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Privacy, defined as the protection of private data, ensures that a malicious adversary cannot obtain sensitive information while data is in transit [33]. At the moment, privacy is most vulnerable since the data of end users is sent directly to the cloud. From this point of view, edge and fog paradigms enforce privacy by moving the computation closer to the user. In doing so, data can be processed locally and the user can control what third parties are accessing his private data based on a defined role-based access control policy. However, some privacy challenges remain open, such as (i) the awareness of privacy in the community where, for example, almost 80% of WiFi user still use their default passwords for their routers and (ii) the lack of efficient tools for security and privacy for constrained devices [3].
2.5.3 Network Management
The network management plays the most important role in both edge and fog paradigms since it represents the means of connecting all smart devices at the edge and ultimately providing available resources by deploying more nodes. Since the nature of an IoT network consists of heterogeneous devices, which are highly dispersed across large areas, an engaging task is to manage and maintain connectivity. Newly emerging technologies like software-defined networks (SDNs) and network function virtualization (NFV) are seen as a possible solution that may have a significant impact in implementing and maintaining the network increasing the scalability and reducing cost [19].
Considering the volatile nature of the network, providing a seamless connectivity mechanism is critical since both mobile and stationary devices coexist in the network. Therefore, another aspect of network management is related to connectivity. This mechanism must be able to provide the possibility of connecting/disconnecting easily from the network such that the uncertainty introduced by mobile devices is accommodated. Moreover, providing this encourages an increased deployment of smart devices by users and manufacturers alike, without extra cost or expert knowledge.
An effort in this direction is made by the I3: the intelligent IoT integrator, developed by USC [34], having the purpose of creating a marketplace where users can share their private data with application developers and receive incentives for it. There are two main advantages of designing the marketplace like this: first, the users are encouraged to deploy more edge devices, which in return extends the IoT network with more resources that app developers can use; and second, there is a pool of data that developers can utilize to improve their IoT applications.
2.6 Conclusion
The never-ending increase in interconnected IoT devices and the stringent requirements of new IoT applications has posed severe challenges to the current cloud computing state-of-the-art architecture, such as network congestion and privacy of data. As a result, researchers have proposed a new solution to tackle these challenges by migrating some computational resources closer to the user. The approach taken in this solution made the cloud more efficient by extending its computational capabilities at the end of the network, solving its challenges in the process.
Continuing to improve this solution, multiple paradigms appeared, having as their underlying vision the same goal of deploying more resources at the edge of the network. Besides their common vision, some paradigms were influenced by their considered use case, e.g. MEC paradigm enables constrained devices like smartphones to offload parts of the applications to save resources. However, two of the most popular paradigms (i.e. fog and edge computing) are widely used in research today.
These two paradigms were designed to enable processing IoT applications at the endpoints of the network, sharing more similarities than others. Other than the naming convention, the difference at the beginning for the two, i.e. fog computing extends the cloud creating a cloud-to-things continuum and edge computing places the application directly on the edge devices, was represented by the location where computations are performed. Since in the past couple of years there were tremendous advances for edge devices, this difference between the two has disappeared, both fog and edge aiming to deploy applications as close as possible to the edge of the network. Considering the similarities they share, we argue that there is no difference between their purpose of them.
Acknowledgment
The research leading to these results has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 764785, FORA (Fog Computing for Robotics and Industrial Automation). This publication was partially supported by the TUW Research Cluster Smart CT.
References
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