Fog Computing and the Internet of Things: A Review
Abstract
:1. Introduction
- Discussing recent articles which investigate the integration of fog computing with different IoT applications.
- Investigating IoT challenges and how they will be resolved by integrating the IoT with fog computing.
- Providing various IoT applications that benefit from the integration of the fog with the IoT.
- Discussing the challenges that result from integrating the IoT with fog computing.
- Discussing future research directions regarding fog computing and the IoT.
2. Challenges of the Cloud of Things
3. Fog Computing
3.1. Definition of Fog Computing
3.2. Characteristics of Fog Computing
- Location awareness and low latency: Fog computing supports location awareness in which fog nodes can be deployed in different locations. In addition, as the fog is closer to end devices, it provides lower latency when processing the data of end devices.
- Geographical distribution: In contrast to the centralized cloud, the services and applications provided by the fog are distributed and can be deployed anywhere.
- Scalability: There are large-scale sensor networks which monitor the surrounding environment. The fog provides distributed computing and storage resources which can work with such large-scale end devices.
- Support for mobility: One of the important aspects of fog applications is the ability to connect directly to mobile devices and therefore enable mobility methods, such as locator ID separation protocol (LISP) which needs a distributed directory system.
- Real-time interactions: Fog computing applications provide real-time interactions between fog nodes rather than the batch processing employed in the cloud.
- Heterogeneity: Fog nodes or end devices are designed by different manufacturers and thus come in different forms and need to be deployed according to their platforms. The fog has the ability to work on different platforms.
- Interoperability: Fog components can interoperate and work with different domains and across different service providers.
- Support for on-line analytics and interplay with the cloud: The fog is placed between the cloud and end devices to play an important role in the absorption and processing of the data close to end devices.
3.3. Benefits of Fog Computing
- Greater business agility: With the use of the right tools, fog computing applications can be quickly developed and deployed. In addition, these applications can program the machine to work according to the customer needs [17].
- Low latency: The fog has the ability to support real-time services (e.g., gaming, video streaming) [19].
- Geographical and large-scale distribution: Fog computing can provide distributed computing and storage resources to large and widely distributed applications [19].
- Lower operating expense: Saving network bandwidth by processing selected data locally instead of sending them to the cloud for analysis [17].
- Flexibility and heterogeneity: Fog computing allows the collaboration of different physical environments and infrastructures among multiple services [20].
- Scalability: The closeness of fog computing to end devices enables scaling the number of connected devices and services [19].
3.4. Architecture of Fog Computing
4. Fog Computing with IoT
5. Applications of Fog with the IoT
5.1. Connected Car
5.2. Smart Traffic Lights
5.3. Smart Home
5.4. Wireless Sensor and Actuator Networks
5.5. Healthcare and Activity Tracking
5.6. IoT and Cyber–Physical Systems
5.7. Augmented Reality
6. Challenges of Fog with the IoT
- (1)
- Scalability: The number of IoT devices is in the order of billions, which generates a huge amount of data and requires a huge amount of resources such as processing power and storage. Therefore, fog servers should be able to support all these devices with adequate resources. The real challenge will be the capability to respond to the rapid growth of IoT devices and applications [53,54].
- (2)
- Complexity: Since there are many IoT devices and sensors designed by different manufacturers, choosing the optimal components is becoming very complicated, especially with different software and hardware configurations and personal requirements. In addition, in some cases, applications with high-security requirements require specific hardware and protocols to function, which increases the difficulty of the operation [53].
- (3)
- Dynamicity: One of the important features of IoT devices is the ability to evolve and dynamically change their workflow composition. This challenge will alter the internal properties and performance of IoT devices. In addition, handheld devices suffer from software and hardware aging, which will result in changing workflow behaviour and device properties. Therefore, fog nodes will need automatic and intelligent reconfiguration of the topological structure and assigned resources [53].
- (4)
- Heterogeneity: There are many IoT devices and sensors which are designed by different manufacturers. These devices have various capabilities in communication radios, sensors, computing powers, storage, etc. The management and coordination of networks such heterogeneous IoT devices and the selection of the appropriate resources will become a big challenge [14].
- (5)
- Latency: One of the main reasons to replace the cloud with fog computing is providing low latency, especially for time-sensitive applications. However, there are many factors presenting a high latency of application or service performance on fog computing platforms. The fog with high latency will lead to user dissatisfaction [54].
- (6)
- Security: Although fog nodes will need to be protected by using the same policy, controls and procedures and use the same physical security and cybersecurity solutions [17], the fog environment itself is vulnerable and less secure than cloud computing. Existing security and privacy measurements of cloud computing cannot be directly applied to the fog due to its mobility, heterogeneity and large-scale geo-distribution [55]. Many research studies focus on cryptography and authentication to improve network security to protect against cyber-attacks in fog computing [14,53].
- (7)
- Resource management: Fog end devices are often network devices equipped with additional storage and computing power. However, it is difficult for such devices to match the resource capacity of traditional servers, let alone the cloud. Therefore, sensible management of fog resources is required for efficient operation of the fog computing environment [27,35].
- (8)
- Energy consumption: The fog environment involves a large number of fog end devices; the computation is distributed and can be less energy-efficient than the centralized cloud model of computation. Therefore, reducing energy consumed in fog computing is an important challenge that needs to be addressed [56].
