Internet of Things (IoT) aims to bring every object (e.g. smart cameras, wearable, environmental sensors, home appliances, and vehicles) online, hence generating massive amounts of data that can overwhelm storage systems and data analytics applications. Cloud computing offers services at the infrastructure level that can scale to IoT storage and processing requirements. However, there are applications such as health monitoring and emergency response that require low latency, and delay caused by transferring data to the cloud and then back to the application can seriously impact their performances. To overcome this limitation, Fog computing paradigm has been proposed, where cloud services are extended to the edge of the network to decrease the latency and network congestion. To realize the full potential of Fog and IoT paradigms for real-time analytics, several challenges need to be addressed. The first and most critical problem is designing resource management techniques that determine which modules of analytics applications are pushed to each edge device to minimize the latency and maximize the throughput. To this end, we need a evaluation platform that enables the quantification of performance of resource management policies on an IoT or Fog computing infrastructure in a repeatable manner. In this paper we propose a simulator, called iFogSim, to model IoT and Fog environments and measure the impact of resource management techniques in terms of latency, network congestion, energy consumption, and cost. We describe two case studies to demonstrate modeling of an IoT environment and comparison of resource management policies. Moreover, scalability of the simulation toolkit in terms of RAM consumption and execution time is verified under different circumstances.
Since traditional wireless sensor networks and IoT simulators do not focus on modeling of large scale deployments, @cite_4 proposed a simulation methodology for IoT systems with a large number of interconnected devices. It is designed to study low-level networking aspects. In summary, the main advantages of their approach are 1) simulation of IoT systems with geographically distributed devices; 2) simulation of are IoT devices with multiple network interfaces and protocols, as well as different mobility, network, and energy consumption models.
@cite_4
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The Internet of Things (IoT) refers to the interconnection of billions of IP-enabled devices, denoted as "smart objects", with limited capabilities, in terms of computational power and memory capacity, which typically operate in constrained environments, in an Internet-like structure. Large-scale systems and applications that rely on such a high number of devices, due to their complexity, need careful analysis and test, before being deployed to target environments. Traditional IoT simulators do not focus on the simulation of large scale deployments, as they are intended to evaluate and analyze low-level networking aspects, with groups of smart objects arranged in specific topologies. In this paper, we illustrate an efficient simulation methodology, which is particularly suitable to test IoT systems with a large number of interconnected devices in Urban environments from an application-layer perspective. The main advantages of such an approach are: i) the capability to simulate large-scale systems with thousands of geographically distributed devices; ii) the maximization of code reuse; and iii) the high generality of simulated nodes, which can be characterized by multiple network interfaces and protocols, as well as different mobility, network, and energy consumption models.