Deep Learning Semantic Compression: IoT Support over LORA Use Case Long Range (LORA) networks are serious candidates to support Internet of Things (IoT). Despite the scalability and range of LORA, yet many IoT devices need to send much more data than what is possible in this band. In this paper, a deep learning compression method to squeeze data and transfer its semantic is presented. Data from IoT equipment is considered as a time series and trains a neural network. Resulting neural network weights are periodically sent instead of sending all the IoT raw data. Anomalies are locally detected by a similar neural network and sent separately. The resulting architecture makes it feasible to use LORA for IoT devices that generate very large amounts of data.

Memory and CommunicationAware Model Compression for Distributed Deep Learning Inference on IoT. Model compression has emerged as an important area of research for deploying deep learning models on InternetofThings (IoT). However, for extremely memoryconstrained scenarios, even the compressed models cannot fit within the memory of a single device and, as a result, must be distributed across multiple devices. This leads to a distributed inference paradigm in which memory and communication costs represent a major bottleneck. Yet, existing model compression techniques are not communicationaware. Therefore, we propose Network of Neural Networks (NoNN), a new distributed IoT learning paradigm that compresses a large pretrained u0027teacheru0027 deep network into several disjoint and highlycompressed u0027studentu0027 modules, without loss of accuracy. Moreover, we propose a network sciencebased knowledge partitioning algorithm for the teacher model, and then train individual students on the resulting disjoint partitions. Extensive experimentation on five image classification datasets, for userdefined memoryperformance budgets, show that NoNN achieves higher accuracy than several baselines and similar accuracy as the teacher model, while using minimal communication among students. Finally, as a case study, we deploy the proposed model for CIFAR10 dataset on edge devices and demonstrate significant improvements in memory footprint (up to 24x), performance (up to 12x), and energy per node (up to 14x) compared to the large teacher model. We further show that for distributed inference on multiple edge devices, our proposed NoNN model results in up to 33x reduction in total latency w.r.t. a stateoftheart model compression baseline.