Search

Scientific advances build on reproducible researches which need publicly available benchmark data sets. The computer vision and speech recognition communities have led the way in establishing benchmark data sets. There are much less data sets available in mobile computing, especially for rich locomotion and transportation analytics. This paper presents a highly versatile and precisely annotated large-scale data set of smartphone sensor data for multimodal locomotion and transportation analytics of mobile users. The data set comprises seven months of measurements, collected from all sensors of four smartphones carried at typical body locations, including the images of a body-worn camera, while three participants used eight different modes of transportation in the south-east of the U.K., including in London. In total, 28 context labels were annotated, including transportation mode, participant’s posture, inside/outside location, road conditions, traffic conditions, presence in tunnels, social interactions, and having meals. The total amount of collected data exceed 950 GB of sensor data, which corresponds to 2812 h of labeled data and 17 562 km of traveled distance. We present how we set up the data collection, including the equipment used and the experimental protocol. We discuss the data set, including the data curation process, the analysis of the annotations, and of the sensor data. We discuss the challenges encountered and present the lessons learned and some of the best practices we developed to ensure high quality data collection and annotation. We discuss the potential applications which can be developed using this large-scale data set. In particular, we present how a machine-learning system can use this data set to automatically recognize modes of transportations. Many other research questions related to transportation analytics, activity recognition, radio signal propagation and mobility modeling can be addressed through this data set. The full data set is being made available to the community, and a thorough preview is already published.

Transportation and locomotion mode recognition from multimodal smartphone sensors is useful for providing just-in-time context-aware assistance. However, the field is currently held back by the lack of standardized datasets, recognition tasks, and evaluation criteria. Currently, the recognition methods are often tested on the ad hoc datasets acquired for one-off recognition problems and with different choices of sensors. This prevents a systematic comparative evaluation of methods within and across research groups. Our goal is to address these issues by: 1) introducing a publicly available, large-scale dataset for transportation and locomotion mode recognition from multimodal smartphone sensors; 2) suggesting 12 reference recognition scenarios, which are a superset of the tasks we identified in the related work; 3) suggesting relevant combinations of sensors to use based on energy considerations among accelerometer, gyroscope, magnetometer, and global positioning system sensors; and 4) defining precise evaluation criteria, including training and testing sets, evaluation measures, and user-independent and sensor-placement independent evaluations. Based on this, we report a systematic study of the relevance of statistical and frequency features based on the information theoretical criteria to inform recognition systems. We then systematically report the reference performance obtained on all the identified recognition scenarios using a machine-learning recognition pipeline. The extent of this analysis and the clear definition of the recognition tasks enable future researchers to evaluate their own methods in a comparable manner, thus contributing to further advances in the field. The dataset and the code are available online. http://www.shl-dataset.org/.

The Sussex-Huawei Transportation-Locomotion (SHL) Recognition Challenge 2018 aims to recognize eight transportation activities (Still, Walk, Run, Bike, Bus, Car, Train, Subway) from the inertial and pressure sensor data of a smartphone. In this chapter, we, as part of competition organizing team, present reference recognition performance obtained by applying various classical and deep-learning classifiers to the testing dataset. The classical classifiers include naive Bayes, decision tree, random forest, K-nearest neighbours and support vector machine, while the deep-learning classifiers include fully-connected and convolutional deep neural networks. We feed different types of input to the classifier, including hand-crafted features, raw sensor data in the time domain, and in the frequency domain. We additionally employ a post-processing scheme, which smoothens the predictions in order and improves the recognition performance. Results show that convolutional neural network operating on frequency-domain raw data achieves the best performance among all the classifiers. Finally, we achieve a benchmark result with F1 score 92.9%, which is comparable to the best result from the team that won the competition (achieving F1 score 93.9%). The competition dataset and the benchmark implementation is made available online (http://www.shl-dataset.org/).

In this paper we, as part of the Sussex-Huawei Locomotion-Transportation (SHL) Recognition Challenge organizing team, present reference recognition performance obtained by applying various classical and deep-learning classifiers to the testing dataset. We aim to recognize eight modes of transportation (Still, Walk, Run, Bike, Bus, Car, Train, Subway) from smartphone inertial sensors: accelerometer, gyroscope and magnetometer. The classical classifiers include naive Bayesian, decision tree, random forest, K-nearest neighbour and support vector machine, while the deep-learning classifiers include fully-connected and convolutional deep neural networks. We feed different types of input to the classifier, including hand-crafted features, raw sensor data in the time domain, and in the frequency domain. We employ a post-processing scheme to improve the recognition performance. Results show that convolutional neural network operating on frequency-domain raw data achieves the best performance among all the classifiers.

