Rajiv Ranjan (rajivranjan.net) is a Chair Professor in Computing Science and Internet of Things at Newcastle University, United Kingdom. He has received two IEEE research excellence awards (2018 IEEE TCCPS Early Career Award and 2016 IEEE TCSC Award for Excellence in Scalable Computing), which recognized his leading expertise in algorithms, resource management models and distributed system architectures for Cloud computing, Internet of Things (IoT) and Data Science. Another testimonial of his international research leadership is his appointment by IEEE Computer Society as the Advisory Board Chair and Lead Editor for the Blue Skies department of IEEE Cloud Computing. In this appointment, Prof Ranjan’s main role is to develop a vision for the research community to guide future research at the intersection of Cloud computing, IoT and Data Science. Additionally, he also serves on the editorial boards of top quality international journals including IEEE Transactions on Cloud computing, IEEE Transactions on Computers (2014-2016), IEEE Cloud Computing, Springer Computing, The Computer Journal (Oxford University Press), among many others.
His research outcomes include 250+ academic peer-reviewed articles and multiple open source software toolkits – stemming from funded research projects worth over $12 Million AUD (£6 Million GBP). He is one of the highly cited authors (top 0.05%) in computer science and software engineering worldwide (h-index=46, g-index=114, and 12200+ google scholar citations).
The Internet of Things (IoT) is the latest web evolution incorporating potentially billions of devices (such as cameras, sensors, RFIDs, smart phones, and wearables), owned by different organizations, and by people who are deploying and using them for their own purposes. %Federations of such IoT devices (which we refer to as IoT ``things") can deliver the information needed to solve internet-scale problems that have been too difficult to harness before.
There are currently 6.4 billion IoT devices in use around the world. Their number, capabilities, as well as their scope of use, keeps growing & changing rapidly. Gartner forecasts the number of IoT devices will reach 20.8 billion by 2020, and projects that by then IoT service spending will reach $1,534 billion and hardware spending $1,477 billion.
Similarly, the volume of generated data and computing requirements of IoT applications will continue to increase, with the increasing pervasiveness of IoT technologies. Thus, new computing paradigms, such as Edge computing or IoT-Cloud Computing, have been investigated in literature to extend IoT resources into centralized datacenters (e.g., Cloud datacenters) or at the edge of IoT systems (e.g., Edge micro-datacenters). The main issue arising in using several computing models to support IoT applications is the management of different physical/virtual infrastructures (e.g., datacenters, edge devices, IoT devices & gateways) according to specific application/service requirements (e.g., latency, data volume, responsivity, and processing delay etc). In particular, it is often hard to determine apriori how to deploy the microelements composing IoT applications into different infrastructures -- since resources' availability, system load and connectivity features can unpredictably change over the time.
In this scenario, the``Osmotic Computing'' paradigm which provides convergence of IoT, Edge and Cloud computing technologies. Osmotic Computing aims to dynamically manage resources available in IoT, Edge and Cloud systems driven by specific applications requirements (of QoS, security, and privacy). The need for such a new computing paradigm arises from data, application and service management requirements and cost/economic factors relevant to current and emerging IoT applications.
邀请人： 肖晶 陈丹