# Cost-Aware IoT Extension of DISSECT-CF
## 1 Introduction  
Internet of Things (IoT) is a rapidly emerging concept where sensors, actuators and smart devices are often connected to cloud systems. Clouds are used in scenarios in which data from a large set of sensors is processed and often fed back to actuators or smart devices. As the number of devices with connected sensors and actuators are reaching new heights every day, the way they are integrated to the cloud computing ecosystem is also rapidly changing. As a result, IoT systems integrators often need new experimental techniques--e.g., simulators--which allow them to understand the behaviour of systems of previously unprecedented scale.  
Recently, a wide range of IoT oriented simulators have risen [1; 2; 3]. Regrettably, these are use case limited (e.g., they only focus on big data processing). In addition, they are often focused on very specific sensors or sensor behaviour, neglecting the financial side of operating large scale IoT systems. Finally, these simulators are rarely scaling to match the number of devices foreseen in IoT systems of tomorrow.  
In this paper, we lay the foundations for flexible and scalable modelling of IoT systems and their related cost models through our extensions to the simulator: DISCreate event baSed Energy Consumption simulaTor for Clouds and Federations (DISSECT-CF) [4]. The main contributions of this paper are: (i) the analysis of operating costs of IoT scenarios by introducing a model of providerpricing schemes, (ii) the comparison of usage costs of a real world meteorological application at four providers, (iii) the extension of a state-of-the-art simulator for modelling IoT sensors and applying provider pricing models on them, and finally (iv) the evaluation of the considered meteorological case study in the simulator. Although scaling the simulation over several nodes is a relevant topic to meet the demands of the newest IoT scenarios, this topic is out of scope here as it was discussed before by [5]. Similarly, detailed information on sensor produced data (e.g., how accurate it is) and algorithmic reactions to the data are out of scope of this paper, as these would require such level of detail that it would hinder our objective to support large scale IoT-Cloud systems.  
One of the earliest use of connected sensors is from the field of weather forecasting. The findings of the paper are evaluated through five weather forecasting scenarios: we used the public data available on the sensors operated by the crowdsourced meteorological service of Hungary called Idokep.hu [6]. Using the extended DISSECT-CF, we set up an extensive network of simulated sensors (with over 400 devices encompassing over 3000 individual sensors) and evaluated data collection and analysis techniques, as if they would be executed in a state-of-the-art infrastructure as a service system.  
The structure of the paper is the following. First, in Section 2, we continue with the discussion of the state of the art. Next, in Section 3, we discuss the extensions applied to the DISSECT-CF simulator. Later, Section 4 discusses our weather forecasting case study and evaluates our extensions with real-life scenarios. Finally, Section 5 concludes our work.