Journal articles on the topic 'Location-aware computing'

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1

Patterson, C. A., R. R. Muntz, and C. M. Pancake. "Challenges in location-aware computing." IEEE Pervasive Computing 2, no. 2 (April 2003): 80–89. http://dx.doi.org/10.1109/mprv.2003.1203757.

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Ackerman, Mark S., Tao Dong, Scott Gifford, Jungwoo Kim, Mark W. Newman, Atul Prakash, Sarah Qidwai, et al. "Location-Aware Computing, Virtual Networks." IEEE Pervasive Computing 8, no. 4 (October 2009): 28–32. http://dx.doi.org/10.1109/mprv.2009.78.

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Banerjee, S., S. Agarwal, K. Kamel, A. Kochut, C. Kommareddy, T. Nadeem, P. Thakkar, et al. "Rover: scalable location-aware computing." Computer 35, no. 10 (October 2002): 46–53. http://dx.doi.org/10.1109/mc.2002.1039517.

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Huber, Manuel, Benjamin Becker, and Gudrun Klinker. "Location aware computing using RFID infrastructure." International Journal of Autonomous and Adaptive Communications Systems 3, no. 1 (2010): 23. http://dx.doi.org/10.1504/ijaacs.2010.030310.

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Anthony, Denise, Tristan Henderson, and David Kotz. "Privacy in Location-Aware Computing Environments." IEEE Pervasive Computing 6, no. 4 (October 2007): 64–72. http://dx.doi.org/10.1109/mprv.2007.83.

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6

Hazas, M., J. Scott, and J. Krumm. "Location-aware computing comes of age." Computer 37, no. 2 (February 2004): 95–97. http://dx.doi.org/10.1109/mc.2004.1266301.

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7

Sashima, Akio, Noriaki Izumi, and Koichi Kurumatani. "Location‐aware middle agents in pervasive computing." International Journal of Pervasive Computing and Communications 2, no. 2 (May 2007): 149–62. http://dx.doi.org/10.1108/17427370780000149.

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8

Want, R., and B. Schilit. "Expanding the horizons of location-aware computing." Computer 34, no. 8 (August 2001): 31–34. http://dx.doi.org/10.1109/mc.2001.940010.

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Gupta, Ajay Kumar, and Udai Shanker. "A Literature Review of Location-Aware Computing Policies." International Journal of Mobile Human Computer Interaction 12, no. 3 (July 2020): 21–45. http://dx.doi.org/10.4018/ijmhci.2020070102.

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Location-aware computing systems implementation has the challenge of maintaining its consistency due to frequent disconnections in the mobile environment. Many other exciting issues associated with this domain provide a fertile ground for many mobile database researchers to tackle. This work contributes by taking up-to-date literature on current location-aware mobile data management practices. The paper includes taxonomy for caching, map-matching, and location privacy protection with spatial-temporal queries, unlike other review papers, which only address a specific aspect of location-dependent data management. First, an in-depth analysis of state-of-the-art and classified literature is presented, taking into account the domain of application models and approaches for evaluation. Secondly, it provides a theoretical and analytical investigation of location-conscious computing approaches available in the literature, examining the merit and demerit of each method and the evolving processes between them. Finally, the paper highlighted the outstanding opportunities for future research directions.
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Souza, Paulo S., Tiago C. Ferreto, Fabio D. Rossi, and Rodrigo N. Calheiros. "Location-Aware Maintenance Strategies for Edge Computing Infrastructures." IEEE Communications Letters 26, no. 4 (April 2022): 848–52. http://dx.doi.org/10.1109/lcomm.2022.3150243.

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Mehra, Pankaj. "Context-Aware Computing: Beyond Search and Location-Based Services." IEEE Internet Computing 16, no. 2 (March 2012): 12–16. http://dx.doi.org/10.1109/mic.2012.31.

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12

Qi, Qi, Jianxin Liao, and Yufei Cao. "Cloud service-aware location update in mobile cloud computing." IET Communications 8, no. 8 (May 22, 2014): 1417–24. http://dx.doi.org/10.1049/iet-com.2013.1142.

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13

Linbing, MA, Zhang Xinchang, and Tao Haiyan. "An integrated and layered architecture for location-aware computing." Geo-spatial Information Science 8, no. 2 (January 2005): 144–47. http://dx.doi.org/10.1007/bf02826855.

