Reliability-based condition assessment of steel containment and liners

Steel containments and liners in nuclear power plants may be exposed to aggressive environments that may cause their strength and stiffness to decrease during the plant service life. Among the factors recognized as having the potential to cause structural deterioration are uniform, pitting or crevice corrosion; fatigue, including crack initiation and propagation to fracture; elevated temperature; and irradiation. The evaluation of steel containments and liners for continued service must provide assurance that they are able to withstand future extreme loads during the service period with a level of reliability that is sufficient for public safety. Rational methodologies to provide such assurances can be developed using modern structural reliability analysis principles that take uncertainties in loading, strength, and degradation resulting from environmental factors into account. The research described in this report is in support of the Steel Containments and Liners Program being conducted for the US Nuclear Regulatory Commission by the Oak Ridge National Laboratory. The research demonstrates the feasibility of using reliability analysis as a tool for performing condition assessments and service life predictions of steel containments and liners. Mathematical models that describe time-dependent changes in steel due to aggressive environmental factors are identified, and statistical data supporting the use of these models in time-dependent reliability analysis are summarized. The analysis of steel containment fragility is described, and simple illustrations of the impact on reliability of structural degradation are provided. The role of nondestructive evaluation in time-dependent reliability analysis, both in terms of defect detection and sizing, is examined. A Markov model provides a tool for accounting for time-dependent changes in damage condition of a structural component or system. 151 refs

New Waves of IoT Technologies Research – Transcending Intelligence and Senses at the Edge to Create Multi Experience Environments

The next wave of Internet of Things (IoT) and Industrial Internet of Things (IIoT) brings new technological developments that incorporate radical advances in Artificial Intelligence (AI), edge computing processing, new sensing capabilities, more security protection and autonomous functions accelerating progress towards the ability for IoT systems to self-develop, self-maintain and self-optimise. The emergence of hyper autonomous IoT applications with enhanced sensing, distributed intelligence, edge processing and connectivity, combined with human augmentation, has the potential to power the transformation and optimisation of industrial sectors and to change the innovation landscape. This chapter is reviewing the most recent advances in the next wave of the IoT by looking not only at the technology enabling the IoT but also at the platforms and smart data aspects that will bring intelligence, sustainability, dependability, autonomy, and will support human-centric solutions.acceptedVersio

Model-free non-invasive health assessment for battery energy storage assets

With the increasing application of battery energy storage in buildings, networks and transportation, an emerging challenge to overall system resilience is in understanding the constituent asset health. Current battery energy storage considerations focus on adhering to the technical specification of the service in the short term, rather than the long-term consequences to battery health. However, accurately determining battery health generally requires invasive measurements or computationally expensive physics-based models which do not scale up to a fleet of assets cost-effectively. This paper alternatively proposes capturing cumulative maloperation through a physics model-free proxy for cell health, articulated via the strong influence misuse has on the internal chemical state. A Hidden Markov Chain approach is used to automatically recognize violations of chemistry specific usage preferences from sequences of observed charging actions. The resulting methodology is demonstrated on distribution network level electrical demand and generation data, accurately predicting maloperation under a number of battery technology scenarios

Measuring Large-Scale Social Networks with High Resolution

This paper describes the deployment of a large-scale study designed to measure human interactions across a variety of communication channels, with high temporal resolution and spanning multiple years-the Copenhagen Networks Study. Specifically, we collect data on face-to-face interactions, telecommunication, social networks, location, and background information (personality, demographics, health, politics) for a densely connected population of 1 000 individuals, using state-of-the-art smartphones as social sensors. Here we provide an overview of the related work and describe the motivation and research agenda driving the study. Additionally, the paper details the data-types measured, and the technical infrastructure in terms of both backend and phone software, as well as an outline of the deployment procedures. We document the participant privacy procedures and their underlying principles. The paper is concluded with early results from data analysis, illustrating the importance of multi-channel high-resolution approach to data collection

