An overview of safety and security analysis frameworks for the Internet of Things

YesThe rapid progress of the Internet of Things (IoT) has continued to offer humanity numerous benefits, including many security and safety-critical applications. However, unlocking the full potential of IoT applications, especially in high-consequence domains, requires the assurance that IoT devices will not constitute risk hazards to the users or the environment. To design safe, secure, and reliable IoT systems, numerous frameworks have been proposed to analyse the safety and security, among other properties. This paper reviews some of the prominent classical and model-based system engineering (MBSE) approaches for IoT systems’ safety and security analysis. The review established that most analysis frameworks are based on classical manual approaches, which independently evaluate the two properties. The manual frameworks tend to inherit the natural limitations of informal system modelling, such as human error, a cumbersome processes, time consumption, and a lack of support for reusability. Model-based approaches have been incorporated into the safety and security analysis process to simplify the analysis process and improve the system design’s efficiency and manageability. Conversely, the existing MBSE safety and security analysis approaches in the IoT environment are still in their infancy. The limited number of proposed MBSE approaches have only considered limited and simple scenarios, which are yet to adequately evaluate the complex interactions between the two properties in the IoT domain. The findings of this survey are that the existing methods have not adequately addressed the analysis of safety/security interdependencies, detailed cyber security quantification analysis, and the unified treatment of safety and security properties. The existing classical and MBSE frameworks’ limitations obviously create gaps for a meaningful assessment of IoT dependability. To address some of the gaps, we proposed a possible research direction for developing a novel MBSE approach for the IoT domain’s safety and security coanalysis framework

Opportunities and obligations for physical computing systems

The recent confluence of embedded and real-time systems with wireless, sensor, and networking technologies is creating a nascent infrastructure for a technical, economic, and social revolution. Based on the seamless integration of computing with the physical world via sensors and actuators, this revolution will accrue many benefits. Potentially, its impact could be similar to that of the current Internet. We believe developers must focus on the physical, real-time, and embedded aspects of pervasive computing. We refer to this domain as physical computing systems. For pervasive computing to achieve its promise, developers must create not only high-level system software and application solutions, but also low-level embedded systems solutions. To better understand physical computing\u27s advantages, we consider three application areas: assisted living, emergency response systems for natural or man-made disasters, and protecting critical infrastructures at the national level

Annual Meeting of the Lunar Exploration Analysis Group : October 22-24, 2014 Laurel, Maryland

The focus for this year's meeting is the topic of lunar volatiles — which species are present, their abundance on the surface and interior, their sources and formation processes, their mobility and temporary storage on the surface, and their ultimate fate (be it loss from the lunar environment or “permanent” sequestration in surface reservoirs).Institutional Support: NASA Lunar Exploration Analysis Group, The Johns Hopkins University/Applied Physics Laboratory, Universities Space Research Association (USRA), Lunar and Planetary Institute, National Aeronautics and Space Administration ; Conveners: Samuel Lawrence, Arizona State University, Stephen Mackwell, Lunar and Planetary Institute, Clive Neal, University of Notre Dame, Jeffrey Plescia, The Johns Hopkins University/Applied Physics Laboratory.PARTIAL CONTENTS: Solar Wind Implantation into Lunar Regolith: Hydrogen Retention in a Surface with Defects / W M Farrell, D. M Hurley, and MI. Zimmerman--Lunar Surface Models / H. Fink.--The Geology oflnferno Chasm, Idaho: A Terrestrial Analog for Lunar Rilles? / W B. Gany, S. S. Hughes, S. E. Kobs Nawotniak, C. D. Neish, C. W Haberle, J L. Heldmann, D. S. S. Lim, and FINESSE Team--Spectral and Therrnophysical Properties of Lunar Swirls from the Diviner Lunar Radiometer / T D. Glotch, J L. Bandfield, P. G. Lucey, P. O. Hayne, B. T Greenhagen, J A. Arnold, R. R. Ghent, and D. A. Paige--The Benefits of Sample Return: Connecting Apollo Soils and Diviner Lunar Radiometer Remote Sensing Data / B. T. Greenhagen, K. L. Donaldson Hanna, I. R. Thomas, N. E. Bowles, C. C. Allen, C. M Pieters, and D. A. Paige--International Strategy for the Exploration of Lunar Polar Volatiles / J E. Gruener and N. H. Suzuki--Why Do We Need the Moon: Next Steps Forward for Moon Exploration / U. G. Guven--Space Mission to the Moon with a Low Cost Moon Probe Nanosatellite: University Project Feasibility Analysis and Design Concepts / U G. Guven, G. V. Velidi, and L. D. Datta--ARTEMIS Observations of the Space Environment Around the Moon and its Interaction with the Atmosphere and Surface / J S. Halekas and ARTEMIS Team

