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Proceedings - 2020 19th acm/ieee international conference on information processing in sensor networks, ipsn 2020

Proceedings - 2020 19th ACM/IEEE International Conference on...

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19th acm/ieee international conference on information processing in sensor networks, ipsn 2020

ISBN: (纸本)9781728154978

the proceedings contain 57 papers. the topics discussed include: wireless glasses for non-contact facial expression monitoring;fireworks: channel estimation of parallel backscattered signals;a weakly supervised tracking of hand hygiene technique;bootstrapping batteryless networks using fluorescent light properties;a novel modeling involved security approach for LoRa Key generation;distributed slot scheduling for QoS guarantee over TSCH-based IoT networks via adaptive parameterization;distributed human trajectory sensing and partial similarity queries;federated learning for speech emotion recognition applications;C-Sync: the resilient time synchronization protocol;and passive activity classification of smart homes through wireless packet sniffing.

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A study on the health status of cherry production in multi-factor facilities based on the attention mechanism Bi-LSTM 23

A study on the health status of cherry production in multi-f...

引用

23rd ieee international conference on High Performance Computing and Communications, 7th ieee international conference on Data Science and Systems, 19th ieee international conference on Smart City and 7th ieee international conference on Dependability in sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Hu, Lingyan Zhang, Chao Wang, Zumin Liu, Yan Guo, Zhanjun Li, Guoqiang College of Information Engineering Dalian University Dalian China

ISBN: (纸本)9781665494571

High environmental humidity leading to cherry splitting is a major concern for farmers. Forecasting models that consider only major single-factor conditions tend to ignore the interaction of important multi-factor parameters when addressing complex agricultural environmental parameters, resulting in low forecasting accuracy. A multi-factor Attention-Bi-LSTM model is proposed to define the cracking rate function, obtain objective and accurate multi-factor key feature parameters by processing the cracking rate function matrix through the entropy weight method, further extract feature information through different scales of Bi-LSTM, and later strengthen the weight parameters of key feature information by the attention mechanism to achieve efficient allocation of information resources. In this paper, the model uses binary classification output to correspond ambient humidity to fruit cracking rate, and after experimental comparison, the three-scale A-Bi-LSTM network, which considers a multi-factor improvement, outperforms traditional RNN, LSTM and Bi-LSTM networks in prediction, Compared with RNN, LSTM and Bi-LSTM models, the three-scale A-Bi-LSTM model reduced RMSE by 13.443, 10.503 and 4.247, MAE by 9.366, 6.045 and 5.289, and MAPE by 0.231, 0.189 and 0.161. © 2021 ieee.

关键词： Recurrent neural networks information resources Smart cities Production Humidity Predictive models Feature extraction

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PhD Forum Abstract: Activity Classification at the Edge 19

PhD Forum Abstract: Activity Classification at the Edge

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19th acm/ieee international conference on information processing in sensor networks (ipsn)

作者： Hosseininoorbin, Seyedehfaezeh Univ Queensland Brisbane Qld Australia CSIRO DATA61 Brisbane Qld Australia

ISBN: (纸本)9781728154978

this study investigates activity classification using deep learning for developing an energy efficient and high-performance behavioural model on edge (resource constrained) devices. the specific focus of this research is on cattle activity classification. Considering accuracy and efficiency of approaches, different deep neural network structures and data representations will be explored. the developed model will be capable of identifying multiple cattle behavioural patterns accurately. However, there are some challenges to develop energy efficient and computationally light systems.

关键词： Activity classification Behaviour model Deep learning Time-frequency distribution Tensor processing Unit

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Optimal sensor channel selection for resource-efficient deep activity recognition 20

Optimal sensor channel selection for resource-efficient deep...

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20th acm/ieee international conference on information processing in sensor networks (ipsn)

作者： Souza Leite, Clayton Frederick Xiao, Yu Aalto Univ Dept Commun & Networking Espoo Finland

ISBN: (纸本)9781450380980

Deep learning has permitted unprecedented performance in sensor-based human activity recognition (HAR). However, deep learning models often present high computational overheads, which poses challenges to their implementation on resource-constraint devices such as microcontrollers. Usually, the computational overhead increases with the input size. One way to reduce the input size is by constraining the number of sensor channels. We refer to sensor channel as a specific data modality (e.g. accelerometer) placed on a specific body location (e.g. chest). Identifying and removing irrelevant and redundant sensor channels is feasible via exhaustive search only in cases where few candidates exist. In this paper, we propose a smarter and more efficient way to optimize the sensor channel selection during the training of deep neural networks for HAR. Firstly, we propose a light-weight deep neural network architecture that learns to minimize the use of redundant and irrelevant information in the classification task, while achieving high performance. Secondly, we propose a sensor channel selection algorithm that utilizes the knowledge learned by the neural network to rank the sensor channels by their contribution to the classification task. the neural network is then trimmed by removing the sensor channels with the least contribution from the input and pruning the corresponding weights involved in processing them. the pipeline that consists of the above two steps iterates until the optimal set of sensor channels has been found to balance the trade-off between resource consumption and classification performance. Compared with other selection methods in the literature, experiments on 5 public datasets showed that our proposal achieved significantly higher F1-scores at the same time as utilizing from 76% to 93% less memory, with up to 75% faster inference time and as far as 76% lower energy consumption.

