Open Access Open Access  Restricted Access Subscription or Fee Access

A Complexity in Design of Cyber-Physical Systems: Context framework

Arun Kumar

Abstract


Cyber-bodily structures play a key role in a huge quantity of application domain names which include transportation, energy, health and nicely-being, manufacturing, and indeed as a part of clever infrastructures and towns—consequently now not best referring to business domain names. This paper makes a specialty of the complexity of destiny CPS, intuitively interpreted as a machine feature making it tough, and sometimes even not possible, as it should be predicted conduct over time, mainly in terms of knowledge all applicable interactions among CPS factors and with the surroundings. We trust unparalleled competencies and possibilities could be executed by a corresponding remarkable technological complexity, where unknown or poorly understood such interactions may lead to sudden and undesired results which include effort overruns, poor operational overall performance, or maybe gadget disasters. New era cell gadgets have emerged as inevitable to be hired in the realm of ubiquitous sensing. Mainly, smartphones have gained significance to be used for Human activity recognition (HAR) primarily based research considering its miles believed that spotting the human-centric hobby patterns correctly enough may want to provide better expertise of human behaviors, and also extra drastically, it is able to supply a threat for helping people for you to enhance the pleasant of lives. However, the mixing and awareness of HAR based totally cellular services stand as a massive project on aid-confined mobile embedded platforms. In this way, this chapter proposes a novel Discrete-Time Inhomogeneous Hidden Semi-Markov model (DT-IHS-MM) primarily based prevalent framework to address a better awareness of HAR-based cell context-focus. Further to that, the chapter presents power-efficient sensor control strategies including 3 intuitive solutions, and additionally confined Markov selection process (CMDP) and partially Observable Markov choice procedure (POMDP) primarily based most suitable solutions in order to respond to the tradeoff defined between the accuracy in context-conscious offerings and the strength intake because of the carrier operations.


Keywords: Context inference module, cyber-physical systems, CMDP, DT-IHS-MM, POMDP


Full Text:

PDF

References


Jatobá LC, Grossmann U, Kunze C, Ottenbacher J, Stork W. Context-

aware mobile health monitoring: evaluation of different pattern recognition methods for classification of physical activity. Annu Int Conf IEEE Eng Med Biol Soc conference of the IEEE Eng in Medicine and Biology Society (EMBS). aug 2008;2008:5250–3. doi: 10.1109/IEMBS.2008.4650398, PMID 19163901.

Miluzzo E, Lane ND, Lu H, Campbell AT. Research in the app store era: experiences from the cenceme app deployment on the iPhone.

Zappi P, Lombriser C, Stiefmeier T, Farella E, Roggen D, Benini L, Trister G. Activity recognition from on-body sensors: accuracy-power trade-off by dynamic sensor selection. In: European conference on Wireless Sensor Networks. Springer-Verlag; 2008. p. 17–33.

Chen Y-P, Yang J-Y, Liou S-N, Lee G-Y, Wang J-S. Online classifier construction algorithm for human activity detection using a tri-axial accelerometer. Appl Math Comput. 2008;205(2):849–60. doi: 10.1016/j.amc.2008.05.099.

Maurer U, Smailagic A, Siewiorek D, Deisher M. Activity recognition and monitoring using multiple sensors on different body positions. Int'l Workshop on Wearable and Implantable Body Sensor Networks (BSN). Apr 2006, P. 4 P.-116.

Zhu C, Sheng W. Human daily activity recognition in robot-assisted living using multisensor fusion. In: IEEE int’ conference on Robotics and Automation (ICRA). Vol. l; May 2009. p. 2154–9.

Berchtold M, Budde M, Gordon D, Schmidtke H, Beigl M. Actiserv: activity recognition service for mobile phones. Int'l Symposium on Wearable Computers (ISWC). oct 2010:1–8.

Kao T, Lin C, Wang J. Development of a portable activity detector for daily activity recognition. In: IEEE int’ Symposium on Industrial Electronics (ISIE). Vol. l; 2009. p. 115–20.