New visualization model for large scale biosignals analysis

Benefits of long-term monitoring have drawn considerable attention in healthcare. Since the acquired data provides an important source of information to clinicians and researchers, the choice for long-term monitoring studies has become frequent. However, long-term monitoring can result in massive datasets, which makes the analysis of the acquired biosignals a challenge. In this case, visualization, which is a key point in signal analysis, presents several limitations and the annotations handling in which some machine learning algorithms depend on, turn out to be a complex task. In order to overcome these problems a novel web-based application for biosignals visualization and annotation in a fast and user friendly way was developed. This was possible through the study and implementation of a visualization model. The main process of this model, the visualization process, comprised the constitution of the domain problem, the abstraction design, the development of a multilevel visualization and the study and choice of the visualization techniques that better communicate the information carried by the data. In a second process, the visual encoding variables were the study target. Finally, the improved interaction exploration techniques were implemented where the annotation handling stands out. Three case studies are presented and discussed and a usability study supports the reliability of the implemented work

Cross-Layer Energy Optimization for IoT Environments: Technical Advances and Opportunities

[EN] Energy efficiency is a significant characteristic of battery-run devices such as sensors, RFID and mobile phones. In the present scenario, this is the most prominent requirement that must be served while introducing a communication protocol for an IoT environment. IoT network success and performance enhancement depend heavily on optimization of energy consumption that enhance the lifetime of IoT nodes and the network. In this context, this paper presents a comprehensive review on energy efficiency techniques used in IoT environments. The techniques proposed by researchers have been categorized based on five different layers of the energy architecture of IoT. These five layers are named as sensing, local processing and storage, network/communication, cloud processing and storage, and application. Specifically, the significance of energy efficiency in IoT environments is highlighted. A taxonomy is presented for the classification of related literature on energy efficient techniques in IoT environments. Following the taxonomy, a critical review of literature is performed focusing on major functional models, strengths and weaknesses. Open research challenges related to energy efficiency in IoT are identified as future research directions in the area. The survey should benefit IoT industry practitioners and researchers, in terms of augmenting the understanding of energy efficiency and its IoT-related trends and issues.Kumar, K.; Kumar, S.; Kaiwartya, O.; Cao, Y.; Lloret, J.; Aslam, N. (2017). Cross-Layer Energy Optimization for IoT Environments: Technical Advances and Opportunities. Energies. 10(12):1-40. https://doi.org/10.3390/en10122073S1401012Zanella, A., Bui, N., Castellani, A., Vangelista, L., & Zorzi, M. (2014). Internet of Things for Smart Cities. IEEE Internet of Things Journal, 1(1), 22-32. doi:10.1109/jiot.2014.2306328Kamalinejad, P., Mahapatra, C., Sheng, Z., Mirabbasi, S., M. Leung, V. C., & Guan, Y. L. (2015). Wireless energy harvesting for the Internet of Things. IEEE Communications Magazine, 53(6), 102-108. doi:10.1109/mcom.2015.7120024Kaiwartya, O., Abdullah, A. H., Cao, Y., Altameem, A., Prasad, M., Lin, C.-T., & Liu, X. (2016). Internet of Vehicles: Motivation, Layered Architecture, Network Model, Challenges, and Future Aspects. IEEE Access, 4, 5356-5373. doi:10.1109/access.2016.2603219Grieco, L. A., Rizzo, A., Colucci, S., Sicari, S., Piro, G., Di Paola, D., & Boggia, G. (2014). IoT-aided robotics applications: Technological implications, target domains and open issues. Computer Communications, 54, 32-47. doi:10.1016/j.comcom.2014.07.013Aijaz, A., & Aghvami, A. H. (2015). Cognitive Machine-to-Machine Communications for Internet-of-Things: A Protocol Stack Perspective. IEEE Internet of Things Journal, 2(2), 103-112. doi:10.1109/jiot.2015.2390775Lin, Y.