Abstract
Pervasive computing (PerC) systems are now being integrated into everyday life which is deployed in homes, offices, hospitals, universities. The sensor data can be integrated with different range of sources in pervasive systems which also offers an extensible, open and portfolio services. Due to remarkable progress in various domains such as smart phones, computing power, sensor, network and embedded devices, wireless communications which are combined with social networking paradigms, cloud computing and data mining techniques, that enabled the users for creating PerC systems with global accessibility. The major challenge of PerC is to provide the suitable consistent adaptive behaviors and context-aware systems in a vast amount of sensor data for these services which need to improve the accuracy, precision and dynamism. This research work provides an inclusive analysis of characteristics of data, then the complexities of the existing technique are reviewed which are mostly used in inferring situations from sensor data. The extensive experiments are carried out on benchmark dataset to validate the efficiency of existing techniques namely multi-context, mechanisms of user-side publish/subscribe by using the metrics such as accuracy, f-measure, precision, recall and communication overhead. Many open research opportunities are identified in this area by comparing and contrasting the existing techniques, which are discussed in this research work.
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References
Al-Shargabi, A.A., Siewe, F.: Resolving context conflicts using association rules (RCCAR) to improve quality of context-aware systems. In: 8th International Conference on Computer Science and Education. IEEE (2013)
Avenoğlu, B., Eren, P.E.: A context-aware and workflow-based framework for pervasive environments. J. Ambient Intell. Human. Comput. 10, 215–237 (2019)
Bansod, G., Pisharoty, N., Patil, A.: BORON: an ultra-lightweight and low power encryption design for pervasive computing. Front. Inf. Technol. Electron. Eng. 18, 317–331 (2017)
Bodaghi, A.: A novel pervasive computing method to enhance efficiency of walking activity. Health Technol. 6, 269–276 (2016)
Bordel, B., Alcarria, R., Robles, T., Martín, D.: Cyber–physical systems: extending pervasive sensing from control theory to the Internet of Things. Pervasive Mob. Comput. 40, 156–184 (2017)
Cassales, G.W., Charão, A.S., Kirsch-Pinheiro, M., Souveyet, C., Steffenel, L.A.: Improving the performance of Apache Hadoop on pervasive environments through context-aware scheduling. J. Ambient Intell. Human. Comput. 7, 333–345 (2016)
Chabridon, S., Conan, D., Abid, Z., Taconet, C.: Building ubiquitous QoC-aware applications through model-driven software engineering. Sci. Comput. Progr. 78, 1912–1929 (2013)
Conti, M., Das, S.K., Bisdikian, C., Kumar, M., Ni, L.M., Passarella, A., Zambonelli, F.: Looking ahead in pervasive computing: challenges and opportunities in the era of cyber–physical convergence. Pervasive Mob. Comput. 8, 2–21 (2012)
Doukas, C., Maglogiannis, I.: Bringing IoT and cloud computing towards pervasive healthcare. In: Proceeding of IEEE Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (2012)
D’Angelo, G., Palmieri, F., Rampone, S.: Detecting unfair recommendations in trust-based pervasive environment. Inf. Sci. 486, 31–51 (2019)
D’Angelo, G., Rampone, S., Palmieri, F.: Developing a trust model for pervasive computing based on Apriori association rules learning and Bayesian classification. Soft. Comput. 21, 6297–6315 (2017)
Jayalakshmi, M., Gomathi, V.: Pervasive health monitoring through video-based activity information integrated with sensor-cloud oriented context-aware decision support system. Multimed. Tools Appl. (2018). https://doi.org/10.1007/s11042-018-6716-8
Karthik, N., Ananthanarayana, V.S.: Context aware trust management scheme for pervasive healthcare. Wirel. Pers. Commun. 105, 725–763 (2019)
Lee, E., Jin-Hee, L., Shin, B.: Vertex relocation: a feature-preserved terrain rendering method for pervasive computing environments. Multimed. Tools Appl. 75, 14057–14073 (2016)
Riboni, D.: Opportunistic pervasive computing: adaptive context recognition and interfaces. CCF Trans. Pervasive Comput. Interact. (2019). https://doi.org/10.1007/s42486-018-00004-9
Roth, F.M., Becker, C., Vega, G., Lalanda, P.: XWARE—a customizable interoperability framework for pervasive computing systems. Pervasive Mob. Comput. 47, 13–30 (2018)
Roy, N., Misra, A., Das, S.K., Julien, C.: Determining quality-and energy-aware multiple contexts in pervasive computing environments. IEEE/ACM Trans. Netw. TON 24, 3026–3042 (2016)
Serral, E., Sernani, P., Dalpiaz, F.: Personalized adaptation in pervasive systems via non-functional requirements. J. Ambient Intell. Human. Comput. 9, 1729–1743 (2018)
Wang, Z., Luo, T., Yang, L.: An energy-and space-efficient object representation model in pervasive computing systems. IEEE Syst. J. 12, 1456–1466 (2016)
Yu, C., Yao, D., Yang, L.T., Jin, H.: Energy conservation in progressive decentralized single-hop wireless sensor networks for pervasive computing environment. IEEE Syst. J. 11, 823–834 (2014)
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Gollagi, S.G., Math, M.M. & Daptardar, A.A. A survey on pervasive computing over context-aware system. CCF Trans. Pervasive Comp. Interact. 2, 79–85 (2020). https://doi.org/10.1007/s42486-020-00030-6
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DOI: https://doi.org/10.1007/s42486-020-00030-6