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Journal articles on the topic 'Context-aware architecture'

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Author: Grafiati

Published: 10 December 2022

Last updated: 29 January 2023

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1

Augusto, Juan C., Mario J. Quinde, Chimezie L. Oguego, and José Giménez Manuel. "Context-Aware Systems Architecture (CaSA)." Cybernetics and Systems 53, no. 4 (October 16, 2021): 319–45. http://dx.doi.org/10.1080/01969722.2021.1985226.

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2

Ganguly, Sukanta. "CADRA: context aware data retrieval architecture." International Journal of Advanced Intelligence Paradigms 3, no. 1 (2011): 82. http://dx.doi.org/10.1504/ijaip.2011.038117.

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3

Adel, Alti, Sébastien Laborie, and Philippe Roose. "Semantic Context-aware Adaptation Platform Architecture." Procedia Computer Science 32 (2014): 959–64. http://dx.doi.org/10.1016/j.procs.2014.05.518.

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4

Devarakonda, G. K. Mohan. "DEVICE ENABLED ARCHITECTURE FOR CONTEXT AWARE APPLICATIONS." International Journal of Advanced Research in Computer Science 8, no. 8 (August 30, 2017): 214–16. http://dx.doi.org/10.26483/ijarcs.v8i8.4600.

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5

Shakshuki, Elhadi M., Wael Alghamdi, and Tarek Sheltami. "Integrated Context-Aware Driver Assistance System Architecture." Mobile Information Systems 9, no. 3 (2013): 225–40. http://dx.doi.org/10.1155/2013/205426.

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Recently, significant improvements have been made in the area of vehicular communication systems. Furthermore, vehicle-to-vehicle communication is considered a key concept for keeping roads safe. An efficient implementation of these systems is necessary to ensure the safety of driving situations and to reduce the collision rates. This paper proposes a Context-Aware Driver Assistance System that links drivers with the physical environment surrounding them using multiple types of sensors and traffic systems as well as considering the senior driver's difficulties and the system processing time. This is achieved by developing a warning system that assists drivers to avoid collisions and improve their response times. The proposed system architecture consists of a set of components to process the user's request such as parking assistance, and to provide responses and advices when needed. These components include communication, knowledge exchange, knowledge update, and context-history. Also, it includes other processes such as context-history manipulation, hazard detection, and hazard detection control. The main goal of the proposed system is to reduce the number of car accidents and improve driver's decisions. The NXT Robotic environment is used to demonstrate the feasibility of the proposed system.

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Rehman, Kasim, Frank Stajano, and George Coulouris. "An Architecture for Interactive Context-Aware Applications." IEEE Pervasive Computing 6, no. 1 (January 2007): 73–80. http://dx.doi.org/10.1109/mprv.2007.5.

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7

Zaragoza, Mechelle Grace, and Haeng-Kon Kim. "Intelligent Agent Architecture for Context Aware Service." International Journal of Control and Automation 11, no. 10 (October 31, 2018): 45–54. http://dx.doi.org/10.14257/ijca.2018.11.10.05.

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Gochhayat, Sarada Prasad, Pallavi Kaliyar, Mauro Conti, Prayag Tiwari, V. B. S. Prasath, Deepak Gupta, and Ashish Khanna. "LISA: Lightweight context-aware IoT service architecture." Journal of Cleaner Production 212 (March 2019): 1345–56. http://dx.doi.org/10.1016/j.jclepro.2018.12.096.

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9

Han, Jialong, Aixin Sun, Haisong Zhang, Chenliang Li, and Shuming Shi. "CASE: Context-Aware Semantic Expansion." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 7871–78. http://dx.doi.org/10.1609/aaai.v34i05.6293.