We explain how to obtain a highly versatile and precisely annotated dataset for the multimodal locomotion of mobile users. After presenting the experimental setup, data management challenges and potential applications, we conclude with the best practices for assuring data quality and reducing loss. The dataset currently comprises 7 months of measurements, collected by smartphone's sensors and a body-worn camera, while the 3 participants used 8 different modes of transportation. It comprises 950 GB of sensor data, which corresponds to 750 hours of labelled data. The obtained data will be useful for a wide range of research questions related to activity recognition, and will be made available to the community1.

We have completed the collection of one of the richest accurately annotated mobile dataset of modes of transportation and locomotion. To do this, we developed a highly reliable Android application called DataLogger capable of recording multisensor data from multiple synchronized smartphones simultaneously. The application allows real-time data annotation. We explain how we designed the app to achieve high reliability and ease of use. We also present an evaluation of the application in a big-data collection (750 hours, 950 GB of data, 17 different sensor modalities), analysing the data loss (less than 0.4%) and battery consumption (a 6% on average per hour). The application is available as open source.

In this paper, a novel approach for improving channel prediction is proposed. Its main novelty consists in optimizing the post processing of acquired measurements intended to assess channel quality. To this end, a Multi-Armed Bandit based algorithm is designed to choose in an adaptive and online manner the optimal filtering parameters, taking into account the target usage in the network as well as the employed prediction algorithm. Performance evaluation is made following 3GPP assumptions and compared to conventional fixed filtering operation. Substantial gains in terms of prediction accuracy and UE throughput are obtained, especially in cases where the user context is fast varying.

In non-reciprocal wireless channels, the performance of link adaptation heavily deteriorates with outdated measurement reports. To provide an effective solution to this problem, we construct an accurate channel gain predictor by combining the Kalman Filter (KF) with Expectation-Maximization (EM). This joint design minimizes the mismatch between the autoregressive model of the KF and the true channel gain. After deriving the filter parameters in closed form, we compare the accuracy of our solution to two relevant alternatives in a 3GPP-compliant scenario. Impressive gains at low computational complexity make our solution a promising candidate to improve the next generation of link adaptation functions.

Part 8: Cryptography (Short Papers); International audience; In this paper, we answer the question of what are the necessary conditions under which Correlation Power Attack (CPA), that essentially targets embedded cryptographic implementations, is optimal with regards to attacks that exploit the same leakage model. For this purpose, we offer an in-depth theoretical study which aims at determining the conditions under which the Pearson correlation coefficient is maximized. Moreover, we propose theoretical metrics to practically verify the validity of those conditions. Besides, we illustrate our theoretical study by an experiment on real electromagnetic traces acquired from a DES cryptographic implementation.

Electromagnetic attacks (EMA) pose real threats to embedded devices containing a secret information. Such attacks are of great concern since they are completely passive, low cost and easily mounted in practice. In this paper, we propose two innovative techniques to enhance electromagnetic attacks by reducing the number of measurements needed to succeed an attack on cryptographic implementations. The first method is based on the electromagnetic shielding theory which aims at decreasing the contribution of external noise sources. Whereas, the second provides an algorithmic solution to preprocess electromagnetic signals using the Kalman filtering (KF).

Power analysis attacks are non intrusive and easily mounted. As a consequence, there is a growing interest in efficient implementation of these attacks against block cipher algorithms such as Data Encryption Standard (DES) and Advanced Encryption Standard (AES). In our paper we propose a new technique based on the Kalman theory. We show how this technique could be useful for the cryptographic domain by making power analysis attacks faster. Moreover we prove that the Kalman filter is more powerful than the High Order Statistics technique.

This paper treats the digital design of a probabilistic energy equalizer for impulse radio (IR) UWB receiver in high data rate (100 Mbps). The aim of this study is to bypass certain complex mathematical function as a chi-squared distribution and reduce the computational complexity of the equalizer for a low cost hardware implementation. As in Sub-MAP algorithm, the max* operation is investigated for complexity reduction and tested by computer simulation with fixed point data types under 802.15.3a channel models. The obtained results prove that the complexity reduction involves a very slight algorithm deterioration.

Submmitted in : The Eleventh IEEE International Conference on Communications Systems (ICCS 2008); This paper addresses the hardware implementation feasibility assessment of a probabilistic energy equalizer for UWB impulse radio (IR) transmission. The receiver is based on energy detection and targets data rates greater than 100Mbps. The aim of this study is to bypass certain complex mathematical functions like the \emph{chi-squared} ($\chi^2$) distribution and approximate it by functions easier to implement. Fixed point data types simulations are achieved and compared to the results obtained from the reference floating point non-approximated function. Simulation results, under IEEE 802.15.3a channel models , confirm our choice which allows an easy hardware implementation for these complex functions.