-B., Lin, Y.-W., Chih, C.-Y., Li, T.-Y., Tai, C.-C., Wang, Y.-C., … Hsu, S.-C. (2015). EasyConnect: A Management System for IoT Devices and Its Applications for Interactive Design and Art. IEEE Internet of Things Journal, 2(6), 551-561. doi:10.1109/jiot.2015.2423286Bello, O., & Zeadally, S. (2016). Intelligent Device-to-Device Communication in the Internet of Things. IEEE Systems Journal, 10(3), 1172-1182. doi:10.1109/jsyst.2014.2298837Atzori, L., Iera, A., & Morabito, G. (2010). The Internet of Things: A survey. Computer Networks, 54(15), 2787-2805. doi:10.1016/j.comnet.2010.05.010Kaur, N., & Sood, S. K. (2017). An Energy-Efficient Architecture for the Internet of Things (IoT). IEEE Systems Journal, 11(2), 796-805. doi:10.1109/jsyst.2015.2469676Erol-Kantarci, M., & Mouftah, H. T. (2015). Energy-Efficient Information and Communication Infrastructures in the Smart Grid: A Survey on Interactions and Open Issues. IEEE Communications Surveys & Tutorials, 17(1), 179-197. doi:10.1109/comst.2014.2341600Machine-to-Machine Communications (M2M). M2M Service Requirementshttp://www.etsi.org/deliver/etsi_ts/102600_102699/102689/01.01.01_60/ts_102689v010101p.pdfKhan, M., Silva, B. N., & Han, K. (2016). Internet of Things Based Energy Aware Smart Home Control System. IEEE Access, 4, 7556-7566. doi:10.1109/access.2016.2621752Huang, S.-C., Chen, B.-H., Chou, S.-K., Hwang, J.-N., & Lee, K.-H. (2016). Smart Car [Application Notes]. IEEE Computational Intelligence Magazine, 11(4), 46-58. doi:10.1109/mci.2016.2601758Kant, K., & Pal, A. (2017). Internet of Perishable Logistics. IEEE Internet Computing, 21(1), 22-31. doi:10.1109/mic.2017.19Roopaei, M., Rad, P., & Choo, K.-K. R. (2017). Cloud of Things in Smart Agriculture: Intelligent Irrigation Monitoring by Thermal Imaging. IEEE Cloud Computing, 4(1), 10-15. doi:10.1109/mcc.2017.5Tröster, G. (2011). Smart Clothes—The Unfulfilled Pledge? IEEE Pervasive Computing, 10(2), 87-89. doi:10.1109/mprv.2011.32Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015). Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications. IEEE Communications Surveys & Tutorials, 17(4), 2347-2376. doi:10.1109/comst.2015.2444095Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H., & Zhao, W. (2017). A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications. IEEE Internet of Things Journal, 4(5), 1125-1142. doi:10.1109/jiot.2017.2683200Perera, C., Liu, C. H., Jayawardena, S., & Min Chen. (2014). A Survey on Internet of Things From Industrial Market Perspective. IEEE Access, 2, 1660-1679. doi:10.1109/access.2015.2389854Kamilaris, A., & Pitsillides, A. (2016). Mobile Phone Computing and the Internet of Things: A Survey. IEEE Internet of Things Journal, 3(6), 885-898. doi:10.1109/jiot.2016.2600569Arcadius Tokognon, C., Gao, B., Tian, G. Y., & Yan, Y. (2017). Structural Health Monitoring Framework Based on Internet of Things: A Survey. IEEE Internet of Things Journal, 4(3), 619-635. doi:10.1109/jiot.2017.2664072Razzaque, M. A., Milojevic-Jevric, M., Palade, A., & Clarke, S. (2016). Middleware for Internet of Things: A Survey. IEEE Internet of Things Journal, 3(1), 70-95. doi:10.1109/jiot.2015.2498900Luong, N. C., Hoang, D. T., Wang, P., Niyato, D., Kim, D. I., & Han, Z. (2016). Data Collection and Wireless Communication in Internet of Things (IoT) Using Economic Analysis and Pricing Models: A Survey. IEEE Communications Surveys & Tutorials, 18(4), 2546-2590. doi:10.1109/comst.2016.2582841Perera, C., Zaslavsky, A., Christen, P., & Georgakopoulos, D. (2014). Context Aware Computing for The Internet of Things: A Survey. IEEE Communications Surveys & Tutorials, 16(1), 414-454. doi:10.1109/surv.2013.042313.00197Khan, A. A., Rehmani, M. H., & Rachedi, A. (2017). Cognitive-Radio-Based Internet of Things: Applications, Architectures, Spectrum Related Functionalities, and Future Research Directions. IEEE Wireless