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In this paper, we define and study a new task called Context-Aware Semantic Expansion (CASE). Given a seed term in a sentential context, we aim to suggest other terms that well fit the context as the seed. CASE has many interesting applications such as query suggestion, computer-assisted writing, and word sense disambiguation, to name a few. Previous explorations, if any, only involve some similar tasks, and all require human annotations for evaluation. In this study, we demonstrate that annotations for this task can be harvested at scale from existing corpora, in a fully automatic manner. On a dataset of 1.8 million sentences thus derived, we propose a network architecture that encodes the context and seed term separately before suggesting alternative terms. The context encoder in this architecture can be easily extended by incorporating seed-aware attention. Our experiments demonstrate that competitive results are achieved with appropriate choices of context encoder and attention scoring function.

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Anusha, D. J., R. Anandan, and P. Venkata Krishna. "Modified Context Aware Middleware Architecture for Precision Agriculture." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 7 (July 31, 2022): 112–20. http://dx.doi.org/10.17762/ijritcc.v10i7.5635.

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The opportunities for researchers are enhanced with the progression in the technology of communication and computing. The ease in life of many people like farmers, educators, administers, managers, etc., is increased with more inventions of the researchers using this new technology. The progressive technology for data management is providing more amount of information. However, is the user able to access the needed information when required? This question rises to the more questions like, how to identify whether the information is as per the requirement? Whether a user is authorized or not? The answer for all the questions is to make the support aware of the context. Therefore, the present technology needs to be modified to make the system aware about the context. The process of demonstrating the services based on the context using Wireless Sensor Networks (WSN) with the help of illustration on mango crop is emphasized in this paper. There are many serious problems like unsuitable fertilizer use, wrong selection of crops in wrong seasons, water waste, poor publicizing in the case of farming. These problems are addressed using the ubiquitous context aware middleware architecture for precision agriculture in mango crop.

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Tair, H. Al, M. J. Zemerly, M. Al-Qutayri, and M. Leida. "Architecture for Context-Aware Pro-Active Recommender System." International Journal of Multimedia and Image Processing 2, no. 3/4 (September 1, 2012): 125–33. http://dx.doi.org/10.20533/ijmip.2042.4647.2012.0016.

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12

Betances, Addison, Kenneth Mark Hopkinson, and Mark Silvius. "Context aware routing management architecture for airborne networks." IET Networks 5, no. 4 (July 2016): 85–92. http://dx.doi.org/10.1049/iet-net.2015.0064.

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13

Mamo, Beza, and Dejene Ejigu. "A Generic Layered Architecture for Context Aware Applications." Procedia Computer Science 34 (2014): 619–24. http://dx.doi.org/10.1016/j.procs.2014.07.085.

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14

Roy, Patrice, Bessam Abdulrazak, and Yacine Belala. "A Distributed Architecture for Micro Context-Aware Agents." Procedia Computer Science 5 (2011): 296–303. http://dx.doi.org/10.1016/j.procs.2011.07.039.

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15

Gui, Ning, Vincenzo De Florio, Hong Sun, and Chris Blondia. "Toward architecture-based context-aware deployment and adaptation." Journal of Systems and Software 84, no. 2 (February 2011): 185–97. http://dx.doi.org/10.1016/j.jss.2010.09.017.

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16

Doulkeridis, Christos, Nikos Loutas, and Michalis Vazirgiannis. "A System Architecture for Context-Aware Service Discovery." Electronic Notes in Theoretical Computer Science 146, no. 1 (January 2006): 101–16. http://dx.doi.org/10.1016/j.entcs.2005.11.010.

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Espada, Jordán Pascual, Rubén González Crespo, Oscar Sanjuán Martínez, B. Cristina Pelayo G-Bustelo, and Juan Manuel Cueva Lovelle. "Extensible architecture for context-aware mobile web applications." Expert Systems with Applications 39, no. 10 (August 2012): 9686–94. http://dx.doi.org/10.1016/j.eswa.2012.02.151.

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LLUÍS ARCOS, JOSEP, and ENRIC PLAZA. "CONTEXT AWARE PERSONAL INFORMATION AGENTS." International Journal of Cooperative Information Systems 11, no. 03n04 (September 2002): 245–64. http://dx.doi.org/10.1142/s0218843002000583.

