A Quality-of-Things model for assessing the Internet-of-Things' nonfunctional properties
Ayesha Qamar
Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Pakistan
Search for more papers by this authorCorresponding Author
Muhammad Asim
Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Pakistan
Muhammad Asim, Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad-44000, Pakistan.
Email: [email protected]
Search for more papers by this authorZakaria Maamar
College of Technological Innovation, Zayed University, Dubai, UAE
Search for more papers by this authorSaad Saeed
Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Pakistan
Search for more papers by this authorThar Baker
Department of Computer Science, Liverpool John Moores University, Liverpool, UK
Search for more papers by this authorAyesha Qamar
Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Pakistan
Search for more papers by this authorCorresponding Author
Muhammad Asim
Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Pakistan
Muhammad Asim, Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad-44000, Pakistan.
Email: [email protected]
Search for more papers by this authorZakaria Maamar
College of Technological Innovation, Zayed University, Dubai, UAE
Search for more papers by this authorSaad Saeed
Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Pakistan
Search for more papers by this authorThar Baker
Department of Computer Science, Liverpool John Moores University, Liverpool, UK
Search for more papers by this authorAbstract
The Internet of Things (IoT) is in a “desperate” need for a practical model that would help in differentiating things according to their nonfunctional properties. Unfortunately, despite IoT growth, such properties either lack or ill-defined resulting into ad hoc ways of selecting similar functional things. This paper discusses how things' nonfunctional properties are combined into a Quality-of-Things (QoT) model. This model includes properties that define the performance of things' duties related to sensing, actuating, and communicating. Since the values of QoT properties might not always be available or confirmed, providers of things can tentatively define these values and submit them to an Independent Regulatory Authority (IRA) whose role is to ensure fair competition among all providers. The IRA assesses the values of nonfunctional properties of things prior to recommending those that could satisfy users' needs. To evaluate the technical doability of the QoT model, a set of comprehensive experiments are conducted using real data sets. The results depict an acceptable level of the QoT estimation accuracy.
REFERENCES
- 1Andročec D, Tomaš B, Kišasondi T. Interoperability and lightweight security for simple IoT devices. Paper presented at: 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO); 2017; Opatija, Croatia.
- 2Martínez-Ballesté A, Pérez-Martínez PA, Solanas A. The pursuit of citizens' privacy: a privacy-aware smart city is possible. IEEE Commun Mag. 2013; 51(6): 136-141.
- 3Eisa M, Younas M, Basu K. Analysis and representation of QoS attributes in cloud service selection. Paper presented at: 2018 IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA); 2018; Krakow, Poland.
- 4Rittinghouse J, Ransome J. Cloud Computing: Implementation, Management, and Security. Boca Raton, FL: CRC Press; 2009.
10.1201/9781439806814 Google Scholar
- 5Uriarte RB, Tiezzi F, Nicola RD. SLAC: a formal service-level-agreement language for cloud computing. In: Proceedings of the 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing (UCC); 2014; London, UK.
- 6Perera C, Liu CH, Jayawardena S, Chen M. A survey on Internet of Things from industrial market perspective. IEEE Access. 2014; 2: 1660-1679.
- 7Maamar Z, Baker T, Sellami M, Asim M, Ugljanin E, Faci N. Cloud vs edge: who serves the Internet-of-Things better? Internet Technol Lett. 2018; 1(5):e66.
- 8Sundareswaran S, Squicciarini A, Lin D. A brokerage-based approach for cloud service selection. Paper presented at: 2012 IEEE 5th International Conference on Cloud Computing; 2012; Honolulu, HI.
- 9Issarny V, Bouloukakis G, Georgantas N, Billet B. Revisiting service-oriented architecture for the IoT: a middleware perspective. In: Service-Oriented Computing: 14th International Conference, ICSOC 2016, Banff, AB, Canada, October 10-13, 2016, Proceedings. Cham, Switzerland: Springer International Publishing; 2016: 3-17.
10.1007/978-3-319-46295-0_1 Google Scholar
- 10Mzahm AM, Ahmad MS, Tang AY. Agents of Things (AoT): an intelligent operational concept of the Internet of Things (IoT). Paper presented at: 2013 13th International Conference on Intelligent Systems Design and Applications; 2013; Bangi, Malaysia.