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14

Yu, Chen, Dezhong Yao, Xi Li, Yan Zhang, Laurence T. Yang, Naixue Xiong, and Hai Jin. "Location-aware private service discovery in pervasive computing environment." Information Sciences 230 (May 2013): 78–93. http://dx.doi.org/10.1016/j.ins.2012.08.010.

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15

Song, MoonBae, Sang-Won Kang, and KwangJin Park. "On the Design of Energy-Efficient Location Tracking Mechanism in Location-Aware Computing." Mobile Information Systems 1, no. 2 (2005): 109–27. http://dx.doi.org/10.1155/2005/172540.

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The battery, in contrast to other hardware, is not governed by Moore's Law. In location-aware computing, power is a very limited resource. As a consequence, recently, a number of promising techniques in various layers have been proposed to reduce the energy consumption. The paper considers the problem of minimizing the energy used to track the location of mobile user over a wireless link in mobile computing. Energy-efficient location update protocol can be done by reducing the number of location update messages as possible and switching off as long as possible. This can be achieved by the concept of mobility-awareness we propose. For this purpose, this paper proposes a novel mobility model, called state-based mobility model (SMM) to provide more generalized framework for both describing the mobility and updating location information of complexly moving objects. We also introduce the state-based location update protocol (SLUP) based on this mobility model. An extensive experiment on various synthetic datasets shows that the proposed method improves the energy efficiency by 2 ∼ 3 times with the additional 10% of imprecision cost.
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Smailagic, A., and D. Kogan. "Location sensing and privacy in a context-aware computing environment." IEEE Wireless Communications 9, no. 5 (October 2002): 10–17. http://dx.doi.org/10.1109/mwc.2002.1043849.

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17

Heilig, Leonard, Rajkumar Buyya, and Stefan Voß. "Location-aware brokering for consumers in multi-cloud computing environments." Journal of Network and Computer Applications 95 (October 2017): 79–93. http://dx.doi.org/10.1016/j.jnca.2017.07.010.

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18

Nelson, Giles J. "User interaction with machines on the move: Location aware computing." Computers in Industry 29, no. 1-2 (July 1996): 63–70. http://dx.doi.org/10.1016/0166-3615(95)00055-0.

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19

Ficco, Massimo, and Stefano Russo. "A hybrid positioning system for technology-independent location-aware computing." Software: Practice and Experience 39, no. 13 (September 10, 2009): 1095–125. http://dx.doi.org/10.1002/spe.919.

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20

Manabe, Yusuke, and Kenji Sugawara. "Sitting Posture Recognition and Location Estimation for Human-Aware Environment." International Journal of Software Science and Computational Intelligence 3, no. 1 (January 2011): 34–49. http://dx.doi.org/10.4018/jssci.2011010103.

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Realization of human-computer symbiosis is an important idea in the context of ubiquitous computing. Symbiotic Computing is a concept that bridges the gap between situations in Real Space (RS) and data in Digital Space (DS). The main purpose is to develop an intelligent software application as well as establish the next generation information platform to develop the symbiotic system. In this paper, the authors argue that it is necessary to build ’Mutual Cognition’ between human and system. Mutual cognition consists of two functions: ’RS Cognition’ and ’DS Cognition’. This paper examines RS Cognition, which consists of many software functions for perceiving various situations like events or humans’ activities in RS. The authors develop two perceptual functions, sitting posture recognition and human’s location estimation for a person, as RS perception tasks. In the resulting experiments, developed functions are quite competent to recognize a human’s activities.
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Svahn, Fredrik, and Ola Henfridsson. "Situated Knowledge in Context-Aware Computing." International Journal of Advanced Pervasive and Ubiquitous Computing 1, no. 3 (July 2009): 23–41. http://dx.doi.org/10.4018/japuc.2009090802.