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We present a society of personal information agents that work for a community of users and that are aware of the physical and social context of their users. We show how context-awareness is a feature that allows the agents to improve their performance when they work with limited resources in information spaces with a large amount of information. The use of context information allows the agents to focus their information search and, as a result of this, increase the quantity and quality of information delivered to the user. Moreover, we propose an implemented agent architecture with context-aware capabilities. We discuss this architecture in detail, focusing on the capability of exploiting the windows of opportunity provided by the awareness of the users' activity. The agents use these windows of opportunity to furnish the user with information and advice in the situation where it can be most useful. In particular, we show how context-aware information agents can assist a community of attendees to big conferences and fairs. In this application, an information agent gathers relevant information based on a model of specific interests of a user. Given the multiplicity of interesting events, and their distribution in time and space, an information agent has to deliver the gathered information in a few hours and comply to the schedule constraints. Finally, we report some experimentation results to illustrate how context-awareness improve the service a society of information agents provides to a community of users in the conference application.

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R, Suresh Kumar, Mohandas R, and Jerome Christhudass A. "A Brief Overview of Context Aware System." Journal of Computational Science and Intelligent Technologies 2, no. 2 (August 16, 2021): 17–23. http://dx.doi.org/10.53409/mnaa/jcsit/2203.

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Context Awareness emerged as a concept from Ubiquitous computing, which is becoming a reality by emphasizing the integration of the data space and the physical space. With its aid, people may receive and analyze data at any time and from any location using a device that can connect to the internet. As a result, it can reduce the complexity of using the gadget and make people's life easier and more efficient. Context aware systems (CAS) are an effective approach for dealing with day-today tasks. Context aware frameworks provide up completely new possibilities for applications developers and end users by collecting contexts data and changing system behaviour respectively. This survey presents an overview of context aware systems. This study analyzes the concept of context aware systems, network architecture, application, and user interface, as well as thorough information of every layer of context aware system. As a consequence of the survey, a general procedure layout in CASs is provided, and the architectural contemplations of CASs are clarified

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20

Basler, Gérard, Michele Mazzucchi, Thomas Wahl, and Daniel Kroening. "Context-aware counter abstraction." Formal Methods in System Design 36, no. 3 (June 11, 2010): 223–45. http://dx.doi.org/10.1007/s10703-010-0096-7.

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21

Williamson, Carey, and Qian Wu. "Context-aware TCP/IP." ACM SIGMETRICS Performance Evaluation Review 30, no. 1 (June 2002): 262–63. http://dx.doi.org/10.1145/511399.511371.

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22

Huang, Li-Shing, Jui-Yuan Su, and Tsang-Long Pao. "A Context Aware Smart Classroom Architecture for Smart Campuses." Applied Sciences 9, no. 9 (May 3, 2019): 1837. http://dx.doi.org/10.3390/app9091837.

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The Smart campus is a concept of an education institute using technologies, such as information systems, internet of things (IoT), and context-aware computing, to support learning, teaching, and administrative activities. Classrooms are important building blocks of a school campus. Therefore, a feasible architecture for building and running smart classrooms is essential for a smart campus. However, most studies related to the smart classroom are focused on studying or addressing particular technical or educational issues, such as networking, AI applications, lecture quality, and user responses to technology. In this study, an architecture for building and running context-aware smart classrooms is proposed. The proposed architecture consists of three parts including a prototype of a context-aware smart classroom, a model for technology integration, and supporting measures for the operation of smart classrooms in this architecture. The classroom prototype was designed based on our study results and a smart classroom project in Ming Chuan University (MCU). The integration model was a layered model uses Raspberry Pi in the bottom layer of the model to integrate underlying technologies and provide application interfaces to the higher layer applications for the ease of building context-aware smart classroom applications. As a result, application interfaces were implemented using Raspberry Pi based on the proposed technology integration model, and a context-aware energy-saving smart classroom application was implemented based on the proposed classroom prototype and the implemented web application interface. The result shows that, in terms of technology, the proposed architecture is feasible for building context-aware smart classrooms in smart campuses.

