Abstract
This paper introduces the concept of tomography, a mechanism to lower management overhead for component-based IoT applications. Previous research has shown the advantages of component-based software engineering, wherein applications are built and reconfigured at runtime through the composition of components. While this approach promotes code-reuse and dynamic reconfiguration, the introspection and reconfiguration of distributed applications is cumbersome and inefficient. Tomography addresses this problem by reimagining the visitor design pattern for distributed component based compositions. We evaluate the performance of this approach in a case-study of discovering/introspecting and reconfiguring a real-world IoT application. We show that in comparison to classic management operations, tomography reduces both the number of explicit queries and the volume of network messages. This significantly reduces management effort and energy consumption. © 2015 ACM.

Abstract

Abstract
We propose an abstract framework for modelling state-based systems with internal behaviour as e.g. given by silent or epsilon-transitions. Our approach employs monads with a parametrized fixpoint operator dagger to give a semantics to those systems and implement a sound procedure of abstraction of the internal transitions, whose labels are seen as the unit of a free monoid. More broadly, our approach extends the standard coalgebraic framework for state-based systems by taking into account the algebraic structure of the labels of their transitions. This allows to consider a wide range of other examples, including Mazurkiewicz traces for concurrent systems and non-deterministic transducers.

Abstract
The prospect of distributed generation investment planning (DGIP) is especially relevant in insular networks because of a number of reasons such as energy security, emissions and renewable integration targets. In this context, this paper presents a DGIP model that considers various DG types, including renewables. The planning process involves an economic analysis considering the costs of emissions, reliability and other relevant cost components. In addition, a comprehensive sensitivity analysis is carried out in order to investigate the effect of variability and uncertainty of model parameters on DG investment decisions. The ultimate goal is to identify the parameters that significantly influence the decision-making process and to quantify their degree of influence. The results show that uncertainty has a meaningful impact on DG investment decisions. In fact, the degree of influence varies from one parameter to another. However, in general, ignoring or inadequately considering uncertainty and variability in model parameters has a quantifiable cost. The analyses made in this paper can be very useful to identify the most relevant model parameters that need special attention in planning practices.

Abstract
TIEC 61499 defines an execution model for distributed industrial control applications, i.e. a single application distributed among several devices. In such an environment partial failures are likely to occur. In order to avoid probable system malfunctions and breakdowns due to partial failures, the authors have previously proposed a framework where the concept of replication may be applied to the IEC 61499 execution model. This paper focuses on describing an implementation of this replication framework on the FORTE IEC 61499 execution platform, along with the results of the first tests of the implementation. A set-up for the full validation of the approach is also described.

Abstract
Modelling wind power uncertainty is a critic aspect in the optimal management of power systems with high integration of this renewable resource. It is typically carried out by considering a limited number of representative scenarios that incorporate relevant properties such as hourly auto-correlation and diurnal forecasting profile. Considering a large amount of scenarios improves the wind power modelling, but increases the computational effort. To deal with this problem, a method to incorporate a big set of scenarios in stochastic unit commitment (UC) problem is presented in this paper. The effectiveness of the proposed methodology is evaluated by means of the analysis of a case study and the results are compared to those obtained from a stochastic programming method, concluding that the method presented in this paper offers an approximated solution in a reduced computational time.