Towards Run-Time Verification of Compositions in the Web of Things using Complex Event Processing

Following the vision of the Internet of Things, physical world entities are integrated into virtual world things. Things are expected to become active participants in business and social processes. Then, the Internet of Things could benefit from the Web Service architecture like today’s Web does, so Future ser-vice-oriented Internet things will offer their functionality via service-enabled in-terfaces. In previous work, we demonstrated the need of considering the behav-iour of things to develop applications in a more rigorous way, and we proposed a lightweight model for representing such behaviour. Our methodology relies on the service-oriented paradigm and extends the DPWS profile to specify the order with which things can receive messages. We also proposed a static verifi-cation technique to check whether a mashup of things respects the behaviour, specified at design-time, of the composed things. However, a change in the be-haviour of a thing may cause that some compositions do not fulfill its behaviour anymore. Moreover, given that a thing can receive requests from instances of different mashups at run-time, these requests could violate the behaviour of that thing, even though each mashup fulfills such behaviour, due to the change of state of the thing. To address these issues, we present a proposal based on me-diation techniques and complex event processing to detect and inhibit invalid invocations, so things only receive requests compatible with their behaviour.Work partially supported by projects TIN2008-05932, TIN2012-35669, CSD2007-0004 funded by Spanish Ministry MINECO and FEDER; P11-TIC-7659 funded by Andalusian Government; and Universidad de Málaga, Campus de Excelencia Internacional Andalucía Tec

Machine Learning-Based Elastic Cloud Resource Provisioning in the Solvency II Framework

The Solvency II Directive (Directive 2009/138/EC) is a European Directive issued in November 2009 and effective from January 2016, which has been enacted by the European Union to regulate the insurance and reinsurance sector through the discipline of risk management. Solvency II requires European insurance companies to conduct consistent evaluation and continuous monitoring of risks—a process which is computationally complex and extremely resource-intensive. To this end, companies are required to equip themselves with adequate IT infrastructures, facing a significant outlay. In this paper we present the design and the development of a Machine Learning-based approach to transparently deploy on a cloud environment the most resource-intensive portion of the Solvency II-related computation. Our proposal targets DISAR®, a Solvency II-oriented system initially designed to work on a grid of conventional computers. We show how our solution allows to reduce the overall expenses associated with the computation, without hampering the privacy of the companies’ data (making it suitable for conventional public cloud environments), and allowing to meet the strict temporal requirements required by the Directive. Additionally, the system is organized as a self-optimizing loop, which allows to use information gathered from actual (useful) computations, thus requiring a shorter training phase. We present an experimental study conducted on Amazon EC2 to assess the validity and the efficiency of our proposal

Middleware Technologies for Cloud of Things - a survey

The next wave of communication and applications rely on the new services provided by Internet of Things which is becoming an important aspect in human and machines future. The IoT services are a key solution for providing smart environments in homes, buildings and cities. In the era of a massive number of connected things and objects with a high grow rate, several challenges have been raised such as management, aggregation and storage for big produced data. In order to tackle some of these issues, cloud computing emerged to IoT as Cloud of Things (CoT) which provides virtually unlimited cloud services to enhance the large scale IoT platforms. There are several factors to be considered in design and implementation of a CoT platform. One of the most important and challenging problems is the heterogeneity of different objects. This problem can be addressed by deploying suitable "Middleware". Middleware sits between things and applications that make a reliable platform for communication among things with different interfaces, operating systems, and architectures. The main aim of this paper is to study the middleware technologies for CoT. Toward this end, we first present the main features and characteristics of middlewares. Next we study different architecture styles and service domains. Then we presents several middlewares that are suitable for CoT based platforms and lastly a list of current challenges and issues in design of CoT based middlewares is discussed.Comment: http://www.sciencedirect.com/science/article/pii/S2352864817301268, Digital Communications and Networks, Elsevier (2017