Abstract
Devices in Internet of Things (IoT) often offer services that allow tenants to access data of different metrics collected from sensors. These sensors can be built-in or remotely connected to such devices. Given that such monitoring services are usually invoked within devices that have limited IT resource capacities, it is impossible to collect data of all metrics in the application’s context with a very high frequency. In this paper, we propose a framework that determines which metrics to monitor, monitoring start times, the optimal allocation of metrics to devices, and the optimal monitoring frequency of these metrics, without exceeding different device-specific time-varying resource capacities. Our approach is also adaptive; it gives updated solutions whenever a trigger happens in the system necessitating the need for a change in the previous optimal decisions. We provide an implementation of our approach and present numerical results showing its usage and limitations. At the heart of our approach is an integer programming optimization model that might be hard to solve for large-sized IoT systems. Thus, we present another predictive model that predicts for the user whether our optimization-based approach would be appropriate for her system or not. That is, whether the optimization model is predicted to give optimal solutions within some user-given optimality gaps in a time less than or equal to some user-given maximum allowed time.
Similar content being viewed by others
References
Aceto G, Botta A, de Donato W, Pescapé A (2013) Cloud monitoring: a survey. Comput Netw 57(9):2093–2115. https://doi.org/10.1016/j.comnet.2013.04.001
Addo-Tenkorang R, Helo PT (2016) Big data applications in operations/supply-chain management: a literature review. Comput Ind Eng 101(Supplement C):528–543. https://doi.org/10.1016/j.cie.2016.09.023
Aggarwal CC, Ashish N, Sheth A (2013) The Internet of Things: a survey from the data-centric perspective. In: Aggarwal C (ed) Managing and mining sensor data. Springer, Boston, MA, pp 383–428
Barcelo M, Correa A, Llorca J, Tulino AM, Vicario JL, Morell A (2016) IoT-Cloud service optimization in next generation smart environments. IEEE J Sel Areas Commun 34(12):4077–4090. https://doi.org/10.1109/JSAC.2016.2621398
Brandt J, Gentile A, Mayo J, Pebay P, Roe D, Thompson D, Wong M (2009) Resource monitoring and management with OVIS to enable HPC in cloud computing environments. In: IEEE international symposium on parallel distributed processing. IEEE, pp 1–8. https://doi.org/10.1109/IPDPS.2009.5161234
Burns B, Grant B, Oppenheimer D, Brewer E, Wilkes J (2016) Borg, omega, and kubernetes. Queue 14(1):70–93
Clayman S, Galis A, Mamatas L (2010) Monitoring virtual networks with Lattice. In: Network operations and management symposium workshops (NOMS Wksps). IEEE, pp 239–246. https://doi.org/10.1109/NOMSW.2010.5486569
Eckman DJ, Maillart LM, Schaefer AJ (2016) Optimal pinging frequencies in the search for an immobile beacon. IIE Trans 48(6):489–500. https://doi.org/10.1080/0740817X.2015.1110270
Entreprises N (2014) Nagios documentation. http://www.nagios.org/documentation. Accessed 8 Aug 2018
Friedman J, Hastie T, Tibshirani R (2001) The elements of statistical learning, vol 1. Springer, New York
Gil D, Ferrández A, Mora-Mora H, Peral J (2016) Internet of things: a review of surveys based on context aware intelligent services. Sensors 16(7):1069
Gubbi J, Buyya R, Marusic S, Palaniswami M (2013) Internet of things (IoT): a vision, architectural elements, and future directions. Future Gener Comput Syst 29(7):1645–1660. https://doi.org/10.1016/j.future.2013.01.010
Hindman B, Konwinski A, Zaharia M, Ghodsi A, Joseph AD, Katz RH, Shenker S, Stoica I (2011) Mesos: a platform for fine-grained resource sharing in the data center. In: 2011 USENIX conference on networked systems design and implementation (NSDI). USENIX, pp 295–308
IBM ILOG CPLEX (2016) CPLEX 12.7 user’s manual. International Business Machines Corporation
Issarny V, Bouloukakis G, Georgantas N, Billet B (2016) Revisiting service-oriented architecture for the IoT: a middleware perspective. In: International conference on service-oriented computing (SCC). Springer, pp 3–17
Jeswani D, Natu M, Ghosh RK (2013) Adaptive monitoring: a framework to adapt passive monitoring using probing. In: International conference on network and service management (CNSM). International Federation for Information Processing, pp 350–356
Jeswani D, Natu M, Ghosh RK (2015) Adaptive monitoring: application of probing to adapt passive monitoring. J Netw Syst Manag 23(4):950–977. https://doi.org/10.1007/s10922-014-9330-8
Kannan R, Monma CL (1978) On the computational complexity of integer programming problems. In: Henn R, Korte B, Oettli W (eds) Optimization and operations research. Lecture notes in economics and mathematical systems, vol 157. Springer, Berlin, Heidelberg, pp 161–172