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A central feature of ubiquitous computing applications is their capability to automatically react on context changes so as to support users in their mobility. Such context awareness relies on models of specific use contexts, embedded in ubiquitous computing environments. However, since most such models are based merely on location and identity parameters, context-aware applications seldom cater for users’ situated knowledge and experience of specific contexts. This is a general user problem in well-known, but yet dynamic, user environments. Drawing on a sequential multimethod study of in-car navigation, this paper explores the role of situated knowledge in designing and using context-aware applications. This focus is motivated by the current lack of empirical investigations of context-aware applications in actual use settings. In-car navigation systems are a type of context-aware application that includes a set of contextual parameters for supporting route guidance in a volatile context. The paper outlines a number of theoretical and practical implications for context-aware application design and use.
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Yu, Mengshan, Guisheng Fan, Huiqun Yu, and Liang Chen. "Location-based and Time-aware Service Recommendation in Mobile Edge Computing." International Journal of Parallel Programming 49, no. 5 (April 9, 2021): 715–31. http://dx.doi.org/10.1007/s10766-021-00702-5.

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23

Liu, Jianhua, Zibo Wu, Jiajia Liu, and Xiaoguang Tu. "Distributed Location-Aware Task Offloading in Multi-UAVs Enabled Edge Computing." IEEE Access 10 (2022): 72416–28. http://dx.doi.org/10.1109/access.2022.3189682.

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24

Choi, Ho-Young, Chang-Yeol Choi, and Sung-Soo Kim. "A Self-optimizing Mechanism of Location Aware Systems for Ubiquitous Computing." KIPS Transactions:PartA 12A, no. 4 (August 1, 2005): 273–80. http://dx.doi.org/10.3745/kipsta.2005.12a.4.273.

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25

Hernández, José L., M. Victoria Moreno, Antonio J. Jara, and Antonio F. Skarmeta. "A soft computing based location-aware access control for smart buildings." Soft Computing 18, no. 9 (April 13, 2014): 1659–74. http://dx.doi.org/10.1007/s00500-014-1278-9.

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26

Xiao, Ruo gui, and Tong qiang Guo. "A two-stage multimodal speaker location-aware approach in pervasive computing." International Journal of Computer Applications in Technology 38, no. 1/2/3 (2010): 118. http://dx.doi.org/10.1504/ijcat.2010.034147.

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27

Li, Yin, Yuyin Ma, and Ziyang Zeng. "A Novel Approach to Location-Aware Scheduling of Workflows Over Edge Computing Resources." International Journal of Web Services Research 17, no. 3 (July 2020): 56–68. http://dx.doi.org/10.4018/ijwsr.2020070104.

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Edge computing is pushing the frontier of computing applications, data, and services away from centralized nodes to the logical extremes of a network. A major technological challenge for workflow scheduling in the edge computing environment is cost reduction with service-level-agreement (SLA) constraints in terms of performance and quality-of-service requirements because real-world workflow applications are constantly subject to negative impacts (e.g., network congestions, unexpected long message delays, shrinking coverage, range of edge servers due to battery depletion. To address the above concern, we propose a novel approach to location-aware and proximity-constrained multi-workflow scheduling with edge computing resources). The proposed approach is capable of minimizing monetary costs with user-required workflow completion deadlines. It employs an evolutionary algorithm (i.e., the discrete firefly algorithm) for the generation of near-optimal scheduling decisions. For the validation purpose, the authors show that our proposed approach outperforms traditional peers in terms multiple metrics based on a real-world dataset of edge resource locations and multiple well-known scientific workflow templates.
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Alshattnawi, Sawsan. "Utilizing Cloud Computing in Developing a Mobile Location-Aware Tourist Guide System." International Journal of Advanced Pervasive and Ubiquitous Computing 5, no. 2 (April 2013): 9–18. http://dx.doi.org/10.4018/japuc.2013040102.

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Cloud Computing technologies support mobile applications and overcome the low computation resources and limited data storage by providing an on-demand access with pay-as-use rule to large number of computing resources. In this paper, the author describes the architecture of an electronic tourist guide system (Trip@Cloud) as mobile cloud computing application wherein the tourists can access cloudy information in ubiquitous and pervasive manner. The information needs not to be over the user's mobile device, but it will be downloaded to user's device according to user's location and the Internet status connection. The interaction between the mobile device and the cloud is done when possible and transparently from the user. The existing architectures of partitioning the application between the device and the cloud depends deeply on the CPU workloads. The author’s application's nature is different from these applications and this represents the main contribution. The idea is to download the data to the user's device according to the current user location. Therefore the partitioning is done for the data to be sent to the mobile device. This idea is applied by developing a tourist guide application where the data to be installed is very huge and the mobile storage device is very limited.
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29

Md Sahabul Hossain, Nafis Irtija, Maria Diamanti, Fisayo Sangoleye, Eirini Eleni Tsiropoulou, and Symeon Papavassiliou. "Reconfigurable intelligent surfaces-enabled edge computing: A location-aware task offloading framework." ITU Journal on Future and Evolving Technologies 3, no. 3 (December 9, 2022): 830–43. http://dx.doi.org/10.52953/fltj9889.

