Building Enterprise-Grade Internet of Things Applications

Abstract

The last few years have seen two key trends maturing in the industry: IoT and Big Data. These trends build on more than a decade of research in both academia and industry. As the cost of instrumentation and microprocessor chips has declined, it is now possible to monitor the environment on a widening scale. The cost decline is matched by cloud computing (exposed as web services) that provides infrastructure for storage and processing. Furthermore, on top of cloud, advances in big data tools/techniques provide a platform to analyze and understand the massive amount of data. But data generated from IoT is expected not only to be Big (volume) but also Fast (velocity). Data analysis and machine learning will play a key role in unlocking the value generated by IoT data. The Internet of Things (IoT) can be thought of in terms of connecting and combining the above mentioned elements. Combination of all these elements at various levels (e.g., physical objects, cloud service, mobile with rich user interfaces, analytics) will allow access and analysis of an enormous amount of fast data, which could be used to improve efficiency and performance of the whole enterprise. Moreover, it opens the possibilities of developing IoT applications in novel scenarios such as smart metering, smart electric car recharge stations, retail & logistics, and so on. An important challenge that needs to be addressed is to enable the rapid development of IoT applications. Similar challenges have already been addressed in the closely related fields of Wireless Sensor and Actuator Networks (WSANs) and Pervasive/Ubiquitous computing. While the main challenge in the former is largely limited to similar nodes, the primary concern in the latter largely has been the heterogeneity of physical objects. The upcoming field of IoT will include both WSANs as well as heterogeneous physical objects, in addition to this it brings heterogeneity at various levels (e.g., physical objects, cloud services, smart phones with rich user interfaces, analytics). Therefore, Software Engineering (SE) support for IoT applications is needed to develop methodologies, abstractions, and techniques. Nevertheless, so far this topic has received very little attention by SE community.