Elsevier

Computer Networks

Volume 143, 9 October 2018, Pages 221-246
Computer Networks

Review article
IoT survey: An SDN and fog computing perspective

https://doi.org/10.1016/j.comnet.2018.07.020Get rights and content

Abstract

Recently, there has been an increasing interest in the Internet of Things (IoT). While some analysts disvalue the IoT hype, several technology leaders, governments, and researchers are putting serious efforts to develop solutions enabling wide IoT deployment. Thus, the huge amount of generated data, the high network scale, the security and privacy concerns, the new requirements in terms of QoS, and the heterogeneity in this ubiquitous network of networks make its implementation a very challenging task. SDN, a new networking paradigm, has revealed its usefulness in reducing the management complexities in today's networks. Additionally, SDN, having a global view of the network, has presented effective security solutions. On the other hand, fog computing, a new data service platform, consists of pushing the data to the network edge reducing the cost (in terms of bandwidth consumption and high latency) of “big data” transportation through the core network. In this paper, we critically review the SDN and fog computing-based solutions to overcome the IoT main challenges, highlighting their advantages, and exposing their weaknesses. Thus, we make recommendations at the end of this paper for the upcoming research work.

Introduction

One of the all-time most impactful innovations is the Internet. Internet has permitted the interconnection of all traditional computing devices and it was natural for this desire for access and control to extend to non-traditional devices. Here came the evolution into Internet of Things (IoT). Mentioned seventeen years ago by Kevin Ashton [1], IoT draws the lines of the second digital revolution [2,3]. Cisco expected that, by 2020, 50 billion objects would be connected to the Internet [4]. This large scale is one of the unavoidable challenges for the IoT domain. The high scalability is accompanied with an increased complexity in the management of this large number of things/gateways, and network devices. Managing all these devices in the traditional way (manually and each device separately) is no longer viable.

As the Metcalfe's law states, the importance of a communication network increases exponentially with the number of connected devices [5]. Therefore, with billions of connected things in the future network, the IoT value is extremely high [6]. In addition, IoT is depicted as one of the most disruptive technologies [7,8]. Many firms and technology leaders (Intel, Microsoft, Cisco, InterDigital, etc.) have taken note of the IoT economical value [9], and put serious efforts to enable IoT real deployment (Table 1 lists some of the important ongoing projects). However, this drive to develop IoT solutions has resulted in proposing disjoint ones. Lacking interoperability between the different IoT platforms limits their potential. We all know that the root enabler of the Internet success and wide adoption is its openness and its standardized architecture. Having different IoT architectures and platform resulted in having heterogeneous silos of networks. In addition to this kind of heterogeneity, different formats of data are used, and different types of communication technologies are invoked. This makes the IoT a vertically fragmented network. Therefore, the heterogeneity is another important challenge facing IoT.

Moreover, the large number of connected devices will naturally result in enormous amount of data, which challenges the ability of today's networks to handle. The current centralized paradigm of data processing and storage is not feasible. New ways to analyze, filter and aggregate this data at the network edges will be essential in any upcoming IoT solution. The IoT Big Data” is not only about the size of the generated data, but it is more about the variety of this data in terms of type, semantic, frequency, place and time.

Finally, security and privacy guarantees present one of the most important challenges that effectively hinders any real IoT wide deployment. In addition to the current security vulnerabilities, IoT poses new ones.

In the light of the cited challenges, there is a need for new approach to networking. Software Defined Networking (SDN), a new networking paradigm, aims to separate the control and data planes. This separation provides the network controller with a global view of the network, facilitating traffic engineering and network management at runtime [10]. On the other hand, fog computing (a cloud computing complement) aims at bringing the cloud to the network edge making it more scalable and more responsive. In this survey, we will investigate how these technologies have been applied in the IoT field and how their application will enable the IoT wide deployment.

In the literature, several surveys have tackled different IoT related subjects (see Table 2): IoT applications, challenges, and opportunities [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], IoT frameworks [32], [33], [34], [35], [36], [37], IoT security [38], [39], [40], [41], [42], [43], IoT standardization [44], [45], [46], [47], [48], SDN application in IoT [49], [50], [51], [52], [53], [54], IoT and cloud integration [55,56].

