Exploring Implicit Parallelism in Class Diagrams

As multicore processors are becoming more wide-spread, leveraging of parallelism is once again becoming an important concern during the software development process. Substantial refactoring is required to parallelize legacy sequential software in order to exploit the advantages offered by parallel processing. In this study, guidelines are offered to aid in parallelizing object-oriented programs by analyzing their designs as represented in UML class diagrams. We define often occurring patterns of class-dependencies and demonstrate their characteristics in class diagrams by investigating their properties. We present example instances exhibiting the usage of these patterns in class diagrams. Through analyzing the runtime aspects of these instances, we have identified how they impact the parallelization of object oriented software. Taking these lessons into account when refactoring existing object-oriented software can significantly reduce time and effort required. We have evaluated our method by applying it to three popular design patterns and a real-world case study

Analysis for Embedded Systems: Experiments with Priced Timed Automata

AbstractAnalysis of resource consumption of embedded systems is a major challenge in the industry since the number of components that can be included in a single chip keeps getting bigger. In this paper, we consider simple models of embedded systems and the automated analysis about timing and memory access costs of those models. In order to achieve this, a basic model is built using priced timed automata and some resource consumption scenarios are verified. Even though the experiments are performed on small and basic models, we believe we have taken a basis step in showing that it is promising to use priced timed automata and Uppaal Cora as a model checking tool in reasoning about resource consumption of embedded systems

Sustainable environmental monitoring via energy and information efficient multi-node placement

The Internet of Things is gaining traction for sensing and monitoring outdoor environments such as water bodies, forests, or agricultural lands. Sustainable deployment of sensors for environmental sampling is a challenging task because of the spatial and temporal variation of the environmental attributes to be monitored, the lack of the infrastructure to power the sensors for uninterrupted monitoring, and the large continuous target environment despite the sparse and limited sampling locations. In this paper, we present an environment monitoring framework that deploys a network of sensors and gateways connected through low-power, long-range networking to perform reliable data collection. The three objectives correspond to the optimization of information quality, communication capacity, and sustainability. Therefore, the proposed environment monitoring framework consists of three main components: (i) to maximize the information collected, we propose an optimal sensor placement method based on QR decomposition that deploys sensors at information- and communication-critical locations; (ii) to facilitate the transfer of big streaming data and alleviate the network bottleneck caused by low bandwidth, we develop a gateway configuration method with the aim to reduce the deployment and communication costs; and (iii) to allow sustainable environmental monitoring, an energy-aware optimization component is introduced. We validate our method by presenting a case study for monitoring the water quality of the Ergene River in Turkey. Detailed experiments subject to real-world data show that the proposed method is both accurate and efficient in monitoring a large environment and catching up with dynamic changes