RSS FINGERPRINT MENGGUNAKAN SENSOR FUSION UNTUK ESTIMASI LOKASI DI DALAM GEDUNG

Sebagian besar penelitian estimasi lokasi dalam gedung berdasarkan pada penggunaan Receive Signal Strength (RSS). Salah satu tahapan yang dilakukan adalah fingerprint. Tahap ini merupakan tahap pengumpulan informasi RSS yang diterima oleh instrument pengukur di koordinat tertentu. Tujuan Penelitian ini adalah untuk memperoleh tingkat akurasi di posisi yang presisi dengan menggunakan sensor- fusion dalam hal ini sebuah ponsel untuk mendapatkan RSS Sinyal Global System for Mobile Communication (GSM) dan laptop untuk mendapatkan sinyal IEEE 802.11g. Selanjutnya data hasil pengumpulan fingerprint dianalisis dan diuji dengan menggunakan algoritma K- Nearest Neighbour (KNN)

MobiGroup: Enabling Lifecycle Support to Social Activity Organization and Suggestion with Mobile Crowd Sensing

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This paper presents a group-aware mobile crowd sensing system called MobiGroup, which supports group activity organization in real-world settings. Acknowledging the complexity and diversity of group activities, this paper introduces a formal concept model to characterize group activities and classifies them into four organizational stages. We then present an intelligent approach to support group activity preparation, including a heuristic rule-based mechanism for advertising public activity and a context-based method for private group formation. In addition, we leverage features extracted from both online and offline communities to recommend ongoing events to attendees with different needs. Compared with the baseline method, people preferred public activities suggested by our heuristic rule-based method. Using a dataset collected from 45 participants, we found that the context-based approach for private group formation can attain a precision and recall of over 80%, and the usage of spatial-temporal contexts and group computing can have more than a 30% performance improvement over considering the interaction frequency between a user and related groups. A case study revealed that, by extracting the features such as dynamic intimacy and static intimacy, our cross-community approach for ongoing event recommendation can meet different user needs

PENENTUAN POSISI OBJEK DI DALAM GEDUNG BERDASARKAN GSM MENGGUNAKAN METODE SUPPORT VECTOR MACHINE

Sebagian besar penelitian penentuan  posisi objek dalam gedung berdasarkan pada penggunaan sinyal jarak  pendek, seperti WiFi, Bluetooth, ultra sound, dan infrared. Dalam penelitian ini dibahas penentuan posisi objek dalam gedung menggunakan Global System for Mobile Communication (GSM). Penggunaan GSM mempunyai kelebihan pada jangkauan area yang luas. Penentuan posisi objek menggunakan Receive Signal Strength (RSS) GSM fingerprinting. Skenario percobaan dilakukan dengan luasan 2 m2 dan menggunakan 4 Cell-ID. Estimasi posisi pada tahap positioning menggunakan metode Support vector Machine (SVM) yang hasilnya  dibandingkan  dengan  metode  Naïve Bayes (NB).  Hasil penentuan  posisi menunjukkan adanya perbedaan jarak kesalahan rata-rata minimum. Dengan  menggunakan  metode  SVM  akurasi  sebesar  12.45. Kesalahan tersebut lebih baik daripada menggunakan metode Naïve Bayes dengan akurasi sebesar 14.6 meter pada jumlah Cell-ID yang sama yaitu 4 Cell-ID

PENENTUAN POSISI OBJEK DALAM GEDUNG MENGGUNAKAN RSS FINGERPRINT BERDASARKAN TEKNOLOGI GSM DAN IEEE 820.11g

Sebagian besar penelitian penentuan posisi objek dalam gedung berdasarkan pada penggunaan ReceiveSignal Strength (RSS). Salah satu tahapan yang dilakukan adalah fingerprint. Tahap ini merupakan tahappengumpulan informasi RSS yang diterima oleh instrument pengukur di koordinat tertentu. Tujuan Penelitian iniadalah untuk memperoleh tingkat akurasi di posisi yang presisi dengan menggunakan sensor dalam hal ini sebuahponsel untuk mendapatkan RSS Sinyal Global System for Mobile Communication (GSM) dan laptop untukmendapatkan sinyal IEEE 802.11g . Selanjutnya data hasil pengumpulan fingerprint dianalisis dan diuji denganmenggunakan algoritma Naïve Bayes (NB).Hasil percobaan menunjukkan jarak kesalahan rata-rata minimumsebesar 5.11 meter dengan fusion sensor antara GSM dan IEEE 820.11g

Distribution-Matching Embedding for Visual Domain Adaptation

Domain-invariant representations are key to addressing the domain shift problem where the training and test examples follow different distributions. Existing techniques that have attempted to match the distributions of the source and target domains typically compare these distributions in the original feature space. This space, however, may not be directly suitable for such a comparison, since some of the features may have been distorted by the domain shift, or may be domain specific. In this paper, we introduce a Distribution-Matching Embedding approach: An unsupervised domain adaptation method that overcomes this issue by mapping the data to a latent space where the distance between the empirical distributions of the source and target examples is minimized. In other words, we seek to extract the information that is invariant across the source and target data. In particular, we study two different distances to compare the source and target distributions: the Maximum Mean Discrepancy and the Hellinger distance. Furthermore, we show that our approach allows us to learn either a linear embedding, or a nonlinear one. We demonstrate the benefits of our approach on the tasks of visual object recognition, text categorization, and WiFi localization

Privacy-Preserving Individual-Level COVID-19 Infection Prediction via Federated Graph Learning

Accurately predicting individual-level infection state is of great value since its essential role in reducing the damage of the epidemic. However, there exists an inescapable risk of privacy leakage in the fine-grained user mobility trajectories required by individual-level infection prediction. In this paper, we focus on developing a framework of privacy-preserving individual-level infection prediction based on federated learning (FL) and graph neural networks (GNN). We propose Falcon, a Federated grAph Learning method for privacy-preserving individual-level infeCtion predictiON. It utilizes a novel hypergraph structure with spatio-temporal hyperedges to describe the complex interactions between individuals and locations in the contagion process. By organically combining the FL framework with hypergraph neural networks, the information propagation process of the graph machine learning is able to be divided into two stages distributed on the server and the clients, respectively, so as to effectively protect user privacy while transmitting high-level information. Furthermore, it elaborately designs a differential privacy perturbation mechanism as well as a plausible pseudo location generation approach to preserve user privacy in the graph structure. Besides, it introduces a cooperative coupling mechanism between the individual-level prediction model and an additional region-level model to mitigate the detrimental impacts caused by the injected obfuscation mechanisms. Extensive experimental results show that our methodology outperforms state-of-the-art algorithms and is able to protect user privacy against actual privacy attacks. Our code and datasets are available at the link: https://github.com/wjfu99/FL-epidemic.Comment: accepted by TOI