IKTPLUSS-IKT og digital innovasjon

The pervasion of the Internet of Things (IoT) which connects numerous sensors, actuators, appliances, vehicles, etc., will strongly impact the evolution of more intelligent and greener cities and environmental monitoring. A basic tenet underlying all critical functionalities of the IoT is situational awareness, i.e., the ability to capture events and derive accurate, critical information to facilitate decision-making to enable timely action in a heterogeneous and highly dynamic environment. This calls for an intelligent infrastructure that is autonomous, dependable, and resilient to natural or artificial disturbances. A critical component of such an infrastructure comprises myriads of information-gathering sensors deployed throughout many points of concern in the city. The sensors deployed in smart cities' IoT constitute critical data sources on which the ensuing analytics and control actions depend. Those sensors are interconnected through the internet, forming an essential part of IoT, and most likely powered only by batteries. In order to ensure that sensors function effectively, we take a holistic approach to designing secure sensor networks with energy-efficient practical algorithms, starting with sensing, followed by data processing and communication to ensure reliable decision-making to enable timely actions that make possible long-lasting, secure, and dependable functionality. This project aims to go beyond state-of-the-art solutions and take a holistic approach that starts with smart sensors, intelligent inference, and secure two-way communication among all the devices in the network. During the project's first three years, we developed new distributed machine learning algorithms for estimation and control tasks that reduce the amount of communication in IoT/CPS. We have also accounted for system malfunctioning or security threats, e.g., noisy and spiky sensor readings or data falsification. In particular, we have developed inference methods to promptly identify malicious data tampering and privacy-preserving solutions to avoid information leakage when devices collaboratively solve tasks. In the last year, we have implemented efficient scheduling algorithms for channel-constrained remote estimation scenarios utilizing age-of-information, which indicates the "freshness" of information. We have also focused on designing privacy-preserving distributed learning algorithms that protect individual network nodes from leaking private information to internal and external adversaries.

This project develops efficient detection and estimation schemes to improve data quality and security of the physical-layer signals in IoT. The project contributes to ICT as an enabling technology, and has a strong international component; students hired will spend part of the studies at University of Notre Dame, U.S.A. and at Aalto University, Finland. The pervasion of the Internet of Things (IoT) which connects numerous sensors, actuators, appliances, vehicles etc, will have a strong impact on the evolution of smarter and greener cities as well as on environmental monitoring. A basic tenet underlying all key functionalities of the IoT is situational awareness, i.e., the ability to capture events and derive accurate critical information to facilitate decision making to enable timely action in a heterogeneous and highly dynamic environment. This calls for an intelligent infrastructure that is autonomous, dependable, and resilient to natural or man-made disturbances. A critical component of such an infrastructure comprises myriads of information-gathering sensors deployed throughout many points of concerns in the city. The sensors deployed in smart cities' IoT constitute critical data sources, on which the ensuing analytics and control actions depend. Those sensors are interconnected through the internet, forming an important part of IoT, and most likely powered only by batteries. To ensure that the sensors function effectively, we need to take a holistic approach to designing secure sensor networks with energy-efficient functional algorithms starting with sensing, followed by data processing and communication to ensure reliable decision making to enable timely actions that make possible long lasting secure and dependable functionality. This project aims to go beyond state-of-the-art solutions and take a holistic approach that starts with smart sensors, smart inference, and secure two-way communication among all the devices in the network.