However, the existing surveys do not comprehensively review the main IoT challenges. The Internet already presents QoS and security related challenges, but in the IoT case, some of the existing challenges become more crucial. Thus, the existing work includes specific and non-specific-IoT challenges. In addition, the IoT challenges are listed without including the related proposed solutions. However, in this work, we presented the four main IoT-specific challenges and we reviewed the proposed solutions coping with these challenges. In this context, new technologies emerge in the network, communication, and IT domains. These technologies can enable innovative IoT applications and help in coping with many of the IoT challenges. However, the existing work does not cover the most recent technologies and the role of these technologies in alleviating the IoT challenges. Thus, this work presents the most recent enabling technologies, and how these technologies can be employed to cope with the presented IoT challenges. Specifically, this paper reviews the application of SDN, NFV, cloud computing, and fog computing to handle the main IoT challenges.

This paper is organized as follows: In Section 2, we discuss the most relevant IoT definitions and we list the main related IoT concepts. In Section 3, we investigate the IoT enabling technologies. In Section 4, we review the most important IoT, SDN, NFV, and edge computing standardization efforts. In the subsequent sections, we present the IoT main challenges (as shown in Table 3): IoT security, IoT Big Data, IoT heterogeneity, and IoT scalability, and the corresponding SDN/NFV and cloud/edge-based solutions. Thus, in Section 5, we review the IoT security related work and we show how SDN can alleviate the IoT security concerns. Section 6 reviews the IoT “Big Data” and the application of cloud and fog computing to manage it. The IoT gateway is an essential part to cope with the heterogeneity challenge, so in Section 7, we review the propositions of IoT gateways. IoT scalability imposes new architectural considerations, so Section 8 reviews the most known IoT architectures and the SDN integration into a general IoT architecture. In Section 9, we present the main limitations of the current IoT solutions and we make some recommendations for the future research directions. Finally, we conclude in Section 10.

Section snippets

IoT definition

Beyond the IoT hype, a real definition is essential to highlight the characteristics of this new concept [57]. Several definitions have been proposed resulting in a storm of terms and definitions. Important work is being done by the IEEE Internet initiative in order to find a conceptual IoT definition [58]. ITU defines IoT as being an infrastructure that will connect physical and virtual devices [59]. IETF defines IoT as being the Internet that considers TCP/IP and Non-TCP/IP suites at the same

Enabling technologies

In this section, we present the recent technologies designated to play an essential role in the IoT realization.

IoT standardization

Standardization is key to achieve any new technology's wide adoption. Having disjoint platforms, architectures and protocols undermine their utility. A standardized IoT architecture is key for the IoT wide deployment in addition to the standardization at the communication level [168]. The TCP/IP standard was the enabler of the Internet revolution. Revisiting its architecture, we found that most of its protocols at different layers are not designed for the IoT case. The “things” in IoT might be

IoT security and privacy: an engineering perspective

The security and privacy issues hinder the IoT realization. Although, some of the IoT security breaches/vulnerabilities are common with the current Internet network [172], IoT presents new security concerns that make it the “Internet of Vulnerabilities” [173]. Some analysts argue that the security concerns in IoT outweigh its benefits. DY intruder, DoS/DDoS, physical attacks, privacy attacks, eavesdropping, data mining, and traffic analysis are primary IoT attacks [174]. Additionally, new types

IoT Big Data: a management perspective

It is not about “things”; it is about data. Effectively, the IoT innovative value lays on a collected data foundation [234]. The IoT “Big Data” is about 3 V's: Volume, Variety, and Value [235,236]. To an extent, we can say that there is no IoT without the sense of data. It is not about the size of collected/generated data; it is more about the diversity, heterogeneity, dispersity of this data. Having the data shared between different entities poses security and privacy concerns [237,238].

IoT gateway

Owing to the massive heterogeneity in the IoT domain and the presence of vertically integrated domains and applications, the call for a gateway layer is crucial [266]. Thus, the IoT gateway has to perform multiple functions such as: protocols translation (NATing [267]), service chaining, security related functions (firewall, authentication, access control, etc.), data mining, QoS management, mobility and handover management, and routing and forwarding packets (Fig. 9) [268].