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In this paper, an energy efficient task offloading mechanism in a Multiaccess Edge Computing (MEC) environment is introduced, based on the principles of contract theory. The technology of Reconfigurable Intelligent Surfaces (RISs) is adopted and serves as the enabler for energy efficient task offloading, from the perspective of location-awareness and improved communication environment. Initially a novel positioning, navigation, and timing solution is designed, based on the RIS technology and an artificial intelligent method that selects a set of RISs to perform the multilateration technique and determine the Internet of Things (IoT) nodes' positions in an efficient and accurate manner is introduced. Being aware of the nodes' positions, a maximization problem of the nodes' sum received signal strength at the MEC server where the nodes offload their computing tasks is formulated and solved, determining each RIS element's optimal phase shifts. Capitalizing on these enhancements, a contract-theoretic task offloading mechanism is devised enabling the MEC server to incentivize the IoT nodes to offload their tasks to it for further processing in an energy efficient manner, while accounting for the improved nodes' communications and computing characteristics. The performance evaluation of the proposed framework is obtained via modeling and simulation under different operation scenarios.
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Xu, Yu Bin, Mu Zhou, and Lin Ma. "An Effective Adaptive Autocorrelation-Based Neighboring Matching Location-Aware Computing in WLANs." Advanced Materials Research 204-210 (February 2011): 2011–14. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.2011.

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The recent advances of ubiquitous wireless infrastructures and requirements for high speed context-aware computing have created the opportunities to supply the high efficient location based service (LBS) in indoor wireless local area network (WLAN) environment. Because of the serious multi-path effect, unpredictable co-channel interference and inherent equipment noise, the measured signal strengths vary a lot in the real-world indoor environment. And this strength variation will also result in the performance deterioration of radio map-based neighboring matching algorithm. In response to this compelling problem, we propose the adoption of adaptive autocorrelation-based signal preprocessing method as a specific solution by effectively eliminating the singular strength from the original fingerprint set. Finally, the feasibility and effectiveness of autocorrelation-based preprocessing are also verified by decreasing about 33.4% and 32.9% of errors in k nearest neighbor (KNN) and weighted KNN (WKNN).
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Bellavista, Paolo, Antonio Corradi, Rebecca Montanari, and Cesare Stefanelli. "A mobile computing middleware for location- and context-aware internet data services." ACM Transactions on Internet Technology 6, no. 4 (November 2006): 356–80. http://dx.doi.org/10.1145/1183463.1183465.

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32

Olowolayemo, Akeem, and Teddy Mantoro. "Human Linguistic Perception of Distances for Location-Aware Systems." International Journal of Mobile Computing and Multimedia Communications 10, no. 2 (April 2019): 19–41. http://dx.doi.org/10.4018/ijmcmc.2019040102.

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Location referencing relative to landmarks or between two points of interest is often presented by navigation systems (e.g., GPS, Google Maps) in quantitative terms (e.g., 100m, 2km, etc.). However, humans refer to distances between points of interests in linguistic forms, such as very close, far, almost there, nearby, etc. When location information is presented to humans in quantitative terms, they often reprocess the quantities into linguistic terms and articulate it in linguistic labels because quantitative articulations are not directly in line with the natural human cognition. Therefore, this research seeks to evaluate the possibility of applying perceptive computing to reprocess quantitative location references from landmarks or two points of interest into linguistic labels easily understood by humans. A comparative analysis between the perception of quantitative distances and similar physical distances in an environment familiar to the subjects has been carried out, and there is a clear disparity between the perceptions in these two contexts.
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Rodríguez-Pupo, Luis, Carlos Granell, and Sven Casteleyn. "An Analytics Platform for Integrating and Computing Spatio-Temporal Metrics." ISPRS International Journal of Geo-Information 8, no. 2 (January 26, 2019): 54. http://dx.doi.org/10.3390/ijgi8020054.