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23

Alharbi, Olayan, and Mafawez Alharbi. "Context-aware architecture for Industry 4.0-ready manufacturing facility." International Journal of ADVANCED AND APPLIED SCIENCES 8, no. 8 (August 2021): 20–30. http://dx.doi.org/10.21833/ijaas.2021.08.003.

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The industry 4.0 revolution is empowering the manufacturing sector with several advantages from the production to consumption stage of products, or beyond that. Recently, operators in factories have been accumulating extensive data from machine sensors and other organizational and operational technologies such as company enterprise and planning systems. Notably, having access to extensive data is a double-edged sword. To the best of our knowledge, there is not any work in the literature that proposed architecture for industry 4.0 based on a context-aware system. The aim of this research is to provide the context-aware architecture to enhance decision-making in factories and reduce the exposure of operators to the necessary and related findings. The proposed system is contextually aware of three aspects, operator feedback for previous similar findings, specifications of products under production, and historical data of manufacturing machines. The proposed system is proactive which attracted operator attention only when the findings were contextually related, based on the aforementioned aspects. The contributions of this research an intelligent architecture, a case study, and a mathematical model.

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24

Hu, Xiping, Xitong Li, Edith Ngai, Victor Leung, and Philippe Kruchten. "Multidimensional context-aware social network architecture for mobile crowdsensing." IEEE Communications Magazine 52, no. 6 (June 2014): 78–87. http://dx.doi.org/10.1109/mcom.2014.6829948.

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25

Shapsough, Salsabeel Y., and Imran A. Zualkernan. "A Generic IoT Architecture for Ubiquitous Context-Aware Learning." IEEE Transactions on Learning Technologies 13, no. 3 (July 1, 2020): 449–64. http://dx.doi.org/10.1109/tlt.2020.3007708.

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Lee, Tae-Dong, Byung Moo Lee, and Wonjong Noh. "Hierarchical Cloud Computing Architecture for Context-Aware IoT Services." IEEE Transactions on Consumer Electronics 64, no. 2 (May 2018): 222–30. http://dx.doi.org/10.1109/tce.2018.2844724.

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27

Qiu, Qinru, Zhe Li, Khadeer Ahmed, Wei Liu, Syed Faisal Habib, Hai (Helen) Li, and Miao Hu. "A Neuromorphic Architecture for Context Aware Text Image Recognition." Journal of Signal Processing Systems 84, no. 3 (November 4, 2015): 355–69. http://dx.doi.org/10.1007/s11265-015-1067-4.

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28

Hervás, Ramón, and José Bravo. "COIVA: context-aware and ontology-powered information visualization architecture." Software: Practice and Experience 41, no. 4 (September 22, 2010): 403–26. http://dx.doi.org/10.1002/spe.1011.

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29

Wang, Hongli, Bin Guo, Jiaqi Liu, Sicong Liu, Yungang Wu, and Zhiwen Yu. "Context-aware Adaptive Surgery." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, no. 3 (September 9, 2021): 1–22. http://dx.doi.org/10.1145/3478073.

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Deep Neural Networks (DNNs) have made massive progress in many fields and deploying DNNs on end devices has become an emerging trend to make intelligence closer to users. However, it is challenging to deploy large-scale and computation-intensive DNNs on resource-constrained end devices due to their small size and lightweight. To this end, model partition, which aims to partition DNNs into multiple parts to realize the collaborative computing of multiple devices, has received extensive research attention. To find the optimal partition, most existing approaches need to run from scratch under given resource constraints. However, they ignore that resources of devices (e.g., storage, battery power), and performance requirements (e.g., inference latency), are often continuously changing, making the optimal partition solution change constantly during processing. Therefore, it is very important to reduce the tuning latency of model partition to realize the real-time adaption under the changing processing context. To address these problems, we propose the Context-aware Adaptive Surgery (CAS) framework to actively perceive the changing processing context, and adaptively find the appropriate partition solution in real-time. Specifically, we construct the partition state graph to comprehensively model different partition solutions of DNNs by import context resources. Then "the neighbor effect" is proposed, which provides the heuristic rule for the search process. When the processing context changes, CAS adopts the runtime search algorithm, Graph-based Adaptive DNN Surgery (GADS), to quickly find the appropriate partition that satisfies resource constraints under the guidance of the neighbor effect. The experimental results show that CAS realizes adaptively rapid tuning of the model partition solutions in 10ms scale even for large DNNs (2.25x to 221.7x search time improvement than the state-of-the-art researches), and the total inference latency still keeps the same level with baselines.