Communications, 24(3), 17-25. doi:10.1109/mwc.2017.1600404Ahmed, E., Yaqoob, I., Gani, A., Imran, M., & Guizani, M. (2016). Internet-of-things-based smart environments: state of the art, taxonomy, and open research challenges. IEEE Wireless Communications, 23(5), 10-16. doi:10.1109/mwc.2016.7721736Cao, Y., Jiang, T., & Han, Z. (2016). A Survey of Emerging M2M Systems: Context, Task, and Objective. IEEE Internet of Things Journal, 3(6), 1246-1258. doi:10.1109/jiot.2016.2582540Rajandekar, A., & Sikdar, B. (2015). A Survey of MAC Layer Issues and Protocols for Machine-to-Machine Communications. IEEE Internet of Things Journal, 2(2), 175-186. doi:10.1109/jiot.2015.2394438Botta, A., de Donato, W., Persico, V., & Pescapé, A. (2016). Integration of Cloud computing and Internet of Things: A survey. Future Generation Computer Systems, 56, 684-700. doi:10.1016/j.future.2015.09.021Risteska Stojkoska, B. L., & Trivodaliev, K. V. (2017). A review of Internet of Things for smart home: Challenges and solutions. Journal of Cleaner Production, 140, 1454-1464. doi:10.1016/j.jclepro.2016.10.006Liu, C. H., Fan, J., Branch, J. W., & Leung, K. K. (2014). Toward QoI and Energy-Efficiency in Internet-of-Things Sensory Environments. IEEE Transactions on Emerging Topics in Computing, 2(4), 473-487. doi:10.1109/tetc.2014.2364915Du, R., Gkatzikis, L., Fischione, C., & Xiao, M. (2015). Energy Efficient Sensor Activation for Water Distribution Networks Based on Compressive Sensing. IEEE Journal on Selected Areas in Communications, 33(12), 2997-3010. doi:10.1109/jsac.2015.2481199Chen, Y., Chiotellis, N., Chuo, L.-X., Pfeiffer, C., Shi, Y., Dreslinski, R. G., … Kim, H. S. (2016). Energy-Autonomous Wireless Communication for Millimeter-Scale Internet-of-Things Sensor Nodes. IEEE Journal on Selected Areas in Communications, 34(12), 3962-3977. doi:10.1109/jsac.2016.2612041Akgül, Ö. U., & Canberk, B. (2016). Self-Organized Things (SoT): An energy efficient next generation network management. Computer Communications, 74, 52-62. doi:10.1016/j.comcom.2014.07.004Ahn, J. H., & Lee, T.-J. (2018). ALLYS: All You can Send for Energy Harvesting Networks. IEEE Transactions on Mobile Computing, 17(4), 775-788. doi:10.1109/tmc.2017.2740929Mondal, S., & Paily, R. (2017). Efficient Solar Power Management System for Self-Powered IoT Node. IEEE Transactions on Circuits and Systems I: Regular Papers, 64(9), 2359-2369. doi:10.1109/tcsi.2017.2707566Qureshi, F. F., Iqbal, R., & Asghar, M. N. (2017). Energy efficient wireless communication technique based on Cognitive Radio for Internet of Things. Journal of Network and Computer Applications, 89, 14-25. doi:10.1016/j.jnca.2017.01.003Nguyen, T. D., Khan, J. Y., & Ngo, D. T. (2017). Energy harvested roadside IEEE 802.15.4 wireless sensor networks for IoT applications. Ad Hoc Networks, 56, 109-121. doi:10.1016/j.adhoc.2016.12.003Khanouche, M. E., Amirat, Y., Chibani, A., Kerkar, M., & Yachir, A. (2016). Energy-Centered and QoS-Aware Services Selection for Internet of Things. IEEE Transactions on Automation Science and Engineering, 13(3), 1256-1269. doi:10.1109/tase.2016.2539240Afzal, B., Alvi, S. A., Shah, G. A., & Mahmood, W. (2017). Energy efficient context aware traffic scheduling for IoT applications. Ad Hoc Networks, 62, 101-115. doi:10.1016/j.adhoc.2017.05.001Song, L., Chai, K. K., Chen, Y., Schormans, J., Loo, J., & Vinel, A. (2017). QoS-Aware Energy-Efficient Cooperative Scheme for Cluster-Based IoT Systems. IEEE Systems Journal, 11(3), 1447-1455. doi:10.1109/jsyst.2015.2465292Energy-Efficient Probabilistic Routing Algorithm for Internet of Thingshttp://www.ietf.org/rfc/rfc3561.txtMachado, K., Rosário, D., Cerqueira, E., Loureiro, A., Neto, A., & de Souza, J. (2013). A Routing Protocol Based on Energy and Link Quality for Internet of Things Applications. Sensors, 13(2), 1942-1964. doi:10.3390/s130201942Chelloug, S. A. (2015). Energy-Efficient Content-Based Routing in Internet of Things. Journal of Computer and Communications, 03(12), 9-20. doi:10.4236/jcc.2015.312002Zhao, M., Ho, I. W.