Internet of Things (IoT): A vision, architectural elements, and future directions
Introduction
The next wave in the era of computing will be outside the realm of the traditional desktop. In the Internet of Things (IoT) paradigm, many of the objects that surround us will be on the network in one form or another. Radio Frequency IDentification (RFID) and sensor network technologies will rise to meet this new challenge, in which information and communication systems are invisibly embedded in the environment around us. This results in the generation of enormous amounts of data which have to be stored, processed and presented in a seamless, efficient, and easily interpretable form. This model will consist of services that are commodities and delivered in a manner similar to traditional commodities. Cloud computing can provide the virtual infrastructure for such utility computing which integrates monitoring devices, storage devices, analytics tools, visualization platforms and client delivery. The cost based model that Cloud computing offers will enable end-to-end service provisioning for businesses and users to access applications on demand from anywhere.
Smart connectivity with existing networks and context-aware computation using network resources is an indispensable part of IoT. With the growing presence of WiFi and 4G-LTE wireless Internet access, the evolution towards ubiquitous information and communication networks is already evident. However, for the Internet of Things vision to successfully emerge, the computing paradigm will need to go beyond traditional mobile computing scenarios that use smart phones and portables, and evolve into connecting everyday existing objects and embedding intelligence into our environment. For technology to disappear from the consciousness of the user, the Internet of Things demands: (1) a shared understanding of the situation of its users and their appliances, (2) software architectures and pervasive communication networks to process and convey the contextual information to where it is relevant, and (3) the analytics tools in the Internet of Things that aim for autonomous and smart behavior. With these three fundamental grounds in place, smart connectivity and context-aware computation can be accomplished.
The term Internet of Things was first coined by Kevin Ashton in 1999 in the context of supply chain management [1]. However, in the past decade, the definition has been more inclusive covering wide range of applications like healthcare, utilities, transport, etc. [2]. Although the definition of ‘Things’ has changed as technology evolved, the main goal of making a computer sense information without the aid of human intervention remains the same. A radical evolution of the current Internet into a Network of interconnected objects that not only harvests information from the environment (sensing) and interacts with the physical world (actuation/command/control), but also uses existing Internet standards to provide services for information transfer, analytics, applications, and communications. Fueled by the prevalence of devices enabled by open wireless technology such as Bluetooth, radio frequency identification (RFID), Wi-Fi, and telephonic data services as well as embedded sensor and actuator nodes, IoT has stepped out of its infancy and is on the verge of transforming the current static Internet into a fully integrated Future Internet [3]. The Internet revolution led to the interconnection between people at an unprecedented scale and pace. The next revolution will be the interconnection between objects to create a smart environment. Only in 2011 did the number of interconnected devices on the planet overtake the actual number of people. Currently there are 9 billion interconnected devices and it is expected to reach 24 billion devices by 2020. According to the GSMA, this amounts to $1.3 trillion revenue opportunities for mobile network operators alone spanning vertical segments such as health, automotive, utilities and consumer electronics. A schematic of the interconnection of objects is depicted in Fig. 1, where the application domains are chosen based on the scale of the impact of the data generated. The users span from individual to national level organizations addressing wide ranging issues.
This paper presents the current trends in IoT research propelled by applications and the need for convergence in several interdisciplinary technologies. Specifically, in Section 2, we present the overall IoT vision and the technologies that will achieve it followed by some common definitions in the area along with some trends and taxonomy of IoT in Section 3. We discuss several application domains in IoT with a new approach in defining them in Section 4 and Section 5 provides our Cloud centric IoT vision. A case study of data analytics on the Aneka/Azure cloud platform is given in Section 6 and we conclude with discussions on open challenges and future trends in Section 7.
