Projects 2017-2019

Ambient Re-Scatter Inspired Machine Type Communications for Heterogeneous IoT Systems

Prof. Riku Jäntti, Aalto University, Finland
Prof. Lempiäinen, Jukka, Tampere University of Technology, Finland
Assist. Prof. Miao Pan, University of Houston, USA

This project targets at IoT applications through constructing new IoT connectivity solutions by combining existing and emerging Machine Type Communication (MTC) systems with a layer of ultra-low power or passive Ambient Re-Scatter (ARS) devices, and then uncovering the challenges of radio resource control signaling design, performance tradeoff, security between ARS and other networks. ARS benefits the overall IoT architecture in two ways: firstly, it provides local access to ultra-low power or passive `things’ that cannot supply even the low power transceivers, and secondly it can improve the capacity and reliability of the other wireless IoT solutions with zero or negligible cost in terms of energy and complexity. The project is divided into four work packages: WP1 System Architecture and Protocol Design, WP2 Distributed System Optimization, WP3 Hierarchical Security Enhancement, and WP4 Demonstration for Public Good using a software defined radio implementation of the NB-IoT system.

Efficient and Robust Cognitive IoT Systems using Unreliable Sensors: Fundamental Limits and Practical Strategies

Assoc. Prof. Teemu Roos, University of Helsinki, Finland
Assist. Prof. Pulkit Grover, Carnegie Mellon University, USA

Improving reliability and efficiency of Internet of Things (IoT) is necessary to harness their full potential. IoT systems will sense, communicate, and process data to produce reliable decisions and inferences. Each IoT device will be limited in several ways that the proposed research will overcome: (a) limited “field of view” of each sensor; (b) the severe energy limitations; (c) unreliability of sensing and communication. The severe energy constraints provide a compelling incentive for cross-layer design of “cognitive” IoT systems that jointly sense, compress, communicate, and compute. This proposal aims at providing a systematic understanding and designs of cross-layer cognitive IoT strategies by providing (i) fundamental performance limits; (ii) theoretical analyses of performance of designed strategies; and (iii) validation through a simulation platform on practical inference problems.

Internet of Cognitive Things for Personalised Healthcare

Prof. Pasi Liljeberg, University of Turku, Finland
Principal Scientist Juha-Pekka Soininen, VTT Technical Research Centre of Finland, Finland
Prof. Nikil Dutt, University of California-Irvine, USA
Adj. Prof. Amir Rahmani, University of California-Irvine, USA
Assist. Prof. Marco Levorato, University of California-Irvine, USA

Achieving consistent end-user Quality of Experience (QoE) poses huge challenges in the face of resource constraints and dynamic variations at multiple scales of the IoT system stack: at the application, network, resource, and device levels. This proposal outlines a self-aware cognitive architecture – the Internet of Cognitive Things (IoCT) – that delivers acceptable QoE by adapting to dynamic variations in compute, communication and resource needs, while also synergistically learning and adapting to end user behavior. The proposed IoCT system is the first example of architecture where a network of algorithms communicates and collaborates synergistically to achieve system-wide objectives. The project demonstrates in collaboration with the Turku University Hospital a personalized ubiquitous healthcare framework using the Early Warning Score (EWS) system for human health monitoring. The framework and services are also applicable to a broad range of other IoT application domains.

Low Overhead Wireless Access Solutions for Massive and Dynamic IoT Connectivity

Prof. Jyri Hämäläinen, Aalto University, Finland
Prof. Xiaojun Lin, Purdue University, USA
Prof. Zhi Ding, University of California-Davis, USA

Connecting billions of physical devices, the new era of Internet-of-Things (IoT) is poised to fundamentally change every aspect of our lives and human society. This development has created a pressing need to develop fundamentally new wireless access protocols and transmission technologies specifically aimed at the next-generation IoT wireless applications. The objective of the project ‘Low Overhead Wireless Access Solutions for Massive and Dynamic IoT Connectivity’ is therefore to develop a novel IoT network framework, control algorithms, and optimization tools to support high-performance and low-overhead connectivity for massive number of heterogenous IoT devices, many of which are energy and/or latency limited.