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In large-scale context-aware applications, a central design concern is capturing, managing and acting upon location and context data. The ability to understand the collected data and define meaningful contextual events, based on one or more incoming (contextual) data streams, both for a single and multiple users, is hereby critical for applications to exhibit location- and context-aware behaviour. In this article, we describe a context-aware, data-intensive metrics platform —focusing primarily on its geospatial support—that allows exactly this: to define and execute metrics, which capture meaningful spatio-temporal and contextual events relevant for the application realm. The platform (1) supports metrics definition and execution; (2) provides facilities for real-time, in-application actions upon metrics execution results; (3) allows post-hoc analysis and visualisation of collected data and results. It hereby offers contextual and geospatial data management and analytics as a service, and allow context-aware application developers to focus on their core application logic. We explain the core platform and its ecosystem of supporting applications and tools, elaborate the most important conceptual features, and discuss implementation realised through a distributed, micro-service based cloud architecture. Finally, we highlight possible application fields, and present a real-world case study in the realm of psychological health.
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Benali, Asmae, and Bouchra El Asri. "A Context-Aware and Self-Adaptation Strategy for Cloud Service Selection and Configuration in Run-Time." Computer and Information Science 13, no. 1 (December 20, 2019): 10. http://dx.doi.org/10.5539/cis.v13n1p10.

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Day after day, the number of mobile applications deployed on cloud computing continues in increasing because of smartphone capabilities improvement. Cloud computing has already succeeded in the web-based application, for that reason, the demand for context-aware services provided by cloud computing increases. To customize a cloud service that takes into account the consumer requirements, which depend on information change, it brings to light many recent challenges to cloud computing about environment-aware, location-aware, time-aware. The cloud provider, moreover, has to manage personalized applications and the constraints of mobile devices in matters of interaction abilities and communication restrictions. This paper proposes a strategy for selecting automatically an appropriate cloud environment that runs out whole requirements, defines a configuration for the associated cloud environment and able to easily adapt to the change of the environment on either the user or the cloud side or both. This process builds on the principles of dynamic software product lines, Agent-oriented software engineering, and the MAPE-k model to select and configure cloud environments according to the consumer needs and the context change.
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KIM, Jangseong, Taeshik SHON, and Kwangjo KIM. "Location-Aware and Privacy-Preserving Approach for Child Safety in Ubiquitous Computing Environment." IEICE Transactions on Communications E94-B, no. 3 (2011): 686–89. http://dx.doi.org/10.1587/transcom.e94.b.686.

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Lu, Tianjun, Xian Zhong, Luo Zhong, and RuiqiLuo. "A location-aware feature extraction algorithm for image recognition in mobile edge computing." Mathematical Biosciences and Engineering 16, no. 6 (2019): 6672–82. http://dx.doi.org/10.3934/mbe.2019332.

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37

Kasaki, Nagato, Shuichi Kurabayashi, and Yasushi Kiyoki. "A Geo-Location Context-Aware Mobile Learning System with Adaptive Correlation Computing Methods." Procedia Computer Science 10 (2012): 593–600. http://dx.doi.org/10.1016/j.procs.2012.06.076.

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38

SONG, M. "Mobility-Awareness: An Efficient Approach to Improve Energy Efficiency in Location-Aware Computing." IEICE Transactions on Information and Systems E89-D, no. 5 (May 1, 2006): 1662–71. http://dx.doi.org/10.1093/ietisy/e89-d.5.1662.

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Jiang, Shenghao, Saeed Mashdoor, Hamid Parvin, Bui Anh Tuan, and Kim-Hung Pho. "An Adaptive Location-Aware Swarm Intelligence Optimization Algorithm." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 29, no. 02 (April 2021): 249–79. http://dx.doi.org/10.1142/s0218488521500128.