7. Open Issues of Fog with the IoT
7.1. Communications between the Fog and the Cloud
7.2. Communications between Fog Servers
7.3. Fog Computing Deployment
7.4. Parallel Computation Algorithm
7.5. Security
7.6. End User Privacy
8. Conclusions
Acknowledgments
Conflicts of Interest
References
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Items | Cloud Computing | Fog Computing |
---|---|---|
Latency | High | Low |
Hardware | Scalable storage and computing power | Limited storage and computing power |
Location of server nodes | Within the Internet | At the edge of the local network |
Distance between client and server | Multiple hops | One hop |
Working environment | Warehouse-size building with air conditioning systems | Outdoor (e.g., Streets, gardens) or indoor (e.g., Restaurants) |
Security measures | Defined | Hard to define |
Attack on data | Less probability | High probability |
Deployment | Centralized | Distributed |
Location awareness | No | Yes |
IoT Challenge | How the Fog Can Solve the Challenge |
---|---|
Latency constraints | The fog performs all computation operation such as managing and analyzing data and other time-sensitive actions close to end users, which is the ideal solution to meet latency constraints of some of IoT applications. |
Network bandwidth constraints | Fog computing enables hierarchical data processing along the cloud to IoT devices. This allows data processing to be carried out depending on application demands, available networking and computing resources. This, in turn, reduces the amount of data required to be uploaded to the cloud, which will save network bandwidth. |
Resource-constrained devices | Fog computing can be used to perform operations that need huge resources on behalf of resource-constrained devices when such operations cannot be uploaded to the cloud. Therefore, this allows reducing devices’ complexity, lifecycle costs and power consumption. |
Uninterrupted services | Fog computing can run independently to ensure continuous services even when it has irregular network connectivity to the cloud. |
IoT security challenges | Resource-constrained devices have limited security functions; therefore, fog computing acts as the proxy for these devices to update the software of these devices and security credentials. The fog can also be used to monitor the security status of nearby devices. |
Author | Year | Summary of Contribution |
---|---|---|
F. Bonomi et al. [6] | 2012 | Propose a fog computing platform to support resource-constrained IoT devices |
Hong et al. [37] | 2013 | Propose mobile fog (MF) that allows IoT applications to aggregate and process data locally and support load balancing |
F. Bonomi et al. [20] | 2014 | Propose a hierarchical distributed architecture for the fog and provide use cases of smart traffic light system and wind farm to demonstrate the key features of the proposed architecture |
Stojmenovic and Wen [45] | 2014 | Provide security and privacy issues of the current fog computing paradigm with an example of the man-in-the-middle attack |
K. Lee et al. [41] | 2015 | Investigate security and privacy issues resulting from integrating fog computing |
K. Saharan and A. Kumar [32] | 2015 | Provide an overview of fog computing as an extension of cloud computing |
N. Peter [7] | 2015 | Provide a summary of opportunities for fog computing in real-time applications and how to resolve problems related to congestion and latency |
M. Aazam and E. Huh [23] | 2015 | Propose a model for management of resources through fog computing. This model provides a dynamic way to manage resources and can adapt to different requirements of cloud service providers |
S. Yi et al. [33] | 2015 | Provide a survey of fog computing and challenges that might arise while implementing fog computing systems |
V. Gazis et al. [39] | 2015 | Propose an adaptive operations platform (AOP) to provide end-to-end manageability for fog computing regarding the operational demands of the industrial process |
Y. Shi et al. [21] | 2015 | Provide essential characteristics of fog computing in the healthcare system |
Dastjerdi et al. [35] | 2016 | Propose a reference architecture for fog computing which serves IoT requests in the local fog rather than involving the cloud |
M. Chiang and T. Zhang [10] | 2016 | Provide a summary of the opportunities and challenges of fog computing focusing primarily on the networking context of the IoT |
O. Skarlat et al. [31] | 2016 | Propose a framework for fog resource provisioning to provide delay-sensitive utilization of available fog-based computational resources |
X. Hou et al. [42] | 2016 | Propose VFC architecture to facilitate collaboration between end users to perform communication and computation based on resources of each vehicle |
A. Alrawais et al. [40] | 2017 | Propose a mechanism that employs the fog to improve the distribution of certificate revocation information among IoT devices for security enhancement |
A. Yousefpour et al. [38] | 2017 | Propose a framework to understand, evaluate and model service delay in IoT–fog–cloud application scenarios |
C. Puliafito et al. [34] | 2017 | Discuss mobility support in a fog environment and investigate the main challenges of mobility support with presenting three scenarios |
M. Sookhak et al. [43] | 2017 | Propose a cross-layer architecture of VFC to explain the procedures of the decision-making process and how different types of services are distributed among vehicles |
S. Khan et al. [44] | 2017 | Provide a review of fog computing applications to identify common security issues |
Mahmud et al. [46] | 2018 | Provide a taxonomy of fog computing according to challenges and features of fog computing |
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Atlam, H.F.; Walters, R.J.; Wills, G.B. Fog Computing and the Internet of Things: A Review. Big Data Cogn. Comput. 2018, 2, 10. https://doi.org/10.3390/bdcc2020010
Atlam HF, Walters RJ, Wills GB. Fog Computing and the Internet of Things: A Review. Big Data and Cognitive Computing. 2018; 2(2):10. https://doi.org/10.3390/bdcc2020010
Chicago/Turabian StyleAtlam, Hany F., Robert J. Walters, and Gary B. Wills. 2018. "Fog Computing and the Internet of Things: A Review" Big Data and Cognitive Computing 2, no. 2: 10. https://doi.org/10.3390/bdcc2020010
APA StyleAtlam, H. F., Walters, R. J., & Wills, G. B. (2018). Fog Computing and the Internet of Things: A Review. Big Data and Cognitive Computing, 2(2), 10. https://doi.org/10.3390/bdcc2020010