Millimeter Wave-based Wearable Networks in High-end IoT Applications

Adj. Prof. Antti Tölli, University of Oulu, Finland
Dr. Olga Galinina, Tampere University of Technology, Finland
Prof. Robert W. Heath, University of Texas at Austin, USA

Next-generation wearables create a new powerful user interface to the Internet of Things (IoT). This research provides a rigorous and systematic design of the mmWave wearable PHY layer with an emphasis on its MIMO operation, developing an in-depth understanding of the propagation environment in crowded areas, and devising new MAC algorithms and protocols tailored to the mmWave and wearable environment. New mathematical tools are developed for estimating mmWave channels and new algorithms are devised for communicating in mmWave MIMO channels incorporating the array constraints. A major outcome of this research will be rapidly reconfigurable and robust high bandwidth mmWave communication links for wearables. Practical channel models are created that incorporate array geometry, orientation, and blockage effects tailored to the wearable setting. Finally, testbed results are expected to validate the theoretical hypotheses and provide refinements to the measurements and models.

Scalable Edge Architecture for Massive Location-Aware Heterogeneous IoT Systems

Prof. Mika Ylianttila, University of Oulu, Finland
Research Prof. Heikki Ailisto, VTT Technical Research Centre of Finland, Finland
Prof. Henning Schulzrinne, Columbia University, USA
Prof. Theodore S. Rappaport, New York University, USA

The fusion of sensor data from different sources can improve the efficiency of many existing systems significantly. At the same time, data fusion enables studying more innovative and smarter solutions for emerging systems. For example, autonomous traffic, to be safer and more efficient, needs combined data from various sources to indicate locations and speeds of vehicles and pedestrians, road conditions, traffic light statuses, various obstacles, and weather reliably. This project focuses on system design of reliable large-scale IoT systems by combining simulation and physical environments. It defines an efficient security-enhancing edge architecture that moves data processing close to users, thus, minimizing data transfers in networks. Through semantic interoperability capabilities, system programmability and virtualization, and millimeter-wave communications, complex and delay-sensitive processes on location-aware smart traffic can be brought into practice.

Secure Inference in the Internet of Things

Prof. Visa Koivunen, Aalto University, Finland
Prof. H. Vincent Poor, Princeton University, USA
Prof. Rick S. Blum, Lehigh University, USA

The next generation of communication networks is predicted to provide an Internet of Things (IoT) that
interconnects up to 1 trillion sensors, products, machines, and devices by 2022. The proposed research addresses the critical problem of security for these networks.

Securing Lifecycle of Internet-of-Things

Prof. N. Asokan, Aalto University, Finland
Prof. Gene Tsudik, University of California-Irvine, USA
Assoc. Prof. Patrick Traynor, University of Florida, USA

Develop novel techniques to secure Internet of Things devices through all stages of their lifecycles, focusing on effective detection of vulnerable devices and deploying suitable reaction mechanisms to mitigate the impact of detected vulnerabilities.

Ultra-low Latency and High Reliability for Wireless IoT

Prof. Savo Glisic, University of Oulu, Finland
Prof. Eytan Modiano, Massachusetts Institute of Technology, USA
Prof. Randall A. Berry, Northwestern University, USA

The goal of this project is to develop new architectures and control algorithms for wireless networks to support ultra low latency and high reliability IoT applications. For each IoT application, we assume that it has to accomplish certain tasks triggered by the occurrence of external events. These tasks need to be accomplished within a given latency and with a specified reliability of qi, which gives the probability that the latency requirement is met,
The latency of conveying the message to the destination depends on if this message is sent in one or more packets, how these packets are mapped into physical layer transmissions, the number of hops the message needs to take to reach the destination, how many times it is re-transmitted, and any scheduling or queueing delays that are incurred. Our proposed effort will address all of these factors so as to best exploit the interdependencies among them..