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Optimization is an important and decisive task in science. Many optimization problems in science are naturally too complicated and difficult to be modeled and solved by the conventional optimization methods such as mathematical programming problem solvers. Meta-heuristic algorithms that are inspired by nature have started a new era in computing theory to solve the optimization problems. The paper seeks to find an optimization algorithm that learns the expected quality of different places gradually and adapts its exploration-exploitation dilemma to the location of an individual. Using birds’ classical conditioning learning behavior, in this paper, a new particle swarm optimization algorithm has been introduced where particles can learn to perform a natural conditioning behavior towards an unconditioned stimulus. Particles are divided into multiple categories in the problem space and if any of them finds the diversity of its category to be low, it will try to go towards its best personal experience. But if the diversity among the particles of its category is high, it will try to be inclined to the global optimum of its category. We have also used the idea of birds’ sensitivity to the space in which they fly and we have tried to move the particles more quickly in improper spaces so that they would depart these spaces as fast as possible. On the contrary, we reduced the particles’ speed in valuable spaces in order to let them explore those places more. In the initial population, the algorithm has used the instinctive behavior of birds to provide a population based on the particles’ merits. The proposed method has been implemented in MATLAB and the results have been divided into several subpopulations or parts. The proposed method has been compared to the state-of-the-art methods. It has been shown that the proposed method is a consistent algorithm for solving the static optimization problems.
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Shaheen, Qaisar, Muhammad Shiraz, Muhammad Usman Hashmi, Danish Mahmood, Zhu zhiyu, and Rizwan Akhtar. "A Lightweight Location-Aware Fog Framework (LAFF) for QoS in Internet of Things Paradigm." Mobile Information Systems 2020 (September 16, 2020): 1–15. http://dx.doi.org/10.1155/2020/8871976.

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Realization of Internet of Things (IoT) has revolutionized the scope of connectivity and reachability ubiquitously. Under the umbrella of IoT, every object which is smart enough to communicate with other object leads to the enormous data generation of varying sizes and nature. Cloud computing (CC) employs centralized data centres for the provisioning of remote services and resources. However, for the reason of being far away from client devices, CC has their own limitations especially for time and resource critical applications. The remote and centralized characteristics of CC often result in creating bottle necks, being latent, and hence deteriorate the quality of service (QoS) in the provisioning of services. Here, the concept of fog computing (FC) emerges that tends to leverage CC and end devices for data congestion and processing locally in a distributed and decentralized way. However, addressing latency and bottleneck issues for time critical applications are still challenging. In this work, a lightweight framework is proposed which employs the concept of fog head node that keeps track of other fog nodes in terms of user registrations and location awareness. The proposed lightweight location-aware fog framework (LAFF) persistently satisfies QoS by providing an accurate location-aware algorithm. A comparative analysis is also presented to analyse network usage, service time, latency, and RAM and CPU utilization. The comparison results depicts that the LAFF reduces latency, network use, and service time by 11.01%, 7.51%, and 14.8%, respectively, in contrast to the state-of-the-art frameworks. Moreover, considering RAM and CPU utilization, the proposed framework supersedes IFAM and TPFC targeting IoT applications. The RAM consumption and CPU utilization are reduced by 8.41% and 16.23% as compared with IFAM and TPFC, respectively, making the framework lightweight. Hence, the proposed LAFF improves QoS while accessing remote computational servers for the outsourced applications in fog computing.
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Bansal, Maggi, Inderveer Chana, and Siobhan Clarke. "Enablement of IoT Based Context-Aware Smart Home with Fog Computing." Journal of Cases on Information Technology 19, no. 4 (October 2017): 1–12. http://dx.doi.org/10.4018/jcit.2017100101.

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The recent advent of Internet of Things (IoT), has given rise to a plethora of smart verticals- smart homes being one of them. Smart Home is a classic example of IoT, wherein smart appliances connected via home gateways constitute a local home network to assist people in activities of daily life. Smart Home involves IoT-based automation (such as smart lighting, heating, surveillance etc.), remote monitoring and control of smart appliances. Besides automation, human-in-the-loop is a unique characteristic of Smart home to offer personalized services. Understanding the human behavior requires context processing. Thus, enablement of Smart home involves two prominent technologies IoT and context-aware computing. Further, local devices lying in the smart home have the implicit location and situational information, hence fog computing can offer real-time smart home services. In this paper, the authors propose ICON (IoT-based CONtext-aware) framework for context-aware IoT applications such as smart home, further ICON leverages fog-based IoT middleware to perform context-aware processing.
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42

Fongen, Anders, Christian Larsen, Gheorghita Ghinea, Simon J. E. Taylor, and Tacha Serif. "Location Based Mobile Computing–A Tuplespace Perspective." Mobile Information Systems 2, no. 2-3 (2006): 135–49. http://dx.doi.org/10.1155/2006/847256.

