# Modeling Dew Computing in DISSECT-CF-Fog
###### Abstract  
Fog computing provides an effective solution to various problems by extending the cloud's functionality to typically more limited computing units closer to user devices. Fog computing can provide a higher level of user experience due to its geographic and network topology location and distribution. IoT services also need to be managed seamlessly to ensure adequate QoS (due to the mobility of devices or the temporary periods without an internet connection). Such domains are combined under the auspices of Dew computing, as in critical cases, extending an IoT service to the end user's device is a feasible task. Such scenarios can hardly be investigated at a large scale due to the lack of dedicated simulation environments. In this paper, we present an extension of the DISSECT-CF-Fog simulator with a Dew computing model, to enable the simulation of IoT-Dew-Fog systems in a cost-effective manner. In particular, we focus on service migration options for mobile devices and cases with temporary internet access limitations. Finally, we performed measurements of real-world use cases with the extended simulator as an evaluation. Our simulation results show that the proposed proactive strategy reduces the processing time of IoT data, exploiting an IoT-Dew-Fog environment.  
Dew computing; fog computing; Internet of Things; simulation +
Footnote †: journal: Applied  
processed potentially need several network hops to reach their destinations. Furthermore, increased latencies and unpredictable response times degrade the quality of service (QoS). One possible way to reduce the negative impact, as much as possible, is to dynamically migrate the IoT services to a more optimal (preferably closer) location. Movement-induced service migration is a challenging problem that needs to be addressed from many different angles. For example, the distance between the device and the node (both physical and network distance), the size of the service to be migrated, the available bandwidth, or the current load on the destination node.  
Cloud-influenced domains, such as fog, edge, and mobile edge computing [2], are often utilized to outsource cloud computing features and services to the perimeters of mobile networks. Such complex networks strongly rely on (typically the internet) connections; therefore, another novel paradigm was introduced, namely Dew computing, to deal with the lack of internet access. Dew computing compounds the basic capabilities of end devices and the cloud model. In this area, IoT data can be discovered, processed, and stored locally in the case of offline modes. When a network connection is established, the end devices may initiate synchronization with the fog and cloud services. With the Dew concept, various challenges arise, such as resource and network utilization, limited storage, and energy consumption [3].  
The concept of Dew computing can be applied to various domains [4], such as air transportation systems, including flying objects (meteorological drones, airplanes, etc.), healthcare systems aimed at real-time monitoring of patients and interactions with doctors, smart manufacturing focusing on Industry 4.0, and traffic control relying on distributed surveillance systems.  
The main contributions of the paper are as follows:  
* We present an overview of Dew computing and extend the DISSECT-CF-Fog simulator with a Dew model to represent IoT-Dew-Fog systems.
* We introduce realistic, Dew-based IoT use cases exploiting service migration and offline data management.
* We evaluate the use cases in the extended simulator, pinpointing some technical challenges that Dew computing can bring to these complex systems.  
The remainder of the paper is as follows. In Section 2, we briefly discuss the Dew computing domain and related simulation approaches, in Section 3, we introduce the simulator and its components, in Section 4, we present two IoT-Dew-Fog use cases for exemplifying its utilization. Finally, in Section 5, we conclude our work.