关键词： sensor channel selection human activity recognition deep learning

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Demo Abstract: Human Activity Detection with Loose-Fitting Smart Jacket 19

Demo Abstract: Human Activity Detection with Loose-Fitting S...

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19th acm/ieee international conference on information processing in sensor networks (ipsn)

作者： Lin, Qi Wu, Yuezhong Liu, Jun Hu, Wen Hassan, Mahbub Univ New South Wales Sydney NSW Australia Data61 CSIRO Sydney NSW Australia

ISBN: (纸本)9781728154978

We demonstrate a human activity detection with casual loose-fitting smart garment system. By employing a new type of highly sensitive, stretchable, optical transparent and low-cost strain sensor and a deep learning model enabled by CNN-LSTM, the loose-fitting jacket is able to recognize 5 activities with 90.9% accuracy when the system is trained with the user data, and 73.5% accuracy when an unseen user wears the smart jacket, which is comparable with tight-fitting smart garment system. In the demonstration, we will showcase activity recognition of three activities: walk, sit, and stand.

关键词： Smart garment deep learning CNN-LSTM piezo-resistive strain sensor posture detection

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E-Jacket: Posture Detection with Loose-Fitting Garment using a Novel Strain sensor 19

E-Jacket: Posture Detection with Loose-Fitting Garment using...

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19th acm/ieee international conference on information processing in sensor networks (ipsn)

作者： Lin, Qi Peng, Shuhua Wu, Yuezhong Liu, Jun Hu, Wen Hassan, Mahbub Seneviratne, Aruna Wang, Chun H. Univ New South Wales Sydney NSW Australia Data61 CSIRO Sydney NSW Australia

ISBN: (纸本)9781728154978

We address the problem of human posture detection with casual loose-fitting smart garments by fabricating a new type of highly sensitive;strachablc, optical transparent and low-cost strain sensor enabled by uniquely designed microcracks within a hybrid conductive thin film. In terms of sensitivity and stretchability, the developed sensor outperformed most of the works reported in recent: :literature, and has a gauge, factor of 103 at the high strain of 58%. By attaching these sensors to art off-the-self casual jacket, we implement L-Jacket, a smart loose-titling sensing garment prototype. To detect postures from sensor data, we implement a conventional deep learning model;CNN-LSTM, capable of overcoming the noise induced by the loose-fitting of the sensors to the :human skin. To evaluate E-jacket, we conducted three case studies in experimental CIIVirOIMICIILS: recognition of daily activities, recognition of stationary postures with random hand movements, and slouch detection. Our evaluation results demonstrate the feasibility of the proposed E-Jacket smart garment system for different posture, recognition applications.

关键词： Smart garment deep learning CNN-LSTM piezo-resistive strain sensor posture detection

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Quick (and Dirty) Aggregate Queries on Low-Power WANs 19

Quick (and Dirty) Aggregate Queries on Low-Power WANs

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19th acm/ieee international conference on information processing in sensor networks (ipsn)

作者： Gadre, Akshay Yi, Fan Rowe, Anthony Iannucci, Bob Kumar, Swarun Carnegie Mellon Univ Pittsburgh PA 15213 USA Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9781728154978

Low-Power Wide-Area networks (LP-WANs) are seeing wide-spread deployments connecting millions of sensors, each powered by a ten-year AA battery to radio infrastructure, often miles away. fly design, iteratively querying all sensors in an LP-WAN may take several hours or even days, given the stringent battery limits of client, radios. this precludes obtaining evert art approximate real-time view of sensed information across LP-WAN devices over a large area, say in the event of a disaster, fault or simply for diagnostics. this paper presents QuAiL(1), a system that provides a coarse aggregate view of sensed data across LP-WAN devices over a wide area within a time span of just, one LP-WAN packet. QuAiL achieves this by coordinating multiple LP-WAN radios to transmit their information synchronously in time and frequency despite their power constraints. We design each client's transmission so that the base station can retrieve an approximate heatmap of sensed data, by exploiting the spatial correlation of this data across clients. We further show how our system can be optimized for statistical and machine learning queries, all while maintaining the security and privacy of sensed data from individual clients. Our deployment over a 3 sq. km. LP-WAN deployment around CMU campus in Pittsburgh demonstrates a 4x faster information retrieval versus the state-of-the-art statistical methods to retrieve the spatial sensor heatmap at a desired resolution.