Section snippets
Ubiquitous computing in the next decade
The effort by researchers to create a human-to-human interface through technology in the late 1980s resulted in the creation of the ubiquitous computing discipline, whose objective is to embed technology into the background of everyday life. Currently, we are in the post-PC era where smart phones and other handheld devices are changing our environment by making it more interactive as well as informative. Mark Weiser, the forefather of Ubiquitous Computing (ubicomp), defined a smart environment
Definitions
As identified by Atzori et al. [8], Internet of Things can be realized in three paradigms—internet-oriented (middleware), things oriented (sensors) and semantic-oriented (knowledge). Although this type of delineation is required due to the interdisciplinary nature of the subject, the usefulness of IoT can be unleashed only in an application domain where the three paradigms intersect.
The RFID group defines the Internet of Things as–
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The worldwide network of interconnected objects uniquely
Applications
There are several application domains which will be impacted by the emerging Internet of Things. The applications can be classified based on the type of network availability, coverage, scale, heterogeneity, repeatability, user involvement and impact [21]. We categorize the applications into four application domains: (1) Personal and Home; (2) Enterprize; (3) Utilities; and (4) Mobile. This is depicted in Fig. 1, which represents Personal and Home IoT at the scale of an individual or home,
Cloud centric Internet of Things
The vision of IoT can be seen from two perspectives—‘Internet’ centric and ‘Thing’ centric. The Internet centric architecture will involve internet services being the main focus while data is contributed by the objects. In the object centric architecture [43], the smart objects take the center stage. In our work, we develop an Internet centric approach. A conceptual framework integrating the ubiquitous sensing devices and the applications is shown in Fig. 4. In order to realize the full
IoT Sensor data analytics SaaS using Aneka and Microsoft Azure
Microsoft Azure is a cloud platform, offered by Microsoft, includes four components as summarized in Table 3 [44]. There are several advantages for integrating Azure and Aneka. Aneka can launch any number of instances on the Azure cloud to run their applications. Essentially, it provides the provisioning infrastructure. Similarly, Aneka provides advanced PaaS features as shown in Fig. 6. It provides multiple programming models (Task, Thread, MapReduce), runtime execution services, workload
Open challenges and future directions
The proposed Cloud centric vision comprises a flexible and open architecture that is user centric and enables different players to interact in the IoT framework. It allows interaction in a manner suitable for their own requirements, rather than the IoT being thrust upon them. In this way, the framework includes provisions to meet different requirements for data ownership, security, privacy, and sharing of information.
Some open challenges are discussed based on the IoT elements presented
Summary and conclusions
The proliferation of devices with communicating–actuating capabilities is bringing closer the vision of an Internet of Things, where the sensing and actuation functions seamlessly blend into the background and new capabilities are made possible through access of rich new information sources. The evolution of the next generation mobile system will depend on the creativity of the users in designing new applications. IoT is an ideal emerging technology to influence this domain by providing new
Acknowledgments
There have been many contributors for this to take shape and the authors are thankful to each of them. We specifically would like to thank Mr. Kumaraswamy Krishnakumar, Mr. Mohammed Alrokayan, Dr. Jiong Jin, Dr. Yee Wei Law, Prof. Mike Taylor, Prof. D. Nandagopal, Mr. Aravinda Rao and Prof. Chris Leckie. The work is partially supported by Australian Research Council’s LIEF (LE120100129), Linkage grants (LP120100529) and Research Network on Intelligent Sensors, Sensor networks and Information
Jayavardhana Gubbi received the Bachelor of Engineering degree from Bangalore University, Bengaluru, India, in 2000, the Ph.D. degree from the University of Melbourne, Melbourne, Vic., Australia, in 2007. For three years, he was a Research Assistant at the Indian Institute of Science, where he was engaged in speech technology for Indian languages. Dr. Gubbi is a Research Fellow in the Department of Electrical and Electronic Engineering at the University of Melbourne. Currently, from 2010 to
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Jayavardhana Gubbi received the Bachelor of Engineering degree from Bangalore University, Bengaluru, India, in 2000, the Ph.D. degree from the University of Melbourne, Melbourne, Vic., Australia, in 2007. For three years, he was a Research Assistant at the Indian Institute of Science, where he was engaged in speech technology for Indian languages. Dr. Gubbi is a Research Fellow in the Department of Electrical and Electronic Engineering at the University of Melbourne. Currently, from 2010 to 2014, he is an ARC Australian Postdoctoral Fellow - Industry (APDI) working on an industry linkage grant in video processing. His current research interests include Video Processing, Internet of Things and ubiquitous healthcare devices. He has coauthored more than 40 papers in peer reviewed journals, conferences, and book chapters over the last ten years. Dr. Gubbi has served as Conference Secretary and Publications Chair in several international conferences in the area of wireless sensor networks, signal processing and pattern recognition.