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Location based or ”context aware” computing is becoming increasingly recognized as a vital part of a mobile computing environment. As a consequence, the need for location-management middleware is widely recognized and actively researched. Location-management is frequently offered to the application through a “location API” (e.g. JSR 179) where the mobile unit can find out its own location as coordinates or as “building, floor, room” values. It is then up to the application to map the coordinates into a set of localized variables, e.g. direction to the nearest bookshop or the local timezone. It is the opinion of the authors that a localization API should be more transparent and more integrated: The localized values should be handed to the application directly, and the API for doing so should be the same as the general storage mechanisms. Our proposed middleware for location and context management is built on top of Mobispace. Mobispace is a distributed tuplespace made for mobile units (J2me) where replication between local replicas takes place with a central server (over GPRS) or with other mobile units (using Bluetooth). Since a Bluetooth connection indicates physical proximity to another node, a set of stationary nodes may distribute locality information over Bluetooth connections, and this information may be retrieved through the ordinary tuplespace API. Besides the integration with the general framework for communication and coordination the middleware offers straightforward answers to questions like:Where is node X located? Which nodes are near me? What is the trace of node Y?
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43

Uddin, Shabbir, Amitava Ray, Karma Sonam Sherpa, and Sandeep Chakravorty. "Design of an Expert System for Distribution Planning System using Soft Computing Techniques." International Journal of Energy Optimization and Engineering 5, no. 2 (April 2016): 45–63. http://dx.doi.org/10.4018/ijeoe.2016040103.

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An expert system for distribution planning is proposed in this paper. Choosing a best location of a distribution substation and grouping the various load points to be fed from a particular distribution substation has always been a concern to the distribution planners. Here in this paper the authors present a hybridization of K-means clustering method with fuzzy context aware decision algorithm for choosing the optimum location of distribution substation and its feeder layout. K means clustering has been applied to various loads which are at different location to form a cluster with load points in closer proximity so that a substation could be placed for each cluster for the distribution of power. Fuzzy Context Aware Decision Algorithm based on the Analytical Hierarchy process (AHP) is then applied on each cluster to decide on the feeder layout connecting the load points in each cluster. The feeder layout is based on the various reliability factors and thus the result obtained will lead to optimum feeder path and will hence lower long range distribution expenses.
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44

Zeng, Wen, Reem Bashir, Trevor Wood, Francois Siewe, Helge Janicke, and Isabel Wagner. "How Location-Aware Access Control Affects User Privacy and Security in Cloud Computing Systems." EAI Endorsed Transactions on Cloud Systems 6, no. 18 (September 7, 2020): 165236. http://dx.doi.org/10.4108/eai.13-7-2018.165236.

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45

A. Almusaylim, Zahrah, and NZ Jhanjhi. "Comprehensive Review: Privacy Protection of User in Location-Aware Services of Mobile Cloud Computing." Wireless Personal Communications 111, no. 1 (October 17, 2019): 541–64. http://dx.doi.org/10.1007/s11277-019-06872-3.

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46

Shi, Gang, and Yuechen Yang. "Analysis of an Automatic Early Warning System Based on Fog Architecture." Journal of Physics: Conference Series 2095, no. 1 (November 1, 2021): 012046. http://dx.doi.org/10.1088/1742-6596/2095/1/012046.

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Abstract In the process of analysing and processing terminal sensor information, a large number of terminal sensors are needed to collect front-end information. These front-end data collection, analysis and processing require high real-time, and need the support of location aware mobile computing services. Traditional cloud computing architecture is not the best choice for service scenarios with high real-time requirements. The fog computing architecture is to extend cloud computing services to the edge of the sensor network, coupled with appropriate fitness algorithms, can effectively improve the information analysis and early warning response speed of the geological disaster information early warning system.
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47

Khalfi, Mohammed Fethi, and Sidi Mohamed Benslimane. "Spontaneous Service-Providing using WS4D in Smart Environments." International Journal of Advanced Pervasive and Ubiquitous Computing 6, no. 4 (October 2014): 71–87. http://dx.doi.org/10.4018/ijapuc.2014100106.