关键词： LPWAN Aggregate Queries sensor networks machine learning

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Electric Vehicle Battery Energy information is Enough to Track You 19

Electric Vehicle Battery Energy Information is Enough to Tra...

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19th acm/ieee international conference on information processing in sensor networks (ipsn)

作者： Kang, Liuwang Shen, Haiying Univ Virginia Charlottesville VA 22904 USA

ISBN: (纸本)9781728154978

Pure electric vehicles (EVs) are more and more popular in current automotive markets. Many services for EVs such as intelligent battery charging systems and mobile apps are developed to monitor battery energy information (energy consumption time series) for users. However, it is neglected that such EV energy consumption time series may lead to driver privacy related problems such as the disclosure of driving path. In this paper, we propose the first battery energy based path inference attacks (Bepath), which can estimate EV driving paths only based on EV energy consumption time series. Bepath firstly estimates appliance states of an EV including vehicle speed state, air condition state and intersection turn state based on EV energy consumption time series. And then, Bepath figures out the driving path based on the estimated appliance states and map information. We used real-life daily driving data with different participants to test the performance of Bepath and compared inferred path results between Bepath and an existing method to evaluate the path inference accuracy of Bepath. the experimental results show that Bepath can estimate appliance states accurately and infers 50% trips with distance errors within 0.5 km, while the previous work that infers paths based on vehicle speed from insurance companies can only infer 26% trips with distance errors within 0.5 km.

关键词： Electric vehicle Battery energy information Appliance state estimation Path inference

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Selective Sampling for sensor Type Classification in Buildings 19

Selective Sampling for Sensor Type Classification in Buildin...

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19th acm/ieee international conference on information processing in sensor networks (ipsn)

作者： Ma, Jing Hong, Dezhi Wang, Hongning Univ Virginia Charlottesville VA 22904 USA Univ Calif San Diego La Jolla CA 92093 USA

ISBN: (纸本)9781728154978

A key barrier to applying any smart technology to a building is the requirement of locating and connecting to the necessary resources among the thousands of sensing and control points, i.e., the metadata mapping problem. Existing solutions depend on exhaustive manual annotation of sensor metadata - a laborious, costly, and hardly scalable process. To reduce the amount of manual effort required, this paper presents a multi-oracle selective sampling framework to leverage noisy labels from information sources with unknown reliability such as existing buildings, which we refer to as weak oracles, for metadata mapping. this framework involves an interactive process, where a small set of sensor instances are progressively selected and labeled for it to learn how to aggregate the noisy labels as well as to predict sensor types. Two key challenges arise in designing the framework, namely, weak oracle reliability estimation and instance selection for querying. To address the first challenge, we develop a clustering-based approach for weak oracle reliability estimation to capitalize on the observation that weak oracles perform differently in different groups of instances. For the second challenge, we propose a disagreement-based query selection strategy to combine the potential effect of a labeled instance on both reducing classifier uncertainty and improving the quality of label aggregation. We evaluate our solution on a large collection of real-world building sensor data from 5 buildings with more than 11, 000 sensors of 18 different types. the experiment results validate the effectiveness of our solution, which outperforms a set of state-of-the-art baselines.

关键词： Selective sampling label aggregation sensor type classification smart buildings

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TagAlong: efficient integration of battery-free sensor tags in standard wireless networks 19

TagAlong: efficient integration of battery-free sensor tags ...

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19th acm/ieee international conference on information processing in sensor networks, ipsn 2020

作者： Perez-Penichet, Carlos Piumwardane, Dilushi Rohner, Christian Voigt, thiemo Uppsala University RISE SICS Sweden

ISBN: (纸本)9781728154978

New battery-free sensor tags that interoperate with unmodified standard IoT devices can extend a sensor network's capabilities in a scalable and cost-effective manner. the tags achieve battery-free operation through backscatter-related techniques, while the standard IoT devices can provide the necessary unmodulated carrier, avoiding additional dedicated infrastructure. However, this approach presents multiple challenges: It requires coordination between nodes transmitting, receiving and generating carrier, adds extra latency and energy consumption to already constrained devices, and increases interference and contention in shared spectrum. We present TagAlong, a medium access mechanism for interoperable sensor tags that, besides coordinating, optimizes the use of carrier generators, minimizing the disruption caused to the operation of the regular nodes. We accomplish this by parallelizing communications with battery-free tags when possible, sharing carriers for multiple tags concurrently and synchronizing communications with tags that share carrier generators. We demonstrate the feasibility of TagAlong in a testbed deployment. In our evaluation we find that it can reduce the duration of the tags' schedule by 60% while improving the energy and spectrum usage by 30% when compared to sequential interrogation with no difference in reliability. © 2020 ieee.

关键词： Internet of things

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