Internet of Everything (IoE) orchestrates the Internet of Things (IoT) into a coherent networked system. Whilst physical connectivity in IoT enables machine and device connectivity, IoE uses their data and other data sets to drive a cohesive cyber-physical system. As we accelerate into the 21st century, we are seeing increased human digital activity through the compounded effects of urbanisation and proliferation of smart devices. Beyond these statistical trends, networks are also experiencing increased data demand complexity from people and machines. Enabling real-time understanding and exploitation of these complexities is important. Over the past decade, wireless and social networks have increasingly made available meta-data with spatial components of varying resolutions. Connected network devices under the IoE paradigm, can potentially leverage on a combination of real-time data streams and other databases (i.e. geographical information system data, commercial data, census data, engineering data), to derive social context and improve the performance of underlying 4G/5G/B5G wireless network performance. Wireless networks are a fundamental cornerstone of the global digital economy and a key component in our daily lives. Each year, the mobile cellular network industry contributes 3.6% to the global GDP ($2.4 trillion), supports 10.5 million jobs, and has contributed to $336 billion of public funding.
In this project, we exploit heterogeneous big data analytics to optimize both the deployment and operations of wireless networks. We design protocols that enable future Data Aware Wireless Networks (DAWN) for enabling a new age of IoE. The proposal has been developed to address the following open issues in data driven flexible wireless systems:
• How to characterize user mobility and wireless data traffic patterns using heterogeneous data sets.
• How to infer user Quality-of-Experience (QoE) from combining data sets.
• How to use data analytics to assist network planning.
• How to use data driven techniques to optimise the network using Self-Organising-Network (SON) algorithms.
• How to optimally cache data to accelerate and optimise data storage and transmission.
The research objectives of the DAWN4IoE project are as follows:
• Develop urban propagation guided spatial-temporal filters to combine data sets and infer digital data demand patterns.
• Develop natural language processing (NLP) techniques to understand consumer experience.
• Design algorithms to integrate the new heterogeneous data analytics techniques with current state-of-the-art deployment techniques to assist HetNet planning, performance prediction, and deployment.
• Design mechanisms to integrate data analytics and drive SON algorithms.
• Design algorithms to optimally cache data leveraging on mobile edge computing (MEC).
Achieving the above objectives will provide crucial inputs for 5G/IoE data-driven flexible wireless network design and both increase network capacity by 50% and decrease operation costs by 20-30% (compared with non-data driven networks).
