Research Article Clustering Approaches for Pragmatic Two-Layer IoT Architecture J. Sathish Kumar and Mukesh A. Zaveri Computer Engineering Department, SVNIT, Surat, India Correspondence should be addressed to J. Sathish Kumar; sathish613@gmail.com Received 13 September 2017; Revised 8 February 2018; Accepted 11 March 2018; Published 19 April 2018 Academic Editor: Mauro Femminella Copyright © 2018 J. Sathish Kumar and Mukesh A. Zaveri. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Connecting all devices through Internet is now practical due to Internet of ings. IoT assures numerous applications in everyday life of common people, government bodies, business, and society as a whole. Collaboration among the devices in IoT to bring various applications in the real world is a challenging task. In this context, we introduce an application-based two-layer architectural framework for IoT which consists of sensing layer and IoT layer. For any real-time application, sensing devices play an important role. Both these layers are required for accomplishing IoT-based applications. e success of any IoT-based application relies on efficient communication and utilization of the devices and data acquired by the devices at both layers. e grouping of these devices helps to achieve the same, which leads to formation of cluster of devices at various levels. e clustering helps not only in collaboration but also in prolonging overall network lifetime. In this paper, we propose two clustering algorithms based on heuristic and graph, respectively. e proposed clustering approaches are evaluated on IoT platform using standard parameters and compared with different approaches reported in literature. 1. Introduction e success of wireless sensor network in the form of technol- ogy and applications in different areas like home automation, industrial applications, security and military surveillance, and many more raises the further need for machine-to- machine connectivity and availability of the data or infor- mation anytime and anywhere [1]. is requirement leads to the new technology development in the form of Internet of ings (IoT). e IoT allows the connectivity among the devices and helps in acquisition of data or information at any time and from any place. ere are numerous applications of IoT coming up using different technologies [2]. Also, IoT enables innovative services in numerous applications like smart transportation, smart home, smart city, smart lifestyle, smart retail, smart agriculture, smart industries, smart emergency, smart health care, smart environment, and many more [3, 4]. e use of these applications and their demand has increased the scope of research and innovation in this domain [2, 5]. e most significant part of above-mentioned applica- tions using IoT needs sensing and monitoring the envi- ronment or acquiring the data from different IP-enabled devices or sensors. e sensor devices used for sensing and monitoring are battery-operated and energy-constrained. is implies that power consumption and energy are critical aspects. IP-based communication effectively utilizes more energy, which leads these low-powered devices to deplete rapidly. ese huge numbers of devices communicate and collaborate with each other in order to accomplish a given job or task. is raises the need for effective connectivity and efficient communication among these devices in an optimized way, which is a very challenging task. In this context, there is a need for a solution that promises maximal connectivity through minimum communication. e most efficient way to fulfill these needs is to collaborate among the devices or sensors and perform the tasks for a given application. One way to achieve this is through grouping the devices in an efficient way in terms of energy usage and computational complexity. Hindawi Wireless Communications and Mobile Computing Volume 2018, Article ID 8739203, 16 pages https://doi.org/10.1155/2018/8739203