ ์‹œํ‹ฐ ์ •๋ณด ์„œ๋น„์Šค์˜ ํŠน์„ฑ์€ ์–ธ์–ด, ์ ‘๊ทผ ๋ชจ๋“œ, ์„œ๋น„์Šค ์†Œ์œ ๊ถŒ, ์ƒํ˜ธ์šด์šฉ์„ฑ ๋ฐ ๋ณด์•ˆ์œผ๋กœ ๊ตฌ์„ฑ๋œ๋‹ค. ์ง€๋ถˆ ์˜์ง€๋Š” ์ตœ์ข… ์‚ฌ์šฉ์ž์˜ ์„ ํƒ์— ๋”ฐ๋ฅธ ๊ฐ€๊ฒฉ ๋ฏผ๊ฐ๋„๋ฅผ ๊ด€์ฐฐํ•˜๊ธฐ ์œ„ํ•ด ์‚ฌ์šฉ๋˜์—ˆ๋‹ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋Š” ์ด์Šฌ๋ผ๋งˆ๋ฐ”๋“œ ์‹œ๋ฏผ๋“ค์ด ๋†’์€ ์ˆ˜์ค€์˜ ๋ณด์•ˆ๊ณผ ํ•จ๊ป˜ ์˜์–ด ์‚ฌ์šฉ์— ๋” ๋†’์€ ํšจ์šฉ์„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์—ฌ์ค€๋‹ค. ๊ฒฐ๋ก ์ ์œผ๋กœ, ์ด ์—ฐ๊ตฌ๋Š” ํŠน๋ณ„ํžˆ ์Šค๋งˆํŠธ ์‹œํ‹ฐ ๊ฐœ๋ฐœ์˜ ์ดˆ๊ธฐ ๋‹จ๊ณ„์— ์žˆ๋Š” ์ •์ฑ… ์ž…์•ˆ์ž๋“ค์„ ์œ„ํ•œ ์ง€์นจ์„ ์ œ๊ณตํ•œ๋‹ค. ์ด์Šฌ๋ผ๋งˆ๋ฐ”๋“œ์‹œ์— ๋Œ€ํ•œ ์ˆ˜์š” ์ธก๋ฉด ์—ฐ๊ตฌ๋Š” ์Šค๋งˆํŠธ ์‹œํ‹ฐ ์„œ๋น„์Šค์˜ ์‹ ์†ํ•œ ์ฑ„ํƒ์„ ์ง€์›ํ•˜๋Š” ๊ธฐ์กด ์ถ”์„ธ์— ๋Œ€ํ•œ ๊ท€์ค‘ํ•œ ํ†ต์ฐฐ๋ ฅ์„ ์ œ๊ณตํ•œ๋‹ค. ์ฃผ์š” ๋‹จ์–ด: ์Šค๋งˆํŠธ ์‹œํ‹ฐ, ์ง€์  ์ž๋ณธ, ์ธ์  ์ž๋ณธ, ๊ตฌ์กฐ์  ์ž๋ณธ, ์ •๋ณด ์„œ๋น„์ŠคChapter 1 Introduction 1 1.1 Overview 1 1.2 Purpose of the Research 9 1.3 Contribution of the Research 12 1.4 Research Outline 15 Chapter 2 Literature Review 18 2.1 Smart Cities 18 2.1.1 Smart City Definitions 19 2.1.2 Smart City Components 22 2.1.3 Smart City Systems Architecture 28 2.2 Intellectual Capital 30 2.2.1 Existing Studies on Intellectual Capital 32 2.2.2 Intellectual Capital and Smart Cities 37 2.2.3 Intellectual Capital Components 39 Chapter 3 Study on the Role of Human Capital for Smart City Success 50 3.1 Model 52 3.1.1 Personal Qualifications 54 3.1.2 Personal Traits 57 3.1.3 Culture 58 3.1.4 Social Factors 59 3.2 Methodology 60 3.2.1 Survey for Analytic Hierarchy Process 63 3.3 Estimation of Results 66 Chapter 4 Study on Structural Capital Role for Smart City Success 74 4.1 Model 77 4.1.1 Process Elements 77 4.1.2 Relational Elements 81 4.1.3 Infrastructural Elements 82 4.2 Methodology 85 4.2.1 Survey for Analytic Hierarchy Process 85 4.3 Estimation of Results 87 Chapter 5 Adoption of Smart City Information Services 95 5.1 Citizens Preferences Analysis towards the Adoption of Smart City Information Services 95 5.2 Model 97 5.3 Methodology 101 5.3.1 Random Utility Model 101 5.3.2 Willingness to Pay 104 5.4 Survey Design and Data 105 5.4.1 Survey for Discrete Choice Analysis 105 5.5 Estimation of Results 109 Chapter 6 Discussion and Conclusion 115 6.1 Discussion and Implications 115 6.2 Conclusion 128 6.3 Limitations and Future Work 131 References 134 Appendix A: Description of Attributes for AHP Survey 152 Appendix B: Survey Questionnaire for AHP 155 Appendix C: Conjoint Survey for Citizens Preference Analysis 163 ๊ตญ๋ฌธ์ดˆ๋ก 166 Acknowledgments 169Docto
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