Berchtold M, Budde M, Gordon D, Schmidtke H, Beigl M. Actiserv: activity recognition service for mobile phones. Wearable Comput (ISWC). 2010:1–9.

Miluzzo E, Cornelius CT, Ramaswamy A, Choudhury T, Liu Z, Campbell AT. ’Darwin phones: the evolution of sensing and inference on mobile phones,’ ser. MobiSys. ’10.ACM:5–20.

Peebles D, Lu H, Lane ND, Choudhury T, Campbell AT. Community-guided learning: exploiting mobile sensor users to model human behavior. In: AAAI, Press A; 2010.

Siirtola P, Rning J. Recognizing human activities user-independently on smartphones based on accelerometer data. IJIMAI;5:38–45.

Vinh LT, Lee S, Le HX, Ngo HQ, Kim HI, Han M, Lee Y-K. Semi-Markov conditional random fields for accelerometer-based activity recognition. Appl Intell. Oct 2011;35(2):226–41. doi: 10.1007/s10489–010–0216–5.

Lester J, Choudhury T, Borriello G. A practical approach to recognizing physical activities. Lecture Notes in Computer Science. Proceedings of the of pervasive. 2006:1–16. doi: 10.1007/11748625_1.

He Z-Y, Jin L-W. Activity recognition from acceleration data using AR model representation and SVM. Int'l Conf. on Mach. Learning and Cybernetics,. 2008;4(Jul):2245–50.

Riboni D, Bettini C. Cosar: hybrid reasoning for context-aware activity recognition. Personal Ubiquitous Comput. Mar 2011;15(3):271–89. doi: 10.1007/s00779–010–0331–7.

Sun L, Zhang D, Li B, Guo B, Li S. Activity recognition on an accelerometer embedded mobile phone with varying positions and orientations. Lecture Notes in Computer Science conference on Ubiquitous intelligence and computing (UIC). 2010;l:548–62. doi: 10.1007/978–3–642–16355–5_42.

He Z, Jin L. Activity recognition from acceleration data based on discrete consine transform and svm. In: SMC. IEEE int’ conference on Sys., Man and Cybernetics. Vol. l; oct 2009. p. 5041–4.

Hanai Y, Nishimura J, Kuroda T. Haar-like filtering for human activity recognition using 3d accelerometer. DSP/SPE. 2009;09(Jan):675–8.

Minnen D, Westeyn T, Ashbrook D, Presti P, Starner T. Recognizing soldier activities in the field. Int'l Workshop on Wearable and Implantable Body Sensor Networks (BSN).Springer. 2007:236–41. doi: 10.1007/978–3–540–70994–7_40.

Tapia EM, Intille SS, Haskell W, Larson K, Wright J, King A, Friedman R. Realtime recognition of physical activities and their intensities using wireless accelerometers and a heart rate monitor. In: IEEE int’ Symposium on Wearable Computers (ISWC). Vol. l. IEEE Computer Society; 2007. p. 1–4.

Kwapisz JR, Weiss GM, Moore SA. Activity recognition using cell phone accelerometers. SIGKDD Explor Newsl. 2011;12(2):74–82. doi: 10.1145/1964897.1964918.

Bao L, Intille SS. Activity recognition from user-annotated acceleration data. Lecture Notes in Computer Science. 2004:1–17. doi: 10.1007/978–3–540–24646–6_1.

Bandyopadhyay D, Sen J. Internet of things: applications and challenges in technology and standardization. Wirel Personal Commun. 2011;58(1):49–69. doi: 10.1007/s11277–011–0288–5.

Roman M, Hess C, Cerqueira R, Ranganathan A, Campbell RH, Nahrstedt K. A middleware infrastructure for active spaces. IEEE Pervasive Comput. 2002;1(4):74–83. doi: 10.1109/MPRV.2002.1158281.


Refbacks

  • There are currently no refbacks.