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CHEN, HARRY, TIM FININ, and ANUPAM JOSHI. "An ontology for context-aware pervasive computing environments." Knowledge Engineering Review 18, no. 3 (September 2003): 197–207. http://dx.doi.org/10.1017/s0269888904000025.

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This document describes COBRA-ONT, an ontology for supporting pervasive context-aware systems. COBRA-ONT, expressed in the Web Ontology Language OWL, is a collection of ontologies for describing places, agents and events and their associated properties in an intelligent meeting-room domain. This ontology is developed as a part of the Context Broker Architecture (CoBrA), a broker-centric agent architecture that provides knowledge sharing, context reasoning and privacy protection supports for pervasive context-aware systems. We also describe an inference engine for reasoning with information expressed using the COBRA-ONT ontology and the ongoing research in using the DAML-Time ontology for context reasoning.

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31

Leber, Žiga, Matej Črepinšek, Marjan Mernik, and Tomaž Kosar. "RNGSGLR: Generalization of the Context-Aware Scanning Architecture for All Character-Level Context-Free Languages." Mathematics 10, no. 14 (July 13, 2022): 2436. http://dx.doi.org/10.3390/math10142436.

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The limitations of traditional parsing architecture are well known. Even when paired with parsing methods that accept all context-free grammars (CFGs), the resulting combination for any given CFG accepts only a limited subset of corresponding character-level context-free languages (CFL). We present a novel scanner-based architecture that for any given CFG accepts all corresponding character-level CFLs. It can directly parse all possible specifications consisting of a grammar and regular definitions. The architecture is based on right-nulled generalized LR (RNGLR) parsing and is a generalization of the context-aware scanning architecture. Our architecture does not require any disambiguation rules to resolve lexical conflicts, it conceptually has an unbounded parser and scanner lookahead and it is streaming. The added robustness and flexibility allow for easier grammar development and modification.

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Karolus, Jakob, and Paweł W. Woźniak. "Proficiency-aware systems: Designing for user reflection in context-aware systems." it - Information Technology 63, no. 3 (April 8, 2021): 167–75. http://dx.doi.org/10.1515/itit-2020-0039.

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Abstract In an increasingly digital world, intelligent systems support us in accomplishing many everyday tasks. With the proliferation of affordable sensing devices, inferring user states from collected physiological data paves the way to tailor-made adaptation. While estimating a user’s abilities is technically possible, such proficiency assessments are rarely employed to benefit the user’s task reflection. In our work, we investigate how to model and design for proficiency estimation as part of context-aware systems. In this paper, we present the definition and conceptual architecture of proficiency-aware systems. The concept is not only applicable to current adaptive systems but provides a stepping stone for systems which actively aid in developing user proficiency during interaction.

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33

Guo, Chenjuan, Bin Yang, Jilin Hu, Christian S. Jensen, and Lu Chen. "Context-aware, preference-based vehicle routing." VLDB Journal 29, no. 5 (March 11, 2020): 1149–70. http://dx.doi.org/10.1007/s00778-020-00608-7.

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MARTÍNEZ-CARRERAS, M. ANTONIA, ANDRÉS MUÑOZ, JUAN BOTÍA, and ANTONIO F. GÓMEZ-SKARMETA. "CREATING CONTEXT-AWARE COLLABORATIVE WORKING ENVIRONMENTS." International Journal on Artificial Intelligence Tools 20, no. 01 (February 2011): 195–207. http://dx.doi.org/10.1142/s0218213011000085.