- 11Awan I, Younas M, Naveed W. Modelling QoS in IoT applications. Paper presented at: 2014 17th International Conference on Network-Based Information Systems; 2014; Salerno, Italy.
- 12Wang S, Zhao Y, Huang L, Xu J, Hsu C-H. QoS prediction for service recommendations in mobile edge computing. J Parallel Distributed Comput. 2019; 127: 134-144.
- 13Ling J, Tang M, Zheng Z, Liu XF, Lyu S. Location-aware and personalized collaborative filtering for web service recommendation. IEEE Trans Serv Comput. 2016; 9(5): 686-699.
- 14Chen X, Zheng Z, Yu Q, Lyu MR. Web service recommendation via exploiting location and QoS information. IEEE Trans Parallel Distributed Syst. 2014; 25(7): 1913-1924.
- 15Yu C, Huang L. A web service QoS prediction approach based on time- and location-aware collaborative filtering. SOCA. 2016; 10(2): 135-149.
- 16Hu Y, Peng Q, Hu X. A time-aware and data sparsity tolerant approach for web service recommendation. Paper presented at: 2014 IEEE International Conference on Web Services; 2014; Anchorage, AK.
- 17Lin S-Y, Lai C-H, Wu C-H, Lo C-C. A trustworthy QoS-based collaborative filtering approach for web service discovery. J Syst Softw. 2014; 93: 217-228.
- 18Qiu W, Zheng Z, Wang X, Yang X, Lyu MR. Reputation-aware QoS value prediction of web services. Paper presented at: 2013 IEEE International Conference on Services Computing; 2013; Santa Clara, CA.
- 19Wu C, Qiu W, Zheng Z, Wang X, Yang X. QoS prediction of web services based on two-phase K-means clustering. Paper presented at: 2015 IEEE International Conference on Web Services; 2015; New York, NY.
- 20White G, Palade A, Cabrera C, Clarke S. Quantitative evaluation of QoS prediction in IoT. Paper presented at: 2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W); 2017; Denver, CO.
- 21White G, Palade A, Cabrera C, Clarke S. IoTpredict: collaborative QoS prediction in IoT. Paper presented at: 2018 IEEE International Conference on Pervasive Computing and Communications (PerCom); 2018; Athens, Greece.
- 22Menascé DA. QoS issues in web services. IEEE Internet Comput. 2002; 6(6): 72-75.
- 23Tang M, Zhang T, Liu J, Chen J. Cloud service QoS prediction via exploiting collaborative filtering and location-based data smoothing. Concurrency Computat Pract Exper. 2015; 27(18): 5826-5839.
- 24Zheng X, Da Xu L, Chai S. QoS recommendation in cloud services. IEEE Access. 2017; 5: 5171-5177.
- 25Xu M, Watanachaturaporn P, Varshney PK, Arora MK. Decision tree regression for soft classification of remote sensing data. Remote Sens Environ. 2005; 97(3): 322-336.
- 26White G, Palade A, Clarke S. Forecasting QoS attributes using LSTM networks. Paper presented at: 2018 International Joint Conference on Neural Networks (IJCNN); 2018; Rio de Janeiro, Brazil.
- 27Batra N, Parson O, Berges M, Singh A, Rogers A. A comparison of non-intrusive load monitoring methods for commercial and residential buildings. arXiv:1408.6595. 2014.
- 28Zheng Z, Zhang Y, Lyu MR. Investigating QoS of real-world web services. IEEE Trans Serv Comput. 2014; 7(1): 32-39.
- 29White G, Nallur V, Clarke S. Quality of service approaches in IoT: a systematic mapping. J Syst Softw. 2017; 132: 186-203.
- 30Sarwar B, Karypis G, Konstan J, Riedl J. Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web (WWW); 2001; New York, NY.
- 31Zheng Z, Ma H, Lyu MR, King I. QoS-aware web service recommendation by collaborative filtering. IEEE Trans Serv Comput. 2011; 4(2): 140-152.
