Keynote Speakers

A wireless sensor network (WSN) is a communication network with ad hoc configuration consisting of tiny, lowpower, low-cost sensors which are normally distributed in a decentralized fashion and have limited processing capability. WSNs have found a wide range of applications such as industrial process control, healthcare monitoring, surveillance, forest fire detection, natural disaster detection, target tracking, among many other applications. It is known that WSNs are resourceconstrained, hence, energy efficiency is crucial for all applications of WSNs to extend the life span of the sensors' batteries. The most energy consuming operation in WSN is data communication, hence, it is important to reduce amount of data transmission through WSNs without significantly affecting the transferred information. In this presentation we will focus on two directions of data-efficient signal representations that are expected to provide WSNs with sufficient energy control. The first direction is the use of intelligent short word-length (SWL) systems via embedded sigma-delta modulation, and the second direction is to use compressive sensing (CS) with chaotic sequences. If security is a factor, then CS via chaos can support secure communication in addition to its main function as a technique for data compression. Bio: Zahir M. Hussain got his BSc and MSc degrees from the University of Baghdad and his PhD from Queensland University of Technology (Australia) in 2002. In 2001 he joined the School of Electrical & Computer Engineering, RMIT, Australia and led a 3G communication project 2001-2002. He has over 250 publications. While at RMIT, he received an ARC Discovery Grant (2005-2008) jointly with Professor Peter O'Shea to finalize research on short word-length processing. In 2005 he was promoted to Associate Professor at RMIT. He got RMIT Publication Awards for 2005 and 2006, and RMIT Teaching Award for 2007. He has been a senior member of the IEEE and the Australian Computer Society (ACS); also, he got the title of Chartered Scientist from the British Science Council in 2020. He attended over 50 international conferences; worked on the TPC of many leading conferences, and served as a reviewer for leading journals, including MDPI Sensors, at which he is currently an Editor. He examined over 70 PhD Theses, and supervised 27 PhD's. In 2010 he joined the University of Kufa, Najaf, Iraq, as a Professor of Signal Processing, while RMIT granted him Adjunct Professorship. In 2014 he joined Edith Cowan University (Australia) as Adjunct Professor at the School of Engineering. On 15th April 2012 he got the "Distinguished Academic Award" from the ministry of Higher Education, Iraq. On 20th September 2017 he was awarded the "Scientific Excellence Medal" by the Minister of Higher Education, Iraq. 20 20 3 0t h In te rn at io na l T el ec om m un ic at io n N et w or ks a nd A pp lic at io ns C on fe re nc e (I TN A C ) | 9 78 -1 -7 28 188 27 -0 /2 0/ $3 1. 00 © 20 20 IE EE | D O I: 10 .1 10 9/ IT N A C 50 34 1. 20 20 .9 31 51 20 Professor Shui Yu, University of Technology Sydney Keynote Topic: Networking and Big Data: Challenges and Opportunities Abstract: Big Data is one of the hottest topics in data science community. However, we found that the fancy learning and mining applications would be impossible without the support from the underneath layers, which is generally named as Networking for Big Data or Big Data Networking. It is an emerging and attractive topic in networking and communication communities. In this talk, we will firstly overview the current landscape of this energetic area, and then present the unprecedented challenges in this new domain, and finally discuss the current research directions in the field. We humbly hope this talk will shed light for forthcoming researchers to further explore the uncharted part of this promising land. Big Data is one of the hottest topics in data science community. However, we found that the fancy learning and mining applications would be impossible without the support from the underneath layers, which is generally named as Networking for Big Data or Big Data Networking. It is an emerging and attractive topic in networking and communication communities. In this talk, we will firstly overview the current landscape of this energetic area, and then present the unprecedented challenges in this new domain, and finally discuss the current research directions in the field. We humbly hope this talk will shed light for forthcoming researchers to further explore the uncharted part of this promising land. Bio: Shui Yu is a Professor of School of Computer Science, University of Technology Sydney, Australia. Dr Yu's research interest includes Security and Privacy, Networking, Big Data, and Mathematical Modelling. He has published two monographs and edited two books, more than 300 technical papers, including top journals and top conferences, such as IEEE TPDS, TC, TIFS, TMC, TKDE, TETC, ToN, and INFOCOM. Dr Yu initiated the research field of networking for big data in 2013. His hindex is 47. He is currently serving a number of prestigious editorial boards, including IEEE Communications Surveys and Tutorials(Area Editor), IEEE Communications Magazine. He is a Senior Member of IEEE, a member of AAAS and ACM, and a Distinguished Lecturer of IEEE Communication Society. Mr Matt Fowler, Senior Manager, Sales Engineering, Juniper Networks Keynote Topic: How Mist AI is Simplifying Network Operations Abstract: AIOps (artificial intelligence and operations) is staring to gain traction across the industry and for good reason. AIOps solutions use the same kind of machine learning and advanced analytics technologies behind Google Maps or Uber's predictive ride pricing models to help IT departments anticipate and fix problems before users even realise they've happened. Juniper is leading this transition, driven by Mist AI, which is being adopted by IT operations to address the growing data and complexity in networking and, at the same time, the continuing pressure on IT budgets. AIOps holds especially exciting promise in assuring speed and reliability of wireless networks. Wi-Fi has taken its place alongside water, power and light as a must-have technology on which businesses are building mission-critical services for consumers and employees in today's highly mobile, app-driven world. Thus, it must be more predictable, measurable and easily managed than ever. In this session you will learn and experience how AI and ML concepts are being applied to solve real-world problems in wired and wireless networks that are being built by businesses today. AIOps (artificial intelligence and operations) is staring to gain traction across the industry and for good reason. AIOps solutions use the same kind of machine learning and advanced analytics technologies behind Google Maps or Uber's predictive ride pricing models to help IT departments anticipate and fix problems before users even realise they've happened. Juniper is leading this transition, driven by Mist AI, which is being adopted by IT operations to address the growing data and complexity in networking and, at the same time, the continuing pressure on IT budgets. AIOps holds especially exciting promise in assuring speed and reliability of wireless networks. Wi-Fi has taken its place alongside water, power and light as a must-have technology on which businesses are building mission-critical services for consumers and employees in today's highly mobile, app-driven world. Thus, it must be more predictable, measurable and easily managed than ever. In this session you will learn and experience how AI and ML concepts are being applied to solve real-world problems in wired and wireless networks that are being built by businesses today. Bio: Matt, Senior Manager, Sales Engineering at Juniper Networks, leads an accomplished team of sales engineers for Juniper across the Asia Pacific region and works directly with organisations and businesses to realise the practical benefits of AI/ML in networking. A 14-year networking industry veteran, Matt understands first-hand the complexity and challenges of operating enterprise networks and thus the true benefits that a network driven by Mist AI can bring.