Over the past years, a considerable amount of effort has been devoted, both in industry and academia, towards the development of basic technology as well as innovative applications for the Internet of Things. Adaptive Middleware for the Internet of Things introduces a scalable, interoperable and privacy-preserving approach to realize IoT applications and discusses abstractions and mechanisms at the middleware level that simplify the realization of services that can adapt autonomously to the behavior of their users. Technical topics discussed in the book include: - Behavior-driven Autonomous Services - GAMBAS Middleware Architecture - Generic and Efficient Data Acquisition - Interoperable and Scalable Data Processing - Automated Privacy Preservation Adaptive Middleware for the Internet of Things summarizes the results of the GAMBAS research project funded by the European Commission under Framework Programme 7. It provides an in-depth description of the middleware system developed by the project consortium. In addition, the book describes several innovative mobility and monitoring applications that have been built, deployed and operated to evaluate the middleware under realistic conditions with a large number of users. Adaptive Middleware for the Internet of Things is ideal for personnel in the computer and communication industries as well as academic staff and research students in computer science interested in the development of systems and applications for the Internet of Things.

This book constitutes selected papers from the Second International Workshop on IoT Streams for Data-Driven Predictive Maintenance, IoT Streams 2020, and First International Workshop on IoT, Edge, and Mobile for Embedded Machine Learning, ITEM 2020, co-located with ECML/PKDD 2020 and held in September 2020. Due to the COVID-19 pandemic the workshops were held online. The 21 full papers and 3 short papers presented in this volume were thoroughly reviewed and selected from 35 submissions and are organized according to the workshops and their topics: IoT Streams 2020: Stream Learning; Feature Learning; ITEM 2020: Unsupervised Machine Learning; Hardware; Methods; Quantization.