VERSO IL CONCETTO DI SMART CITY: SOLUZIONI IOT EDGE-CLOUD

Since the term was coined by Kevin Ashton in 1999, the Internet of Things (IoT) did not gain considerable popularity until 2010 where it became a strategic priority for governments, companies, and research centers. Despite this large-scale interest, IoT only reached mass markets in 2014 in the form of wearable devices and fitness trackers, home automation, industrial asset monitoring, and smart energy meters. The ‘things’ refer to sensors and other smart devices with the ability to monitor an object’s state, or even control it using actuators. Ashton envisaged that when such sensors and smart devices were on a ubiquitous network – the Internet – they would have far more value. Trending data-centric technologies in the IoT involve security and data governance, infrastructure (edge & cloud analytics), data processing, advanced analytics, and data integrating and messaging. These technologies are supported by cloud computing service models that include three major layers – Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). Of the three, IaaS is the foundation while SaaS is the top layer functioning off both PaaS and IaaS. Interestingly enough, although SaaS is normally represented in graphics as the smallest layer of Cloud infrastructure, it is anything but. The IaaS layer of Cloud Computing is comprised of all the hardware needed to make Cloud Computing possible. The PaaS layer of the Cloud is a framework for developers that they can build upon and use to create customized applications. Built on top of both IaaS and PaaS, Software as a Service provides applications, programs, software, and web tools to the public for free or for a price. By the year 2020, trillions of gigabytes of data will be generated through the Internet of Things. This is no doubt difficult to comprehend easily. However, with the growing number of connected devices it is not surprising that by 2020, more than ten billion sensors and devices will be connected to the internet. Furthermore, all of these devices will gather, analyze, share, and transmit data in real-time. Hence, without the data, IoT devices would not hold the functionalities and capabilities which have made them achieve so much worldwide attention. If organizations are not in a position to somehow ingest, process and analyze these data, then it becomes worthless, and the IoT project will be considered a failure. Unlike a traditional IT system, IoT systems are cyber-physical systems involving both humans and machines as end-users. Their interaction forms a complex web of M2M (Machine to Machine) and H2M (Human to Machine) transactions. Right from device firmware, to network interfaces, extending all the way to business logic defined in cloud application and user app, software remains the most critical driver in IoT. Similarly, Edge computing presents great opportunities to achieve ubiquitous computation in the Internet ecosystem. It is proposed to overcome the intrinsic challenges of computing on the cloud side. Edge computing offers to gather more sensory data, reducing the response time, freeing up network bandwidth, and ultimately reducing the workload on the cloud. In the effort to elevate support for technologies that are directed toward IoT in smart cities concept, support for developers and service providers is critical especially regarding fast and feasible deployment of IoT solutions and assets. To that end, I focused during my research on ways and methods to exploit generic IoT solutions; Application Programming Interfaces (APIs) and edge engines. In this book, I present Atmosphere, a novel edge-to-cloud solution for supporting development and deployment by IoT developers and service providers. Atmosphere cloud is a SaaS deployment-ready model, while Atmosphere edge is a lightweight edge engine for IoT device management. Needless to say, testing the various software components is essential to ensure a safe and reliable IoT system. The solutions I contributed to were tested in multiple projects of varying volumes and challenges. In some projects, using the generic concept was straight forward, while in others, where the structure of the IoT data was complicated and restrictions were established by the partners, the integration was challenging.