-H., & Chong, P. H. J. (2016). An Energy-Efficient Region-Based RPL Routing Protocol for Low-Power and Lossy Networks. IEEE Internet of Things Journal, 3(6), 1319-1333. doi:10.1109/jiot.2016.2593438Qiu, T., Lv, Y., Xia, F., Chen, N., Wan, J., & Tolba, A. (2016). ERGID: An efficient routing protocol for emergency response Internet of Things. Journal of Network and Computer Applications, 72, 104-112. doi:10.1016/j.jnca.2016.06.009Liu, Y., Liu, A., Hu, Y., Li, Z., Choi, Y.-J., Sekiya, H., & Li, J. (2016). FFSC: An Energy Efficiency Communications Ap-proach for Delay Minimizing in Internet of Things. IEEE Access, 1-1. doi:10.1109/access.2016.2588278Qiu, S., Haselmayr, W., Li, B., Zhao, C., & Guo, W. (2017). Bacterial Relay for Energy-Efficient Molecular Communications. IEEE Transactions on NanoBioscience, 16(7), 555-562. doi:10.1109/tnb.2017.2741669Biason, A., Pielli, C., Rossi, M., Zanella, A., Zordan, D., Kelly, M., & Zorzi, M. (2017). EC-CENTRIC: An Energy- and Context-Centric Perspective on IoT Systems and Protocol Design. IEEE Access, 5, 6894-6908. doi:10.1109/access.2017.2692522Huang, Z., Lin, K.-J., Yu, S.-Y., & Hsu, J. Y. (2014). Co-locating services in IoT systems to minimize the communication energy cost. Journal of Innovation in Digital Ecosystems, 1(1-2), 47-57. doi:10.1016/j.jides.2015.02.005Kwak, J., Kim, Y., Lee, J., & Chong, S. (2015). DREAM: Dynamic Resource and Task Allocation for Energy Minimization in Mobile Cloud Systems. IEEE Journal on Selected Areas in Communications, 33(12), 2510-2523. doi:10.1109/jsac.2015.2478718Abu Sharkh, M., & Shami, A. (2017). An evergreen cloud: Optimizing energy efficiency in heterogeneous cloud computing architectures. Vehicular Communications, 9, 199-210. doi:10.1016/j.vehcom.2017.02.004Bui, D.-M., Yoon, Y., Huh, E.-N., Jun, S., & Lee, S. (2017). Energy efficiency for cloud computing system based on predictive optimization. Journal of Parallel and Distributed Computing, 102, 103-114. doi:10.1016/j.jpdc.2016.11.011Liu, A., Zhang, Q., Li, Z., Choi, Y., Li, J., & Komuro, N. (2017). A green and reliable communication modeling for industrial internet of things. Computers & Electrical Engineering, 58, 364-381. doi:10.1016/j.compeleceng.2016.09.005Kim, J. (2015). Energy-Efficient Dynamic Packet Downloading for Medical IoT Platforms. IEEE Transactions on Industrial Informatics, 11(6), 1653-1659. doi:10.1109/tii.2015.2434773Chiu, T.-C., Shih, Y.-Y., Pang, A.-C., & Pai, C.-W. (2017). Optimized Day-Ahead Pricing With Renewable Energy Demand-Side Management for Smart Grids. IEEE Internet of Things Journal, 4(2), 374-383. doi:10.1109/jiot.2016.2556006Gandotra, P., Jha, R. K., & Jain, S. (2017). Green Communication in Next Generation Cellular Networks: A Survey. IEEE Access, 5, 11727-11758. doi:10.1109/access.2017.2711784Li, J., Peng, M., Yu, Y., & Ding, Z. (2016). Energy-Efficient Joint Congestion Control and Resource Optimization in Heterogeneous Cloud Radio Access Networks. IEEE Transactions on Vehicular Technology, 65(12), 9873-9887. doi:10.1109/tvt.2016.2531184Kaiwartya, O., Abdullah, A. H., Cao, Y., Lloret, J., Kumar, S., Shah, R. R., … Prakash, S. (2018). Virtualization in Wireless Sensor Networks: Fault Tolerant Embedding for Internet of Things. IEEE Internet of Things Journal, 5(2), 571-580. doi:10.1109/jiot.2017.2717704Garcia, M., Sendra, S., Lloret, J., & Canovas, A. (2011). Saving energy and improving communications using cooperative group-based Wireless Sensor Networks. Telecommunication Systems, 52(4), 2489-2502. doi:10.1007/s11235-011-9568-3kaiwartya, omprakash, Abdullah, A., Cao, Y., Rao, R. S., Kumar, S., Lobiyal, D. K., … Shah, R. R. (2016). T-MQM: Testbed based Multi-metric Quality Measurement of Sensor Deployment for Precision Agriculture-A Case Study. IEEE Sensors Journal, 1-1. doi:10.1109/jsen.2016.2614748Alrajeh, N. A., Khan, S., Lloret, J., & Loo, J. (2013). Secure Routing Protocol Using Cross-Layer Design and Energy Harvesting in Wireless Sensor Networks. International Journal of Distributed Sensor Networks, 9(1), 374796. doi:10.1155/2013/374796Mehmood, A., Khan, S., Shams, B., & Lloret, J. 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Statistical Degradation Models for Electronics

