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MobiCom 2020 Papers

ACM International Conference on Mobile Computing and Networking

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62papers

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TinyLink 2.0: integrating device, cloud, and client development for IoT applications

Gaoyang Guan, Borui Li, Yi Gao, Yuxuan Zhang, Jiajun Bu, Wei Dong

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The recent years have witnessed the rapid growth of IoT (Internet of Things) applications. A typical IoT application usually consists of three essential parts: the device side, the cloud side, and the client side. The development of a complete IoT application is very difficult for non-expert developers because it involves drastically different technologies and complex interactions between different sides. Unlike traditional IoT development platforms which use separate approaches for these three sides, we present TinyLink 2.0, an integrated IoT development approach with a single coherent language. It achieves high expressiveness for diverse IoT applications by an enhanced IFTTT rule design and a virtual sensor mechanism which helps developers express application logic with machine learning. Moreover, TinyLink 2.0 optimizes the IoT application performance by using both static and dynamic optimizers, especially for resource-constrained IoT devices. We implement TinyLink 2.0 and evaluate it with eight case studies, a user study, and a detailed evaluation of the proposed programming language as well as the performance optimizers. Results show that TinyLink 2.0 can speed up IoT development significantly compared with existing approaches from both industry and academia, while still achieving high expressiveness.

DMM: fast map matching for cellular data

Zhihao Shen, Wan Du, Xi Zhao, Jianhua Zou

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Map matching for cellular data is to transform a sequence of cell tower locations to a trajectory on a road map. It is an essential processing step for many applications, such as traffic optimization and human mobility analysis. However, most current map matching approaches are based on Hidden Markov Models (HMMs) that have heavy computation overhead to consider high-order cell tower information. This paper presents a fast map matching framework for cellular data, named as DMM, which adopts a recurrent neural network (RNN) to identify the most-likely trajectory of roads given a sequence of cell towers. Once the RNN model is trained, it can process cell tower sequences as making RNN inference, resulting in fast map matching speed. To transform DMM into a practical system, several challenges are addressed by developing a set of techniques, including spatial-aware representation of input cell tower sequences, an encoder-decoder framework for map matching model with variable-length input and output, and a reinforcement learning based model for optimizing the matched outputs. Extensive experiments on a large-scale anonymized cellular dataset reveal that DMM provides high map matching accuracy (precision 80.43% and recall 85.42%) and reduces the average inference time of HMM-based approaches by 46.58×.