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Context-aware systems are intended for providing services adapted to the needs of people, by taking into account their state and the information related to their environment. One alternative to represent this context information resides in the use of Semantic Web ontologies. They provide a formal vocabulary which allows to easily express and share knowledge. Additionally, several types of automatic knowledge manipulation and reasoning processes become available thanks to the formal features of such ontologies. The inclusion of context information through ontologies in Collaborative Working Environments (CWEs) may bring important benefits to team work inside an organization, such as an automatic selection between different collaborative services according to the team members' preferences and their current state. This paper describes the design and implementation of a context-reasoning system which has been integrated into a CWE architecture to take advantage of context-awareness.

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35

Vahdat-Nejad, Hamed, Mohammad Sadeq Navabi, and Hosein Khosravi-Mahmouei. "A Context-Aware Museum-Guide System Based on Cloud Computing." International Journal of Cloud Applications and Computing 8, no. 4 (October 2018): 1–19. http://dx.doi.org/10.4018/ijcac.2018100101.

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This paper proposes a context-aware museum-guide system, which is able to present information on exhibits in accordance with visitor needs. Other features of the system include planning and directing a visit as well as providing locations, facilities and additional services. The architecture is designed based on the attribute-driven design method. It consists of two components including mobile and server components. The server component is located in the cloud and is regarded as the backup for the mobile component. The software architecture analysis method is employed to evaluate the proposed architecture. To this end, some scenarios have been codified for system functionalities by consulting museum experts. The proposed architecture satisfies the scenarios by resolving the issues arisen in each one. The CIF-based questionnaire method indicates a high usability score for the proposed system. Finally, comparison of the capabilities of the proposed system with related research reveals that it benefits from a comprehensive design.

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Falcarin, Paolo, Massimo Valla, Jian Yu, Carlo Alberto Licciardi, Cristina Frà, and Luca Lamorte. "Context data management: an architectural framework for context-aware services." Service Oriented Computing and Applications 7, no. 2 (July 4, 2012): 151–68. http://dx.doi.org/10.1007/s11761-012-0115-1.

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Manotumruksa, Jarana. "Effective neural architectures for context-aware venue recommendation." ACM SIGIR Forum 53, no. 2 (December 2019): 102–3. http://dx.doi.org/10.1145/3458553.3458568.