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Pervasive environments are characterized by a large number of embedded devices offering their services to the user. Which of the available services are of most interest to the user considerably depends on the user's current context. Spontaneous service discovery and selection is one of the most important fields of research in pervasive computing. In this paper the authors will present an enhancement of ubiquitous computing discovery mechanisms adding context handling capabilities to Web Services for Devices in Pervasive Computing using UPnP as an infrastructure to address these implicit requests. User preferences, network and location are described by a formal context model ontology that is based on two levels: a generic level and a domain specific level. As compared with previous research, the authors' method uses location aware, UPnP infrastructure, web service for devices and the notion of proactivity in pervasive computing to continuously present the Spontaneous most relevant services to the user or device in response to changes of context, services or user preferences.
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48

Song, MoonBae. "Sleepwalk: Scalable and Energy-Efficient Processing of Continuous Range Queries for Location-Aware Mobile Computing." International Journal of Distributed Sensor Networks 2015 (2015): 1–16. http://dx.doi.org/10.1155/2015/273131.

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Recently, monitoring queries are getting attention for various real-life applications such as safety, security, and personalization services. This work proposes a distributed sensing and monitoring technique (calledSleepwalk) for continuous range queries with energy- and computation-efficient optimizations. In our scheme, each mobile client (MC) is aware of its nearby monitoring queries by leveraging its processing power. The proposed Sleepwalk has three major contributions. First, withpiecewiselinear movement assumption and motion vectorv̅, it can locally preevaluate every possible query result in advancein bulkand sends them to the server at once. We also provide a timestamp-based invalidation technique for efficiently removing failed preevaluated results by computing the smallest valid timestamp. Second, an energy-conserving technique that repeatedly sleeps off MCs whenever possible is proposed by calculating thesafely sleepabletime. Third, we provide a set of localized query optimization techniques for MCs’ local query subset using plane-sweeping, which effectively minimize search space. Extensive experiments indicate that Sleepwalk technique remarkably outperforms existing state-of-the-art techniques in terms of server scalability, communication cost, and energy consumption of MCs.
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49

Zavala, Laura, Pradeep K. Murukannaiah, Nithyananthan Poosamani, Tim Finin, Anupam Joshi, Injong Rhee, and Munindar P. Singh. "Platys: From Position to Place-Oriented Mobile Computing." AI Magazine 36, no. 2 (June 21, 2015): 50–62. http://dx.doi.org/10.1609/aimag.v36i2.2584.

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The Platys project focuses on developing a high-level, semantic notion of location called place. A place, unlike a geospatial position, derives its meaning from a user’s actions and interactions in addition to the physical location where they occur. Our aim is to enable the construction of a large variety of applications that take advantage of place to render relevant content and functionality and thus, improve user experience. We consider elements of context that are particularly related to mobile computing. The main problems we have addressed to realize our place-oriented mobile computing vision, are representing places, recognizing places, engineering place-aware applications. We describe the approaches we have developed for addressing these problems and related subproblems. A key element of our work is the use of collaborative information sharing where users’ devices share and integrate knowledge about places. Our place ontology facilitates such collaboration. Declarative privacy policies allow users to specify contextual features under which they prefer to share or not share their information.
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Yin, Yuyu, Song Aihua, Gao Min, Xu Yueshen, and Wang Shuoping. "QoS Prediction for Web Service Recommendation with Network Location-Aware Neighbor Selection." International Journal of Software Engineering and Knowledge Engineering 26, no. 04 (May 2016): 611–32. http://dx.doi.org/10.1142/s0218194016400040.

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Web service recommendation is one of the key problems in service computing, especially in the case of a large number of service candidates. The QoS (quality of service) values are usually leveraged to recommend services that best satisfy a user’s demand. There are many existing methods using collaborative filtering (CF) to predict QoS missing values, but very limited works can leverage the network location information in the user side and service side. In real-world service invocation scenario, the network location of a user or a service makes great impact on QoS. In this paper, we propose a novel collaborative recommendation framework containing three novel prediction models, which are based on two techniques, i.e. matrix factorization (MF) and network location-aware neighbor selection. We first propose two individual models that have the capability of using the user and service information, respectively. Then we propose a unified model that combines the results of the two individual models. We conduct sufficient experiments on a real-world dataset. The experimental results demonstrate that our models achieve higher prediction accuracy than baseline models, and are not sensitive to the parameters.
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