In [269], Datta

IoT scalability: an architectural perspective

Having billions of things connected to the Internet in the future network, the network architecture needs to be rethought. Many IoT architectures have been proposed in the literature (Table 8). This situation is similar to have multiple remote controls (for managing different types of devices the DVD, TV, AC, etc.) all functioning the same way, but no one can replace the other [303]. For enabling the IoT wide deployment, we need a common agreed upon architecture as the case of the TCP/IP

Limitations

Based on the review presented in this paper, we can list some of the limitations of the current IoT solutions:

Lack of Interoperability: different solutions have been proposed to overcome the different IoT challenges. However, most of them do not consider the existing IoT solutions and this makes the adoption of the new solutions a complicated task. Interoperability between the different IoT solutions (devices, architectures, protocols, etc.) helps in revealing the IoT value in enabling

Conclusion

The network and telecommunication networks are in continuous evolution. Internet of Things is expected to take advantage of this evolution to be widely deployed. While some IoT applications are already there, its wide realization still encumbered by many challenges such as the high scalability and management complexity, the heterogeneity and interoperability support, the big data handling, and the security and privacy guarantee. These main challenges need new architectural and design based

Acknowledgements

Research funded by the AUB University Research Board, the Lebanese National Council for Scientific Research, and TELUS Corp., Canada.

Ola Salman received her M.E. degree in Computer and Communications Engineering from the Lebanese University in 2013. In September 2014, she joined the PhD accelerated track program in the Electrical and Computer Engineering (ECE) department at the American University of Beirut (AUB). Her research interests are in the area of Information Security and Networks, Software Defined Networks, Edge Computing, Artificial Intelligence, and Internet of things. In 2017, she received the CNRS-L/AUB doctoral

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    Ola Salman received her M.E. degree in Computer and Communications Engineering from the Lebanese University in 2013. In September 2014, she joined the PhD accelerated track program in the Electrical and Computer Engineering (ECE) department at the American University of Beirut (AUB). Her research interests are in the area of Information Security and Networks, Software Defined Networks, Edge Computing, Artificial Intelligence, and Internet of things. In 2017, she received the CNRS-L/AUB doctoral scholarship award from the Lebanese National Council for Scientific Research (CNRS) in recognition of her research work.

    Imad H. Elhajj received his Bachelor of Engineering in Computer and Communications Engineering, with distinction, from the American University of Beirut in 1997 and the M.S. and Ph.D. degrees in Electrical Engineering from Michigan State University in 1999 and 2002, respectively. He is currently an Associate Professor with the Department of ECE at AUB. Imad received Best Research Paper Award at the Third International Conference on Cognitive and Behavioral Psychology (CBP), Best Paper award at the IEEE Electro Information Technology Conference in June 2003, and at the International Conference on Information Society in the 21st Century in November 2000. Dr. Elhajj is recipient of the Teaching Excellence Award at the American University of Beirut, June 2011, the Kamal Salibi Academic Freedom Award, 2014, and the most Outstanding Graduate Student Award from the ECE Department at Michigan State University, April 2001.

    Ali M. Chehab received his Bachelor degree in EE from AUB in 1987, the Master's degree in EE from Syracuse University in 1989, and the PhD degree in ECE from the University of North Carolina at Charlotte, in 2002. From 1989 to 1998, he was a lecturer in the ECE Department at AUB. He rejoined the ECE Department at AUB as an Assistant Professor in 2002, and became a Full Professor in 2014. He received the AUB Teaching Excellence Award in 2007. He teaches courses in Programming, Electronics, Digital Systems Design, Computer Organization, Cryptography, and Digital Systems Testing. His research interests include: Wireless Communications Security, Cloud Computing Security, Multimedia Security, Trust in Distributed Computing, Low Energy VLSI Design, and VLSI Testing. He has more than 180 publications. He is a senior member of IEEE and a senior member of ACM.

    Ayman Kayssi studied electrical engineering and received the BE degree, with distinction, in 1987 from the American University of Beirut (AUB), and the MSE and PhD degrees from the University of Michigan, Ann Arbor, in 1989 and 1993, respectively. In 1993, he joined the Department of Electrical and Computer Engineering (ECE) at AUB, where he is currently a full professor. From 2004 to 2007, he served as chairman of the ECE Department at AUB. He teaches courses in electronics and in networking, and has received AUB's Teaching Excellence Award in 2003. His research interests are in information security and networks, and in integrated circuit design and test. He has published more than 200 articles in the areas of security, networking, and VLSI. He is a senior member of IEEE, and a member of ACM, ISOC, and the Beirut OEA.

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