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Users in Location-Based Social Networks (LBSNs), such as Yelp and Foursquare, can search for interesting venues such as restaurants and museums to visit, or share their location with their friends by making an implicit feedback (e.g. checking in at venues they have visited). The users can also leave explicit feedback on the venues they have visited by providing ratings and/or comments. Such explicit and implicit feedback by the users provide rich information about both users and venues, and thus can be leveraged to study the users' movement in urban cities, as well as enhance the quality of personalised venue recommendations. Unlike traditional recommendation systems (e.g. book and movie recommendation systems), making effective venue recommendations is more challenging because we need to take into account the users' current context (e.g. time of the day, user's current location as well as his recently visited venues). In this thesis, based upon Matrix Factorisation (MF) and Bayesian Personalised Ranking (BPR) models, we aim to generate effective context-aware venue recommendation that a user may wish to visit based on the user's historical explicit and implicit feedbacks, the user's contextual information (e.g. the user's current location and time of the day) and additional information (e.g. the geographical location of venues and users' social relationships). To achieve this goal, we need to address the following challenges: namely (C1) modelling the users' preferences and the characteristic of venues, (C2) capturing the complex structure of user-venue interactions in a Collaborative Filtering manner, (C3) modelling the users' short-term ( dynamic ) preferences from the sequential order of user's observed feedback as well as the contextual information associated with the successive feedback, (C4) generating accurate top-K venue recommendations based on the users' preferences using a pairwise ranking-based model and (C5) appropriately sampling potential negative instances to train a ranking-based model. First, to address challenge C1 , we leverage the users' explicit feedback (e.g. their ratings and the textual content of the comments) and additional information (e.g. users' social relationships) to effectively model the users' preferences and the characteristics of venues. In particular, we propose a novel regularisation technique [1] and a factorisation-based model [2] that leverages the users' explicit feedback and the additional information to improve the rating prediction accuracy of the traditional MF model. Experiments conducted on a large scale rating dataset on LBSN demonstrate that the textual content of comments plays an important role in enhancing the accuracy of rating prediction. Second, we investigate how to leverage the users' implicit feedback and additional information such as the users' social relationship and the geographical location of venues to improve the quality of top-K venue recommendations. In particular, to address challenges C4 and C5 , we propose a novel pairwise ranking-based framework for top-K venue recommendations [3] that can incorporate multiple sources of additional information (e.g. the users' social relationship and the geographical location of venues) to effectively sample the potential negative instances. Experimental results on three large scale checkin and rating datasets from LBSNs demonstrate that the social correlations and the geographical influences play an important role to the quality of sampled negative instances and hence can improve the quality of top-K venue recommendations. Finally, to address challenges C2 and C3 , we propose a framework for sequential-based venue recommendations [4] that exploits Deep Neural Network (DNN) models to effectively capture the complex structure of user-venue interactions and the users' long-term ( dynamic ) preferences from their sequential order of checkins. Moreover, we propose a novel Recurrent Neural Network (RNN) architecture [5] that can effectively incorporate the contextual information associated with the successive implicit feedback (e.g. the time interval and the geographical distance between two successive checkins) to generate high quality context-aware venue recommendations. Experimental results on three large scale checkin and rating datasets from LBSNs demonstrate the effectiveness and robustness of our proposed framework and architecture for context-aware venue recommendations. Supervisors Dr. Craig Macdonald (University of Glasgow), Prof. Iadh Ounis (University of Glasgow) Available from : http://theses.gla.ac.uk/76735/

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38

Mohana Roopa, Y., M. Ramesh Babu, Jetti Kumar, and D. Kishore Babu. "Optimal component architecture using particle swarm optimization algorithm for self-adaptive software architecture." International Journal of Engineering & Technology 7, no. 1.6 (June 1, 2018): 23. http://dx.doi.org/10.14419/ijet.v7i1.6.11387.

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The component-based software engineering (CBSE) ensue the procedure of reconfiguration and reusability of components to reap the higher productivity. The context-aware structures are portion of CBSE, which observes the functionality of the system and adopt automatically according to the execution context. In this paper, we are focusing on the aware context guidelines that automatically adapt to the given context given by the customers and remodel the software architecture based totally on the requirements. The component repository turned into added, in which it carries the wide variety of reusable components. The fuzzy logic becomes carried out to the component evaluation in the component repository. The Particle Swarm Optimization (PSO) algorithm applied, to optimize component architecture. The Hospital management system is used to test the adaptability of the system.

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39

Rehena, Zeenat, Marijn Janssen, and Samiran Chattopadhyay. "A Reference Architecture for Context-Aware Intelligent Traffic Management Platforms." International Journal of Electronic Government Research 14, no. 4 (October 2018): 65–79. http://dx.doi.org/10.4018/ijegr.2018100105.

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Smart cities have been heralded for improving traffic management by utilizing data for making better traffic management decisions. Multi-sided platforms collect data from sensors and citizen-generated data on one side and can provide input for decision-making using data analytics by governments and the public on the other side. However, there is no guidance for creating developing Intelligent Traffic Management Systems (ITMS) platforms. The involvement of various actors having different interest and heterogeneous datasets hampers development. In this article, the authors design a reference architecture (RA) to support intelligent traffic management systems for providing better a commute, and safety and security during travel based on real-time information. The main three layers of this RA are datasets, processes, and actors. The RA for ITMS provides guidance for designing and overcoming the challenges with: 1) heterogeneous datasets; 2) data gathering; 3) data processing; 4) data management; and 5) supporting various types of data users. The illustration and evaluation of the architecture shows possible solutions of the aforementioned challenges. The RA helps to integrate the activities performed by the various actors. In this way it can be used to reduce traffic queues, increase the efficient use of resources, smooth and safe commute of the citizens.