With increasing presence of electronics in modern systems and in every-day products, their reliability is inextricably dependent on that of their electronics. We develop reliability models for failure-time prediction under small failure-time samples and information on individual degradation history. The development of the model extends the work of Whitmore et al. 1998, to incorporate two new data-structures common to reliability testing. Reliability models traditionally use lifetime information to evaluate the reliability of a device or system. To analyze small failure-time samples within dynamic environments where failure mechanisms are unknown, there is a need for models that make use of auxiliary reliability information. In this thesis we present models suitable for reliability data, where degradation variables are latent and can be tracked by related observable variables we call markers. We provide an engineering justification for our model and develop parametric and predictive inference equations for a data-structure that includes terminal observations of the degradation variable and longitudinal marker measurements. We compare maximum likelihood estimation and prediction results obtained by Whitmore et. al. 1998 and show improvement in inference under small sample sizes. We introduce modeling of variable failure thresholds within the framework of bivariate degradation models and discuss ways of incorporating covariates. In the second part of the thesis we investigate anomaly detection through a Bayesian support vector machine and discuss its place in degradation modeling. We compute posterior class probabilities for time-indexed covariate observations, which we use as measures of degradation. Lastly, we present a multistate model used to model a recurrent event process and failure-times. We compute the expected time to failure using counting process theory and investigate the effect of the event process on the expected failure-time estimates

A Framework to Quantify Network Resilience and Survivability

The significance of resilient communication networks in the modern society is well established. Resilience and survivability mechanisms in current networks are limited and domain specific. Subsequently, the evaluation methods are either qualitative assessments or context-specific metrics. There is a need for rigorous quantitative evaluation of network resilience. We propose a service oriented framework to characterize resilience of networks to a number of faults and challenges at any abstraction level. This dissertation presents methods to quantify the operational state and the expected service of the network using functional metrics. We formalize resilience as transitions of the network state in a two-dimensional state space quantifying network characteristics, from which network service performance parameters can be derived. One dimension represents the network as normally operating, partially degraded, or severely degraded. The other dimension represents network service as acceptable, impaired, or unacceptable. Our goal is to initially understand how to characterize network resilience, and ultimately how to guide network design and engineering toward increased resilience. We apply the proposed framework to evaluate the resilience of the various topologies and routing protocols. Furthermore, we present several mechanisms to improve the resilience of the networks to various challenges