Novel backscatter communication techniques enable battery-free sensor tags to interoperate with unmodified standard IoT devices, extending a sensor network’s capabilities in a scalable manner. Without requiring additional dedicated infrastructure, the battery-free tags harvest energy from the environment, while the IoT devices provide them with the unmodulated carrier they need to communicate. A schedule coordinates the provision of carriers for the communications of battery-free devices with IoT nodes. Optimal carrier scheduling is an NP-hard problem that limits the scalability of network deployments. Thus, existing solutions waste energy and other valuable resources by scheduling the carriers suboptimally. We present DeepGANTT, a deep learning scheduler that leverages graph neural networks to efficiently provide near-optimal carrier scheduling. We train our scheduler with optimal schedules of relatively small networks obtained from a constraint optimization solver, achieving a performance within 3% of the optimum. Without the need to retrain, our scheduler generalizes to networks 6 × larger in the number of nodes and 10 × larger in the number of tags than those used for training. DeepGANTT breaks the scalability limitations of the optimal scheduler and reduces carrier utilization by up to compared to the state-of-the-art heuristic. As a consequence, our scheduler efficiently reduces energy and spectrum utilization in backscatter networks.

Dynamic Adaptive Streaming over HTTP (DASH) is a standard for delivering video in segments and adapting each segment’s bitrate (quality), to adjust to changing and limited network bandwidth. We study segment prefetching, informed by machine learning predictions of bitrates of client segment requests, implemented at the network edge. We formulate this client segment request prediction problem as a supervised learning problem of predicting the bitrate of a client’s next segment request, in order to prefetch it at the mobile edge, with the objective of jointly improving the video streaming experience for the users and network bandwidth utilization for the service provider. The results of extensive evaluations showed a segment request prediction accuracy of close to 90% and reduced video segment access delay with a cache hit ratio of 58%, and reduced transport network load by lowering the backhaul link utilization by 60.91%.

This paper presents a low-resolution infrared thermal dataset of people and thermal objects, such as a working laptop, in indoor environments. The dataset was collected by a far infrared thermal camera (32x24 pixels), which can capture the position and shape information of thermal objects without privacy issues that enable trustworthy computer vision applications. The dataset consists of 1770 thermal images with high-quality annotation collected from an indoor room with around 15°C. We implemented a privacy-preserving human detection method and trained a multiple object detection (MOD) model based on the dataset. The human detection method reaches 90.3% accuracy. On the other hand, the MOD model achieved 56.8% mean average precision (mAP). Researchers can implement interesting applications based on our dataset, for example, privacy-preserving people counting systems, occupancy estimation systems for smart buildings, and social distance detectors.

Streaming high-quality video over dynamic radio networks is challenging. Dynamic adaptive streaming over HTTP (DASH) is a standard for delivering video in segments, and adapting its quality to adjust to a changing and limited network bandwidth. We present a machine learning-based predictive pre-fetching and caching approach for DASH video streaming, implemented at the multi-access edge computing server. We use ensemble methods for machine learning (ML) based segment request prediction and an integer linear programming (ILP) technique for pre-fetching decisions. Our approach reduces video segment access delay with a cache-hit ratio of 60% and alleviates transport network load by reducing the backhaul link utilization by 69%. We validate the ML model and the pre-fetching algorithm, and present the trade-offs involved in pre-fetching and caching for resource-constrained scenarios.

Product variety and its induced manufacturing complexity remains to increase and therefore greatens challenges for design of joining elements. Historically, joining element design was a paper-based process with incomplete variety documentation and is digitalized only by replacing paper for 3D space. Currently, joining element design remains an ambiguous manual task with limited automation, resulting in long iterative, error prone development trajectories and costly reworks. Thus, processes in practice conflict with required capabilities. Artificial intelligence helps to solve such conflicts by taking over repetitive tasks, preventing human errors, optimizing designs and enabling designers to focus on their core competencies. This paper proposes a novel artificial intelligence method toolbox as a foundation to automate joining element design in manufacturing industries. The methodology aims to incorporate multiple lifecycle requirements including large product variety.