About The Forum
Calendar 2017 Poster Sessions : Scaling Wireless Networks to the Next Trillion Devices
Abstract:
The next exponential growth in connectivity is projected to be no longer in access between people but in connecting objects and machines in the age of ``Internet of Everything'' (IoE). Projections show sensor demand growing from billions to trillions within the next decade or two. This tremendous growth is fueled by the emergence of tiny and low-cost wireless devices that combine communication, computation, and sensing. These wireless devices are expected to form the fabric of smart technologies and cyberphysical systems, enabling a plethora of exciting applications from in-body and personal health monitoring, to smart homes and transportation systems, to automation and monitoring in smart grids. Realizing this vision presents unprecedented challenges to wireless system and circuit designers. Technologies which brought about the last generation of connectivity between people, such as WiFi and LTE, are not scalable to address the IoE domain requirements. In this poster, we demonstrate a proof-of-concept for a massive network of ultra-low cost, fully-integrated transceivers with no off-chip components, which are powered wirelessly via low cost power beacons. The transceivers operate in a mesh network topology, with bidirectional multi-hop communication links, subject to minimal synchronization provided by the power beacons. This enables seamless, scalable, and low-cost dense deployment of tera-scale networks, eliminating the need for complex IoE central nodes, which would become well-suited for large-scale ubiquitous sensing and communication applications.
Bio:
Peter Kairouz is a postdoctoral scholar at Stanford University. He received his MS in ECE, MS in Maths, and PhD in ECE all from the University of Illinois at Urbana-Champaign. For his masters, he was mainly interested in signal processing and wireless communication. He interned twice at Qualcomm (in 2012 and 2013), and was awarded The 2012 Roberto Padovani Scholarship from Qualcomm's Research Center. For his PhD, he chose to work on privacy, security, and Internet of Things (IoT), winning the Best Paper Award at SIGMETRICS 2015 for “Spy vs. Spy: Rumor Source Obfuscation”. He recently interned at Google, where he designed privacy-aware machine learning algorithms. His primary research interests include statistical data and metadata privacy, machine learning, and big data.
The next exponential growth in connectivity is projected to be no longer in access between people but in connecting objects and machines in the age of ``Internet of Everything'' (IoE). Projections show sensor demand growing from billions to trillions within the next decade or two. This tremendous growth is fueled by the emergence of tiny and low-cost wireless devices that combine communication, computation, and sensing. These wireless devices are expected to form the fabric of smart technologies and cyberphysical systems, enabling a plethora of exciting applications from in-body and personal health monitoring, to smart homes and transportation systems, to automation and monitoring in smart grids. Realizing this vision presents unprecedented challenges to wireless system and circuit designers. Technologies which brought about the last generation of connectivity between people, such as WiFi and LTE, are not scalable to address the IoE domain requirements. In this poster, we demonstrate a proof-of-concept for a massive network of ultra-low cost, fully-integrated transceivers with no off-chip components, which are powered wirelessly via low cost power beacons. The transceivers operate in a mesh network topology, with bidirectional multi-hop communication links, subject to minimal synchronization provided by the power beacons. This enables seamless, scalable, and low-cost dense deployment of tera-scale networks, eliminating the need for complex IoE central nodes, which would become well-suited for large-scale ubiquitous sensing and communication applications.
Bio:
Peter Kairouz is a postdoctoral scholar at Stanford University. He received his MS in ECE, MS in Maths, and PhD in ECE all from the University of Illinois at Urbana-Champaign. For his masters, he was mainly interested in signal processing and wireless communication. He interned twice at Qualcomm (in 2012 and 2013), and was awarded The 2012 Roberto Padovani Scholarship from Qualcomm's Research Center. For his PhD, he chose to work on privacy, security, and Internet of Things (IoT), winning the Best Paper Award at SIGMETRICS 2015 for “Spy vs. Spy: Rumor Source Obfuscation”. He recently interned at Google, where he designed privacy-aware machine learning algorithms. His primary research interests include statistical data and metadata privacy, machine learning, and big data.