Katsaros G, Gallizo G, Kübert R, Wang T, Fitó JO, Henriksson D (2011) A multi-level architecture for collecting and managing monitoring information in cloud environments. In: Leymann F, Ivanov II, van Sinderen M, Shishkov B (eds) Proceedings of the third international conference on cloud computing and services science, CLOSER. SciTePress, pp 232–239
Kelly SDT, Suryadevara NK, Mukhopadhyay SC (2013) Towards the implementation of IoT for environmental condition monitoring in homes. IEEE Sens J 13(10):3846–3853. https://doi.org/10.1109/JSEN.2013.2263379
Luo J, Chen Y, Tang K, Luo J (2009) Remote monitoring information system and its applications based on the internet of things. In: International conference on future BioMedical information engineering (FBIE). pp 482–485
Massie ML, Chun BN, Culler DE (2004) The ganglia distributed monitoring system: design, implementation, and experience. Parallel Comput 30(7):817–840
Merkel D (2014) Docker: lightweight linux containers for consistent development and deployment. Linux J. 2014(239):2
Mohamed M (2014) Generic monitoring and reconfiguration for service-based applications in the cloud. Ph.D., Institut National des Télécommunications. https://tel.archives-ouvertes.fr/tel-01123740. Accessed 8 Aug 2018
MQTT Community: MQTT: A M2M/Internet of Things Connectivity Protocol. (2017) http://mqtt.org/. Accessed 8 Aug 2018
Munawar MA, Reidemeister T, Jiang M, George A, Ward PAS (2008) Adaptive monitoring with dynamic differential tracing-based diagnosis. In: De Turck F, Kellerer W, Kormentzas G (eds) Managing large-scale service deployment. Lecture notes in computer science, vol 5273. Springer, Berlin, Heidelberg, pp 162–175
Paek J, Kim J, Govindan R (2010) Energy-efficient rate-adaptive gps-based positioning for smartphones. In: International conference on mobile systems, applications, and services (MobiSys ’10). ACM, pp 299–314. https://doi.org/10.1145/1814433.1814463
Perera C, Jayaraman P, Zaslavsky A, Christen P, Georgakopoulos D (2013) Dynamic configuration of sensors using mobile sensor hub in internet of things paradigm. In: 2013 IEEE international conference on intelligent sensors, sensor networks and information processing. IEEE, pp 473–478
Qiu X, Luo H, Xu G, Zhong R, Huang GQ (2015) Physical assets and service sharing for IoT-enabled supply hub in industrial park (ship). Int J Prod Econ 159(Supplement C):4–15. https://doi.org/10.1016/j.ijpe.2014.09.001
Rohokale VM, Prasad NR, Prasad R (2011) A cooperative internet of things (IoT) for rural healthcare monitoring and control. In: 2011 International conference on wireless communication, vehicular technology, information theory and aerospace electronic systems technology (Wireless VITAE). pp 1–6
Tao F, Zuo Y, Xu LD, Zhang L (2014) IoT-based intelligent perception and access of manufacturing resource toward cloud manufacturing. IEEE Trans Ind Inf 10(2):1547–1557. https://doi.org/10.1109/TII.2014.2306397
Tata S, Mohamed M, Megahed A (2017) An optimization approach for adaptive monitoring in IoT environments. In: 2017 IEEE international conference on services computing (SCC). IEEE, pp 378–385
Trihinas D, Pallis G, Dikaiakos MD (2015) AdaM: an adaptive monitoring framework for sampling and filtering on IoT devices. In: IEEE international conference on big data. IEEE, 717–726. https://doi.org/10.1109/BigData.2015.7363816
Vinyals M, Rodriguez-Aguilar JA, Cerquides J (2011) A survey on sensor networks from a multiagent perspective. Comput J 54(3):455–470. https://doi.org/10.1093/comjnl/bxq018
Xiang F, Hu YF (2012) Cloud manufacturing resource access system based on internet of things. In: Frontiers of manufacturing and design science II, applied mechanics and materials, vol 121. Trans Tech Publications, pp 2421–2425. https://doi.org/10.4028/www.scientific.net/AMM.121-126.2421
Yigitoglu E, Mohamed M, Liu L, Ludwig H (2017) Foggy: a framework for continuous automated IoT application deployment in fog computing. In: 2017 IEEE international conference on AI & mobile services (AIMS). IEEE, pp 38–45
Zhang L, Luo Y, Tao F, Li BH, Ren L, Zhang X, Guo H, Cheng Y, Hu A, Liu Y (2014) Cloud manufacturing: a new manufacturing paradigm. Enterp Inf Syst 8:167–187. https://doi.org/10.1080/17517575.2012.683812
Zhengxia W, Laisheng X (2010) Modern logistics monitoring platform based on the internet of things. In: 2010 International conference on intelligent computation technology and automation, vol 2. pp 726–731. https://doi.org/10.1109/ICICTA.2010.650
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Megahed, A., Pazour, J.A., Nazeem, A. et al. Monitoring services in the Internet of Things: an optimization approach. Computing 101, 1119–1145 (2019). https://doi.org/10.1007/s00607-018-0658-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00607-018-0658-x