Rajkumar Buyya is Professor of Computer Science and Software Engineering; and Director of the Cloud Computing and Distributed Systems (CLOUDS) Laboratory at the University of Melbourne, Australia. He is the founding CEO of Manjrasoft, a spin-off company of the university, commercializing its innovations in Cloud Computing. He has authored over 430 publications and four textbooks. He also edited several books including “Cloud Computing: Principles and Paradigms” (Wiley Press, USA, Feb 2011). He is one of the highly cited authors in computer science and software engineering worldwide (h-index = 66 and 21300+ citations).
Software technologies for Grid and Cloud computing developed under Dr. Buyya’s leadership have gained rapid acceptance and are in use at several academic institutions and commercial enterprizes in 40 countries around the world. Dr. Buyya has led the establishment and development of key community activities, including serving as foundation Chair of the IEEE Technical Committee on Scalable Computing and five IEEE/ACM conferences. These contributions and the international research leadership of Dr. Buyya are recognized through the award of the “2009 IEEE Medal for Excellence in Scalable Computing”. Manjrasoft’s Aneka Cloud technology developed under his leadership has received the “2010 Asia Pacific Frost & Sullivan New Product Innovation Award” and “2011 Telstra Innovation Challenge, People’s Choice Award”. He is currently serving as the first Editor-in-Chief (EiC) of IEEE Transactions on Cloud Computing. For further information on Dr. Buyya, please visit his cyberhome: www.buyya.com.
Slaven Marusic is a Senior Research Fellow in Sensor Networks at the Department of Electrical and Electronic Engineering, at the University of Melbourne. Completing his Ph.D. at La Trobe University specializing in signal and image processing, before taking a Senior Lecturer role at the University of New South Wales, Dr Marusic returned to Melbourne also taking up the Role of Program Manager for the ARC Research Network on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP). In this capacity he has facilitated numerous international research collaborations across academia and industry. He was the General Co-Chair of the 6th International Conference on ISSNIP, Brisbane 2010, and has served on numerous organizing and technical program committees. His research work has encompassed multidisciplinary contributions in the areas of image and video processing, sensor networks and biomedical signal processing, applied variously to environmental monitoring, healthcare, smart grids and more recently, urban living.
M. Palaniswami received his B.E. (Hons) from the University of Madras, M.E. from the Indian Institute of Science, India, and Ph.D. from the University of Newcastle, Australia before joining the University of Melbourne, where he is a Professor of Electrical Engineering and Director/Convener of a large ARC Research Network on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP) with about 200 researchers and interdisciplinary themes as the focus for the center. Previously, he was a Co-Director of the Center of Expertise on Networked Decision & Sensor Systems. He served on various international boards and advisory committees including being a panel member for the National Science Foundation (NSF). He has published more than 340 refereed journal and conference papers, including a number of books, edited volumes and book chapters. He was given a Foreign Specialist Award by the Ministry of Education, Japan in recognition of his contributions to the field of Machine Learning. He received the University of Melbourne Knowledge Transfer Excellence Award and Commendation Awards. He served as an Associate Editor for journals/transactions including IEEE Transactions on Neural Networks and Computational Intelligence for Finance. He is the Subject Editor for the International Journal on Distributed Sensor Networks. Through his research, he supported various local and international companies. As an international investigator, he is involved in FP6 and FP7 initiatives in the areas of Smart City and Internet of Things (IoT). In order to develop a new research capacity, he founded the international conference series on sensors, sensor networks and information processing. His research interests include smart sensors and sensor networks, machine learning, neural networks, support vector machines, signal processing, biomedical engineering and control. He is a Fellow of the IEEE.