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40

Lee, Seung-Wook, Jong-Tae Kim, Bong-Ki Sohn, Keon-Myung Lee, Jun-Dong Keon-Myung, Jee-Hyung Lee, and Jae-Wook Jeon. "Real-Time Rule-Based System Architecture for Context-Aware Computing." Journal of Korean Institute of Intelligent Systems 14, no. 5 (August 1, 2004): 587–92. http://dx.doi.org/10.5391/jkiis.2004.14.5.587.

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Miraoui, Moeiz, Sherif El-etriby, Abdulasit Zaid Abid, and Chakib Tadj. "Agent-Based Context-Aware Architecture for a Smart Living Room." International Journal of Smart Home 10, no. 5 (May 31, 2016): 39–54. http://dx.doi.org/10.14257/ijsh.2016.10.5.05.

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Garcia De Prado, Alfonso, Guadalupe Ortiz, and Juan Boubeta-Puig. "CARED-SOA: A Context-Aware Event-Driven Service-Oriented Architecture." IEEE Access 5 (2017): 4646–63. http://dx.doi.org/10.1109/access.2017.2679338.

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Kim, Geonwoo, Seongju Kang, Jiwoo Park, and Kwangsue Chung. "An MQTT-Based Context-Aware Autonomous System in oneM2M Architecture." IEEE Internet of Things Journal 6, no. 5 (October 2019): 8519–28. http://dx.doi.org/10.1109/jiot.2019.2919971.

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Paspallis, Nearchos, and George A. Papadopoulos. "A pluggable middleware architecture for developing context-aware mobile applications." Personal and Ubiquitous Computing 18, no. 5 (October 8, 2013): 1099–116. http://dx.doi.org/10.1007/s00779-013-0722-7.

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Hasswa, Ahmed, Nidal Nasser, and Hossam Hassanein. "A seamless context-aware architecture for fourth generation wireless networks." Wireless Personal Communications 43, no. 3 (April 12, 2007): 1035–49. http://dx.doi.org/10.1007/s11277-007-9262-7.

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Kapsalis, Vassilis, Loukas Hadellis, Dimitris Karelis, and Stavros Koubias. "A dynamic context-aware access control architecture for e-services." Computers & Security 25, no. 7 (October 2006): 507–21. http://dx.doi.org/10.1016/j.cose.2006.05.004.

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Kim, Hyejin. "Multi-Agent Architecture for Context-Aware Collaborative U-Learning System." International Journal of Future Generation Communication and Networking 10, no. 7 (July 31, 2017): 77–84. http://dx.doi.org/10.14257/ijfgcn.2017.10.7.07.

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Medina Acosta, Marco Antonio, Eduardo Lopez Dominguez, German Gomez Castro, Saul Eduardo Pomares Hernandez, and Maria Auxilio Medina Nieto. "Two-Level Software Architecture for Context-Aware Mobile Distributed Systems." IEEE Latin America Transactions 13, no. 4 (April 2015): 1205–9. http://dx.doi.org/10.1109/tla.2015.7106376.

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Kashevnik, A. M., A. V. Ponomarev, and A. V. Smirnov. "A multimodel context-aware tourism recommendation service: Approach and architecture." Journal of Computer and Systems Sciences International 56, no. 2 (March 2017): 245–58. http://dx.doi.org/10.1134/s1064230717020125.

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Neves, Ana Régia de M., Álvaro Marcos G. Carvalho, and Célia G. Ralha. "Agent-based architecture for context-aware and personalized event recommendation." Expert Systems with Applications 41, no. 2 (February 2014): 563–73. http://dx.doi.org/10.1016/j.eswa.2013.07.081.

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