A Capacity Planning Tool For Batch Parallel Processing Systems

System capacity planning is a very useful method to predict which hardware will be necessary to run an IT system with high performance. The proposed tool in this paper is developed to ease this hard and important job, automating the analysis of the software in different ways. It uses the discrete-event type simulation based on a queue theory model to represent the hardware and software interaction. The model presented in this paper represents a simplification of any batch IT systems. In this type of system, commonly we have a lot of transactions that need to be processed by the system without human interaction and frequently use the benefits of parallel processing for better performance. We can apply these ideas to represent the workload in the simulation model as simple tasks that enter the system to be processed. Results show that it is possible to use this model to represent the real system with good fidelity.139144Menascé, D.A., Almeida, V.A.F., Capacity Planning for Web Services: Metrics, Models (2001) and Methods. 1st, , Prentice HallIngalls, R.G., Introduction to simulation. In Proceedings of the 33nd Conference on Winter Simulation (Arlington, Virginia, December 09 - 12, 2001) (2001) Winter Simulation Conference, pp. 7-16. , IEEE Computer Society, Washington, DCRose, F., Carpenter, T., Kumar, S., Shackleton, J., Honeywell, T.S., A Model for the Coanalysis of Hardware and Software Architectures. In Proceedings of the 4th international Workshop on Hardware/Software Co-Design (March 18 - 20, 1996) (1996) International Conference on Hardware Software Codesign, p. 94. , IEEE Computer Society, Washington, DCXu, J., Chung, M.J., Predicting the performance of synchronous discrete event simulation systems. In Proceedings of the 2001 IEEE/ACM international Conference on Computer-Aided Design (San Jose, California, November 04 - 08, 2001) (2001) International Conference on Computer Aided Design, pp. 18-23. , IEEE Press, Piscataway, NJSchriber, T.J., Brunner, D.T., Inside discrete-event simulation software: How it works and why it matters. In Proceedings of the 38th Conference on Winter Simulation (Monterey, California, December 03 - 06, 2006) (2006) Winter Simulation Conference. Winter Simulation Conference, pp. 118-128. , L. F. Perrone, B. G. Lawson, J. Liu, and F. P. Wieland, EdsAlexander, C.W., Discrete event simulation for batch processing. In Proceedings of the 38th Conference on Winter Simulation (Monterey, California, December 03 - 06, 2006) (1929) Winter Simulation Conference. Winter Simulation Conference, , L. F. Perrone, B. G. Lawson, J. Liu, and F. P. Wieland, Eds, 2006Menascé, D.A., Almeida, V.A.F., Dowdy, L.W., (2004) Performance by Design: Computer Capacity Planning by Example, , Prentice HallBose, S., (2002) An Introduction to Queueing Systems, , Kluwer Academic PublishersLo, T.L., Computer Capacity Planning using Queueing Network Models (1980) Proc. Performance 80 Corn r, pp. 145-152. , TorontoJ.Niño, F. Hosch. An introduction to programming and Object Oriented design using Java 1.5. Wiley 2005. 2nd editio